From 5514df0077630685f406525498f09194f2d527ec Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Fri, 7 Oct 2022 15:52:49 +0200 Subject: [PATCH 01/62] Start branch From 4d0136c550de477e5b618fbfcf7f968a74cb3f93 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Tue, 11 Oct 2022 10:45:47 +0200 Subject: [PATCH 02/62] Start T029 talktorial on proteochemometrics (PCM) --- .../README.md | 80 +++ .../data/README.md | 6 + .../images/PCM_model_text-01.png | Bin 0 -> 202774 bytes .../images/README.md | 5 + .../images/papyrus_workflow.png | Bin 0 -> 80810 bytes .../talktorial.ipynb | 571 ++++++++++++++++++ 6 files changed, 662 insertions(+) create mode 100644 teachopencadd/talktorials/T029_compound_activity_proteochemometrics/README.md create mode 100644 teachopencadd/talktorials/T029_compound_activity_proteochemometrics/data/README.md create mode 100644 teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/PCM_model_text-01.png create mode 100644 teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/README.md create mode 100644 teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/papyrus_workflow.png create mode 100644 teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/README.md b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/README.md new file mode 100644 index 00000000..499f7ef6 --- /dev/null +++ b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/README.md @@ -0,0 +1,80 @@ +
+ +Thank you for contributing to TeachOpenCADD! + +
+ + +
+ +Set up your PR: Please check out our issue on how to set up a PR for new talktorials, including standard checks and TODOs. + +
+ + +# T000 · Talktorial topic title + +**Note:** This talktorial is a part of TeachOpenCADD, a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects. + +Authors: + +- First and last name, year(s) of contribution, lab, institution +- First and last name, year(s) of contribution, lab, institution + + +*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).* + + +
+ +Cross-referencing talktorials: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: Talktorial T000. + +
+ + +## Aim of this talktorial + +Add a short summary of this talktorial's content. + + +### Contents in *Theory* + +_Add Table of Contents (TOC) for Theory section._ + +* ChEMBL database +* Compound activity measures + + +
+ +Sync TOC with section titles: These points should refer to the headlines of your Theory section. + +
+ + +### Contents in *Practical* + +_Add Table of Contents (TOC) for Practical section._ + +* Connect to ChEMBL database +* Load and draw molecules + + +
+ +Sync TOC with section titles: These points should refer to the headlines of your Practical section. + +
+ + +### References + +* Paper +* Tutorial links +* Other useful resources + +*We suggest the following citation style:* +* Keyword describing resource: Journal (year), volume, pages (link to resource) + +*Example:* +* ChEMBL web services: [Nucleic Acids Res. (2015), 43, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881) diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/data/README.md b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/data/README.md new file mode 100644 index 00000000..cc6d1e4c --- /dev/null +++ b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/data/README.md @@ -0,0 +1,6 @@ +# Data + +This folder stores input and output data for the Jupyter notebook. + +- `xxx.csv`: Describe data. +- `xxx.sdf`: Describe data. diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/PCM_model_text-01.png b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/PCM_model_text-01.png new file mode 100644 index 0000000000000000000000000000000000000000..7fd70185c5da6ba6551be57954c820709376c87f GIT binary patch literal 202774 zcmeEu_dnI||NnIyd!|>>XKL3j(NQ0zMhMYeR#H?)h_)6X 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a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb new file mode 100644 index 00000000..e03bd58a --- /dev/null +++ b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb @@ -0,0 +1,571 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "

\n", + "\n", + "Thank you for contributing to TeachOpenCADD!\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Set up your PR: Please check out our issue on how to set up a PR for new talktorials, including standard checks and TODOs.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# T029 · Compound activity: Proteochemometrics\n", + "\n", + "**Note:** This talktorial is a part of TeachOpenCADD, a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects.\n", + "\n", + "Authors:\n", + "\n", + "- Marina Gorostiola González, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands)\n", + "- Olivier J.M. Béquignon, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands)\n", + "- Willem Jespers, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Cross-referencing talktorials: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: Talktorial T000.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aim of this talktorial\n", + "\n", + "While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be solved leveraging similarities and differences between proteins. In this talktorial, we use Proteochemometrics modelling (PCM) to enrich our activity models with protein data to predict the activity of novel compounds against the four adenosine receptor isoforms (A1, A2A, A2B, A3)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Contents in *Theory*\n", + "\n", + "* Data preparation\n", + " * Papyrus dataset\n", + " * Molecule encoding: molecular descriptors\n", + " * Protein encoding: protein descriptors\n", + "\n", + "* Proteochemometrics (PCM)\n", + " * Machine learning (ML): regression model\n", + " * Applications in drug discovery" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Contents in *Practical*\n", + "\n", + "* Downlaod Papyrus dataset\n", + "* Data preparation\n", + " * Filter activity data for targets of interest\n", + " * Align target sequences\n", + " * Calculate protein descriptors\n", + " * Calculate compound descriptors\n", + "* Proteochemometrics\n", + " * Helper functions\n", + " * XGBoost regressor" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "* Papyrus scripts [github](https://github.com/OlivierBeq/Papyrus-scripts)\n", + "* Papyrus dataset preprint: [ChemRvix (2021)](https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0)\n", + "* Molecular descriptors (Modred): [J. Cheminf., 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y)\n", + "* Proteochemometrics review: [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub)\n", + "\n", + "* Tutorial links\n", + "* Other useful resources\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Theory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To successfully apply PCM modelling, we need a large dataset of molecule-protein pairs with known bioactivity values, a way of describing molecules and proteins, and a ML algorithm to train a model. Then, we can make predictions for new molecule-protein pairs.\n", + "\n", + "NOTE: PCM modelling is an extension of ligand-based modelling with ML described in Talktorial T007. Explore that talktorial to know more about the basic principle of activity prediction using ML." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "*Figure 1:*\n", + "Proteochemometrics modelling construction from protein and molecular descriptors for which protein-compound pair bioactivity data is known.\n", + "Figure made by Marina Gorostiola González." + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Data preparation" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Papyrus dataset" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "The Papyrus dataset is a highly curated compilation of bioactivity data intended for modelling in drug discovery. Apart from the bioactivity data contained in the ChEMBL database (see also Talktorial T001), the Papyrus dataset contains binary data for classification tasks from the ExCAPE-DB, and bioactivity data from a number of kinase-specific papers (Figure 1).\n", + "\n", + "The bioactivity data aggregated is standardized, repaired, and normalised to form the Papyrus dataset, which is updated with every new version of ChEMBL released. The Papyrus dataset contains \"high quality\" data associated to pChEMBL values for regression tasks and \"low quality\" data associated to an active/inactive label for classification tasks (read more about ML applications in Talktorial T007)." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "\n", + "*Figure 2:*\n", + "Papyrus dataset generation scheme.\n", + "Figure taken from: [ChemRvix (2021)](https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0)." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Molecule encoding: molecular descriptors" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "For the ML models used in PCM, molecules need to be converted into a list of features. In Talktorial T007, molecular fingerprints were introduced. In this talktorial, we will use a different type of representation that is often used on its own or in combination with fingerprints: molecular descriptors.\n", + "\n", + "Molecular descriptors are the \"final result of a logical and mathematical procedure, which transforms chemical information encoded within a symbolic representation of a molecule into a useful number or the result of some standardized experiment\" ([J. Cheminf., 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y)). These descriptors can be, for example, molecular weight, ring count, Eccentric Connectivity Index (calculated from the 2D structure), or Geometrical Index (calculated from the 3D structure).\n", + "\n", + "In this talktorial, we use Modred as a software engine to calculate molecular descriptors. Modred calculates more than 1,800 molecular descriptors, including the ones implemented in RDKit, including an automatic preprocessing step that is common for all descriptors calculated. For simplicity, here we calculate only 4 types of descriptors from the vast list of possibilities from Modred, excluding their 3D representation. These include:\n", + "\n", + "* ABC Index: 2 descriptors that represent the atom-bond connectivity index or the Graovac-Ghorbani atom-bond connectivity index (see Modred ABCIndex [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.ABCIndex.html))\n", + "* Acid-Base: 2 descriptors that count acidic and basic groups, respectively (see Modred AcidBase [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.AcidBase.html?highlight=acidbase))\n", + "* Atom count: 16 descriptors that represent a count of different types of atoms (see Modred AtomCount [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.AtomCount.html?highlight=atomcount))\n", + "* Balaban J index: 1 descriptor (included in RDkit), which represents a topological index (see Modred BalabanJ [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.BalabanJ.html?highlight=balaban#module-mordred.BalabanJ))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Protein encoding: protein descriptors" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "As done for molecules, the proteins of interest need to be converted to a list of features or protein descriptors. Protein descriptors used in PCM applications are commonly based on the protein sequence and represent physicochemical characteristics of the amino acids that make up the sequence (e.g. Z-scales). Other protein descriptors represent topological (e.g. St-scales) or electrostatic properties (e.g. MS-WHIM) of the protein sequence. Moreover, if structural information is available, protein descriptors can be derived from the 3D structure of the protein (e.g. sPairs) or the ligand-protein interaction in 3D (e.g. interaction fingerprints). Finally, with the widespread use of deep learning, protein embeddings can be obtained after parsing the protein sequence through the network (e.g. UniRep, AlphaFold embeddings).\n", + "\n", + "For protein descriptors based on the protein sequence, an aspect to take into account is that for ML the length of the protein descriptor needs to be the same. However, most proteins do not have the same sequence length. To solve this issue, there are two main approaches:\n", + "* Multiple sequence alignment: when the whole protein wants to be incorporated to the model, a multiple sequence alignment can be performed. The final descriptor will have as many features as the number of features per amino acid multiplied by the number of aligned positions. To take into account, gaps in the alignment will receive zeroes in the descriptor.\n", + "* Binding pocket selection: To avoid unnecessary features, a binding pocket of the same length can be selected for each protein. Normally, the binding pocket selection is preceded by a multiple sequence alignment and driven by known structural or mutagenesis data.\n", + "\n", + "Other options are available when proteins are not of the same family or do not share a binding pocket (see [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub))\n", + "\n", + "In this talktorial, we will focus on physicochemical protein descriptors, mainly Z-scales ([J. Med. Chem, 30 (1987)](https://pubs.acs.org/doi/10.1021/jm00390a003)). The Z-scales descriptor assigns three pre-determined values (Z1, Z2, Z3) to each amino acid in the sequence. The Z1, Z2, and Z3 values are the first principal components of a principal component analysis (PCA) including 29 different physicochemical variables to characterize the amino acids.\n", + "Since we are calculating activity for four proteins with very high sequence similarity (Adenosine receptors A1, A2A, A2B, and A3), we will use multiple sequence alignment prior to calculation of the Z-scales." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Proteochemometrics (PCM)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "The ML principles for proteochemometric modelling are equivalent to those explained in Talktorial T007. However, in this talktorial we will explore the other type of supervised ML application: regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values.\n", + "\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Applications in drug discovery" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "* Multi-target activity prediction\n", + "* Selectivity\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Practical" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Add short summary of what will be done in this practical section." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Sync section titles with TOC: Please make sure that all section titles in the Practical section are synced with the bullet point list provided in the Aim of this talktorial > Contents in Practical section.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " \n", + "Beware of section levels: Please check if you are using the correct subsection levels. The section Practical is written in Markdown as ## Practical, so every subsection within Practical is ### or lower.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "RDKit WARNING: [16:28:01] Enabling RDKit 2019.09.3 jupyter extensions\n" + ] + } + ], + "source": [ + "from pathlib import Path\n", + "import math\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.lines import Line2D\n", + "import matplotlib.patches as mpatches\n", + "from rdkit import Chem\n", + "from rdkit.Chem import Descriptors, Draw, PandasTools" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Imports: Please add all your imports on top of this section, ordered by standard library / 3rd party packages / our own (teachopencadd.*). \n", + "Read more on imports and import order in the \"PEP 8 -- Style Guide for Python Code\".\n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "HERE = Path(_dh[-1])\n", + "DATA = HERE / \"data\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Relative paths: Please define all paths relative to this talktorial's path by using the global variable HERE.\n", + "If your talktorial has input/output data, please define the global DATA, which points to this talktorial's data folder (check out the default folder structure of each talktorial).\n", + " \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Connect to ChEMBL database" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_Explain what you will do and why here in the Markdown cell. This includes everything that has to do with the talktorial's storytelling._" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Add comments in the code cell if you want to comment on coding decisions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Functions: \n", + "\n", + "
    \n", + "
  • Please add numpy docstrings to your functions.
    • \n", + "
  • Please expose all variables used within a function in the function's signature (i.e. they must be function parameters), unless they are created within the scope of the function.
    • \n", + "
  • Please add comments to the steps performed in the function.
    • \n", + "
  • Please use meaningful function and parameter names. This applies also to variable names.
    • \n", + "
\n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def calculate_ro5_properties(smiles):\n", + " \"\"\"\n", + " Test if input molecule (SMILES) fulfills Lipinski's rule of five.\n", + "\n", + " Parameters\n", + " ----------\n", + " smiles : str\n", + " SMILES for a molecule.\n", + "\n", + " Returns\n", + " -------\n", + " pandas.Series\n", + " Molecular weight, number of hydrogen bond acceptors/donor and logP value\n", + " and Lipinski's rule of five compliance for input molecule.\n", + " \"\"\"\n", + " # RDKit molecule from SMILES\n", + " molecule = Chem.MolFromSmiles(smiles)\n", + " # Calculate Ro5-relevant chemical properties\n", + " molecular_weight = Descriptors.ExactMolWt(molecule)\n", + " n_hba = Descriptors.NumHAcceptors(molecule)\n", + " n_hbd = Descriptors.NumHDonors(molecule)\n", + " logp = Descriptors.MolLogP(molecule)\n", + " # Ro5 conditions fulfilled\n", + " conditions = [molecular_weight <= 500, n_hba <= 10, n_hbd <= 5, logp <= 5]\n", + " ro5_fulfilled = sum(conditions) >= 3\n", + " # Return True if no more than one out of four conditions is violated\n", + " return pd.Series(\n", + " [molecular_weight, n_hba, n_hbd, logp, ro5_fulfilled],\n", + " index=[\"molecular_weight\", \"n_hba\", \"n_hbd\", \"logp\", \"ro5_fulfilled\"],\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load and draw molecules" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "_Explain what you will do and why here in the Markdown cell. This includes everything that has to do with the talktorial's storytelling._" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Add comments in the code cell if you want to comment on coding decisions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Discussion\n", + "\n", + "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quiz\n", + "\n", + "Ask three questions that the user should be able to answer after doing this talktorial. Choose important take-aways from this talktorial for your questions.\n", + "\n", + "1. Question\n", + "2. Question\n", + "3. Question" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Useful checks at the end: \n", + " \n", + "
    \n", + "
  • Clear output and rerun your complete notebook. Does it finish without errors?
    • \n", + "
  • Check if your talktorial's runtime is as excepted. If not, try to find out which step(s) take unexpectedly long.
    • \n", + "
  • Flag code cells with # NBVAL_CHECK_OUTPUT that have deterministic output and should be tested within our Continuous Integration (CI) framework.
    • \n", + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.4" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file From 4267bcc1d9df1869127dde6fa644573983a264e9 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Tue, 11 Oct 2022 22:33:16 +0200 Subject: [PATCH 03/62] Add environment file --- .../T029_env.yml | 22 +++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 teachopencadd/talktorials/T029_compound_activity_proteochemometrics/T029_env.yml diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/T029_env.yml b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/T029_env.yml new file mode 100644 index 00000000..3ba92f04 --- /dev/null +++ b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/T029_env.yml @@ -0,0 +1,22 @@ +name: teachopencadd_t029 +channels: + - conda-forge + - defaults +dependencies: + - python>=3.8 + - pip + - jupyter + - jupyterlab>=3 + - nglview>=3 + - pandas + - numpy + - biopython<=1.77 + - rdkit==2021.09.5 + - scikit-learn + - scipy + - seaborn + # Dependencies for PCM and papyrus scripts + - mordred + - pip: + - prodec + - https://github.com/OlivierBeq/Papyrus-scripts/tarball/master From d3efd75000019af09221eaf56ed115432c6eb276 Mon Sep 17 00:00:00 2001 From: gorostiolam Date: Wed, 12 Oct 2022 11:14:58 +0200 Subject: [PATCH 04/62] Completed Theory draft --- .../talktorial.ipynb | 152 ++++++++++++++++-- 1 file changed, 135 insertions(+), 17 deletions(-) diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb index e03bd58a..e31628a0 100644 --- a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb @@ -76,8 +76,11 @@ " * Protein encoding: protein descriptors\n", "\n", "* Proteochemometrics (PCM)\n", - " * Machine learning (ML): regression model\n", - " * Applications in drug discovery" + " * Machine learning principles: regression\n", + " * Splitting methods\n", + " * Regression evaluation metrics\n", + " * ML algorithm: Random Forest\n", + " * Applications of PCM in drug discovery" ] }, { @@ -94,7 +97,7 @@ " * Calculate compound descriptors\n", "* Proteochemometrics\n", " * Helper functions\n", - " * XGBoost regressor" + " * Model training and validation" ] }, { @@ -106,10 +109,11 @@ "* Papyrus scripts [github](https://github.com/OlivierBeq/Papyrus-scripts)\n", "* Papyrus dataset preprint: [ChemRvix (2021)](https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0)\n", "* Molecular descriptors (Modred): [J. Cheminf., 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y)\n", + "* Protein descriptors (ProDEC) [github](https://github.com/OlivierBeq/ProDEC)\n", + "* Regression metrics [(Scikit learn)](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics)\n", + "* XGBoost [Documentation](https://xgboost.readthedocs.io/en/stable/index.html)\n", "* Proteochemometrics review: [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub)\n", - "\n", - "* Tutorial links\n", - "* Other useful resources\n" + "\n" ] }, { @@ -235,7 +239,7 @@ "Other options are available when proteins are not of the same family or do not share a binding pocket (see [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub))\n", "\n", "In this talktorial, we will focus on physicochemical protein descriptors, mainly Z-scales ([J. Med. Chem, 30 (1987)](https://pubs.acs.org/doi/10.1021/jm00390a003)). The Z-scales descriptor assigns three pre-determined values (Z1, Z2, Z3) to each amino acid in the sequence. The Z1, Z2, and Z3 values are the first principal components of a principal component analysis (PCA) including 29 different physicochemical variables to characterize the amino acids.\n", - "Since we are calculating activity for four proteins with very high sequence similarity (Adenosine receptors A1, A2A, A2B, and A3), we will use multiple sequence alignment prior to calculation of the Z-scales." + "Since we are calculating activity for four proteins with very high sequence similarity (Adenosine receptors A1, A2A, A2B, and A3), we will use multiple sequence alignment prior to calculation of the Z-scales. To calculate Z-scales we will use ProDEC, an open source resource that compiles a large number of protein descriptors." ], "metadata": { "collapsed": false, @@ -256,7 +260,92 @@ { "cell_type": "markdown", "source": [ - "The ML principles for proteochemometric modelling are equivalent to those explained in Talktorial T007. However, in this talktorial we will explore the other type of supervised ML application: regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values.\n", + "#### Machine learning principles: regression" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Proteochemometrics (PCM) consists in the modelling via supervised machine learning algorithms of a biological endpoint (e.g. compound activity) based on a series of features derived from chemical compounds and target proteins. The ML principles for proteochemometric modelling are equivalent to those explained in Talktorial T007. However, in this talktorial we will explore a supervised ML application other than classification, this is regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Splitting methods" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Similarly to classification tasks, in supervised ML regression applications the model is first fitted to a training set and subsequently the predictive performance is evaluated on a test set. Therefore, the original dataset needs to be split between training and test sets. The split needs to ensure that the fitting process has enough data, and that the test set is representative. Normally, the distribution between train and test set is 80/20 or 70/30. Depending on the applicability domain, the split can be done in multiple ways. In PCM modelling, some of the most common splitting methods are:\n", + "* Random split: This method is not particularly relevant in drug discovery applications as it does not refflect the reality of a drug discovery campaign and it will most likely lead to data leaks between the training and test set. This is, very similar data will be found in both sets, which will lead to an overestimation of the rpedictive performance of the model. This type of split is commonly used, however, as a baseline and point of reference for other splitting methods, or as a starting point for quick model comparisons.\n", + "* Leave one target out (LOTO) split: To evaluate the ability of the model to extrapolate to targets not previously seen, one of the targets can be completely moved to the test set.\n", + "* Leave one compound cluster out (LOCCO) split: This method evaluates the ability of the model to extrapolate to compounds with properties not previously seen by the model. Clustering can be done based on different molecular characteristics, such as physicochemical properties, scaffold, or approval status, for example (see Talktorial T005 to learn more about clustering). One (or several) clusters can then be left out for testing. This methods prevents data leaking in terms of chemistry between training and test sets.\n", + "* Temporal split: This method was developed in order to account for the usual timeline of drug discovery campaigns, where chemical series are populated sequentially over time. In this approach, the molecules included in the training set are those released until certain date and the rest (most novel) are included in the test set.\n", + "* Stratified split per target: This method can be applied to any of the splitting methods described above (except LOTO), and aims to include data of all targets in both the training and test set, so that additional target-compound interactions can be extracted by the model." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "\n", + "*Figure 3:*\n", + "Overview of splitting methods, including target-stratified random and temporal splits and leave one target out approach.\n", + "Figure made by Marina Gorostiola González." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Regression evaluation metrics" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "To evaluate the predictive performance of a regression model, there are several metrics that in simple terms measure the differences between the true target values and the predictions made by the model. These metrics can be used in cross-validation on the training set (see Talktorial T007) or in the test set. The most commonly used metrics include:\n", + "\n", + "* Coefficient of determination ($R^{2}$ score): Represents the portion of variance of the target variable that has been explained by the independent variables (features) in the model. $R^{2}$ score varies between 1.0 (best score) and minus infinite, where 0.0 represents a model that always predicts the average target variable. As the variance is dataset dependent, it might not be meaningful metric to compare between datasets. When dealing with linear regression, and model fitting and evaluation are performed on a single dataset, $R^{2}$ is equivalent to the square of the Pearson correlation coefficient, described below, and can be noted as $r^{2}$.\n", + "* Pearson's correlation coefficient (Pearson's $r$): Is a measure of the linear correlation between the true and predicted values of the target variable. It is calculated as the covariance of the two variables divided by the product of their standard deviation. Pearson's $r$ can vary between 1.0 (a perfect positive correlation) and -1.0 (a perfect negative correlation), where 1.0 would represent a perfect prediction.\n", + "* Mean squared error (MSE): Measures the average of the squares of the difference between the predicted and the true values. It varies between 1.0 (worst) and 0.0 (best).\n", + "* Root mean square error (RMSE): Also called root mean square deviation (RMSD), it is the square root of the MSE and represents the standard deviation of the prediction errors respect to the line of best fit. RMSE is a measure of accuracy and it cannot be applied to compare between datasets, as it is scale-dependent. It varies between 1.0 (worst) and 0.0 (best).\n", + "\n", + "To learn more about evaluation metrics, you can consult scikit learn's regression metrics [Docs](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics).\n", + "\n", + "\n", "\n" ], "metadata": { @@ -266,7 +355,7 @@ { "cell_type": "markdown", "source": [ - "#### Applications in drug discovery" + "##### ML algorithm: Random Forest" ], "metadata": { "collapsed": false, @@ -278,8 +367,38 @@ { "cell_type": "markdown", "source": [ - "* Multi-target activity prediction\n", - "* Selectivity\n" + "Different ML algorithms can be used to train PCM models. Some of them include support vector machines (SVM), random forest (RF), and neural networks (NN), that were described in Talktorial T007. In this talktorial, we will use RF. RF is a decision tree-based algorithm, more in detail a bagging ensemble method. This means that there are multiple decision trees trained independently with subsets of features and data and the final prediction is made from a consensus between the independent predictions.\n", + "RF models have been used extensively in PCM applications due to their efficiency in large datasets and resistance to overfitting with more features. " + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Applications of PCM in drug discovery" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "The possibility to predict bioactivity for multiple targets in one model with PCM is very interesting in drug discovery. Some applications of this technique are listed below and help answer the following questions in drug discovery:\n", + "\n", + "* Poly-pharmacology: Is it possible to target several proteins of interest simultaneously with one single drug?\n", + "* Off-target prediction: What other proteins do these compounds target apart from the intended therapeutic target? Are maybe these off-targets responsible for side effects?\n", + "* Selectivity prediction: Do certain novel compounds target one protein isoform while avoiding others (off-targets) known to cause adverse effects?\n", + "\n", + "To know more about applications of PCM in drug discovery, have a look at this review [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub)." ], "metadata": { "collapsed": false, @@ -499,7 +618,8 @@ "source": [ "## Discussion\n", "\n", - "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges." + "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges.\n", + "Compared to purely ligand-based compound activity prediction models, PCM modelling has certain advantages and limitations." ] }, { @@ -508,11 +628,9 @@ "source": [ "## Quiz\n", "\n", - "Ask three questions that the user should be able to answer after doing this talktorial. Choose important take-aways from this talktorial for your questions.\n", - "\n", - "1. Question\n", - "2. Question\n", - "3. Question" + "1. What types of features are needed for PCM?\n", + "2. How many types of training/test set splitting methods commonly used in PCM modelling do you know?\n", + "3. Which applications do you know of PCM in drug discovery?" ] }, { From af8bdb0b76b17feb20ae09bf1fde17a1a6e4ea55 Mon Sep 17 00:00:00 2001 From: gorostiolam Date: Thu, 13 Oct 2022 09:26:27 +0200 Subject: [PATCH 05/62] Added first practical part to download, read and filter Papyrus dataset --- .../talktorial.ipynb | 473 ++++++++++++++---- 1 file changed, 362 insertions(+), 111 deletions(-) diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb index e31628a0..d97c55a2 100644 --- a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb @@ -95,7 +95,7 @@ " * Align target sequences\n", " * Calculate protein descriptors\n", " * Calculate compound descriptors\n", - "* Proteochemometrics\n", + "* Proteochemometrics modelling\n", " * Helper functions\n", " * Model training and validation" ] @@ -368,7 +368,7 @@ "cell_type": "markdown", "source": [ "Different ML algorithms can be used to train PCM models. Some of them include support vector machines (SVM), random forest (RF), and neural networks (NN), that were described in Talktorial T007. In this talktorial, we will use RF. RF is a decision tree-based algorithm, more in detail a bagging ensemble method. This means that there are multiple decision trees trained independently with subsets of features and data and the final prediction is made from a consensus between the independent predictions.\n", - "RF models have been used extensively in PCM applications due to their efficiency in large datasets and resistance to overfitting with more features. " + "RF models have been used extensively in PCM applications due to their efficiency in large datasets and resistance to overfitting with more features." ], "metadata": { "collapsed": false, @@ -418,191 +418,442 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Add short summary of what will be done in this practical section." + "In the practical section of this talktorial we will create a PCM regression model for the four adenosine receptors (A1, A2A, A2B, A3) with data from the Papyrus dataset and molecular and protein descriptors as features." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 2, "metadata": {}, + "outputs": [], "source": [ - "
\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import pandas as pd\n", + "import re\n", "\n", - "Sync section titles with TOC: Please make sure that all section titles in the Practical section are synced with the bullet point list provided in the Aim of this talktorial > Contents in Practical section.\n", + "from papyrus_scripts.download import download_papyrus\n", + "from papyrus_scripts.reader import read_papyrus, read_protein_set\n", + "from papyrus_scripts.preprocess import *\n", + "from papyrus_scripts.utils.IO import get_num_rows_in_file\n", "\n", - "
" + "from Bio.Seq import Seq\n", + "from Bio.SeqIO import SeqRecord, write as SeqIO_write, parse as SeqIO_parse\n", + "from Bio.Align.Applications import ClustalOmegaCommandline\n", + "\n", + "from prodec import ProteinDescriptors, Transform\n", + "from rdkit import Chem\n", + "from mordred import Calculator, descriptors\n", + "\n", + "from sklearn.preprocessing import RobustScaler\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import r2_score,mean_absolute_error\n", + "from scipy.stats import pearsonr\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 4, "metadata": {}, + "outputs": [], "source": [ - "
\n", - " \n", - "Beware of section levels: Please check if you are using the correct subsection levels. The section Practical is written in Markdown as ## Practical, so every subsection within Practical is ### or lower.\n", - "\n", - "
" + "# Set path to this notebook\n", + "HERE = Path(_dh[-1])\n", + "DATA = HERE / \"data\"" ] }, + { + "cell_type": "markdown", + "source": [ + "### Download Payrus dataset" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "By default, the download_papyrus function retrieves bioactivity, target and other information for the latest version of the Papyrus dataset. The data retrieved consists of the highest quality continuous bioactivity data (Papyrus++) without stereochemistry annotated." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, + "execution_count": 18, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "RDKit WARNING: [16:28:01] Enabling RDKit 2019.09.3 jupyter extensions\n" + "Latest version: 05.5\n", + "Number of files to be donwloaded: 6\n", + "Total size: 118MB\n" ] + }, + { + "data": { + "text/plain": "Donwloading version 05.5: 0%| | 0.00/118M [00:00\n", - "\n", - "Imports: Please add all your imports on top of this section, ordered by standard library / 3rd party packages / our own (teachopencadd.*). \n", - "Read more on imports and import order in the \"PEP 8 -- Style Guide for Python Code\".\n", - " \n", - "" + "### Data preparation" ] }, + { + "cell_type": "markdown", + "source": [ + "#### Filter activity data for targets of interest" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "From the Papyrus dataset, we want to extract continuous activity data for all compound-protein pairs for the four human Adenosine receptors. In the Papyrus set, unique compound-target activity values are reported by aggregating data from different assays available. To account for the different types of bioactivity units available, a canonical activity metric defined by ChEMBL is used, pchembl value, which is defined as $-log_{10}(molar IC_{50}, XC_{50}, EC_{50}, AC_{50}, Ki, Kd, or potency)$. From the activity aggregation strategies available with the Papyrus set, we will use the Mean (pchembl_value_Mean variable). \n", + "\n", + "|Receptor|Uniprot accession|\n", + "|---|---|\n", + "|A1|P30542|\n", + "|A2A|P29274|\n", + "|A2B|P29275|\n", + "|A3|P0DMS8|" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": 76, "outputs": [], "source": [ - "HERE = Path(_dh[-1])\n", - "DATA = HERE / \"data\"" - ] + "def filter_explore_activity_data(papyrus_version, targets):\n", + " \"\"\"\n", + " Filter Papyrus dataset for targets of interest and explore the statistics of the resulting dataset\n", + "\n", + " Parameters\n", + " ----------\n", + " papyrus_version : str\n", + " Version of the Papyrus dataset to read\n", + " targets : dict\n", + " Dictionary with target labels as keys and Uniprot accession codes as values\n", + "\n", + " Returns\n", + " -------\n", + " pandas.DataFrame\n", + " Filtered bioactivity dataset for input targets\n", + " \"\"\"\n", + " # Read downloaded Papyrus dataset in chunks, as it does not fit in memory\n", + " CHUNKSIZE = 100000\n", + " data = read_papyrus(version=papyrus_version, chunksize=CHUNKSIZE, source_path=DATA)\n", + "\n", + " # Create filter for targets of interest\n", + " target_accession_list = targets.values()\n", + " filter = keep_accession(data, target_accession_list)\n", + "\n", + " # Iterate through chunks and apply the filter defined\n", + " filtered_data = consume_chunks(filter, total=round(get_num_rows_in_file('bioactivities', False) / CHUNKSIZE))\n", + " # Add column named 'Target' for easier data visualization\n", + " filtered_data['Target'] = filtered_data['accession'].apply(lambda x: [i for i in targets.keys() if targets[i]==x][0])\n", + "\n", + " # Print number of bioactivity datapoints per target\n", + " for target,accession in zip(targets.keys(), targets.values()):\n", + " print('Number of bioactivity datapoints')\n", + " print(f'{target} ({accession}) : {filtered_data[filtered_data[\"accession\"]==accession].shape[0]}')\n", + "\n", + " # Plot distribution of activity values (pchembl_value_Mean) per target\n", + " g = sns.displot(filtered_data, x='pchembl_value_Mean', hue='Target', element='step', hue_order=targets.keys())\n", + "\n", + " return filtered_data" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } }, { - "cell_type": "markdown", - "metadata": {}, + "cell_type": "code", + "execution_count": 81, + "outputs": [ + { + "data": { + "text/plain": " 0%| | 0/12 [00:00", + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "
\n", + "# Define the set of receptors of interest with a label and their Uniprot accession\n", + "adenosine_receptors = {'A1': 'P30542',\n", + " 'A2A': 'P29274',\n", + " 'A2B': 'P29275',\n", + " 'A3': 'P0DMS8'}\n", "\n", - "Relative paths: Please define all paths relative to this talktorial's path by using the global variable HERE.\n", - "If your talktorial has input/output data, please define the global DATA, which points to this talktorial's data folder (check out the default folder structure of each talktorial).\n", - " \n", - "
" - ] + "# Filter the Papyrus bioactivity dataset and plot the distribution of activity values for the targets of interest\n", + "ar_data = filter_explore_activity_data('05.5', adenosine_receptors)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } }, { "cell_type": "markdown", - "metadata": {}, "source": [ - "### Connect to ChEMBL database" - ] + "#### Align target sequences" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } }, { "cell_type": "markdown", - "metadata": {}, "source": [ - "_Explain what you will do and why here in the Markdown cell. This includes everything that has to do with the talktorial's storytelling._" - ] + "In order to ensure protein descriptors are of the same length, we first need to align the target sequences. We do this by using the software ClustalW. The first step is to install the software." + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "execution_count": 79, "outputs": [], "source": [ - "# Add comments in the code cell if you want to comment on coding decisions" - ] + "# Download Clustal W and unzip\n", + "%%capture\n", + "!wget -O \"clustalo-1.2.4\" \"http://www.clustal.org/omega/clustal-omega-1.2.2-win64.zip\"\n", + "!chmod +x ./clustalo-1.2.4" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } }, { "cell_type": "markdown", "metadata": {}, "source": [ - "
\n", - "\n", - "Functions: \n", - "\n", - "
    \n", - "
  • Please add numpy docstrings to your functions.
    • \n", - "
  • Please expose all variables used within a function in the function's signature (i.e. they must be function parameters), unless they are created within the scope of the function.
    • \n", - "
  • Please add comments to the steps performed in the function.
    • \n", - "
  • Please use meaningful function and parameter names. This applies also to variable names.
    • \n", - "
\n", - " \n", - "
" + "#### Calculate protein descriptors" ] }, + { + "cell_type": "markdown", + "source": [ + "Explain" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": null, "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", "source": [ - "def calculate_ro5_properties(smiles):\n", - " \"\"\"\n", - " Test if input molecule (SMILES) fulfills Lipinski's rule of five.\n", - "\n", - " Parameters\n", - " ----------\n", - " smiles : str\n", - " SMILES for a molecule.\n", - "\n", - " Returns\n", - " -------\n", - " pandas.Series\n", - " Molecular weight, number of hydrogen bond acceptors/donor and logP value\n", - " and Lipinski's rule of five compliance for input molecule.\n", - " \"\"\"\n", - " # RDKit molecule from SMILES\n", - " molecule = Chem.MolFromSmiles(smiles)\n", - " # Calculate Ro5-relevant chemical properties\n", - " molecular_weight = Descriptors.ExactMolWt(molecule)\n", - " n_hba = Descriptors.NumHAcceptors(molecule)\n", - " n_hbd = Descriptors.NumHDonors(molecule)\n", - " logp = Descriptors.MolLogP(molecule)\n", - " # Ro5 conditions fulfilled\n", - " conditions = [molecular_weight <= 500, n_hba <= 10, n_hbd <= 5, logp <= 5]\n", - " ro5_fulfilled = sum(conditions) >= 3\n", - " # Return True if no more than one out of four conditions is violated\n", - " return pd.Series(\n", - " [molecular_weight, n_hba, n_hbd, logp, ro5_fulfilled],\n", - " index=[\"molecular_weight\", \"n_hba\", \"n_hbd\", \"logp\", \"ro5_fulfilled\"],\n", - " )" - ] + "#### Calculate compound descriptors" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } }, { "cell_type": "markdown", - "metadata": {}, "source": [ - "### Load and draw molecules" - ] + "Explain" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } }, { "cell_type": "markdown", - "metadata": {}, "source": [ - "_Explain what you will do and why here in the Markdown cell. This includes everything that has to do with the talktorial's storytelling._" - ] + "### Proteochemometrics modelling" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Explain" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Helper functions" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Explain" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": null, "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Model training and validation" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ - "# Add comments in the code cell if you want to comment on coding decisions" + "Explain" ] }, { From 11ff43f334c58c6aef976d1119654dcc9dbf4cd7 Mon Sep 17 00:00:00 2001 From: gorostiolam Date: Thu, 13 Oct 2022 09:27:25 +0200 Subject: [PATCH 06/62] Added figure with splitting methods --- .../images/splitting_methods.png | Bin 0 -> 99213 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/splitting_methods.png diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/splitting_methods.png b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/splitting_methods.png new file mode 100644 index 0000000000000000000000000000000000000000..9b6f4570db4c8e76d02b0c22fb47329312e550b0 GIT binary 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z0AzOL@^W0371H37{#oMbB(G8jJQ^hh7KO94MsX1|Fe9VRfu~Gd`wmdU#;vUux-_GH zzCLao(USIqr>0E83_->~%{>rywoUH=-poS=lg3i#(i7)RRYK%TM%_N*lUsBD1-W9Q z-x7B4!~ap2_u%vp3om?T`pb`2LP$e+ypRI2tQ3+R8mMJnx)g$BX0iM!*`K|pVi@Cv zCl|rVq&#=4=MPV&;4_UMKi+|W?`GKTAcAgLSY1mL~KYelL2%~F7{nk^G zsQ(owHTM+R;-^>w)idj;ZmPm_0>_bJ9S*_SvlI@3ZH~%J498!jZloyRh?;&X9io8zcaa4~@r)C|&6w;F3#`%h@5x_y;i9Pm z$Enc5(e>|0(Hbq^7}Fd)Dg$fUe4ELwma0-eG0NFqji(8w{nh!AjusJ<^OZP&VPw!B zz1Nb5Jc^l6kwlu@XefFT9q#LT+a2)?@9{C2)tl&D0x)#FbXW~#DR8u0u?`=VNW7N+ zzJ Date: Thu, 13 Oct 2022 12:39:43 +0200 Subject: [PATCH 07/62] Add section to download clustalO (Win,Unix,Mac) and perform MSA --- .../talktorial.ipynb | 291 +++++++++++++++++- 1 file changed, 283 insertions(+), 8 deletions(-) diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb index d97c55a2..8b5a6245 100644 --- a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb @@ -431,6 +431,8 @@ "import numpy as np\n", "import pandas as pd\n", "import re\n", + "import wget\n", + "import zipfile\n", "\n", "from papyrus_scripts.download import download_papyrus\n", "from papyrus_scripts.reader import read_papyrus, read_protein_set\n", @@ -440,6 +442,9 @@ "from Bio.Seq import Seq\n", "from Bio.SeqIO import SeqRecord, write as SeqIO_write, parse as SeqIO_parse\n", "from Bio.Align.Applications import ClustalOmegaCommandline\n", + "import Bio.AlignIO\n", + "import rich\n", + "from rich_msa import RichAlignment\n", "\n", "from prodec import ProteinDescriptors, Transform\n", "from rdkit import Chem\n", @@ -523,6 +528,21 @@ } } }, + { + "cell_type": "code", + "execution_count": 90, + "outputs": [], + "source": [ + "# Let's specify the Papyrus version for the rest of the work\n", + "PAPYRUS_VERSION = '05.5'" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, { "cell_type": "markdown", "metadata": {}, @@ -659,7 +679,7 @@ " 'A3': 'P0DMS8'}\n", "\n", "# Filter the Papyrus bioactivity dataset and plot the distribution of activity values for the targets of interest\n", - "ar_data = filter_explore_activity_data('05.5', adenosine_receptors)" + "ar_data = filter_explore_activity_data(PAPYRUS_VERSION, adenosine_receptors)" ], "metadata": { "collapsed": false, @@ -683,21 +703,276 @@ { "cell_type": "markdown", "source": [ - "In order to ensure protein descriptors are of the same length, we first need to align the target sequences. We do this by using the software ClustalW. The first step is to install the software." + "In order to ensure protein descriptors are of the same length, we first need to align the target sequences. We do this by using the software ClustalW. The first step is to install the software. Choose one of the following download options depending on your system (Windows, Unix, or MacOS)." ], "metadata": { "collapsed": false } }, + { + "cell_type": "markdown", + "source": [ + "##### Clustal Omega downlaod for Windows" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 96, + "outputs": [], + "source": [ + "# Define url of download file and paths to download\n", + "clustalo_url = \"http://www.clustal.org/omega/clustal-omega-1.2.2-win64.zip\"\n", + "clustalo_zip_path = str(Path(DATA, 'clustalo.zip'))\n", + "clustalo_path = str(Path(DATA, 'clustalo'))\n", + "# Download .zip installation file\n", + "wget.download(clustalo_url, out=clustalo_zip_path)\n", + "# Unzip installation file\n", + "with zipfile.ZipFile(clustalo_zip_path, 'r') as zip_ref:\n", + " zip_ref.extractall(clustalo_path)\n", + "# Define path to executable\n", + "clustalo_exe = os.path.join(clustalo_path, 'clustal-omega-1.2.2-win64', 'clustalo.exe')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "##### clustalo download for Unix" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Define url of download file and paths to download\n", + "clustalo_url = \"http://www.clustal.org/omega/clustalo-1.2.4-Ubuntu-x86_64\"\n", + "clustalo_path = str(Path(DATA, 'clustalo'))\n", + "# Download executable file\n", + "wget.download(clustalo_url, out=clustalo_path)\n", + "# Give file executable permission\n", + "os.chmod(clustalo_path, 0755)\n", + "# Define path to executable\n", + "clustalo_exe = clustalo_path" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "##### clustalo download for MacOS" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Define url of download file and paths to download\n", + "clustalo_url = \"http://www.clustal.org/omega/clustal-omega-1.2.3-macosx\"\n", + "clustalo_path = str(Path(DATA, 'clustalo'))\n", + "# Download executable file\n", + "wget.download(clustalo_url, out=clustalo_path)\n", + "# Give file executable permission\n", + "os.chmod(clustalo_path, 0755)\n", + "# Define path to executable\n", + "clustalo_exe = clustalo_path" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Next, we obtain the protein sequences from the target files in Papyrus." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 91, + "outputs": [ + { + "data": { + "text/plain": " target_id HGNC_symbol UniProtID Status Organism \\\n80 P30542_WT ADORA1 AA1R_HUMAN reviewed Homo sapiens (Human) \n81 P29274_WT ADORA2A AA2AR_HUMAN reviewed Homo sapiens (Human) \n47 P29275_WT ADORA2B AA2BR_HUMAN reviewed Homo sapiens (Human) \n82 P0DMS8_WT ADORA3 AA3R_HUMAN reviewed Homo sapiens (Human) \n\n Classification Length \\\n80 Membrane receptor->Family A G protein-coupled ... 326 \n81 Membrane receptor->Family A G protein-coupled ... 412 \n47 Membrane receptor->Family A G protein-coupled ... 332 \n82 Membrane receptor->Family A G protein-coupled ... 318 \n\n Sequence \n80 MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC... \n81 MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV... \n47 MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL... \n82 MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT... ", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
target_idHGNC_symbolUniProtIDStatusOrganismClassificationLengthSequence
80P30542_WTADORA1AA1R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...326MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...
81P29274_WTADORA2AAA2AR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...412MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV...
47P29275_WTADORA2BAA2BR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...332MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL...
82P0DMS8_WTADORA3AA3R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...318MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT...
\n
" + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "protein_data = read_protein_set(version=PAPYRUS_VERSION)\n", + "sequences = pd.concat(protein_data[protein_data.target_id.str.startswith(x)] for x in adenosine_receptors.values())\n", + "sequences" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "In order to align the sequences with Clustal Omega, we first need to write them into a FASTA file." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 94, + "outputs": [], + "source": [ + "records = []\n", + "for index, row in sequences.reset_index(drop=True).iterrows():\n", + " records.append(SeqRecord(seq=Seq(row.Sequence),\n", + " id=str(index),\n", + " name=row.target_id,\n", + " description=' '.join([row.UniProtID, row.Organism, row.Classification])))\n", + "sequences_path = os.path.join(DATA, 'sequences.fasta')\n", + "_ = SeqIO_write(records, sequences_path, 'fasta')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Now, we use Clustal Omega to align the sequences and write out the alignment file." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 97, + "outputs": [], + "source": [ + "alignment_file = os.path.join(DATA, 'aligned_sequences.fasta')\n", + "clustalomega_cline = ClustalOmegaCommandline(cmd=clustalo_exe, infile=sequences_path, outfile=alignment_file, auto=True)\n", + "_ = clustalomega_cline()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Finally we parse the aligned sequences." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 101, "outputs": [], "source": [ - "# Download Clustal W and unzip\n", - "%%capture\n", - "!wget -O \"clustalo-1.2.4\" \"http://www.clustal.org/omega/clustal-omega-1.2.2-win64.zip\"\n", - "!chmod +x ./clustalo-1.2.4" + "aligned_sequences = [str(seq.seq) for seq in SeqIO_parse(alignment_file, 'fasta')]" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "And we visualize the MSA." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 112, + "outputs": [ + { + "data": { + "text/plain": "╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA1R_HU… \u001B[1;36m 1\u001B[0m 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╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA1R_HU…     1  ---MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFCFIVSLAVADVAVGALVIPLAILINIGPQTYFHTCLMVACPVLI  │\n│ 1 AA2AR_H…     1  ------MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLV  │\n│ 2 AA2BR_H…     1  -----MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVAVGLFAIPFAITISLGFCTDFYGCLFLACFVLV  │\n│ 3 AA3R_HU…     1  MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTTTFYFIVSLALADIAVGVLVMPLAIVVSLGITIHFYSCLFMTCLLLI  │\n│                                                                                                                 │\n│ 0 AA1R_HU…    90  LTQSSILALLAIAVDRYLRVKIPLRYKMVVTPRRAAVAIAGCWILSFVVGLTPMFGWNNLSAVER----AW---AANGSMGEPVIKCEFEKV  │\n│ 1 AA2AR_H…    87  LTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWN-------NCGQPKEGKNHSQGCGEGQVACLFEDV  │\n│ 2 AA2BR_H…    88  LTQSSIFSLLAVAVDRYLAICVPLRYKSLVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCC---LVKCLFENV  │\n│ 3 AA3R_HU…    93  FTHASIMSLLAIAVDRYLRVKLTVRYKRVTTHRRIWLALGLCWLVSFLVGLTPMFGWNMKLTSEYHR-------------NVTFLSCQFVSV  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   175  ISMEYMVYFNFFVWVLPPLLLMVLIYLEVFYLIRKQLNKKVSAS--SGDPQKYYGKELKIAKSLALILFLFALSWLPLHILNCITLFCPSC-  │\n│ 1 AA2AR_H…   172  VPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDCS  │\n│ 2 AA2BR_H…   177  VPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTEL----MDHSRTTLQREIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQG  │\n│ 3 AA3R_HU…   172  MRMDYMVYFSFLTWIFIPLVVMCAIYLDIFYIIRNKLSLNLSN---SKETGAFYGREFKTAKSLFLVLFLFALSWLPLSIINCIIYFNG---  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   264  -HKPSILTYIAIFLTHGNSAMNPIVYAFRIQKFRVTFLKIWNDHFRCQPAPPIDEDLPEERPDD----------------------------  │\n│ 1 AA2AR_H…   264  -HAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHP  │\n│ 2 AA2BR_H…   265  KNKPKWAMNMAILLSHANSVVNPIVYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQ----------AGVQPALGVGL--------------  │\n│ 3 AA3R_HU…   258  -EVPQLVLYMGILLSHANSMMNPIVYAYKIKKFKETYLLILKACVVCHPSDSLDTSIEKNSE------------------------------  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   327  ----------------------------------------------------------                                    │\n│ 1 AA2AR_H…   355  ERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS                                    │\n│ 2 AA2BR_H…   333  ----------------------------------------------------------                                    │\n│ 3 AA3R_HU…   319  ----------------------------------------------------------                                    │\n╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n
\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Read MSA\n", + "msa = Bio.AlignIO.read(alignment_file, \"fasta\")\n", + "viewer = RichAlignment(\n", + " names=[record.description for record in msa],\n", + " sequences=[str(record.seq) for record in msa],\n", + ")\n", + "# Visualize MSA\n", + "panel = rich.panel.Panel(viewer, title=\"Multiple sequence alignment\")\n", + "rich.print(panel)" ], "metadata": { "collapsed": false, @@ -716,7 +991,7 @@ { "cell_type": "markdown", "source": [ - "Explain" + "Now that our protein sequences are aligned, we can calculate protein descriptors" ], "metadata": { "collapsed": false, From 161a3bfa6d11c97f87bcd178579eec9591aeac16 Mon Sep 17 00:00:00 2001 From: gorostiolam Date: Thu, 13 Oct 2022 14:25:42 +0200 Subject: [PATCH 08/62] Add libraries for installing ClustalO and visualizing MSA --- .../T029_compound_activity_proteochemometrics/T029_env.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/T029_env.yml b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/T029_env.yml index 3ba92f04..35f49962 100644 --- a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/T029_env.yml +++ b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/T029_env.yml @@ -19,4 +19,6 @@ dependencies: - mordred - pip: - prodec + - rich-msa + - wget - https://github.com/OlivierBeq/Papyrus-scripts/tarball/master From a460d8e4bd372df43b3a9f4643fe4e06235453f9 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Mon, 17 Oct 2022 16:02:02 +0200 Subject: [PATCH 09/62] Update tutorial number to 032 --- .../README.md | 0 .../T032_env.yml} | 0 .../data/README.md | 0 .../images/PCM_model_text-01.png | Bin .../images/README.md | 0 .../images/papyrus_workflow.png | Bin .../images/splitting_methods.png | Bin 7 files changed, 0 insertions(+), 0 deletions(-) rename teachopencadd/talktorials/{T029_compound_activity_proteochemometrics => T032_compound_activity_proteochemometrics}/README.md (100%) rename teachopencadd/talktorials/{T029_compound_activity_proteochemometrics/T029_env.yml => T032_compound_activity_proteochemometrics/T032_env.yml} (100%) rename teachopencadd/talktorials/{T029_compound_activity_proteochemometrics => T032_compound_activity_proteochemometrics}/data/README.md (100%) rename teachopencadd/talktorials/{T029_compound_activity_proteochemometrics => T032_compound_activity_proteochemometrics}/images/PCM_model_text-01.png (100%) rename teachopencadd/talktorials/{T029_compound_activity_proteochemometrics => T032_compound_activity_proteochemometrics}/images/README.md (100%) rename teachopencadd/talktorials/{T029_compound_activity_proteochemometrics => T032_compound_activity_proteochemometrics}/images/papyrus_workflow.png (100%) rename teachopencadd/talktorials/{T029_compound_activity_proteochemometrics => T032_compound_activity_proteochemometrics}/images/splitting_methods.png (100%) diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md similarity index 100% rename from teachopencadd/talktorials/T029_compound_activity_proteochemometrics/README.md rename to teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/T029_env.yml b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/T032_env.yml similarity index 100% rename from teachopencadd/talktorials/T029_compound_activity_proteochemometrics/T029_env.yml rename to teachopencadd/talktorials/T032_compound_activity_proteochemometrics/T032_env.yml diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/data/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/README.md similarity index 100% rename from teachopencadd/talktorials/T029_compound_activity_proteochemometrics/data/README.md rename to teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/README.md diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/PCM_model_text-01.png b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/PCM_model_text-01.png similarity index 100% rename from teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/PCM_model_text-01.png rename to teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/PCM_model_text-01.png diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/README.md similarity index 100% rename from teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/README.md rename to teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/README.md diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/papyrus_workflow.png b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/papyrus_workflow.png similarity index 100% rename from teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/papyrus_workflow.png rename to teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/papyrus_workflow.png diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/splitting_methods.png b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/splitting_methods.png similarity index 100% rename from teachopencadd/talktorials/T029_compound_activity_proteochemometrics/images/splitting_methods.png rename to teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/splitting_methods.png From 45a60d1b762ef6e0eeb6d6e1a51520e4c32b9cb4 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Mon, 17 Oct 2022 16:02:36 +0200 Subject: [PATCH 10/62] Add PCM and QSAR training and validation and update tutorial number to 032 --- .../talktorial.ipynb | 1215 ----------- .../talktorial.ipynb | 1870 +++++++++++++++++ 2 files changed, 1870 insertions(+), 1215 deletions(-) delete mode 100644 teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb create mode 100644 teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb diff --git a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb deleted file mode 100644 index 8b5a6245..00000000 --- a/teachopencadd/talktorials/T029_compound_activity_proteochemometrics/talktorial.ipynb +++ /dev/null @@ -1,1215 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Thank you for contributing to TeachOpenCADD!\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Set up your PR: Please check out our issue on how to set up a PR for new talktorials, including standard checks and TODOs.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# T029 · Compound activity: Proteochemometrics\n", - "\n", - "**Note:** This talktorial is a part of TeachOpenCADD, a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects.\n", - "\n", - "Authors:\n", - "\n", - "- Marina Gorostiola González, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands)\n", - "- Olivier J.M. Béquignon, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands)\n", - "- Willem Jespers, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Cross-referencing talktorials: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: Talktorial T000.\n", - "\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Aim of this talktorial\n", - "\n", - "While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be solved leveraging similarities and differences between proteins. In this talktorial, we use Proteochemometrics modelling (PCM) to enrich our activity models with protein data to predict the activity of novel compounds against the four adenosine receptor isoforms (A1, A2A, A2B, A3)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Contents in *Theory*\n", - "\n", - "* Data preparation\n", - " * Papyrus dataset\n", - " * Molecule encoding: molecular descriptors\n", - " * Protein encoding: protein descriptors\n", - "\n", - "* Proteochemometrics (PCM)\n", - " * Machine learning principles: regression\n", - " * Splitting methods\n", - " * Regression evaluation metrics\n", - " * ML algorithm: Random Forest\n", - " * Applications of PCM in drug discovery" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Contents in *Practical*\n", - "\n", - "* Downlaod Papyrus dataset\n", - "* Data preparation\n", - " * Filter activity data for targets of interest\n", - " * Align target sequences\n", - " * Calculate protein descriptors\n", - " * Calculate compound descriptors\n", - "* Proteochemometrics modelling\n", - " * Helper functions\n", - " * Model training and validation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### References\n", - "\n", - "* Papyrus scripts [github](https://github.com/OlivierBeq/Papyrus-scripts)\n", - "* Papyrus dataset preprint: [ChemRvix (2021)](https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0)\n", - "* Molecular descriptors (Modred): [J. Cheminf., 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y)\n", - "* Protein descriptors (ProDEC) [github](https://github.com/OlivierBeq/ProDEC)\n", - "* Regression metrics [(Scikit learn)](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics)\n", - "* XGBoost [Documentation](https://xgboost.readthedocs.io/en/stable/index.html)\n", - "* Proteochemometrics review: [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Theory" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To successfully apply PCM modelling, we need a large dataset of molecule-protein pairs with known bioactivity values, a way of describing molecules and proteins, and a ML algorithm to train a model. Then, we can make predictions for new molecule-protein pairs.\n", - "\n", - "NOTE: PCM modelling is an extension of ligand-based modelling with ML described in Talktorial T007. Explore that talktorial to know more about the basic principle of activity prediction using ML." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "*Figure 1:*\n", - "Proteochemometrics modelling construction from protein and molecular descriptors for which protein-compound pair bioactivity data is known.\n", - "Figure made by Marina Gorostiola González." - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Data preparation" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Papyrus dataset" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "The Papyrus dataset is a highly curated compilation of bioactivity data intended for modelling in drug discovery. Apart from the bioactivity data contained in the ChEMBL database (see also Talktorial T001), the Papyrus dataset contains binary data for classification tasks from the ExCAPE-DB, and bioactivity data from a number of kinase-specific papers (Figure 1).\n", - "\n", - "The bioactivity data aggregated is standardized, repaired, and normalised to form the Papyrus dataset, which is updated with every new version of ChEMBL released. The Papyrus dataset contains \"high quality\" data associated to pChEMBL values for regression tasks and \"low quality\" data associated to an active/inactive label for classification tasks (read more about ML applications in Talktorial T007)." - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "\n", - "*Figure 2:*\n", - "Papyrus dataset generation scheme.\n", - "Figure taken from: [ChemRvix (2021)](https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0)." - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Molecule encoding: molecular descriptors" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "For the ML models used in PCM, molecules need to be converted into a list of features. In Talktorial T007, molecular fingerprints were introduced. In this talktorial, we will use a different type of representation that is often used on its own or in combination with fingerprints: molecular descriptors.\n", - "\n", - "Molecular descriptors are the \"final result of a logical and mathematical procedure, which transforms chemical information encoded within a symbolic representation of a molecule into a useful number or the result of some standardized experiment\" ([J. Cheminf., 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y)). These descriptors can be, for example, molecular weight, ring count, Eccentric Connectivity Index (calculated from the 2D structure), or Geometrical Index (calculated from the 3D structure).\n", - "\n", - "In this talktorial, we use Modred as a software engine to calculate molecular descriptors. Modred calculates more than 1,800 molecular descriptors, including the ones implemented in RDKit, including an automatic preprocessing step that is common for all descriptors calculated. For simplicity, here we calculate only 4 types of descriptors from the vast list of possibilities from Modred, excluding their 3D representation. These include:\n", - "\n", - "* ABC Index: 2 descriptors that represent the atom-bond connectivity index or the Graovac-Ghorbani atom-bond connectivity index (see Modred ABCIndex [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.ABCIndex.html))\n", - "* Acid-Base: 2 descriptors that count acidic and basic groups, respectively (see Modred AcidBase [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.AcidBase.html?highlight=acidbase))\n", - "* Atom count: 16 descriptors that represent a count of different types of atoms (see Modred AtomCount [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.AtomCount.html?highlight=atomcount))\n", - "* Balaban J index: 1 descriptor (included in RDkit), which represents a topological index (see Modred BalabanJ [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.BalabanJ.html?highlight=balaban#module-mordred.BalabanJ))" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Protein encoding: protein descriptors" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "As done for molecules, the proteins of interest need to be converted to a list of features or protein descriptors. Protein descriptors used in PCM applications are commonly based on the protein sequence and represent physicochemical characteristics of the amino acids that make up the sequence (e.g. Z-scales). Other protein descriptors represent topological (e.g. St-scales) or electrostatic properties (e.g. MS-WHIM) of the protein sequence. Moreover, if structural information is available, protein descriptors can be derived from the 3D structure of the protein (e.g. sPairs) or the ligand-protein interaction in 3D (e.g. interaction fingerprints). Finally, with the widespread use of deep learning, protein embeddings can be obtained after parsing the protein sequence through the network (e.g. UniRep, AlphaFold embeddings).\n", - "\n", - "For protein descriptors based on the protein sequence, an aspect to take into account is that for ML the length of the protein descriptor needs to be the same. However, most proteins do not have the same sequence length. To solve this issue, there are two main approaches:\n", - "* Multiple sequence alignment: when the whole protein wants to be incorporated to the model, a multiple sequence alignment can be performed. The final descriptor will have as many features as the number of features per amino acid multiplied by the number of aligned positions. To take into account, gaps in the alignment will receive zeroes in the descriptor.\n", - "* Binding pocket selection: To avoid unnecessary features, a binding pocket of the same length can be selected for each protein. Normally, the binding pocket selection is preceded by a multiple sequence alignment and driven by known structural or mutagenesis data.\n", - "\n", - "Other options are available when proteins are not of the same family or do not share a binding pocket (see [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub))\n", - "\n", - "In this talktorial, we will focus on physicochemical protein descriptors, mainly Z-scales ([J. Med. Chem, 30 (1987)](https://pubs.acs.org/doi/10.1021/jm00390a003)). The Z-scales descriptor assigns three pre-determined values (Z1, Z2, Z3) to each amino acid in the sequence. The Z1, Z2, and Z3 values are the first principal components of a principal component analysis (PCA) including 29 different physicochemical variables to characterize the amino acids.\n", - "Since we are calculating activity for four proteins with very high sequence similarity (Adenosine receptors A1, A2A, A2B, and A3), we will use multiple sequence alignment prior to calculation of the Z-scales. To calculate Z-scales we will use ProDEC, an open source resource that compiles a large number of protein descriptors." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "### Proteochemometrics (PCM)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Machine learning principles: regression" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Proteochemometrics (PCM) consists in the modelling via supervised machine learning algorithms of a biological endpoint (e.g. compound activity) based on a series of features derived from chemical compounds and target proteins. The ML principles for proteochemometric modelling are equivalent to those explained in Talktorial T007. However, in this talktorial we will explore a supervised ML application other than classification, this is regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values." - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "##### Splitting methods" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Similarly to classification tasks, in supervised ML regression applications the model is first fitted to a training set and subsequently the predictive performance is evaluated on a test set. Therefore, the original dataset needs to be split between training and test sets. The split needs to ensure that the fitting process has enough data, and that the test set is representative. Normally, the distribution between train and test set is 80/20 or 70/30. Depending on the applicability domain, the split can be done in multiple ways. In PCM modelling, some of the most common splitting methods are:\n", - "* Random split: This method is not particularly relevant in drug discovery applications as it does not refflect the reality of a drug discovery campaign and it will most likely lead to data leaks between the training and test set. This is, very similar data will be found in both sets, which will lead to an overestimation of the rpedictive performance of the model. This type of split is commonly used, however, as a baseline and point of reference for other splitting methods, or as a starting point for quick model comparisons.\n", - "* Leave one target out (LOTO) split: To evaluate the ability of the model to extrapolate to targets not previously seen, one of the targets can be completely moved to the test set.\n", - "* Leave one compound cluster out (LOCCO) split: This method evaluates the ability of the model to extrapolate to compounds with properties not previously seen by the model. Clustering can be done based on different molecular characteristics, such as physicochemical properties, scaffold, or approval status, for example (see Talktorial T005 to learn more about clustering). One (or several) clusters can then be left out for testing. This methods prevents data leaking in terms of chemistry between training and test sets.\n", - "* Temporal split: This method was developed in order to account for the usual timeline of drug discovery campaigns, where chemical series are populated sequentially over time. In this approach, the molecules included in the training set are those released until certain date and the rest (most novel) are included in the test set.\n", - "* Stratified split per target: This method can be applied to any of the splitting methods described above (except LOTO), and aims to include data of all targets in both the training and test set, so that additional target-compound interactions can be extracted by the model." - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "\n", - "*Figure 3:*\n", - "Overview of splitting methods, including target-stratified random and temporal splits and leave one target out approach.\n", - "Figure made by Marina Gorostiola González." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "##### Regression evaluation metrics" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "\n", - "To evaluate the predictive performance of a regression model, there are several metrics that in simple terms measure the differences between the true target values and the predictions made by the model. These metrics can be used in cross-validation on the training set (see Talktorial T007) or in the test set. The most commonly used metrics include:\n", - "\n", - "* Coefficient of determination ($R^{2}$ score): Represents the portion of variance of the target variable that has been explained by the independent variables (features) in the model. $R^{2}$ score varies between 1.0 (best score) and minus infinite, where 0.0 represents a model that always predicts the average target variable. As the variance is dataset dependent, it might not be meaningful metric to compare between datasets. When dealing with linear regression, and model fitting and evaluation are performed on a single dataset, $R^{2}$ is equivalent to the square of the Pearson correlation coefficient, described below, and can be noted as $r^{2}$.\n", - "* Pearson's correlation coefficient (Pearson's $r$): Is a measure of the linear correlation between the true and predicted values of the target variable. It is calculated as the covariance of the two variables divided by the product of their standard deviation. Pearson's $r$ can vary between 1.0 (a perfect positive correlation) and -1.0 (a perfect negative correlation), where 1.0 would represent a perfect prediction.\n", - "* Mean squared error (MSE): Measures the average of the squares of the difference between the predicted and the true values. It varies between 1.0 (worst) and 0.0 (best).\n", - "* Root mean square error (RMSE): Also called root mean square deviation (RMSD), it is the square root of the MSE and represents the standard deviation of the prediction errors respect to the line of best fit. RMSE is a measure of accuracy and it cannot be applied to compare between datasets, as it is scale-dependent. It varies between 1.0 (worst) and 0.0 (best).\n", - "\n", - "To learn more about evaluation metrics, you can consult scikit learn's regression metrics [Docs](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics).\n", - "\n", - "\n", - "\n" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "##### ML algorithm: Random Forest" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Different ML algorithms can be used to train PCM models. Some of them include support vector machines (SVM), random forest (RF), and neural networks (NN), that were described in Talktorial T007. In this talktorial, we will use RF. RF is a decision tree-based algorithm, more in detail a bagging ensemble method. This means that there are multiple decision trees trained independently with subsets of features and data and the final prediction is made from a consensus between the independent predictions.\n", - "RF models have been used extensively in PCM applications due to their efficiency in large datasets and resistance to overfitting with more features." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Applications of PCM in drug discovery" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "The possibility to predict bioactivity for multiple targets in one model with PCM is very interesting in drug discovery. Some applications of this technique are listed below and help answer the following questions in drug discovery:\n", - "\n", - "* Poly-pharmacology: Is it possible to target several proteins of interest simultaneously with one single drug?\n", - "* Off-target prediction: What other proteins do these compounds target apart from the intended therapeutic target? Are maybe these off-targets responsible for side effects?\n", - "* Selectivity prediction: Do certain novel compounds target one protein isoform while avoiding others (off-targets) known to cause adverse effects?\n", - "\n", - "To know more about applications of PCM in drug discovery, have a look at this review [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub)." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Practical" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the practical section of this talktorial we will create a PCM regression model for the four adenosine receptors (A1, A2A, A2B, A3) with data from the Papyrus dataset and molecular and protein descriptors as features." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "import numpy as np\n", - "import pandas as pd\n", - "import re\n", - "import wget\n", - "import zipfile\n", - "\n", - "from papyrus_scripts.download import download_papyrus\n", - "from papyrus_scripts.reader import read_papyrus, read_protein_set\n", - "from papyrus_scripts.preprocess import *\n", - "from papyrus_scripts.utils.IO import get_num_rows_in_file\n", - "\n", - "from Bio.Seq import Seq\n", - "from Bio.SeqIO import SeqRecord, write as SeqIO_write, parse as SeqIO_parse\n", - "from Bio.Align.Applications import ClustalOmegaCommandline\n", - "import Bio.AlignIO\n", - "import rich\n", - "from rich_msa import RichAlignment\n", - "\n", - "from prodec import ProteinDescriptors, Transform\n", - "from rdkit import Chem\n", - "from mordred import Calculator, descriptors\n", - "\n", - "from sklearn.preprocessing import RobustScaler\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.ensemble import RandomForestRegressor\n", - "from sklearn.metrics import r2_score,mean_absolute_error\n", - "from scipy.stats import pearsonr\n", - "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# Set path to this notebook\n", - "HERE = Path(_dh[-1])\n", - "DATA = HERE / \"data\"" - ] - }, - { - "cell_type": "markdown", - "source": [ - "### Download Payrus dataset" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "By default, the download_papyrus function retrieves bioactivity, target and other information for the latest version of the Papyrus dataset. The data retrieved consists of the highest quality continuous bioactivity data (Papyrus++) without stereochemistry annotated." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 18, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Latest version: 05.5\n", - "Number of files to be donwloaded: 6\n", - "Total size: 118MB\n" - ] - }, - { - "data": { - "text/plain": "Donwloading version 05.5: 0%| | 0.00/118M [00:00pchembl_value_Mean variable). \n", - "\n", - "|Receptor|Uniprot accession|\n", - "|---|---|\n", - "|A1|P30542|\n", - "|A2A|P29274|\n", - "|A2B|P29275|\n", - "|A3|P0DMS8|" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 76, - "outputs": [], - "source": [ - "def filter_explore_activity_data(papyrus_version, targets):\n", - " \"\"\"\n", - " Filter Papyrus dataset for targets of interest and explore the statistics of the resulting dataset\n", - "\n", - " Parameters\n", - " ----------\n", - " papyrus_version : str\n", - " Version of the Papyrus dataset to read\n", - " targets : dict\n", - " Dictionary with target labels as keys and Uniprot accession codes as values\n", - "\n", - " Returns\n", - " -------\n", - " pandas.DataFrame\n", - " Filtered bioactivity dataset for input targets\n", - " \"\"\"\n", - " # Read downloaded Papyrus dataset in chunks, as it does not fit in memory\n", - " CHUNKSIZE = 100000\n", - " data = read_papyrus(version=papyrus_version, chunksize=CHUNKSIZE, source_path=DATA)\n", - "\n", - " # Create filter for targets of interest\n", - " target_accession_list = targets.values()\n", - " filter = keep_accession(data, target_accession_list)\n", - "\n", - " # Iterate through chunks and apply the filter defined\n", - " filtered_data = consume_chunks(filter, total=round(get_num_rows_in_file('bioactivities', False) / CHUNKSIZE))\n", - " # Add column named 'Target' for easier data visualization\n", - " filtered_data['Target'] = filtered_data['accession'].apply(lambda x: [i for i in targets.keys() if targets[i]==x][0])\n", - "\n", - " # Print number of bioactivity datapoints per target\n", - " for target,accession in zip(targets.keys(), targets.values()):\n", - " print('Number of bioactivity datapoints')\n", - " print(f'{target} ({accession}) : {filtered_data[filtered_data[\"accession\"]==accession].shape[0]}')\n", - "\n", - " # Plot distribution of activity values (pchembl_value_Mean) per target\n", - " g = sns.displot(filtered_data, x='pchembl_value_Mean', hue='Target', element='step', hue_order=targets.keys())\n", - "\n", - " return filtered_data" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 81, - "outputs": [ - { - "data": { - "text/plain": " 0%| | 0/12 [00:00", - "image/png": 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- }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Define the set of receptors of interest with a label and their Uniprot accession\n", - "adenosine_receptors = {'A1': 'P30542',\n", - " 'A2A': 'P29274',\n", - " 'A2B': 'P29275',\n", - " 'A3': 'P0DMS8'}\n", - "\n", - "# Filter the Papyrus bioactivity dataset and plot the distribution of activity values for the targets of interest\n", - "ar_data = filter_explore_activity_data(PAPYRUS_VERSION, adenosine_receptors)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Align target sequences" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "In order to ensure protein descriptors are of the same length, we first need to align the target sequences. We do this by using the software ClustalW. The first step is to install the software. Choose one of the following download options depending on your system (Windows, Unix, or MacOS)." - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "##### Clustal Omega downlaod for Windows" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 96, - "outputs": [], - "source": [ - "# Define url of download file and paths to download\n", - "clustalo_url = \"http://www.clustal.org/omega/clustal-omega-1.2.2-win64.zip\"\n", - "clustalo_zip_path = str(Path(DATA, 'clustalo.zip'))\n", - "clustalo_path = str(Path(DATA, 'clustalo'))\n", - "# Download .zip installation file\n", - "wget.download(clustalo_url, out=clustalo_zip_path)\n", - "# Unzip installation file\n", - "with zipfile.ZipFile(clustalo_zip_path, 'r') as zip_ref:\n", - " zip_ref.extractall(clustalo_path)\n", - "# Define path to executable\n", - "clustalo_exe = os.path.join(clustalo_path, 'clustal-omega-1.2.2-win64', 'clustalo.exe')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "##### clustalo download for Unix" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "# Define url of download file and paths to download\n", - "clustalo_url = \"http://www.clustal.org/omega/clustalo-1.2.4-Ubuntu-x86_64\"\n", - "clustalo_path = str(Path(DATA, 'clustalo'))\n", - "# Download executable file\n", - "wget.download(clustalo_url, out=clustalo_path)\n", - "# Give file executable permission\n", - "os.chmod(clustalo_path, 0755)\n", - "# Define path to executable\n", - "clustalo_exe = clustalo_path" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "##### clustalo download for MacOS" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "# Define url of download file and paths to download\n", - "clustalo_url = \"http://www.clustal.org/omega/clustal-omega-1.2.3-macosx\"\n", - "clustalo_path = str(Path(DATA, 'clustalo'))\n", - "# Download executable file\n", - "wget.download(clustalo_url, out=clustalo_path)\n", - "# Give file executable permission\n", - "os.chmod(clustalo_path, 0755)\n", - "# Define path to executable\n", - "clustalo_exe = clustalo_path" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Next, we obtain the protein sequences from the target files in Papyrus." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 91, - "outputs": [ - { - "data": { - "text/plain": " target_id HGNC_symbol UniProtID Status Organism \\\n80 P30542_WT ADORA1 AA1R_HUMAN reviewed Homo sapiens (Human) \n81 P29274_WT ADORA2A AA2AR_HUMAN reviewed Homo sapiens (Human) \n47 P29275_WT ADORA2B AA2BR_HUMAN reviewed Homo sapiens (Human) \n82 P0DMS8_WT ADORA3 AA3R_HUMAN reviewed Homo sapiens (Human) \n\n Classification Length \\\n80 Membrane receptor->Family A G protein-coupled ... 326 \n81 Membrane receptor->Family A G protein-coupled ... 412 \n47 Membrane receptor->Family A G protein-coupled ... 332 \n82 Membrane receptor->Family A G protein-coupled ... 318 \n\n Sequence \n80 MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC... \n81 MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV... \n47 MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL... \n82 MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT... ", - "text/html": "
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target_idHGNC_symbolUniProtIDStatusOrganismClassificationLengthSequence
80P30542_WTADORA1AA1R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...326MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...
81P29274_WTADORA2AAA2AR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...412MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV...
47P29275_WTADORA2BAA2BR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...332MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL...
82P0DMS8_WTADORA3AA3R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...318MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT...
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" - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "protein_data = read_protein_set(version=PAPYRUS_VERSION)\n", - "sequences = pd.concat(protein_data[protein_data.target_id.str.startswith(x)] for x in adenosine_receptors.values())\n", - "sequences" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "In order to align the sequences with Clustal Omega, we first need to write them into a FASTA file." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 94, - "outputs": [], - "source": [ - "records = []\n", - "for index, row in sequences.reset_index(drop=True).iterrows():\n", - " records.append(SeqRecord(seq=Seq(row.Sequence),\n", - " id=str(index),\n", - " name=row.target_id,\n", - " description=' '.join([row.UniProtID, row.Organism, row.Classification])))\n", - "sequences_path = os.path.join(DATA, 'sequences.fasta')\n", - "_ = SeqIO_write(records, sequences_path, 'fasta')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Now, we use Clustal Omega to align the sequences and write out the alignment file." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 97, - "outputs": [], - "source": [ - "alignment_file = os.path.join(DATA, 'aligned_sequences.fasta')\n", - "clustalomega_cline = ClustalOmegaCommandline(cmd=clustalo_exe, infile=sequences_path, outfile=alignment_file, auto=True)\n", - "_ = clustalomega_cline()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Finally we parse the aligned sequences." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 101, - "outputs": [], - "source": [ - "aligned_sequences = [str(seq.seq) for seq in SeqIO_parse(alignment_file, 'fasta')]" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "And we visualize the MSA." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 112, - "outputs": [ - { - "data": { - "text/plain": "╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA1R_HU… \u001B[1;36m 1\u001B[0m 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╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA1R_HU…     1  ---MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFCFIVSLAVADVAVGALVIPLAILINIGPQTYFHTCLMVACPVLI  │\n│ 1 AA2AR_H…     1  ------MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLV  │\n│ 2 AA2BR_H…     1  -----MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVAVGLFAIPFAITISLGFCTDFYGCLFLACFVLV  │\n│ 3 AA3R_HU…     1  MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTTTFYFIVSLALADIAVGVLVMPLAIVVSLGITIHFYSCLFMTCLLLI  │\n│                                                                                                                 │\n│ 0 AA1R_HU…    90  LTQSSILALLAIAVDRYLRVKIPLRYKMVVTPRRAAVAIAGCWILSFVVGLTPMFGWNNLSAVER----AW---AANGSMGEPVIKCEFEKV  │\n│ 1 AA2AR_H…    87  LTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWN-------NCGQPKEGKNHSQGCGEGQVACLFEDV  │\n│ 2 AA2BR_H…    88  LTQSSIFSLLAVAVDRYLAICVPLRYKSLVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCC---LVKCLFENV  │\n│ 3 AA3R_HU…    93  FTHASIMSLLAIAVDRYLRVKLTVRYKRVTTHRRIWLALGLCWLVSFLVGLTPMFGWNMKLTSEYHR-------------NVTFLSCQFVSV  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   175  ISMEYMVYFNFFVWVLPPLLLMVLIYLEVFYLIRKQLNKKVSAS--SGDPQKYYGKELKIAKSLALILFLFALSWLPLHILNCITLFCPSC-  │\n│ 1 AA2AR_H…   172  VPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDCS  │\n│ 2 AA2BR_H…   177  VPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTEL----MDHSRTTLQREIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQG  │\n│ 3 AA3R_HU…   172  MRMDYMVYFSFLTWIFIPLVVMCAIYLDIFYIIRNKLSLNLSN---SKETGAFYGREFKTAKSLFLVLFLFALSWLPLSIINCIIYFNG---  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   264  -HKPSILTYIAIFLTHGNSAMNPIVYAFRIQKFRVTFLKIWNDHFRCQPAPPIDEDLPEERPDD----------------------------  │\n│ 1 AA2AR_H…   264  -HAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHP  │\n│ 2 AA2BR_H…   265  KNKPKWAMNMAILLSHANSVVNPIVYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQ----------AGVQPALGVGL--------------  │\n│ 3 AA3R_HU…   258  -EVPQLVLYMGILLSHANSMMNPIVYAYKIKKFKETYLLILKACVVCHPSDSLDTSIEKNSE------------------------------  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   327  ----------------------------------------------------------                                    │\n│ 1 AA2AR_H…   355  ERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS                                    │\n│ 2 AA2BR_H…   333  ----------------------------------------------------------                                    │\n│ 3 AA3R_HU…   319  ----------------------------------------------------------                                    │\n╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n
\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Read MSA\n", - "msa = Bio.AlignIO.read(alignment_file, \"fasta\")\n", - "viewer = RichAlignment(\n", - " names=[record.description for record in msa],\n", - " sequences=[str(record.seq) for record in msa],\n", - ")\n", - "# Visualize MSA\n", - "panel = rich.panel.Panel(viewer, title=\"Multiple sequence alignment\")\n", - "rich.print(panel)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Calculate protein descriptors" - ] - }, - { - "cell_type": "markdown", - "source": [ - "Now that our protein sequences are aligned, we can calculate protein descriptors" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Calculate compound descriptors" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Explain" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "### Proteochemometrics modelling" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Explain" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Helper functions" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Explain" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "#### Model training and validation" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "markdown", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "Explain" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Discussion\n", - "\n", - "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges.\n", - "Compared to purely ligand-based compound activity prediction models, PCM modelling has certain advantages and limitations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quiz\n", - "\n", - "1. What types of features are needed for PCM?\n", - "2. How many types of training/test set splitting methods commonly used in PCM modelling do you know?\n", - "3. Which applications do you know of PCM in drug discovery?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Useful checks at the end: \n", - " \n", - "
    \n", - "
  • Clear output and rerun your complete notebook. Does it finish without errors?
    • \n", - "
  • Check if your talktorial's runtime is as excepted. If not, try to find out which step(s) take unexpectedly long.
    • \n", - "
  • Flag code cells with # NBVAL_CHECK_OUTPUT that have deterministic output and should be tested within our Continuous Integration (CI) framework.
    • \n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.4" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} \ No newline at end of file diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb new file mode 100644 index 00000000..09077376 --- /dev/null +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -0,0 +1,1870 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Thank you for contributing to TeachOpenCADD!\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Set up your PR: Please check out our issue on how to set up a PR for new talktorials, including standard checks and TODOs.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# T032 · Compound activity: Proteochemometrics\n", + "\n", + "**Note:** This talktorial is a part of TeachOpenCADD, a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects.\n", + "\n", + "Authors:\n", + "\n", + "- Marina Gorostiola González, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands)\n", + "- Olivier J.M. Béquignon, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands)\n", + "- Willem Jespers, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Cross-referencing talktorials: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: Talktorial T000.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Aim of this talktorial\n", + "\n", + "While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be solved leveraging similarities and differences between proteins. In this talktorial, we use Proteochemometrics modelling (PCM) to enrich our activity models with protein data to predict the activity of novel compounds against the four adenosine receptor isoforms (A1, A2A, A2B, A3)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Contents in *Theory*\n", + "\n", + "* Data preparation\n", + " * Papyrus dataset\n", + " * Molecule encoding: molecular descriptors\n", + " * Protein encoding: protein descriptors\n", + "\n", + "* Proteochemometrics (PCM)\n", + " * Machine learning principles: regression\n", + " * Splitting methods\n", + " * Regression evaluation metrics\n", + " * ML algorithm: Random Forest\n", + " * Applications of PCM in drug discovery" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Contents in *Practical*\n", + "\n", + "* Downlaod Papyrus dataset\n", + "* Data preparation\n", + " * Filter activity data for targets of interest\n", + " * Align target sequences\n", + " * Calculate protein descriptors\n", + " * Calculate compound descriptors\n", + "* Proteochemometrics modelling\n", + " * Helper functions\n", + " * Model training and validation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### References\n", + "\n", + "* Papyrus scripts [github](https://github.com/OlivierBeq/Papyrus-scripts)\n", + "* Papyrus dataset preprint: [ChemRvix (2021)](https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0)\n", + "* Molecular descriptors (Modred): [J. Cheminf., 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y)\n", + "* Protein descriptors (ProDEC) [github](https://github.com/OlivierBeq/ProDEC)\n", + "* Regression metrics [(Scikit learn)](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics)\n", + "* XGBoost [Documentation](https://xgboost.readthedocs.io/en/stable/index.html)\n", + "* Proteochemometrics review: [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Theory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To successfully apply PCM modelling, we need a large dataset of molecule-protein pairs with known bioactivity values, a way of describing molecules and proteins, and a ML algorithm to train a model. Then, we can make predictions for new molecule-protein pairs.\n", + "\n", + "NOTE: PCM modelling is an extension of ligand-based modelling with ML described in Talktorial T007. Explore that talktorial to know more about the basic principle of activity prediction using ML." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "*Figure 1:*\n", + "Proteochemometrics modelling construction from protein and molecular descriptors for which protein-compound pair bioactivity data is known.\n", + "Figure made by Marina Gorostiola González." + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Data preparation" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Papyrus dataset" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "The Papyrus dataset is a highly curated compilation of bioactivity data intended for modelling in drug discovery. Apart from the bioactivity data contained in the ChEMBL database (see also Talktorial T001), the Papyrus dataset contains binary data for classification tasks from the ExCAPE-DB, and bioactivity data from a number of kinase-specific papers (Figure 1).\n", + "\n", + "The bioactivity data aggregated is standardized, repaired, and normalised to form the Papyrus dataset, which is updated with every new version of ChEMBL released. The Papyrus dataset contains \"high quality\" data associated to pChEMBL values for regression tasks and \"low quality\" data associated to an active/inactive label for classification tasks (read more about ML applications in Talktorial T007)." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "\n", + "*Figure 2:*\n", + "Papyrus dataset generation scheme.\n", + "Figure taken from: [ChemRvix (2021)](https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0)." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Molecule encoding: molecular descriptors" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "For the ML models used in PCM, molecules need to be converted into a list of features. In Talktorial T007, molecular fingerprints were introduced. In this talktorial, we will use a different type of representation that is often used on its own or in combination with fingerprints: molecular descriptors.\n", + "\n", + "Molecular descriptors are the \"final result of a logical and mathematical procedure, which transforms chemical information encoded within a symbolic representation of a molecule into a useful number or the result of some standardized experiment\" ([J. Cheminf., 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y)). These descriptors can be, for example, molecular weight, ring count, Eccentric Connectivity Index (calculated from the 2D structure), or Geometrical Index (calculated from the 3D structure).\n", + "\n", + "In this talktorial, we use Modred as a software engine to calculate molecular descriptors. Modred calculates more than 1,800 molecular descriptors, including the ones implemented in RDKit, including an automatic preprocessing step that is common for all descriptors calculated. For simplicity, here we calculate only 4 types of descriptors from the vast list of possibilities from Modred, excluding their 3D representation. These include:\n", + "\n", + "* ABC Index: 2 descriptors that represent the atom-bond connectivity index or the Graovac-Ghorbani atom-bond connectivity index (see Modred ABCIndex [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.ABCIndex.html))\n", + "* Acid-Base: 2 descriptors that count acidic and basic groups, respectively (see Modred AcidBase [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.AcidBase.html?highlight=acidbase))\n", + "* Atom count: 16 descriptors that represent a count of different types of atoms (see Modred AtomCount [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.AtomCount.html?highlight=atomcount))\n", + "* Balaban J index: 1 descriptor (included in RDkit), which represents a topological index (see Modred BalabanJ [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.BalabanJ.html?highlight=balaban#module-mordred.BalabanJ))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Protein encoding: protein descriptors" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "As done for molecules, the proteins of interest need to be converted to a list of features or protein descriptors. Protein descriptors used in PCM applications are commonly based on the protein sequence and represent physicochemical characteristics of the amino acids that make up the sequence (e.g. Z-scales). Other protein descriptors represent topological (e.g. St-scales) or electrostatic properties (e.g. MS-WHIM) of the protein sequence. Moreover, if structural information is available, protein descriptors can be derived from the 3D structure of the protein (e.g. sPairs) or the ligand-protein interaction in 3D (e.g. interaction fingerprints). Finally, with the widespread use of deep learning, protein embeddings can be obtained after parsing the protein sequence through the network (e.g. UniRep, AlphaFold embeddings).\n", + "\n", + "For protein descriptors based on the protein sequence, an aspect to take into account is that for ML the length of the protein descriptor needs to be the same. However, most proteins do not have the same sequence length. To solve this issue, there are two main approaches:\n", + "* Multiple sequence alignment: when the whole protein wants to be incorporated to the model, a multiple sequence alignment can be performed. The final descriptor will have as many features as the number of features per amino acid multiplied by the number of aligned positions. To take into account, gaps in the alignment will receive zeroes in the descriptor.\n", + "* Binding pocket selection: To avoid unnecessary features, a binding pocket of the same length can be selected for each protein. Normally, the binding pocket selection is preceded by a multiple sequence alignment and driven by known structural or mutagenesis data.\n", + "\n", + "Other options are available when proteins are not of the same family or do not share a binding pocket (see [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub))\n", + "\n", + "In this talktorial, we will focus on physicochemical protein descriptors, mainly Z-scales ([J. Med. Chem, 30 (1987)](https://pubs.acs.org/doi/10.1021/jm00390a003)). The Z-scales descriptor assigns three pre-determined values (Z1, Z2, Z3) to each amino acid in the sequence. The Z1, Z2, and Z3 values are the first principal components of a principal component analysis (PCA) including 29 different physicochemical variables to characterize the amino acids.\n", + "Since we are calculating activity for four proteins with very high sequence similarity (Adenosine receptors A1, A2A, A2B, and A3), we will use multiple sequence alignment prior to calculation of the Z-scales. To calculate Z-scales we will use ProDEC, an open source resource that compiles a large number of protein descriptors." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Proteochemometrics (PCM)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Machine learning principles: regression" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Proteochemometrics (PCM) consists in the modelling via supervised machine learning algorithms of a biological endpoint (e.g. compound activity) based on a series of features derived from chemical compounds and target proteins. The ML principles for proteochemometric modelling are equivalent to those explained in Talktorial T007. However, in this talktorial we will explore a supervised ML application other than classification, this is regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Splitting methods" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Similarly to classification tasks, in supervised ML regression applications the model is first fitted to a training set and subsequently the predictive performance is evaluated on a test set. Therefore, the original dataset needs to be split between training and test sets. The split needs to ensure that the fitting process has enough data, and that the test set is representative. Normally, the distribution between train and test set is 80/20 or 70/30. Depending on the applicability domain, the split can be done in multiple ways. In PCM modelling, some of the most common splitting methods are:\n", + "* Random split: This method is not particularly relevant in drug discovery applications as it does not refflect the reality of a drug discovery campaign and it will most likely lead to data leaks between the training and test set. This is, very similar data will be found in both sets, which will lead to an overestimation of the rpedictive performance of the model. This type of split is commonly used, however, as a baseline and point of reference for other splitting methods, or as a starting point for quick model comparisons.\n", + "* Leave one target out (LOTO) split: To evaluate the ability of the model to extrapolate to targets not previously seen, one of the targets can be completely moved to the test set.\n", + "* Leave one compound cluster out (LOCCO) split: This method evaluates the ability of the model to extrapolate to compounds with properties not previously seen by the model. Clustering can be done based on different molecular characteristics, such as physicochemical properties, scaffold, or approval status, for example (see Talktorial T005 to learn more about clustering). One (or several) clusters can then be left out for testing. This methods prevents data leaking in terms of chemistry between training and test sets.\n", + "* Temporal split: This method was developed in order to account for the usual timeline of drug discovery campaigns, where chemical series are populated sequentially over time. In this approach, the molecules included in the training set are those released until certain date and the rest (most novel) are included in the test set.\n", + "* Stratified split per target: This method can be applied to any of the splitting methods described above (except LOTO), and aims to include data of all targets in both the training and test set, so that additional target-compound interactions can be extracted by the model." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "\n", + "*Figure 3:*\n", + "Overview of splitting methods, including target-stratified random and temporal splits and leave one target out approach.\n", + "Figure made by Marina Gorostiola González." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Regression evaluation metrics" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "To evaluate the predictive performance of a regression model, there are several metrics that in simple terms measure the differences between the true target values and the predictions made by the model. These metrics can be used in cross-validation on the training set (see Talktorial T007) or in the test set. The most commonly used metrics include:\n", + "\n", + "* Coefficient of determination ($R^{2}$ score): Represents the portion of variance of the target variable that has been explained by the independent variables (features) in the model. $R^{2}$ score varies between 1.0 (best score) and minus infinite, where 0.0 represents a model that always predicts the average target variable. As the variance is dataset dependent, it might not be meaningful metric to compare between datasets. When dealing with linear regression, and model fitting and evaluation are performed on a single dataset, $R^{2}$ is equivalent to the square of the Pearson correlation coefficient, described below, and can be noted as $r^{2}$.\n", + "* Pearson's correlation coefficient (Pearson's $r$): Is a measure of the linear correlation between the true and predicted values of the target variable. It is calculated as the covariance of the two variables divided by the product of their standard deviation. Pearson's $r$ can vary between 1.0 (a perfect positive correlation) and -1.0 (a perfect negative correlation), where 1.0 would represent a perfect prediction.\n", + "* Mean squared error (MSE): Measures the average of the squares of the difference between the predicted and the true values. It varies between 1.0 (worst) and 0.0 (best).\n", + "* Root mean square error (RMSE): Also called root mean square deviation (RMSD), it is the square root of the MSE and represents the standard deviation of the prediction errors respect to the line of best fit. RMSE is a measure of accuracy and it cannot be applied to compare between datasets, as it is scale-dependent. It varies between 1.0 (worst) and 0.0 (best).\n", + "\n", + "To learn more about evaluation metrics, you can consult scikit learn's regression metrics [Docs](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics).\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "##### ML algorithm: Random Forest" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Different ML algorithms can be used to train PCM models. Some of them include support vector machines (SVM), random forest (RF), and neural networks (NN), that were described in Talktorial T007. In this talktorial, we will use RF. RF is a decision tree-based algorithm, more in detail a bagging ensemble method. This means that there are multiple decision trees trained independently with subsets of features and data and the final prediction is made from a consensus between the independent predictions.\n", + "RF models have been used extensively in PCM applications due to their efficiency in large datasets and resistance to overfitting with more features." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Applications of PCM in drug discovery" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "The possibility to predict bioactivity for multiple targets in one model with PCM is very interesting in drug discovery. Some applications of this technique are listed below and help answer the following questions in drug discovery:\n", + "\n", + "* Poly-pharmacology: Is it possible to target several proteins of interest simultaneously with one single drug?\n", + "* Off-target prediction: What other proteins do these compounds target apart from the intended therapeutic target? Are maybe these off-targets responsible for side effects?\n", + "* Selectivity prediction: Do certain novel compounds target one protein isoform while avoiding others (off-targets) known to cause adverse effects?\n", + "\n", + "To know more about applications of PCM in drug discovery, have a look at this review [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub)." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Practical" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the practical section of this talktorial we will create a PCM regression model for the four adenosine receptors (A1, A2A, A2B, A3) with data from the Papyrus dataset and molecular and protein descriptors as features." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os.path\n", + "from pathlib import Path\n", + "import numpy as np\n", + "import pandas as pd\n", + "import re\n", + "import wget\n", + "import zipfile\n", + "import random\n", + "\n", + "from papyrus_scripts.download import download_papyrus\n", + "from papyrus_scripts.reader import read_papyrus, read_protein_set\n", + "from papyrus_scripts.preprocess import *\n", + "from papyrus_scripts.utils.IO import get_num_rows_in_file\n", + "\n", + "from Bio.Seq import Seq\n", + "from Bio.SeqIO import SeqRecord, write as SeqIO_write, parse as SeqIO_parse\n", + "from Bio.Align.Applications import ClustalOmegaCommandline\n", + "import Bio.AlignIO\n", + "import rich\n", + "from rich_msa import RichAlignment\n", + "\n", + "from prodec import ProteinDescriptors, Transform\n", + "from rdkit import Chem\n", + "from mordred import Calculator, descriptors\n", + "\n", + "from sklearn.preprocessing import RobustScaler\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.metrics import r2_score,mean_absolute_error\n", + "from scipy.stats import pearsonr\n", + "\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Set path to this notebook\n", + "HERE = Path(_dh[-1])\n", + "DATA = HERE / \"data\"" + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Download Payrus dataset" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "By default, the download_papyrus function retrieves bioactivity, target and other information for the latest version of the Papyrus dataset. The data retrieved consists of the highest quality continuous bioactivity data (Papyrus++) without stereochemistry annotated." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "# Let's specify the Papyrus version for the rest of the work\n", + "PAPYRUS_VERSION = '05.5'" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of files to be donwloaded: 6\n", + "Total size: 118MB\n" + ] + }, + { + "data": { + "text/plain": "Donwloading version 05.5: 0%| | 0.00/118M [00:00pchembl_value_Mean variable). \n", + "\n", + "|Receptor|Uniprot accession|\n", + "|---|---|\n", + "|A1|P30542|\n", + "|A2A|P29274|\n", + "|A2B|P29275|\n", + "|A3|P0DMS8|" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [], + "source": [ + "def filter_explore_activity_data(papyrus_version, targets):\n", + " \"\"\"\n", + " Filter Papyrus dataset for targets of interest and explore the statistics of the resulting dataset\n", + "\n", + " Parameters\n", + " ----------\n", + " papyrus_version : str\n", + " Version of the Papyrus dataset to read\n", + " targets : dict\n", + " Dictionary with target labels as keys and Uniprot accession codes as values\n", + "\n", + " Returns\n", + " -------\n", + " pandas.DataFrame\n", + " Filtered bioactivity dataset for input targets\n", + " \"\"\"\n", + " # Read downloaded Papyrus dataset in chunks, as it does not fit in memory\n", + " CHUNKSIZE = 100000\n", + " data = read_papyrus(version=papyrus_version, chunksize=CHUNKSIZE, source_path=DATA)\n", + "\n", + " # Create filter for targets of interest\n", + " target_accession_list = targets.values()\n", + " filter = keep_accession(data, target_accession_list)\n", + "\n", + " # Iterate through chunks and apply the filter defined\n", + " filtered_data = consume_chunks(filter, total=round(get_num_rows_in_file('bioactivities', False) / CHUNKSIZE))\n", + " # Add column named 'Target' for easier data visualization\n", + " filtered_data['Target'] = filtered_data['accession'].apply(lambda x: [i for i in targets.keys() if targets[i]==x][0])\n", + "\n", + " # Print number of bioactivity datapoints per target\n", + " for target,accession in zip(targets.keys(), targets.values()):\n", + " print('Number of bioactivity datapoints')\n", + " print(f'{target} ({accession}) : {filtered_data[filtered_data[\"accession\"]==accession].shape[0]}')\n", + "\n", + " # Plot distribution of activity values (pchembl_value_Mean) per target\n", + " g = sns.displot(filtered_data, x='pchembl_value_Mean', hue='Target', element='step', hue_order=targets.keys())\n", + "\n", + " return filtered_data" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": " 0%| | 0/12 [00:00", + "image/png": 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+ }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Define the set of receptors of interest with a label and their Uniprot accession\n", + "adenosine_receptors = {'A1': 'P30542',\n", + " 'A2A': 'P29274',\n", + " 'A2B': 'P29275',\n", + " 'A3': 'P0DMS8'}\n", + "\n", + "# Filter the Papyrus bioactivity dataset and plot the distribution of activity values for the targets of interest\n", + "ar_data = filter_explore_activity_data(PAPYRUS_VERSION, adenosine_receptors)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "For PCM modelling, we keep from our bioactivity dataset three variables:\n", + "* Bioactivity (pchembl_value_mean), which is our target variable to predict\n", + "* Target IDs (target_id), to link the protein descriptors\n", + "* Compound IDs (SMILES), to link the compound descriptors" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 57, + "outputs": [ + { + "data": { + "text/plain": " SMILES accession \\\n222 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n223 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n383 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n462 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n464 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n... ... ... \n1238255 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... P30542 \n1238605 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P0DMS8 \n1238606 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29274 \n1238607 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29275 \n1238608 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P30542 \n\n pchembl_value_Mean \n222 8.6800 \n223 6.6800 \n383 4.8200 \n462 7.1515 \n464 5.6500 \n... ... \n1238255 5.1000 \n1238605 7.6100 \n1238606 7.3500 \n1238607 5.1500 \n1238608 7.3400 \n\n[12719 rows x 3 columns]", + "text/html": "
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SMILESaccessionpchembl_value_Mean
222Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.6800
223Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.6800
383Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.8200
462O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.1515
464O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.6500
............
1238255Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc...P305425.1000
1238605CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P0DMS87.6100
1238606CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292747.3500
1238607CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292755.1500
1238608CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P305427.3400
\n

12719 rows × 3 columns

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" + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ar_dataset = ar_data[['SMILES', 'accession', 'pchembl_value_Mean']]\n", + "ar_dataset" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Align target sequences" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "In order to ensure protein descriptors are of the same length, we first need to align the target sequences. We do this by using the software ClustalO. The first step is to install the software. Choose one of the following download options depending on your system (Windows, Unix, or MacOS)." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Clustal Omega downlaod for Windows" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [], + "source": [ + "# Define url of download file and paths to download\n", + "clustalo_url = \"http://www.clustal.org/omega/clustal-omega-1.2.2-win64.zip\"\n", + "clustalo_zip_path = str(Path(DATA, 'clustalo.zip'))\n", + "clustalo_path = str(Path(DATA, 'clustalo'))\n", + "\n", + "if not os.path.isfile(clustalo_zip_path):\n", + " # Download .zip installation file\n", + " wget.download(clustalo_url, out=clustalo_zip_path)\n", + " # Unzip installation file\n", + " with zipfile.ZipFile(clustalo_zip_path, 'r') as zip_ref:\n", + " zip_ref.extractall(clustalo_path)\n", + "# Define path to executable\n", + "clustalo_exe = os.path.join(clustalo_path, 'clustal-omega-1.2.2-win64', 'clustalo.exe')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "##### clustalo download for Unix" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Define url of download file and paths to download\n", + "clustalo_url = \"http://www.clustal.org/omega/clustalo-1.2.4-Ubuntu-x86_64\"\n", + "clustalo_path = str(Path(DATA, 'clustalo'))\n", + "if not os.path.isfile(clustalo_path):\n", + " # Download executable file\n", + " wget.download(clustalo_url, out=clustalo_path)\n", + " # Give file executable permission\n", + " os.chmod(clustalo_path, 0755)\n", + "# Define path to executable\n", + "clustalo_exe = clustalo_path" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "##### clustalo download for MacOS" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Define url of download file and paths to download\n", + "clustalo_url = \"http://www.clustal.org/omega/clustal-omega-1.2.3-macosx\"\n", + "clustalo_path = str(Path(DATA, 'clustalo'))\n", + "if not os.path.isfile(clustalo_path):\n", + " # Download executable file\n", + " wget.download(clustalo_url, out=clustalo_path)\n", + " # Give file executable permission\n", + " os.chmod(clustalo_path, 0755)\n", + "# Define path to executable\n", + "clustalo_exe = clustalo_path" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Next, we obtain the protein sequences from the target files in Papyrus." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 65, + "outputs": [ + { + "data": { + "text/plain": " target_id HGNC_symbol UniProtID Status Organism \\\n80 P30542_WT ADORA1 AA1R_HUMAN reviewed Homo sapiens (Human) \n81 P29274_WT ADORA2A AA2AR_HUMAN reviewed Homo sapiens (Human) \n47 P29275_WT ADORA2B AA2BR_HUMAN reviewed Homo sapiens (Human) \n82 P0DMS8_WT ADORA3 AA3R_HUMAN reviewed Homo sapiens (Human) \n\n Classification Length \\\n80 Membrane receptor->Family A G protein-coupled ... 326 \n81 Membrane receptor->Family A G protein-coupled ... 412 \n47 Membrane receptor->Family A G protein-coupled ... 332 \n82 Membrane receptor->Family A G protein-coupled ... 318 \n\n Sequence accession \n80 MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC... P30542 \n81 MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV... P29274 \n47 MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL... P29275 \n82 MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT... P0DMS8 ", + "text/html": "
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target_idHGNC_symbolUniProtIDStatusOrganismClassificationLengthSequenceaccession
80P30542_WTADORA1AA1R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...326MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...P30542
81P29274_WTADORA2AAA2AR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...412MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV...P29274
47P29275_WTADORA2BAA2BR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...332MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL...P29275
82P0DMS8_WTADORA3AA3R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...318MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT...P0DMS8
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" + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "protein_data = read_protein_set(version=PAPYRUS_VERSION)\n", + "protein_data['accession'] = protein_data['target_id'].apply(lambda x: x.split('_')[0])\n", + "targets = pd.concat(protein_data[protein_data.target_id.str.startswith(x)] for x in adenosine_receptors.values())\n", + "targets" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "In order to align the sequences with Clustal Omega, we first need to write them into a FASTA file." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "records = []\n", + "for index, row in targets.reset_index(drop=True).iterrows():\n", + " records.append(SeqRecord(seq=Seq(row.Sequence),\n", + " id=str(index),\n", + " name=row.accession,\n", + " description=' '.join([row.UniProtID, row.Organism, row.Classification])))\n", + "sequences_path = os.path.join(DATA, 'sequences.fasta')\n", + "_ = SeqIO_write(records, sequences_path, 'fasta')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Now, we use Clustal Omega to align the sequences and write out the alignment file." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 30, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Alignment file already exists\n" + ] + } + ], + "source": [ + "alignment_file = os.path.join(DATA, 'aligned_sequences.fasta')\n", + "if not os.path.isfile(alignment_file):\n", + " clustalomega_cline = ClustalOmegaCommandline(cmd=clustalo_exe, infile=sequences_path, outfile=alignment_file, auto=True)\n", + " _ = clustalomega_cline()\n", + "else:\n", + " print('Alignment file already exists')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Finally we parse the aligned sequences." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 31, + "outputs": [], + "source": [ + "aligned_sequences = [str(seq.seq) for seq in SeqIO_parse(alignment_file, 'fasta')]" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "And we visualize the MSA." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 61, + "outputs": [ + { + "data": { + "text/plain": "╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 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╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA1R_HU…     1  ---MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFCFIVSLAVADVAVGALVIPLAILINIGPQTYFHTCLMVACPVLI  │\n│ 1 AA2AR_H…     1  ------MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLV  │\n│ 2 AA2BR_H…     1  -----MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVAVGLFAIPFAITISLGFCTDFYGCLFLACFVLV  │\n│ 3 AA3R_HU…     1  MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTTTFYFIVSLALADIAVGVLVMPLAIVVSLGITIHFYSCLFMTCLLLI  │\n│                                                                                                                 │\n│ 0 AA1R_HU…    90  LTQSSILALLAIAVDRYLRVKIPLRYKMVVTPRRAAVAIAGCWILSFVVGLTPMFGWNNLSAVER----AW---AANGSMGEPVIKCEFEKV  │\n│ 1 AA2AR_H…    87  LTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWN-------NCGQPKEGKNHSQGCGEGQVACLFEDV  │\n│ 2 AA2BR_H…    88  LTQSSIFSLLAVAVDRYLAICVPLRYKSLVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCC---LVKCLFENV  │\n│ 3 AA3R_HU…    93  FTHASIMSLLAIAVDRYLRVKLTVRYKRVTTHRRIWLALGLCWLVSFLVGLTPMFGWNMKLTSEYHR-------------NVTFLSCQFVSV  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   175  ISMEYMVYFNFFVWVLPPLLLMVLIYLEVFYLIRKQLNKKVSAS--SGDPQKYYGKELKIAKSLALILFLFALSWLPLHILNCITLFCPSC-  │\n│ 1 AA2AR_H…   172  VPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDCS  │\n│ 2 AA2BR_H…   177  VPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTEL----MDHSRTTLQREIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQG  │\n│ 3 AA3R_HU…   172  MRMDYMVYFSFLTWIFIPLVVMCAIYLDIFYIIRNKLSLNLSN---SKETGAFYGREFKTAKSLFLVLFLFALSWLPLSIINCIIYFNG---  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   264  -HKPSILTYIAIFLTHGNSAMNPIVYAFRIQKFRVTFLKIWNDHFRCQPAPPIDEDLPEERPDD----------------------------  │\n│ 1 AA2AR_H…   264  -HAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHP  │\n│ 2 AA2BR_H…   265  KNKPKWAMNMAILLSHANSVVNPIVYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQ----------AGVQPALGVGL--------------  │\n│ 3 AA3R_HU…   258  -EVPQLVLYMGILLSHANSMMNPIVYAYKIKKFKETYLLILKACVVCHPSDSLDTSIEKNSE------------------------------  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   327  ----------------------------------------------------------                                    │\n│ 1 AA2AR_H…   355  ERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS                                    │\n│ 2 AA2BR_H…   333  ----------------------------------------------------------                                    │\n│ 3 AA3R_HU…   319  ----------------------------------------------------------                                    │\n╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n
\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Read MSA\n", + "msa = Bio.AlignIO.read(alignment_file, \"fasta\")\n", + "viewer = RichAlignment(\n", + " names=[record.description for record in msa],\n", + " sequences=[str(record.seq) for record in msa],\n", + ")\n", + "# Visualize MSA\n", + "panel = rich.panel.Panel(viewer, title=\"Multiple sequence alignment\")\n", + "rich.print(panel)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate protein descriptors" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Now that our protein sequences are aligned, we can calculate protein descriptors using ProDEC. For that, let's parse all default descriptors available. Since we are focusing on Z-scale descriptors in this talktorial, we can explore the details about this descriptor, and in case we want some extra information we can look at the article where it is first described." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 15, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Available ProDEC descriptors: ['ADFQ', 'BLOSUM', 'c-scales', 'CBFQ', 'CDFQ', 'Combined descriptors', 'Contact energies', 'CUFQ', 'DPPS', 'E-scale', 'FASGAI', 'G-scales', 'GH-scale', 'GRID tscore', 'HESH', 'HPI', 'HSEHPCSV', 'Independent descriptors', 'ISA-ECI', 'Kidera', 'MS-WHIM', 'P-scale', 'PhysChem', 'ProtFP hash', 'ProtFP PCA', 'PSM', 'QCP', 'Raychaudhury', 'Sneath', 'SSIA AM1', 'SSIA DFT', 'SSIA HF', 'SSIA PM3', 'STscale', 'SVEEVA', 'SVGER', 'SVHEHS', 'SVMW', 'SVRDF', 'SVRG', 'SVWG', 'SZOTT', 'Tscale', 'V-scale', 'VARIMAX', 'VHSE', 'VHSEH', 'VSGETAWAY', 'VSTPV', 'VSTV', 'VSW', 'VTSA', 'Zscale binary', 'Zscale Hellberg', 'Zscale Jonsson', 'Zscale Sandberg', 'Zscale Sjöström', 'Zscale van Westen']\n" + ] + } + ], + "source": [ + "# Parse ProDEC descriptors\n", + "desc_factory = ProteinDescriptors()\n", + "# Print available descriptors\n", + "print('Available ProDEC descriptors: ', desc_factory.available_descriptors)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "More about Z-Scales:\n" + ] + }, + { + "data": { + "text/plain": "{'Authors': 'Hellberg, Sjöström, Skagerberg, Wold',\n 'Year': 1987,\n 'Journal': 'Journal of Medicinal Chemistry',\n 'DOI': '10.1021/jm00390a003',\n 'PMID': None,\n 'Patent': None}" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Print information about Z-scales\n", + "print('More about Z-Scales:')\n", + "desc_factory.get_descriptor('Zscale Hellberg').Info" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 62, + "outputs": [], + "source": [ + "def calculate_protein_descriptor(targets, aligned_sequences, protein_descriptor):\n", + " \"\"\"\n", + " Calculate protein descriptor of choice for aligned proteins of interest\n", + "\n", + " Parameters\n", + " ----------\n", + " targets : pandas.Dataframe\n", + " Pandas dataframe with information about targets of interest\n", + " aligned_sequences : list\n", + " List of aligned sequences read from fasta file produced with Clustal Omega\n", + " protein_descriptor : str\n", + " Protein descriptor label as described in ProDEC\n", + "\n", + " Returns\n", + " -------\n", + " pandas.DataFrame\n", + " Dataset with accession and features for the protein descriptor of interest for the targets in the input\n", + " \"\"\"\n", + " # Get protein descriptor from ProDEC\n", + " prodec_descriptor = desc_factory.get_descriptor(protein_descriptor)\n", + "\n", + " # Calculate descriptor features for aligned sequences of interest\n", + " protein_features = prodec_descriptor.pandas_get(aligned_sequences)\n", + "\n", + " # Insert protein labels in the obtained features\n", + " protein_features.insert(0, 'accession', targets.accession.reset_index(drop=True))\n", + "\n", + " return protein_features" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 66, + "outputs": [ + { + "data": { + "text/plain": " 0%| | 0/4 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
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\n" + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "protein_features = calculate_protein_descriptor(targets, aligned_sequences, 'Zscale Hellberg')\n", + "protein_features" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Calculate compound descriptors" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "The final step to prepare our dataset for PCM is to calculate compound descriptors. For this, we first have to convert the molecules in our dataset to chemical entities from their text representation (SMILES). Afterwards, we use Mordred to calculate 2D molecular descriptors." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 19, + "outputs": [], + "source": [ + "def calculate_molecular_descriptors(bioactivity_dataset, moldred_descriptors):\n", + " \"\"\"\n", + " Calculate compound molecular descriptors of choice for unique molecules in the bioactivity dataset\n", + "\n", + " Parameters\n", + " ----------\n", + " bioactivity_dataset : pandas.Dataframe\n", + " Pandas dataframe with bioactivity dataset for PCM\n", + " moldred_descriptors : list\n", + " List of descriptors from Moldred to calculate\n", + " Use ['all'] for calculate all possible descriptors\n", + "\n", + " Returns\n", + " -------\n", + " pandas.DataFrame\n", + " Dataset with SMILES and features for the compound descriptors of interest for molecules in the bioactivity dataset\n", + " \"\"\"\n", + " # Extract unique molecules from the bioactivity dataset\n", + " molecules = [Chem.MolFromSmiles(x) for x in bioactivity_dataset.SMILES.unique()]\n", + "\n", + " # Use Moldred to calculate molecular descriptors of interest\n", + " if moldred_descriptors == ['all']:\n", + " molecular_descriptor = Calculator(descriptors, ignore_3D=True).pandas(molecules, pynb=False)\n", + " else:\n", + " moldred_list = [descriptors.__dict__[descriptor] for descriptor in moldred_descriptors]\n", + " molecular_descriptor = Calculator(moldred_list, ignore_3D=True).pandas(molecules,ipynb=False)\n", + "\n", + " # Clean descriptors by: renaming duplicated columns; replacing values bigger than 2,147,483,647 by 0;\n", + " # rounding values to 3 decimals; converting to minimal memory footprint; inserting SMILES in first column\n", + " mordred_descs_names = {\n", + " str(x): re.sub(r'(.*F?)A(H?Ring)$', r'\\1aliph\\2', re.sub(r'(.*F?)a(H?Ring)$', r'\\1arom\\2', str(x))) for x in\n", + " Calculator(descriptors, ignore_3D=True).descriptors}\n", + "\n", + " molecular_descriptor = pd.DataFrame(molecular_descriptor.fill_missing(np.NAN).rename(mordred_descs_names)).\\\n", + " astype(np.float32).replace([np.inf, -np.inf], np.NAN).round(3)\n", + " molecular_descriptor.fillna(value=0, inplace=True)\n", + " molecular_descriptor = molecular_descriptor.convert_dtypes()\n", + " molecular_descriptor.insert(0, 'SMILES', bioactivity_dataset.SMILES.unique())\n", + "\n", + " return molecular_descriptor" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 34, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 6898/6898 [00:10<00:00, 676.78it/s]\n" + ] + }, + { + "data": { + "text/plain": " SMILES ABC ABCGG \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... 21.041 17.684 \n1 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... 20.701 15.635 \n2 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 23.23 17.455999 \n3 CCNC(=O)C1OC(n2cnc3c2ncnc3Nc2ccc(OCC(=O)Nc3ccc... 31.336 22.212999 \n4 NC(=NC(=O)Cn1c(O)c2CCCCc2c1O)Nc1nc2c(cccc2)s1 21.408001 17.066 \n... ... ... ... \n6893 CC(C)(C)C1(O)CCN2CC3c4c(cccc4)CCc4cccc(c43)C2C1 22.177999 16.375 \n6894 CNC(=O)C12CC1C(n1cnc3c(NC(C)C)nc(C#Cc4ccc(Cl)s... 26.351 22.591999 \n6895 CCCOc1ccc(C=Cc2cc3c(c(=O)n(C)c(=O)n3C)n2C)cc1 20.021999 15.893 \n6896 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... 23.736 18.441999 \n6897 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 18.511 15.661 \n\n nAcid nBase nAtom nHeavyAtom nSpiro nBridgehead nHetero ... nN \\\n0 0 1 51 27 0 0 8 ... 6 \n1 0 0 42 26 0 0 8 ... 4 \n2 0 0 43 29 0 0 8 ... 6 \n3 0 0 66 40 0 0 14 ... 7 \n4 0 3 46 27 0 0 9 ... 5 \n... ... ... ... ... ... ... ... ... .. \n6893 0 1 58 27 0 0 2 ... 1 \n6894 0 0 56 33 0 0 11 ... 6 \n6895 0 0 49 26 0 0 6 ... 3 \n6896 0 0 52 30 0 0 9 ... 4 \n6897 0 0 43 24 0 0 8 ... 5 \n\n nO nS nP nF nCl nBr nI nX BalabanJ \n0 2 0 0 0 0 0 0 0 1.631 \n1 3 1 0 0 0 0 0 0 1.307 \n2 2 0 0 0 0 0 0 0 1.328 \n3 6 0 0 0 1 0 0 1 1.043 \n4 3 1 0 0 0 0 0 0 1.234 \n... .. .. .. .. ... ... .. .. ... \n6893 1 0 0 0 0 0 0 0 1.46 \n6894 3 1 0 0 1 0 0 1 1.303 \n6895 3 0 0 0 0 0 0 0 1.479 \n6896 3 0 0 2 0 0 0 2 1.318 \n6897 3 0 0 0 0 0 0 0 1.68 \n\n[6898 rows x 23 columns]", + "text/html": "
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" + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "molecular_features = calculate_molecular_descriptors(ar_dataset, ['ABCIndex', 'AcidBase', 'AtomCount', 'BalabanJ'])\n", + "molecular_features" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "### Proteochemometrics modelling" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "When our dataset is complete with all the descriptors for proteins and compounds, we can start with the modelling part. Here, we will use a Random Forest (RF) ML regression model to predict the bioactivity of our compound-target pairs.\n", + "\n", + "We will try two methods to split our dataset between training and test set:\n", + "* Random split\n", + "* Leave one target out split\n", + "\n", + "Additionally, we will compare our PCM model to four independent models trained only on compound data, and finally we will comment on the results.\n", + "\n", + "Ultimately, we want a model that can predict compound activity data towards a target of interest for compound-target pairs that it has never seen before. By combining several targets in one model, we expect the model to be able to learn the similarities and differences between targets and use the additional data to make better predictions.\n", + "\n", + "We start by defining a few functions that will help us split the data (split_train_test) and train and validate a PCM regression model (train_validate_model). The validation will be done on the test set and the performance will be assessed using regression metrics such as $R^{2}$ and $MSE$. Finally, we will define a function (benchmark_models_performance) to plot the correlation between true and predicted values in order to compare the performance of different models, either trained on different splits, or a PCM model to individual models trained only on compound descriptors." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Helper functions" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Function to split the data using one of the methods described in theory." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 99, + "outputs": [], + "source": [ + "def split_train_test(pcm_dataset, test_size, split_method, loto_accession='None'):\n", + " \"\"\"\n", + " Split a dataset for PCM modelling in train and test set based on the split method of choice\n", + "\n", + " Parameters\n", + " ----------\n", + " bioactivity_dataset : pandas.Dataframe\n", + " Pandas dataframe with bioactivity dataset for PCM\n", + " test_size : float\n", + " Ratio of the data to include in the test set\n", + " split_method : str\n", + " 'random' for random split\n", + " 'loto' for leave one target out split\n", + "\n", + " Returns\n", + " -------\n", + " train: pandas.DataFrame\n", + " Training dataset\n", + " test : pandas.DataFrame\n", + " Testing dataset\n", + " \"\"\"\n", + " # Random split\n", + " if split_method == 'random':\n", + " train, test = train_test_split(pcm_dataset, test_size=test_size, random_state=1234)\n", + "\n", + " # Leave one target out\n", + " elif split_method == 'loto':\n", + " if loto_accession != None:\n", + " # Leave out defined accession\n", + " test_target = loto_accession\n", + " else:\n", + " # Make a random selection of the target to leave out\n", + " targets = pcm_dataset.accession.unique()\n", + " test_target = random.choice(targets)\n", + " print(f'Target left out for testing is {test_target}')\n", + "\n", + " # Move data associated to target to test set and rest to training set\n", + " train = pcm_dataset[pcm_dataset['accession'] != test_target]\n", + " test = pcm_dataset[pcm_dataset['accession'] == test_target]\n", + "\n", + " # Print statistics of training and test sets\n", + " print(f'Training set has {train.shape[0]} datapoints')\n", + " print(f'Test set has {test.shape[0]} datapoints ({round(100*test.shape[0]/pcm_dataset.shape[0], 3)} %)')\n", + "\n", + " return train,test" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 101, + "outputs": [], + "source": [ + "def train_validate_pcm_model(targets_dict, train,test):\n", + " \"\"\"\n", + " Train PCM RF regression model and validate on test set, calculating performance metrics\n", + "\n", + " Parameters\n", + " ----------\n", + " train: pandas.DataFrame\n", + " Training dataset\n", + " test : pandas.DataFrame\n", + " Testing dataset\n", + "\n", + " Returns\n", + " -------\n", + " dict:\n", + " r2_score and MSE on test set\n", + " \"\"\"\n", + " # Store keys of training and test sets\n", + " train_keys = train[['SMILES', 'accession']]\n", + " test_keys = test[['SMILES', 'accession', 'pchembl_value_Mean']].reset_index(drop=True)\n", + "\n", + " # Remove identifiers\n", + " train = train.drop(columns=['SMILES', 'accession'])\n", + " test = test.drop(columns=['SMILES', 'accession'])\n", + "\n", + " # Set model parameter for random forest\n", + " param = {\n", + " \"n_estimators\": 100, # number of trees to grows\n", + " \"criterion\": \"squared_error\", # cost function to be optimized for a split\n", + " }\n", + " model_RF = RandomForestRegressor(**param)\n", + "\n", + " # Fit model\n", + " model_RF.fit(train.iloc[:, 1:], train.iloc[:, 0])\n", + "\n", + " # Make predictions on test set\n", + " predictions = model_RF.predict(test.iloc[:, 1:])\n", + "\n", + " # Calculate model performance with regression metrics\n", + " model_performance = {}\n", + " model_performance['pearson_r'] = pearsonr(test.iloc[:, 0], predictions)\n", + " model_performance['r2_score'] = r2_score(test.iloc[:, 0], predictions)\n", + " model_performance['mse'] = mean_absolute_error(test.iloc[:, 0], predictions)\n", + "\n", + " # Add column named 'Target' for easier data visualization\n", + " test_keys['Target'] = test_keys['accession'].apply(lambda x: [i for i in targets_dict.keys() if targets_dict[i]==x][0])\n", + "\n", + " # Calculate model performance per target\n", + " test_keys['prediction'] = pd.Series(predictions)\n", + "\n", + " for target,accession in targets_dict.items():\n", + " # Define true values and predictions per target\n", + " true_target = test_keys[test_keys['accession'] == accession]['pchembl_value_Mean']\n", + " prediction_target = test_keys[test_keys['accession'] == accession]['prediction']\n", + "\n", + " try:\n", + " # Calculate r2 score\n", + " r2_target = r2_score(true_target, prediction_target)\n", + "\n", + " # Plot correlation between true and predicted values\n", + " ax = sns.scatterplot(y=true_target, x=prediction_target, label=(f'{target} r2= {r2_target:.2f}'))\n", + " _ = sns.lineplot(x=(0, 14), y=(0, 14))\n", + " _ = ax.set_xlim((0,14))\n", + " _ = ax.set_ylim((0,14))\n", + " _ = ax.set_xlabel('Predicted')\n", + " _ = ax.set_ylabel('Observed')\n", + " except ValueError:\n", + " # Performance can only be plotted for the left out target in LOTO split\n", + " pass\n", + "\n", + " return model_performance" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 89, + "outputs": [], + "source": [ + "def train_validate_qsar_model(pcm_dataset,target,accession,test_size):\n", + " \"\"\"\n", + " Train PCM RF regression model and validate on test set, calculating performance metrics\n", + "\n", + " Parameters\n", + " ----------\n", + "\n", + " target_id : str\n", + " Target id to perform qsar on\n", + "\n", + " Returns\n", + " -------\n", + " dict:\n", + " r2_score and MSE on test set\n", + " \"\"\"\n", + " # Extract target-specific dataset\n", + " target_dataset = pcm_dataset[pcm_dataset['accession'] == accession]\n", + "\n", + " # Remove identifiers\n", + " target_dataset = target_dataset.drop(columns=['SMILES', 'accession'])\n", + "\n", + " # Random-split in training and test set\n", + " train, test = train_test_split(target_dataset, test_size=test_size, random_state=1234)\n", + "\n", + " # Set model parameter for random forest\n", + " param = {\n", + " \"n_estimators\": 100, # number of trees to grows\n", + " \"criterion\": \"squared_error\", # cost function to be optimized for a split\n", + " }\n", + " model_RF = RandomForestRegressor(**param)\n", + "\n", + " # Fit model\n", + " model_RF.fit(train.iloc[:, 1:], train.iloc[:, 0])\n", + "\n", + " # Make predictions on test set\n", + " predictions = model_RF.predict(test.iloc[:, 1:])\n", + "\n", + " # Calculate model performance with regression metrics\n", + " model_performance = {}\n", + " model_performance['pearson_r'] = pearsonr(test.iloc[:, 0], predictions)\n", + " model_performance['r2_score'] = r2_score(test.iloc[:, 0], predictions)\n", + " model_performance['mse'] = mean_absolute_error(test.iloc[:, 0], predictions)\n", + " print(model_performance)\n", + "\n", + " # Plot correlation between true and predicted values\n", + " ax = sns.scatterplot(y=test.iloc[:, 0], x=predictions, label=(f'{target} R2= {model_performance[\"r2_score\"]:.2f}'))\n", + " _ = sns.lineplot(x=(0, 14), y=(0, 14))\n", + " _ = ax.set_xlim((0,14))\n", + " _ = ax.set_ylim((0,14))\n", + " _ = ax.set_xlabel('Predicted')\n", + " _ = ax.set_ylabel('Observed')\n", + "\n", + "\n", + " return model_performance" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Preprocessing" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 70, + "outputs": [ + { + "data": { + "text/plain": " SMILES accession \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... 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" + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Add protein and molecular features to bioactivity dataset\n", + "ar_pcm_dataset = ar_dataset.merge(protein_features, on='accession')\n", + "ar_pcm_dataset = ar_pcm_dataset.merge(molecular_features, on='SMILES')\n", + "\n", + "ar_pcm_dataset" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 92, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Random split ==\n", + "Training set has 10175 datapoints\n", + "Test set has 2544 datapoints (20.002 %)\n" + ] + } + ], + "source": [ + "# Split dataset in training and test set (random split)\n", + "print('== Random split ==')\n", + "train_random,test_random = split_train_test(ar_pcm_dataset, 0.20, 'random')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "#### Model training and validation" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Random split PCM model" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": "{'pearson_r': PearsonRResult(statistic=0.6826149493447694, pvalue=0.0),\n 'r2_score': 0.46133002529077327,\n 'mse': 0.6410782145163035}" + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "train_validate_pcm_model(adenosine_receptors,train_random,test_random)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Random split QSAR models" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 95, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Performance of QSAR model of A1\n", + "{'pearson_r': PearsonRResult(statistic=0.631807112055607, pvalue=1.038314406824121e-79), 'r2_score': 0.39891177879746675, 'mse': 0.5714550824100995}\n", + "Performance of QSAR model of A2A\n", + "{'pearson_r': PearsonRResult(statistic=0.6319152879729949, pvalue=2.7528986689869012e-90), 'r2_score': 0.39771817563716727, 'mse': 0.6995484350458305}\n", + "Performance of QSAR model of A2B\n", + "{'pearson_r': PearsonRResult(statistic=0.6987524591740315, pvalue=1.4124503125187107e-59), 'r2_score': 0.4866612157523956, 'mse': 0.5785976646732139}\n", + "Performance of QSAR model of A3\n", + "{'pearson_r': PearsonRResult(statistic=0.6832772944521972, pvalue=6.913722782939143e-90), 'r2_score': 0.4660144268069807, 'mse': 0.6527746663023826}\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for target,accession in adenosine_receptors.items():\n", + " print(f'Performance of QSAR model of {target}')\n", + " train_validate_qsar_model(ar_pcm_dataset,target,accession,0.20)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Leave one target out split PCM model" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 103, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Leave one target out split ==\n", + "Target left out for testing is P30542\n", + "Training set has 9200 datapoints\n", + "Test set has 3519 datapoints (27.667 %)\n", + "== Leave one target out split ==\n", + "Target left out for testing is P29274\n", + "Training set has 8728 datapoints\n", + "Test set has 3991 datapoints (31.378 %)\n", + "== Leave one target out split ==\n", + "Target left out for testing is P29275\n", + "Training set has 10731 datapoints\n", + "Test set has 1988 datapoints (15.63 %)\n", + "== Leave one target out split ==\n", + "Target left out for testing is P0DMS8\n", + "Training set has 9498 datapoints\n", + "Test set has 3221 datapoints (25.324 %)\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for target,accession in adenosine_receptors.items():\n", + " # Split dataset in training and test set (leave one target out split)\n", + " print('== Leave one target out split ==')\n", + " train_loto, test_loto = split_train_test(ar_pcm_dataset, 0.20, 'loto', accession)\n", + " # Train and validate PCM model, every time leaving a different target out for validation\n", + " train_validate_pcm_model(adenosine_receptors,train_loto,test_loto)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Discussion\n", + "\n", + "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges.\n", + "Compared to purely ligand-based compound activity prediction models, PCM modelling has certain advantages and limitations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quiz\n", + "\n", + "1. What types of features are needed for PCM?\n", + "2. How many types of training/test set splitting methods commonly used in PCM modelling do you know?\n", + "3. Which applications do you know of PCM in drug discovery?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Useful checks at the end: \n", + " \n", + "
    \n", + "
  • Clear output and rerun your complete notebook. Does it finish without errors?
    • \n", + "
  • Check if your talktorial's runtime is as excepted. If not, try to find out which step(s) take unexpectedly long.
    • \n", + "
  • Flag code cells with # NBVAL_CHECK_OUTPUT that have deterministic output and should be tested within our Continuous Integration (CI) framework.
    • \n", + "
\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.4" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file From 74eee293b2b03f2c4a04311ffdadce704343661c Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Tue, 18 Oct 2022 11:19:55 +0200 Subject: [PATCH 11/62] Change dependencies to use ClustalO REST API --- .../T032_env.yml | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/T032_env.yml b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/T032_env.yml index 35f49962..f6005bcf 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/T032_env.yml +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/T032_env.yml @@ -1,4 +1,4 @@ -name: teachopencadd_t029 +name: teachopencadd_t032 channels: - conda-forge - defaults @@ -18,7 +18,9 @@ dependencies: # Dependencies for PCM and papyrus scripts - mordred - pip: + - https://github.com/OlivierBeq/Papyrus-scripts/tarball/master - prodec - rich-msa - - wget - - https://github.com/OlivierBeq/Papyrus-scripts/tarball/master + # Dependency for ClustalO webservice (also conda installable via -c bioconda) + - xmltramp2 + From 1c122e5fa16a7b56b4998f5dc72994d23f655eb6 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Tue, 18 Oct 2022 11:20:29 +0200 Subject: [PATCH 12/62] Update code to use ClustalO REST API instead of binary download --- .../talktorial.ipynb | 279 +++++------------- 1 file changed, 71 insertions(+), 208 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index 09077376..bebe2ccc 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -11,17 +11,6 @@ "" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Set up your PR: Please check out our issue on how to set up a PR for new talktorials, including standard checks and TODOs.\n", - "\n", - "
" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -37,24 +26,6 @@ "- Willem Jespers, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).*" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Cross-referencing talktorials: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: Talktorial T000.\n", - "\n", - "
" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -432,8 +403,6 @@ "import numpy as np\n", "import pandas as pd\n", "import re\n", - "import wget\n", - "import zipfile\n", "import random\n", "\n", "from papyrus_scripts.download import download_papyrus\n", @@ -524,7 +493,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "ead0c3239b2c4cc19c3773938899f0c6" + "model_id": "b11c3201b52d4c9da9482b7f0d837916" } }, "metadata": {}, @@ -641,7 +610,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "80483e4a49574c68b12e4deea9a9e415" + "model_id": "9a43f70ddf1244f5a34109b760c2f08f" } }, "metadata": {}, @@ -704,14 +673,14 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 7, "outputs": [ { "data": { "text/plain": " SMILES accession \\\n222 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n223 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n383 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n462 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n464 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n... ... ... \n1238255 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... P30542 \n1238605 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P0DMS8 \n1238606 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29274 \n1238607 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29275 \n1238608 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P30542 \n\n pchembl_value_Mean \n222 8.6800 \n223 6.6800 \n383 4.8200 \n462 7.1515 \n464 5.6500 \n... ... \n1238255 5.1000 \n1238605 7.6100 \n1238606 7.3500 \n1238607 5.1500 \n1238608 7.3400 \n\n[12719 rows x 3 columns]", "text/html": "
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SMILESaccessionpchembl_value_Mean
222Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.6800
223Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.6800
383Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.8200
462O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.1515
464O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.6500
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1238255Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc...P305425.1000
1238605CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P0DMS87.6100
1238606CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292747.3500
1238607CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292755.1500
1238608CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P305427.3400
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" }, - "execution_count": 57, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -742,142 +711,22 @@ { "cell_type": "markdown", "source": [ - "In order to ensure protein descriptors are of the same length, we first need to align the target sequences. We do this by using the software ClustalO. The first step is to install the software. Choose one of the following download options depending on your system (Windows, Unix, or MacOS)." + "In order to ensure protein descriptors are of the same length, we first need to align the target sequences. We do this by using the software ClustalO. To begin with, we extract the protein sequences from the target files in Papyrus. The sequences could also be obtained from Uniprot, but tis way we ensure we are always retrieving the canonical isoform sequence." ], "metadata": { "collapsed": false } }, - { - "cell_type": "markdown", - "source": [ - "##### Clustal Omega downlaod for Windows" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, { "cell_type": "code", - "execution_count": 9, - "outputs": [], - "source": [ - "# Define url of download file and paths to download\n", - "clustalo_url = \"http://www.clustal.org/omega/clustal-omega-1.2.2-win64.zip\"\n", - "clustalo_zip_path = str(Path(DATA, 'clustalo.zip'))\n", - "clustalo_path = str(Path(DATA, 'clustalo'))\n", - "\n", - "if not os.path.isfile(clustalo_zip_path):\n", - " # Download .zip installation file\n", - " wget.download(clustalo_url, out=clustalo_zip_path)\n", - " # Unzip installation file\n", - " with zipfile.ZipFile(clustalo_zip_path, 'r') as zip_ref:\n", - " zip_ref.extractall(clustalo_path)\n", - "# Define path to executable\n", - "clustalo_exe = os.path.join(clustalo_path, 'clustal-omega-1.2.2-win64', 'clustalo.exe')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "##### clustalo download for Unix" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "# Define url of download file and paths to download\n", - "clustalo_url = \"http://www.clustal.org/omega/clustalo-1.2.4-Ubuntu-x86_64\"\n", - "clustalo_path = str(Path(DATA, 'clustalo'))\n", - "if not os.path.isfile(clustalo_path):\n", - " # Download executable file\n", - " wget.download(clustalo_url, out=clustalo_path)\n", - " # Give file executable permission\n", - " os.chmod(clustalo_path, 0755)\n", - "# Define path to executable\n", - "clustalo_exe = clustalo_path" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "##### clustalo download for MacOS" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "# Define url of download file and paths to download\n", - "clustalo_url = \"http://www.clustal.org/omega/clustal-omega-1.2.3-macosx\"\n", - "clustalo_path = str(Path(DATA, 'clustalo'))\n", - "if not os.path.isfile(clustalo_path):\n", - " # Download executable file\n", - " wget.download(clustalo_url, out=clustalo_path)\n", - " # Give file executable permission\n", - " os.chmod(clustalo_path, 0755)\n", - "# Define path to executable\n", - "clustalo_exe = clustalo_path" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } - }, - { - "cell_type": "markdown", - "source": [ - "Next, we obtain the protein sequences from the target files in Papyrus." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } - }, - { - "cell_type": "code", - "execution_count": 65, + "execution_count": 8, "outputs": [ { "data": { "text/plain": " target_id HGNC_symbol UniProtID Status Organism \\\n80 P30542_WT ADORA1 AA1R_HUMAN reviewed Homo sapiens (Human) \n81 P29274_WT ADORA2A AA2AR_HUMAN reviewed Homo sapiens (Human) \n47 P29275_WT ADORA2B AA2BR_HUMAN reviewed Homo sapiens (Human) \n82 P0DMS8_WT ADORA3 AA3R_HUMAN reviewed Homo sapiens (Human) \n\n Classification Length \\\n80 Membrane receptor->Family A G protein-coupled ... 326 \n81 Membrane receptor->Family A G protein-coupled ... 412 \n47 Membrane receptor->Family A G protein-coupled ... 332 \n82 Membrane receptor->Family A G protein-coupled ... 318 \n\n Sequence accession \n80 MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC... P30542 \n81 MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV... P29274 \n47 MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL... P29275 \n82 MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT... P0DMS8 ", "text/html": "
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target_idHGNC_symbolUniProtIDStatusOrganismClassificationLengthSequenceaccession
80P30542_WTADORA1AA1R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...326MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...P30542
81P29274_WTADORA2AAA2AR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...412MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV...P29274
47P29275_WTADORA2BAA2BR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...332MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL...P29275
82P0DMS8_WTADORA3AA3R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...318MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT...P0DMS8
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" }, - "execution_count": 65, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -901,15 +750,12 @@ "In order to align the sequences with Clustal Omega, we first need to write them into a FASTA file." ], "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } + "collapsed": false } }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "outputs": [], "source": [ "records = []\n", @@ -931,7 +777,7 @@ { "cell_type": "markdown", "source": [ - "Now, we use Clustal Omega to align the sequences and write out the alignment file." + "Now, we use ClustalO to align the sequences and write out the alignment file. We do this by calling the CLustalO webservice from the command line." ], "metadata": { "collapsed": false, @@ -942,23 +788,35 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 10, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Alignment file already exists\n" + "FINISHED\n", + "Creating result file: aligned_sequences.out.txt\n", + "Creating result file: aligned_sequences.sequence.txt\n", + "Creating result file: aligned_sequences.aln-fasta.fasta\n", + "Creating result file: aligned_sequences.tree.dnd\n", + "Creating result file: aligned_sequences.phylotree.ph\n", + "Creating result file: aligned_sequences.pim.pim\n", + "Creating result file: aligned_sequences.submission.params\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "JobId: clustalo-R20221018-101740-0985-45571475-p1m\n" ] } ], "source": [ - "alignment_file = os.path.join(DATA, 'aligned_sequences.fasta')\n", - "if not os.path.isfile(alignment_file):\n", - " clustalomega_cline = ClustalOmegaCommandline(cmd=clustalo_exe, infile=sequences_path, outfile=alignment_file, auto=True)\n", - " _ = clustalomega_cline()\n", - "else:\n", - " print('Alignment file already exists')" + "os.chdir(DATA) # Move to data folder to generate ClustalO results there\n", + "# Query ClustalO webservice from command line\n", + "!python clustalo.py --email m.gorostiola.gonzalez@lacdr.leidenuniv.nl --stype protein --sequence sequences.fasta --outfmt fa --outfile aligned_sequences --order input\n", + "os.chdir(HERE) # Move back to main notebook directory" ], "metadata": { "collapsed": false, @@ -981,9 +839,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 11, "outputs": [], "source": [ + "alignment_file = os.path.join(DATA, 'aligned_sequences.aln-fasta.fasta')\n", "aligned_sequences = [str(seq.seq) for seq in SeqIO_parse(alignment_file, 'fasta')]" ], "metadata": { @@ -1007,12 +866,12 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 12, "outputs": [ { "data": { - "text/plain": "╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA1R_HU… \u001B[1;36m 1\u001B[0m 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\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m │\n│ 3 AA3R_HU… \u001B[1;36m319\u001B[0m 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╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA1R_HU…     1  ---MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFCFIVSLAVADVAVGALVIPLAILINIGPQTYFHTCLMVACPVLI  │\n│ 1 AA2AR_H…     1  ------MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLV  │\n│ 2 AA2BR_H…     1  -----MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVAVGLFAIPFAITISLGFCTDFYGCLFLACFVLV  │\n│ 3 AA3R_HU…     1  MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTTTFYFIVSLALADIAVGVLVMPLAIVVSLGITIHFYSCLFMTCLLLI  │\n│                                                                                                                 │\n│ 0 AA1R_HU…    90  LTQSSILALLAIAVDRYLRVKIPLRYKMVVTPRRAAVAIAGCWILSFVVGLTPMFGWNNLSAVER----AW---AANGSMGEPVIKCEFEKV  │\n│ 1 AA2AR_H…    87  LTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWN-------NCGQPKEGKNHSQGCGEGQVACLFEDV  │\n│ 2 AA2BR_H…    88  LTQSSIFSLLAVAVDRYLAICVPLRYKSLVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCC---LVKCLFENV  │\n│ 3 AA3R_HU…    93  FTHASIMSLLAIAVDRYLRVKLTVRYKRVTTHRRIWLALGLCWLVSFLVGLTPMFGWNMKLTSEYHR-------------NVTFLSCQFVSV  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   175  ISMEYMVYFNFFVWVLPPLLLMVLIYLEVFYLIRKQLNKKVSAS--SGDPQKYYGKELKIAKSLALILFLFALSWLPLHILNCITLFCPSC-  │\n│ 1 AA2AR_H…   172  VPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDCS  │\n│ 2 AA2BR_H…   177  VPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTEL----MDHSRTTLQREIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQG  │\n│ 3 AA3R_HU…   172  MRMDYMVYFSFLTWIFIPLVVMCAIYLDIFYIIRNKLSLNLSN---SKETGAFYGREFKTAKSLFLVLFLFALSWLPLSIINCIIYFNG---  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   264  -HKPSILTYIAIFLTHGNSAMNPIVYAFRIQKFRVTFLKIWNDHFRCQPAPPIDEDLPEERPDD----------------------------  │\n│ 1 AA2AR_H…   264  -HAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHP  │\n│ 2 AA2BR_H…   265  KNKPKWAMNMAILLSHANSVVNPIVYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQ----------AGVQPALGVGL--------------  │\n│ 3 AA3R_HU…   258  -EVPQLVLYMGILLSHANSMMNPIVYAYKIKKFKETYLLILKACVVCHPSDSLDTSIEKNSE------------------------------  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   327  ----------------------------------------------------------                                    │\n│ 1 AA2AR_H…   355  ERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS                                    │\n│ 2 AA2BR_H…   333  ----------------------------------------------------------                                    │\n│ 3 AA3R_HU…   319  ----------------------------------------------------------                                    │\n╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n
\n" + "text/plain": "╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA1R_HU… \u001B[1;36m 1\u001B[0m \u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;31mM\u001B[0m\u001B[1;31mP\u001B[0m\u001B[1;31mP\u001B[0m\u001B[1;32mS\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;32mS\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mF\u001B[0m\u001B[1;32mQ\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;32mY\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;32mG\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;34mE\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;32mS\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mP\u001B[0m\u001B[1;32mG\u001B[0m\u001B[1;32mN\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;31mW\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;38;5;129mK\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;32mN\u001B[0m\u001B[1;32mQ\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;38;5;129mR\u001B[0m\u001B[1;34mD\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;32mT\u001B[0m\u001B[1;31mF\u001B[0m\u001B[1;32mC\u001B[0m\u001B[1;31mF\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;32mS\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;34mD\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;32mG\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;31mP\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;32mN\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;32mG\u001B[0m\u001B[1;31mP\u001B[0m\u001B[1;32mQ\u001B[0m\u001B[1;32mT\u001B[0m\u001B[1;32mY\u001B[0m\u001B[1;31mF\u001B[0m\u001B[1;32mH\u001B[0m\u001B[1;32mT\u001B[0m\u001B[1;32mC\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mM\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;32mC\u001B[0m\u001B[1;31mP\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mI\u001B[0m │\n│ 1 AA2AR_H… \u001B[1;36m 1\u001B[0m 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│\n│ 3 AA3R_HU… \u001B[1;36m319\u001B[0m 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│\n╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n", + "text/html": "
╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA1R_HU…     1  ---MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFCFIVSLAVADVAVGALVIPLAILINIGPQTYFHTCLMVACPVLI  │\n│ 1 AA2AR_H…     1  ------MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLV  │\n│ 2 AA2BR_H…     1  -----MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVAVGLFAIPFAITISLGFCTDFYGCLFLACFVLV  │\n│ 3 AA3R_HU…     1  MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTTTFYFIVSLALADIAVGVLVMPLAIVVSLGITIHFYSCLFMTCLLLI  │\n│                                                                                                                 │\n│ 0 AA1R_HU…    90  LTQSSILALLAIAVDRYLRVKIPLRYKMVVTPRRAAVAIAGCWILSFVVGLTPMFGWNNLSAVER----AWA---ANGSMGEPVIKCEFEKV  │\n│ 1 AA2AR_H…    87  LTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWNN-------CGQPKEGKNHSQGCGEGQVACLFEDV  │\n│ 2 AA2BR_H…    88  LTQSSIFSLLAVAVDRYLAICVPLRYKSLVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCC---LVKCLFENV  │\n│ 3 AA3R_HU…    93  FTHASIMSLLAIAVDRYLRVKLTVRYKRVTTHRRIWLALGLCWLVSFLVGLTPMFGWNMKLTSEYH-------------RNVTFLSCQFVSV  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   175  ISMEYMVYFNFFVWVLPPLLLMVLIYLEVFYLIRKQLNKKVSAS--SGDPQKYYGKELKIAKSLALILFLFALSWLPLHILNCITLFCPSC-  │\n│ 1 AA2AR_H…   172  VPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDC-  │\n│ 2 AA2BR_H…   177  VPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTEL----MDHSRTTLQREIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQG  │\n│ 3 AA3R_HU…   172  MRMDYMVYFSFLTWIFIPLVVMCAIYLDIFYIIRNKLSLNLSN---SKETGAFYGREFKTAKSLFLVLFLFALSWLPLSIINCIIYFNG---  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   264  -HKPSILTYIAIFLTHGNSAMNPIVYAFRIQKFRVTFLKIWNDHFRCQPAPPIDEDLPEE--------------------------------  │\n│ 1 AA2AR_H…   263  SHAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHP  │\n│ 2 AA2BR_H…   265  KNKPKWAMNMAILLSHANSVVNPIVYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQ----------AGVQPALGVGL--------------  │\n│ 3 AA3R_HU…   258  -EVPQLVLYMGILLSHANSMMNPIVYAYKIKKFKETYLLILKACVVCHPSDSLDTSIEKNSE------------------------------  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   323  --RPDD----------------------------------------------------                                    │\n│ 1 AA2AR_H…   355  ERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS                                    │\n│ 2 AA2BR_H…   333  ----------------------------------------------------------                                    │\n│ 3 AA3R_HU…   319  ----------------------------------------------------------                                    │\n╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n
\n" }, "metadata": {}, "output_type": "display_data" @@ -1057,7 +916,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "outputs": [ { "name": "stdout", @@ -1082,7 +941,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "outputs": [ { "name": "stdout", @@ -1095,7 +954,7 @@ "data": { "text/plain": "{'Authors': 'Hellberg, Sjöström, Skagerberg, Wold',\n 'Year': 1987,\n 'Journal': 'Journal of Medicinal Chemistry',\n 'DOI': '10.1021/jm00390a003',\n 'PMID': None,\n 'Patent': None}" }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -1114,7 +973,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 18, "outputs": [], "source": [ "def calculate_protein_descriptor(targets, aligned_sequences, protein_descriptor):\n", @@ -1155,7 +1014,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 19, "outputs": [ { "data": { @@ -1163,7 +1022,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "25ee7751d787413f9e104cba22304dbb" + "model_id": "4905f9dc32d3430cae804eebc4e189d1" } }, "metadata": {}, @@ -1174,7 +1033,7 @@ "text/plain": " accession Zscale_1 Zscale_2 Zscale_3 Zscale_4 Zscale_5 Zscale_6 \\\n0 P30542 0.00 0.00 0.00 0.00 0.00 0.00 \n1 P29274 0.00 0.00 0.00 0.00 0.00 0.00 \n2 P29275 0.00 0.00 0.00 0.00 0.00 0.00 \n3 P0DMS8 -2.49 -0.27 -0.41 -1.22 0.88 2.23 \n\n Zscale_7 Zscale_8 Zscale_9 ... Zscale_1269 Zscale_1270 Zscale_1271 \\\n0 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n1 0.00 0.00 0.00 ... 0.09 2.23 -5.36 \n2 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n3 3.22 1.45 0.84 ... 0.00 0.00 0.00 \n\n Zscale_1272 Zscale_1273 Zscale_1274 Zscale_1275 Zscale_1276 \\\n0 0.0 0.00 0.00 0.00 0.00 \n1 0.3 -2.69 -2.53 -1.29 1.96 \n2 0.0 0.00 0.00 0.00 0.00 \n3 0.0 0.00 0.00 0.00 0.00 \n\n Zscale_1277 Zscale_1278 \n0 0.00 0.00 \n1 -1.63 0.57 \n2 0.00 0.00 \n3 0.00 0.00 \n\n[4 rows x 1279 columns]", "text/html": "
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accessionZscale_1Zscale_2Zscale_3Zscale_4Zscale_5Zscale_6Zscale_7Zscale_8Zscale_9...Zscale_1269Zscale_1270Zscale_1271Zscale_1272Zscale_1273Zscale_1274Zscale_1275Zscale_1276Zscale_1277Zscale_1278
0P305420.000.000.000.000.000.000.000.000.00...0.000.000.000.00.000.000.000.000.000.00
1P292740.000.000.000.000.000.000.000.000.00...0.092.23-5.360.3-2.69-2.53-1.291.96-1.630.57
2P292750.000.000.000.000.000.000.000.000.00...0.000.000.000.00.000.000.000.000.000.00
3P0DMS8-2.49-0.27-0.41-1.220.882.233.221.450.84...0.000.000.000.00.000.000.000.000.000.00
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4 rows × 1279 columns

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" }, - "execution_count": 66, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1216,7 +1075,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "outputs": [], "source": [ "def calculate_molecular_descriptors(bioactivity_dataset, moldred_descriptors):\n", @@ -1269,13 +1128,13 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 21, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 6898/6898 [00:10<00:00, 676.78it/s]\n" + "100%|██████████| 6898/6898 [00:10<00:00, 655.93it/s]\n" ] }, { @@ -1283,7 +1142,7 @@ "text/plain": " SMILES ABC ABCGG \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... 21.041 17.684 \n1 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... 20.701 15.635 \n2 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 23.23 17.455999 \n3 CCNC(=O)C1OC(n2cnc3c2ncnc3Nc2ccc(OCC(=O)Nc3ccc... 31.336 22.212999 \n4 NC(=NC(=O)Cn1c(O)c2CCCCc2c1O)Nc1nc2c(cccc2)s1 21.408001 17.066 \n... ... ... ... \n6893 CC(C)(C)C1(O)CCN2CC3c4c(cccc4)CCc4cccc(c43)C2C1 22.177999 16.375 \n6894 CNC(=O)C12CC1C(n1cnc3c(NC(C)C)nc(C#Cc4ccc(Cl)s... 26.351 22.591999 \n6895 CCCOc1ccc(C=Cc2cc3c(c(=O)n(C)c(=O)n3C)n2C)cc1 20.021999 15.893 \n6896 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... 23.736 18.441999 \n6897 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 18.511 15.661 \n\n nAcid nBase nAtom nHeavyAtom nSpiro nBridgehead nHetero ... nN \\\n0 0 1 51 27 0 0 8 ... 6 \n1 0 0 42 26 0 0 8 ... 4 \n2 0 0 43 29 0 0 8 ... 6 \n3 0 0 66 40 0 0 14 ... 7 \n4 0 3 46 27 0 0 9 ... 5 \n... ... ... ... ... ... ... ... ... .. \n6893 0 1 58 27 0 0 2 ... 1 \n6894 0 0 56 33 0 0 11 ... 6 \n6895 0 0 49 26 0 0 6 ... 3 \n6896 0 0 52 30 0 0 9 ... 4 \n6897 0 0 43 24 0 0 8 ... 5 \n\n nO nS nP nF nCl nBr nI nX BalabanJ \n0 2 0 0 0 0 0 0 0 1.631 \n1 3 1 0 0 0 0 0 0 1.307 \n2 2 0 0 0 0 0 0 0 1.328 \n3 6 0 0 0 1 0 0 1 1.043 \n4 3 1 0 0 0 0 0 0 1.234 \n... .. .. .. .. ... ... .. .. ... \n6893 1 0 0 0 0 0 0 0 1.46 \n6894 3 1 0 0 1 0 0 1 1.303 \n6895 3 0 0 0 0 0 0 0 1.479 \n6896 3 0 0 2 0 0 0 2 1.318 \n6897 3 0 0 0 0 0 0 0 1.68 \n\n[6898 rows x 23 columns]", "text/html": "
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SMILESABCABCGGnAcidnBasenAtomnHeavyAtomnSpironBridgeheadnHetero...nNnOnSnPnFnClnBrnInXBalabanJ
0Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...21.04117.684015127008...6200000001.631
1Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...20.70115.635004226008...4310000001.307
2O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc123.2317.455999004329008...6200000001.328
3CCNC(=O)C1OC(n2cnc3c2ncnc3Nc2ccc(OCC(=O)Nc3ccc...31.33622.2129990066400014...7600010011.043
4NC(=NC(=O)Cn1c(O)c2CCCCc2c1O)Nc1nc2c(cccc2)s121.40800117.066034627009...5310000001.234
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6893CC(C)(C)C1(O)CCN2CC3c4c(cccc4)CCc4cccc(c43)C2C122.17799916.375015827002...1100000001.46
6894CNC(=O)C12CC1C(n1cnc3c(NC(C)C)nc(C#Cc4ccc(Cl)s...26.35122.5919990056330011...6310010011.303
6895CCCOc1ccc(C=Cc2cc3c(c(=O)n(C)c(=O)n3C)n2C)cc120.02199915.893004926006...3300000001.479
6896Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc...23.73618.441999005230009...4300200021.318
6897CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc1218.51115.661004324008...5300000001.68
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6898 rows × 23 columns

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" }, - "execution_count": 34, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1359,7 +1218,7 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 22, "outputs": [], "source": [ "def split_train_test(pcm_dataset, test_size, split_method, loto_accession='None'):\n", @@ -1417,7 +1276,7 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 30, "outputs": [], "source": [ "def train_validate_pcm_model(targets_dict, train,test):\n", @@ -1462,6 +1321,7 @@ " model_performance['pearson_r'] = pearsonr(test.iloc[:, 0], predictions)\n", " model_performance['r2_score'] = r2_score(test.iloc[:, 0], predictions)\n", " model_performance['mse'] = mean_absolute_error(test.iloc[:, 0], predictions)\n", + " print(model_performance)\n", "\n", " # Add column named 'Target' for easier data visualization\n", " test_keys['Target'] = test_keys['accession'].apply(lambda x: [i for i in targets_dict.keys() if targets_dict[i]==x][0])\n", @@ -1500,7 +1360,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 24, "outputs": [], "source": [ "def train_validate_qsar_model(pcm_dataset,target,accession,test_size):\n", @@ -1579,14 +1439,14 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 25, "outputs": [ { "data": { "text/plain": " SMILES accession \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n1 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n2 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n3 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n4 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n... ... ... \n12714 Cn1cc(Nc2nc(-c3ccco3)c(-c3ncncc3)cn2)ccc1=O P29275 \n12715 N#Cc1c(-c2ccc(OCC3CC3)cc2)c(C#N)c(SCC(N)=O)nc1N P29275 \n12716 O=C(Cc1cccc2c1cccc2)Nc1nc2nn(CCc3ccccc3)cc2c2n... P29275 \n12717 COc1c(OCC(=O)O)ccc(-c2cc3c([nH]2)c(=O)n(C)c(=O... P29275 \n12718 CCCn1c(=O)c2c([nH]c(-c3cc(OCC4CC(=O)N(c5ccc(F)... P29275 \n\n pchembl_value_Mean Zscale_1 Zscale_2 Zscale_3 Zscale_4 Zscale_5 \\\n0 8.6800 0.00 0.00 0.00 0.00 0.00 \n1 6.6800 0.00 0.00 0.00 0.00 0.00 \n2 4.8200 0.00 0.00 0.00 0.00 0.00 \n3 5.6500 0.00 0.00 0.00 0.00 0.00 \n4 7.1515 -2.49 -0.27 -0.41 -1.22 0.88 \n... ... ... ... ... ... ... \n12714 7.5515 0.00 0.00 0.00 0.00 0.00 \n12715 7.5100 0.00 0.00 0.00 0.00 0.00 \n12716 7.3672 0.00 0.00 0.00 0.00 0.00 \n12717 6.5700 0.00 0.00 0.00 0.00 0.00 \n12718 6.6800 0.00 0.00 0.00 0.00 0.00 \n\n Zscale_6 Zscale_7 ... nN nO nS nP nF nCl nBr nI nX BalabanJ \n0 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.631 \n1 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.631 \n2 0.00 0.00 ... 4 3 1 0 0 0 0 0 0 1.307 \n3 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.328 \n4 2.23 3.22 ... 6 2 0 0 0 0 0 0 0 1.328 \n... ... ... ... .. .. .. .. .. ... ... .. .. ... \n12714 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.368 \n12715 0.00 0.00 ... 5 2 1 0 0 0 0 0 0 1.613 \n12716 0.00 0.00 ... 7 2 0 0 0 0 0 0 0 0.998 \n12717 0.00 0.00 ... 3 6 0 0 0 0 0 0 0 1.608 \n12718 0.00 0.00 ... 6 5 0 0 1 0 0 0 1 1.103 \n\n[12719 rows x 1303 columns]", "text/html": "
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SMILESaccessionpchembl_value_MeanZscale_1Zscale_2Zscale_3Zscale_4Zscale_5Zscale_6Zscale_7...nNnOnSnPnFnClnBrnInXBalabanJ
0Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.68000.000.000.000.000.000.000.00...6200000001.631
1Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.68000.000.000.000.000.000.000.00...6200000001.631
2Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.82000.000.000.000.000.000.000.00...4310000001.307
3O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.65000.000.000.000.000.000.000.00...6200000001.328
4O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.1515-2.49-0.27-0.41-1.220.882.233.22...6200000001.328
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12714Cn1cc(Nc2nc(-c3ccco3)c(-c3ncncc3)cn2)ccc1=OP292757.55150.000.000.000.000.000.000.00...6200000001.368
12715N#Cc1c(-c2ccc(OCC3CC3)cc2)c(C#N)c(SCC(N)=O)nc1NP292757.51000.000.000.000.000.000.000.00...5210000001.613
12716O=C(Cc1cccc2c1cccc2)Nc1nc2nn(CCc3ccccc3)cc2c2n...P292757.36720.000.000.000.000.000.000.00...7200000000.998
12717COc1c(OCC(=O)O)ccc(-c2cc3c([nH]2)c(=O)n(C)c(=O...P292756.57000.000.000.000.000.000.000.00...3600000001.608
12718CCCn1c(=O)c2c([nH]c(-c3cc(OCC4CC(=O)N(c5ccc(F)...P292756.68000.000.000.000.000.000.000.00...6500100011.103
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12719 rows × 1303 columns

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" }, - "execution_count": 70, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1607,7 +1467,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 26, "outputs": [ { "name": "stdout", @@ -1656,21 +1516,21 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "text/plain": "{'pearson_r': PearsonRResult(statistic=0.6826149493447694, pvalue=0.0),\n 'r2_score': 0.46133002529077327,\n 'mse': 0.6410782145163035}" + "text/plain": "{'pearson_r': PearsonRResult(statistic=0.6885449652658857, pvalue=0.0),\n 'r2_score': 0.4684208657515193,\n 'mse': 0.641552897297109}" }, - "execution_count": 102, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "text/plain": "
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\n" + "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" @@ -1694,26 +1554,26 @@ }, { "cell_type": "code", - "execution_count": 95, + "execution_count": 28, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Performance of QSAR model of A1\n", - "{'pearson_r': PearsonRResult(statistic=0.631807112055607, pvalue=1.038314406824121e-79), 'r2_score': 0.39891177879746675, 'mse': 0.5714550824100995}\n", + "{'pearson_r': PearsonRResult(statistic=0.623175021660205, pvalue=5.575802632228035e-77), 'r2_score': 0.3877742318018689, 'mse': 0.5715224726715452}\n", "Performance of QSAR model of A2A\n", - "{'pearson_r': PearsonRResult(statistic=0.6319152879729949, pvalue=2.7528986689869012e-90), 'r2_score': 0.39771817563716727, 'mse': 0.6995484350458305}\n", + "{'pearson_r': PearsonRResult(statistic=0.6322514381507167, pvalue=2.075307940603518e-90), 'r2_score': 0.3984961116776179, 'mse': 0.7001518465280718}\n", "Performance of QSAR model of A2B\n", - "{'pearson_r': PearsonRResult(statistic=0.6987524591740315, pvalue=1.4124503125187107e-59), 'r2_score': 0.4866612157523956, 'mse': 0.5785976646732139}\n", + "{'pearson_r': PearsonRResult(statistic=0.6960602722481648, pvalue=6.029910702826532e-59), 'r2_score': 0.4831210039190027, 'mse': 0.5876251514924776}\n", "Performance of QSAR model of A3\n", - "{'pearson_r': PearsonRResult(statistic=0.6832772944521972, pvalue=6.913722782939143e-90), 'r2_score': 0.4660144268069807, 'mse': 0.6527746663023826}\n" + "{'pearson_r': PearsonRResult(statistic=0.6872475301950196, pvalue=2.5399695303435347e-91), 'r2_score': 0.47123053951136107, 'mse': 0.6503513180863987}\n" ] }, { "data": { "text/plain": "
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\n" 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\n" }, "metadata": {}, "output_type": "display_data" @@ -1745,7 +1605,7 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 31, "outputs": [ { "name": "stdout", @@ -1755,24 +1615,28 @@ "Target left out for testing is P30542\n", "Training set has 9200 datapoints\n", "Test set has 3519 datapoints (27.667 %)\n", + "{'pearson_r': PearsonRResult(statistic=0.25042850038047726, pvalue=1.8412015539714736e-51), 'r2_score': -0.09210483491448596, 'mse': 0.8522890475855737}\n", "== Leave one target out split ==\n", "Target left out for testing is P29274\n", "Training set has 8728 datapoints\n", "Test set has 3991 datapoints (31.378 %)\n", + "{'pearson_r': PearsonRResult(statistic=0.19724573427085657, pvalue=2.6943474596637424e-36), 'r2_score': -0.04664854887508518, 'mse': 0.9690074906568571}\n", "== Leave one target out split ==\n", "Target left out for testing is P29275\n", "Training set has 10731 datapoints\n", "Test set has 1988 datapoints (15.63 %)\n", + "{'pearson_r': PearsonRResult(statistic=-0.009883169222359389, pvalue=0.6596514784653168), 'r2_score': -0.28866658578888793, 'mse': 0.9936770590148246}\n", "== Leave one target out split ==\n", "Target left out for testing is P0DMS8\n", "Training set has 9498 datapoints\n", - "Test set has 3221 datapoints (25.324 %)\n" + "Test set has 3221 datapoints (25.324 %)\n", + "{'pearson_r': PearsonRResult(statistic=0.09172545645648542, pvalue=1.840667237645006e-07), 'r2_score': -0.2699995305753564, 'mse': 1.0547617517398264}\n" ] }, { "data": { "text/plain": "
", - "image/png": 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hqu2oqKgA4LfffuPFF19k9+7d/PTTT+j1enr37k1paek1Pd+VsnTpUpYvX87atWv5448/CAkJoVevXhQXFzu8Jjk5mUGDBjFs2DBSUlIYNmwYAwcOZM+ePVbntWrVigsXLli+Dh8+fEOfRSAQCK6H0uJCPhkeyd3v/oyXFtIDQf/GawyY83Ftm1a7XPco0ZucGz31XJIkSdLkS1LOcUk694ck5fxjen2DKSkpkby8vKRjx45JgwYNkubMmVPtNWPGjJG6detmc3zEiBHSlClTpO+++05q3Lix1VRxe8yaNUuKiIiQEhISpEaNGkkymczuNdnZ2RIg/fbbb1f+YFeJ0WiUQkJCpMWLF1uOlZWVST4+PlJ8fLzD6wYOHCj16dPH6thDDz0kDR482PLa/JxXg5h6LhAIaouUxK+l77q1kI42M00r3zLoXqkgN6O2zbpmanLqufDsXC+F6bD1WVjbHt7rAWvvh89Gmo7fQLZs2UKzZs1o1qwZQ4cOZf369UjVDLAvLCzEz8/P6lhxcTFbt25l6NCh9OrVi9LSUnbs2FHt/U+ePMmnn37Ktm3bOHjwoMP7AVb3jI2NxdPT0+lXVS+VM1JTU8nMzKR3796WY25ubnTp0oWkpCSH1yUnJ1tdA/DQQw/ZXHPixAlCQ0Np1KgRgwcP5vTp01dsm0AgEPxbfLE0lvIxE2mQIaFxhaNPtWfgJ/uo41+3tk27KRAJyteDtgC+Ggunf7E+fmo7/Hcc9E8Ale8NuXVCQgJDhw4FoE+fPpSUlLB9+3Z69uxp9/zk5GQ+/fRTvvnmG6vjn3zyCU2bNqVVq1YADB48mISEBLp16+b0/jqdjg8//JDAwEC765IkERcXR6dOnbjnnnssx+fOncvEiROd7h0aGup0vTKZmZkABAcHWx0PDg7m7NmzTq+zd415P4AOHTqwceNG7r77brKyspg/fz5RUVEcOXIEf3//K7ZRIBAIbhQlhfl8M6YPbfaZwvbng2X4zZxDvx4Datmymwshdq6H0hxboWPm1HbT+g0QO8ePH2fv3r18/vnnACiVSgYNGsT7779vV+wcOXKEvn37MnPmTHr16mW1Vlk0AQwdOpTOnTtz8eJF6tSp49CGBg0aOBQ6AGPHjuXQoUPs2rXL6nhQUBBBQUFX8pg2fPTRR4waNcry+rvvvkOhUAAgk8mszpUkyeZYVaq75uGHH7Z837p1ayIjI7nrrrv44IMPiIuLu6ZnEAgEgpriwG9fkD3rNdpkmrz6h+/zpM/ab/D2vbZ/Y29nhNi5HsqKrm/9GklISECv1xMWdjmrXpIkXFxcKCgowNf3ssA6evQo3bt35/nnn2f69OlW+xw9epQ9e/bwxx9/MHnyZMtxg8HA5s2bGT16tEMbPDw8HK6NGzeO//73v+zcuZN69epZrcXGxrJp0yanz3f06FHCw8Ntjj/xxBN06NDB8josLIwLFy4AJk9N3bqX3bXZ2dk2npvKhISEWHlxruQaDw8PWrduzYkTJ5zaLxAIBDeabQuepcGnyYSXQ6kbpA2OZuDU92rbrJsWIXauB3fv61u/BvR6PRs3bmTZsmU2OSf9+vXjo48+YuzYsYDJo9O9e3eGDx/OggULbPZKSEigc+fOvPnmm1bHP/zwQxISEpyKHXtIksS4ceP44osv2LFjB40a2bYgv54wlpeXF15eXlbHGjVqREhICD/99BPt2rUDTCG23377jSVLlji8R2RkJD/99BOvvPKK5diPP/5IVFSUw2vKy8v5+++/efDBB53aLxAIBDeKooJsvh/zCK0PmKpc0+rKCJ67iP97sG8tW3ZzI8TO9eARCHf1MIWsqnJXD9N6DfP1119TUFDAyJEj8fHxsVrr378/CQkJjB07liNHjtCtWzd69+5NXFycxYuhUCgIDAykoqKCDz/8kLlz51rl1AA899xzLF26lJSUFCIiIq7YthdffJGPP/6Yr776Ci8vL8s9fXx8UKlUwPWFsewhk8kYP348CxcupGnTpjRt2pSFCxeiVqt56qmnLOfFxMQQFhbGokWLAHj55Zfp3LkzS5YsoW/fvnz11Vf8/PPPVmG3iRMn8vjjjxMeHk52djbz58+nqKiI4cOH15j9AoFAcKX8uf0TCubOpXWWKWx16H5vHlv3Ix5ePtVcKRDVWNeDyheeWGMSNpW5q4fp+A3I10lISKBnz542QgdMnp2DBw+yf/9+tm7dSk5ODh999BF169a1fLVv3x6A//73v+Tl5fHkk0/a7NO0aVNat25NQkLCVdm2bt06CgsL6dq1q9U9t2zZcm0Pe4VMmjSJ8ePHM2bMGO6//37S09P58ccfrbxAaWlplpAXQFRUFJ988gnr16+nTZs2bNiwgS1btliFyc6fP8+QIUNo1qwZ//d//4erqyu7d++mQYMGN/R5BLc2hRodp7JLOJBWwKmcEgo1uto2SXAb8NncGGSvzKFelkSxCo6P7MKgTXuE0LlCZFJ19cq3OEVFRfj4+FBYWIi3t3VYqaysjNTUVBo1aoS7u/u130RbYEpGLisyha48Am9YFZbg5qbGfqYEtyQZF7VM3naI30/kWo51bhrA4n5tCK2j+ldsKNToyC3RUVRWgbfKhQAPV3zUrv/KvQU1z8W8C/w05lHuSdECcDZMRr0Fy7in48PVXHnr4+zz+2oRYayaQOUrxI1AcIdTqNHZCB2AnSdymbLtEGuGtLvhouNmEFuCmmPP9xspWbCIe3JMr1M61KHvWz+g8qj5fNDbHRHGEggEghogt0RnI3TM7DyRS27JjQ1nVSe2RDjt1uLTGYNxmbSI0BwoUsOJUb0Y/EGyEDrXiPDsCAQCQQ1QVFbhdL24mvXr5UrElghn3fzkZ53jlxefoPVfZQCk1pPTaNEqOrS33zBWcGXUqmdn586dPP7444SGhiKTyfjyyy8taxUVFUyePJnWrVvj4eFBaGgoMTExZGRk1J7BAoFA4ABvdxen617VrF8vtS22BNdP8tcJHOzXm1Z/lWEEUqL86PG/P2ghhM51U6tip7S0lIiICLuTuDUaDfv372fGjBns37+fzz//nH/++YcnnniiFiwVCAQC5wR4utK5aYDdtc5NAwjwvLFeldoWW4Lr49Op/VBNfYO6uVCohtSxjzL4/UTcVOraNu22oFbDWA8//LBVS/7K+Pj48NNPP1kdW7NmDQ888ABpaWl2O+wKBAJBbeGjdmVxvzZM2XaInVUShJf0a3PDQ0hmsbXTTijr3xBbgmsj90Iqv734JK2PlgNwOlxOk6Xr6Ni2cy1bdntxS+XsFBYWIpPJnM5sKi8vp7y83PK6qOjGjGwQCASCqoTWUbFmSDtyS3QUl1Xg5e5CgOe/U/pd22JLcPX8/vmbGN5YS8t8MMrgcKdAnlz9vfDm3ABuGbFTVlbGlClTeOqpp5zW2y9atIg5c+b8i5YJBALBZXzUtdfXpjbFluDKMej1fDa1H82/+wdXPVz0hPyR/2Hw6EW1bdptyy0hdioqKhg8eDBGo5G33nrL6blTp061mkhdVFRE/fr1b7SJAoFAcFNQm2JLUD2ZaSdIeqk/bY6ZWgGcbCin5evvEtna8Vw+wfVz04udiooKBg4cSGpqKr/88ku1XRTd3Nxwc3P7l6wTCAQCgeDK2LFlJbKVb9OiAAwyONwlhP6rv8fFVXxm3Whu6qaCZqFz4sQJfv75Z/z9/WvbpJuKpKQkFAoFffr0sVlLSUlhyJAh1K9fH5VKRYsWLVi1apXVOTt27EAmk1m+VCoVrVq14p133rkme959910efPBBfH198fX1pWfPnuzdu/ea9rpa3nrrLcuIhvvuu4/ff//d6fmff/45vXr1IjAwEG9vbyIjI/nhhx8cnv/JJ58gk8n4z3/+U8OWCwSC2x2DXs8ncY/iO/dtggog3wvSJw5iSPyvQuj8S9Sq2CkpKeHgwYMcPHgQgNTUVA4ePEhaWhp6vZ7+/fvz559/8tFHH2EwGMjMzCQzMxOdTnQCBXj//fcZN24cu3btIi0tzWpt3759BAYGsmnTJo4cOcJrr73G1KlT7Zb5Hz9+nAsXLnD06FFGjRrF6NGj2b7dziR3BxgMBoxGIzt27GDIkCH8+uuvJCcnEx4eTu/evUlPT7/uZ3XGli1bGD9+PK+99hoHDhzgwQcf5OGHH7Z5Tyqzc+dOevXqxbfffsu+ffvo1q0bjz/+OAcOHLA59+zZs0ycOJEHH3zwRj6GQCC4DUlPPcp/n7yXiG9P42qAE40VhG38kIdGzq5t0+4spFrk119/lQCbr+HDh0upqal21wDp119/veJ7FBYWSoBUWFhos6bVaqWjR49KWq32up7jYtlF6fTF01JKdop0+uJp6WLZxeva70ooKSmRvLy8pGPHjkmDBg2S5syZU+01Y8aMkbp162Z5bX7/CwoKrM5r3LixtHTpUof7rF+/XvLx8ZH+97//SS1atJAUCoV0+vRpm/P0er3k5eUlffDBB1f+YNfAAw88IMXGxloda968uTRlypSr2qdly5Y276Ner5eio6Ol9957Txo+fLjUt29fp3vU1M+UQCC49fl50xJp5wPNpaPNmkuHWjSXPn6xh6SvqKhts24ZnH1+Xy21mrPTtWtXJCdD152t3SxklmYyK2kWSRlJlmPRodHMjppNiEfIDbvvli1baNasGc2aNWPo0KGMGzeOGTNmIJPJHF5TWFiIn5+fw3VJkvjhhx84d+4cHTp0cHp/jUbDokWLeO+99/D39ycoKMjuORUVFVb3XLhwIQsXLnS693fffXfFXhSdTse+ffuYMmWK1fHevXuTlJTk4CpbjEYjxcXFNu/P3LlzCQwMZOTIkdWGxgQCgQBMYautcY/S6uc0lEbI8wbNmKcZMmJ6bZt2x3LTJyjfzBSWF9oIHYDEjERmJ81mSecl+Lj53JB7JyQkMHToUAD69OlDSUkJ27dvp2dP+23Fk5OT+fTTT/nmm29s1urVqweYehQZjUbmzp1L587OG1pVVFTw1ltvERER4fCcKVOmEBYWZmVTbGwsAwcOdLp3WFiY0/XK5ObmYjAYCA4OtjoeHBxMZmbmFe+zbNkySktLrWxLTEwkISHBEmYVCASC6kg7kcL+V4YScVIPwPEmSu5dsYnwpo7/rRTceITYuQ7yy/JthI6ZxIxE8svyb4jYOX78OHv37uXzzz8HQKlUMmjQIN5//327YufIkSP07duXmTNn0qtXL5v133//HS8vL8rLy9m7dy9jx47Fz8+P0aNHO7TB1dWVNm3aOFxfunQpmzdvZseOHbi7u1uO+/n5OfUuOeP333+36rj99ttv061bNwAbj5YkSU69XJXZvHkzs2fP5quvvrJ4qIqLixk6dCjvvvsuAQH2RwAIBAJBZX7cMB/1Wx/RrAgqFHCkZwMGLvsahVJ81NY24v/AdVCsK76u9WslISEBvV5v5QGRJAkXFxcKCgrw9fW1HD969Cjdu3fn+eefZ/p0+y7URo0aWbpSt2rVij179rBgwQKnYkelUjkUE2+88QYLFy7k559/thFE1xPGuv/++628LMHBwbi5uaFQKGy8ONnZ2TbeHnts2bKFkSNHsnXrViuheOrUKc6cOcPjjz9uOWY0GgGTuDx+/Dh33XVXtfsLBILbH4Nez9aXH6LVrxkojZDrAxUvj2TIUxNr2zTBJYTYuQ68XL2ua/1a0Ov1bNy4kWXLltG7d2+rtX79+vHRRx8xduxYwOTR6d69O8OHD2fBggVXfA+FQoFWq70m+15//XXmz5/PDz/8wP3332+zfj1hLJVKRZMmTWyO33ffffz00088+eSTlmM//fQTffv2dXqfzZs38+yzz7J582YeffRRq7XmzZtz+PBhq2PTp0+nuLiYVatWiUaVAoEAgDN//8mhuBFEpBoAOHa3Cw+s+oSwRi1r2TJBZYTYuQ783P2IDo0mMSPRZi06NBo/92sL1zjj66+/pqCggJEjR+LjYx0i69+/PwkJCYwdO5YjR47QrVs3evfuTVxcnMXzoVAoCAwMtLouOzubsrIySxjrww8/pH///ldt29KlS5kxYwYff/wxDRs2tNzT09MTT09P4PrCWI6Ii4tj2LBh3H///URGRvLOO++QlpZGbGys5ZypU6eSnp7Oxo0bAZPQiYmJYdWqVXTs2NFiq0qlwsfHB3d3d+655x6r+5i9X1WPCwSCO5Mf3puF19uf0rQYdAr4+6HGDFj6lQhb3YTc1E0Fb3Z83HyYHTWb6NBoq+Pmaqwbka+TkJBAz549bYQOmDw7Bw8eZP/+/WzdupWcnBw++ugj6tata/lq3769zXXNmjWjbt26NGnShMmTJzNq1CjWrFlz1ba99dZb6HQ6+vfvb3XPN95445qe9UoZNGgQK1euZO7cubRt25adO3fy7bff0qBBA8s5Fy5csOq78/bbb6PX63nxxRetbH355ZdvqK0CgeDWp0JXzubYroQt+xTfYsj2hYLZYxi8/BshdG5SZNKtUN99HRQVFeHj40NhYaHNqImysjJSU1MtnXevlcLyQvLL8inWFePl6oWfu98Nq8IS3NzU1M+UQCC4OTl1OImjrz5PkzOmHL6/m7sStfozQsKb1rJltx/OPr+vFiFBawAfNx8hbgQCgeA255t1U/FL+JImJVCuhOOPNKP/ws+EN+cWQPwfEggEAoHACeVaDV+Me4jWibnIJcj0A+WrLzHoSccVq4KbCyF2BAKBQCBwwPEDOzg5+UUi0kxhq6Ot3Oiy9gsC6jaqZcsEV4MQOwKBQCAQ2OHrNRMIWP8tjTVQ7gL/PN6KgQs/q22zBNeAEDsCgUAgEFSiXKvhixd70TopHzlwIQBUUyYy8LGRtW2a4BoRYkcgEAgEgkv8/cfPpE59iYjzpkLlI63d6fHW1/gGXvnMPsHNhxA7AoFAIBAA/13xEiEbf6KRFrSucLJvBAPnfVLbZglqACF2BAKBQHBHoy0t4qsxDxGx5yIAGYHg+dpUBvaJqV3DBDWGEDsCgeC60BcWYsjLw1hcjNzLG4W/H0o7Hb5vCbQFUJoDZUXg7gMeAaDyrf46wS3LoaRvyJj+KhEZprDVXxFqer31NXX869ayZYKaRIgdgUBwzVRcyCRj+nQ0iZfnw6k7dSJ03jxc6obUomXXQGE6fDUWTv9y+dhdPeCJNeAj8jVuR758PZawj36jQRloXCG1f3sGzNxY22YJbgBiNtYtTFJSEgqFgj59+tis5eXl0adPH0JDQ3Fzc6N+/fqMHTuWoqKiq75PSkoKQ4YMoX79+qhUKlq0aMGqVatq4hGq5fDhw3Tp0gWVSkVYWBhz587F2YSTM2fOMHLkSBo1aoRKpeKuu+5i1qxZ6HQ6m3M3bNhAmzZtcHd3JyQkxDItXnBl6AsLbYQOgGbXLjJmzEBfWFhLll0D2gJboQNwajv8d5xpXXDbUFpcyJahD9As4Tc8y+B8sAxpxSz6C6Fz2yI8O7cw77//PuPGjeO9994jLS2N8PBwy5pcLqdv377Mnz+fwMBATp48yYsvvkh+fj4ff/zxFd+joqKCffv2ERgYyKZNm6hfvz5JSUm88MILKBSKGyoQioqK6NWrF926deOPP/7gn3/+YcSIEXh4eDBhwgS71xw7dgyj0cjbb79NkyZN+Ouvv3j++ecpLS21Gki6fPlyli1bxuuvv06HDh0oKyvj9OnTN+xZbkcMeXk2QseMZtcuDHl5t044qzTHVuiYObXdtC7CWbcFB377guxZr9Em0/RH0+F7Penz5jd4+wbVsmWCG4kQOzVAbeQslJaW8umnn/LHH3+QmZnJhg0bmDlzpmXd19eX0aMvtzJv0KABY8aM4fXXX3e6r0wmY926dXz33Xf8/PPPTJw4kTlz5lid07hxY5KTk/n8889vqNj56KOPKCsrY8OGDbi5uXHPPffwzz//sHz5cuLi4pDJZDbX9OnTx8rT1bhxY44fP866dessYqegoIDp06fzv//9jx49eljObdWq1Q17ltsRY3FxNesl/5IlNUBZNR7P6tYFtwSfLxxJ+JYkwsuh1A3SBkUxcFpCbZsl+BcQYazrpOJCJulxEzj9yKOcGTSY0488QvqEiVRcyLyh992yZQvNmjWjWbNmDB06lPXr1zsN72RkZPD555/TpUuXaveeNWsWffv25fDhwzz77LN2zyksLMTPz8/yOi0tDU9PT6dfsbGxV/WMycnJdOnSBTc3N8uxhx56iIyMDM6cOXPF+1S19aeffsJoNJKenk6LFi2oV68eAwcO5Ny5c1dl352O3MurmnXPf8mSGsC9monK1a0LbmqKCrL59Kn2tNiYhEc5pIXIUKxeyP8JoXPHIDw710F1OQthy964YR6ehIQEhg4dCpi8GSUlJWzfvp2ePXtanTdkyBC++uortFotjz/+OO+99161ez/11FMORQ6YRMinn37KN998YzkWGhrKwYMHne7r7X11HxiZmZk0bNjQ6lhwcLBlrVGj6mfTnDp1ijVr1rBs2TLLsdOnT2M0Glm4cCGrVq3Cx8eH6dOn06tXLw4dOoSrq+tV2XmnovD3R92pE5pdu2zW1J06ofD3rwWrrgFtAciV0Li7/VDWXT3AI/Dft0tQI/y5/RMK5s6ldZbpj8FD93vx6Jvf4+njV82VgtsJ4dm5Dq4kZ+FGcPz4cfbu3cvgwYMBUCqVDBo0iPfff9/m3BUrVrB//36+/PJLTp06RVxcXLX733///Q7Xjhw5Qt++fZk5cya9evWyHFcqlTRp0sTpV1CQ45h4q1atLB6ghx9+2HK8aqjK7L2yF8KqSkZGBn369GHAgAE899xzluNGo5GKigpWr17NQw89RMeOHdm8eTMnTpzg119/rXZfgQmljw+h8+ah7tTJ6ri6UydC58+7NfJ1CtNh67MQ3wk6vACNu1qvm6uxRL7OLclnc2OQvTKHelkSJe5w7NkuDNq0VwidOxDh2bkOaitnISEhAb1eT1jY5XJYSZJwcXGhoKAAX9/L/zCHhIQQEhJC8+bN8ff358EHH2TGjBnUreu4h4SHh4fd40ePHqV79+48//zzTJ8+3WotLS2Nli1bOrV76NChxMfH21379ttvqaioAEClUllsz8y0DgdmZ2cDlz08jsjIyKBbt25ERkbyzjvvWK2Zn72yvYGBgQQEBJCWluZ0X4E1LnVDCFv2xqWctRLkXp4o/P1vDaFTtQJr20joONr0BVCnIXgFC6FzC3Ix7wI/jXmUe1K0AJwNlRE6/3WejHq0li0T1BZC7FwHtZGzoNfr2bhxI8uWLaN3795Wa/369eOjjz5ymDRs9oqUl5df9X2PHDlC9+7dGT58OAsWLLBZv94wVoMGDWyORUZGMm3aNHQ6nSW09OOPPxIaGmoT3qpMeno63bp147777mP9+vXI5dYOzOjoaMDkIatXrx4A+fn55Obm2rVD4Bylj8+tIW6qUrUCS1cKOy9X7DH2DyF0bkH2/riJonkLuCfH9DqlQx0eX/s9Hl634M+ooMYQYuc6qI2cha+//pqCggJGjhyJT5UPmP79+5OQkMDYsWP59ttvycrKon379nh6enL06FEmTZpEdHS0U6FgjyNHjtCtWzd69+5NXFycxduiUCgIDDTlMpjDWDXJU089xZw5cxgxYgTTpk3jxIkTLFy4kJkzZ1rCWHv37iUmJobt27cTFhZGRkYGXbt2JTw8nDfeeIOcnBzLfiEhpiZ3d999N3379uXll1/mnXfewdvbm6lTp9K8eXO6detWo88guIkRFVi3HVtnDuGuLw8SpoMiNWQN68XgV1bXtlmCmwCRs3Md1EbOQkJCAj179rQROmDy7Bw8eJD9+/ejUql499136dSpEy1atGD8+PE89thjfP3111d9z61bt5KTk8NHH31E3bp1LV/t27eviUdyiI+PDz/99BPnz5/n/vvvZ8yYMcTFxVnlHWk0Go4fP24Jgf3444+cPHmSX375hXr16lnZW5mNGzfSoUMHHn30Ubp06YKLiwvff/89Li4uN/SZBLWAtgBy/4Hzf0LuicsNAkUF1m1DQU46n/Vvxz2fHkSlg9R6MrzfXsMTQugILiGTnNUr3wYUFRXh4+NDYWGhTRilrKyM1NRUGjVqhLu7+zXf43KfnVssZ0FQ49TUz9QdyY2YS+VsBISrGj4baWoaWJW7ekD/BBHGugVI/joB7eI3qJsLRuBwlB9913yHykOI1VsdZ5/fV4sIY9UAt2zOgkBQm1QWN64ecG4v/DDVlDsD1z+XqroREP0TTPv/d5y14Ll030LJg9zsEorKKvBWuRDg4YqPWrQluJn4dFp/7v7fEepUQKEacp95hMHjllV/oeCOQ4gdgUDw72PP49K4K/RLMFVF6UqtRcm1eFiuZAREwN2m/S0eJW/wCORCuTuTNh/g9xO5lks6Nw1gcb82hNZRXb0tghol90Iqv419ktZHTMUWp8PlNFnyJh3bda1VuwQ3L0LsCASCfxdHHpfTO0z/7Tj6clXU9cylutIEZJWv1f6FGh2Tth1g39kCXutel17hclwNxeiUbhz8JxWPe5oID08t8vsX69C/vpqW+WCUweHoAJ5c8wNuKnVtmya4iRFiRyAQ/Ls487ic3nG5z42Za62Kqppg7Oph2rtee9CXg4sK8k+D9iK4eVlyhHJLdOw7W8DWIeE03zsNRdLlRpP1GnXHcNcqUIcj+Hcx6PV8Nq0/zb49jpseLnpC/sj/MHj0oto2TXALIMQOOJ0pJRBcDeJnyQ5VE4+NRpPwMOfmVEVfpQ/UtVZFeQSa8m9ObTfdr18C7Im37qXTuDv0mA7rH4aQNvDEGkrK1bzSKcgkdFKtO2orU39B8c14GFBDycs3Iin7NiQz7QRJL/WnzTEdAKcayGnxxrtEto6qZcsEtwp3tNgxlxlrNBpL116B4HrQaDQAooTdjN1qqO7WuTlmXD3Qt3sRg+IujD02I1e7odBloLzWuVQq38sJyGHtTELHHCozc/oXwAhPbYUPHoX/jqPBI/H4hMutPDqVkZ2+jtBaZZxVil1rUvZtyI6ta5Atf4sWBWCQweEuwfRf/QMurm7VXywQXOKOFjsKhYI6depYRhCo1eormrkkEFRFkiQ0Gg3Z2dnUqVMHhUJR2ybVPg6roX4ByWidm+PqQcXDG8hY8wmapAGWU9XR0YTO74VLdX+LOPKQ+ISZEpCLMqw9OpU5vQM6FVns8TIUYJBpnN/vehsOXkml2B3u4THo9Wyd1JcWP5zG1QAFXlA8aiBDnptT26YJbkHuaLEDl7vqmgWPQHA91KlTx/IzdcdTXW5OpwkWAaJv9+IloZNsdZr2wAFKdu1C1a4tUmkpci9vFP5+1q0eqvOQqHwh75RzW8sumnJ5AIWuGJ861XQ/v96Gg1dSKXYHi5301KPsfXkwEf+YmoWeaKSgzfINNGzheEixQOCMO17syGQy6tatS1BQkKULr0BwLbi4uAiPTmWq8364uJvmT5UVYdB6oUn6j9WyTK0mbNkb5G/8kMwZMyzH1Z06ETpvHi51Q67cQ1KdOFG6Xc4VcvdG6RGIdFcPZI4aDl5raM2MGFXhkF8+fgPl6gSaXwS9HP7qHsbAld+jUN7xH1eC60D89FxCoVCIDyqBoCapTmCo6pj63ADGlBSbZb+YGPI3fogm2drbo9m1i4wZMwhb9gbKiiv0kHgEmnKFTtk5t3FXOP8HNOkFMf8zVWchQ/b4KvhuMhz/5vK5Zo/R9XpdxKgKGwx6PZ9OeIxWP5/FxQB53qAZ8zRDRkyvbdMEtwFC7AgEghtD5WqoqlTxjsi9vGxOUbWNIC8+3u7Wml27MOTloXS9il46j62E/71knaTcuCt0iIV9G8GnPvzvZWsbH1sBveZAWaGl4WCNhJeu4r25E0g7kcL+V4bS9qQegH/uUtJu5SbCm0bUsmWC2wUxCFQgENwYzNVQd/WwPm7HO6Lw97cZqCuVVylBr4KxuOTqPCS+DeDx1RDzXxi4EZ7aAvXuNwmdyBfhh2nW157aDl+/Ykp2rne/yQtVU3k0V/He3O78vHEhaU8PptlJPXo5pPQO57GvDgihI6hRhGdHIBDcOMzVUFXGMVT9MFf6+BA6bx4ZM2ag2bULAJmb89JiuZcnePheuYdEWwDfTYGQlpcbC9ZrD80fgw2P2u/7cyOTha/wvbldMej1fDq+D/f8ko7SCLk+oHvpGQY/Pam2TRPchgixIxAIbixVxjE4wqVuCGHL3sCQl4exuAS5bx3U0dFoEhNtzlV3ikahlkN+KjzyBnw70e4wT6v7lubAP9+aviozcKPjBodAhaYQjUZ3Y0ZEXOF7c7uRdnw/B1+Joe1pAwDHmrrwwOpPCGvUspYtE9yu1GoYa+fOnTz++OOEhoYik8n48ssvrdYlSWL27NmEhoaiUqno2rUrR44cqR1jBQLBlaEtgNx/4PyfkHvC9PoKUfr44Na4MaqINriFhxM6f75NeEsdHU1o3EiU70fBu93g7U7Qsi+MSYbntpsqvPon2Dbmc1ThpHTuQdLgjvbCMfRpf9g8T6FGx6nsEg6kFXAqp4RCje6Kn/VO5YeE2Zwb+jRNTxvQKSDlkcY88cV+IXQEN5Ra9eyUlpYSERHBM888Q79+/WzWly5dyvLly9mwYQN333038+fPp1evXhw/fhwvOwmNAoGgdtAXFpo8MoUXkcvLUGT8ivLAmyaPyXV0Bbbx9niqUWQnofyy32VvjK7UlHh8Vw/nzfgc5fec/8OUqFy1uzIgNe6OV/Y+fL6ukrj8xBouSH5M2nZITEa/Qip05Xz2Uh9a/5aJQoJsX5DGj2LwoPG1bZrgDkAm3STDfGQyGV988QX/+c9/AJNXJzQ0lPHjxzN58mQAysvLCQ4OZsmSJYwaNeqK9i0qKsLHx4fCwkK8ve+8ck6B4EZTcSGTjOnTrcJN6qiOhI4bgst3Iy4LnmvtCly5O7KrBxScAZkC9FpQusP5vbB7nek+Y/+wlLPb3eezkbb5Pa4eMGQL/L4MTl8eEWFs3B3ZgxOQbR5oE+aS7urBd83mM+bzVJvbdG4awJoh7a4t7HWbzso6dTiJo68+T5MzRgD+bu5K1OrPCAlvWsuWCW5mavLz+6bN2UlNTSUzM5PevXtbjrm5udGlSxeSkpIcip3y8nLKK1VxFBXduc25BIIaxc4HsV4ntxE6AJqk3WQAYQNeRLln6bUn+lbujlx5mGfV8nHzrC1nzfgss7LGWvfbqdceKrQQ3hE6xqJ38eF8uQpfLzU+H3S1m88jO7Wdlu1n2r3NzhO55JZcQ47PbTor69v4afi+9wVNSkCnhGOPNKP/ws9Ek0DBv8pN+9OWmZkJQHBwsNXx4OBgzp496/C6RYsWMWeOmJ0iENQoDj6IDZGLbYSOTK3GLyYGVdsIdB5qDE9EIpfrMRYYMaan2B/5YA9zd+Tze6DzRFPTP+1F6DjGVApu9uaYhU/H0dWXovuEQZ/FUHFp9pWuFBQucOpXSFoNulKyB3xD1w/TSRrmjY+TxGVXQ4nDteKyq+zGfhvOyirXavjipT603pWDXIIsP1C8+hKDnhxd26YJ7kBuWrFjpupgTkmSnA7rnDp1KnFxcZbXRUVF1K9f/4bZJxDc9jj5IDY2OW91qPKIh8oNAdVRUfgNG0rG9Bn4DhiAR8cO6FxdUXh7onCXULorwMPftnrq/J7L3pzKgzwre3PMgqfThCtrxidXws+zHXqH5O7eQCE6hfO8QJ3CEyi0u+blfpVT72+zWVnHD+7k5KTRRKSZwlZHW7rR5c0vCKjbqJYtE9yp3LRixzxMMTMzk7p161qOZ2dn23h7KuPm5oZbNf05BALBVeDkg1juYrR67XDEQ1ISMldXwtetI3vFiipC6FJ+z7GZ8Mjrl0M2ZZcmkVcNW4G1N8csgpSuUJxpqphS+drPd9EWwDcT4fwf6DtMwhDYAaNWh1zthiI/HeXDr+PpX5fOTbX8lGbkmUbdUabaPrt0Vw+OFtkPU3VuGkCA55WFsArLC8kvy6dYX4TX8M/xO7sbn6Q3bUNnt9CsrK/XTiTg/W9orIFyF/jnsZYMXLStts0S3OHctGKnUaNGhISE8NNPP9GuXTsAdDodv/32G0uWLKll6wSCOwgnH7SKnD1WvXCcjXhwb96c7FUr7Qghc35PK5RVh3fWa2/t0anM6R0msWPGoIO3Ol5+XTnfxZxvpMmHyBepYBYZi1ahSdpkOV0d1ZHQOTPxrBPImiE+5JXq0LVbieL7V6wHgt7VA9kTa2hr9OXj54K4qK3A3UXB/rQCjl8oYm7fe64oXyezNJNZSbNIykiyHIsO6cDsQR8QsmW4teC5BWZllWs1fPFiL1on5SMHLgSA+6Q4Bj7xfG2bJhDUrtgpKSnh5MmTltepqakcPHgQPz8/wsPDGT9+PAsXLqRp06Y0bdqUhQsXolareeqpp2rRaoHg9sZSRl5cbMqvUQegdPWwm6irPPAmoXOSyJg1H01iotMRD05nXSXtRj9hAhXhD6MpKuRCSToeLir8vOvi4+DeALh6mpoCqnxN55jP9QiEB54HbT7oSkydky95p/QdJpGx9RSapN02NmTMXkDY8mX4+PhcEiyelPV9B7kmF8pNXY6NqgAuGj2Y/Ll12fmDTQNY9GRr6jorO78kugqBWQfeICnDWvglZu5hNrAk6kV8diw1HbwFZmX9/cfPpE59mYjzJk/fkXvc6f7mf/ELFikEgpuDWhU7f/75J926dbO8NufaDB8+nA0bNjBp0iS0Wi1jxoyhoKCADh068OOPP4oeOwLBDcJuGXmnaELjtuFSubeNmfodcfFU4jZ3IrrcochdAhzuXd2sK935dNJfHo9bdCRSXAyDDk7i3qB29j0dlotK4NMY0/fmvJvvJpvmXn0/xZTIfP5PqzCYIbCDlUenMprERNOA0UvJ0xkXtUzedqqSqMmhc9NCxnRrwr6z1s0Sfz+Ry7QvDjsuO6+U5J0//HMboWMmMXMP+Z2H4AO3xKys/658meCNP9JIA1pXONk3goHzPqltswQCK2pV7HTt2hVnbX5kMhmzZ89m9uzZ/55RAsEdiL6wEENhIZlz5qBJTLJa0+xKJEOCsOeWoPxh7OWFu3rA4yvh21fR9prO/x0cxytNn6drdCTlibYf5Ipqqq/Ms7DKE5PxQsa3U9+nODsdfZk3mic2oP7vCGvB07irqSGgGbOgGfCBSeiYw1xVwmBGrfMux8ZiU5VVoUbH5CpNA8FUWm6QJJ7t1Ii1v5g802pXBa90CqJXuBx1zkHTzK7KOUNVkryLDc5tKFb5UDZqD2Wufshc6lBN3VqtoC0t4qsxDxGx5yIAGYHg+dpUBvaJqV3DBAI73LQ5OwKB4N/B7M3xGzbURuiY0SQmYpg2BeXYP6yHVpbmwj/f4tf1VaJDOhCfuon2cUvxRma1lzoyErmnJ+qoKDRJtvdQR0aiPZhiea1NTML//DByYsdTBhiio6k77TcMmaeRuytR6NJR+ofAZyOsNzq9A3orLwsfva03Sa5ynk8j9/IEILdEZyN0zCSezOPZaFNlkdpVwdYh4TTfOw1F0uWmhFY5Q1WSvL0Uzm3IK3Wn+bungFM3ZVfmv3Z/x/nXJhCRbvpj9a8IFb3e+oY6/nWruVIgqB2E2BEI7mD0hYWWsJXvwAFOzzWWaOCuNtYH806BqwceOiXLG75CQZ0cyowKgmbNwJiRiaGwEJmbG9qDKaSNiiV0/jwAK8GjjozEL2YY6RMmWm1dOeylPXCA0n0HcG/VCn1pKUbv+zG4uKPo8CrKPa9be3wqJ1TbmXulyNmDOqqjTc4OgLpTJxT+/gB4SiX89kx9/D3kuMklyjXF5Bk9+PGMgRW7sinXm/JTXukUZBI6qb9ab1a5R06VJG+/s7uJDulAYuYeGxsi60ZxKM1geb3zRC5Tth269q7MNcyXr8cS+vFvNNCawlan/+8+Bsy2HxYUCG4WhNgRCO5gDHl5lvwcWTUtG8weD6g8C0uG7P9+QvvHUbIWLUbSXGrW9+kW0kY8Y7NH+oSJ+MXEEDxpEsaSEgxaDej1IJcTumghcjd3NAcPkr9xo8Weyr17MmfOsuyljowkYHQs0qNtcPlm2GXBU7lyyc7cK+WBNwkdt4EMmQxNpXCbOjqS0DkzTPk6hekEbZ+I7L4Y+MVU+u4CeALPNe7Oky8s5e9S0z+fvcLl1h6dyph75FSppvJJepPZgz9ktlxOYqXcneiQDsxqMYKLpa586OnKqAf86RUux9VQjLo4FWRBtZa/U1pcyNeje9PmT5NwOx8sw3fmTPr3GFwr9ggEV4MQOwLBHUTVSivJaESmViNpNGgPppjCSSkplg7IUnk5cjd3KnJyLB4Pu0nMkZGELXuD9AkTkTQaKi51QK+KpNGQFx+PKiKCsqNHUd9/P7nvr7cqR1dHRlJ/3To0f/4JOOndk5xMLuD9cB+82pnGUkiNu2N08UTeuCuy0ztMXZb7JZguMAseXSkuf79P2Ny5GDLPYdSUI1e5osjZg3LnFHhsOXw1Fll4B7s9fmSnf8GfibR/eDVqVwWuhmLnb3pZEfjfZQprmUvYdaWEnPuDJQGdyG8yiGKDDi+Fq6nPzof9CAzrwPcj3sB3+wTHobF/kYO/f0XWzKm0uWAKWx1u50Gft77F2zfoX7VDILhWhNgRCO4Q7IqU6GiLSMnfuJGwlSuQu7mTGx9v3fgvOhrPTp3Qg/1ZWMnJIJfRYOMHVKSn4xJWzYexzNQNPfedt+2LGLkMVbt7gWpK1pOTCRw3Fr2qFTqfjhj86/HfAyUMeXQlsm/Gm4TKtpGmROVOcaawVnkxeASgLD2L8n/9bTft/pqpc3PPWbBjkX3zT+/ArfgM373QGh9ZNQ3/3L0rzeUad1nw1G2Lz8eD7CYfK1N/wV93Hpmz0Ni/5OH5fNFzhH+SSHg5lLrB2YGRDHzt/X/l3gJBTSHEjkBwB1A5N6cymsREkCT8YmLIi49He+gw2v37bQVIYiIZM2YQPHmyzR6Xz0lCHxND2d/HUAYFUW/dOmQymSUsZQ5xmZORVW0jyFu71uFefsOGIVOrkbk4H71gKCzk7FNjLK87RUdTcfdruEW+BL3moa9QYiiTYywoRC7Tosjai7JhBKjqmGZu7V5nurDjaKjXHkl7EdmwL6DM/igIM7Kyi0i6HD520mnZqkeOT5hJpFwapioZ9DgefGPa3y7/0viIksJ8vh39EK33m6rTzoXICJyzgH5dnryh9xUIbgTy2jZAIBDceCrn5lRFk5SER8cOAKjuaWW3WgpAs2sXFYXOBYBcpUKbksLZIU9xfvRozsXGok1JIWzZG8jUatTR0fjFDDOJn2r67kg6nek6hcL5w1WZladJTCRzzjz0LoFUZGWTPvt1Tj/xJGeeHsHpp0aTvvUkFaq7TZPOz/8J/TdA//Wm7z8ehGzDI5DQ2/k9AZRuuBpKWLErm+MPLEDfqLv1ur0eOSpfCLibDM9WZOnV1e7vkBs8PmLf9q0kP9HJInQO3e9F1Fe7aCeEjuAWRXh2BII7AGOx87wSmbs7jb/9BkN+vtPzFOpqPqCNRrthKXOIS6ZUcnbkc/jFxOBSr57TrVwbNCRr6RJUrdugjoy02RdsS9Yt90xMRl82kaw1m+13Sp63lLC4QShP74DWA8CoN3l27n8WlO5wfi+cS7ZJbrZwqcePLvRxNLpCBmxO45VOMxjWexGu+mL0Ll4UyOpQqvPEX6OzqqIy9+/pXN/Fyeyt7sgq9xCqyg0cH7FtbgwNP/uDejoocYfzQzozaPLbN+x+AsG/gfDsCAR3APJquo4rvL1xa9wYhZ+f0/MktTtu0ZF219TR0ZTuti2lBlNYCpkMfV4eDRLewyMqEpmrK+roKPt7RUUhc3VBk5hE/saN+MUMQx1pfd/KXiJ7GIpLnfcNUtY1hZjC7oMjX8DHg0zdmD8eaPLy1G0HHV+ExlU8No27QodY9BeO8tOlqd4anYEFv1zgrCyU4T8auXvlKTqs2Ef3Zb8xbvMBMi5qLZeb+/c48ghJd/VA9vgqyPrbru03anzExbwLbB10Hy0//gO1Ds6GynB76w2eFEJHcBsgPDsCwR2Awt8fdadOaHbtslmr3FvG4F0H9+hoyuyEvNyiosiWV+D+2isoFlRpGhgVRfDkSZwZPMShDRXnz5MxdZqljNwc3sIoWVdjRUcRPHkyhgLTOAZJo7GUrPsNj0EqL0fm5oZrgwak9ut/udy9CnKV8yZ8xvwsU5jp+6mOp6qHR0LveaAZD2UXTaGl839g3LeR4/fOYMXmNMslDzYNYH/aRbsdlyv3ySkqqwBMAsnsEerVYRauhhJ0Ck/UviEE1QkxTYDXl11OaIYbNj5i74+bKJq/gHuyTa9TOtTh8bXf4+F1M/ZuFgiuHiF2BILbgKol5XJPD4ylpRiLikzDPP39CJ03j4wZM6wEj7pTJ0Lnz7PMgsqUXCl+8VW8JInyyo3/oqMJmDUTqaKcovyLeE6Lw7ciDnmpFoW7CiQwarUOhQeY+vj4jxyJPifnsmhRKvGLGYbfiOFglFD6+1H8y6+cGTyEsOXLLNeaS9Yr0+i7b1C1a2c3F6mqF8gechcJvOrCaQc9cszjJooyTNVZ9dojSWBs2Y+fvPsTt/ksGp2p+d+DTQKY9Xgr0gu0jO3ehPd3pVrWwCR4cktM4Sxv98sJ12aP0ALLkUK2xzUhCGwSmi1dq2tY6GydOYS7vjxImA6KVXBhaE8GT1hTo/cQCGobIXYEglscuyXlUVH4DRtq6XtjEjXzCVv2xiVRVILcyxOFv79F6AAUl1XwW2E5T8x+jbCycowlJejULkiuLuQuXozml8vCwKNbNwJfHIM+NxeprAzXBg1QR0c7FB/av47g/VBvshYusumrY+6gHLZ8mUXUmPv+2M3ViY4CZAS/OpEsJGsvU3Q0wVOnYNRqUUdH2Q1lqaOjwCsI7YUy5I9/jiJ7N8oDb9oMG5UkKPJsiD7ME28fPzakaHh7bzqDHwhn7VP3onZVYDBKJJ/O44m1u9DoDEQ38Wf1kHa8tPmAleApvuTRCfB0pXPTAHbaGUXRuWkAAZ6VuiSrfG9Y1VVBTjrbxzzGPYfLADgTJiN84Ur6driC5GyB4BZDiB2B4BbGYUl5UpJVSblm1y4ypk8nbNkbuDVu7HC/Ot4aDunW8u6vlwXG0nazaPn6d1aiQaZW4ztwANnLllvEiEytpn78OnLBpuGgX8wwyo7+TdbixfYTmDE1D6xcoZW/cSNhK5aDvErILDoKv5gYzg4dRr2VK/Hu0we/YcMs4S19Tg4yhQJ9djZBcXGUPdSHrMWXuzuro6MIGDWK1CHPWI55dOtCSNz3SEU5piaDajcU2bsxeIcT98NFZj0egVLlwu/nDpBbomPtLycZ270JB9IKSDyZZ/U85teVB4UCeF3y6PioXVncrw1Tth2yEjydmwawpF+bf2UkRNI36ylbtJRWuWAEDkf60Xftd6g8blzis0BQm8gkZ2PHbwOKiorw8fGhsLAQb2/xiyy4vSg/fZrTjzzqcL1e/DrOx462vG787TcOxU5heSGTdk4iKcPaE/J52zXoB422OuZ/qaS8qnCR+/tTb8VyFF5eGIqKUHh7I/P0pDw1FRd/f870s9PEz2zr2/Eo/f1NnqLycuQqFQp/f8qOH0fp53dZzGRnowwJMfUE2rfPYZWWKsLUjFAdHUXItGkYLuYi9/BAe+goWYuXWIRO5XEU1rlDkQTNmUOxdyBB3u6gLUBflE3hxTyKUSPzCOThdw5beW8qkzD8fkZ+YOoC3blpgM1sq0KNjtwSHcVlFXi5uxDg6fqvCJ1Pp/Xn7v8dwa0CCtWQM+JhHn9p+Q2/r0BwtdTk57fw7AgEtzDVlZRX7WVjLC5xeG5+Wb6N0AFQlpajxyQKzGMk5Go16rZtUUVEWBoGytRqQufPI3ddvLVoiIoiaPx4KrKz8Y+NvTyGQq1GujQXSyorwyUoCO3hv6y9MOZwXNwEq3wgdWQkgePGOm5KmJyM3/AY0/eJSWTOX0DwrBnIdFrLfC3z83j17EH2ihV2Gikmkz17rimJujAPvhqL8vQv+AP+gLFxD7YOmc+AzWl2BY95UKgjj42PunpxYxZERWUVeKtcCPC4dkGUeyGV38Y+Sesjpp+J0/XlNFn6Jh3bdb2m/QSCWwkhdgSCW5jqSsqVgYGW2Vem8z0dnlusK0alVDGs5TDaBLSh3FCOu9Idj2Ivyit5P6zGSFSaieVwhlVSEtkyCJkxg4JL1zv0plSZsVU1HGfZMzkZaXSs02evLPQ0yclQrkMyGAlbtdLkNfLzI2ftm6jatXVcor5rF4bcHJR7ZlAYfj/5D461mmPVbP88Xun0Kgt+uWBzbUN/NdvjulyzxybjopbJ2w5ZVXd1bhrA4n5tCK3jvNKsKru+eoeKpStomQdGGRyODuDJNT/gpqqmb5JAcJsgxI5AcAvjtKQ8MpLykyct4kF1772WEnMATV42xoKLGIqLUHh5E+ZVh2VdlrHx6EbeOfSO5byl7WbRceoUh8M4AYvHx+EMq8Qk9NnZqCIi8Bseg8LXl5zVqx02ILQSPJW8NJWRV9PgsOoU94qMC5wffTkcZ/YayZTO/xk0FBWR2eFZZp3cQtLOTyzHo0M6MLvjs/TWKytVU5no3DSAsDoqi8gpLC8kvyyfYl0xXq5e+Ln74ePmuKzb3HiwujL26jDo9Wx7bQB3f3MMNz1c9ID8kU8weMySaq8VCG4nRFNBgeAWRunjQ+i8eaijo62Om5OCsxYvIX/jhwRPnWpVYq5JP0f2pCmce6wvGUOGce6xvpRMm0uYxo2UnBT83P1YH7WG3yM/oYOhIaqICLu5MWASKObQlCNkajWKOnXQpqRwPnY0hoICJw3/knAJDqbBxg+QXxJn9vaWubo6LDG321m5yiAqTVIS+Rs/RFlJANpD7q5CX+ZNB682qJSXPSqJmXuYfXIL7t7Ws7uqhq0ySzOZtHMST3z5BE9/+zRPfPkEk3ZOIrUgndM5JRRqdDb3NDcetIe5jL06stNP8VX/+2j9lUnonGogJ+D993hUCB3BHYjw7AgEtzgudUMImTUT3enTliRe7cEUK89I8GvTcAkJAUweneyZs9Am2g77dFtiZPyo53k4pCuFcxZx4dI5YatXObXBfF9H+MXEkLVkiUUwVTcXS3f+PAWfbCF83TrOjhhhs7c6OgpcXAieNpWsxUvsVn+lT5hodczuWInkZHBxcTqOouiHHymOj6drdCTt45Yy8uAktHpTR+TEzD2UtpezPa6L3UTjwvJCZiXNssmFSspIYsHeOQxpOI3MQk9clXJ8PVwtOTnmxoOOKK5mfcfWNciWv0WLAjDI4HCXYPqv/gEXVyfztgSC2xghdgSC2wBjQYFV1ZXNelFxpXMv2ggdM+WJyTw2aQL5cxZZnSNzdR4ycQ0PR+bh4bDPjkfHDlYhLmfCCEAZEGDJ9QmeMsVKqJg7LOvOnKH86N8EvTIe4l5BKitD7uWFNuWy0DOdH43f0KetxE9lDAUF+MUMA3DY/8f83ngA705cilpnStw2eLijLlcQUtd+LpSjpG+APZnJPNdCy7PvHWNk5xDubaTkXGkZ9Xz88Pf2Ru2qcFjp5eVufxK8Qa/ns8n/ofn3p3A1QIEXFL3QnyHPz7N7vkBwpyDEjkBwG1BdorKkK0dfWIjSxwdDseOJ2TK1GleZC/7DYpAGDrJUTCkDgxw3DIyORubhSdbChfgNfdpmGKg6MtImL8Zpw8DISIu40h44SMj06Rg1GjyiIlH4+CBzdUWfl49raBiGnBzOxgy/3C+ne3eCXn6JsBXLkcrKkLm5oQwIsDrH5r1TqcBoJHD8y8gmTkDSajGUlqI9cNBKNIFJ8Nz1/AucG/EMevOx6Ggq5s/HpW6Izd6F5c6r5UoqSlk7rDEfn36DDbsuvxdRoVGsf24Sz7x3wkbw2DQevMSFs8fYPW4gbf4xeX1ONFJwz7L3iWr5gFMbBII7ASF2BILbAIW/v9PuxaW796AMCkLp44PCy9SvwlJK3q4tMqUShZ8fcnd3MufPR5OYZFUxpb7/fgJGvUBuVSETHUXdWTPJXLyI0h070OzdazXDSuHjg1GrxaDRWtmUv3GjqaQb+94UQ3Excn9/QufPs9hT9ZxzL7yAKiLCKpm59JdfyCovx/uh3pYSc1O5e1tTZVfV9yY6mrIjRyznmp/Jb+hQS0l9VYyFhVavNYmJZMyYQdiyN6y6UWdc1FJWbt8DYybE04+VBxezJ9Na9Jm8QUuZ1fcVJm89ZTnuqIz9l83LUK56j+YXQS+HI11D6bfyWxG2EgguIZoKCgS3CeWpqWTOnecwFNNg/XpUEW1MOTszZuM/YIBVhVXlRoFmoSNXqTAUFuJSrz4lO3aAUoFnp07os7NBLkcZEIBMriD1yScd2lUvfh3agyk2TQjNYsujYwckgwGpogLtwRTyN26k/ltvIvf0tOrQXBmrpoGVvjfTcNtnABhLSpB7eyNTKm3HVFzqpHwudrSNqLG3Z+XnsRcyrNywsVCjY+zmA7Rt4MbfhrdsxAxAh5BIXmwTR8yPAxy+d1888RWyiiCHjQcNej1bJz5Gy5/O4mKAPG/QjHmK3iNmONxTILhVEE0FBQKBXcyl3fYSlc09dtT+QdR9bRr6c+fxHTwI/2dGIBkMKENCcG/RHP/nRqIMCjKNdqjiUQl65RVy1qyldMcO/GNjcalfD6WfbTWTVQNClQr1ve3w7tOH7NWrKL00X0vSaNCmpKCKaGPJi/GLiaH+W28iGQzIFApUERFoU1JsxIhV00A7pen67GzOjx5jee3RtStBE+IwlpQiGfTIFAoUQUGc6T/ArvfGUbm7R9euIEnUi19naozo5o7m4EHyN25EV1xC/rksvMpKkJcUMzPCk53Zeobd/SrwupXgiQqN4qlGE9EaHIcUAc4X5nN3nbrcFWSbE3Tu1F/se3kIESdNAbV/7lLSbuUmwptGON1TILgTEWJHILhNUPj5of3rL7veCHWnTpYeO2XpGWTNnGUtZC71nLkwdx7h77xtI3TAJACy5TJU7e7FvXlzvB7qDUYj+pwcq/NkjhoQRkcTMmM60ssvmzwuHh5oDx+2CJ3qmhZWFSWVK7qqVncpfHxsBEnOm28RPGUyurQ0NH/8aSqXdzKlnSo+b4/u3Ql6ZbzdQaZhK1cg8/KibNY0LlYKl3WLiqJ03GTauI7l6U4voZM0uMrU1PcOIr+sBEmhxxmSwd1uX52fNy7Efe2HNCu6FLbqGc6A5d+gqKZnkEBwpyJ+MwSC2wRzz52MGTOsmgyaJp6beuzoCwvJmjnTVshc6lQcumghMpncaQ+c4FdfJXvlKjx7dEcqK7NJNnbYSTkxkcw5cy3emoAXx6Bq25aw5cucNxm8tGdVEVe5oqvy9+roKMpPnrQaCxE8ZTJevXqiz87GJTgY176PYSy1ziOyeT/9/Wi47TMq0tORubqiDAgga5H9QabeDz9M0QcfUFYlL6g8KQlv2VKemDWNbf8UkbAzH40uhx2Tgnnv2BLuDWrH0nazaCIFWKq7dpamEJ+6iTYBbdmfqrf01fFRu5rCVuMfptUv51EaIdcHdC89w+CnJzl9FoHgTkeIHYHgFkRfWIghLw9jcTFyL28U/n4ofXxwqRtC2LI3Lq2VIPfyROHvb0mcNeTl2U1iBtOHdvCM6VScPev03hXZOQSOfRGZiyuGsjLU97bDq2cPcta+SemOHc47KV8KD+XFx5MLBE+exPnY0dSLX+dYYNkJKVXum2P1fXQ0AaNe4NylnJrKXibrJORogsa/TMjCBZYho5VDUqp2bSn+5ddLYTbT89R7O95ukjOAMijQiUBMRFmQxt+Gj1k7bCKbkwoxyIo5mH2A8fWG4vHGBlOH6Uvnd42OpOvktZyT+zJ6oyk5ubisgrTj+zn4SgwRp03VWcebKmm/egthjVrava9AILiMEDsCwS1GxYVMMqZPt26k16kTofPm4VI3BKWPj1VVUGWqGxxKWRnIZA6XZWo1LqF1yVq0yG4+j++wobj4+9vNaTGHjMwhJ01yMkaNBnV0dLVNBiuvq6Oj8IuJIf2VOFNobNo09AX5eHTsiDIkmNT/62e5lzMvUzbg3echq2RjdWQk9detQ+Hny5lBg5E0GovQksrKrsg+eyg05ezJTEbGMuY8uZDc8nTGN32eun9lohw2DN+BA63F1pJ38J4w1XL9yf8to+6GbTQtBp0C/n6oMQOWfiXCVgLBFSJ+UwSCWwh9YaGN0AHTwEp75c9Vqa4fj1GjcdoDJ/i1aTZCB0zCJcfNjcAxY8h6/XUbIVQ576ZyyElfUEDIzBlIpaVO7XKpV4+wVSuRubmhz8lBGRhI+HvvUn7yFLr08+Rv+ICQWTMx5OZa5eE4n9eViN+woTbPkSuXEVRpyrpZyDhrhFhdk0Sj2jRmYndmEuVSEd6u3jwZ2IPsdfMdDkINMOawcnAYZ18fQ/OvclFIkO0L0vhRDB403un9BAKBNULsCAS3EE7DULt2oc/OsZxXNcSlLywEpdJpPx6ZSnW5B45cZiNaVK1akfnadLv3d2/enOxVK+0KITB5WbQpKZaQk0ytxjU8nMy5c1G1buO0yWDxTz9biZYGmz/GUFpK5syZNPryC/xHPouk1SL38LCa8n41HiOLvYlJSC++aHltFjLagymoo6LshrJ0OTmO39eoKNy8/FApVWj1Wop1xTSRh5A9f6rTHKUTf++jLOEtOp0xAvB3Mxei1mwjJLyp02cSCAS2CLEjENxCVBeGMlwswFB4kXMvjDJ5UdRqgqdORRXRhoqMDGQKBUEvv0x21eaAl6qxSnbtQtWurakvz8YP0A8bZlXGbihyXCpdXa5OwKgXrMrMg6dMtlR9lR07Tvjb8RhLSjAUFlpCOmXHjuE7aKDNqAfDxUJcwsKQqdXozp4l/eXxl551Cg03bUJ3/hxyVzfk3s49WY48Mvpc0xDOyvlAmn+O4TdjCob5iyivNErDLSqK9KYR3PVAR3LnzLYSQ+rISPyGDSX3jTeJHTqUFSfexcvVC0VeidMcpYONFQQt2UXTEtAp4VifpvRf/LkIWwkE14j4zREIbiGqC0NhMKBLS7uUq7LxcnLujMtN5sw9Z/R5ecjVaowaDS716lGRkYHbXXfh1aMHWYsWU/zzdpMnJiXF0jNH4Wl/BhRcgRfFYLCEstSRkajuaU3mzFnI1GpC58+zaSCojo4i6OXxpI22bfqHDLKWLMEvJgaZm5t1IvKMmZbTQubOQR0dZVdYOBoOCqZZYKroKPynTyUn+wyePdfjGhDEfl0ajeZMwL9Yh764CJ86IXz4j4bVn58meWgTVG3a4BczzG6fowdHDWJvaDR+7n4Yi8/Yva8eONTGlbYf70IuQZYf5I0ZxKChs52+twKBwDlC7AgENzFVq67knp7VjoVQtY1AGRSEH/aTc0t37EAqL0d13314de1C3vvrCYgdBQYDGAzoL1wgePIkKrKy8H70EQwFBeSuiycvPp6Gn211GG5SOMkVAtNwz4abNmHUlJpEm0xmaT5oP4k4iWyjhO+AATa9d7QHU9AkJhLw/HOU7t7jcI+sxUuoH7+OXGTWCd3RUYRMm0Z5air14+OtkqjV0VGU1wvgl5ERxCc9ZZlwvrb7Wsb9Og6AjnU78nSLpwlXSSzelQGArKTEoWcLQF0Os6Nm4+PmQ7kd0XohwBWNqoJ7D+kAONRczuqHlGx6fLjT91UgEFSPEDsCwU2Kvaorjx49CHltGpnzbBNbzWMh6rVfjVGrRXXfvQCWjsqVq300yckEjhtL7rvvERT3ik0TwXrr3uL86DH4jx2Ldv9+y71KfttJQGwsuWDjhVH4+jrOu4mORnvokJXXRR0dfSk3SF5tqbq95wRALjd5sJYvs7uHpNFwLnY0DTZ+gPH550AuR+7hQdmRI6QOGHh5MvqlxOCCrZ/h9+or5GadobNHBDSC+NRNaPVayg2XPVe7L+wGYGp7U3+bB5v4I3Pi9QLw86+HysM0LFTh74+6UydLP6SUVh40PFNK3Vwod4G/ezWm/fBJzJDl4ufu53RfgUBQPULsCAQ3IeaqK+2BA5cGWUZYBItRo3E6FkKmUODasCEYJfJTUhx3JNbrcWvUyKojsHn4pktoKP6xsXh160re2rWW6/MSEnBvfQ/eD/exur8+L8+UM2OnWstUHj6Vou9/QO7vj++AAZefR6UGueNSdwC5Wm2pxKr8nOa1esuXg4v1P2WVx1VI5eUgk1G6ew8u4fUp+vobmyRjTXIyyOUETXqVs/0GWfbvGh1J+7iljDw4CTeFdX7P7gu7MQBbXmyMi6uOMsnFsdetUydcAgItr5U+PgTPnU3a9On8UbyPtodLkQOZ/sCgfrTb8B0V38bSIjoa9fxoqOvcayYQCJwjxI5AcBNiyMtDe+CA3REKIXPnoD182P5YiEuhLJewUIq+/95ptY9c7YFn5wct+8j9/WmwYT3Zy5YTOGYM2pQU3CPaUG/dWyiDgkwjHjy90OfmoL94EfdWrag4fx6AinPnKffzo/zo3wTFTUA2aRIVGRdAZqpiSh0wEFVEhGX/yraHb1jv/M0wGkl/ebzdZy3+eTt58fFWezgcVxEZifejj5A1f4Hd22gSE9FnDrPKDypPTMYDmPPyJA7lHrK5JqMkg7G/jAVApVTxyeS1qJdg2wPpUgdrM5mlmaz4aSpd0v7k3vOmuRRHWrnRpkN/jBu+sNjgaKK6QCC4OoTYEQhuQozFxVeQh1LlQ7VSiCds+TKn1T4Bo15A7uWJ7nS25XjoooVoDxwgcNxYspcvp+yff3Br1IjMOXNtKreCp06h+Ndf0ezZgyYxCZlaTcMtn1Cw8UPT9PIqE87N981atBhVmzaU7thhOV66e4/Dkm5zeCx8wwZKd+++nFdz6Vkzps/APzYWmasr9datQyaTIUlGCrZ8av/+ixfbHT1hxl7jwPLEZCInT2DWH0vtXmNGq9cy+I+xzH15Eh0nTUKh0eDq423VwRqgsLyQ9YuH8X/fnMdbA2UucOShJtz39UkMRz6y2VezaxeGvDwhdgSC60CIHYHgJkTu5eWwlNuchxK++WMMF4baDWVVVxklc3Ula8kSfAcNMr1Wq3GtX5/89RtQBgWhSUyi3rq3bIQOmOZoZS1aTMCY0Xh2jCTbKKGKiCBr0WK0KSkEjhtLXny8TSjJnDNkziUyY+nrI6uSRBwVhd/QoZwZ8pQlcbjhJ5tBkij67nsyps8gdP48OwNHTddp9u61nZaemITfsGGO3xcHpehFF7OJbTSUBz3aWGZYnZDl8Hf+3zbnZpRkoFS1RiaTATKk8nLKU1MxFhVR7qLguyXP8cSeQuRARiAkPOzBgq5jKft6/GU7qrx3kk5n6pOE/R5KAoHAOULsCAQ3IQp/fzib5nBd0miQDEarUQeVqa6jr1ztgXvLVigDAlBHRaG6914y55uSnn0HmwSQMijIbrIxmASPYtKrnB35HL4DBuDVq6dFtBgKC52Hkh552Lrxn0ZD+oSJhK9/H+n555C5uCBVVFC6e49Vfo4m0SSygidPttxLrlLhO3gQ/sOHX06+TkwCo+TYgyPZHjLbZq8UXaZWExzUiG7zPqQscZ1lhlXL6GjunTaeze6byS/LR6VUkdB2KZ7LNnIuad3lfS/1MEpeNAWDvoi2puIt9t+jZHVP0KjK0Xu4Wd3P0dR489wvS2J1pTEhAoHAMfLaNkAgEFhjLjd3CQl2ep7CQ406Otr+Hjk5qKOj7K6pIyMxlpehPbAfY1k5Qa9OxLtXT0vYyyyUjCUlTu9vKCrCmJdHXnw8FefPW8JuyGSOy8mTky39cSojaTQYCgpIG/EMcg8P0kY8Q158vK1nJjkZZNBgwwbyN20ibcQzpL88nnOxsWhTUghb9gYytRpNcjKqthF27XYJrWvz3qijowkYHUv+xo025wdPmUzmvHmUJVYtjU+kbMEKvo/8gP81W8E37d4m9Eg25VUEkyYpiW82zaVOThENM0DrCvujAyiPex6NaYoEv5cewi06EnA+zyt3XbzVe2ceE2L2+ggEAvsIz45AcBNRudzcPzbWcSl3p04o1GoCRr1AbtVuyJGRqO+7D2VQEBgluyXqhqIitAcOgl5P2eG/UAZdrhQqO3KUkLlzqu2bI/fwsHwvc3OzhN1UERF4dOzgZCZVEn4jRlhVmSl86mDUmjo+G6uZkwWQvWKF0+TrvPh4u6E8dXQ0ktFI0CtxSC++iD43F5mbGy716iHpdCaxJJNfHsgZEYF7q1ZWE9Ot7pmUhDEjE/XuI+TGx9vMAStTwLEWLrRNzATgfBDo3N25NzEX5UttWHFpn/jUTbSPW4onsiuaGm91TOT0CATVclOLHb1ez+zZs/noo4/IzMykbt26jBgxgunTpyOXC6eU4Pai6pBPSy4LVXraXKruMZaUcC52NH4xMTZl6EXffY/2yBHbEvW/jqAvKMCtYUPqvbkWhY83qjatMZSUUP+dt5EAl9BQshYtoiLjgmOxFRUFkmQJR2kPpqCKaGOx2/PBTg6fU6ZW4xIcbFsWHx1Fgw0bkLm5On2fJL3ecXitkhioKtZMOUBPc/bpoZYkZ/9nRqAICESbkoJbw4bIPTyQe3jg0/cJvB99BGNhEUat1qk9hsJCVG0jkKnVqCIikKtU1FuzmiPHkri4aT1t/6oA4FCEiiZ/l6HWmZKg1ZW0mFavJf7CNl6dOQN5bp7z57cj4ozFzr1wAsGdzhWLndWrV1/xpi+99NI1GVOVJUuWEB8fzwcffECrVq34888/eeaZZ/Dx8eHll1+ukXsIBDcLVYd8mnNZzGJG4eWNwreOpbpHm5KCpNHY9QLI1GoafbaVzPnzLOsytZr669aR+87baA8cJGzZG2QtWWolHELmziX/gw/QJCZZzgHsVmPlvPkWYcveIGP6DGQqd1xCwwjf+IFpGKfSxeFz+sXEkLVkif2OyRKEzJ7ldMSD4eJFp++jVF5uCtVptdSLX4dUXo5rgwZIegMlv/12+X6XeusET3qVom++se4NdCnPxlzZ5gyZmxuSXm+VZ7O/jZpm/2gILwONGxxr4ckTsa9b5VjV8QtjdafN6CQNrjI1+1P1/Pd0EU8GVlR7v6rIvZw3NBQI7nSuWOysWLHC6nVOTg4ajYY6deoAcPHiRdRqNUFBQTUmdpKTk+nbty+PPvooAA0bNmTz5s38+eefNbK/QHAzYW/IZ2Ux03DLFtwaN7asOZuTJWk0SPpywgY0Qf/qRHRp53EJCyN7+Qo0SUn4x8Za5YXI1Gr8R45E1aY1mTNnWvaoLLbMoqHiwgXOjngGY14ekq6chps+JHP+fHJXrLTcP2TeXIcN9pyGuJKS0GdcIGT6dDLnzrMbgpP0ervXmlH4+FhK8M05P+EbPyAtZrhNmEmTmIih4DnbSe1JSSCZkpy1B1OcTmTXHkzBq2cPspctJ/+P3RxvqeTeQ6b7ngsGRdPmPNJ7iFXys7pTJ4rcvHnmTet/y9Y/34CfsvbSNTrSatho1ftZHevUyZTQLhAIHHLFsaDU1FTL14IFC2jbti1///03+fn55Ofn8/fff3Pvvfcyb968GjOuU6dObN++nX/++QeAlJQUdu3axSOPPFJj9xAIbhaqG/JZ9a93hb+/wwRlt6go8HBHuWcpyjNfUvDpp+hzcy29bFRtI6yETtjKFajvv5+KCxcsx/xjYwlbvgz3Fs2RIaPs72Pozp/n/OgxGPNMoRb3u5uZqriqiIWsRYsJGPWCbSJwVBQyF8deHwBD4UXTuIuICOrFryNs1Uoafb6N4BnTkYxGlP4BDp9bHR2FUau1EjoAskvTwjXJyeRv/NAqyVcyGJCp1TZ7mZOc8zduxC9mGOrISOt7XRJfZceOIel0HD13gBx/iYijJjF2sJWSwDwZobuO4d6qlSX5WR0dTejc2ZS5e1S9JTpJQ3zqJkrjYiwJy5WfrWoStb2GhQKBwJZrytmZMWMGn332Gc2aNbMca9asGStWrKB///48/fTTNWLc5MmTKSwspHnz5igUCgwGAwsWLGDIkCEOrykvL6e8Uky7qKioRmwRCG40VeclVcbeX+9KHx/8Z8/BMGsm5ZUa8rlFRVE0dhIyyZX6jbqjPPAmdaf9Rnnaecs55rwPc5mzsk4d9Hl5KIOCqu1AXLlsvLpeQA0/2QxGI4biEhRensjc3e027quMzM0NY5WhmuEb1pP2f/0sNjf8aBNZkmTViNDcl6eq0FFHR1GSdNlLUjXJV6ZQWHl7rJ6jvNzKwxU0cQIV589bcqMKtnyK76CB/O/jeTTJKUNdDqXucPxutcW7AyBpNTRYNQ+5yhVFzh6Uv71KeJ8l9GwRxM9/X27s6CpTo9VrGXlwErEjh/LgSzEoNOUY1G78pv2bRxvVpdFnWzEWlyD38rRpWCgQCOxzTWLnwoULVFTYxpUNBgNZWVnXbZSZLVu2sGnTJj7++GNatWrFwYMHGT9+PKGhoQwfbn8S8KJFi5gzZ06N2SAQ/FsofXwInTePjBkzrASPs7/eDf6BHH32VZqO1lNHKqfcVcW358tZ/WUqbz11L5oHFtBs72vIT32BMqSP5TqZm9tlUbNpk8Uz4x8bS/CUKY7LxhcvocHGjRT//LOpm7GT5oWSRoMuNRWZqxvnR1/OVQmZO8fp5HbtwRQ8Oz94+VhUFKW791jte/a552nwfgKGggJTXx93d5T+/uS8+ZaN0PGLiSH9lThr2y7ZbR6voU1JsduXx5wfYw4nynp35mKIG0qNjoA+PdH6efHtmpdoc8mbc7YuIHO3EjoAClkJbtut/0iT68uY2/tNtBUGEk+aPGX7U/V0DIlid2YSK068S+XkgejQaPp6P4VboBA3AsHVck1ip0ePHjz//PMkJCRw3333IZPJ+PPPPxk1ahQ9e/asMeNeffVVpkyZwuDBgwFo3bo1Z8+eZdGiRQ7FztSpU4mLu/wPW1FREfXr168xmwSCG4lL3RBTY768vCv+671pk1DOF2jJkMnw93Rl78F/0OgMpKRfpJmPjHo9l+Ah06Ew+ljGMmgPphA8ZbKNqMnfuJEGH2605O1URZOYiH7YUEtPG7nKNvxTGXvJtFmLl9j3zFwKCxVs3QqXwk7q6Cj8hg9He+iwJdnY3In57LMjabjpQxR16mAsLUXS6/EdPIigl1/GWFKMoaTE1FX6lTgbj43Mzc1qvIak0diUdFfNj1FFR/G/i7+z4sS7ALTN9GT4Nxdpc8kxczDCjeZ/leNusPZcuUdHoQvwp+o7ITu1nYoHsmgX7suz0Y0o1xtRuyjoVWcaEgvYk3n5/0t0aDSzo2bj43Z9QqdQoyO3REdRWQXeKhcCPFzxUTuvfhMIbgeuSey8//77DB8+nAceeACXS/F3vV7PQw89xHvvvVdjxmk0GpsSc4VCgdFodHiNm5sbbtV0jxUIbmaUPj5XFJrIuKhl8rZD/H4i13LswaYBzHmiFS9119HYrQivH15B9vUvAJQP2oHfsKEgSZVEjXX/GEmjoSI93el9pfLyS839ZITMme20ckqfnW0aCFrlHmefe56GH21Cn5lp8syYw0JbPyMoLg7dmTPUi1+Hwt8fQ34+2n37rKavq6OjaLD+fYxlZRT/8KOlJ05AbCzIZchUavLffMt+UnF0FMqAAFQREVahq8peqsrVWOZrSibEEH/gVVRKFSMP1KH9j+dQ6aBYBWcHRNHmPzGoln1glVjsFh1JyYRh6D1csJeR5aIvYe0v1u+32lXByM5jmPjYZHTGUrxcvfBz97tuoWPv56Vz0wAW92tDaB3Vde0tENzsXJPYCQwM5Ntvv+Wff/7h2LFjSJJEixYtuPvuu2vUuMcff5wFCxYQHh5Oq1atOHDgAMuXL+fZZ5+t0fsIBLcahRqdzQcXwO8ncpnx1V+8Eh2Ex69xyFJ/tawZczNJnzDdUl3lqHmfzNX5X/pmb40mKQndmTMExMaSi8xmKGnA6FiMZWVkLV5is4cxL4/ykycpO3Ycz6hIMBrx6tkDn75PoDlwgKz5C5A0Ghp+tpX8DR/YLVPPWrQYVUSExcuUPmEiue+8TfCkSSBJBIwZbRqWWrWia+hQzsYMt/H2uDZoQINNm5Cr1ci8PDFqNNR7920qPFz538VE1h54FbUWxnyvo/Vf5wA4EwpKoxsPnJJRIpOz6/n2RFbKs9lReoj4A5NY130t9tApPAHr7scanYE1P6fznzZdaB5UMyXljn5edp7IZcq2Q6wZ0k54eAS3NdfVVLBhw4ZIksRdd92FUlnz/QnXrFnDjBkzGDNmDNnZ2YSGhjJq1ChmOnCxCwR3CrklOpsPLjOJJ/N49xEfFJWEDoBc5WJVyl4vfp29y6+o1NqMQqVCkssJmTUTqawMY1ExcrUalAokg8FqjlNVZC4uqFq1JGfNWmtBEh1No62fUvT9D1fUQND8POacG2NpKTlr3yRkzmy8H3nYUjavDAyk/OQpMqbPsBlQWpGdhfboUZS+vqS98AKqtm0JmDOTT3S/c7fn3fyetp+7U408+30pdXPBCKS0dqHVkQpcjeVoMhPxQILn7+f/Do6zsdXLxbbySrqrB0eL7AuMzk0DCPCsOfHh7Odl54lcckt0QuwIbmuuSaFoNBrGjRvHBx98AMA///xD48aNeemllwgNDWXKlCk1YpyXlxcrV65k5cqVNbKfQHC7UFTmvPGcvNy2ClGRswd1VEc0SbsBx6Imf+NG6sevI1cut/HWmHNczBi0WtDryawqWCIjCZoQhyoiwqFoQiZDn5OD3/AYfAcPsuTi5G/cSOb8BXg/8jAKLy+r6i8z5qngyoAAwlatRO7mjjK8PjIXF/QFBaYxDsXFVGRlo2rdGt3ZsxhKS3Fv2ZJ6K5aTuy7edlJ6TAwFmz+5PG4i/QJyo4aJhyYy6oAf9/5QinsFFKnhdGM17Q5b26RNTOKJyXGsVa5Hq7/cdblLvS7ULZNT/tg2jFodcrUbCl0GF+pGoirzJrpJkSVBGaBTE38WPtm6RsVHdT8vxdWsCwS3OtckdqZOnUpKSgo7duygT5/LFR49e/Zk1qxZNSZ2BAKBfTzdnP/qylS2+R3KA28SOvVbMhbL0CQmm8ZRrFgOVUSNqm1bjGVlBMXFIb04BkNhIchkpmTfSjku6shI0OttkpzNYxOMGg0hr00jc9Ei6+7EkZH4PTMCl9BQ8tdvsBFJ5pBU0MQJ6NLOEZ7wHiW/7TRVf2k0TqaCRxHwwiiUIcE0+HQLMrkcdTvTsxhKS9GfT8e7R3eMJcX2J6UDQa/EAaacJmNhIV29WuK1rZx7jp4FILW+DNcyV9r+Zd9bVXoxh2Eth/HOoXcAk9BZ3GwSebPmWL8H0dEUje3Aq9+kMPiBcEuCsptSzoFzF9EZHOclXgve7s57G3lVsy4Q3Opck9j58ssv2bJlCx07dkQmk1mOt2zZklOnTtWYcQKBwD6uCjnRTfytPAJmopv4Uyivg1/TR1DWbQn12oO+HJTuuHgpCB0/FMOrkzCWlCD39CJkxnR0Z88ilZfjEhaGTKEke+VK6jz5HzKmTjMJiw822u1mjFxu04XZq3cvshYvNpVqX/LABDz/PMjlyFxcTE0FZXKyFi1Cm5JiNRDUHFLyHzmSivPnSX95PAAeXbvSYOMH6PPykCmVyBRKS76OWXxpEpPINUoET5lMzspV1pVe0dGETJtK1tKllP7yq9VzmMWVJjEJ/bBh5H+4iQYbNpC881Nkmz/jnjwwyuBQpB8PPbOArOcvl9FXReMGvev3JqpuJF6uXtQpU5MzfTZlSdYJ3JrERDykJTz3f2NZ/MtJm316Ng+6gp+CKyfA05XOTQPYaSeUVdMhM4HgZuSaxE5OTg5BQba/jKWlpVbiRyAQ3BguanU8E90IwErwdG8eyPQu9XErKEbXYiIGRTmKM6aePQb/+zAUlSEZPChN/sHiKVFHm5rxZUyZCoD/yJEEjn0RmVJpd2SES1gYxT9vJ33CREIXLQQuNyfU5+SQtXCRRQCZc4TyLk0EV0WYmhDWi19nmb1lr3lh8LSpVGRmWvb2HTSQ7GXLHXqBLIInORnDpTBWZTSJiWQuWIiqTRsrsWNvUnpJ4i5+Nxzlnj8LcNPDRQ/IfKoHUUe16Pc5yWeKjiZHDa4VGu77cDCU5lD25LfkJdlWqgGUJyXRbfQrLLazVtOeFh+1K4v7tWHKtkNWgqdz0wCW9Gsj8nUEtz3XJHbat2/PN998w7hxpkQ8s8B59913iazSUl0gENQ8nm4uDHl3D892amTVo6WpXINu3nQyLn3AytRqU/7N22+jSVxvuV4dHUXDTzajz8pCs28/BZ9utXzg565ZQ+6aNdR7O97S/C9/40b8MCX1GjUa1G3bWvYHk1jI3/ghfsNjnCcUP/sM/rGxKAMCCF//PjmrVztoXrgYv6FDrRsf2jnPfO/KYslQaF3dZDk/KcnkjbJn16UeO/naAo41VXDf7gIATjWQ885jnnzcazTn3u3veDhqdBT+0ybx+pnVvHLfKxQO+wKf9x9CKrJvixml1rYi7kZ5WkLrqFgzpB25JTqKyyrwcnchwFP02RHcGVyT2Fm0aBF9+vTh6NGj6PV6Vq1axZEjR0hOTua3SlOFBQLBjSHA05X7G/iytlIIZEqnUBp8vtYqZOIXE0PuuvhqS7f9YoYh9/CwEg1ylQq/mGHI3FzxHTDArgfGq0d3PLp3s4yN8B08yKHNMrUal+Bg8lNSLN4de/15zPb5DR9xqXGhyvF5VUY/gP1GhmYcdXyWyss5+WATvJbMoWUBGGRwqKMvjzy7iMhAP5RyFxp+thUAo0ZD0MQJgIyKCxnIlEoUfn5kaLL47fxv6Aw6pt/7Cj4dRyNXORcSASE+vP2cDDe5B/tOV3AsvYK5fe+5YQLERy3EjeDO5IoHgVYmKiqKxMRENBoNd911Fz/++CPBwcEkJydz33331bSNAoGgCuawROemAZZj3YJdbHJDKg/8rIp50KV5OKbc09M0eHP1Khpu+wyZQkH6K3H4DhlC/qZNlvwa83BO/+HDKTt2jKDx41EGBFAvfh0uYWEObfaLiSFryZLLIS4HwsM8hFQZYJoFJvfywj821u6wzqr7qKOjbaaCW+1tRwgZkfjm62WEJ50kuAAKPOFIczVPTngHN78APD3qYCgsJHvZcs70H0BazHDO9OtP9vJlyFxcSI+bgFzpwl7tMQCSLySjwQj12lsq4OzhHh3FttwfmZg4knG/D+YfaR2z/68edUWDP4Ggxrnm5jitW7e2lJ4LBIJ/n6phidC8s5yvco6z2VWV1zXJyaZwtEKBa8OGZC1Zgt8wU8hH6efnNL9Gff/95Kxbh1uDhnj16ulw7pVHxw5W19oTHs6GkDoa1mme8xU8dQqqiAgqMjKoHx9vqbSyVI9FRdkIoXwvBVkh0PYnU5PAkw3leJd50LHOPRT/vJ28+HhC5s6l6Ifv7XrHAIKnTEZ75C/uaX15MLK2QgP6clMF3LgNZMgUVu+Je3QUJROGEX9gkuVYUkYS83bPYUnnJdfdLVkgEFhzTWKnW7duDB06lP79++MjJu4KBDcUfWHhpVlZxci9vFH4+1nGSVQOS5TrC2yudRbSqbpuKCxEu28/+SkpJq9P6zYET5mMPjvbkpNjN7/m9dcJjB1N9ooVpnL2ZW+A0Wid0xIVBVVGv2j/OmIzasLZfczrVcvNlYGBNNr2GZnz55M543LD0coCSRURQciM6WQtfd2yfrSpmsAcDS1OgF4OhzsF8YCuIf6PPIoyMNDST0gZFOg03BYUF8fZYTE0/nij5bi3TA5KFehKcTm2gbClKzAUlqIvKkavdmNb7o/EH5hk1Y8HIDEjkfyyfCF2BIIa5prETuvWrZk+fTpjx47lkUceYdiwYTzyyCO4VtNmXiAQXB0VFzLJmD7durlfdDR1Z87AUFSE3MPTIn4U/v6oO3Wymph+Nd2QjboKvHr2QNU2wtTkT6VCGRRERWamJSfHHu53NyN71UrLPSpXbyFJKOrUwVBUhMLb23KNTK1G1aY1Hu3bk2uULNc6u0/V/BxzFVnJjt/Q/PmnfYEkl9Fo22cYSzUYy8pwb9UK737/x7efLeCe3zNxMUC+N1x8sjv9Bk/kzNBhuDVpQtpzz9udmWWPivR0JI0GqdiUbBwVGkVA1jEIaAnNHoVHXkfpE4zSH85nl3Cy6KhlmKg9inXFTu8nhnkKBFfPNYmd1atXs3LlSn7++Wc+/vhjhg8fjkKhoH///jz99NN06dKlpu0UCO449IWFNkIHTGXUF2bPsZRxqzt1InTePFzqhhA6bx4ZM2ZYBM+VdkNWR0ai9PMle8UKKy9GvXVvoU05hCqijUM7qwqUyiMpAOq9+w7aAwdRVgpx+cXEkL9+w6Xk6Mtl7XIHeTlm5B4ehG/8AIwSRq2pLD5s+TJy16yxe74mMQldWhrnR8Wijo6irE9n/l7+Mu1Om5r2/dNQhm+BgsYf/ELmyTIabFiP7vx5q1BZtd6xS3/kybw86Fi3IzPbjccn/xzsXAqPrQCvEMu5RWUVuMqcP6OXq72RoSbEME+B4Nq45pwduVxO79696d27N/Hx8fzvf/9jwYIFJCQkYDAYatJGgeCOxJCXZzf3Bay9HJpdu8iYMYOwZW/gUjeEwHnzkBcUUJGVCTIZmj/+RNWuHQHPP4/MxQWpQkfp7j2W/Bd1dBRBL48nZ+2btuEamcwyIR0uj2mwNAFUq1H4+1Mvfp2lKaDm4EEKtm7Fd8AAVPfdi2toKPkbNlwOcSFZCSQrYeRgXpcZY2kp+Rs/JCjuFc6NGWPyqFSXl1RWBsAfOSkEv5FEs0JT2CqltRvtUsqQI5nex8QkshYvIWTGdKsRFU69Y9HRKAMCCZk7F723B3M6TCf00Gfw+zLQlUKvOVZix9vdhV/+0dMhJJI9mbb7RYVG4efuZ/c5xDBPgeDaue7pnZmZmXzyySds2rSJQ4cO0b59+5qwSyC44zEWOw9nVP6Q1+zahSEvD6WPD7LycrJef93mwzkPUyfioNem4dXnIdxbNEfm5oYyIAB9QQGlO3bY3EN7MAVVRATFv+7Ao3s3qxJ0czJx9rJlViLJo2tXGmxYT9bCRQDkv7/eKsTVYOMHGB0MB72SsJsmKQn9iBEW0aUMCLCz02XkISEciPSl9Z4ClEbI9YGsYA/uSykFrJugahIT0WdmWuUGXfaOyWxGPvjFDONsTAyqdu0I6fgAqtOpaGmB/KEPUWTvRl5WTGp2iSXk5OmuJDXLwFMPTATesBI8HUOimNlxtsN8ndwSHfvOFjC2exPa1a9Dud6Iu4uC/WkFvL8rleziciF2BAIHXJPYKSoqYtu2bXz88cfs2LGDxo0b89RTT/HJJ5/QpEmTmrZRILgjkXs5DmeAbXjFWFxi+karcVxuvncvMr0e7eHDFH37HZrkZNO4BgdhKrM35uJ//0vQhAkYcnIsc6UkyUjBlk9tvEHuzZtbuihXnkoOphBX8c/b8ejYwen9qs7rqhp2U6jVqCLaIMM0TNSRQCqOjmD7lKdol2oKWx1voqBV7EzaeQbbVGuZMRQWomobcfneHTqgCAgg+LXXoKwMo1aL3NOT4l27SH8lDkmjMXVonj0XVZs2ludVR3UkuJUfg95JJrdEB0CvFkEsfagRpVk5LPR9AddGceS5ychTSNTzDiTUy9/u+wJQUl7B6iHtWJ+YatVfKbqJP6uHtCO9QIuHm1KEswQCO1yT2AkODsbX15eBAweycOFC4c0RCG4A9hKOzVRNLgaQe3kCYCwrs5k3Zf5g94uJIXP+fKtOwJXDVFWRNBoyps+g4ccfkTl3bhXPhilBWLN3r5VgqByishdiyt+4Ea+ePVBHRdmMdZA0Ggq2bjXN6zpzBqm8HJmbm80QUkNJMedHjwFMnqTgaVOtxlQA/P1gA4JTUri7CCoUcOg+H9rtvYh+4izO4bicXebmhlytJmzVSlMjxJAQshYssB1mWqUbc9UOzZqk3WTNWcCn0+fx2PoU1K4K5nb0p+i1yTYeojZz5+LufVnoFJYXkl+WT7GuGA+lJy544+7qygeJqTbz0MyvJ/dpLsJZAoEDrlrsSJLEqlWrGDp0KOpqkgkFAsG1o/TxsUk4BlsvB4C6UycU/qYPS6WvL9pLXYorXxO27A1QKi3HLVVTI4YjU6vx6NYN92bNbESSS3h9G6EDl/rMGCWbcvDKAsdecq+k0ZA2KpbwdevIlslsKs38R4yg8Otv0O7bd0VVZKU7dpAN+MUMw294DAY3F75NmELrxLMojZBTBy62bsx9v5+mctjKXjm7OtrUi8ez84Om/j1yOYa8fFSt26A9cNBqBlfVa6s+u+k9SsSjsIBRXRozsL4rukVz7fTrSSRr1kzCli1D6eNDZmkms5JmkZRx+f3uEBLJ5Ptnsi/tos37ASbBo9Mb2Xkil9wSnRA7AkEVrknsjB07lm7dutG0adMbYZNAILiES90Qy4DNivPpKAMDKDtyxMoboY6Kou6c2Sh9fNAXFpI5b77DPjVBE+IsxypXTdVLeI+guFfIWrjIWiRFReHzn75kzV9g177qxjU4ysEx5uWR89ZbBL0yHtmrr2IoKkTh7Y328GHOxZqmitudQWVH6IFJ8PgOHsS+aS9R7G2g3RlT2OrvJnLaPDuNwGnzq7VfHRlJ8OTJZK9chUtYqMOePZUFz5WMqqgoKuaJxuF4ZZ3lnKPw4q5EdLnZlLpjI3QA9mQm8/q+eYzsPJo1P6fb3SOnxCS0issq7K4LBHcyVy125HI5TZs2JS8vT4gdgeBfQOnjgyEvj/OXRib4xcQQtnyZVYhH0plyQqqr4JIp7U/TVnh62oSBwBSayZw338aDUZmq3gztwZTLXqJ2bfGIioRRoyjdvfvypPXISIImxJG9bLklMbre2/EWgSFTq9EeOUrguLFIo2ORe3ggV6sp+vY7u12UAX7duYkAXQVNzoBOCYdauXNvipYgjwDsywMTcpXaNIX9YAqGixcJemU8+sxM/GNjLfZeiSdHHRlJ2ZGjNiFEbXAASl0pRgcDSs0U5KVT5i+3ETpmki8kMaTTOIfXh/uq2TY6Cl8P4dURCKpyTTk7S5cu5dVXX2XdunXcc889NW2TQCCogrkyq2oPGzNe3bpZnecIfWmJxdMiU6vxHzkSzy6dkSmVjpOaExPxGzbU4Z4udetalWqXp6YSMnUKF2bNrtLtOJpGWz9Fl5EBer2V0JGp1chVKsv3lpERa9daXe839GnbZwJS2rjSdnMiCgmy/KDAV839KRpAVm2fHKNWw/nY0aijo1FFtOFc7GiLIKvszXHmyVFHReE3YjhIEvkbPrB6blV0NCHTplFRTdjfo04gF8oKeLPHm6TkpPDh0Q9tOizLFGV2r41u4s93RzJZ+8tJHmwawBLRd0cgsOKaxM7QoUPRaDRERETg6uqKSmX9S5Wfn18jxgkEAhPVVma5u6MvLKz2vAoPV/xHxyJzc8P3qSHI3dzJXrbc6bRygEutaGxQR0ZSdvw4DT/dgu7MGWRKJXK1mguz59jNTcmcvwBVRASqthFWpe5+MTFgNFq+tzsyIjERJOscoQsBrmhUFdx3yOTZOtrchfavrkR6/yM0p0weEu3BFLvJ0Gb7zet+Q5+2CVOZ7bGXcK2OjkYZFETDz7eB0UjZ8eMUff2Njd3axESyFi7Eb+jTTvv1fJ67nRXJps7KHet2ZGnnpUzaaT1SItCjDtFN9FZJytFN/HkmuhEvbT4AwO+i745AYMM1iZ2VK1fWsBkCgcAZ1VVmFf/8M8qgINTt73c4iFMdHYXe14uLinICRz5DxZmzlvLzqh6LqiiDAm0+qCvnz6giIlBFRKBNSSFo4gS7wgJMAiJoQhxGrekD3ByW8+rVk+KffkYdGel8ZERSEsETJwDwS9LHNDxVRN1cKFfC0ei6tN2bj5/MHU27e/EbNswU6lOr8X6oN1lvLKuSDB1F8OTJSJeaoNoLj1V9byyenMhI/IY+Tc7qNQRNiOPsiGeov2a1U+9Y8IQ4AkbH2ob0oqJwm/oy8XtGWs7ffWE3AMNaDuOdQ+8ApiTln/8qpV24Ly92bYJSIae4rIID5y7y0uYDaHSXm7mKRGWBwJprEjvDhw+vaTsEAoETnFZmPTPCEj7JWrTI/iDO6CgMk0fRf8dwhjQfwjCfrigDAy3nVNfMr/jXHagiIi7NuzIlThvLyzEWFV3yxGwk6JIIqUh3nCEjU6uRDAZTaffqVbiEhVF27BiS0Xg5v0cmc3g9QPHpk/y8ZyMRhzXIJcj0h2J/H9r9dsH0Xvn64f1Qb4wlJehzcpAZjBRt/4WQGTOQNKXozp1D5u4ORiMVWVnIXV1Rt20LYLfvjtmbo46ORuHra8nvMYujrIoK/D+Ix1BQ6tRu3blzpL883vL/o+EnmzFcvEhxgJpBf7xoE7LafWE3Q1uYwocdQiJ5qvFExn54mvsb+PJkuzBOZpcw6sN9jt8nkagsEFi45g7Kp06dYv369Zw6dYpVq1YRFBTE999/T/369WnVqlVN2igQCKhUmZWZaQoZXUpO1h46bFWmbT2IE1wCvSj1duXh30ei1Wt58+CbPNS0Ef6V8oovj3JwXP1UWQTUi1/H+UtVU+bclooLF8iLj3c48sGci5OzerVNv56A+vUtDfqcjYxID3QjddUU2p0zxdWOtHSlTccBGN7/CACP7t2RqVW2PYEiI/F5uA8oFGRMnWbJCar6rI767pjzhdKeedbW+5OYiK50KFTjRKmcO6RJTCJryRI850zjid+G2AgdMyqlBx/0/gxXvMGoYtvoELQ6PXqDkbo+7qhdFVYencp4udtPRhcI7kSuSez89ttvPPzww0RHR7Nz504WLFhAUFAQhw4d4r333uOzzz6raTsFAgEmD0/FmTMWDwGYhEflRN6qScyNN60h3b3C8oGqUqrwCgrDxUdmdU3G9BmEvx0PMhnGklKMWo1NMz/L+ZVHVVwSDMHTptLws63IVCq7oTTHuThJ5Fbq1+PIy3TwHjWNT2vwzoEyF/irpYr+Mz/k7LDLpeOBY0aTOXsO2pQU28aK+/ejat+e4KlT7NvhoO+Oa4MGBE96lTNDnrJbBQag0JRz3KOYltFRtvPFuJwbVHW2mEupnthGQ4lP3WRX8JRq3Hnxg9RLnZOPWeXqPNjEn4Th9zPygz9tBE/npgEEeIoQlkBgRn4tF02ZMoX58+fz008/4ep6+ReqW7duJDuIWQsEgpqhahJydYMwjdoKvBSm31M/dz/+2yGB0kUrLDkyZnwHDCB72XL0OTmWCqW8+Hi7H/BVK5w0yclgMFJ29CgVGRkET56EOjrK6hyPjh0c57QkJ1tGNJg6PQ+z2KaTw4F7XGjzlwZvDWQEwPlQFfenaJHKyghdtJB68etQRUSgz8tDm5JC2LI30KakcD52NOkvj+dcbCxF336HDFC1bXtFdqgjIwkYNQqUSpAky/sgU6vxj42lXvw6wlatpH58PMF1m5Iuu4hxiqmqqzJm71jB1q02dp3p+x+6JaSQ0HYpKqV1oUeHkEj2p+p5tlMj1tvpnPz7yTze+vUU0x9tYXXcXI0l8nUEgstck2fn8OHDfPzxxzbHAwMDycvLs3OFQCCoKaomK1dXWi0LbIDf+a/pHtaZqY1HUz5vOZrkZKuREeYP+bz4eFQREbiE1q12IGdVDMXFFH33vaWs3S8mhoDnnweFAplS6XD4pxmzaJM0GksoLv+xTmS+9Qbt/jLlnxxurqTRGQOhuZcSnN3dkbm5IZWXm/JulErHHqTkZDLnzSNkxgybKe2Vc3XkHh7Ui1+HPjsbhZ8fWYsW4X53M9NzXxJS5mGolvckOponp0+k3M8L2YxZ6HILkWtKqRvkQ8n3P1x+Hjt2lSUm4QnEjhzKihOmaqzKOTprhrSzmoVVmd9P5vLaYy349qVOaHQGfFQuBHm5CaEjEFThmsROnTp1uHDhAo0aNbI6fuDAAcLCwmrEMIFAYJ+qycrVTgo/fIT/b+++w5ssuweOfzPaNOmie7EVZbYslbaIvA7cmyWjoDhYsveWKcgSEBAHiOOnoq/jdeMAtQUHW4aIbEp36UqaZjy/P9KGhqYtOwXO57q8Lpvx5PCozfG+z7mPf50YZtS9B83JQnJLX1c+qQjuk+Q85yZn7VpiFi8itH9/sji7E4wBsFkrbB8pxWYsGen4NG+OUs2heuWTNsVo5Ptf3+b6f4uobwKTN+xpoqfNjtNbPYbERIp37yZtylTnY3XXrK66mys5BWtamrPeqOzPVL5WR+Pvj9pgQG0wYMvOxueGG8ldt47omTOwZt5TeVv8DAXd+Mnssxno+9ERAJY/VItmu3ahGI1VxlWcnMJjY0YTE96OQG9/ikx6Br+9D2OJDbPVXuV9Ky6x0bJuUJWvEeJad17JTo8ePRg7dizr1q1DpVJht9tJTk5m1KhRJCVV3cIqhLhwZcXKtuxs7EVGAh95mLQZM9wOqzwxchSG999BV6LCnHfS5TouIyNKC4MVo5ETw4YT0q+fc7yE3WhEU6tWpTU8hsREiv7c4n7VIz4ew003Yfpr91mtFhVrYF8TL1r95ehuOh4Oyg030ubXv10+L2LUKCxZmcS8vNi5QmPcshWfpk2rvHe2M5Ku8rU6pp07seXnczTpdMepIT6e6JkzSJ00uer28pQUQrLTuD66Pl8NaY/eS0OwrzeGmTNJnTyp2u3GnIx8nvsyE8jk3advcdbh6LRVVxtIIbIQ1TuvZGfWrFn07duXmJgYFEWhadOm2Gw2evTowaRJky52jEIIN7SBgWgDAwEw7dqFvkXs6bNlzpgUbk8/BIbQKre8TNt3OAuLFaORrKVLyVq6FIDI6S9Q8NMGgrp2QR8X57rak5BA5ORJ5H/5VeXbRzNnYWjd2jkV3LUtPpHQ557l+PAR5DxxN4UbvqNl6bbVzhY6brqtL1Ed70Q1QoOtsACVlxeakFDSZ892OZjQkJhA5PjxKFZrlffN3T0wbtpE6HPPEvjgAxxO6lPhOdQq6r66EntR1e3l9rw8/GoV0H7NFtpfH8LsR1sQGBVJzIIFWNPSqnxvcHggBu8cjCU2dFo1t14fyi8Hsth27BSJ14dUqNkBKUQW4mydV7Lj5eXFu+++y4wZM9i6dSt2u51WrVrJrCwhPETt61vpFgmAulYQ+Iei1RkxVNIxVLxvHxHjxpI+50WXQwEN8fH4NGtG2pSpGH/7zbnt5TKby2zGp1lTZ3J0prKREydGjCQ4KYmI0aMoOX4cbWgoKp0PCgp/3dWAep98S3AxGHWwt7GBW/1aEnpTAkeS+jhXkyKnTyd/2TK3U9jTZs0mdED/yg9WrKTeCEDl7c3hpD7Y3dQdGpNTsPbuXen9LaMNCwO7lR/uC8Wm92XH3mP4xNYnvDQpNbRPxPhrxbh0ifGkFO+iX4frWfr9CfKLrcTVrUXfxPpoVCoebx3D1M928/M/Wc73dJBCZCHO2nmfswPQsGFDGjZsiM1mY9euXeTm5hIUJHvHQlxuVZ2w7Hv77aiCanNy6jSK9/3tWKF45hlseXnO7Z/iffsI6taVY8NHUOflxWA2YysoRFMrENPOnc6DAiubzeV3a/tKR0qUUfv5UWflCtR+ftjNJai8vMhcuoycPzazv5mO2B2OuU/HIsDm5UObHUaMuLalg+M0Z3fJGjhWYexPPUnoc8866o1cTkxOdI6EcBufXu820SmjmM0U791XxciHBMwHDrjUEDVLSMDwwnSsih+2nBwiJkwgfdYsl/h1ifEUjUxi2rYxzG33Jrc3DkNRGWlznYUS5QgatS9bjmmZ3yWOgmIrBcUW/H28CPXzlkRHiLN0XsnOsGHDaNGiBf369cNms3HbbbeRkpKCwWDgiy++oGPHjhc5TCFEVSo9YTkxkciJEzg5Zaqz+ypjwcIKpyuHDx3GsREjiJo4gfSZs5zPqwwG6qxaBdaqT+NVG3xR+1U9l8teWMiJESOJWTAfr6gobKfgZMKN5B/c5Ex0djTTcuPfNnyspwdelh/ZoDIYnKcYV9ZNpRiNHBs6jPrvvYsyYjjWzEy8oqNRLFYyly5120pvSExAsdkqnINT/voqna7ywxcTEwh97jmOlSt8BjCnpJA9bSoB99xN2uQpzuuHPPMMNq2aTK9iNhRtZ+U2xwwslaaY8Q9GM/fP6fyWdvr6t0TGc1PDqVwXLg0gQpyP80p2PvroI3r1chxj/r///Y+DBw+yb98+1q5dy8SJE0l2s3wshLi0vKIiiZr+ApajR7Hl5Tm3mCzHj2NMTib0+eexZmYS3CeJoO7dXL7IMxYtImriBLc1N3ZjEWqDoYqZW4mo/P1AUSrdIjMkJmLNznYUML/zDsbkFLbGGmj8t5E6Zijygb8bGWi9y317etmMK7cnMJ/RTaXS6VCMRkqOHCH3w3WEDx/G0YGDCHr0USJGjyLNYqm44pPUm8KUFOqsWEHWypUVCqzrrFiB8c8/K3awGQxgt6ONiuLQo4+5TaTKT40vWxnLXrkSXWI8P/dr6Ww3BwjzDaiQ6AD8lraJ2b9PZ16HeQTqAt3eIyFE5c4r2cnKyiIyMhKAr776iq5du3LDDTfQr18/lixZclEDFEKcHWteHicnT6mQkPg0aYzKYMC/012kz55T6YgEr6gxFRKd4KQkclavcZwvs2ghoFRINEKfexbryTRSX3iBukuXkDZzlmsykZBA5KRJWLOzyVqxnKw/NnGgqZbWOx2JwdEoCHryWVrPXlXpn80rpjb11q4lY+HCCkNGXbqpduxw1uRow8IwbdsGKhX1Xl1J2uzZZL/xhiNR6d0LFAWviEgUFI706k3wU0+RtepVtwXWWWoV+latAdetvHrvvcuRHj2p/9G6Sk9XBvcHP5qTN3HrkCQWlf4cH52AYtdWSHTKpKSmkFOcI8mOEOfhvJKdiIgI9uzZQ1RUFN988w3Lly8HwGg0otFoLmqAQojKWfPyHO3nBQWo9Hr0LVpg2ratwmyn4KQk0l98scoRCbaCAuqsXOmyLVR2NozKYACVioB77qnQ8XWs/wD0cXHUfnEO6XPnoW/RguDevVDMZjSBgWijozk6cBC1577IX8e24l1LIXaPo2Nqe3MvGu+1EJOjxlTZqlBCAqBgzc6ucpp66HPPoo+LdbTax8djPnDA0Z6fn+9IkkqvfeaqTdkAU7+EeJexGy7XT04h+IwCZUN8PKrSE+SrOzCxsi44jdGRBLWLasf4myZyIj+nyusUlBRU+bwQwr3zSnaefPJJunbtSlRUFCqVirvuuguA3377jcaNG1/UAIUQ7llOppE6adIZWzIJNPjgA/K/+47sN95wPKgo+Ma3q/ygvdKaGHtREcf7D3Cu9qROmuyoj3l1JWq9Ho2/P6at20if82LFYZibNmEvLKRowwaXdnBHTIlEjh7F529OoFFaMfoSKNDDgesNtCrdtspZu5YGH39E2oyZFTrBgnv3InPZK86toMooNhsnRo5yTGcvPV9IHxdH5ORJVRY0oyiOpKiaQw/Lr86UnWFkKW0nV2m153ziNEBQSAzLbl/GzqydLNgynxFtRlYZg7931XVRQgj3zivZmTZtGs2bN+fYsWN06dIFXen/tWg0GsaNG3dRAxRCVGTNy6uQ6EBp+/Xs2QTcew8xixeBopD7f+8T+nS/qi+oKJh27HRcY9MmVDoddVesIOPlxVXWx5RXWbKQsSWF3/O2EPuXo+j4cDSo7T7ORAccW0P24mL0sbEEJ7k/Kyjs+cFV/hE0gYHELFzg8h7jpk0oVisqg6HSbSaluJictW87i6Ar41W7NjEvL3aJK2bhAgzx8RT98QfhI0dSvHcP2ohI1D46NAGBoAJUKiwnT6IOCXHp9tIlxvNJ1o8uNTtDWg0hITqBlNSKyVlidCLBPsFVxiiEcO+8W887d+5c4bE+ffq4eaUQ4mKzZWe7LRaG0ys11rQ08r/5FmNKSrWJgjY8nJy1a50/+zRuXCHRKbs2uE4GL+Nuq+ZAPT2GIhPN/yrGDuxs6UOzAyoie/Vx6XiyZGagFLlvay+jWK1Vrp4UfP+D2/dbTp6k/vv/hzU9HeOWrS6dWwDqgADHbLC4uKqvv/77Cltg1owMgvv2AY0Gu8nomA125spUUm9yP1xHvTWrOdL3SezZ2c5285Xbxrh8TrHNyAsJLzAtZRrJqaf/+SZGJzItYZrU6whxns472fnhhx9YtGgRe/fuRaVS0bhxY4YNG8add955MeMTQrhhL6i6dkMxm9FGRDi/eKtMFBITKPhpg0sCUOV8qXKt4OWvUbx7j8tcrK++WcqN3/+LjwXyDXDwen8enfV/2LKyyVq5snS6uaPNWxsahtrfj5D+/SskI2Vs+fmVnsBc1fk5Gn9/0ue8iD4uzjnIs2zlx5CYCCoVQBVt5Y4Tnsu3lRsSE4mcMJ78b77FmpmFNizUOQT1zHsFoI+LI33Oi9R79x2O5R1mQ9FOZ7t5ef7e/kT6RjK3w1xyinMoKCnA39ufYJ9gSXSEuADnlewsW7aM4cOH07lzZ4YOHQrA5s2bue+++1i4cCGDB1f9f5FCiAuj9q+6dkOl06HSnv7Pu9JEIT6e4F69KiQK1c1xOrN+JXz4COwmI1nLV3DorVUcrasi7m/HbKdDdVSE/uchWq79DKWkhKxXX618enhCgtttMt+OHcFqBa2W8JEjUGk0WHNy0ATWwpqZQe6H7ruhyoqIyxK0ss8KTkrCtGsXkZMnoZSUOLe5nG3lTz2JJiAAlVaLUlwM/n7EfPwB9pISvFFjLSrEarOgjYrEp3FjUBSXwwTLK//Z9mIT89LfZfPJzRVeV36bKlAXKMmNEBfReSU7c+bMYdGiRS5JzZAhQ0hMTGTWrFmS7AhxiVV1YnJZQaz/XadXWU1btlK8b5+jeLd01IM2LAzzgQNu6280gVV/0Z5Zv1KY/CvG339ne9oOAgx2mv8NdmB7C29u8m9NnYd7U2CIQqVWOwZm9u/vfo5W6UpU+W0y39v/Q/iIEaTPml0hUYucPAm8vQkfPoz0khLXYu3SLSRbQYEjmSlN0IybNhE+aiT+d97BocceR9+qJXVWruBY/wEoRqNjhSculszFL7serrhyBTmvvupaw5SYiHft2thOnaryfpV9tq2ggJ5NegK4JDwJ0QmyTSXEJXReyU5+fj733HNPhcc7derE2LFjLzgoIUT1oiZP4uT0GW6/4E+MHIX/nXc4t65y161zjIkoLHSOiSjatBnDzTehb9WqwjXUfn5VbHslUvDTBrBa0beMw6dJY9SRkXz706s0P2ZCZ4U8Xzhc35fWu4qwsRlrUiamXbsIfPAB6ry2yrkyVZZ4lT/g0JiSQvjIEY7zgXQ61L6+pM+Z43aLKG3WLPQtYjlRuiUW+szToFZjN5kwbdvOiZGjqLP8Fcf2VLmVLsvx44CjMNqYnEIWEDFuLGlTpjrOFjojEQtOSiJrxcqKMSQnk2W3V1sTVVbPpPL3ZczPg+ndtDe9mvTCbDOj0+io7V+bSN/IKq8hhDh/55XsPPTQQ3zyySeMHj3a5fHPPvuMBx988KIEJoRwr6zl3LRtGyHPPUvE6FFYTqaBCmeXkL5VS1TeXgQn9Ual0xHUraubMRGJ+HW8jYixY7CkpqI2GLAbjZi27+Doc/2JnjkDqFi/Ejl5EiVHj5Kzeg3ZK1eSZ9Bwoq6aNvscIyX+raPCYPIibvfpCeGK2YwxOdkx/bxtW/w73YVpx44KBb9lW1iW48c5MXQYALVXrqi8dbz0/Btl5emTiQ3x8ejj4px/X7T5N0w7d7q0rp9ZTG1MTiFs0CBqr1yBNjS0Qr1SdTVMqpEjqm09NyQk8K+SiclqYtVO1wMUP3/kc7fXFkJcHGed7JQ/GblJkybMmjWLDRs2EB8fDzhqdpKTkxk5supzIs7ViRMnGDt2LF9//TUmk4kbbriBN954gzZt2lzUzxGiJis7PNCWl49SYkbfogXF+/bhc8MNZK5YSegzT2MvLMSnSWNn+7Vp118U/PADQd26OkY0uFmVyEAhYvRojvcfQMjgwZi2bXUmFuXHIqAoaAIDKUzZBBoNOavXYNy0iX3XGQjKNdJ0nw27Cra18CZuZwlaSlw+qyy5MG7aRPjIEdUecFg+GTmX+qGy6wT3SXJZ5VKMRmdRdVkXlSX1pOs9zszkxNBhxCx5udrPOJMlLa3ymqjSbqyQKRNI+u2pCu9NiE6QlnIhLrGzTnYWLVrk8nNQUBB79uxhz549zsdq1arFm2++yaRJky5KcLm5uSQmJvKf//yHr7/+mvDwcP79919q1ap1Ua4vxJXA7eGB8fHUXbGCzOXLHW3iZ6zawOk6E+z2KldG7IOMjkJelYrQZ58jy644zqcpHYtgSEgguG8fClM2oW/eDFt6OrV69iRZ+y/NNmXgbYVcP0htWYc2vx6r8BnuDtWr6pC/0OeepWjzb6f/HJWcPlzV82qDAX1cnEs9klJcjCExkYhxY1GsViwZmS7n75RdRxsaelaf4fK8VsuJESMdyeGTT5aesxOAolJhw86pod055pXGDUE3uNTqtItMYEo7qdUR4lI762Tn0KFDFR7LyspCpVIREhJyUYMqM3fuXOrUqcPq1audj9WvX/+SfJYQNVGlhwdu2kSGWoW+RWylWyyK0cix/gOo+/prFZ5z+YysLIKTeqPW6zk2YIBzNUexWvGKikKl0WArKMC3bVuKNm/mwEfvkFbLSKsDdgAO1FMRYPbj7p7jybG97XZlo3y3V3WjFVCrXc78sWZmVj5gtJLTie3Gimf2eNWuTfjwYRzu1t3Rdl5u20zfqhXWjAwAVN7eFbakTNt3VLtNVX5mli4xng39WrKpcBc9m/RkzM9jaBfVjqGxE+l5fRFmexGBOn/qBoYT7X9pfn8KIU4755qdU6dOMXHiRD744ANyc3MBxypP9+7dmTlz5kVddfn888+5++676dKlCxs3biQmJoaBAwfyzDPPVPoes9mMudySc35+/kWLR4jLrcrDA8vqVarZYlH7+lb5vMrbmxMjRlJ76RLnF3bZhPGM+QtcvuD/ubUhtayFNDkANhXsaONLx1ZPkP/a645tr6eeImLcWMcWUbkaorLVE0N8vEtLvDt2k+n06xMSMLRpg1dEBJSuOJUxJCQQ3Lti27y7BKjsUECf5s2cZ/soJSVoQ0Opv+5D7Pn5oFJR/6N12M1mx5aUWuVMsHLWrnVMRC/3GDjOFwp97rkKZ/AUjuzNym2jMVlNqFQqVt+9Gm+NDp0tDLtXIL46L0L9vAk0eFd5L4QQF8c5JTs5OTnEx8dz4sQJevbsSZMmTVAUhb1797JmzRp++OEHUlJSCAoKuijBHTx4kBUrVjBixAgmTJjA77//zpAhQ9DpdCQluT/afc6cObzwwgsX5fOF8LSzOTywqi2W4KeewrRzZ5WdVdbsbBSjEcViOf2+MzqS7Chsi9MTm3IQLxvk+ENqtIE2exSCx91NQJs2KFYrugYNSF+wgKAuXch5y/0qT2HKpspXahITHW3ty5ai8fNDGxlJxstLCO7ZA32bNoSPGonl+HFUPj5oQ0LIXPaKS9u82wMASz83ddJk6t1/H7nvvlehMLr8LK2ICeM5PmQodV9dibXc0FPjn3+ib9XamWB61a5NwU8bMP7xJzELF4CiUBxRiz+t/+IXoHZef1PqJno27om9JITVG/7ixcdjia6lr/KfqxDi4lIpiqKc7YuHDRvGDz/8wPfff09ERITLc2lpaXTq1Ik77rijQn3P+fL29qZt27aklDt+fciQIfzxxx9scvOLG9yv7NSpU4e8vDwCAgIuSlxCXC7mgwc5eN/9lT5fd81qR7fRjh0Vkhl1SAj13nidwz16ErNooaOt+4w5V6ED+qMNDcOanYW9sNCZ4NReuYLjpQlDrr+W9HAbjf91/KrYX1/FDU+ORjV3meNgwHfewZjsODunLA6VwXB6BaV0+rndZHKuwtT/4H3SZ89xX8y7bh0Ro0eT/9XX+MS24PgzzzrO2hk5EiwWLCfTUKlUGP/6C5VKhU+zps7P0AQFYdq9G21wsMt8rZy1a4kYP67COIfyn+3s4EpMIODuu7GknnR7X8GxqqSPjXXZtioamUS/0lOR20W1IzYs1tl1tez2ZWw/UItF3x6nQ6NQlj7RSlZ1hKhGfn4+gYGBF+X7+5xWdj799FNeffXVCokOQGRkJPPmzaN///4XLdmJioqiadOmLo81adKEjz/+uNL36HQ652BSIa50tkA/DImJbreyDIkJaKMiKd7/d4VOIJXBQL3Vb2LLy0MxGin+azcBd9/tXJUoSwKO9R+APi4OfVwcxfv/JnLqFNJmzHRuje2+wZeI9CIa/wtWNWxvoaP1jmJiakVTfMbqT/naIcVNzUz9jz+izsoVjm0qq5XgpN6EPvcstry8CkM/08wlBNx3L6YtW1EZDAR16UL67NkVkrXgpN6cGDESfVwcoQP6Y8nMRBsSQs6atyokUvrYWNImT3F7n8uPwDAmpxAxZgwFGza477BKSCBy2lQsJUYKb2mMzaCrMP5h88nNDGw5kLf3vI3JaiLcEOU4ZRH4+Z8ssgpLJNkR4jI6p2Tn5MmTNGvWrNLnmzdvTlpa2gUHVSYxMZG///7b5bH9+/dTr169i/YZQtRUaUVpzN4xm2dG9MIXO+bk8l+47Yge3A2vD+8k+sEnsYX6EDFuFNgV7IV5aMKjUIod7d91Vq5EExrCkaQ+bkcqlB9nkG5XCO7VE3VICH/G6Wi5qwitHbIDIC3Sl7Y7igAVKp2uQmF0te3Zx4+j8tZxYuRI6q19C7Vez9G+T7p9rTE5mYgxo8lYuMjtIX9lcaNW0+Djj7AXFVG48Wey33gD4HSRddl20/rvK53KXkbl5UXtlStQzGYUq5WgLl3Ax4fIKVNQzMXY8gtQ+/liTU8nY9FCvIb15eH9jpUqvVZP/wa9uNU3Fm2RGZuvD9oSbxbctoAP96/ju50mmkefPjSwoNhSWRhCiEvgnJKd0NBQDh8+TO3atd0+f+jQoYvamTV8+HASEhKYPXs2Xbt25ffff2fVqlWsWrWq+jcLcQXLM+cxNWUqKakpbD65mf79enHrkCQ0RjNe/oGEkI/X532hpAht5u9oG7aGjx8HwHLvGlKnz6xQSOtu5lTZdpM2NJSYlxej1vlwojiLfeMG0/agYyni74YqQrPUNNvvOCSwrABYHxfnEvPZtIhrAgKov+5DMhYsIKRv3ypfb0lNJahLl6oP9EtORiku5nDnLi6Pl399zMuLyV65koBOd1Udn0bDsX5PO382JCYQOWkS6S/No+jHn04/XrqipLL4AI5E542W8/BbsJbilBVYS1/nlZhAzNjnGNN2DP/bnovVfrpiwN/Hq8pYhBAX1zklO/fccw8TJ05k/fr1eHu7LsGazWYmT57sdozE+brpppv45JNPGD9+PNOnT6dBgwYsXryYnj17XrTPEKImyinOISXVkayYrCYW/fMa5TeHP31gHQ17fISqJB+O/wEf94OSIqy3jCF16f9hTHEdNGlMTgG74jJzqqzjqvwwzl03GohJNXJDAVg0sKtNIC1/P4Uaxxe1ITGRyIkTsBcXo9K4/vqorj3bmpmJNjwcLBaKfvyJoG7dqr4JKpWz5qcqdpPJ5bycCpfR6TAkJICPT+VbggkJLmf7gOOepc2YiT421iXZKfvzhY0fS7uodsT7tShNdM48tDEF3VwF+wsj2W5eRqfIyRi8NbStF0Son2xhCXE5nVOy88ILL9C2bVsaNWrEoEGDaNy4MQB79uxh+fLlmM1m3n777Ysa4AMPPMADDzxwUa8pRE1QdiqyvaAAtX8AmpBgtKUDOAtKqu7CKiopQOWthzX3ujxuC7sFY8o7bt9Tvi4FXDuurMCOOB1xu4xo7ZBZC4oe/A+dkyagFBZgNxpR+/lh2rmTQ527oBiNhPTv79JVlbN2LTEL5qPS6fBp3LhccXItNEG1sGRmcqRXb6LnzAbAtG175clH6eqRT5PG1Q4lVUpKiBg/zm09juO05Ewixo+j4KcNhD77LFl2+xkjMBLcTn4Hx2DSsrqdM++lWlFY2GAkaouNoykr3MZmTt5EpFFhU2oK85jB1IeH0+G6elKvI8Rldk7JTu3atdm0aRMDBw5k/PjxlDVyqVQq7rrrLpYtW0adOnUuSaBCXE3cnorcvj3RM2bgFRWJv7d/le/3L86HQynQsCMc3OB83G4qqfQ9AJTrvSzbHkoP9iLf30qbHY4VlL3Xq4lIV9H+1iegxEz6vJfQx8VV6EzKWbvWpatKMRod7d2vv0b6/Pmu7d2JCQSXHhehMhgI6d8ffZvWBD78EGnTZ7hc17djR8IGD8KanY3Gzw+1vz+RM6aTPufFCqs3hsQEVHo9+tjYCq8xJCYSOWkiJceOcaTvk0TPmul6aGLpOTtqX18OP9Gj0pWhylaWbLmnON73SWJeXlz1PS90XHdTagpj244lStrOhbjszvlQwQYNGvD111+Tm5vLP//8A8D1119PcLDMdhHibFR6KvKvv5I6eTIxC+YT7BNMYnQiyakVVz0SI28h+Mhm2LwCHncU5HJwA3j7ogqq2ClZnjYkmNqvrkSt14NKxc4mBuocM9LoCJRoYGcLH1pvN6FGhVrvg1JSQvjIEaDRVKibUYxGCr5bT8C995wuBo6JIf2l+RXau53baE89hTawlnMIaOjzz7u8X2UwoA2s5XZoaf1338GSlYViNKLW+WDJyEAbGcHRJ59ynIicmECDj9ZhPnjQcUZPRASHHu98OolRFLddYrVXrqg00YHKa5HspUlQdbVKFv3p+pwia2GVrxVCXBrnNfUcHKcm33zzzRczFiGuCVWeivzrr9iyswls2JAXEl5g27+/cp0S4uzwOUAGrSJvJDA/HR5pDhofuHM6VvywFlpRSixV1qUU/PgT2StXYtP7sKu1Ly32GdEokB4EucEG2m43AioA7EVFHO2d5Gi1njjBbV1M9htvOOp+3n4bY3KKY0K5m3NswLH1E/b8YDIWLXImMs73v7UW46ZNLmf1uLw3OZl0RXE526b8apHjNSmkzZqFvoXjNfU//oiYhQucrfaaSs7pqLLWKCHB7TgKQ3w8pm3bq39/YgLf5J2un6puxU4IcWmoq3+JEOJiqu5UZHuB4//+g/MVmrz8FdZuAyh+ahiWbv1psuRbahXoMRd6YT3uOJbBcsrEicmzOfTQIxxJ6kNwr56OgtxyygZgGtq0pqjnwxwOM9MyORuNAnsaqfExa2j8b7mTiMuNXDCmpJA2e45LYlFGMRo5MXIUERMnUv/DD6odTaFYrS5JQdn79XFx1F65goC7O7lNGsri0Lc83QFmTE4hZ81bFRKestfYi4pQoaJ47z5OjBiJ+cABDIkJFa6bs3YtoaX1R673LIGI8eMoPuP4C0NiIsFJvZ3zu3LWriU4qTeG+HiX1+kTEygcmcSyfx2z/WS6uRCec94rO0KI86P2r/r/7tX+fuW2us7cDkombdYcRw3Nrr+JmvgQJxe85HxdWfIQnJRE6HPPotJqUSwWijb/xuHuT7C9gYp6h4u4rgjMWtjX6TpuPhWK+Z/TnUjuhncak5MJfeZpty3g+pYtUUwmMl5+meDeFYt5Xf5sBkOVz9sKq97mObN+5syia3AULINjGOjx/gPw7diR+u+9h2Kz4V2/PjzzLEWbN5Ozdi2K0Yg+Lg67udhlFIRKp0MTFETmslcIGzSQkL59UKxWFIsF7/r1OfTY46enqZe758F9klD8DBDoz0eZ653zseKjExjXdopMNxfCQyTZEeIy04SEYGjfHuOvv1Z4ztC+PZqQkKq3usodAmjJOFUhISpfl2LatRNjcgoWFexq4U3LXSWoFUgLgZKO7bn/niRMO3YSNXI0dqMRe1FRheGdZVReXhW2awyJiUROnkT6vHmOVZUWsVW2n5/pzPb32ivddzU5X++mPubMBMgrJgbfjh0xbd/hOH25W1fS586tEHeDdR9SkpqKactWTgwbXuHPW//jj9A1aEDmK8sJGzCAI337om/VksDpk9G3auXyz6fsnpdNO48PvofE4Ptp0aADGvT8sq8YxSqJjhCeIsmOEJeZNjCQ6BkzSJ082SXhMbRvT/TMGWgDA7EcPlzlNcq+4Ks6Fbis2yo1TEexroTWOx0rHn/doKHuMWh7V09MW7Zi2rmTnDffpP6773C0t/sBuwAqHx/0cXHOYmLvOnXI//En7EYjQV27Uuvhh1EbDPjffjsZFaaDOzqj7MXFLu3qZ56OXN1ZPe7qZ8onQIbEBIp37yFs8CDHll5lpy8nJ5M2ew4Bd3dyu1rle/t/UOl0BHS6C7/2idjyThE2aQLWVk3IyT5G2MDnCH32GYo2nV4hKpuPtT3tvzTM8Wbg2/swltgA6NAolL63SLu5EJ4iyY4QHuAVFUnMgvml5+wUovb3QxMS4jhnx5SLyuBT5fvLvuCr6gRSzGa2N/el4cEiojOh2At2N9XTZofJ+bz50CEip0wmbfoMLGlpGBIS3A/KTEjAmpHhqE8hCd92t2DNzcX/Px0p3r3bpeXbt2NHIiZMwJaZhS3vlHPu1aHHOwNQZ8UKsjhdX1M+2Sg7qwcqzqMK7l3xLJzyCVDZicclx49jNxqpvXAhmtAQZ23NmYzJyYSPGF4hufK9/XYixo4hbfr0CglbaO065PYfxvFy7e21P/w/0kzpbCjYxva0/9K70TD6vOaa6Mx9PFbO1hHCg85p6vmV6GJOTRWiKnnmPHKKcygoKcDf259gn+Bzr9HIO4Hy2WBsYW058dHBCltU4DqhO6R/f+dWVXklatjbJoAWf+SjBk6GQqGfnkaHTc7X1P/4I6wZGeR+8KHjEMCb2uIdE+M496ZcwmNIcBTqHh0wkKiJE5wrJWWjJnzb3QJqNUpxMcat28hZuxZ9XJxjqOiZ14qPJ/jJvlgzM/Fp3NjR8ZXUxyX28hPT1XoD9mIT2pAQMpe9QtGGDaevlZhIxPhxlBw+jEqrxatuXTLmz3cd7VDuwEB37eX13nsXlY8PKAq2U6dQrFbUegNZr66s9t6Xj8N75jiMPmpCDSFg05NVWEJBsQV/Hy9C/bwl0RHiPFzM729JdoS4CNKK0pyzrMokRicyLWEakb6RVbyzHFMuyrqnUB38Ebx9sdy31jH6wd2k79Ivb9+OHQkbONClnft4pA6rpoT6Jxz/ae9qrKHBYTt+xYrLdfRxcehbxnG8/wDn4+qQEMdWWng4dqMRjZ8fqNVYsrLQ+PmRuWQJxuQUl1qbM6eLl8VXZ/lyijZvxv+uO7GdOoWmVi0UqxVbfj6mLVsdqzgLF7h8fnkqg4F6a9/CmpXlOAAwLAyVlxeWtDRUWi2aoCDnGTsAtVcs5/iAgRWu4y5BKVN3zWrsJpNLMlR75YpKY6rsed/3P0ZbvwHRcmCgEBfNxfz+ltZzIS5Q+aGd5SWnJjMtZRp55qqnbZexFWQ4Eh2AkiK8vkoieugT1H1rDXXXvkXdNavRx8U5v5gN8fEEdevK0QEDnK3bex5vSWCemfonFEzesOvhptwSdLNropOY4GydPrO4156dzfEBAzn8eGdsOTkc7tkLS2oqOa+/gS03t9JamzLGTZvIWfs2wUlJ2PJOOYqojx/nWL+nOfx4Z450687xZ54le+VKFKPRUaPjph1cZTBQZ+UKMhYu4nj/AZwYMpQjT/QgY8FCVBoNJ0aMxJab67pao1K5va/GTZtcWtad9yE+nqLNvznjLVPdLC53z1vyCxj38U7yjNWcYC2E8Aip2RHiApUf2nmm5NRkcopzqt3OSj1lQpebTUj5B0uKsJfA0T59T2/ttGpJnYR4NH5+2C0W7Pn5BHXpQuq7b7HnVyut/rIAcCIcTD56HnhiMiqtFpVaTcmxY3jVrg2KwpGkPihG4+nRDaVzrNQ6H4zbt5Ozdi0qnc6lrbp827i+VcvKJ5Gf0Q5eVV2Rc+TEiy+6rGBFjBtL1quvVjyJedMmUKuImDjBpVjZkOj+8D+nM9avz1whO9t4K3veqvfl53+OkFVYIltWQtRAkuwIcYGqG9pZ3fN5xhLGfryTGYkG12QHsJscyYtiNDq2feJiyVqx0mVFJa19M8x+xbT6y/GNvrOplusO2Gl0UysKN2x0tnSfGDqMkP798YqOcs66Kj+6oYwhMYF6a9ZQfOigs3Mqe+VK5+qIymBwjJuoigKmHY7ko6oOK31cHAXfrSd8+HCUQYMc3WUqFZqAAIxTprq9tDE5hYgxY8hYuMgZb+TEiRzq3KXScLyioxxjIUrP0Dmzvb78as25doTpEhL4Lt3xz6mg2FLVXRFCeIgkO0JcoGqHdlbzfFZhCb/8k8X6OlE81fB2NGVbWYA6ONz59+62jrbGGrjhz91EFoNRB/ua+tF6W6GjqHjUaA737Amc/gLPXbeOuqteRd+8BYrd5lLrU8aYnEKGApFTp6ANDSXg7rvRhoej8vKi7ltrsBuNYK+61M8rKpLcDz4AXDusTDt2OIuPURS04eEU/LQBS1oaqWPGOkdHBHXvVuX1LaknqbP8FWy5uZi27yD/62/Qt2pZaVExKpVLnU35ImjFbMardh1C+vcnZ+1actaudSRGahWmM7uxnnuWY+WuY0iMRzd2Ikve2Q+Av8/pOVhCiJpDkh0hLlCVQzujE6sdEZBfuhqw6NcM2veczY1MRH3wBwA0vl4YEtphTNns0qZdrFWx70YNrXc6ViaORUDgcwN4MORGNEMD0QQFgVaLvmVLjCkpjoRj8SLUPj5kzF+AcdMmxxwrN8kBOEYzWNPT0QYEkPPGm64dVYmJhA8fVsU8qETQ6Qh56kkUsxnjpk2cGDmKkH79iJw0kbTZsyusJAXcczeAc8vMq3btKu8ZKrDl5WPavsOROKk1BNx7D2mzZrkt6C5RbNRZsxp7Xh5qvR5NcDCZy15xjSM+npgF88ldt45j/mYML4zE79QzWAvyCQqJ4XfzfjYWbSF+9QI0RjNe/oEYwiI5pbbQr0Mk+05YCPWTLSwhaiJJdoS4QIG6QKYlTGNayjSXhKesG6u6ep2A0tUAY4mNx989wujbJvNIxxno7SZ0mmKih/QkVaV2brUcru2DxlJMy91WAHY01XLjfhs3XNcOu8lYWnS7lnrvvoOhTRvCRwwHHCcgp8+b50xQqivEVel0ZCxe7GaCeTKZXl6EDx9OBlToxooYOwZLairF23cQMXkSitHo6OwKDiZt9mw3IzBSSJ/zIhETJ5A2cVK5QZ+VDDQt3Urya5/osgWnMhiIGDeW8BEjsJw4gcrbG9P2HeSuW4dvx45klNsWKzu3x/j7786trLJ6oOAZU8n3ymfW9sVsSt3Es7HPsvPwOjaf3FwhlvjoeFqFtWKfbTtTHp0q9TpC1FDSjSXERRDpG8ncDnP5/JHPefe+d/n8kc+Z22HuWbWdh/p506FRKOBIeF5Yn0qr5Yd5e78aLCZUGVsJuPtuvGrXZmusgfDMYuqkQ5HOsY0Vt8eKj1XBbnLMgirrdLIXFuLTtAkZCxZyuHMXLCdPuiQa1RbiarWVrvwUbdiANe+Uswss5uXF1F65wjFnymhEKSwka+lS8j//HxkLFjq2fqzWyleSkpPRN2vu7MzKWbuWyEkTK4yYKFupyVm7FrXBUGGoaNqUqWTMX0Dxnr0c7z8A065dRIwciT0zC1W5AmvHatfbFYabGpNTMOVlk12czaZUx7VjQ2PdJjoAm1I30S66HZvTUpj12/Sz7rwTQlxesrIjxEUSqAs8r0GPgQZvXnw8lnEf7+Tnf7Kcjz94nRbV4Z+xRdzOwVHPcOBGb1rvcqzGHI0ERe3j3MYqW+0oX4uiUqtR+/oRnNSb4L59UPv4UGflSme3VVnbd2V1LrZTp6qMu/wMrvLv87/zDjSBjvtQVq9jzbwHW3XT3osKHcM0+/ZFMRpRLBYC7r3HOZ6ifGGxPi4Ou8V9MbBx0ybCR41E3zLOcXJzl67o4+KIWTDfpSjZ3RBRgKJTmZjL1V+bbVWvgFntjhW2s+28E0JcfpLsCOFJplwoyiTaXMiaR8JQrAHYTAUoPgFoUGDLW2wJ1VFUSyG2NNHZEavjxt1mfGzFwOlTgk/Omk29tW9hLyzElpeHXaNBE1iLrBUrKmw1xSyYT+qkydRdsYIMhYqnHPftgza46lqjsoTG+b7SOKw5OXjXreus6TkxchR1X38NlXfVWzy2ggJy3lpLcFJvUsdPIOblxWjDwsh5a63bgwtt+fmVXsty/Dgnhg5z/lz2/uCnngKr1VmYrA0LcxYmlyVBYcExmP1OX0unqXoFzKA9vWJUXeedEMIzJNkRwgOseXnYMtOxZx5DrVOhCQ6F45uxacKxm0pQGwqg5AQf5TSj4durCSqBAj38c6M/d7XriX5gnHO1wysykiP9nqbuyhVkLFjo/GIP6d8f044dbg/+Awjq0oWjAwZQ99WV2J99BltennP1xJqdjTUjo4oi5ATsJpNLO7c2NJQjSX2o9/ZaSk6ccBkaqlitqLy9q23pdiYlSUlY09Io+GmDy3XK4std9xE+N9xQ6f11t0VXtuKTMX+B28LkEyNHoW/VCp+crURG3EZCdDwpqZvYmbWTdlHt3G5ltYtq5/JzdZ13QgjPkGRHiMvMcjKN1EmTXIpvnW3Nzw5AMRop9FFxqKGWFnscWzVHolWEdO/LwzfcgnH7dk6MGOk8RTk4qTd1V60iY+ECl0TizCGb5ZVt4WSvXEnm8uWEDRrsOETQYsH/zjtQeXlx+Ike7odyxscTMX48h7t2cznBOOblxehbtQRUeIWGkrNrp/Pza69cgfGPPwnt398xBLSSERNlnxX63LPYS0oI6taVnDVvVZhFFT50KJkrV7j9s1U2HR3AmpFRafIXMW4shltu5pjGi+j8dMa1GcVs5SXe3vM28zrMA3BJeNpFtePZ2Gf55cQvwNl13gkhPEOSHSEuI2teXoVEBxwFull2OyH9+rEzbSf8tJEWeyzYgV1t/Gj6tw2vhas5xupyLdIfEdSls3Ob6Mzam+q6rdR6A7VXrsCakwN2G5lLljoHfNZeusTl9OQzV1ZKDh+uMFhTExhIcK9eHOndm5B+/Qh99jmy7ArGTZswbd9B8b59+LRoTsC99xA+srRjys0BfwCKzcaJYY4usrLPx9dAmreRU97AqjcJ7z8Apdh8Rlv86cGfblUxUiJsyPOU5ORw0r8udfx9UeWnEhsWS++mvfH18mVkm5GYbCayTFl4q73JNGZSbC3mzb/ePOvOOyGEZ0iyI8RlZMvOdttODY4D9zaH5XLD1/vwsUC+AQ42NJDg3YLgl06PNnC0SKsJnTKRY492dhTzWq0VrqcyGAgZPBi/hHjHNG9fX1CgcONGst94A7vJyIkRI4lZMN/RYl6a6MQsmI9KowHcFyGDY6WmvLJtrbIYs994w5nYBPdJQrFaCbj/PjIWLcLnhhvxadbMpabmTIrF4kx+yj5f+8EKHtv+PHqtnjeenof69dWEjxjusgWHopD74Tq3E84NiYlVjpSwG40oYWFcH+6H1ktDUOrv7M7Ywaqdq9Br9fRu2pvY0FjAcbZSg8AG5Jvz+eCBD85vwr0Q4rKRZEeIy8heSUdSvl7NsfoWYj/fB8ChGBXeJd60/MuIkdN1LGVf/MbkZExFp/BuFYc5eZPL3CpwJDraWrUwbdtK9rJlzscN8fGE9u+P4eabMW7dSsyC+aj1+goDPvVxcRXqa8o6vXzb3YJiszk7u4r//pvwEcM50vfJ0+MXjEZODBvu6Axr0YKS48exG41EjBmDYjJhLympvBMsMRFNUJBL55h3qzg2FO0EwGQ10W/7GD4Z/TbFRTa8dcHoIiJQTCZsRiMRo0eRVmKucLhg5KSJHHq8c6X/bDRBQfxavJMWKn/QRxHY8A6mhV7PNCA57TdW7VwFQGJ0AtMSXjj7afZCCI+TZEeIy0jtX7GAdX8DAwH5RprtsWMHtrfwosVfFryU09tQ7tqkFaOJohFJ+Jb+XD45CU5KImORmwMBN20iCwi47158Gt9Izpq3XEYzlNX5mHbscKnXKVvxyVn7doXTj8OHDiNz2SsEdenicsBfWQu83WjEu25dAMfsK7udo/2edly/dJvLeb34eCLGj3PWAxni46m9cgVH/M2s3DrS+TqT1cQBWxqjfx/N57e8gXn6i87rlD9c0JqVhTYqEpW3DqWkBH1cXKUF0mg1zNm1lBtOrueF+NlEB0YT6a1n7i2TyLEVU2A14q8LJFgfKqs4QlxhJNkR4jLJM5Zg1fu7nAz8Z0s9LXYb0VkgzxcO1/el9a4it+8/swbHKyCQbTk/c+/I51G03oQOOF38ezbFydrQ0ApJVNlnnFmvowkKInPJEvdztOwK+rg4l0GhbhOj0kJka24u+lYt3dYDWTMyKPj2O5ezcBS1ii3PtMVkNTmv1S6qHTuzdtK/QS/Mcxa7JDrBSUlow8OxZmSgqVULs0Yhza+YerlqgpN6O697Zlw5tnxeSHiBMT+P4WheBr5afwINQQTqg5DURogrmyQ7QlwGqadMjP14J1uO5PLB4DFYmc6/mdtou93xBX6wjor6QycRN2pGpdco306tS4zHS+/Lrav+ICtlCdkGAzGLFxFw370E90mqdiq5YjZjL3QkVeWnfJf/jPL1OlXO0SpLmEpng7obWFr2OgB9mzaEPvccWa++6jYZSn9xrsv7TMkpxA/p7fy5XVQ7ejbpyZifx/Be7AL0LRSCe/dGKSnBKyaG4t27nd1q4NgW048fQElgGHmLPnLbyp617kN+6tWETcd20btpb9QaMyfzHecYBRq8yTOWkFVYQn6xhQC9F6G+3jIaQogriCQ7QlxiecYSxn68k19KT0f++Yf3aLZ3C81ywK6Cne2C6ND8cbwO52KqZh4UOBIdw/jhZM+ZR3FK6Zyr0hqZ+h9+gC0ry1GMXAWVTocmMAA4Yyp5ucSnvOo6u8oO6Ku9cgXa0NBqV5WO9R9AvbVvYe3dG7WvL2qDgYL131foyioTajOw4LYFxPjF8OOxHxnz8xgAovQRZJWbj1V2r8qflmxMTsYwB3z73oLXhDFkTp3h8npdYjxFI5NYuW0MJquJXk164eul57GlKbStF8TMR5oz/Ys9fL83w/meDo1CefHxWKJrVZ1UCiFqBkl2hLjEsgpLHImOYmNQ1hLaf3ECbyuc8oMjdXxpuSmX/E2vozIYqLdmDRmKUqGdOmL8eCxpadT+6nOKiwvxOVWMX9du+Nxwo/P0X8VoxHLyJFitlBw9WuUoCGtGJl516uDbsSNFGzY4t5T0bVoT+MD9pM150SXpOvO05DNpAgMp2rTZeU5PzMuLUev1KDYbaLUoRiNqnQ/Gv/5CExJCzMIF2IuKUKFC4++PoiiVJkgAWRojIzeOZNnty5yFwsMbPUPWnLmVriCdWdBtHjeKsX+/xPBpQ9Dm9EJjNGMz6NhQtNOZ6JTZk7OLfh2uZ+n3J5jwyS5a1g1ySXZ+/ieLcR/vZOkTrWSFR4grgCQ7Qlxi+cUWQmzpjNu3iOb/OFrED9RT4VegJW7v6focxWh0nGj83jvYTqZhL3eicdorywgfNpTM6TMrdBmVX8UwbdlK8b596Fu3InLKFNKmz3A9vDA+ntAB/bGbzWS8NJ+wwYNQzGaMmzY5VnjiYkmfvwB9ixYE9+6FYjajCayFNiqy8inkCQmoAwLwad6s4gGApdPFU8dPcBQcJybge/NNZ2wzJRAxahS+t99O0Y8/Vrx+YgIbTXsB2Jm103my8a2+sRiT3R8s6K6g21JQwMbjG+lyQxcGb3++0n9eYYYwRv88mrnt3gTg1wPZPJnYoMLrfv4ni6zCEkl2hLgCSLIjxCV25IdVLPplDWGnwKaC7YlhxP2aiZaKgyzt2dmcyD/Gfn067UNiseYXob//PgL0etKnTHXbXQWnVzGcgzdzc0l/cS76Vq0IGzTQcc5OaXt68f79+DRugk/jxqDWED5yBEpJCSpvbzIWLcKYnELRhg0un+PbsSPhQ4eSYbe7FvcmJhA5aRL5332HcfNvFVdZUlJAUZzxGZNTyLIrZ6y6pJDOfCKnTiXNZKp4/QkTaGVJRa/VcyB7H1NiBzHDbkdbZKbi6UKnnbn1pvP1Z3ijZ9ibs7fK8Q8bj23EZDVRopzeTjNb7W4/o6DY/TBSIUTNIsmOEJeIzWpl3eiHaPLdIbxtkOMP77S7lbi7B+BrX4Y5xf0ZM0pgDHW8rmfYt8f4cV8m49pH0ys0u0KiU6b8KoZiNHLq088IGziAtAkTKfrpJ5dzdsrUXrkC044dBNx3L4e790LfqhURY0ZXWoRctGEDQT17oI+LI2LsGMd2mUqFafsO8r/6GkOb1mQtWlxtfO5+BkfCo5hMGNq1I3zUSKwZGc7rH+rSlYBWrdgw6f+wakwE/F8P5rbshiakNsfcfqJD+WJrQ3w8BV9/S8ddOzCN7EOzps0A1/EP8dHx9Gjcw1kP5K06fXaRTqt2+xn+Pl5VRCCEqCkk2RHiEjhxaA9/DOlGXOm21f6GGuzDX6ZLnRbsOnGKoufH4qeaR3G5bSGfxES0YyeRq6nFvC/38cuBbAD+E+GFPSfD7eeUKVvFMMTHE/rM01hSU6t9vXHTJtLnzqPB/61Gc/B/WHJOVP2e0u6sgE53cXzAQOfjKoMBw01tzyq+yn4GsKSexOeGRmTMX+CmxT2ZzOmzCLj3Hiy3zSfw+4EYH72j8q21cgXd5WdvKUYjeuDQMzcRGxZLrya9MNvMBOoCMVlNjPnZUbtzS2Q8Ww85/tm1vz6EbcdOVfiMDo1CCfWTLSwhrgSS7Ahxkf3w7jy8l6zmxjywqiG5XTgvho2EH4qBP7i1USiPxDUnbO48NPmnsOYXkKPyJtOuxa/ASEx+LpPj/PgxUsfr27MIthe7neJdnlft2o7VmtKJ5ZXNgCpTdj1jcjIUdEf72zxsD3xc7Xt0ifHYiotdHleMRuwmUyXvcv28yn52PAjasHC3h/7B6RWh1KVvETT9f/x2ah8Jzz1LlruttQkTsGRkoG8ZV2H2ljl5E/FDknistG6nXVQ7hrQewvM/Pu9MdHo0HMXgtw/SoVEoMx9pzowv9rjE0qFRKHMfj5V6HSGuEJLsCHGR2KxW1g2/j2Y/HENrh+wA+LbTPbxtvdPldb/8k8Xkz/5i6ROtMPiqUPlaCDFqsM+cgzE5hbTS13VKSKDn5El4558kb5P7lnAoHa/gbSNj7VsYUzYT8/Jiivfuq/z1Z0wFt5tKANBk/oYhoR3GlIq1LIb4eEoyMygamYTNq2K7tWnb9rP+PHdTycse842Pr/D+MiqDAU1QEMFJfSCtkMSAJqjUdsKGD0M1aiS2U6dQrFZM27ZzqEtXYhYu4Hj/AW6vVdbKHqgLpLZ/baw2K693eh1frR9eBHCqUMP/BkcT6uc4T2d+lziyCksoKLbg7+PlfFwIcWWQZEeIi+DoPzvYNqwXcf86tj7+vl5L5LQ3ePujTLev//NILj6mNPhyKOqwtmSuO1AhyTCnpJAzcwbRE4ZSvP9v96f/JiQQNXYw3p8+RkyXJ7EN7ImijyR1/ASXcQ/O15fb0imj1ju+tLXbXiH6+TWkqtSuHV+JCYRMGs9/09ezbNt8Prv9XXSJ8ZiTT1/XeVaPSuXaNl/ajVX2eYbEREKfe5Zj5ZKQ8jEFdLrL7f0qO5U5c8mSCt1oZe/Vx8U5TkIua8Wv4mwgjX8AjYLCKh3gWTfI9edAgyQ3QlzJVIqiKJ4O4lLKz88nMDCQvLw8AgICPB2OuAp9t2YmhuXvEpIPFg3suaseXeZ/wc7UAh5d7r7gd+LtUTydNgPVwR8xP/AxB3tV3grdcN2bqK0FnFz1X3xubIK+ZVxpS3ggXjFReL9/BxSdTqqst4zhxLoDmLbvJDgpCb8OtzpmUpUW/JYlAwCGhHbEdLke7W/zHG/29sXa42tsWZkUq/zJ0Jr4pWgnKw+9A0D/Br14MPg2fL0MZM6e61Iv43v7f4gYMwZ7URH2ggI0IcGovLUoxRYsqSdBBahUWLOz0QYHu5xgnLN2Lfq4OMJHjnB2hJUX0r8/ph07Kl050sc5xmOU//u6a1ZztO+TFV/fvj0xC+ajrebsICGEZ13M729Z2RHiPFlKzHw8/D6a/ZSK1g6ZtcA6pB/dezhWMQKq6NS5q64aVYrjTJmybaTK2E/lovvlaaIfGoQtrBl2kxF1ZCD2kDBOFuVRr8h19ci5QrMUl3b0nLVvVzisMGrsEGyFOVju+D/UBh2akhNoj3yN9qc5pPb5L4/9PAwAvVbPGy3n4bdgLVkpK8gunUEV+szTqLy8USwWijZv5tBjj5eep5NI9Mh+eK17HADNvWtIXfYBpm3bHbG8tbbCClVw714cfa4/0TMdIzPKJzy+7W6p9lTm8n9vSIzHq05tDO3bY/z119Of07490TNnSKIjxDVGkh0hzsPhvX+yc0Rf4g7ZANh3gxftln5IVL3GzteE+nnToVEoP5eOiSjPX3X6DJeybaTKqPXeUFKE9rd5Lv/BWp79le+OWHmywe1oD5U7jK+kCK+v+xL99IuYx40n71Qq6XoNRaN741vSB7XRhN2gxy8wgrR5L1P040/OtxoSE4h+/gm8vH0JPrKZxKh2JJ/cTP8GvfBbsNZlPEX2ypWO1ZSEBPSxsS7JiDE5mVTFRkyXQWh/m4fX133xm/otJbnZZBVb8Z84AkOJhcK8TGwGH3T+weTOX+E4Z2jkKIKfeorwkaOwpqeDCsdJzFVw2bJSIHrGDLyiY4hZMB9bdjb2gkLU/n5oQkIk0RHiGiTJjhDn6JvXJhOw6iMaFUCJBvbdcx2d536KRuv6n1OgwZsXH49l3Mc7XRKeDo1CCaxVy/lzlYXBCe3QZP7mNg6zsYBFv+bT6cm51FWPQ/XvD87n7LXbsT+oLZlaC8/vrbhFNrzRM/zn5e3O5KWMMTmFVMVOTJdBBKa8wrSk/zJNpeZW31iKUyo5rTglxVlP5Pr4ZmwDezp+yZQUoT34OQvMh0hOq/jnuT2mA3MGPkzEcz0w232w+/rwS8k+0vRpxOubEOVVdYJSvrvLq05tvKJjANAGBkpyI4SQZEeIs2UpMfPx83fT/Od0NApkBIEyYiDdulRebxNdS8/SJ1pV6OTRqorgujvg3x9ObzuBS8JjSEwkenBXtF/3dXttL79g/jc4llp+3qg6v+Go2ynOB58A8giky5JtLEoKdvveqpOX0iSl9u9EGvOZq2+M2u7H8SruTWXFwOW36AJTXmFat7eYplaTnHo6yUqMvIXxDR7B8EEfKCki47k/eWnvUhoFNSI2JpZDNjNeWhX6xARMlcz6cp6p07492rCwKiIVQlyLJNkR4iz8syOZv8c8S9wRx9iAvU28ab/sv4THXFfte8t38ljz8rClHcdUUIA6fi6a6zej/WksXl/3JabLIGxDn8au6FEH1EIT6Iv2u+FQUlTxotfdgS4wguv0fqUPeIP+dAuR2lhC23pBLqcAl1fdqAW7OhDunAZ5JwgMb4LZcnbn9pzJZYuupIjID/ow98mvybm+GwW2Evw13gQf2UxgaaIDoD2VyWN1B/PewQXOoZ96rZ73xy7DMFdVYdZXWTeW1OMIISpzRSU7c+bMYcKECQwdOpTFixd7OhxxjfhyxXiCX/+U64rArIX99zfm8VnrKmxbVcdyMo3USZNcv6zbJxI9LQUvVQ5anR9a37DTSYspF9oNBEsRHNxw+kINO8KtI6r8rLIttJRDR4mPSmDTSdcVEatv1YcUqu15sOpxR3dWq0Eode6h9ooVqFQqjNu3n9HRlVDh3BxwrEypS1xPclZqt8Nf7UXgW49V+tlmlYFR7x/h9d7jGNemhEJLAf7eAQTraqGaMxdVajrWgkL8avmj8vYmP/cUdT78EF1YqNtEJ89YQlZhCfnFFgL0XoT6Shu5ENeaKybZ+eOPP1i1ahWxsbGeDkVcI8wmI588fzctkrNQK5AWDF5jh9P14WfP+VrWvLwKiQ6A8ddkUqfNct8KXZQJ/9cV2g1w/GU1g1YHx/+A97rCsxtcVnPOFF1Lz6P1fWkf2pdpis2lVuZfVRZNKp1iXlon5O2L5d41pC79P4wpvU4/X27Sur5lLFGTxpL20kKXa+gSEsgbPIZ9Nht3DPgdraUAfAJQ+YahAucW3pmU6+7AEBxJ8oBgvL8ciupgucLr6+6Ah5ai3HA9WYUlpJZtC9apg28lyUvqKRNjP97JL2fUTL34eCzRtSoejiiEuDpdEefsFBYW0rp1a5YvX87MmTNp2bLlWa/syDk74nz8vW0DB8YOouFRx7bV7mY6Oi77hNCoBud1PfPBgxy87/5Kn2/41ZfoGjZ0ffD4n/D6HZVf9OkfoHYVM6lMubDuKTj+G3kJg8ip1+701lF+BobQW0md+oJra3ZiIlEj++H96eNYWw3ihJvDDsteFzllMhqdgtZ+Cqu6FrYiK9aCIhRfPwp8/DD5+BJS2SpK3gn4/HnXhKc0mcHb4Ii7fKJT/jWd36gyyXN+hLGEwf+3zSXRKdOhUShLn2glKzxC1GDX3Dk7gwYN4v777+fOO+9k5syZVb7WbDZjLlcsmZ+ff6nDE1eZL5aOJHT1VzQ0gtkL9j/UnK6z1l3QNe0FBdU8X1jxQZ9q/uOu7vmiTGfCELhhHhU2eAb/ScyC+ViysshOz6HIS8/3mTbUJxX+89hXhOGNMeVxt5c2JieDzYY20pGgaUv/Ktsc83P7rnICYxxJS7miasq28LL2u090wJEcFWWeVbKTVVjiNtEB+PmfLLIKSyTZEeIaUeOTnffff5+tW7fyxx9/nNXr58yZwwsvvHCJoxJXI1NRPp8NvpcWm3JQAydDwWf8GLreX/EU3nOl9vev5nk36YFvWKXbPVx3h+P5qhRXk+gX56ENbYQ2MBB1SDSzyrXIvwD8+lB4lW93m6CdC32Q+6Sl2rjP7n9g8ostVT5fUM3zQoirR41Odo4dO8bQoUP57rvv8PHxOav3jB8/nhEjThdv5ufnU6dOnUsVorhK7P7tO45OGEbcCceu7u4WPtyx/AuCwmIuyvU1ISEVTvMtY2jfHk1ISMU36YMc2zqVbfdUt7pxDitD7lrkQwrSqWo9ym2CdjFc6IpWqapOsAbwr+Z5IcTVo0YnO1u2bCEjI4M2bdo4H7PZbPz8888sW7YMs9mMRqNxeY9Op0NXSRusEO58tuB5ot75nvomMHnDv4+0pMv0/7uon6ENDCR6xgxSJ08+t/EFVW33VOccV4bOHHZp1dnOPUG7GC50RatUVSdYd2gUSqifbGEJca2o0QXKBQUFHDlyxOWxJ598ksaNGzN27FiaN29e7TWkQFlUpqggj/8Nvoe4304BcCIMAiZP5OZOvap+4wWw5uVd3vEFVRUCB1a/amU5mVZpguYVGXkpIna4wLjLpJ4yuT3Beu7jsURJN5YQNdrF/P6u0cmOOx07dpRuLHHBdqZ8Seqk0dRLdfzr/1ecgbuWf0GtkCgPR3YJmHLPb2Wo1GVP0MpcYNxlys7ZKX+CtRQmC1HzXXPdWEJcTJ/M60/t9zZSrxiM3nC48010mbLW02FdOpUVAp8lj82XusC4y5y5PSeEuPZcccnOhg0bPB2CuEIV5uXw5cB7iN3iKLs9HqEieMoLPH5HFw9HJoQQ4lK64pIdIc7Hto2fkDF1IrFpjm2rXW38uGfZlwQEVd1eLYQQ4sonyY646v13dj/qfpBCXTMU6eBo90S6jn/d02EJIYS4TCTZEVet/NwMvhl4Hy22OaZpH41SETF9Do/d+rCHIxNCCHE5SbIjrkp//vA+udOn0yLdsW21s20AD6z4Dl9/DxTaCiGE8ChJdsRV56PpSTT46A9ql0CBHlJ73Ea30Ss9HZYQQggPkWRHXDVOZZ9k/cD7ab7DBMCRaBW1Zy/gkXb3ejgyIYQQniTJjrgq/PbNWgpnzaF5puPnHbfU4uHl36L3lYMkhRDiWifJjrjifTi5O9d/toPoEsg3QHrvu+g+fImnwxJCCFFDSLIjrlg56cf4cdBDtPirGIBDtdU0mPMyt9x0p4cjE0IIUZNIsiOuSJu+eAPTi/NplgV2YFdCMI++sh6d3uDp0IQQQtQwkuyIK86H4x/nhi/2UMsCeQbIeup+ug+e7+mwhBBC1FCS7IgrRtbJQ2wc9Cgt9pgBOFhXzfXzVtCuZQcPRyaEEKImk2RHXBF++WQFtpeW0DQH7CrY1T6MR5d8I9tWQgghqiXJjqjRbFYrH41/nMZf78fbCqf8IKffI3QfMMfToQkhhLhCSLIjaqy0o/+QMqQzsftKADhQX03Tl14jvkWChyMTQghxJZFkR9RIGz5YjGrxqzTJBZsKdt0WSecl3+DlrfN0aEIIIa4wkuyIGsVmtbJuzMM0+fYg3jbI9YeC57ryxNMveDo0IYQQVyhJdkSNceLQHn4f0p24fywA/NNAQ+zCNdRv0tbDkQkhhLiSSbIjaoQf3p2H95LVNM4Dqxr+uj2Grou/QaOVf0WFEEJcGPkmER5ls1pZN+J+mn1/FK0dsgPAOLAnT/Sd5OnQhBBCXCUk2REec/SfHWwd3ou4A1YA/r5eS+tF71C3UZyHIxNCCHE1kWRHeMR3a2ZiWP4uN+aDRQO776xH1wVfyLaVEEKIi06+WcRlZbNaWTf0bpr9lIrWDlmBYBnajyd6jPJ0aEIIIa5SkuyIy+bw3j/ZOaIvcYdsAOy7wYubX36fmAZNPRyZEEKIq5kkO+Ky+Pb1qfi9+iGNCqBEA3vvbkiXeZ/JtpUQQohLTr5pxCVlKTHz0ZC7abExHY0CGUGgDHuO7t2GeTo0IYQQ1whJdsQl8++uFPaMfoaWh+0A7G3sTcKSj4is28jDkQkhhLiWSLIjLokvV4wn+I1Pub4QzFr4+74b6Tz7I9m2EkIIcdnJN4+4qMwmI588fzctkrNQK5AWDNrRQ+j26ABPhyaEEOIaJcmOuGj+3raBA2MHEXfUsW21p5mO25Z9QmhUAw9HJoQQ4lomyY64KL5YOpLQ1V/R0AhmL9j/YDO6zv7I02EJIYQQkuyIC2M2Gflk0F20SMlBDZwMBf24UXR9oJ+nQxNCCCEASXbEBdj7x/ccGj+EuOMKALtb+HDH8i8ICovxcGRCCCHEaZLsiPPy+aIhRLy9ngZGMHnDgYfj6DrjfU+HJYQQQlQgyY44J6aifD4beDdxv50CIDUM/CaOp+s9SZ4NTAghhKiEJDvirO1M+ZLUSaOJS3VsW/0VZ+Cu5V9QKyTKw5EJIYQQlZNkR5yVT1/qT8y7G6lXDEZvONT5JrpMWevpsIQQQohqSbIjqlRUkMcXA+4i9s8CAI5HqAiaMoXOd3T3cGRCCCHE2ZFkR1Rq28ZPyJg6kdg0x7bVrtZ+3PPKlwQEhXs4MiGEEOLsSbIj3Prv7H7U/SCFumYo0sHRbgl0nfCGp8MSQgghzpkkO8JFfm4G3wy6nxZbCwE4Gqki/IVZPHbbox6OTAghhDg/kuwIpy0/rCNn+lRapDu2rXa29ef+V77BLzDYw5EJIYQQ50/t6QCqMmfOHG666Sb8/f0JDw/nkUce4e+///Z0WFelj6cnwfAp1E5XKPSBfU/dRrd3fpdERwghxBWvRic7GzduZNCgQWzevJn169djtVrp1KkTRUVFng7tqnEq+yTrurWh6Xt/YCiBI9EqdMvn8+iYlZ4OTQghhLgoVIqiKJ4O4mxlZmYSHh7Oxo0b6dChw1m9Jz8/n8DAQPLy8ggICLjEEV5Zfv/uHfJnzCIm0/Hzjltq8eCyb/D1D/RsYEIIIa55F/P7+4qq2cnLywMgOLjyrRWz2YzZbHb+nJ+ff8njuhKtm/IE1326nZgSKNBDWtJddB++xNNhCSGEEBddjd7GKk9RFEaMGEH79u1p3rx5pa+bM2cOgYGBzr/q1KlzGaOs+XIzT/BR51Y0/3A7+hI4VFuF/6qlPCSJjhBCiKvUFbONNWjQIL788kt+/fVXateuXenr3K3s1KlTR7axgE1fvIHpxflEZYEd2BUfzMPLvkbve23fFyGEEDXPNbeN9fzzz/P555/z888/V5noAOh0OnQ63WWK7Mrx4YTO3PC/3dSyQJ4BMvveS/chCz0dlhBCCHHJ1ehkR1EUnn/+eT755BM2bNhAgwYNPB3SFSfr5CE2Dn6UFrsdq10H66q5fu4rtGvV0aNxCSGEEJdLjU52Bg0axHvvvcdnn32Gv78/aWlpAAQGBqLX6z0cXc33yycrsL60hKY5YFfBrsRQHl36LTq9wdOhCSGEEJdNja7ZUalUbh9fvXo1ffv2PatrXIut5zarlY8mdObGr/5GZ4VTfpDT7xHuHzDH06EJIYQQZ+WaqdmpwXlYjZV29B9ShnQmdl8JAP/WU9Nk/mvEt0jwcGRCCCGEZ9ToZEecmw3rlqJauJwmuWBTwa7bIui85Fu8vKVgWwghxLVLkp2rgM1qZd2Yh2ny7UG8bZDrDwXPdeWJp1/wdGhCCCGEx0myc4U7eWQfm5/vStx+CwD/NNAQu3AN9Zu09XBkQgghRM0gyc4V7Mf35qNd8gaNT4FVDbv/E02Xl79Fo5V/rEIIIUQZ+Va8AtmsVj4c+QDNvj+Clw2yA8A4sAfd+072dGhCCCFEjSPJzhXm6D872Dq8Fy0PWAHYf52WVovfoW6jOA9HJoQQQtRMkuxcQb5fOxufZW9zY37pttWddemy8EvZthJCCCGqIN+SVwCb1cqHw+6h+Y8n0NohKxBKhjxJ955jPB2aEEIIUeNJslPDHf17K9uHJ9HyoA2AfY28uHnJ+8Q0aOrhyIQQQogrgyQ7Ndi3b0zDb+UHNCqAEg3svbshXeZ9JttWQgghxDmQb80ayFJi5qMh99BiYxoaBTKCQBn2HN27DfN0aEIIIcQVR5KdGubfXSnsGf0MLQ/bAdjb2JuEJR8RWbeRhyMTQgghrkyS7NQgX62cQNDrn3B9IZRoYd+9N9B5zseybSWEEEJcAPkWrQHMJiOfDLmHFr9molYgPRg0owbT7bFBng5NCCGEuOJJsuNhf2//mQNjBhB31LFttaepjtte+YTQqAYejkwIIYS4Okiy40FfLBtF6Jtf0tAIZi/Y/0BTus752NNhCSGEEFcVSXY8wGwy8smgu2iRkoMaOBkKPmNG0PWhZzwdmhBCCHHVkWTnMtv7x/ccGj+UuOOObavdzX24/ZXPCY6o4+HIhBBCiKuTJDuX0eeLhxKx9jsaGMHkDQcejqPrjPc9HZYQQghxVZNk5zIwFeXz2cC7ifvtFACpYeA3cTxd70nybGBCCCHENUCSnUvsr81fc3ziSOJOKI6f4/TctfxLaoVEeTgyIYQQ4togyc4l9On8gUS/+xP1TI5tq4OPtaHLtHc8HZYQQghxTZFk5xIoKsjji4GdiP0jH4DjESqCpkyh8x3dPRyZEEIIce2RZOci2/7LZ6RPGU/sSce21a5Wvtyz/CsCgsI9HJkQQghxbZJk5yL675ynqft+MnXNUKSDI13j6TrxTU+HJYQQQlzTJNm5CArzcvhqwN202FoIwLFIFWEvzOLx2x71cGRCCCGEkGTnAm35YR0506fSIt2xbbWzrT/3v/INfoHBHo5MCCGEECDJzgX5eHoS9T/6g9olUOgDx5/oQLexr3o6LCGEEEKUI8nOeTiVfZL1Ax+g+Q4jAEeiVUTPfIlHE+73cGRCCCGEOJMkO+fo9+/eIX/mLJpnOH7ecUstHlz2Db7+gZ4NTAghhBBuSbJzDtZN7cF1n2wjpgQK9HCy1510H7nU02EJIYQQogqS7JyF3MwTfD/wQZrvMgFwOEZF3dmLefiWTh6OTAghhBDVkWSnGilfrqZ4zjyaZ4Ed2BUfzMPLvkbvG+Dp0IQQQghxFiTZqcKHE7vQ6PO/CLJAngEy+95L9yELPR2WEEIIIc6BJDtuZJ08xMbBj9JitxmAg3XUXD/vFdq16ujRuIQQQghx7iTZOcOvn63CMm8RTbPBroJdiaE8uvRbdHqDp0MTQgghxHmQZKeUzWrl44lduOHLfeiscMoXcvo9RPeBcz0dmhBCCCEugCQ7QMaJf/l10GO02FcCwL/11Nw4bxXxcYkejkwIIYQQF+qaT3Y2rFuKauFymuSCTQW7boug85Jv8fLWeTo0IYQQQlwE12yyY7Na+WjsIzT+5l+8bZDrD/nPduaJZ2Z4OjQhhBBCXETXZLJz8sg+Ng/pSuzfFgD+aaCh+YI3SWh6s4cjE0IIIcTFds0lOz/+3wK0L79O41NgVcPujtE8vvgr2bYSQgghrlJqTwdwNpYvX06DBg3w8fGhTZs2/PLLL+d8DZvVyvvD7iF05uuEnYLsADg5pgfdl/8giY4QQghxFavxyc4HH3zAsGHDmDhxItu2bePWW2/l3nvv5ejRo+d0na+faE/cN0fwssH+6zTUffd9OvWdfImiFkIIIURNoVIURfF0EFW55ZZbaN26NStWrHA+1qRJEx555BHmzJlT7fvz8/MJDAzk9+sb4eOlYfcddeiy6Cs02mtuB08IIYS4YpR9f+fl5REQcGHzKGv0N35JSQlbtmxh3LhxLo936tSJlJQUt+8xm82YzWbnz3l5eQAc9bOhHdCD+7qPoMhovHRBCyGEEOKC5efnA3Ax1mRqdLKTlZWFzWYjIiLC5fGIiAjS0tLcvmfOnDm88MILFR7vvP0gPPeC4y8hhBBCXBGys7MJDAy8oGvU6GSnjEqlcvlZUZQKj5UZP348I0aMcP586tQp6tWrx9GjRy/4Zl1t8vPzqVOnDseOHbvgJcKrjdybysm9cU/uS+Xk3lRO7k3l8vLyqFu3LsHBwRd8rRqd7ISGhqLRaCqs4mRkZFRY7Smj0+nQ6Sp2VwUGBsq/SJUICAiQe1MJuTeVk3vjntyXysm9qZzcm8qp1RfeS1Wju7G8vb1p06YN69evd3l8/fr1JCQkeCgqIYQQQlxJavTKDsCIESPo3bs3bdu2JT4+nlWrVnH06FH69+/v6dCEEEIIcQWo8clOt27dyM7OZvr06Zw8eZLmzZvz1VdfUa9evbN6v06nY+rUqW63tq51cm8qJ/emcnJv3JP7Ujm5N5WTe1O5i3lvavw5O0IIIYQQF6JG1+wIIYQQQlwoSXaEEEIIcVWTZEcIIYQQVzVJdoQQQghxVbuqk53ly5fToEEDfHx8aNOmDb/88ounQ/K4OXPmcNNNN+Hv7094eDiPPPIIf//9t6fDqpHmzJmDSqVi2LBhng6lRjhx4gS9evUiJCQEg8FAy5Yt2bJli6fD8jir1cqkSZNo0KABer2ehg0bMn36dOx2u6dDu+x+/vlnHnzwQaKjo1GpVHz66acuzyuKwrRp04iOjkav19OxY0d2797tmWAvs6rujcViYezYsbRo0QJfX1+io6NJSkoiNTXVcwFfRtX9e1Pec889h0qlYvHixef0GVdtsvPBBx8wbNgwJk6cyLZt27j11lu59957OXr0qKdD86iNGzcyaNAgNm/ezPr167FarXTq1ImioiJPh1aj/PHHH6xatYrY2FhPh1Ij5ObmkpiYiJeXF19//TV79uxhwYIF1KpVy9OhedzcuXNZuXIly5YtY+/evcybN4+XXnqJpUuXejq0y66oqIi4uDiWLVvm9vl58+axcOFCli1bxh9//EFkZCR33XUXBQUFlznSy6+qe2M0Gtm6dSuTJ09m69at/Pe//2X//v089NBDHoj08qvu35syn376Kb/99hvR0dHn/iHKVermm29W+vfv7/JY48aNlXHjxnkoopopIyNDAZSNGzd6OpQao6CgQGnUqJGyfv165bbbblOGDh3q6ZA8buzYsUr79u09HUaNdP/99ytPPfWUy2OPPfaY0qtXLw9FVDMAyieffOL82W63K5GRkcqLL77ofKy4uFgJDAxUVq5c6YEIPefMe+PO77//rgDKkSNHLk9QNURl9+b48eNKTEyM8tdffyn16tVTFi1adE7XvSpXdkpKStiyZQudOnVyebxTp06kpKR4KKqaKS8vD+CiDFq7WgwaNIj777+fO++809Oh1Biff/45bdu2pUuXLoSHh9OqVStee+01T4dVI7Rv354ffviB/fv3A7Bjxw5+/fVX7rvvPg9HVrMcOnSItLQ0l9/LOp2O2267TX4vu5GXl4dKpZLVU8But9O7d29Gjx5Ns2bNzusaNf4E5fORlZWFzWarMCw0IiKiwlDRa5miKIwYMYL27dvTvHlzT4dTI7z//vts3bqVP/74w9Oh1CgHDx5kxYoVjBgxggkTJvD7778zZMgQdDodSUlJng7Po8aOHUteXh6NGzdGo9Fgs9mYNWsWTzzxhKdDq1HKfve6+7185MgRT4RUYxUXFzNu3Dh69Oghw0FxbBVrtVqGDBly3te4KpOdMiqVyuVnRVEqPHYtGzx4MDt37uTXX3/1dCg1wrFjxxg6dCjfffcdPj4+ng6nRrHb7bRt25bZs2cD0KpVK3bv3s2KFSuu+WTngw8+4J133uG9996jWbNmbN++nWHDhhEdHU2fPn08HV6NI7+Xq2axWOjevTt2u53ly5d7OhyP27JlCy+//DJbt269oH9PrsptrNDQUDQaTYVVnIyMjAr/V3Gtev755/n888/56aefqF27tqfDqRG2bNlCRkYGbdq0QavVotVq2bhxI0uWLEGr1WKz2TwdosdERUXRtGlTl8eaNGlyzRf8A4wePZpx48bRvXt3WrRoQe/evRk+fDhz5szxdGg1SmRkJID8Xq6CxWKha9euHDp0iPXr18uqDvDLL7+QkZFB3bp1nb+Xjxw5wsiRI6lfv/5ZX+eqTHa8vb1p06YN69evd3l8/fr1JCQkeCiqmkFRFAYPHsx///tffvzxRxo0aODpkGqMO+64g127drF9+3bnX23btqVnz55s374djUbj6RA9JjExscIRBfv37z/rgbxXM6PRiFrt+qtUo9Fck63nVWnQoAGRkZEuv5dLSkrYuHHjNf97GU4nOv/88w/ff/89ISEhng6pRujduzc7d+50+b0cHR3N6NGj+fbbb8/6OlftNtaIESPo3bs3bdu2JT4+nlWrVnH06FH69+/v6dA8atCgQbz33nt89tln+Pv7O/8vKzAwEL1e7+HoPMvf379C7ZKvry8hISHXfE3T8OHDSUhIYPbs2XTt2pXff/+dVatWsWrVKk+H5nEPPvggs2bNom7dujRr1oxt27axcOFCnnrqKU+HdtkVFhZy4MAB58+HDh1i+/btBAcHU7duXYYNG8bs2bNp1KgRjRo1Yvbs2RgMBnr06OHBqC+Pqu5NdHQ0nTt3ZuvWrXzxxRfYbDbn7+bg4GC8vb09FfZlUd2/N2cmfl5eXkRGRnLjjTee/YdceKNYzfXKK68o9erVU7y9vZXWrVtLe7XiaOtz99fq1as9HVqNJK3np/3vf/9Tmjdvruh0OqVx48bKqlWrPB1SjZCfn68MHTpUqVu3ruLj46M0bNhQmThxomI2mz0d2mX3008/uf390qdPH0VRHO3nU6dOVSIjIxWdTqd06NBB2bVrl2eDvkyqujeHDh2q9HfzTz/95OnQL7nq/r050/m0nqsURVHOKQUTQgghhLiCXJU1O0IIIYQQZSTZEUIIIcRVTZIdIYQQQlzVJNkRQgghxFVNkh0hhBBCXNUk2RFCCCHEVU2SHSGEEEJc1STZEUJcMaZNm0bLli2dP/ft25dHHnnkssdx+PBhVCoV27dvv+yfLYQ4d5LsCCEuWN++fVGpVKhUKry8vGjYsCGjRo2iqKjokn7uyy+/zJo1a87qtZKgCHHtumpnYwkhLq977rmH1atXY7FY+OWXX3j66acpKipixYoVLq+zWCx4eXldlM8MDAy8KNcRQlzdZGVHCHFR6HQ6IiMjqVOnDj169KBnz558+umnzq2nN998k4YNG6LT6VAUhby8PJ599lnCw8MJCAjg9ttvZ8eOHS7XfPHFF4mIiMDf359+/fpRXFzs8vyZ21h2u525c+dy/fXXo9PpqFu3LrNmzQIcU7cBWrVqhUqlomPHjs73rV69miZNmuDj40Pjxo1Zvny5y+f8/vvvtGrVCh8fH9q2bcu2bdsu4p0TQlxqsrIjhLgk9Ho9FosFgAMHDvDhhx/y8ccfo9FoALj//vsJDg7mq6++IjAwkFdffZU77riD/fv3ExwczIcffsjUqVN55ZVXuPXWW3n77bdZsmQJDRs2rPQzx48fz2uvvcaiRYto3749J0+eZN++fYAjYbn55pv5/vvvadasmXOS9GuvvcbUqVNZtmwZrVq1Ytu2bTzzzDP4+vrSp08fioqKeOCBB7j99tt55513OHToEEOHDr3Ed08IcVFd4LBSIYRQ+vTpozz88MPOn3/77TclJCRE6dq1qzJ16lTFy8tLycjIcD7/ww8/KAEBAUpxcbHLda677jrl1VdfVRRFUeLj45X+/fu7PH/LLbcocXFxbj83Pz9f0el0ymuvveY2xrLJ0tu2bXN5vE6dOsp7773n8tiMGTOU+Ph4RVEU5dVXX1WCg4OVoqIi5/MrVqxwey0hRM0k21hCiIviiy++wM/PDx8fH+Lj4+nQoQNLly4FoF69eoSFhTlfu2XLFgoLCwkJCcHPz8/516FDh/j3338B2Lt3L/Hx8S6fcebP5e3duxez2cwdd9xx1jFnZmZy7Ngx+vXr5xLHzJkzXeKIi4vDYDCcVRxCiJpHtrGEEBfFf/7zH1asWIGXlxfR0dEuRci+vr4ur7Xb7URFRbFhw4YK16lVq9Z5fb5erz/n99jtdsCxlXXLLbe4PFe23aYoynnFI4SoOSTZEUJcFL6+vlx//fVn9drWrVuTlpaGVqulfv36bl/TpEkTNm/eTFJSkvOxzZs3V3rNRo0aodfr+eGHH3j66acrPF9Wo2Oz2ZyPRUREEBMTw8GDB+nZs6fb6zZt2pS3334bk8nkTKiqikMIUfPINpYQ4rK78847iY+P55FHHuHbb7/l8OHDpKSkMGnSJP78808Ahg4dyptvvsmbb77J/v37mTp1Krt37670mj4+PowdO5YxY8awdu1a/v33XzZv3swbb7wBQHh4OHq9nm+++Yb09HTy8vIAx0GFc+bM4eWXX2b//v3s2rWL1atXs3DhQgB69OiBWq2mX79+7Nmzh6+++or58+df4jskhLiYJNkRQlx2KpWKr776ig4dOvDUU09xww030L17dw4fPkxERAQA3bp1Y8qUKYwdO5Y2bdpw5MgRBgwYUOV1J0+ezMiRI5kyZQpNmjShW7duZGRkAKDValmyZAmvvvoq0dHRPPzwwwA8/fTTvP7666xZs4YWLVpw2223sWbNGmerup+fH//73//Ys2cPrVq1YuLEicydO/cS3h0hxMWmUmRDWgghhBBXMVnZEUIIIcRVTZIdIYQQQlzVJNkRQgghxFVNkh0hhBBCXNUk2RFCCCHEVU2SHSGEEEJc1STZEUIIIcRVTZIdIYQQQlzVJNkRQgghxFVNkh0hhBBCXNUk2RFCCCHEVU2SHSGEEEJc1f4foN3uxh6C/pIAAAAASUVORK5CYII=\n" 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\n" }, "metadata": {}, "output_type": "display_data" @@ -1799,7 +1663,6 @@ "source": [ "## Discussion\n", "\n", - "Wrap up the talktorial's content here and discuss pros/cons and open questions/challenges.\n", "Compared to purely ligand-based compound activity prediction models, PCM modelling has certain advantages and limitations." ] }, @@ -1833,7 +1696,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [] From 054ccfec1683a46306631e19286fd6d5b77c54df Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Tue, 18 Oct 2022 11:21:13 +0200 Subject: [PATCH 13/62] Add ClustalO REST API client --- .../data/clustalo.py | 678 ++++++++++++++++++ 1 file changed, 678 insertions(+) create mode 100644 teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/clustalo.py diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/clustalo.py b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/clustalo.py new file mode 100644 index 00000000..78746b73 --- /dev/null +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/clustalo.py @@ -0,0 +1,678 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +############################################################################### +# +# Copyright 2012-2022 EMBL - European Bioinformatics Institute +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# +# Python Client Automatically generated with: +# https://github.com/ebi-wp/webservice-clients-generator +# +# Clustal Omega (REST) web service Python client using xmltramp2. +# +# For further information see: +# https://www.ebi.ac.uk/Tools/webservices/ +# +############################################################################### + +from __future__ import print_function + +import os +import sys +import time +import requests +import platform +from xmltramp2 import xmltramp +from optparse import OptionParser + +try: + from urllib.parse import urlparse, urlencode + from urllib.request import urlopen, Request + from urllib.error import HTTPError + from urllib.request import __version__ as urllib_version +except ImportError: + from urlparse import urlparse + from urllib import urlencode + from urllib2 import urlopen, Request, HTTPError + from urllib2 import __version__ as urllib_version + +# allow unicode(str) to be used in python 3 +try: + unicode('') +except NameError: + unicode = str + +# Base URL for service +baseUrl = u'https://www.ebi.ac.uk/Tools/services/rest/clustalo' +version = u'2022-09-13 12:15' + +# Set interval for checking status +pollFreq = 3 +# Output level +outputLevel = 1 +# Debug level +debugLevel = 0 +# Number of option arguments. +numOpts = len(sys.argv) + +# Process command-line options +parser = OptionParser(add_help_option=False) + +# Tool specific options (Try to print all the commands automatically) +parser.add_option('--guidetreeout', action='store_true', help=('Output guide tree.')) +parser.add_option('--addformats', action='store_true', help=('Output additional output formats')) +parser.add_option('--dismatout', action='store_true', help=('Output distance matrix. This is only calculated if the mBed-like' + 'clustering guide tree is set to false.')) +parser.add_option('--dealign', action='store_true', help=('Remove any existing alignment (gaps) from input sequences.')) +parser.add_option('--mbed', action='store_true', help=('This option uses a sample of the input sequences and then represents' + 'all sequences as vectors to these sequences, enabling much more rapid' + 'generation of the guide tree, especially when the number of sequences' + 'is large.')) +parser.add_option('--mbediteration', action='store_true', help=('Use mBed-like clustering during subsequent iterations.')) +parser.add_option('--iterations', type=int, help=('Number of (combined guide-tree/HMM) iterations.')) +parser.add_option('--gtiterations', type=int, help=('Having set the number of combined iterations, this parameter can be' + 'changed to limit the number of guide tree iterations within the' + 'combined iterations.')) +parser.add_option('--hmmiterations', type=int, help=('Having set the number of combined iterations, this parameter can be' + 'changed to limit the number of HMM iterations within the combined' + 'iterations.')) +parser.add_option('--outfmt', type=str, help=('Format for generated multiple sequence alignment.')) +parser.add_option('--order', type=str, help=('The order in which the sequences appear in the final alignment')) +parser.add_option('--stype', type=str, help=('Defines the type of the sequences to be aligned')) +parser.add_option('--sequence', type=str, help=('Three or more sequences to be aligned can be entered directly into' + 'this box. Sequences can be in GCG, FASTA, EMBL (Nucleotide only),' + 'GenBank, PIR, NBRF, PHYLIP or UniProtKB/Swiss-Prot (Protein only)' + 'format. Partially formatted sequences are not accepted. Adding a' + 'return to the end of the sequence may help certain applications' + 'understand the input. Note that directly using data from word' + 'processors may yield unpredictable results as hidden/control' + 'characters may be present. There is currently a sequence input limit' + 'of 4000 sequences and 4MB of data.')) +# General options +parser.add_option('-h', '--help', action='store_true', help='Show this help message and exit.') +parser.add_option('--email', help='E-mail address.') +parser.add_option('--title', help='Job title.') +parser.add_option('--outfile', help='File name for results.') +parser.add_option('--outformat', help='Output format for results.') +parser.add_option('--asyncjob', action='store_true', help='Asynchronous mode.') +parser.add_option('--jobid', help='Job identifier.') +parser.add_option('--polljob', action="store_true", help='Get job result.') +parser.add_option('--pollFreq', type='int', default=3, help='Poll frequency in seconds (default 3s).') +parser.add_option('--status', action="store_true", help='Get job status.') +parser.add_option('--resultTypes', action='store_true', help='Get result types.') +parser.add_option('--params', action='store_true', help='List input parameters.') +parser.add_option('--paramDetail', help='Get details for parameter.') +parser.add_option('--quiet', action='store_true', help='Decrease output level.') +parser.add_option('--verbose', action='store_true', help='Increase output level.') +parser.add_option('--version', action='store_true', help='Prints out the version of the Client and exit.') +parser.add_option('--debugLevel', type='int', default=debugLevel, help='Debugging level.') +parser.add_option('--baseUrl', default=baseUrl, help='Base URL for service.') + +(options, args) = parser.parse_args() + +# Increase output level +if options.verbose: + outputLevel += 1 + +# Decrease output level +if options.quiet: + outputLevel -= 1 + +# Debug level +if options.debugLevel: + debugLevel = options.debugLevel + +if options.pollFreq: + pollFreq = options.pollFreq + +if options.baseUrl: + baseUrl = options.baseUrl + + +# Debug print +def printDebugMessage(functionName, message, level): + if (level <= debugLevel): + print(u'[' + functionName + u'] ' + message, file=sys.stderr) + + +# User-agent for request (see RFC2616). +def getUserAgent(): + printDebugMessage(u'getUserAgent', u'Begin', 11) + # Agent string for urllib2 library. + urllib_agent = u'Python-urllib/%s' % urllib_version + clientRevision = version + # Prepend client specific agent string. + try: + pythonversion = platform.python_version() + pythonsys = platform.system() + except ValueError: + pythonversion, pythonsys = "Unknown", "Unknown" + user_agent = u'EBI-Sample-Client/%s (%s; Python %s; %s) %s' % ( + clientRevision, os.path.basename(__file__), + pythonversion, pythonsys, urllib_agent) + printDebugMessage(u'getUserAgent', u'user_agent: ' + user_agent, 12) + printDebugMessage(u'getUserAgent', u'End', 11) + return user_agent + + +# Wrapper for a REST (HTTP GET) request +def restRequest(url): + printDebugMessage(u'restRequest', u'Begin', 11) + printDebugMessage(u'restRequest', u'url: ' + url, 11) + try: + # Set the User-agent. + user_agent = getUserAgent() + http_headers = {u'User-Agent': user_agent} + req = Request(url, None, http_headers) + # Make the request (HTTP GET). + reqH = urlopen(req) + resp = reqH.read() + contenttype = reqH.info() + + if (len(resp) > 0 and contenttype != u"image/png;charset=UTF-8" + and contenttype != u"image/jpeg;charset=UTF-8" + and contenttype != u"application/gzip;charset=UTF-8"): + try: + result = unicode(resp, u'utf-8') + except UnicodeDecodeError: + result = resp + else: + result = resp + reqH.close() + # Errors are indicated by HTTP status codes. + except HTTPError as ex: + result = requests.get(url).content + printDebugMessage(u'restRequest', u'End', 11) + return result + + +# Get input parameters list +def serviceGetParameters(): + printDebugMessage(u'serviceGetParameters', u'Begin', 1) + requestUrl = baseUrl + u'/parameters' + printDebugMessage(u'serviceGetParameters', u'requestUrl: ' + requestUrl, 2) + xmlDoc = restRequest(requestUrl) + doc = xmltramp.parse(xmlDoc) + printDebugMessage(u'serviceGetParameters', u'End', 1) + return doc[u'id':] + + +# Print list of parameters +def printGetParameters(): + printDebugMessage(u'printGetParameters', u'Begin', 1) + idList = serviceGetParameters() + for id_ in idList: + print(id_) + printDebugMessage(u'printGetParameters', u'End', 1) + + +# Get input parameter information +def serviceGetParameterDetails(paramName): + printDebugMessage(u'serviceGetParameterDetails', u'Begin', 1) + printDebugMessage(u'serviceGetParameterDetails', u'paramName: ' + paramName, 2) + requestUrl = baseUrl + u'/parameterdetails/' + paramName + printDebugMessage(u'serviceGetParameterDetails', u'requestUrl: ' + requestUrl, 2) + xmlDoc = restRequest(requestUrl) + doc = xmltramp.parse(xmlDoc) + printDebugMessage(u'serviceGetParameterDetails', u'End', 1) + return doc + + +# Print description of a parameter +def printGetParameterDetails(paramName): + printDebugMessage(u'printGetParameterDetails', u'Begin', 1) + doc = serviceGetParameterDetails(paramName) + print(unicode(doc.name) + u"\t" + unicode(doc.type)) + print(doc.description) + if hasattr(doc, 'values'): + for value in doc.values: + print(value.value) + if unicode(value.defaultValue) == u'true': + print(u'default') + print(u"\t" + unicode(value.label)) + if hasattr(value, u'properties'): + for wsProperty in value.properties: + print(u"\t" + unicode(wsProperty.key) + u"\t" + unicode(wsProperty.value)) + printDebugMessage(u'printGetParameterDetails', u'End', 1) + + +# Submit job +def serviceRun(email, title, params): + printDebugMessage(u'serviceRun', u'Begin', 1) + # Insert e-mail and title into params + params[u'email'] = email + if title: + params[u'title'] = title + requestUrl = baseUrl + u'/run/' + printDebugMessage(u'serviceRun', u'requestUrl: ' + requestUrl, 2) + + # Get the data for the other options + requestData = urlencode(params) + + printDebugMessage(u'serviceRun', u'requestData: ' + requestData, 2) + # Errors are indicated by HTTP status codes. + try: + # Set the HTTP User-agent. + user_agent = getUserAgent() + http_headers = {u'User-Agent': user_agent} + req = Request(requestUrl, None, http_headers) + # Make the submission (HTTP POST). + reqH = urlopen(req, requestData.encode(encoding=u'utf_8', errors=u'strict')) + jobId = unicode(reqH.read(), u'utf-8') + reqH.close() + except HTTPError as ex: + print(xmltramp.parse(unicode(ex.read(), u'utf-8'))[0][0]) + quit() + printDebugMessage(u'serviceRun', u'jobId: ' + jobId, 2) + printDebugMessage(u'serviceRun', u'End', 1) + return jobId + + +# Get job status +def serviceGetStatus(jobId): + printDebugMessage(u'serviceGetStatus', u'Begin', 1) + printDebugMessage(u'serviceGetStatus', u'jobId: ' + jobId, 2) + requestUrl = baseUrl + u'/status/' + jobId + printDebugMessage(u'serviceGetStatus', u'requestUrl: ' + requestUrl, 2) + status = restRequest(requestUrl) + printDebugMessage(u'serviceGetStatus', u'status: ' + status, 2) + printDebugMessage(u'serviceGetStatus', u'End', 1) + return status + + +# Print the status of a job +def printGetStatus(jobId): + printDebugMessage(u'printGetStatus', u'Begin', 1) + status = serviceGetStatus(jobId) + if outputLevel > 0: + print("Getting status for job %s" % jobId) + print(status) + if outputLevel > 0 and status == "FINISHED": + print("To get results: python %s --polljob --jobid %s" + "" % (os.path.basename(__file__), jobId)) + printDebugMessage(u'printGetStatus', u'End', 1) + + +# Get available result types for job +def serviceGetResultTypes(jobId): + printDebugMessage(u'serviceGetResultTypes', u'Begin', 1) + printDebugMessage(u'serviceGetResultTypes', u'jobId: ' + jobId, 2) + requestUrl = baseUrl + u'/resulttypes/' + jobId + printDebugMessage(u'serviceGetResultTypes', u'requestUrl: ' + requestUrl, 2) + xmlDoc = restRequest(requestUrl) + doc = xmltramp.parse(xmlDoc) + printDebugMessage(u'serviceGetResultTypes', u'End', 1) + return doc[u'type':] + + +# Print list of available result types for a job. +def printGetResultTypes(jobId): + printDebugMessage(u'printGetResultTypes', u'Begin', 1) + if outputLevel > 0: + print("Getting result types for job %s" % jobId) + + resultTypeList = serviceGetResultTypes(jobId) + if outputLevel > 0: + print("Available result types:") + for resultType in resultTypeList: + print(resultType[u'identifier']) + if hasattr(resultType, u'label'): + print(u"\t", resultType[u'label']) + if hasattr(resultType, u'description'): + print(u"\t", resultType[u'description']) + if hasattr(resultType, u'mediaType'): + print(u"\t", resultType[u'mediaType']) + if hasattr(resultType, u'fileSuffix'): + print(u"\t", resultType[u'fileSuffix']) + if outputLevel > 0: + print("To get results:\n python %s --polljob --jobid %s\n" + " python %s --polljob --outformat --jobid %s" + "" % (os.path.basename(__file__), jobId, + os.path.basename(__file__), jobId)) + printDebugMessage(u'printGetResultTypes', u'End', 1) + + +# Get result +def serviceGetResult(jobId, type_): + printDebugMessage(u'serviceGetResult', u'Begin', 1) + printDebugMessage(u'serviceGetResult', u'jobId: ' + jobId, 2) + printDebugMessage(u'serviceGetResult', u'type_: ' + type_, 2) + requestUrl = baseUrl + u'/result/' + jobId + u'/' + type_ + result = restRequest(requestUrl) + printDebugMessage(u'serviceGetResult', u'End', 1) + return result + + +# Client-side poll +def clientPoll(jobId): + printDebugMessage(u'clientPoll', u'Begin', 1) + result = u'PENDING' + while result == u'RUNNING' or result == u'PENDING': + result = serviceGetStatus(jobId) + if outputLevel > 0: + print(result) + if result == u'RUNNING' or result == u'PENDING': + time.sleep(pollFreq) + printDebugMessage(u'clientPoll', u'End', 1) + + +# Get result for a jobid +# Allows more than one output file written when 'outformat' is defined. +def getResult(jobId): + printDebugMessage(u'getResult', u'Begin', 1) + printDebugMessage(u'getResult', u'jobId: ' + jobId, 1) + if outputLevel > 1: + print("Getting results for job %s" % jobId) + # Check status and wait if necessary + clientPoll(jobId) + # Get available result types + resultTypes = serviceGetResultTypes(jobId) + + for resultType in resultTypes: + # Derive the filename for the result + if options.outfile: + filename = (options.outfile + u'.' + unicode(resultType[u'identifier']) + + u'.' + unicode(resultType[u'fileSuffix'])) + else: + filename = (jobId + u'.' + unicode(resultType[u'identifier']) + + u'.' + unicode(resultType[u'fileSuffix'])) + # Write a result file + + outformat_parm = str(options.outformat).split(',') + for outformat_type in outformat_parm: + outformat_type = outformat_type.replace(' ', '') + + if outformat_type == 'None': + outformat_type = None + + if not outformat_type or outformat_type == unicode(resultType[u'identifier']): + if outputLevel > 1: + print("Getting %s" % unicode(resultType[u'identifier'])) + # Get the result + result = serviceGetResult(jobId, unicode(resultType[u'identifier'])) + if (unicode(resultType[u'mediaType']) == u"image/png" + or unicode(resultType[u'mediaType']) == u"image/jpeg" + or unicode(resultType[u'mediaType']) == u"application/gzip"): + fmode = 'wb' + else: + fmode = 'w' + + try: + fh = open(filename, fmode) + fh.write(result) + fh.close() + except TypeError: + fh.close() + fh = open(filename, "wb") + fh.write(result) + fh.close() + if outputLevel > 0: + print("Creating result file: " + filename) + printDebugMessage(u'getResult', u'End', 1) + + +# Read a file +def readFile(filename): + printDebugMessage(u'readFile', u'Begin', 1) + fh = open(filename, 'r') + data = fh.read() + fh.close() + printDebugMessage(u'readFile', u'End', 1) + return data + + +def print_usage(): + print("""\ +EMBL-EBI Clustal Omega Python Client: + +Multiple sequence alignment with Clustal Omega. + +[Required (for job submission)] + --email E-mail address. + --stype Defines the type of the sequences to be aligned. + --sequence Three or more sequences to be aligned can be entered + directly into this box. Sequences can be in GCG, FASTA, EMBL + (Nucleotide only), GenBank, PIR, NBRF, PHYLIP or + UniProtKB/Swiss-Prot (Protein only) format. Partially + formatted sequences are not accepted. Adding a return to the + end of the sequence may help certain applications understand + the input. Note that directly using data from word + processors may yield unpredictable results as hidden/control + characters may be present. There is currently a sequence + input limit of 4000 sequences and 4MB of data. + +[Optional] + --guidetreeout Output guide tree. + --addformats Output additional output formats. + --dismatout Output distance matrix. This is only calculated if the mBed- + like clustering guide tree is set to false. + --dealign Remove any existing alignment (gaps) from input sequences. + --mbed This option uses a sample of the input sequences and then + represents all sequences as vectors to these sequences, + enabling much more rapid generation of the guide tree, + especially when the number of sequences is large. + --mbediteration Use mBed-like clustering during subsequent iterations. + --iterations Number of (combined guide-tree/HMM) iterations. + --gtiterations Having set the number of combined iterations, this parameter + can be changed to limit the number of guide tree iterations + within the combined iterations. + --hmmiterations Having set the number of combined iterations, this parameter + can be changed to limit the number of HMM iterations within + the combined iterations. + --outfmt Format for generated multiple sequence alignment. + --order The order in which the sequences appear in the final + alignment. + +[General] + -h, --help Show this help message and exit. + --asyncjob Forces to make an asynchronous query. + --title Title for job. + --status Get job status. + --resultTypes Get available result types for job. + --polljob Poll for the status of a job. + --pollFreq Poll frequency in seconds (default 3s). + --jobid JobId that was returned when an asynchronous job was submitted. + --outfile File name for results (default is JobId; for STDOUT). + --outformat Result format(s) to retrieve. It accepts comma-separated values. + --params List input parameters. + --paramDetail Display details for input parameter. + --verbose Increase output. + --version Prints out the version of the Client and exit. + --quiet Decrease output. + --baseUrl Base URL. Defaults to: + https://www.ebi.ac.uk/Tools/services/rest/clustalo + +Synchronous job: + The results/errors are returned as soon as the job is finished. + Usage: python clustalo.py --email [options...] + Returns: results as an attachment + +Asynchronous job: + Use this if you want to retrieve the results at a later time. The results + are stored for up to 24 hours. + Usage: python clustalo.py --asyncjob --email [options...] + Returns: jobid + +Check status of Asynchronous job: + Usage: python clustalo.py --status --jobid + +Retrieve job data: + Use the jobid to query for the status of the job. If the job is finished, + it also returns the results/errors. + Usage: python clustalo.py --polljob --jobid [--outfile string] + Returns: string indicating the status of the job and if applicable, results + as an attachment. + +Further information: + https://www.ebi.ac.uk/Tools/webservices and + https://github.com/ebi-wp/webservice-clients + +Support/Feedback: + https://www.ebi.ac.uk/support/""") + + +# No options... print help. +if numOpts < 2: + print_usage() +elif options.help: + print_usage() +# List parameters +elif options.params: + printGetParameters() +# Get parameter details +elif options.paramDetail: + printGetParameterDetails(options.paramDetail) +# Print Client version +elif options.version: + print("Revision: %s" % version) + sys.exit() +# Submit job +elif options.email and not options.jobid: + params = {} + if len(args) == 1 and "true" not in args and "false" not in args: + if os.path.exists(args[0]): # Read file into content + params[u'sequence'] = readFile(args[0]) + else: # Argument is a sequence id + params[u'sequence'] = args[0] + elif len(args) == 2 and "true" not in args and "false" not in args: + if os.path.exists(args[0]) and os.path.exists(args[1]): # Read file into content + params[u'asequence'] = readFile(args[0]) + params[u'bsequence'] = readFile(args[1]) + else: # Argument is a sequence id + params[u'asequence'] = args[0] + params[u'bsequence'] = args[0] + elif hasattr(options, "sequence") or (hasattr(options, "asequence") and hasattr(options, "bsequence")): # Specified via option + if hasattr(options, "sequence"): + if os.path.exists(options.sequence): # Read file into content + params[u'sequence'] = readFile(options.sequence) + else: # Argument is a sequence id + params[u'sequence'] = options.sequence + elif hasattr(options, "asequence") and hasattr(options, "bsequence"): + if os.path.exists(options.asequence) and os.path.exists(options.bsequence): # Read file into content + params[u'asequence'] = readFile(options.asequence) + params[u'bsequence'] = readFile(options.bsequence) + else: # Argument is a sequence id + params[u'asequence'] = options.asequence + params[u'bsequence'] = options.bsequence + + # Pass default values and fix bools (without default value) + if options.stype: + params['stype'] = options.stype + + if not options.guidetreeout: + params['guidetreeout'] = 'true' + if options.guidetreeout: + params['guidetreeout'] = options.guidetreeout + + + if not options.addformats: + params['addformats'] = 'false' + if options.addformats: + params['addformats'] = options.addformats + + + if not options.dismatout: + params['dismatout'] = 'false' + if options.dismatout: + params['dismatout'] = options.dismatout + + + if not options.dealign: + params['dealign'] = 'false' + if options.dealign: + params['dealign'] = options.dealign + + + if not options.mbed: + params['mbed'] = 'true' + if options.mbed: + params['mbed'] = options.mbed + + + if not options.mbediteration: + params['mbediteration'] = 'true' + if options.mbediteration: + params['mbediteration'] = options.mbediteration + + + if not options.iterations: + params['iterations'] = 0 + if options.iterations: + params['iterations'] = options.iterations + + + if not options.gtiterations: + params['gtiterations'] = -1 + if options.gtiterations: + params['gtiterations'] = options.gtiterations + + + if not options.hmmiterations: + params['hmmiterations'] = -1 + if options.hmmiterations: + params['hmmiterations'] = options.hmmiterations + + + if not options.outfmt: + params['outfmt'] = 'clustal_num' + if options.outfmt: + params['outfmt'] = options.outfmt + + + if not options.order: + params['order'] = 'aligned' + if options.order: + params['order'] = options.order + + + + # Submit the job + jobId = serviceRun(options.email, options.title, params) + if options.asyncjob: # Async mode + print(jobId) + if outputLevel > 0: + print("To check status: python %s --status --jobid %s" + "" % (os.path.basename(__file__), jobId)) + else: + # Sync mode + if outputLevel > 0: + print("JobId: " + jobId, file=sys.stderr) + else: + print(jobId) + time.sleep(pollFreq) + getResult(jobId) +# Get job status +elif options.jobid and options.status: + printGetStatus(options.jobid) + +elif options.jobid and (options.resultTypes or options.polljob): + status = serviceGetStatus(options.jobid) + if status == 'PENDING' or status == 'RUNNING': + print("Error: Job status is %s. " + "To get result types the job must be finished." % status) + quit() + # List result types for job + if options.resultTypes: + printGetResultTypes(options.jobid) + # Get results for job + elif options.polljob: + getResult(options.jobid) +else: + # Checks for 'email' parameter + if not options.email: + print('\nParameter "--email" is missing in your command. It is required!\n') + + print(u'Error: unrecognised argument combination', file=sys.stderr) + print_usage() From 73a6c112c2f345c9c3a8f54c6f4021b3bc7576fb Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Tue, 18 Oct 2022 15:25:03 +0200 Subject: [PATCH 14/62] Add modelling interpretation and discussion. Make modelling output neat. --- .../talktorial.ipynb | 450 +++++++++++++----- 1 file changed, 321 insertions(+), 129 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index bebe2ccc..27ece231 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -243,7 +243,7 @@ { "cell_type": "markdown", "source": [ - "Proteochemometrics (PCM) consists in the modelling via supervised machine learning algorithms of a biological endpoint (e.g. compound activity) based on a series of features derived from chemical compounds and target proteins. The ML principles for proteochemometric modelling are equivalent to those explained in Talktorial T007. However, in this talktorial we will explore a supervised ML application other than classification, this is regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values." + "Proteochemometrics (PCM) consists in the modelling via supervised machine learning algorithms of a biological endpoint (e.g. compound activity) based on a series of features derived from chemical compounds and target proteins. PCM is an extension of a more widespread bioactivity modelling technique, Quantitative Structure Activity Relationship (QSAR) modelling, which relies solely on chemical features and that was introduced on Talktorial T007. The ML principles for proteochemometric modelling are equivalent to those explained for QSAR modelling. However, in this talktorial we will explore a supervised ML application other than classification, this is regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values." ], "metadata": { "collapsed": false @@ -311,6 +311,7 @@ "\n", "* Coefficient of determination ($R^{2}$ score): Represents the portion of variance of the target variable that has been explained by the independent variables (features) in the model. $R^{2}$ score varies between 1.0 (best score) and minus infinite, where 0.0 represents a model that always predicts the average target variable. As the variance is dataset dependent, it might not be meaningful metric to compare between datasets. When dealing with linear regression, and model fitting and evaluation are performed on a single dataset, $R^{2}$ is equivalent to the square of the Pearson correlation coefficient, described below, and can be noted as $r^{2}$.\n", "* Pearson's correlation coefficient (Pearson's $r$): Is a measure of the linear correlation between the true and predicted values of the target variable. It is calculated as the covariance of the two variables divided by the product of their standard deviation. Pearson's $r$ can vary between 1.0 (a perfect positive correlation) and -1.0 (a perfect negative correlation), where 1.0 would represent a perfect prediction.\n", + "* Man absolute error (MAE): Measures the average absolute difference between the predicted and the true values. MAE is interpreted based on the scale of the data, and it varies between infinite and 0.0 (best).\n", "* Mean squared error (MSE): Measures the average of the squares of the difference between the predicted and the true values. It varies between 1.0 (worst) and 0.0 (best).\n", "* Root mean square error (RMSE): Also called root mean square deviation (RMSD), it is the square root of the MSE and represents the standard deviation of the prediction errors respect to the line of best fit. RMSE is a measure of accuracy and it cannot be applied to compare between datasets, as it is scale-dependent. It varies between 1.0 (worst) and 0.0 (best).\n", "\n", @@ -403,7 +404,7 @@ "import numpy as np\n", "import pandas as pd\n", "import re\n", - "import random\n", + "import json\n", "\n", "from papyrus_scripts.download import download_papyrus\n", "from papyrus_scripts.reader import read_papyrus, read_protein_set\n", @@ -493,7 +494,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "b11c3201b52d4c9da9482b7f0d837916" + "model_id": "263cdf1d80254e6c86fe1227e2fce90d" } }, "metadata": {}, @@ -610,7 +611,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "9a43f70ddf1244f5a34109b760c2f08f" + "model_id": "6788e7d7b6084ee2a8b1f30835d4d2d4" } }, "metadata": {}, @@ -794,6 +795,8 @@ "name": "stdout", "output_type": "stream", "text": [ + "RUNNING\n", + "RUNNING\n", "FINISHED\n", "Creating result file: aligned_sequences.out.txt\n", "Creating result file: aligned_sequences.sequence.txt\n", @@ -808,7 +811,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "JobId: clustalo-R20221018-101740-0985-45571475-p1m\n" + "JobId: clustalo-R20221018-141634-0284-84527467-p1m\n" ] } ], @@ -895,6 +898,18 @@ } } }, + { + "cell_type": "markdown", + "source": [ + "In the MSA we can observe that the adenosine A2A receptor has a longer C terminus than the rest of the adenosine receptors. Moreover, there are clear parts of the proteins that are very similar in all the receptors (i.e. transmembrane domains), and parts that vary in amino acid composition and length between receptors (i.e. mostly loops)." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, { "cell_type": "markdown", "metadata": {}, @@ -916,7 +931,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "outputs": [ { "name": "stdout", @@ -941,7 +956,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 14, "outputs": [ { "name": "stdout", @@ -954,7 +969,7 @@ "data": { "text/plain": "{'Authors': 'Hellberg, Sjöström, Skagerberg, Wold',\n 'Year': 1987,\n 'Journal': 'Journal of Medicinal Chemistry',\n 'DOI': '10.1021/jm00390a003',\n 'PMID': None,\n 'Patent': None}" }, - "execution_count": 17, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -973,7 +988,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "outputs": [], "source": [ "def calculate_protein_descriptor(targets, aligned_sequences, protein_descriptor):\n", @@ -1014,7 +1029,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 16, "outputs": [ { "data": { @@ -1022,7 +1037,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "4905f9dc32d3430cae804eebc4e189d1" + "model_id": "94ff9603ffa241c293c00e3d0e79265f" } }, "metadata": {}, @@ -1033,7 +1048,7 @@ "text/plain": " accession Zscale_1 Zscale_2 Zscale_3 Zscale_4 Zscale_5 Zscale_6 \\\n0 P30542 0.00 0.00 0.00 0.00 0.00 0.00 \n1 P29274 0.00 0.00 0.00 0.00 0.00 0.00 \n2 P29275 0.00 0.00 0.00 0.00 0.00 0.00 \n3 P0DMS8 -2.49 -0.27 -0.41 -1.22 0.88 2.23 \n\n Zscale_7 Zscale_8 Zscale_9 ... 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" }, - "execution_count": 21, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1177,13 +1192,13 @@ "\n", "We will try two methods to split our dataset between training and test set:\n", "* Random split\n", - "* Leave one target out split\n", + "* Leave one target out (LOTO) split\n", "\n", - "Additionally, we will compare our PCM model to four independent models trained only on compound data, and finally we will comment on the results.\n", + "Additionally, we will compare our PCM model to four independent models trained only on compound data (QSAR), and finally we will comment on the results.\n", "\n", "Ultimately, we want a model that can predict compound activity data towards a target of interest for compound-target pairs that it has never seen before. By combining several targets in one model, we expect the model to be able to learn the similarities and differences between targets and use the additional data to make better predictions.\n", "\n", - "We start by defining a few functions that will help us split the data (split_train_test) and train and validate a PCM regression model (train_validate_model). The validation will be done on the test set and the performance will be assessed using regression metrics such as $R^{2}$ and $MSE$. Finally, we will define a function (benchmark_models_performance) to plot the correlation between true and predicted values in order to compare the performance of different models, either trained on different splits, or a PCM model to individual models trained only on compound descriptors." + "We start by defining a few functions that will help us split the data (split_train_test) and train and validate a PCM regression model (train_validate_pcm_model). The validation will be done on the test set and the performance will be assessed using regression metrics such as Person's $r$, $R^{2}$ and $MAE$. This function will also plot the correlation between true and predicted values, making a distinction between the different targets in the test set to assess whether the PCM model has a different performance per protein. Finally, we will define a function (train_validate_qsar_model) to train a QSAR model for a single target based on a random split. The output of this function will be comparable to that of the PCM model for comparison purposes." ], "metadata": { "collapsed": false, @@ -1207,7 +1222,7 @@ { "cell_type": "markdown", "source": [ - "Function to split the data using one of the methods described in theory." + "Function to split the data using one of these two methods described in theory: random split or leave one target out (LOTO) split." ], "metadata": { "collapsed": false, @@ -1218,22 +1233,26 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 19, "outputs": [], "source": [ - "def split_train_test(pcm_dataset, test_size, split_method, loto_accession='None'):\n", + "def split_train_test(pcm_dataset, test_size, split_method, loto_target=None, loto_accession='None'):\n", " \"\"\"\n", " Split a dataset for PCM modelling in train and test set based on the split method of choice\n", "\n", " Parameters\n", " ----------\n", - " bioactivity_dataset : pandas.Dataframe\n", - " Pandas dataframe with bioactivity dataset for PCM\n", + " pcm_dataset : pandas.Dataframe\n", + " Pandas dataframe with bioactivity dataset for PCM including compound and protein descriptors\n", " test_size : float\n", " Ratio of the data to include in the test set\n", " split_method : str\n", " 'random' for random split\n", " 'loto' for leave one target out split\n", + " loto_target : str\n", + " Target label to leave out for testing in 'loto' split method\n", + " loto_accession : str\n", + " Target Uniprot accession to leave out for testing in 'loto' split method\n", "\n", " Returns\n", " -------\n", @@ -1251,11 +1270,9 @@ " if loto_accession != None:\n", " # Leave out defined accession\n", " test_target = loto_accession\n", + " print(f'Target left out for testing is {loto_target}')\n", " else:\n", - " # Make a random selection of the target to leave out\n", - " targets = pcm_dataset.accession.unique()\n", - " test_target = random.choice(targets)\n", - " print(f'Target left out for testing is {test_target}')\n", + " raise ValueError(\"loto_accession needs to be defined\")\n", "\n", " # Move data associated to target to test set and rest to training set\n", " train = pcm_dataset[pcm_dataset['accession'] != test_target]\n", @@ -1274,17 +1291,31 @@ } } }, + { + "cell_type": "markdown", + "source": [ + "Function to train a PCM RF model on a training set and validate it on a test set. Performance metrics are calculated for the whole test set, and also separately" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 20, "outputs": [], "source": [ - "def train_validate_pcm_model(targets_dict, train,test):\n", + "def train_validate_pcm_model(targets_dict, train, test):\n", " \"\"\"\n", " Train PCM RF regression model and validate on test set, calculating performance metrics\n", "\n", " Parameters\n", " ----------\n", + " targets_dict: dict\n", + " Dictionary of target labels and accession codes in the PCM set\n", " train: pandas.DataFrame\n", " Training dataset\n", " test : pandas.DataFrame\n", @@ -1293,7 +1324,9 @@ " Returns\n", " -------\n", " dict:\n", - " r2_score and MSE on test set\n", + " Pearson's r, R2 score and MAE on test set\n", + " fig:\n", + " Figure with true vs. predicted values colored by target, with r2_score calculated per target\n", " \"\"\"\n", " # Store keys of training and test sets\n", " train_keys = train[['SMILES', 'accession']]\n", @@ -1318,10 +1351,11 @@ "\n", " # Calculate model performance with regression metrics\n", " model_performance = {}\n", - " model_performance['pearson_r'] = pearsonr(test.iloc[:, 0], predictions)\n", - " model_performance['r2_score'] = r2_score(test.iloc[:, 0], predictions)\n", - " model_performance['mse'] = mean_absolute_error(test.iloc[:, 0], predictions)\n", - " print(model_performance)\n", + " model_performance['Pearson r'] = pearsonr(test.iloc[:, 0], predictions)[0]\n", + " model_performance['R2 score'] = r2_score(test.iloc[:, 0], predictions)\n", + " model_performance['MAE'] = mean_absolute_error(test.iloc[:, 0], predictions)\n", + " print('=== PCM model performance ===')\n", + " print(json.dumps(model_performance, indent=4))\n", "\n", " # Add column named 'Target' for easier data visualization\n", " test_keys['Target'] = test_keys['accession'].apply(lambda x: [i for i in targets_dict.keys() if targets_dict[i]==x][0])\n", @@ -1339,7 +1373,7 @@ " r2_target = r2_score(true_target, prediction_target)\n", "\n", " # Plot correlation between true and predicted values\n", - " ax = sns.scatterplot(y=true_target, x=prediction_target, label=(f'{target} r2= {r2_target:.2f}'))\n", + " ax = sns.scatterplot(y=true_target, x=prediction_target, label=(f'{target} R2 = {r2_target:.2f}'))\n", " _ = sns.lineplot(x=(0, 14), y=(0, 14))\n", " _ = ax.set_xlim((0,14))\n", " _ = ax.set_ylim((0,14))\n", @@ -1347,9 +1381,7 @@ " _ = ax.set_ylabel('Observed')\n", " except ValueError:\n", " # Performance can only be plotted for the left out target in LOTO split\n", - " pass\n", - "\n", - " return model_performance" + " pass\n" ], "metadata": { "collapsed": false, @@ -1358,28 +1390,47 @@ } } }, + { + "cell_type": "markdown", + "source": [ + "Function to split a QSAR dataset randomly and train and validate a RF QSAR model for a target of interest." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 21, "outputs": [], "source": [ - "def train_validate_qsar_model(pcm_dataset,target,accession,test_size):\n", + "def train_validate_qsar_model(qsar_dataset, target, accession, test_size):\n", " \"\"\"\n", " Train PCM RF regression model and validate on test set, calculating performance metrics\n", "\n", " Parameters\n", " ----------\n", - "\n", - " target_id : str\n", - " Target id to perform qsar on\n", + " qsar_dataset : pandas.Dataframe\n", + " Pandas dataframe with bioactivity dataset for QSAR including compound descriptors\n", + " target : str\n", + " Target label for QSAR model\n", + " accession: str\n", + " Target Uniprot accession for QSAR model\n", + " test_size: float\n", + " Ratio of the data to include in the test set upon random split\n", "\n", " Returns\n", " -------\n", " dict:\n", - " r2_score and MSE on test set\n", + " Pearson's r, R2 score and MAE on test set\n", + " fig:\n", + " Figure with true vs. predicted values, with r2_score calculated\n", " \"\"\"\n", " # Extract target-specific dataset\n", - " target_dataset = pcm_dataset[pcm_dataset['accession'] == accession]\n", + " target_dataset = qsar_dataset[qsar_dataset['accession'] == accession]\n", "\n", " # Remove identifiers\n", " target_dataset = target_dataset.drop(columns=['SMILES', 'accession'])\n", @@ -1402,21 +1453,19 @@ "\n", " # Calculate model performance with regression metrics\n", " model_performance = {}\n", - " model_performance['pearson_r'] = pearsonr(test.iloc[:, 0], predictions)\n", - " model_performance['r2_score'] = r2_score(test.iloc[:, 0], predictions)\n", - " model_performance['mse'] = mean_absolute_error(test.iloc[:, 0], predictions)\n", - " print(model_performance)\n", + " model_performance['Pearson r'] = pearsonr(test.iloc[:, 0], predictions)[0]\n", + " model_performance['R2 score'] = r2_score(test.iloc[:, 0], predictions)\n", + " model_performance['MAE'] = mean_absolute_error(test.iloc[:, 0], predictions)\n", + " print(f'=== QSAR model performance {target} ===')\n", + " print(json.dumps(model_performance, indent=4))\n", "\n", " # Plot correlation between true and predicted values\n", - " ax = sns.scatterplot(y=test.iloc[:, 0], x=predictions, label=(f'{target} R2= {model_performance[\"r2_score\"]:.2f}'))\n", + " ax = sns.scatterplot(y=test.iloc[:, 0], x=predictions, label=(f'{target} R2 = {model_performance[\"R2 score\"]:.2f}'))\n", " _ = sns.lineplot(x=(0, 14), y=(0, 14))\n", " _ = ax.set_xlim((0,14))\n", " _ = ax.set_ylim((0,14))\n", " _ = ax.set_xlabel('Predicted')\n", - " _ = ax.set_ylabel('Observed')\n", - "\n", - "\n", - " return model_performance" + " _ = ax.set_ylabel('Observed')" ], "metadata": { "collapsed": false, @@ -1437,22 +1486,34 @@ } } }, + { + "cell_type": "markdown", + "source": [ + "For each compound-target pair in our bioactivity dataset, we need to add the protein and molecular features previously calculated. We join the protein features based on Uniprot accession and the molecular features based on SMILES." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 22, "outputs": [ { "data": { "text/plain": " SMILES accession \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n1 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n2 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n3 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n4 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n... ... ... \n12714 Cn1cc(Nc2nc(-c3ccco3)c(-c3ncncc3)cn2)ccc1=O P29275 \n12715 N#Cc1c(-c2ccc(OCC3CC3)cc2)c(C#N)c(SCC(N)=O)nc1N P29275 \n12716 O=C(Cc1cccc2c1cccc2)Nc1nc2nn(CCc3ccccc3)cc2c2n... P29275 \n12717 COc1c(OCC(=O)O)ccc(-c2cc3c([nH]2)c(=O)n(C)c(=O... P29275 \n12718 CCCn1c(=O)c2c([nH]c(-c3cc(OCC4CC(=O)N(c5ccc(F)... P29275 \n\n pchembl_value_Mean Zscale_1 Zscale_2 Zscale_3 Zscale_4 Zscale_5 \\\n0 8.6800 0.00 0.00 0.00 0.00 0.00 \n1 6.6800 0.00 0.00 0.00 0.00 0.00 \n2 4.8200 0.00 0.00 0.00 0.00 0.00 \n3 5.6500 0.00 0.00 0.00 0.00 0.00 \n4 7.1515 -2.49 -0.27 -0.41 -1.22 0.88 \n... ... ... ... ... ... ... \n12714 7.5515 0.00 0.00 0.00 0.00 0.00 \n12715 7.5100 0.00 0.00 0.00 0.00 0.00 \n12716 7.3672 0.00 0.00 0.00 0.00 0.00 \n12717 6.5700 0.00 0.00 0.00 0.00 0.00 \n12718 6.6800 0.00 0.00 0.00 0.00 0.00 \n\n Zscale_6 Zscale_7 ... nN nO nS nP nF nCl nBr nI nX BalabanJ \n0 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.631 \n1 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.631 \n2 0.00 0.00 ... 4 3 1 0 0 0 0 0 0 1.307 \n3 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.328 \n4 2.23 3.22 ... 6 2 0 0 0 0 0 0 0 1.328 \n... ... ... ... .. .. .. .. .. ... ... .. .. ... \n12714 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.368 \n12715 0.00 0.00 ... 5 2 1 0 0 0 0 0 0 1.613 \n12716 0.00 0.00 ... 7 2 0 0 0 0 0 0 0 0.998 \n12717 0.00 0.00 ... 3 6 0 0 0 0 0 0 0 1.608 \n12718 0.00 0.00 ... 6 5 0 0 1 0 0 0 1 1.103 \n\n[12719 rows x 1303 columns]", "text/html": "
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SMILESaccessionpchembl_value_MeanZscale_1Zscale_2Zscale_3Zscale_4Zscale_5Zscale_6Zscale_7...nNnOnSnPnFnClnBrnInXBalabanJ
0Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.68000.000.000.000.000.000.000.00...6200000001.631
1Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.68000.000.000.000.000.000.000.00...6200000001.631
2Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.82000.000.000.000.000.000.000.00...4310000001.307
3O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.65000.000.000.000.000.000.000.00...6200000001.328
4O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.1515-2.49-0.27-0.41-1.220.882.233.22...6200000001.328
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12714Cn1cc(Nc2nc(-c3ccco3)c(-c3ncncc3)cn2)ccc1=OP292757.55150.000.000.000.000.000.000.00...6200000001.368
12715N#Cc1c(-c2ccc(OCC3CC3)cc2)c(C#N)c(SCC(N)=O)nc1NP292757.51000.000.000.000.000.000.000.00...5210000001.613
12716O=C(Cc1cccc2c1cccc2)Nc1nc2nn(CCc3ccccc3)cc2c2n...P292757.36720.000.000.000.000.000.000.00...7200000000.998
12717COc1c(OCC(=O)O)ccc(-c2cc3c([nH]2)c(=O)n(C)c(=O...P292756.57000.000.000.000.000.000.000.00...3600000001.608
12718CCCn1c(=O)c2c([nH]c(-c3cc(OCC4CC(=O)N(c5ccc(F)...P292756.68000.000.000.000.000.000.000.00...6500100011.103
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12719 rows × 1303 columns

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" }, - "execution_count": 25, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# Add protein and molecular features to bioactivity dataset\n", + "# Add protein and molecular features to bioactivity dataset to generate PCM dataset\n", "ar_pcm_dataset = ar_dataset.merge(protein_features, on='accession')\n", "ar_pcm_dataset = ar_pcm_dataset.merge(molecular_features, on='SMILES')\n", "\n", @@ -1465,24 +1526,37 @@ } } }, + { + "cell_type": "markdown", + "source": [ + "For QSAR modelling, we do the same but we do not include the protein descriptors. This results on a dataset for modelling with a significantly reduced number of features." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 23, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "== Random split ==\n", - "Training set has 10175 datapoints\n", - "Test set has 2544 datapoints (20.002 %)\n" - ] + "data": { + "text/plain": " SMILES accession \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n1 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n2 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n3 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n4 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n... ... ... \n12714 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... P30542 \n12715 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P0DMS8 \n12716 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29274 \n12717 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29275 \n12718 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P30542 \n\n pchembl_value_Mean ABC ABCGG nAcid nBase nAtom nHeavyAtom \\\n0 8.6800 21.041 17.684 0 1 51 27 \n1 6.6800 21.041 17.684 0 1 51 27 \n2 4.8200 20.701 15.635 0 0 42 26 \n3 7.1515 23.23 17.455999 0 0 43 29 \n4 5.6500 23.23 17.455999 0 0 43 29 \n... ... ... ... ... ... ... ... \n12714 5.1000 23.736 18.441999 0 0 52 30 \n12715 7.6100 18.511 15.661 0 0 43 24 \n12716 7.3500 18.511 15.661 0 0 43 24 \n12717 5.1500 18.511 15.661 0 0 43 24 \n12718 7.3400 18.511 15.661 0 0 43 24 \n\n nSpiro ... nN nO nS nP nF nCl nBr nI nX BalabanJ \n0 0 ... 6 2 0 0 0 0 0 0 0 1.631 \n1 0 ... 6 2 0 0 0 0 0 0 0 1.631 \n2 0 ... 4 3 1 0 0 0 0 0 0 1.307 \n3 0 ... 6 2 0 0 0 0 0 0 0 1.328 \n4 0 ... 6 2 0 0 0 0 0 0 0 1.328 \n... ... ... .. .. .. .. .. ... ... .. .. ... \n12714 0 ... 4 3 0 0 2 0 0 0 2 1.318 \n12715 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n12716 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n12717 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n12718 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n\n[12719 rows x 25 columns]", + "text/html": "
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SMILESaccessionpchembl_value_MeanABCABCGGnAcidnBasenAtomnHeavyAtomnSpiro...nNnOnSnPnFnClnBrnInXBalabanJ
0Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.680021.04117.6840151270...6200000001.631
1Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.680021.04117.6840151270...6200000001.631
2Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.820020.70115.6350042260...4310000001.307
3O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.151523.2317.4559990043290...6200000001.328
4O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.650023.2317.4559990043290...6200000001.328
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12714Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc...P305425.100023.73618.4419990052300...4300200021.318
12715CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P0DMS87.610018.51115.6610043240...5300000001.68
12716CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292747.350018.51115.6610043240...5300000001.68
12717CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292755.150018.51115.6610043240...5300000001.68
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12719 rows × 25 columns

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" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "# Split dataset in training and test set (random split)\n", - "print('== Random split ==')\n", - "train_random,test_random = split_train_test(ar_pcm_dataset, 0.20, 'random')" + "# Add molecular features to bioactivity dataset to generate QSAR dataset\n", + "ar_qsar_dataset = ar_dataset.merge(molecular_features, on='SMILES')\n", + "\n", + "ar_qsar_dataset" ], "metadata": { "collapsed": false, @@ -1511,39 +1585,104 @@ } }, "source": [ - "Random split PCM model" + "##### Random split PCM model" ] }, + { + "cell_type": "markdown", + "source": [ + "The first PCM model that we train for the four adenosine receptors is based on a random split, where 20 % of the data (2,544 datapoints) is part of the test set for validation." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "== Random split ==\n", + "Training set has 10175 datapoints\n", + "Test set has 2544 datapoints (20.002 %)\n" + ] + } + ], + "source": [ + "# Split dataset in training and test set (random split)\n", + "print('== Random split ==')\n", + "train_random,test_random = split_train_test(ar_pcm_dataset, 0.20, 'random')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 25, "metadata": {}, "outputs": [ { - "data": { - "text/plain": "{'pearson_r': PearsonRResult(statistic=0.6885449652658857, pvalue=0.0),\n 'r2_score': 0.4684208657515193,\n 'mse': 0.641552897297109}" - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "=== PCM model performance ===\n", + "{\n", + " \"Pearson r\": 0.6852262686527153,\n", + " \"R2 score\": 0.4645718547018661,\n", + " \"MAE\": 0.6407882396824323\n", + "}\n" + ] }, { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ + "# Train and validate PCM model\n", "train_validate_pcm_model(adenosine_receptors,train_random,test_random)" ] }, { "cell_type": "markdown", "source": [ - "Random split QSAR models" + "Based on the performance metrics of the PCM model, Pearson's $r$ tells us that the true and rpedicted values are highly correlated .Moreover, the $R^{2}$ tells us that almost 50 % of the variance of the target variable is explained by the model features and that the model is predictive. The $MAE$ tells us that the predictions are on average 0.64 p-value units off, which is an acceptable prediction error.\n", + "An interesting observation is that the $R^{2}$ score is quite similar if we calculate it independently for the test set datapoint corresponding to each target." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "##### Random split QSAR models" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "Now, we want to compare the use of a single PCM model for four targets vs. four independent QSAR models trained for each target solely on chemical compound features." ], "metadata": { "collapsed": false, @@ -1554,35 +1693,52 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Performance of QSAR model of A1\n", - "{'pearson_r': PearsonRResult(statistic=0.623175021660205, pvalue=5.575802632228035e-77), 'r2_score': 0.3877742318018689, 'mse': 0.5715224726715452}\n", - "Performance of QSAR model of A2A\n", - "{'pearson_r': PearsonRResult(statistic=0.6322514381507167, pvalue=2.075307940603518e-90), 'r2_score': 0.3984961116776179, 'mse': 0.7001518465280718}\n", - "Performance of QSAR model of A2B\n", - "{'pearson_r': PearsonRResult(statistic=0.6960602722481648, pvalue=6.029910702826532e-59), 'r2_score': 0.4831210039190027, 'mse': 0.5876251514924776}\n", - "Performance of QSAR model of A3\n", - "{'pearson_r': PearsonRResult(statistic=0.6872475301950196, pvalue=2.5399695303435347e-91), 'r2_score': 0.47123053951136107, 'mse': 0.6503513180863987}\n" + "=== QSAR model performance A1 ===\n", + "{\n", + " \"Pearson r\": 0.595672468880893,\n", + " \"R2 score\": 0.3527408234746222,\n", + " \"MAE\": 0.5948338517708422\n", + "}\n", + "=== QSAR model performance A2A ===\n", + "{\n", + " \"Pearson r\": 0.642402046425395,\n", + " \"R2 score\": 0.4114591741751973,\n", + " \"MAE\": 0.6908901896785428\n", + "}\n", + "=== QSAR model performance A2B ===\n", + "{\n", + " \"Pearson r\": 0.7109948284816284,\n", + " \"R2 score\": 0.4962178778442754,\n", + " \"MAE\": 0.5510204294516342\n", + "}\n", + "=== QSAR model performance A3 ===\n", + "{\n", + " \"Pearson r\": 0.6591265363449496,\n", + " \"R2 score\": 0.432561408555847,\n", + " \"MAE\": 0.6934180112934113\n", + "}\n" ] }, { "data": { "text/plain": "
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\n" + "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ + "# Iterate over targets (adenosine receptors)\n", "for target,accession in adenosine_receptors.items():\n", - " print(f'Performance of QSAR model of {target}')\n", - " train_validate_qsar_model(ar_pcm_dataset,target,accession,0.20)" + " # Train and validate QSAR models\n", + " train_validate_qsar_model(ar_qsar_dataset,target,accession,0.20)" ], "metadata": { "collapsed": false, @@ -1594,7 +1750,7 @@ { "cell_type": "markdown", "source": [ - "Leave one target out split PCM model" + "The four QSAR models trained have quite good performance, with high correlation between the observed and predicted values. Compared to the PCM model, the $R^{2}$ score is less homogeneous between targets and, in general, lower. These results seem to indicate that the PCM model is able to extrapolate certain properties between targets." ], "metadata": { "collapsed": false, @@ -1603,50 +1759,90 @@ } } }, + { + "cell_type": "markdown", + "source": [ + "##### Leave one target out split PCM model" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "The random split PCM model works pretty well. However, a model trained and validated on a random split might overestimate the performance compared to a real life drug discovery scenario.\n", + "Finally, to test whether our PCM model could be used to predict bioactivity data on a target for which we have no previously known bioactivity data, we can train and validate PCM models following the \"leave one target out\" (LOTO) split method. We can do this process for each of the adenosine receptors." + ], + "metadata": { + "collapsed": false + } + }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 26, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "== Leave one target out split ==\n", - "Target left out for testing is P30542\n", + "Target left out for testing is A1\n", "Training set has 9200 datapoints\n", "Test set has 3519 datapoints (27.667 %)\n", - "{'pearson_r': PearsonRResult(statistic=0.25042850038047726, pvalue=1.8412015539714736e-51), 'r2_score': -0.09210483491448596, 'mse': 0.8522890475855737}\n", + "=== PCM model performance ===\n", + "{\n", + " \"Pearson r\": 0.23569780962301712,\n", + " \"R2 score\": -0.13386952750922565,\n", + " \"MAE\": 0.8689090617261386\n", + "}\n", "== Leave one target out split ==\n", - "Target left out for testing is P29274\n", + "Target left out for testing is A2A\n", "Training set has 8728 datapoints\n", "Test set has 3991 datapoints (31.378 %)\n", - "{'pearson_r': PearsonRResult(statistic=0.19724573427085657, pvalue=2.6943474596637424e-36), 'r2_score': -0.04664854887508518, 'mse': 0.9690074906568571}\n", + "=== PCM model performance ===\n", + "{\n", + " \"Pearson r\": 0.18495789114122146,\n", + " \"R2 score\": -0.057385950238118655,\n", + " \"MAE\": 0.9729507524765646\n", + "}\n", "== Leave one target out split ==\n", - "Target left out for testing is P29275\n", + "Target left out for testing is A2B\n", "Training set has 10731 datapoints\n", "Test set has 1988 datapoints (15.63 %)\n", - "{'pearson_r': PearsonRResult(statistic=-0.009883169222359389, pvalue=0.6596514784653168), 'r2_score': -0.28866658578888793, 'mse': 0.9936770590148246}\n", + "=== PCM model performance ===\n", + "{\n", + " \"Pearson r\": 0.005277079367292801,\n", + " \"R2 score\": -0.25410344457937306,\n", + " \"MAE\": 0.9783984679304268\n", + "}\n", "== Leave one target out split ==\n", - "Target left out for testing is P0DMS8\n", + "Target left out for testing is A3\n", "Training set has 9498 datapoints\n", "Test set has 3221 datapoints (25.324 %)\n", - "{'pearson_r': PearsonRResult(statistic=0.09172545645648542, pvalue=1.840667237645006e-07), 'r2_score': -0.2699995305753564, 'mse': 1.0547617517398264}\n" + "=== PCM model performance ===\n", + "{\n", + " \"Pearson r\": 0.08667633542325252,\n", + " \"R2 score\": -0.27063646454770796,\n", + " \"MAE\": 1.053553854194796\n", + "}\n" ] }, { "data": { "text/plain": "
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EKpWKjh07cujQoUqv27p1K40bN8bLy4vGjRvz5Zdf2q3PmDEDmUxm9xUeHn6zHkMgEAhuGF8vGU3x8Fe4J01C7wl/921J7y377kqhA0Ls3NasWbOG0aNHs2vXLs6ePWu3tnfvXkJCQli/fj2HDh3ijTfeYPLkyaxcudJhn127dmEwGOjduzdr1669ajtKS0sB+O2333jppZfYvXs3P/30EyaTiS5dulBcXHxNz3elLFq0iKVLl7Jy5Ur+/PNPwsPDefTRRyksLHR5TVJSEn379mXQoEGkpKQwaNAg+vTpwx9//GF3XpMmTcjIyLB9HTx48KY+i0AgEFwPxYVaPhsczX0f/IyfHtJCwLT4DXrP/LSqTatarnuU6C3OzZ56LkmSJOnyJCn7qCSd+1OSso9ZX99kioqKJD8/P+nIkSNS3759pZkzZ1Z6zciRI6VOnTo5HB8yZIg0adIk6fvvv5fq1q1rN1XcGdOnT5eioqKk1atXS3Xq1JFkMpnTa7KysiRA+u233678wa4Si8UihYeHSwsWLLAdMxgMkkajkeLj411e16dPH6lbt252x7p27Sr169fP9rrsOa8GMfVcIBBUFSkJ30jfd2okHW5gnVa+qe/9Un5OelWbdc3cyKnnwrNzvWjTYMvzsPIB+PBhWNkaPh9qPX4T2bRpEw0aNKBBgwYMHDiQjz76CKmSAfZarZbAwEC7Y4WFhWzZsoWBAwfy6KOPUlxczI4dOyq9/4kTJ9i8eTNbt24lOTnZ5f0Au3sOHz4cX19ft18VvVTuSE1NJTMzky5dutiOeXl50aFDBxITE11el5SUZHcNQNeuXR2uOX78OBEREdSpU4d+/fpx6tSpK7ZNIBAI/i2+XDSckpGvUjtdQucJhwc8QJ/P9lItqHpVm3ZLIBKUrwd9Pnw9Ck79Yn/85Hb432jotRpUATfl1qtXr2bgwIEAdOvWjaKiIrZv384jjzzi9PykpCQ2b97Mt99+a3f8s88+o379+jRp0gSAfv36sXr1ajp16uT2/kajkU8++YSQkBCn65IkMW7cONq1a0fTpk1tx2fNmsWrr77qdu+IiAi36+XJzMwEICwszO54WFgYZ86ccXuds2vK9gNo06YN69at47777uPChQvMmTOHmJgYDh06RFBQ0BXbKBAIBDeLIm0e347sRvO91rD9+TAZgdNm0vPh3lVs2a2FEDvXQ3G2o9Ap4+R26/pNEDtHjx5lz549fPHFFwAolUr69u3LmjVrnIqdQ4cO0aNHD6ZNm8ajjz5qt1ZeNAEMHDiQ9u3bc/HiRapVq+bShtq1a7sUOgCjRo3iwIED7Nq1y+54aGgooaGhV/KYDmzYsIFhw4bZXn///fcoFAoAZDKZ3bmSJDkcq0hl1zz22GO2fzdr1ozo6Gjq1avHxx9/zLhx467pGQQCgeBGsf+3L8ma/gbNM61e/YOtfOm28lv8A67td+ydjBA714Oh4PrWr5HVq1djMpmIjLycVS9JEh4eHuTn5xMQcFlgHT58mM6dO/PCCy8wZcoUu30OHz7MH3/8wZ9//snEiRNtx81mMxs3bmTEiBEubfDx8XG5Nnr0aP73v/+xc+dOatSoYbc2fPhw1q9f7/b5Dh8+TK1atRyOP/XUU7Rp08b2OjIykoyMDMDqqale/bK7Nisry8FzU57w8HA7L86VXOPj40OzZs04fvy4W/sFAoHgZrN17vPU3pxErRIo9oKz/WLpM/nDqjbrlkWInevB2//61q8Bk8nEunXrWLJkiUPOSc+ePdmwYQOjRo0CrB6dzp07M3jwYObOneuw1+rVq2nfvj3vvPOO3fFPPvmE1atXuxU7zpAkidGjR/Pll1+yY8cO6tRxbEF+PWEsPz8//Pz87I7VqVOH8PBwfvrpJ1q2bAlYQ2y//fYbCxcudHmP6OhofvrpJ8aOHWs7tm3bNmJiYlxeU1JSwj///MNDDz3k1n6BQCC4WRTkZ/HDyO4022+tcj1bXUbYrPk881CPKrbs1kaInevBJwTqPWwNWVWk3sPW9RvMN998Q35+PkOHDkWj0dit9erVi9WrVzNq1CgOHTpEp06d6NKlC+PGjbN5MRQKBSEhIZSWlvLJJ58wa9Ysu5wagP/+978sWrSIlJQUoqKirti2l156iU8//ZSvv/4aPz8/2z01Gg0qlQq4vjCWM2QyGWPGjGHevHnUr1+f+vXrM2/ePNRqNQMGDLCdFxcXR2RkJPPnzwfglVdeoX379ixcuJAePXrw9ddf8/PPP9uF3V599VWefPJJatWqRVZWFnPmzKGgoIDBgwffMPsFAoHgSvlr+2fkz5pFswvWsNWB1v48sWobPn6aSq4UiGqs60EVAE+tsAqb8tR72Hr8JuTrrF69mkceecRB6IDVs5OcnMy+ffvYsmUL2dnZbNiwgerVq9u+HnjgAQD+97//kZuby9NPP+2wT/369WnWrBmrV6++KttWrVqFVqulY8eOdvfctGnTtT3sFTJhwgTGjBnDyJEjad26NWlpaWzbts3OC3T27FlbyAsgJiaGzz77jI8++ojmzZuzdu1aNm3aZBcmO3/+PP3796dBgwY888wzeHp6snv3bmrXrn1Tn0dw66HVGTmZVcT+s/mczC5CqzNWtUmCu4zPZ8UhGzuTGhckClVwdGgH+q7/QwidK0QmVVavfJtTUFCARqNBq9Xi728fVjIYDKSmplKnTh28vb2v/Sb6fGsysqHAGrryCblpVViCW5sb9jMluGVIv6hn4tYD/H48x3asff1gFvRsTkQ1VRVaZhVhOUVGCgyl+Ks8CPbxRKP2rFKbBDeWi7kZ/DTycZqm6AE4EymjxtwlNG37WCVX3v64+/y+WkQY60agChDiRiC4A9HqjA5CB2Dn8RwmbT3Aiv4tq0xc3MoiTHBj+OOHdRTNnU/TbOvrlDbV6PHuj6h8bnw+6J2OCGMJBAKBC3KKjA5Cp4ydx3PIKaqacFZlIkyE2W5/Nk/th8eE+URkQ4Eajg97lH4fJwmhc40Iz45AIBC4oMBQ6na9sJL1m8WViDARzro9ybtwjl9eeopmfxsASK0hp8785bR5wHnDWMGVUaWenZ07d/Lkk08SERGBTCbjq6++sq2VlpYyceJEmjVrho+PDxEREcTFxZGenl51BgsEgrsKf28Pt+t+lazfLG5VESa4PpK+WU1yzy40+duABUiJCeTh//uTRkLoXDdVKnaKi4uJiopyOolbp9Oxb98+pk6dyr59+/jiiy84duwYTz31VBVYKhAI7kaCfT1pXz/Y6Vr7+sEE+1aN9+RWFWGCa2fz5J6oJi+meg5o1ZA66nH6rUnAS6WuatPuCKo0jPXYY4/ZteQvj0aj4aeffrI7tmLFCh588EHOnj3rtMOuQCAQ3Eg0ak8W9GzOpK0H2FkhEXhhz+ZVFioqE2E7nYSyqlKECa6enIxUfnvpaZodLgHgVC059y5aRdsW7avYsjuL2ypnR6vVIpPJ3M5sKikpoaSkxPa6oODmjGwQCAR3BxHVVKzo35KcIiOFhlL8vD0I9q3aEu9bVYQJro7fv3gH8+KVNM4DiwwOtgvh6bd/EN6cm8BtI3YMBgOTJk1iwIABbuvt58+fz8yZM/9FywQCwZ2ORn3r9a+5FUWY4Mowm0x8PrknDb8/hqcJLvpC3tD/0G/E/Ko27Y7lthA7paWl9OvXD4vFwrvvvuv23MmTJ9tNpC4oKKBmzZo320SBQCD417kVRZjAPZlnj5P4ci+aH7G2Bzhxj5zGb35AdDPXc/kE188tL3ZKS0vp06cPqamp/PLLL5V2UfTy8sLLy+tfsk4gEAgEgitjx6ZlyJa9R6N8MMvgYIdwer39Ax6e4jPrZnNLNxUsEzrHjx/n559/JigoqKpNuqVITExEoVDQrVs3h7WUlBT69+9PzZo1UalUNGrUiOXLl9uds2PHDmQyme1LpVLRpEkT3n///Wuy54MPPuChhx4iICCAgIAAHnnkEfbs2XNNe10t7777rm1EQ6tWrfj999/dnv/FF1/w6KOPEhISgr+/P9HR0fz4449255SWljJr1izq1auHt7c3UVFR/PDDDzfzMQQCwR2I2WTis3GPEzDrPULzIc8P0l7tS//4X4XQ+ZeoUrFTVFREcnIyycnJAKSmppKcnMzZs2cxmUz06tWLv/76iw0bNmA2m8nMzCQzMxOjUXQHBVizZg2jR49m165dnD171m5t7969hISEsH79eg4dOsQbb7zB5MmTnZb5Hz16lIyMDA4fPsywYcMYMWIE27c7meTuArPZjMViYceOHfTv359ff/2VpKQkatWqRZcuXUhLS7vuZ3XHpk2bGDNmDG+88Qb79+/noYce4rHHHnN4T8qzc+dOHn30Ub777jv27t1Lp06dePLJJ9m/f7/tnClTpvDee++xYsUKDh8+zPDhw3n66aftzhEIBAJ3pKUe5n9P30/Ud6fwNMPxugoi131C16Ezqtq0uwupCvn1118lwOFr8ODBUmpqqtM1QPr111+v+B5arVYCJK1W67Cm1+ulw4cPS3q9/rqe46LhonTq4ikpJStFOnXxlHTRcPG69rsSioqKJD8/P+nIkSNS3759pZkzZ1Z6zciRI6VOnTrZXpe9//n5+Xbn1a1bV1q0aJHLfT766CNJo9FI//d//yc1atRIUigU0qlTpxzOM5lMkp+fn/Txxx9f+YNdAw8++KA0fPhwu2MNGzaUJk2adFX7NG7c2O59rF69urRy5Uq7c3r06CE9++yzLve4UT9TAoHg9ufn9QulnQ82lA43aCgdaNRQ+vSlhyVTaWlVm3Xb4O7z+2qp0pydjh07IrkZuu5u7VYhsziT6YnTSUxPtB2LjYhlRswMwn3Cb9p9N23aRIMGDWjQoAEDBw5k9OjRTJ06FZlM5vIarVZLYGCgy3VJkvjxxx85d+4cbdq0cXt/nU7H/Pnz+fDDDwkKCiI0NNTpOaWlpXb3nDdvHvPmzXO79/fff89DDz3k9pwyjEYje/fuZdKkSXbHu3TpQmJioourHLFYLBQWFtrZWlJS4jC5XKVSsWvXriveVyAQ3H2YTSa2jHucJj+fRWmBXH/QjXyW/kOmVLVpdy23fILyrYy2ROsgdAAS0hOYkTiDhe0XovHS3JR7r169moEDBwLQrVs3ioqK2L59O4884ryteFJSEps3b+bbb791WKtRowZg/XC3WCzMmjWL9u3dN7QqLS3l3XffJSoqyuU5kyZNIjIy0s6m4cOH06dPH7d7R0ZGul0vT05ODmazmbCwMLvjYWFhZGZmXvE+S5Ysobi42M62rl27snTpUtq3b0+9evXYvn07X3/9NWaz+Yr3FQgEdxdnj6ewb+xAok6YADh6r5L731pPrfquf1cKbj5C7FwHeYY8B6FTRkJ6AnmGvJsido4ePcqePXv44osvAFAqlfTt25c1a9Y4FTuHDh2iR48eTJs2jUcffdRh/ffff8fPz4+SkhL27NnDqFGjCAwMZMSIES5t8PT0pHnz5i7XFy1axMaNG9mxY4eddyQwMNCtd8kdv//+u13H7ffee49OnToBOHi0JEly6+Uqz8aNG5kxYwZff/21nYdq+fLlvPDCCzRs2BCZTEa9evV47rnn+Oijj67JfoFAcGezbe0c1O9uoEEBlCrg0CO16bPkGxRK8VFb1YjvwHVQaCy8rvVrZfXq1ZhMJjsPiCRJeHh4kJ+fT0BAgO344cOH6dy5My+88AJTpjh3odapU8fWlbpJkyb88ccfzJ07163YUalULsXE4sWLmTdvHj///LODILqeMFbr1q1tyexg9d54eXmhUCgcvDhZWVkO3h5nbNq0iaFDh7JlyxYHoRgSEsJXX32FwWAgNzeXiIgIJk2aRJ06dSrdVyAQ3D2YTSa2vNKVJr+mo7RAjgZKXxlK/wGvVrVpgksIsXMd+Hn6Xdf6tWAymVi3bh1LliyhS5cudms9e/Zkw4YNjBo1CrB6dDp37szgwYOZO3fuFd9DoVCg1+uvyb4333yTOXPm8OOPP9K6dWuH9esJY6lUKu69916H461ateKnn37i6aefth376aef6NGjh9v7bNy4keeff56NGzfy+OOPuzzP29ubyMhISktL2bp1a6X2CwSCu4fT//zFgXFDiEq1hreP3OfBg8s/I7JO4yq2TFAeIXaug0DvQGIjYklIT3BYi42IJdD72sI17vjmm2/Iz89n6NChaDT2IbJevXqxevVqRo0axaFDh+jUqRNdunRh3LhxNs+HQqEgJCTE7rqsrCwMBoMtjPXJJ5/Qq1evq7Zt0aJFTJ06lU8//ZR77rnHdk9fX198fX2B6wtjuWLcuHEMGjSI1q1bEx0dzfvvv8/Zs2cZPny47ZzJkyeTlpbGunXrAKvQiYuLY/ny5bRt29Zmq0qlsr2vf/zxB2lpabRo0YK0tDRmzJiBxWJhwoQJN9R+gUBwe/Ljh9Pxe28z9QvBqIB/utal96KvRdjqVuS667lucW526XlGUYY0bNswqenapravYduGSRlFGddjtkueeOIJqXv37k7X9u7dKwHS3r17penTpzst269du7bt/Iql/0qlUqpTp4706quvSkVFRS5tKCs9r0jt2rWd3nP69OnX+dSV884770i1a9eWPD09pfvvv1/67bff7NYHDx4sdejQwfa6Q4cOLtselLFjxw6pUaNGkpeXlxQUFCQNGjRISktLc2uHKD0XCO58jCUG6dNhHaSDDa1l5TvaNpR+3fx2VZt1x3EjS89lknQb1HdfBwUFBWg0GrRarcOoCYPBQGpqqq3z7rWiLdGSZ8ij0FiIn6cfgd6BN60KS3Brc6N+pgQCwa3JyYOJHH7tBe49bQHgn4aexLz9OeG16lexZXce7j6/rxbha7sBaLw0QtwIBALBHc63qyYTuPor7i2CEiUc7d6AXvM+F2Gr2wDxHRIIBAKBwA0leh1fju5Ks4Qc5BJkBoLytZfp+7TrilXBrYUQOwKBQCAQuODo/h2cmPgSUWetYavDTbzosPJLgquLFhS3E0LsCAQCgUDghG9WjCf4o++oq4MSDzj2ZBP6zPu8qs0SXANC7AgEAoFAUI4SvY4vX3qUZol5yIGMYFBNepU+TwytatME14gQOwKBQCAQXOKfP38mdfLLRJ23FiofaubNw+9+Q0DIlc/sE9x6CLEjEAgEAgHwv7deJnzdT9TRg94TTvSIos/sz6raLMENQIgdgUAgENzV6IsL+HpkV6L+uAhAegj4vjGZPt3iqtYwwQ1DiB2BQHDdmLRazLm5WAoLkfv5owgKRKm5zXpP6fOhOBsMBeCtAZ9gUAVUfp3gtuZA4rekT3mNqHRr2OrvKDWPvvsN1YKqV7FlghuJEDsCgeC6KM3IJH3KFHQJl2fEqdu1I2L2bDyqh1ehZVeBNg2+HgWnfrl8rN7D8NQK0IhcjTuVr94cTuSG36htAJ0npPZ6gN7T1lW1WYKbgLyqDRBcO4mJiSgUCrp16+awlpubS7du3YiIiMDLy4uaNWsyatQoCgoKrvo+KSkp9O/fn5o1a6JSqWjUqBHLly+/EY9QKQcPHqRDhw6oVCoiIyOZNWsW7iacnD59mqFDh1KnTh1UKhX16tVj+vTpGI1G2zlr165FJpM5/crKyvo3HuuOwaTVOggdAN2uXaRPnYpJq60iy64Cfb6j0AE4uR3+N9q6LrijKC7UsmnggzRY/Ru+BjgfJkN6azq9hNC5YxGenduYNWvWMHr0aD788EPOnj1LrVq1bGtyuZwePXowZ84cQkJCOHHiBC+99BJ5eXl8+umnV3yP0tJS9u7dS0hICOvXr6dmzZokJiby4osvolAoGDVq1M14NMA6F+XRRx+lU6dO/Pnnnxw7dowhQ4bg4+PD+PHjnV5z5MgRLBYL7733Hvfeey9///03L7zwAsXFxSxevBiAvn37OgjEIUOGYDAYCA0NvWnPcydizs11EDpl6Hbtwpybe+uHs4qzHYVOGSe3W9dFOOuOYf9vX5I1/Q2aZ1r/aDp4vy/d3vkW/wDx//6djBA7N4CqyFcoLi5m8+bN/Pnnn2RmZrJ27VqmTZtmWw8ICGDEiMutzGvXrs3IkSN588033e4rk8lYtWoV33//PT///DOvvvoqM2fOtDunbt26JCUl8cUXX9xUsbNhwwYMBgNr167Fy8uLpk2bcuzYMZYuXcq4ceOQyWQO13Tr1s1OyNStW5ejR4+yatUqm9hRqVSoVCrbOdnZ2fzyyy+sXr36pj3LnYqlsLCS9aJ/yZLrwFCJt7OydcFtwxfzhlJrUyK1SqDYC872jaHP6+L/+7sBEca6TkozMkkbN55T3R/ndN9+nOrenbTxr1KakXlT77tp0yYaNGhAgwYNGDhwIB999JHb8E56ejpffPEFHTp0qHTv6dOn06NHDw4ePMjzzz/v9BytVktgYKDt9dmzZ/H19XX7NXz48Kt6xqSkJDp06ICXl5ftWNeuXUlPT+f06dNXvE9FWyuybt061Go1vXr1uir7BCD386tk3fdfsuQ68K5kmnJl64JbnoL8LDYPeIBG6xLxKYGz4TIUb8/jGSF07hqEZ+c6qCxfIXLJ4pvm4Vm9ejUDBw4ErN6MoqIitm/fziOPPGJ3Xv/+/fn666/R6/U8+eSTfPjhh5XuPWDAAJciB6wiZPPmzXz77be2YxERESQnJ7vd19//6j40MjMzueeee+yOhYWF2dbq1Kl8Ns3JkydZsWIFS5YscXnOmjVrGDBggJ23R3BlKIKCULdrh27XLoc1dbt2KIKCqsCqq8QnxJqMfHK741q9h63rgtuWv7Z/Rv6sWTS7YP1j8EBrPx5/5wd8Na7/ABLceQjPznVwJfkKN4OjR4+yZ88e+vXrB4BSqaRv376sWbPG4dy33nqLffv28dVXX3Hy5EnGjRtX6f6tW7d2uXbo0CF69OjBtGnTePTRR23HlUol9957r9svd/kwTZo0sXmAHnvsMdvxiqGqMu+VsxBWRdLT0+nWrRu9e/fmv//9r9NzkpKSOHz4MEOHijbw14JSoyFi9mzU7drZHVe3a0fEnNm3fr4OgFEP7cZB3Y72x8uqsUS+zm3L57PikI2dSY0LEkXecOT5DvRdv0cInbsQ4dm5DqoqX2H16tWYTCYiIy+XxEqShIeHB/n5+QQEXP7lHB4eTnh4OA0bNiQoKIiHHnqIqVOnUr266x4SPj4+To8fPnyYzp0788ILLzBlyhS7tbNnz9K4cWO3dg8cOJD4+Hina9999x2lpaUANg9LeHg4mZn24cCyaqkyD48r0tPT6dSpE9HR0bz//vsuz/vwww9p0aIFrVq1crufwDUe1cOJXLL4Ut5aEXI/XxRBQbeH0NHnw9cvwfk/oO0I65epBJReUHgBPNVVbaHgGriYm8FPIx+naYoegDMRMiLmvMnTMY9XsWWCqkKIneugKvIVTCYT69atY8mSJXTp0sVurWfPnmzYsMFl0nCZV6SkpOSq73vo0CE6d+7M4MGDmTt3rsP69Yaxateu7XAsOjqa119/HaPRiKenJwDbtm0jIiLCIbxVnrS0NDp16kSrVq346KOPkMudOzCLiorYvHkz8+fPd2u3oHKUGs3tIW4qUr4Sa+dix/Xa0cKzc5uxZ9t6CmbPpWm29XVKm2o8ufIHfPxuw59PwQ1DiJ3roCryFb755hvy8/MZOnQomgofLr169WL16tWMGjWK7777jgsXLvDAAw/g6+vL4cOHmTBhArGxsW6FgjMOHTpEp06d6NKlC+PGjbN5WxQKBSEh1nyGsjDWjWTAgAHMnDmTIUOG8Prrr3P8+HHmzZvHtGnTbGGsPXv2EBcXx/bt24mMjCQ9PZ2OHTtSq1YtFi9eTHZ2tm2/8HD7BnebNm3CZDLx7LPP3lC7BbcRohLrjmLLtP7U+yqZSCMUqOHCoEfpN/btqjZLcAsgxM51UJavkD51qp3guZn5CqtXr+aRRx5xEDpg9ezMmzePffv2oVKp+OCDDxg7diwlJSXUrFmTZ555hkmTJl31Pbds2UJ2djYbNmxgw4YNtuO1a9e+qqqoq0Wj0fDTTz/x0ksv0bp1awICAhg3bpxd3pFOp+Po0aO2ENi2bds4ceIEJ06coEaNGnb7VaxWW716Nc8884xd2E9wh+JqFISoxLojyM9OY/uIJ2j6twGA1Boy6sx/mzYPPFLJlYK7BZnkrl75DqCgoACNRoNWq3UIoxgMBlJTU6lTpw7e3t7XfI/LfXZus3wFwQ3nRv1M3XXczLlU7kZBeKrh86GuK7F6rRZhrFucpG9Wo1+wmOo5YAEOxgTSY8X3qHyEUL3dcff5fbUIz84N4LbNVxAIqoKKwkbpDd9NgGPfXT7nRs2lqmwURK/V1vv8b7S94HFTiaXVGckpMlJgKMVf5UGwjycatef12Sm4Jja/3ov7/u8Q1UpBq4ac57rTb7TrNhOCuxchdgQCwb+HMy9L3Y7QZjic/g2MxdZj5cXI9XhWrmQURPB91vvYBJi/tbeOk/umX9QzcesBfj+eYzvWvn4wC3o2J6Ka6NP0b5GTkcpvo56m2SFrscWpWnLuXfgObVt2rFK7BLcuQuwIBIJ/B1dellM7rP9tO8K+IupGzKW60gRkVYDz+5TzQpk9/Uk+bWLvGfvBoDuP5zBp6wFW9G8pPDz/Ar9/uQrTm2/TOA8sMjgYG8zTK37ESyXaBAhcI8SOQCD4d3DnZTm1wyp2KnK91VCVJRh7qCAv1dpbx1hkny9UwQulALrU6cyW/nPpvfEsOqPZts3O4znkFBmF2LmJmE0mPn+9Fw2+O4qXCS76Qt7Q/9BvhGgdIagcIXZwrNIRCK4V8bNUjoq5ORYLePpcDlVVxOSk/9P1VkO5GwVxX3eQyeGbMZe9S2A9/4m34PtJDuJMmfoLDXiDse2mMveXDNtxtacCP6kIctJvTpL1XU7m2eMkvtyL5keMAJysLafR4g+IbhZTxZYJbhfuarHj4eEBWMuXxVwkwY1Ap9MBl3+27lqcVkB1hp6rYetQe8Hj6YOp5UuYFfWwPLwRudoLRdZulBcPXf9cKlWA8wTkuh2hwwT4YZK90AHred+Mgcj77ZOmL6FM/YVH20ynrLWm2lPBlv61CPlxhPOKr+tNsr7L2bFlBbKl79IoH8wyONghjF5v/4iHp1flFwsEl7irxY5CoaBatWq2EQRqtfqKZi4JBBWRJAmdTkdWVhbVqlVDoVBUtUlVh8sKqF9Astjn5nj6UPrYWtJXfIYusbftVHVsNBGzFuBRmWfkSkrWNZHwn3ch+ygYLlpHQZz/81JYbYfzfU/+Am2Gubytp/nyKJix7UJp+OfryE79WmGPG5RkfZdiNpnYMqEHjX48hacZ8v2gcFgf+v93ZlWbJrgNuavFDlzuqlsmeASC66FatWoOnZrvOirLzWk33iZ2TC1fuiR0kmynyNRqVM2iMJ5LpzQrD4VGgyIo0LG9g7v+ORW9KSUFsO4p+2N91rl/DmdhtUsYFb6AFoAn6ylRJP7q/MQbkWR9F5KWepg9r/Qj6pi1WejxOgqaL13LPY1cDykWCNxx14sdmUxG9erVCQ0NtXXhFQiuBQ8Pj7vbo1NGZUnFHt4w6k9rhZPeD13if2xLMrWayCWLyVv3Cbnlhsaq27UjYvZsPKpfEpJX0j+nvMBwZpOykjCIt3OBItV7mGohEXw1sjZ+3h6Elvzjfh8xcuKq+OXTxSjfXk3Di2CSw9+dI+mz7AcUyrv+40pwHYifnksoFArxQSUQ3AgqSypWVbP2tgEsKSl2S4FxceSt+wRdUpLdcd2uXaRPnUrkksVWD8+V9M8pL3ac2XT+T2vujrNQVr2HIbCOY3JzvYeRPbWCappQWpSNvsuppKGoGDlxRZhNJjaPf4ImP5/Bwwy5/qAb+Sz9h0ypatMEdwBC7AgEghuLuwqoeg/bJR3L/fzsllUtouw8OuXR7dqFOTfXKnaudoCnM5t2r7ImTINjNdbji0GXC48vKVeW7qLZ4FU8r8A5Z4+nsG/sQFqcMAFwrJ6SlsvWU6t+VBVbJrhTEGJHIBDcWFxVQDkZwaAICkLdrp1tkK5U4jpPBsBSeCkx+GoHeDqzyVgMe9fCwzOgbTaY9NbQVmEmHNwMv863t9tVVdVVPK/AkZ/XzcN75Sc0KLCGrQ49UoveS78VYSvBDUX8NAkEghuPJvKKRjAoNRoiZs8mfepUdLt2IfNyn0cj9/O1/uNavCnlbdLlWSuzzv8JHz/u2PtnwKbL/76SqqorfF7BZcwmE5vHdKPpL2koLdZooPHl5+j37ISqNk1wByLEjkAguDm4GsFQAY/q4UQuWYw5NxfJIqGOjUWXkOBwnrpdOxRBQZfKzXPgsYXw/QRrmXgZlXlTymw6/xd82te1URUrsa6kquoKn1cAZ4/uI3lsHC1OWbtQH6nvwYNvf0ZkncZVbJngTkVelTffuXMnTz75JBEREchkMr766iu7dUmSmDFjBhEREahUKjp27MihQ4eqxliBQHBt6PMh55hVYOQct76ugFKjwatuXbzvrUfEnDmo27WzW1e3a0fEnNkoKYYtz8PK1vB+B2vjv7j/g+e3WSu8eq2+siZ+lYXBnFVqFedAznEMBTmcyi5i/9l8TmYXodUZK7+fwMaPq2dwbuCz1D9lxqiAlO51eerLfULoCG4qVerZKS4uJioqiueee46ePXs6rC9atIilS5eydu1a7rvvPubMmcOjjz7K0aNH8auQ2CgQCKoWk1aLOTcXS2Ehcj9/a28ciuHrl66qs3B5T4+lsAi5ny+KoCCUnhar0Cnby1hs7dezc7F1z6tp3ucuDFa3ozW8VZGSAvjoMTzqdkb/wFyevTQfS0w9vzJKjSV8/nI3mv2WiUKCrACQxgyjX98xVW2a4C5AJt0iw3xkMhlffvkl//nPfwCrVyciIoIxY8YwceJEAEpKSggLC2PhwoUMG+a6u2l5CgoK0Gg0aLVa/P1FCahAcDMozcgkfcoUu/CTul0sEeOG4vFVT8ecmKsRJ2VdknV5YNBC+n6QAdVbWMNNSm84vwea94Pg+ldutDbN+RiJNsMdR1rU7Qg1Wl9uhlinMx9Vvzwfq339YPdTz6+k0/MdzMmDiRx+7QXuPW0B4J+GnsS8/Tnhta7i+yW467iRn9+3bM5OamoqmZmZdOnSxXbMy8uLDh06kJiY6FLslJSUUFKuoqOgQDT0EghuJiat1kHoAOh2JZBuMRPZ+yWUfyyyv+hKOws765JctzM8NA429r0sSOp2tIqdq0ETaS0xzzl2STSprB6f3xY4Cp0yAXSJivOx3E49v5pOz3cg38W/TsCHX3JvERiVcKR7A3rN+1xUWwn+VW7Zn7bMzEwAwsLC7I6HhYVx5swZl9fNnz+fmTPF7BSB4N/CnH3BTujI1GoC4+JQtYhCKinBVKsGUvUHsFg8sBTrLw/6LClysyuuuySf+gUoN2PL08fqdSlIt16jCrhyz4n+ojVkVeMBa+m5LgdaDYEH/mv1GJUWW9crenqwn48FUGhw0oH9ajs930GU6HV8+XI3mu3KRi7BhUBQvPYyfZ8eUdWmCe5CblmxU0bFwZySJLkd1jl58mTGjRtne11QUEDNmjVvmn0CwV2NPh9L9jnbS5fjHmJiCBwyGP2BVFRNm4B3DB46X3RZ50iX5eOj8CbQAhrk4BNkFQCVzdhqO8IqdHquhj/iLw8XhSv3nHhrrInT5a8t8+SYDG4rtsrPxwLw83Yy6f5qOz3fIRxN3smJCSOIOmsNWx1u7EWHd74kuHqdKrZMcLdyy4qdsmGKmZmZVK9e3XY8KyvLwdtTHi8vL7wq6dUhEAhuEMXZyD0up/25GvegT05G7jUM/b595K5caTuuio1BGjeIvskTuD+4OTPu7Uv4T9Og+5uVd0k2lVgFzx/xjiMfTm63elSeWGodT+FMUOjz4dtX7a/19MEU8iBmgz9mkyeKp79Hkf4ryv3v2Hl2THU689OlD3Kw5uwE+zoJYbl7Bk8ftECeNpVCYyF+nn4Eegei8apk/MQtzjcrXyV4zbfU1UGJBxx7ojF95m+tarMEdzm3rNipU6cO4eHh/PTTT7Rs2RIAo9HIb7/9xsKFC6vYOoFAAIChAEX2H6hj2qJL3O1y3ENgXBw58fGOIighER8khg8dyFvHP2AGsLB6EzT/Gw1d57m/t9LLGn4q75Upz6lfoOgC/PiGVTxV9PJU9Lp4+lD62FrSV2xEl7jedlgdG03EqLV4fD8EjMWY63Ymt/Ni6hf58e6z1QlQe1ArUO08X8dVibunD5l9P2b6/sUkpl9+T2IjYpkRM4Nwn3D3z34LUqLX8eVLj9IsMQ85kBEM3hPG0eepF6raNIGgasVOUVERJ06csL1OTU0lOTmZwMBAatWqxZgxY5g3bx7169enfv36zJs3D7VazYABA6rQaoHg7sJpSbnmkvfB2x/l/neIGL2WdFyPe3A386okIYk+r42H+hCfup68WOv/33lKOYVDvsRPkgg8sxtN4iXviqePVQh5VwOzkzyZ8ugvQvtXYduUS14e15PQTS1fuiR0dtsd1yUkkY6cyJm/ovSWoVME8MbXqfx85IjtnPb1g1nYsznVvQz2VVde/tDgcTj6rd2e2piXmH5yM4kZ9vdKSE9gRuIMFrZfeFt5eP7582dSJ79C1Hmrt+tQU286v/M/AsNECoHg1qBKxc5ff/1Fp06dbK/Lcm0GDx7M2rVrmTBhAnq9npEjR5Kfn0+bNm3Ytm2b6LEjEPxLOC8pb0fE7Nl4VA+3Vi/VbIvH90OI7P0Splo1nO5T6cyrM+fpuDmZB8YtotDblwUlqST+3+XeW7HhbZjR92PCvxgBvdbC74vh1K/2Yx2cIQO2z4ZHplubArqZhG4OaWPn0SmPLiEBs/F1igNrMGrjfn4/nmO3/teZfIz555B+n4isYtXV40us/y4nePLqdSLxF+cVpQnpCeQZ8m4bsfO/Za8Qtm4bdXSg94QTPaLoM/uzqjZLILCjSsVOx44dcdfmRyaTMWPGDGbMmPHvGSUQ3AG49cZcxR7OS8p3kT51KpFLFqP0BNqNg9/ftJWXl4W0ylPZzCuZlxclCUn4IyNsxnTm1HyJYv8+mNRe7CxOIT51PTPkchY+/yOab8ddzrM5/6c1obhizg5cbg546hewTMFsNqEov+4TglTvYWSX+uxY9O47IVsKi8jxNToIHbWngs/616GWlIGs9RBoO9za92f3Kmvu0Lfj4T/vYn54OhZDAWYPP7Ryg9t7FRoL3a7fCuiLC/h6ZFei/rgIQHoI+L4xmT7d4qrWMIHACbdszo5AILg2KvXGXCHm3FynM6rAKnjMubko/U2wsY81UbjTGygVXkQ08SN95lx0CYm2801ZWahjY+yO2WyLjkafnGLdNyERc1o6OUOes60/HBtDnymf8sWFn8i3GNGUFza7V1mrsZBX6MXT0b43jkmPpPBCqyvXC0cVgLH7MhTfvIIy9RfkKhcNAS8h9/OloEJ5udpTwZb+tWj85yR7j07djla7tg6Fk9sx6bQ8983FS0Ipi49eqO32Xn6et7b3+u/d33P+jfFEpVn/WP07SsWj735LtaDqlVwpEFQNQuwIBHcQV+SNuUIPj6XQvXfBnH8R/UUz8q6foMjdi9LbH76fiMf5P4ns9RKmsSMpzS4AmQzDocMExsWBRbJLUvbp2JGQUS9hys0lcvky5F7eyFQqZGo1kk5ntT0hEebM55nhw7AYvKw5O2AVWDUeALPJOhQ0/9Sl5oBejr1xPH2wGIqQPC+C7iLo8sHLF0+ZHB5/E4upBKlE7mYIaSwKtZymJcfZ8VwkP52x8NauLMa2C6XhnteRpf5qf0GZILvUC0h7MZffj1+eCbYv1USb8Gj+yLRP2AZrknKgd6Db974q+erN4UR8+hu19daw1alnWtF7hvPwn0BwqyDEjkBwB3FF3pgrFDtyJ7lxdg0DzSYsOj2FyX9jOHaa6jXy8Dj/JxiLUf6xCHNIG86PGO1wbeir4yk9fx6ZWo2yWjWylr6FLvGyx0cdG0vkW0tJGzvOTvAEv/ACiowcTG1eQ1mjoX1vnRd3WL08rsJZgIe3Gs23w+w8QLL7ukGHSciMhXjJPag+/Q3SZ85FX94rFmsde6FcEwPGYu4BnqvTmZj+c/GVm1AkVhA6ZZT1AgIKJTVwWexs3J3N5yNeZ8HeuSSUS1KOrd6WGQ9ORlOYAxbLLdWDp7hQyzcjutD8L2ti9/kwGQHTptHr4avsXC0QVAFC7AgEdxCWwkKHDsZyL290ycnkrVuHpbCSrsXlUAQFoW7XDt2uXYCbhoGxMYRNnIjx4kXovhaP74aAsdghB0bS6WzX6VNSULVqhX7/PjuhA1wSaxKBzz9v15PHrNWS/9kmwqe9jvL38fbC5viP8NCr1n+XP163o/X4hUPILCaHUnNaDYHtM+DUDmSAp6cPkf9dgOG1VynMLyIwxB9l9m6UFeZ7KVN/oQFvYHh4tvs30VSCVPdhu548AMMeDKLmD5NYGNmMvPb9KDQb8VN4WqvOvp0A4U0hbf8tM1Ii+fevuTBtMs0zrGGrgy196Pbud/gHhFaxZQLBlXHLDAK9WYhBoIK7iZLUVIynTzs09lNHRxMYNwjPe+7Bq86Vd7EtzcgkfepUdLt2ETR8OPqUFIdeOWX7q6Ki0B88SMTofnh8F0dJl3WcGjja4dwy0aQMCuJ0H9cdimt/uoEzA561va4Rv4rzw0dQ58sv8N7U1toAsOVLmEPaYNGXIg+thUKmRSkrhlKdNZxVmAlB9a1dmT/obD/yof2r1u7JTrxB5rqd4cnlKPJTYd1TLm20DE9EHh/jcp24/6NEcw8tlx9GZzTbDu8YEsk9n3Vwfd2ATdbuzVc7zf0m8MX8/1LrswR8SqDYC870iabnG2uqzB7B3cNdMQhUIBBcPXIfH6cdjHVJSSCTEbFg/lXt51E9nMglizHn5iIZjS575eiSkggcHEdufDzpSET2HYOi8B/UsbHo9+938DSV5uWiqKyFhMVCzfh4dMnJGI4csSUxW3Q6Tg/fQaDRn/yZ89AllG8AGEPE9Cl4eGWB0hP8qmPKy8VcYsby0IeX53Ltf8dtQ0LFqV8gPxUMF92aKDMWQdz/gSH/8vT13ausoqpeZwi5D4MikNa1M9hZrorL01xJtZXpUql+FY6UKNLm8d2IrjTbZ/UGnguXETJzLj07PP2v2yIQXC9C7AgEdxCWoiKnnhcAXWIilqIiCL260INSo0Gp0aBPSXF7XlkvHV1CIqbXxoHcg+CRMSirVSNz7rwKoa9Ywl4d73Y/mbc35wYOQh0TQ9jkSZy5VKFl8VXzy6mf6PRhMobECqIuIZH0mXOInPoyyrWPUdp9nbVRYLkqMHVMWyJGr8XD7L7UHMNFq3fIHSaDveenrApr3yfWrs1+4WiABT2bM2nrAZvgMSoqEXrl71vZ2IybwN7tW8ibNZ1mF6yO/wOt/Xj8nR/w1dy6idMCgTuE2BEI7iAqq6C60pwdQ0EOcl0OGAqQqTSYVUFYfHzdXlO+l05pRjbnR4xwGfrSJSTAa6+ijo52GRaznZuYyIV58wno3Rv9wQMYFBba+7TAkLjKqR26hERMxtdgwA+kz17uUO6uS9xNukxO5OzJKNu/Cns/hlaDL00+L7nsofFQw7ndLvv4SHU7Iju90/7gqR0gk8N/VoHf5TL/iGoqVvRvSU6RkUJDKRq1EUvdh5Gf2u74AGX9gcpwNXLiJrF1Vhz3fP4nNYxQ5A3n+7en78T3/lUbBIIbjRA7AsEdhLMKKvt194IFwJh3Do9vRqM4dbnKSFH3YbQdl7ouzS7XKwdAofGnRvwqlMHBLkNfhdt/IXjEcHLAIb8oeMRwin67LCR0SUkED3sR/yceJzP3DEEefmS6eYbStAwID3Ha1wcu9fNJP40y828Y/H/wwyS7kJZUtyOyBt2tQuipFdaD5QSPVLczsjYvXu7jU56Tv0BJgZ3YAdCoPW09fk5lF5H+wBwaSBLKVDf9geo9bO1S/S9wMTeDn0Y+QdMUawXcmQgZEXPe5OmYx/+V+wsENxMhdgSCO4iKFVTlUbdrhyIoyO31hoIcB6EDID+1HT/FZNTT55E5Y5Zdh+Sy5Oe08dZqKHVsDCUnTpA5bTqRy5e5vFfu6tX4de2Cf/fHCBwch1RSgszLC1NWFhaDgdzVq+0vkMsxnjtHaJ26SAa9+zdCJqM0M9vtKRa90Vr19MMkB8+N7NQOJEDWarBVeLQdYf0ylSB5V6NUFYznR4/aJzyXp5LQk1ZfyrMbzzK23VS6tJ1OdU89HpLR6ikq6w9U72Gr0PoX8nX2bFtPwZy5NM2yvk5pU40nV/6Aj9/tMbJCIKgMIXYEgjsIpUZDxJw5FP3+O8qQ4MsJwdlZ+LRpW+n1cl2Og9ABrGXa4Q8gGU2ETZoEFgmLTodkNFK8+w/Sxr+KpNOhjo0l/PXJlKSmUjM+Hrm/a0+TpNNRmpGBZ+3aKDQazAUFyNVqkCTSJ79u67FThsLfH2VQEJmz56Bq1txtCEyfnIKqRZT7Z1V5QpjrJGXZqR1I7cYh27nYdo6pTmeOPjiXYMyEuRI6UGnoSaPy4Pl2dahbsxqHjBZOo8THUkhI5JN49OhEcFAwnpqwf0XobJnWn3pfJRNphEIVZAx8hH7jV9z0+woE/yZC7AgEdxqSRMEPP9iPi4iNQRkaSv7mLVSfMsX12AhnHglPH0ofX0/62xvQJXwAWMvHw6a8gbpFS/y7dsEnNgaFnx/6w4dJ7d3HJlTCZ810OyZCGRBA1pKlDmGsiDmzbQJKplYTNmkSktlMaUYGQYPi0B86ROCQwUCFEFhsDKGvjOHsiBGXXrsIu8W0RZH9B6ibVihhN9pVbJXKvMgc8BvV5AaUag2ZZj8KjN74epZgqtPZPgRVxhWEnjwVcvafzWflLydsx2LvDeK52Dps+ussi3vXxbOS8RXXS352GttHPkHTg9Y5XacjZdSat4webbrc1PsKBFWB6LMjENxBmLRa0saNd5lXo4qKQv/33y7HRhgzj+AZ38Z+zzYTSPv8lE2wlG8uaCc0YmIIHDTQJlLKzq25ahU5779vL76iowkdO5bsd9+leMcOl7bmrVtHzfhV5Lz3nn1FVWwM4ZNfx5h2HgDJYMCzdm1Ks7ORX0qUlkpK8Khdm6y33sKr9j220neFRoNHkBrPr56B/6yitEiyVmyVD83FtCVidH+U1VQcNgTRe+NZdEazTZBM2nqAzwfUpuauidYy9TLKQk9uGgFqdUank9MB2t0bxMKezYkMULu8/kaQ+O1HGOYvonoOWICD0YH0WPk9Kh/xO1Jw63AjP7+F2BEI7iBKTp3iVHfXCaVljfnqfvctXnXrOqwbCnLw+OoFuw/wkie22jUHvJLmgnnr1l3urWM04nnPPUilpZgLClD4+yOZTMg8PTn99DMubb3n8y3IlEouvPmmS8+Q/2PdUIaEkDb+VWq+F09OfLzduT6dOxE88TWyZ81xUn7eH5mnnLQln6JLdPIssdFEjumLIvk9znZaQWqRB75SESHyAnzRofIP5FSBDD/0+KKjWkAQCr/QSkNPJ7OKeHjpby7Xt4/rQL3QyhPJr5XNr/fivv87hFcpaNWQPeQxnnx56U27n0BwrYimggKBwCmVlZ6X9cJxVYLu7R+M8Ym3kX3zMvJLgqds7EPZGAq/Rx/Bu1FDggYPto2hsM2wSkoi8PnniGzZgryP1zn01gkePowzg+KQdDoiV64gaPhwp2MtJJ2O0rQ0ZF5eriuqLjUyzPvYKqwwmx3O9b6vAdkzZjuWvl8qPw+f8gYBAwYQOm48krEEU04Ock8vmx1m47MoT26nRrdiwlXeeH473m66ecM6nUlrv5DSgAYoqqncvvdlVJycXhGtvpSTWUUUGErxV3kQ7HO5iut6yMlI5bdRT9PskPVn4FRNOfcueoe2LTte994Cwa2OEDsCwR1EZaXnMm9vAMw+3hzIPoCfpx+B3oFovC6HtDwDa2J5fAnkHgNTCXJFLddzsaKjiVyy2C50pdBoyF6+3OnMq5xLM6/y1qzBs3Zt8jd86nI/mZeXTZy5QiopsZWlF+/+w2Fd1SLKddfnhESMZ88hUyjIWrLEIW8ocsliLAar0FNIFhTfvmI/WwvrjKxa8knIeq0GrGJHW6Ilz5BHobHQ6fvr7+3h9pkMpWaeWXX5vWtfP5gFPZsTcYViyhm7vn6f0kVv0TgXLDI4GBvM0yt+xEt1c8NlAsGtghA7AsEdhNvS85gYlEFBqDp34mRpOup0oDiDQk01ZGE18Q+OsJ0rN1xEm76XvHuiUSnlhE2e5HoMBRAYF2cTFXJPT7f9bUJGj0bVpDEXFixwuV/Y5Ekog0OQjO7FTlkjQ5mHB3nr1jmsVyaWZEoluR+udmlH+KQxlzYyOwgd2x7lRjpkFmcyPXE6iemXnz82IpYZMTMI9wlHW6IFj1zWDg8Di4q9p0pZvTPTNjer3b1BJJ7Ktdt/5/EcJm09wIr+La/aw2M2mdj6Rm/u+/YIXia46AN5Q5+i38iFV7WPQHC7I8SOQHAHodRoqD5zBhlTp9l5VtTR0QQOGkj2O+8SOnkizF+A/tcdmAADUBobg+fsWXhHWBNrM32DmF6Syv6Er3m+6fMMjupE5tRpTu+pS0oiZPQoa6m3BJYi1yXZMrUahZ8fksHgNjwVNnECZ4b+l9BXXrmiRoYyb2+HUnWw7+rsDEW1ajZh42xaPEpvpAaPI3NXZg5gKEBbonUQOgAJ6QnMSJzBtOhpzEyaabfeJjyalYNeZdQnp2hVO4DBMffw8sb9DtvvPJ5DTpHxqsROVtpJdr30DM2OWL1TJ2vLabDofaKjYq94D4HgTkGIHYHgDkMyGlE1b05g3CBboz59coot1BQ0ZDD6X3fYXWNISOTC1OlELl1CsTdM3zOP/TkHWNJhCUV5Fygt0Lq9p1mr5fyIkYA1sdgVgXFxZM6bR0CfPm73K83IIGLObNKnTKXGW0utXZYrVHOVNTJUR0djKS52WuKuT05BHRPjEFIr20MymQBch+liY6g+cwGe3u49RHj7k2fIcxA6ZSSkJ3Cu8JzD+h+ZSchlS9j60kzUSj8GfvgHz7erQ8ua1SgxWfD2ULDvbD5rdqVSWEmuT3l2bFmBbOm7NMoHswwOdgij19s/4uFZyawvgeAORYgdgeAOw1JQ4DJPBcB88aLT47qEBMy5ueQFyUhMT+SlFi8RqfNCtuw7ZJeGcLpEJrN76arhn0/bNuTGxxM4aFCl++V9vI6A3r05N3wEtT9Zh2z8eEovXAAZNvGmatmS8NcnY9bpCHvtNS6w2E4UGY4cIWzyJC7Mm+/Qjydw4EDMBda+QoFxcc7DdAmJZEyfRdjcGXjV7YjMyYws6nYEL38KDVluH0lb4lwwJmUk0r9eDp/vTmPN0Kbk6nLRGs+g9vJh76lSDqcbebt/S/xV7nN9wBq2+nzif2j4w0k8zZDvBwUv9qL/C7MrvVYguJMRYkcguMOoNEnZTWjHXFBAoZ+cQO9ABgV1R15QhGXECJTh4W49JOXnYhX9tpPg4c5nXqFQAJc8Lq46IMfGoE+2lraHXpqMXpqVheHQYfzaP4SlxIh/1y74d+tK4fZfbE0MfTp3JnT6VCgsovT8eZtH68yQ5wjo3ds2ksKzZk3MRUWcGz6CwLg4a7m820TmBMwXi5C1GW49UF7wlM2yKrEmI7vDS+HmfcfAsIdDWLR3CrszL78nbcKjGRj7KhsSzjDvmeZu9884c4Tdo/vQ/JjVA3S8joKmS9YQ0/hBt9cJBHcDQuwIBHcYbpOUY2PthEkZ8qAgIubMRu6tot55PT+1XUfG1BnokpIIGj4cw7GjBA6OAxkVmvvFEjjwWdtcLLDOvPJu1hT/x7o5zLySq63VP3nr1hG5xDqCoaLHJXzyZE4PHgJA6fnzGI4cQfOfHuSv+4TclSsvn3splFVG8S+/IJWUEPb6ZPI3bbYTZmVCRh0TQ/j0aVhycmz9gCKXLIZK2o1JRYWwzX5GFkov63TyrUMh7n8E+tUnNiKWhHTH/KLoiBgO5BxwuX+4byBvp8y1EzpgDXPBYhrXGEmRwUSYi1Yjv2xcgnL5hzS8CCY5HOoYQc9l34mwlUBwCSF2BII7DKVGQ8Ts2aRPnWoneNTt2hE+dQoX5i+wO19Rowa1P/wAU2YmxjOn8ahRg+I//kCfYhVFqhZWURDQrx+aJ58kbMIEStPTQS7Ho2ZNTvfqbZccLOl0pI0Za032bdoUc1ERFp2O0vQMSrOybbk1aeNfJTAuziaIFBoNFr0eU16ebVyEzMsL74YNyZw564oqwXQJCZhzcggcNBAkyaHDc/Xp09H++AO58e/Z7q3QaJCr3Jd1y3z9rMM5XczRwtsfjZeGGTEzmJE4w07wREfEMPmBKSzd96bTS9uER1NqMZKU4ejlAqvgebbdy05zdswmE1tefYLGP53Bwwy5/qAbOYB+Q6a6fR6B4G5DiB2B4A7Eo3o4kUsWY87NxVJYhNzPF0VQEOb8fAL69kEqKUGfkkLQ0KH4P/E4mTNmOnhY6mz9HIvBgKWoyJrT8tFaAuMGkfXWMrwbNsTvkYfR792LKirKQYhIOh2GY0dRRTVHGRKCOT8fdYsW6P7+m9Cx48iSQJeYaO20TBw+bduAXI5crcZSXEz+ps2ETZpoG+jpMsR0qbFgecwXL5I++XU7IVUW0rIY9OTGv4ek05EbH486Npagl0ZS9MOPbsJqseDrh/m+7iiOfWc9WDZPKywGSRUMuRLmc/vx9fNnTtPp5N2vp7C0kNJSTxKOltB75REW9R2FobSE3Zn21VgD6r5KdvFFt99PH7UBtVc+2hK5rWfPuZN/s/eV/kSdsCZZH6unpOWy9dSq734AqkBwNyLEjkBwh6LUaBzmXxlLzeRv+Rx169aEvT4ZfXIymbOceE0SEq3TxZs3R9Uiyubd8ag+gYC+fchb9wmqFlFcmL/AaTjKp2NHwidOJGPmLIcSeHWLKMKmvIE5JwdFtWpcWLjQobFgYNwglOHhXFiwkIhG89w+Z8VeOh6RkdRYuhRdcjJp48bbeZ28mzS2vVbHxhI8ZTLoDa7DatHRhE2eROa8eQS9Ng+VBMozv1H62Foy3v+CgD6Qt+Itu2u8YmLQj5rAuwdK6N8mnNU796Mzmhn1ySmGth/B0E5j8PA0UlCsYF+qiVGfnGLNf2u7fUa9qZih256z9ez5e+savFd+QoOCS2GrR2rRe+m3KJTiV7pA4AwxG0sguIs4lV1EeFEmJX/uo+D7HwgcHMf54SNcnl8jfpXVu9K6Fcpq1ZApPSjNSEcmk4OHknND/+vQn0bm5QWSRN769c5nWsXG4N+9O8rAQNfnREcTMnYMZ/r0tc3zcsU9n2/BnJOLLjkZw5EjeDdsaPXalCtPLxM4db78Aou+GLmPL7oDB8iav5DAuDj0KSnoU1IcnsOUlYUpO4ecFSvwiolBO2Yitf1kFM5ZgKpZc9czwmJjMU17jWy5hWKditEbjtgaBwL88MpD1u9HTjFeSjkKhQG9/k/uJRhlcQlmH292FqcQn7qeqJAomoc05/0D7yOZLYxO8iEmoRClBXI0YHz5OR5+doLrb7pAcJsiZmMJBALAOuXcGqoqRO7njyIo0Ok08zK0+lIURh1hjeuROS2JgH593e4vlZSQv2ULmqf/4xDqqrXuY7vZVjJk6P85YvWSLF3itoty4KBBIJO5bSwou1SJVVmvnMKft1vFTUwMYZMnceZSmXzFnB51bAySxYLc24MLiy4PFy3z6uRvsk/mlXur8G7cmLPDrFVYJYmJ+I40kp0DlkvP4K6CS3lxIC8kjyYmIob/jZnE6fxclDJr12RDqRkJGLlhH2pPBZuerov/yu/RJSRiurRHx9hoOk5cSZq6hPG/jSdMq2TU9yU0SLXOPztaX8kDb28isk5jpzYIBILLCLEjENymlGZkkj5lin2zvXbtiJg9G4/q4U6v8ff2YNsRE8/6XgQq7zAs8/IioHdvu1CXTK0maOhQlCEh6FNSnM62KjuvYkdi26BPoxFFtWpu713WD8hlr5xynhuw5gBdmDefgN69LycsJyUROn4cPm3booyMALkcU16enciSdDrSp0yl1qpVZC1f5vA8ZcnSkk5HqNyE2WLhApWPolDorOuJ6YkslObZvDNtwqPppZmO2WgVpS8/GIbvioXok5MdBqOWppyhoGV1Yo/I6fWDgcBCMCrgwCM1GLDkexG2EgiuEPF/ikBwG2LSah2EDoBu1y7Sp04lcslipx6eYF9P9mSY6dfGOhbCbb+b6GiQJPwefcQmAMo6DZuys8mc5WSaeFISyGSETZzgdnCoIiAAyWBw+4zKoCBqrfsYmYcHhb/8iqpVK4KHvQhKJZaiIruu0OXvXzFhuTQtjbRXxqCOjSF42DA8qlcn+LXX8GnZAslkQu7jg8xbxYW5cx2Hl1bwDinVHiix9gqqTCj6aUJRKVXoTXp2Z+xmYKOBgLW6at6eWcyOWUD7+sF0CvOgJDnZ6fvl0fZB/vnpNEN/16KQICsA1nfz5fUx7wuhIxBcBfKqNkAgEFw95txcp/OiwCp4zLnWYZImrZaSU6fQp6RQcioVn1I9s3s05YLkjTo21loNFTcIdWyM3R7q2BjCZ0wnf8sWSs+ftx0v6zSsDA11KpDA6mHBw8Pl4NC8T9Yj9/enePcfVkHlBHVsLAU/b6c4MQmzVov3ffXxf7gzcj8/62iK4SPIjY93Og9LMhrtXpeJEl1CIjmr4ilO2o26eTPODHiWs3GDOd2zF+bsLKdhsjKbVS2iUMfGYFBbKFQZbP2KXNofHY380HFWt1iESmktay8xX/YEJaYnUmy6yIKezfEp1Tvt4JwZ6ME/GX/SYmcWCgkO36dkyiBPFDGtCPQOdHpfgUDgHPGngUBwG2IpLHS7brqoRZ55gfQ33rATRT4PP0z4pEl4/bMP73FjMWVlIVMqCYyLI3TcOEzZ2ShDQrDodFyYv4DiHTvs5liVlYFXmuuj07sVQ5LBQP6WLdR6Lx5LURFmrdYW5jIcOULYa6+CBJnz5jnMqgqbNAmZWu1U6AB41qhJzfffQ7dvP4YjR+yaKJZ5fhR+ftSMj7eF1cxa97O/kMA8cRiP/T4UgNXjFiG9s5mgS00NXYXXfFpGMXzoQN46/oFDB+VCYyF1QuqgDwvEVKG8PqWRD7XPFRN+BoxKONi5JpFjJ9LkxBZmxMywlZ8LBIIrQ4gdgeA2pLKREBhLKPp9p4P3R9W4MaXp6RT83zdkvv6G7bg6NpawyZNQhoSQtXQpgYMGUbxjB2Af6irzmlQWwrHo3E8Jl/R6IubMJmvJUof+PqGvjMFcXEz2W8uclsRfWLCAsEkTyZw23WFfdXQ0Bdu22RKWw6dNpTQtDRhuzRXS6ZBKSjBrCzg3fPjlHCO5eye3PDKcdJ8cBjUexCeHP2Fo8gSGDxxIl2o+hE2aiGQyYb54EclkQr8/2RZeK0lI4qGX40iq3tahg3LZeAmP4GBMZ88BYAIONPekxcFi5BJcCISLAWpi+o9H6xPIwvYLhdARCK4BEcYSCG4zTFotKJXWZndOUEdHU7z7D5QhIQ5rvh3akxMf70REJHBh/nxkCgW6hES75FtbqCs62rZnZSEcWSX5JHI/P5eDN7OWLUPu4+PaM5SQiKppM8fQ2yWPSt66ddbzEhPJnD0byWjEcOQIkUsWI1OrkanVKEKCqRG/ioB+fZGr1CiDgvDp2NHp/bxio9mUs40RP4/gQPYBFrVfBMBbxz/gsT+HUlqk5XTPXpwb+l/ODxvuEF5Tl8CzjZ7lk8Of2I7FRsTaQlFKjQaPyAgygj05U1PG/QeMyCU4dJ8cb4OCBid1mNVeVPOqJoSOQHCNCM+OQHAbUVaBZThyhHs+Xkvm3HkuQyiumvHpU1Icqn5s4ZxLU8DLe24knc422kEREIA6OtptE77AuEGUnD6NOjbWaV6ROjYGLBb3YS69++Rlc1Ehqpb3EzhoEHIfHyzFxc4TlhMSCX7hBQL69rF2ZZ7yBh7BwdZkZLsZX9bwWJZcRvEvv9qOe8VGUzw+jvj91j42uzN2AzCo8SDeP/C+9f3xVbu11VsTyISd/0Vv0gPYGgOWFy4/fbOCEL2R6jlQ4gEHm6honay32XBMlku0d0u39xEIBK4RYkcguE0oq8DS799vrYjKy0MVFeUwEqHsA99ZqMliMLitkpL7WkMrhkOHCZ81E2VoqJ0gMuXkXPKeYC3Xfi8eS1ExkmRBGRYGxlLMhQV4BQYSOnYMWUgOg0PDX5+MKS/P7bNWFgZTVKuGd8MGgDUh+fzwES5L3c1FReRv+BRVVBTqFi3JnDvHob+PLiGRC/MXEDZxApbhw5FMJjIURewoTiZ+/wSbUAGr4BnZYiSfHP4EvUlPqUpCHdMWXeJuBzvVsbEY/VVs6L4BuUyOQqYgwDvAJnRK9Dq+HPkIzZLykQOZQVDgr6Z1slWwecVGw8QRtIysJbw6AsF1IMSOQHCbUFaBFTR8uHVcQ1SUQ5+bMtSxsZjy8hw8OIrAQHLeedflUM3w2bMInzsX74YNMGVno9BokHl6Yi4owL9rF2Te3px+ZQwBvXsTNm0q+r/+QhkejkdYmIO3xKdzJ8Jem0BpXCZSSQkekZEYDh8mtXcfIpcucfusMg8P28BQx2eLQf/333hG1uDciBFELl1iK4l3JuL8H+9u7Y48OA6pxOC6kWFiIpLJxNkhz6Fq2ZKdQ5vz1vEPnJ5bUFLAovaL+PLY5yi9IGLqJNLnLLLb2zs2hoLxAxn6y7PoTXoHj86Rvb9wauIoos5bm9j/3UBBVGw/Wj/YzjYYVapdA3OgnxA6AsF1IsSOQHCbUFaBVVYRpU9JcR5Kio0lePp0zGYLBf/3jd2H/z2bN7kMH+lTUqC0lILvviXzjXLJy5dCU6efHYiqZUtqLF3K+XHj0Dz1JAXffY8qKoo8J2MTin/5lQt6A6oo62DK/I2f2c5x298nNgZzURHBLw4jxyI5hMmChw1D9+dfFP78MzXjVyFTKqn57juAzCYAy0JZuqQkLixYQGCc1ftlLipy+x6XpqUh6XToEhJ46OWBvOXm3E+PfMobD07mTM5R/BXehI7ti+Ll/2I0e3HR08hPBXvsvEIJ6QnMSJzBwvYL+W3VNMLWbaOODgwecKixilYpesxHN3B+zQbbPep+9y2+QugIBNeNEDsCwW1CWQVWWfJw+Vya8qEsRUAAxUU6ihYvwlBBTJRmZrrcPzAujsw5TkI8FRrr5QA13lpK5ty51lLuIYPdTiUPmziBshF8ZXbKVSr8HnmY7JXv2Kq+4LKYUQQGcmbwEAJ693YI050bPoLI5cvwbtyInPfesw+TXQrHlc/dKRtPIff2RqZQuH2PZZ6etn/7lSoYW/8FHvJpbjevap/+GAdyDpCUnsTpgrOM2vUaKqWKWU1fo713JBTr7HrqqJQqhtcZyEM+zTHn5vP9C11otqcAOZAeAnqNP12iB6AacckDp1YjmUwgl2POy6MEWaVjQAQCgXuE2BEIbhMUQUGo27VzSB6uKDRqxK9C7edHjpMmeeU/zCuiqtDrpTzlOxPrEhJQvPaqTWRUVnlVmpWFZ2Sk09ESoWPHEvDsAOSeXlj0OpuYUbVsaTf2oXw+TkSjeXiEhKA/cAD9/mQHO+GyMLMhgdzXl8Idv7lOnI6OtvXkkanVBATXpPPstegTVtnmVT0cG8OAqVO4cOEUj7VYiYwAAr0DWdl0Jr5L1nEmcZptv46x0bQZ/ybI5Kjf/Ihjpz7CIjcQlW5d/7u5ivaT41FfLLaF38qH4+w8WpWMAREIBO4RpecCwW2CUqOh+qyZeISHuy071yenIHfRB8ddyTiS+/uXL0c3l2tqWNmMK2VgIJnz5jnNE8p66y30e/dh0evsuiLrEhJQtbCGv8oEgD4lhfPDR5D2yhhS//M0Bd//YCsnr7hv2bVleFQP5+yw4eStWUPYpIkO70HFsvWwSRPJnD0bvbNE5pmzUSX9TWnf4ShnLGdTq5X4rdyEIdH++UoSkvBZso7wgxkkFaYQoDVwTzroPWFvlIoH/VsREF7TTtg466QMl8eAmCprfigQCJwiPDsCwW1CaUYmGVOnod+/n9pr15IlSXYjDso+sPM3bcbvkYed7mErGZfL7QeIRkejDHXsy1Mejxo1LiVHr0NeTmBIJpPb+VqS0XjZC+SkYkoZHkbh9l8cb1gW+nIlAFx5cbAXZurYGAp+3o4lNxd1bCyF235C1aqVtfKqqBiZyhskKPrtN5vN3k2aOG1aWHbfy16uRJgP6mbN0f+6w+Hc/N2J7Nan0PKgNaR2PhSM3t60StGjYxeWggK753LrXbs0BqR8OEtboiXPkEehsRA/Tz8CvQNFMrNA4IRbWuyYTCZmzJjBhg0byMzMpHr16gwZMoQpU6Ygr6TjqUBwJ1Fx8OfZESOsZd8vvoBZq7Xls+Rv2kxA3z4U/bbTToCUFxnIFYROfQOZwYi5sAC5SgXIMOXkuBUthT/9jD4lhZrxq0AmQx0Tgy4xEXNBAYFxg0Auc8ifCYwbhCknx2aD04qp2FgC4wY5jIBQhoaijo6+4vBaecpCfWWdoc8MHoI6OprwKW9weuAgIubM5sKbb6Lfn2x9X1rdj2+H9vg98jCWwkIsxZV0gC43f6ssJ6giZ+/xh5JCovZZ9zoQpaJ1696Ubvzc5kSzFNh7aiqbpG4pvJxgnVmcyfTE6SSmX37Pyyq+wn1EuEsgKM8Vi5233377ijd9+eWXr8mYiixcuJD4+Hg+/vhjmjRpwl9//cVzzz2HRqPhlVdeuSH3EAhuByoO/rTk5nImbrBNwCj8/PHp0gWAtPGvAtgqtcqqthzyQtavty8V79iRsNcnc2HefJeNCiWdjhy5DE3PnlSfOcNavWQ2owgNJXz6dIypqUgGg13Pn7Iyc5cemoQEsFjsPDTq2FiQywkdPw5LJQ0GKwoEdWwMHtWrUyN+FYqAALKWLqXWe/EU/ryd0sxMQl95mfxNm1G1vP/yPLDAQAyHDnFhwUIknY4a8avc3tMjMtJOnFW0YV8LPxocKcDHADovONJAzf0pOpTq44SWS6CW+/rYXVfZGA65ny9g9ehUFDpgX/ElPDwCwWWuWOy89ZZ9EWZ2djY6nY5ql+L1Fy9eRK1WExoaesPETlJSEj169ODxxx8H4J577mHjxo389ddfN2R/geB2wdngz/LJyTU2biS92IylnAekrFIrdPw4st56q/K8kD170B8+TPj0aUgGA8YzZxwaFQLo9ycT/sYbZEyb7jDXKvjFYZwbO87OQ6NPTkEdE3PFHhp1TAyhr7yCKSeH8y+8WKnwKC8Q1NHRBA4ciPHcOdJGjaZG/CqKf/mVgD59yI2PR9WyBd7NmqEMCSHvk/Xkrlxpd21ZJZfb0vjoaAyHDtmJM/ml0JJBKePofQruT7Z+v86FgdnDm/sPXC6FL/se6A8koyhJs2tI6Pa+7dqhCAoCIM+Q5yB0ykhITyDPkCfEjkBQjiuOBaWmptq+5s6dS4sWLfjnn3/Iy8sjLy+Pf/75h/vvv5/Zs2ffMOPatWvH9u3bOXbsGAApKSns2rWL7t2737B7CAS3A5UN/pR8fPn1QileMZfnRZWJIVNOjp0HR9UiyuHDVKZWE7nsLTzDq5M5cxbGM2dIe2WMXdKwTK0maPhwaq/72FZ2Xh5dQiI5779H2KSJdscNR44QNnlSpQnQcpWaGvGrUDVvjkl7EZlcjkytxpSV7TohOzYGZXAwtdZ9bL02Koq08a8iUyrtqqukkhJrUrLFgkyhIO+T9Xb5TmAVInnrPrkkBi/PA7O73yUv14UFC21J0OrYGJSR4aQ/1IjsIImow9bareQmSkJyZdxz3uBwH5+2bYgY3R+Pn0YSMbq/7fls942pMPerXTsi5sy25esUGt1Pva9sXSC427imnJ2pU6fy+eef06BBA9uxBg0a8NZbb9GrVy+effbZG2LcxIkT0Wq1NGzYEIVCgdlsZu7cufTv39/lNSUlJZSUcykXXJr1IxDczpSVnet27XJYU7drR6mfhrf3nCB61AT8WERJ+Q/yCiLDWV5IYFwcpsxMCr7/AX1KCiGjR9mtl8+3UbWIct2FOCGRkJdeokb8KqSSEjxr18ZiNGLR6/GIqO72GcsqsgAily9DrtFQc9UqLKVGwl57lQvOErIHDiR75Tt4N2x4OQQWHY0pK8sWegNQaDSEz5hO1rJlhAwf7iB0bPZf8jBJ8To7z1hpWpqDl0sqKcE7Noawya/w9XvjuHfPP6hLoNgbTj/ZkhZb9rt8VpnMhMf3Q8BYjMf3QwiY9juGLD1KfTEZPn4UjZ6E38gSVEY9QWGBeAQH2yUml01Md0Vl6wLB3cY1iZ2MjAxKS0sdjpvNZi5cuHDdRpWxadMm1q9fz6effkqTJk1ITk5mzJgxREREMHjwYKfXzJ8/n5kzZ94wGwSCWwGlRkPE7NmkT51qJ3jK/uLX+WtoXTuA/l+l8vIzo+g3YRJ52fkEhgRQajbb7eUsL6TMS1GW31Ny4oRdOKV86CugX1+3tpqys0l7ZQwAtdZ9DEDOqnjrbCo3oaEyLwxYxUnx7j/QHzyAquX9pG/cyD0bP8WUnm6fkL15CwF9+9hEjTo2hrDJkyn8cZst/0YdGwNKJabsbIKHDqU0PcOt/eWbNubGx6NqEWV7nvLIa9fgp7710LwxhOYp1i7JZ6rD+91VLH/sZYq2POfyHgqFAYzWxGWpZluKPNXMSskm4UQuYE3ojr03iNGd6+MX7odKbd8fKdA7kNiIWBLSHfsFlZ+oLhAIrFyT2Hn44Yd54YUXWL16Na1atUImk/HXX38xbNgwHnnkkRtm3GuvvcakSZPo168fAM2aNePMmTPMnz/fpdiZPHky48aNs70uKCigZs2aN8wmgaCq8KgeTuSSxZhzc7EUFiH380URFIRSo0EDLOjZnGlf/02Rp5oT3kFog/05g4x71Ga7RnrO8kLKPuDLRE3FURTl820qS6Itvy7z9CR7xQp0SUmux1uUS4AGa86OIiTEml/TqCEeNWqCyYQpLw9FaCiKatUwFxbi360rml49kYqLqbFyBQqNBsPhw5zu09eWM6SOjSEwLg6LTkfexx9bq6Zk7t9nuxygmBg7EVaGV2w0635/l/s++YXILOuxpPs9WNnZQqlHKT8V7KFTbAwGZ7O9YtqiyP7D+qLewxi7L+OpVX/T78FaPB9bhxKTBS+lnP3nLvL82j/5v1Ht0FQQOxovDTNiZjAjcYad4ImJiGFi66nkFijAx+hwnUBwt3JNYmfNmjUMHjyYBx98EA8PD8BaJt61a1c+/PDDG2acTqdzKDFXKBRYLBaX13h5eeFVyS9jgeB2RanRuBwbEFFNxRuPN2LaV3+z8pcTtuOdG4awbPp0mDkDXULi5V47MpktnFP2AV9e1JQfRWEtT7dSWfJumThQR0cjUyptIS+78RZDBlv3tFgo3v2HLTSkjo0lfMobXFi4yH6MRGwMmv/0AJmMzIWLHBKjA+PiOPviMGq9F0/k0iXWwaM1amDKysLw9yF0f/1l9Ur16YPhnyNu53LZ7I+NJXz6NC4sWGB3jldsNAnBubRdcQyVEQpV8NXDPvxfsxLKlFR86noeGLcIf2T2/YxiY4mYOQWlLA8e6A0+IRzJgZwio933rDyFBkcvOkC4TzgL2y8kz5DHRUMBJUZPEo6W8PhbB9EZzbSvH8yCns2JqKZyer1AcDchk8qG1lwDx44d48iRI0iSRKNGjbjvvvtupG0MGTKEn3/+mffee48mTZqwf/9+XnzxRZ5//nkWLlx4RXsUFBSg0WjQarX4+/vfUPsEglsJrc7IqE/38fuJXIe1zS/V40TG7zwV2B5tXgZ6lYJipRkfo5zqJh/kXl4Y/vkHZXAw50eMdLi+RvwqWz6Ny5EG5Tw0qqgoAuMGIZnNpI0a7dRemVpNrbUfgcmEZDIhV6uRqdXok5O5MGeuXUUXWL0sYRMncLr/AMe12Bj8u3en9Nx5m1ir8/VXnHl+KBFz59hsrxG/irRx413aHz5tGiUnTyBTKtEnp1By5gz+Y1+isDAHY8FFtKXFHHt7Js3/tvbZOR0p48iw7qzR/+jwfCqliq86fUJQ+jkseiNylSeKGveiDL/H7ryTWUU8vPQ3p+8RwA+vPIS+1Iy/yoNgH08Hb41WZ2TUxv38fjzH4dr29YNZ0b+l8PAIbktu5Of3dTUVvOeee5AkiXr16qGsZD7OtbBixQqmTp3KyJEjycrKIiIigmHDhjFt2rTKLxYI7gC0OiM5RUYKDKUuP+zKyCkyOhU6APklBeR6GBh7ajG7M3bbra1/cAUBK9YQOnYMmExOry/vzak4gBRAGRICkkRpZib3fPop+sOHMBz+B9+OHVw+m6TTIVMoyHprmYOnps6mTRRs20bu6tWXB3omJmLOz3faMVmXkEjYa69xYe486x7R0ehTUgjo3ds6RPRSU0WFv791xIRCQWDcIAKff85qh5cXpqxsCr77jpwVK+z2NhVokc0cywfb5/PI5qM0zwELsOcBFdGL1uFnuYgyqzafHP7ENuEcQG/SU+RpIuKbngCY63ZGf+/7+FZ4H4J9PWlfP5idTsRKu3uD+OZghs3r48xbk1NkdCp0AHYezyGnSISzBIJr8uzodDpGjx7Nxx9bkw+PHTtG3bp1efnll4mIiGDSpEk33NBrRXh2BLcr6Rf1TNx6wO6DzF1oYv/ZfJ5+13mV0Ucv1EbumcuoX0Y5rAV6B/Jj9DrMmReQ+/iQtXSpQ7WVTK2mZvwqct5732HMRGDcINKnTCWgd+9LDQ79kPv5oU9OxpSdYwshVUQdG4N/165OxzKoo6Pxf/IJvO+7D1NuLpLBgNzLG3lANcwFBej37rMbOaFLTsanXSxnBw6y8zBFLl2CXK3GUlzs3hPVsgWBAwfa9RMqT8oT99Hgx2N4l0KBGs72bUdut1as+XsNepOettXb8myjZ5mwc4JN8LSt3pZ59foRcGw3poiOGEu90Hr4EBQZhjrYPoE4/aKeSVsP2AmedvcGMSS2Di9v3I/OeDnJvKK3xt33HeCrkTG0qBXgcl0guFWpcs/O5MmTSUlJYceOHXTr1s12/JFHHmH69Om3lNgRCG5HtDqjg9AB61/qk7YecBqaUHsqXO63L9VE6wbO1/IMefTb9wqbWq/ElJ5O4MCBYJHsZzZFRSFX++D/eHdCXhqJWasFmQx9cgrpU6YSMW8ueR+vqzAGIoagwYPxbt7McZTEpaqp032cV3bpU1IInzaVzDlzHK4Lf/118jd8ajcRPWzSRBT+/tRevx6ZWgWSRNDQoch9fZF7e5OzKh5dUpLjbC61mjpfbEUqKUEymai9YT2WggLkvn6Ysi5wZNZUTvtdJOoba6+v1BoyPPHlwWMWvHrGslG5Eb1Jb/OWDWo8iPcPvE/b6m2Z1mYaPvlG0j7/FF3CetszWGJjiZgzx26CeUQ1FSv6tySnyEihoRRvDwXfHMxwEDrg6K3x9/Zw+X0H8KtkXSC4G7gmsfPVV1+xadMm2rZti0x2ubShcePGnDx58oYZJxDcrVxtaEKrM7Lv7EVi7w26VL58GbWngqbVvIjwtfa5USlVDK8zkId8muOhM+KjCUHm6YH5ohaZQmEXopJKSmxl3qb8PDJff8OWs5P/2Sa8Gzak1vvvk7V0iWOTvoREQEb4tKn4d+tG4KBBtv1M2dm2rtBBw4c7eGlkHh4OQqdsz8x581E1a4Zuzx6Chr2IX4cOlGZkYkrPQJecTN66daiioggePhxFtWqUpmfYhI7z2VwxBA4caC1j79Pb5t059VB91MZcmh4DiwySm3nygLoF1QcPsVaOzVvGpinv8FTS8zbBM77VeLrW7oKv5Ikq30j27HlOniGB9KlTiVyy2C7ZXKP2tPPWuEpYBvukZXdhsPb1gwn2FSEsgeCaxE52djahoaEOx4uLi+3Ej0AguDYKXFTglFGxQienyMiSbUfZ8N+2zP7mELsuCR61p4Lvh9Sl1u7p6H1e5PeHPkcl87wkJFZhVqtRx8WhbtsGPD2RX/J85K1bhxRvH86JXL4MsObaZMydR+0P3idzzhx82z/kpslgAqaMDDKnOubZ1Vr3sXPxER1N2OuTyV292uWegUMGE9myBXnr1pHz1jK7a8tGPuTEx+Pf/TE8IiIAd7O5EsEioYqKIm/dJ2iefZbtf66nadJxvExw0QcyOjTl/u/+xswe8mQKW95QcNYLbGz/IWmyi6Rkp2AqzqLpmicBKHliK5mu3hcnE8zLczXeGo3akwU9mzuEwdrXD2Zhz+YiX0cg4BrFzgMPPMC3337L6NHWKosygfPBBx8QXaG9ukAguHquNjRRYCil34O1WPjDP7SoFcBzl/q1NAmwUHPnq5ia/Bep0AfpYAoXfvkVVbPmBA4ZgkdYGBcWLrQXGzExNsFQPn9FERBg88LI1WpMF7JQNWuO5CKpuQyzVuv0uKW42Dq2oaL4SEriwoIFThORbbb4+5O9/G2nIx+Qywh8/nlyV64kcMhglJfmSV3JbK5T6z4gLf8vWh2xismTtWTUHTOdgCmLbI2oy8/xMmu1WFQGXkoeTdvqbXmq7pP8PWgLPnIFodnuRUb5CeYVuVpvTcUwmJ+3B8G+rpPZBYK7jWsSO/Pnz6dbt24cPnwYk8nE8uXLOXToEElJSfz2m+sSSoFAcGVc7Yedv7cHLWtWY+UvJ/jlSLbt+I4hkVgCmlJ0NJeC7zcQ+PxzBPTtQ966TwDIS0lxFBuXBER5seHTqRPKsDD0KSkOXhj/S9PWXeGyCaFC6XpsQ0IigXFxrvdUur82dNw48tasQaZUgkyGOjbG6ZiM8uz+6xs8vMw0PmLGfClsFXXASO3gOpytkLQs9/UleOwYPGrVooZcRlKbzzAUXMRwPguDpyfP757A1qi33d5P7ueLtkRLniGPQmMhfp5+BHoHovHSXJO3pnwYTCAQ2HNNYicmJoaEhAQWL15MvXr12LZtG/fffz9JSUk0a9bsRtsoENx1XO2HXbCvJ6dzix328TQXYg5pg1KlRJeURMiYV8hettzmnXDp6UhMJHT8OLwbNUSh0aCsWRNdYiKBg+MI6NfXlluTt24dyGVX1KSvIjKF+znEci8vx4RiL29Ks7IwVzLzzjobKw5loNWrEzZxIpKTETcAFiT2tVDR/KNv8TRDvi+cq6mm1aVJ5QqNhnu2fo754kUwm9EdOIgyMBB9Sgre993nEBoLiY1h9bhF/Fq4n4dcdVFu144Sf28m7JxgN708NiKWGTEzCPcJF94ageAGcs3NcZo1a2YrPRcIBDeeq/mw06g9qRHgWI5uVPhh0RchlVgremRKpe2DuTJPR+n586S9MgaZWk2drZ9T8N33DqXbkUsWU/jbToJHDCcH+zEQPh07EjZxAqasLLwbN0IZEoLMw4PSzEzkSg9beMkVioAAaq5aRU58fIWE4ljCp0xBplY7LRMHQC7Hv1tXkMspTUtHJpMhSRZ8Ona068yc56fgQphE62TrZPITteVEtn+K5p98ZbuXPuUAmeV6e6ljY/B58AG8GzdxmQPkgwQvPEDRuEH4g11Ok3dsLCEzpzPpwAI7oQOQkJ7AjMQZLGy/0ObhEeJGILh+rknsdOrUiYEDB9KrVy80LhLsBALB9VPxw86k1VJy6jyWwkLkfv4oggJtSa7h/t4Ooa9f02BAjdrIMqwDnMwXL9rWrnTGVWBcnDWh2UluDYC6dWuUISH4P/bY5QoutRqP4GDH0vFLvW3OjxtH7XUfu/UIyVQqchYtciImEsicM5uwyZOcJj6ro6NRBgVx4c03He4d9vpksoDiHTs4XF9NSLaORifAJIcDrfx4csy7pL8wDOmSDcHDXuTcpe7Ll++fSI5FImT0KHJXrnT63pUkJBH9chzPJk/gfzNWo9a+SkFeASaVDz9eKOV+qYhfz/3q9NqE9ATyDHlovMTvVoHgRnFNYqdZs2ZMmTKFUaNG0b17dwYNGkT37t3x9BR/gQgEN4vSjEzSp0yxb+rXrh3VZ85AMhrxLChgVYdgTreLJFvyRFdqplmghOLsz5iyVaijo+2SiSubcYUkUSN+Fcrg4EoTe6WSEkrT0/G6tx5mrRZlQAAX3lzsPIEYq4Ay5ecTGDfI7njZvQPj4jDn5rqp8kokbMIEB/vV0dGEjh1L9sp3HEu+LyU+B0+YQILiOE12pOFhhjw/uNCqLs+MXIDM24ua78VbR1d4eHC6X3+n3iNdUhLSiOFObStDoStBb9Kj9fbk6Q9SL/XLsQrR9xqGuL220Fjodl0gEFwd7oPmLnj77bdJS0vj66+/xs/Pj8GDBxMeHs6LL74oEpQFgpuASat1EDpgLWHOmDoN7df/43Tffpx94gnUi2bQQFbEvcZ8qmWmY/auhU+bBwgeMRzThQtWIQPkrVtHYNwg2+syyjwg+Zs2c374CErT0twbJ0lYjEZy4+MxFxRwfsRITDk5rhOIL01R1+/dR/6mzaiioqj96QZqrHqXGvGrULVqBTKZnRfK6XuSk4v/Y92oEb+KyOXLrNdGRWHSXrQLVZXn3ME/+Gl8T1pstwqdY3XkBI5/jcZ7MslavgyZhyfIZJzu2QtjaqrrMBkgV6vd2mdWexFbvS25F+UOjQE9Ze6v9fP0c7suEAiujmsSOwByuZwuXbqwdu1aLly4wHvvvceePXvo3LnzjbRPIBDAJS9HgtM1XWIiqhZRtteGhASMC+dS7fefOP/sIE71H0HmgkV4hPijbvMg4dOmoo6Ntc24UkVFUWvtR9TesJ5aa9cSGDeIrCVLbYKhsnCXMjQUc2EhwaNH41G9OjXiV9lNSXeGVFJC3rp1BPTtg/7gAZtIOj98BJhM5H20Firp2SWZSlGGhJD3ySekvTKG88NHWD1QJrPT8w/d54MkM9PghBmTHPZGeXFvqhn1j7sIjItDl5CI6UImyqAgZGp1pc8NOAjFMrxiozlJNjPq9SHSondY35dqIjoixum1bcNj8FFWq/TeAoHgyrlmsVNGZmYm8fHxLFy4kAMHDtC6desbYZdAIChHWbdhV1RMNq4ogIp/+ZWMOW8iSRKpPXuhataMGvGriFgwH/+uXZCpVJx7+RWKd+9GGRpq5xkpC3c5Qx0bQ9GuBDyCglC3bs2FRYs4P3wEFr3jB3x5ZF5eNrEVOnasnZdE1SIKXVKS+/tGR6M/+DeGw/8Q9sYb3LP1c2pvWE/tTzfgEVHd7lwLEn9FedHgRDHBWsjRwNF7fWiVUoIcmc3TBNa+OZlz5hA2aWIlzx2L4dgx556x2FjCpk0muiCV8E2D8TA5Vo79k1bKqGav0ybc/tro6jFMbD0Vg16kBAgEN5JrytkpKChg69atfPrpp+zYsYO6desyYMAAPvvsM+69994bbaNAcNcj93Mf1nDmhXAQQAmJSHoDkk5Hbny8bXyC/uBBPGvVwpKbS258PN6NG9ldl7dunXVSuJcX3g0b2srAFZpqyH19MJw8if7gQbtqLX1yCurYGKc5N+roaFs5uioqisKft9sJszK7y+4LFXJ6YmOsPXgUCkzp6ZSePVuuLP0C6jZtUMfGoktIIFujJD/ATOsU655H68mp164nwR9vcfpeyby8bH16Cn/51WlOkU/nzoRPnow5Pw+L0UjY1ClgNGK6eBFlYABKDx3KDR3AaG0FUC0giDVD7mPf2XzW7EqlVe0AJnRryIAPdjMo9iXGdp9Anq6AmmhQFZZSfCgNNIVoLaFoQt1XrAkEgivjmsROWFgYAQEB9OnTh3nz5vHAAw/caLsEAkE55L6+tg/wipQXD4CtN41HjRpELl9m1xOntPiyh6hsfII+JcVaGRUTgz45GY/ISLv9JZ2O9ClTqbVqFVnLlznMlQqfOhXjuXN2giBv3Tru2fQZF+bNdzlpXB0bQ/CIERTv/gNlcLBNHJUJtzLPT8U5XcrgYIoSE1FHtaDg+x8c9vesW5fwqVP4du4IwpJPc99pKFXAgVYanp78Eef/+wKWCu+hzMvL7n0sTUvDu2FDUCoJGfMK8okTMF+8iKW0FI/wcDKmTXP6XFnL1hExYTimli9hDmmD2SijOF/GsdQ0juSa+Pbldvx1Op8LFw30e7AWneqH8da3xxjdXIO0ch55l/KcSgBzbCxes2bhHRnh8ufCVVNCgUBgj0ySJKny0y4jSRIffPABAwcORF1Jgt6twI0cES8QVAWlGZlkzJ5NQO9eDn1d1DExBA4aaBvtUH7YpbMPY0N4IIWLl1OSkESN+FXWHBlAHhRErVWrMBw9AhIUfP89+pQUW0M/mYcHMoWS4t27rXOzyiXuqmNjCXl5NGf69rOzO3LlCgx/H7J6goxGPGvVQjKZKM3MRKZUYjh6DN+YaLKWLUO/P9lmtyoqCr2Tzs7l7yX38+PCbMdyeACPtg/yp+dpmu7KQmmB7Gqg792dlopaGI4cwbthQ8cu0I91QxkSYnsfy783ADXefw/9vv34tn8Is7YAmUxmE5Bl74U6OhpVVBT+j3XlwsKF6BJ32673iomhcNQEVhwsoFkNDR3uC+XNH4/wfGwdjp9Mp+sXKzE4SehWx8YSsmge6iDHWYSZxZlMT5zusimhQHC7cyM/v69a7FgsFry9vTl06BD169e/rpv/GwixI7idMWm1pI0bjy4hwaGbsKJaNTxq1CBz3nyKt28HrBPEXQqFmBi0EwYjM1vwfWcTYcOGY87Pt4WA9IcO4RMTzdmh/yVy2VvIvbzJiY936ZkpL3ju2fo5p3v2srtfRcFQ0baKr23P1+p+PMLDubBgoX2Z/aXp5OlTplLrg/c5/UxPh2e8EOhBoZ+Je89Yf639c6+csAsyAgsvJy2Xt0sdG0PY5MkU/riN3NWrkXQ6m2gpE0QytZo6n28hs4K4cvZe1IhfBRKcH2HfmwesgmfbM6OIaVGHhT8cIeFELu8+ez/1DLlYBvV2OL8M38/X4lGntp2A0ZZoHbovlxEbEWtrSigQ3M7cyM/vqw5jyeVy6tevT25u7m0hdgSC25nyVVhluTblqfv9d0TMm4s5dxyWwiJk3l5uR0CoSwYz9Mh0No1bSfb8ZQ5N99QPtEbS6dAfOIh+3z6XjQQrDum0FBbaet6UiRaFvz81Vr2LTCZHl5xM/pYtRMyZDXIZ+v3J+LZ/yG6P8s8nU6up88VWTFlZyLy8kCmVWPR6JL2eWu/FO02APthITY1zOu49A0YlHGim5v79xcixr+qSq9XU+mQdCo0GfUoKp/v0veydiYkhdMwYzgwZYjs/bNJEMufOdfteWMv441AGB2PR6agZH+/g+SlJTKTvhElkyOUkXJpK76WUo9QXY3T6HbNSWniRuYkfsbD9QjCryCkyUqrIcSp0QDQlFAiccU05O4sWLeK1115j1apVNG3a9EbbJBAILlFZFZaloBCvOnVsXZT1Kc7nUJUh1+kZXLMnhiXv4nN/K0LHjrXuo9NZQ1VK668EVdMmLrsDl5/6XYYiMJDgEcPJ9fKyDRqtGCqKmDOb9ClTqbF8GcqAAIxnz7m0U9LpKM20loE75P3ExBA+5Q3buAgTkNLckxYHdSgkuBAAlsF96GoIJHe/E+FnsSD39eXMc88T0Ls3Nd99B5mnJ+aiYvT79mHSXrQLTXk3aULmtOmu34vnnyMyqrnTZ644PT4vK4/Tpb62c/afu0j9UD+3Yse3Wij7Tu0jqziHWV/m8PvxHN77r2hKKBBcDdckdgYOHIhOpyMqKgpPT09UFXpq5OXl3RDjBIK7ncqqsOR+vhVeuz/fTxNKB8mXoP4tkHt5k7VkqZ2QCJ89C5/OnZF5eLjdp3yllzo2FsliwXj+vNULMnu2cy+IXEbtdR9Tev48matWEThokNt7eISFkTnLyV6JiWTOnUvY5EnsXz4HnaqUVgescuFwfTl1Q1sQaQpF1aKJw57qmBgUISGciRtsqz7LjY9HHRuDqllzcuPjqb1+PZFvL0fm6Yk+OaXSpooKf3/bcFWHZ8beC2ZS+eClvNzxY82uVJ55Lgrv2FgMTpLPvWKj2Vt6kkXtF5FZqOX3S6NARFNCgeDquCaxs2zZshtshkAgcIYiKAh1u3bodu1yWFO3a4eiwjBNRVCQ26ot+aHjRD74IPpDexwqmQCyli3nnnUfY8rOdmtXWcWUOjqa4GEvIgMuzJlL5FtL3Y54wGzGIyQUXUIiqmbN7cY9lM9JQpKQSoxOc4/K9kqp70Go3kj1HChRwt9NVcSoowiJG0La2HG2snXb88fGEvrKKzahU3G/MvElmUoxHP7HJlBqxK9y/16UG67qYGdSEqGvjrdOj69WjeMe3vydriX23iASTuSiM5rps+Fvvp44BfnCOXbfN6/YaIrHxzF9/wSiQqJ4JWoykAFYmxK2CY/mj0zH+8ZGxBLoHejWZoHgbuOaxM7gwYNvtB0CgcAJSo2GiNmzSZ861U7wqNu1I2LObFv4qgxJbyB42IvkWCxOe9OkjR2H97vvoAwNdfoBHdC7N5lz5qKKinLdJyc2BmVwMLU/3UDRzt85N3wE6rZtiFyyGGVQkEO5e/lE5tL0dFsDwfJ9dPQpKUQuWUz+ps2AtbGgWat1+p6UyuBgU09afPwbcgkyg4De/6FHi64og4M5O2y4tfS+egS113+C3McHFArw9KTw2++ImDvHlpRd3ka5Sk2ttWux6HX4de1is9Ftz6DYGCSDwamdNnsvTY8HqBYbS+9JU+jetAmz/u8Qv5/IJafIyKPrj/DLnCkYs06j0JVgVnuxo/gA8fsnoDfp2Z2xm5HNLge7Vu/MZOWgV4HFdoKnrBpL5OsIBPZck9gBOHnyJB999BEnT55k+fLlhIaG8sMPP1CzZk2aNHF0HwsEgmvDo3o4kUsWY87NxVJYhNzPF0VQkIPQMWm1pL/xBvr9+x1605iysjD8fQhJp3MpIsAqMnLj49GnpLjukzNwIGfiBhO5dImtOWFA797krV/vkPBcMWdF4e+PZLR+aJfvoxM6fhzZ775rl+/jzKOSFuKF0cvI/Qete/zdQEGtsxL+8V+RF30BdevWRMyZ7Zg/ExND9RnTMRw7Rs6KFZePx8Zwz6bPMJ45g9zXl8KffsJw5Aih48ZiOHKEyCWLSZ8yldprP3L5XpiLitx+/8o3fNQnJCBbMIf8V6bQuk4gQ2LrABDq78Up8ymGJY92uU928eVOzDqjmVGfnGJo+5E82+5lNL5mArz9RZ8dgcAF1yR2fvvtNx577DFiY2PZuXMnc+fOJTQ0lAMHDvDhhx/y+eef32g7BYK7GqVG4yBuKlK+cstZRVbtTzfg3aQxcrXa6u1wQlkujqTTYTxzBlVUlJ1o0ien2MRL2bllzQkry1lRx8Yi9/Oj4NvvbOGrsgosVYsovO9rYLdPxansyU3V1D2lwz8bDB5wpF0ELX5NBy6XgesPHbY1SgwaPtxWpi/38qZ49x9U6/kMRT/+eNnGhEQuzJuPKiqKtFGjyzUHXIZ3w4bkrV9PQP/+/H979x3eVP09cPyd0aZJ96AtbdmgIKMgqLRFRBH3/MkSoag4iiB7ypYlfAVZQsGNE/deiILaggMoIFvZhdJJV9KmSe7vj5DQtGkLWEmB83oeH+29NzcnV2yOn3FO2YmMKp9FSGJi1VOHCfFow+oRmpTkHEEypqQQ+bSR1lGhPP3uVoxmKwnNQxl9Z3C1/34DdP7AmelFo9nK0h/S6doijKUPdiDQIC0mhKjKefXGmjBhArNmzWLt2rV4e5/5D+zGG29kYxVz10KI/1ZNO7es+fkcSxrMkcSBFK79AUN85UaUKp0OlcFAaFISXvXrk5OczLGkwS6NNh2jNI4RC0cvK3ccfacM8fFETBiPNS/Pbbd1pbS00n0c16njrmVrGy/a/WUkwAjHw8A44UnueWSuvYHpO28TPnoUitVKwE03OqfETNu2OWM/mpREwTff4N2gAaoKxVDL98YybtxI7uo38bniSns8KakE3NIDTWQ4OcnJpI8aTcnuPQD4tGpJzMKFqLy8CJ08AX2C6/M8MwqW6IzJ8d5lBYW8lnKQR7vYR3ZS/s7Bi4BKvbIcEqISaBgYTtcWYS7Hu7YIY94D7STREaIG5zWys2PHDt55551Kx+vVq0dOhYV/QogLo6adWOW7iLv0nSpXudeSm0uD5BVkr7CPDJUfWSmvfGuFij24KsVlMKBv147MBQsJHznCbRsIr5gYyo4dc3mdYjSyce4ErNZCOqTbiwT+1UZH+2t7EXVlF470H2AvEPjwI87XRC9eVO1IU8as2TR6602s2dkYt2x1jraU/wyOrfVKaakzOSlN24Zvt27ut9XHx6O78xZSn+xMj9FD8TmZDypcRsEqjnJZ9L6k/H2YR09PYwEcz1XRr+kYVCxgU4a7qsihLH3Qn+wiM4UlZfj7eBHm5y2JjhBn4bySnaCgIE6cOEGTJk1cjm/dupXoCn11hBAXRrU7tyr0zyqfcESMG4u1oABNQABlWVlkr1xp7zp+ejQCKjTiLNeiAkBTw/SarXwxxHFjnQlU+YQhNCkJ387Xubxuaxtfmv9TgL8JTN6wv8cVtPtqH5a/3kJzzwPEJK9Arde7FPBT6XTOdUfuGFNSsAzoz7GkwS5riio2UnVMVYUkJnJy3jxMW9NotPqNSlv14XSy+Oxcbpo8klJTMdluqic7nmHIwER7JeWTZQCUWs506dKqVbybms+M++dQqhS47XcVaJDkRojzcV7TWP369WP8+PFkZGSgUqmw2WykpKQwZswYEhMTa76BEKLWOXZuGbp0cTluSEggJHEAuatXuxx3rJcxHz7MkcSBFPywDm1goHORsSMh0sfG0vijD4levIiYlcmEDOgPWi0NkpNp/NGHoNG4TEm5vHeFJMuam1tpCgugZM8etPXrY4iPp0QDaW286PBXMf4mOBYO2ddeyY0Nbjr9eeKxnp6ys546BYBXVH2iF72A6a+dUEMHHMcojmPKKmLCeJcYwZ7AmdK24dv5Onu3eKMRS05O1dN1qan4ZRYRbK6+PhEKFD09niW/n7R/Fi8NT98czXtDmhISksGEe0Lx1WlpEtiEdvXa0SSwiSw4FqIWnNfIzuzZs3n44YeJjo5GURSuuuoqrFYr/fr1Y/LkybUdoxDiLLnbuYVKxcEHerpsAS/PMaqR++qr+MV1djlXvoVDxZ5bjda8R+aChZjS0tyPACXEEz7ctfWCSq0mb8376GNjqff0UPvOMJUKU9o2zMePk3VXHDkHU2n/l33kY/tVWuLuHUG9Tp0pS0+n4Ruvo42MJHPhCy4LjQ0J8UQ+8wwqLy+0wdUv9C0/iuOog3PyuXln7hUfjzYqipJ9e/GNL7euqIYt5rb8fLwbNar2Gkv9ENafKMFotnJTy3qEBJnYc3g5r/9Yefu4NPMUovacV7Lj5eXF22+/zcyZM9myZQs2m40OHTpIrywh6oCKO7dKjxxB36F9lXViUBRn6wW13n1lXucaH7XKeR+VVutc71NxDY5Kp0MbFoYlL8+l9UJR6kaC+z2IJSMDlU7HscFP2e9lMPBXzu80/mILDUvAqIO/b2nFfU8tIGPWLA7NPVMg0DGNVvzLL857G1NSnfWBHNcY3XURrzDSBFCWnu7SGyti4gSUklKCe/Vy6cGl8vGp7rGj8vFBUauq3JmlS4jjk9z1tG14E9c3D2P6vU2Z9eckNlUoDJhyPIXpqdOlmacQtei86+wANG3alKZNm2K1WtmxYwd5eXkE1/B/VUKIC0sTGEjEmLGcVP6HKS3NpUqxNjzcXk9myWJMadtQ6X3cFtBTjEby3v+AkP79CRkwAKW0FKVcIuCuSWn04kXOfy7fsVzftg0F337nXOtTolWxr4WNdh9sAeBoBFi9fLgx+oYq20WgKJWakTrWxKSPGu128XX5LuXleUVHE714kXM7eVlGBhqD4cy2cscibZut6iQqPh5taChlKhvB05/BOn0WpSmuo1yhkyeiPbmWAION2IZBnCjKYmM1zTxPGu1TXZLwCPHvnVeyM2LECNq2bcugQYOwWq3ccMMNpKamYjAY+PLLL+nWrVsthymEOF/awEAUYyGR06aCxULGnDmVGlZGTp5E3tvvkPvqq/ZEwaZUKqAXMWY0JxcspPjHHwGIWel+EbCDSqfDKzzCXhxQrcaSlUWDpUuw5ucTmpiIJiyUQ00C0BgLaLfNPkW0rbWWK/da8bGUVL/Q+HRiU77FhFJairZePUISEzk+eQrBvXoRMWY0lrw8VBoNxZt+cylwCPYkpHDtD86u5fr2sai9vVH7+RGSmOjs1K7y8UETHGxfr6RUfjYhA/pjMxrxCqjHSYOFXx+/hvvHjoYTmc5pumMP9KFLh1iCpt7GgF/30rZp9S0dDuUfYuGfC2VKS4hacF7Jzocffkj//v0B+OKLLzhw4AB79uxh9erVTJo0iRQ3Q7hCCA8x5eH1y2RKr51OxqzZGDdurJQkWLKyCe7dC+Nvv7lMSaEoaIKDUcxlKFYrYY8NInzY06BSodLpqu3DZcnMRBsWZh9peWEhBV9/7TJitDUhnCuPF2AohWIf2NvCwNU7ziQiNW1pV8xmohc8X2WH9fTRY/C7/npy336H4F49MW3bVinRCUlM5Pgzk9zfJyGBRq+9RllONuGjRlJ2LN3tdJ1ji3nU3Dlkr1xFxIRx3B3UBfP2nWQ9N8/lPUtTNlLw7GyG3T8Eb1X1i5l1Gp1MaQlRS1SKUsPWBTd8fHz4+++/iYmJ4YknnsBgMLBo0SIOHjxIbGwsBQUFNd/kAikoKCAwMJD8/HwCAgI8HY4Q/w1THhRnQUkB+ASCbxjoT08pZ+7CsvkTyhrex6EHeqIyGJxf7pX6Z/Xv7zL6oTIYaPLxR/ZO5uVbQSTEE5Y0GMVkJOeNNyq1iQgbnIQ2NBTLqVOYDx+m4Isvne9l9Fbxd3MN7XZZADhSX0Vg/0cI/N+rznuoDAYavvwSh/s9VOVHbvTO22QtXVZlHSB9bCz6q68GqwVteDiKyWSvRaRSoZjNFK7fQO6rrxJyuuif2/ucTohyX3+DkIGJHEtyv60c7A1DjyUNdv69/LRZxcXh9T/7hMNBKpZvX0iqm6mszvU7065eO1ZtXwXA5/d9TpPAJpWuE+JSVpvf3+c1shMREcGuXbuoX78+3377LcuXLwfAaDSi0Wj+VUBCiHOUnw6fDYVjv2HpMARrveuwlR1FXa8BmuAAtPnHsUbEO9fYVFl073TC0mj1aoo2bEBRFPy73VAp0XFcm21TMHTqZO9tNXw4irkMld4HFIWiDT+T88or6Du0J2L8eDKemQTAgYY+eJeUOBOdtDZe9Bi5FN/AUDIT9mJMSXEmY6UHD1bTgDMBlU5XbeXmsCefQO3vT+bzCyoldRETJlCy094rrPq6PKlEjBljr7IcG3tORRYrFhIsLzf3MI//OYX5XedjU2xsOrHJea5z/c481Oohxv08znms0Fx9dWwhRPXOK9l55JFH6N27N/Xr10elUtGjRw8AfvvtN1q2bFmrAQohqmHKcyY6Zbe/zvGl72JMfct52pCQQNS0Z8BLhZJhX/Ba05e7JTERQ6dOZK9aiT62Hfq27ZyLkst3Cjdu3Ei9p4ei0ulQrFayVix3mxSVpdv7V22O1XPVbhN6MxTq4e/mBjrsMOJdVMLhpwfS+O23OKko6Nu1I3f1mxg6dSLsiSfJdrN+KOzJJ5wNRaui8vYm68UX3SZ1J5+bR/iokQT37ePswl4Va1ExUKHqdBUjYlB5a3vIwMq1x6wGHSaLiXE/j2PAVQN4rO1jWBUrZdYytmdvZ9zP9m7nDv7eNVTHFkJU67ySnenTp9OmTRuOHj1Kr1690J3+j1uj0TBhwoRaDVAIUY3iLDjwI5brxp1OdDa5nDampHB8xhwCbr2VsuPHMcTF1bgWRqXVkn2687nX+Ahyt22rtCbGUXnYml9A+ujRNHpztdsRGIBTRbnsaKmh4zb7l/ehKFDbfOiw40yPLX2H9pTl5BA+ZjQqtZqc5GRCBiZydPBgt2tkjiYNpvE7b1f7OdR6A8U//uT2nDElBUaNJG/NGsJHjKj2PpoAe6Lhrs2FymDAKyqKsmPHiJo7B01QEGpfX9ShoQBEzZqJV2QkDd94HbWfP5bMk+R8/hk/Fm8HwGQxsWr7KlZtX8XLt7zMkz8+Wen9E6ISCPGpfjGzEKJ65731vGfPnpWODRw48F8FI4Q4RyX29XHWete5jOiUZ0xJIWRAf04+9xzRC55HrddXe0tNUBDGjRsJTUri5Lx51XYz1wQGEDpoEJbMTLf3+ruRHsO8SbTNBhuwrY0XrXeV4W0rQWUwEDFxAl6RkYQNHoxiMqHS6Sg7ehSwTwe529LuoFgs1U5z2cqqH/mxFRZSb+hQ0GqrnZ6yL8SOd1ZSdsSjMhhosGIFJ2fPcd3iHh9PozdeB5WKk6cXhJc/FzltCu//mVTpvXQaHQlRCaQcP7Pg21FgUBYnC/HvnFe7CIB169Zx11130axZM5o3b85dd93FDz/8UJuxCSFq4mNftGczlVV7mSNxSB89BpvJZC8m6IYhLg7FYl9P4+j67Y5x40Z8O19H6aFDBNx5B9rwcKIXL6JBcjKhSUmoDAb+jNUTc9xEVDYUGOCvLlF0+KsMb9uZRKHg2285eN/9HOk/gGPDR2DavAWvyEiiFy/CK6aB817uWPLy7HVwKnYbT4gncvIke3XmamgCAlHpdFiysty2sHAsMC7LyCCkf/9KXeIjJk4ge9XKSnV3jKmpnJz7HKbNm93WCDo5Yybzrqo8Ah6oC2Re13l8ft/nvH3H23x+3+fM6zpPtp0LUQvOa2Rn2bJljBw5kp49ezJ8+HAANm3axB133MHChQsZOnRorQYphHDDlAc2G/Rbg4rG1V7qWEfiSHiqqqUTkjgA6+ndlDVNd6HVog0NJWPGsy73scRdzd7GFjpts4+sHIxREXbTPdxoqI++v32ru1d0NJkLX3AmCuV3iGVMnXompoQEGr/3LpaTJzFu3uLsUm5ISMC0bbtzN5VjTZFjmst89CjakJDqFxT/tQNUKnyuuorDg5+qckt5o9WrsQUHETpnBkGmYrxKFSguRu3rS8aUqZXuDWdG09yeS02lmWqUyzHHVFWgLlBGcYT4D5xXsjN37lxeeOEFl6Rm2LBhJCQkMHv2bEl2hPivOXZgHbAX+FM9+EvVX+wJ8Viyspw/l197Ej5yBJacXFDh/HJ3VA2u2Am8IrVeT/aLy13ec18TAwH7ttA65/S0VQcDd4xdiaawGKWkBBUqTLv3oDIYXEZEqt4hlsLJuc+hj411dmHPW/M+oY8+gu10ryp301wxL62iZN8+IsaPo+zECVQqtXNhtT421rklPHrhApTSUvSx7hdtG+LiKNm9i4MdIjGcykH/v1edo13lK0S7U12yqBQWO/85PipepqqE+I+d1zRWQUEBt912W6Xjt9xyS52qsSPEJcmxA+t0ogNgyz5RzVRMIoZrrsEQH4/KYCA0KYnohQvQx7YDLy+0YaHkvrGanORkFKOR3NWrCRuchCUrq+pu5gnxqDQaZ3JiQ2Fzez2NjhqJzIF8X/jn4Rt54LmPyX9xBekjR1Gyew8KCvrYWLzCIwgdOtQ5RaVvH1vtNnLH+dy33iK4T2+OJg22175JrLzTyfemG9EGBVHwxZccvO9+jg1+iqNJSZh2bKfxmvcwdOrkrH2jlJZiM5UQOXkShoSECp8xgfBx4zjVoRl/Zf/lkugANSaD1Z33CghgwQ0LWHbTMsZdM06mqoT4j53XyM4999zDJ598wtixY12Of/bZZ9x99921EpgQogqnd2Dh7eusq6PSR5I+YKD7qZiRo2iwaiXhY0aj1ukqtYvw7daN8NGjsOTk2EdfdDrU/v5oIyOJnDrFXnW5XJVkQ1wcIf37Yz5yBIB8g4b0aIWOafbdVv80UGEweXFzj0FkPPsspq1pVVQojnfu6qqxWrKjdk1Kqn3KymjEmJpK2BOPV9opFj58OCfnzXe/5XzOXPSxsWeKJup0qDRqDvbqfXo6rD8o9u7xBT+sI2vpEoImjuaeoC6cSFnicj9T2rZqRtMSsGRmVToO9kXKFh8vRq8fDdgLBgoh/ltnnewsWXLmP/RWrVoxe/Zs1q9fT9zp//PbtGkTKSkpjB49ulYDTE9PZ/z48XzzzTeYTCauuOIKXnnlFTp27Fir7yNEXWbJz8eak4OtsBC1wRtN/CSUiI4cX/wWxtS3CE1KqnYqRlFsWPXeZM6aU2nRcfH69S5TOSqDgcZr3iP31dcwbdtG4w/eB7OZsowMZ58nxxTQnmYGgvOMXLUfbCrY2tab2O1mtJhR+/phTEklNCmp6iKGNntDzxo7ipcbJSmfGClWKzHJK1ySu7KMDLfNOsG17o0hLg5LVpazDpDznuZSrKfy0be+irx33yX0ZB64KTRfZd2d+HgiJj8D1sqNQx1d1TMy7YmibCsX4sI462TnhRdecPk5ODiYXbt2sWvXLuexoKAgXn31VSZPnlwrweXl5ZGQkMCNN97IN998Q3h4OP/88w9BQUG1cn8hLgZlJzI4Pnmy6+hKQjxhT3bDlGav11LlF+/pRcfHnhxMw3ffrnZ3lSMJiJgwnpNzn3Pep/Db7/C/6UaODX7K2VMrcv48vvx4LlcdNuJtgTw/ONbAQMftxtPxJYBaBdRQxHDjRnttHR+fqjuKl6tODBUSn7KySi0czmYtjf35PYm2Xj0OLRpQ5chTw5XJlOzahXfDhpXvU37t05jR2EpMKL568n0ULBYL6QMfJWrWTMJHj8JWVIzazxdLZiaHH34E9fJZsq1ciAvorJOdgwcPVjqWnZ2NSqUi9HQBrdo2b948GjRowGuvveY81rhx4//kvYSoiyz5+ZUSHTjTrsHRiqBiwTu1wYDNaHSOwihGI0phkdv3cCQw2rAwYlaswKt+fcqOn8CUlmavK/PKKxiu6eTcMbX/zZc59MMq2v9tA+DvRir8C7W03X0m0YmYMN65hb2mKaqyY8c49elnREycwMk5c90ma47qxOUTn4pJkPPz1LCWxismhohJkzClpWHcmkZwr15VjjzZHn8cbXg4xZt+c1vTRzEaMW3bhia6Pp9FZ/DCXy8B8OlNb+Pd8kqODX6q0vsbEhLwq9+UeS2luacQF8o5r9k5deoUkyZNYs2aNeTl5QH2UZ6+ffsya9asWh11+fzzz7n11lvp1asXGzZsIDo6mqeeeorHH3+8yteUlpZSWu6XqyyYFhcza06O267iULkVgaPgncpgoOFrr4LRiE+rlsQsXIhp5040/pVbDpTf8l1VlWT7Ql4zIYmJ/PTO8wTtOkCrU2BVQVqsDzde05+ghAQUqxXFYsGrQQOs2dloAgPtC5nPYiFv8U8/kakohCQOIOThgfbChzYbxZt+c8ZQPvFxjMwcLTeq40javCIjq+3GXrj2B/y6JKBr1BifK69EpdeTu3q1++d/ulZP7urVRL+w0P7c3TQ9NdcPxZy7Fr1Wj8li4rgqn6BRA/ADSspf36WLvapyeOUFyflGM9lFZgpKygjQexHm602gwbvaZyeEODvnlOzk5uYSFxdHeno6Dz30EK1atUJRFHbv3s3rr7/OunXrSE1NJTg4uFaCO3DgACtWrGDUqFE888wz/P777wwbNgydTkeim10YYN8WP2PGjFp5fyE8zVZYfQPIiqMmjuQla8kSly/lyGefxbR9e6UFtVVu+S5XJTl39WpU4fVYt+9zWqcex8sKuf5wPMpAxzQjhfvewUujJeD221BptWTMno0xJdUZiyUzq4Z6Nzvta47ax6I2GFCMJox//ImiKOjbx9IgfoW9BYOvL+YjR2n05moKf1qPraQEfax9l1b5pM05pWezVTlKpG8f65z+clkobTQ6kyZHPGpfX/vI2chRhDz6KPWGDEGxWJw9tYr1au74+UFi68Uyv+t8Ptz3IWlZaby5602SBvWn98TxaIpLUPv7oQkNRRtYeTTn+CkT4z/azi/7s53HurYI47kH2hEVVH3FayFEzVSK4mblXRVGjBjBunXr+OGHH4iIiHA5l5GRwS233EL37t0rre85X97e3nTq1InUcvP4w4YN448//mBjFdtU3Y3sNGjQoFZaxAtxoZUeOMCBO+6s8nxM8gqOJQ12fkH7db0ea36+S10ZxWgkJnkF6aNGOxMCRxLgeH119z+86Sf2//IhLf+xT1vta6wiOE9DvXyLS5LhqIVTPsFQGQyEDhpEwG23kjFnbuVdXQ8PBJWK3Nder5SYhI8ciSX/FBqDwZ5YaLVYCwvR+Ptz8rnnMG1NcyYlmuBglwSvfMKCoqAJDKTo51+cdXYqLuY2xMWhj411Jkrln1FoUhKm7dvdrifSJ8Tz46BYXthvn77qXL8zw68ezqPfPeps5Pn2HW/Trl67Kp9xvtHM0He3uiQ6Dl1bhLH0wQ4ywiMuSwUFBQQGBtbK9/c5JTuNGzdm5cqV3HrrrW7Pf/vttyQlJXHo0KF/FZRDo0aN6NGjBy+//LLz2IoVK5g1axbp6elndY/afFhCXGiW/HzSR4/G+KubKZmEeAJuvZWTz82r9AWtMhiImDAen9atKUtPRxMUxJHEgfYk4NFH8b+xm33USKPhyAD3o6QAhwZ2x+/TdYTlg0UNaW11XL2tBDX2xcehSUnOBKe6xEllMNh3dZWVYT582LlzSuXlhfHPP92P+sTHo2/XzpmUOKaMNCEhqFCRMffMzrKzSdqOJQ12Gd1xbD8vf40pbZvbhC16wfPkvvmW686qhHiKRifyyNaxLh3K37vzPXJKctiWtY03d73JmrvW0CSwSZWx/ZNZRPeFG6o8v27UDTQL96vyvBCXqtr8/j6naawTJ07QunXrKs+3adOGjIyMfxVQeQkJCezdu9fl2L59+2jUqFGtvYcQdVmOtgTruCfQKTZKU8qNfCQkEDXmcVRHN2B49zUy5i92SXTOtF6YBti/yMG+rgeLhcznFxDy6CNo/fycW7fVOh/naJDVWMyWWB/av7kOrQ1yAuDUTR3pmqXDyJkv/PI7raqtGGw0Yv77b6zFxRR89bVzhCcmeQXZS5e6fY0xNZWQxAFnft64kWwg4PbbKMvMIuDWWwkfNYqy9HTnlFJV1AYDTT7+mILvv3dJdFymrHx9Cbj1FvxvvpmiDRvIeeUV+3ql8ruuxo3FVlyEUa/hm4JNvLJjKklN+nO9bzu0xaVYfX1QmayM+XkMsfViebH7izVuLS8oqb6vWWEN54UQNTunZCcsLIxDhw4RExPj9vzBgwdrdWfWyJEjiY+PZ86cOfTu3Zvff/+dVatWsWrVqlp7DyHqqvzSfKamTmVr5laSBvXn+mGJaIylWA06dquyCQo2EPhHCtaQq13W57hbh1O+AJ6+vX26xisigpPPzas0WqH+3zT2LniGTtvsycveZhrCsqD597uJeO9de8G+08lK+QSnpoXImsBAvKJj8AoLA+ytIM62mKCDY1G2NiIC09Y0vBs2dK7nqY5Kq8VaUOAydVXd4uywpCR82rYhfcRIZ8KTk5yM7oYEMpr40/OLnui1el5pPx+/BaspSV2BpdwzfGXUfAaljePlHS8zvfMcsvOLqlx4HODjVW3s/jWcF0LU7JySndtuu41Jkyaxdu1avL1d55BLS0uZMmWK2zYS5+uaa67hk08+YeLEiTz77LM0adKERYsW8dBDD9XaewhRV+WW5JJ63J6IvLD/JSquhPv8hsUExnTCpg5yOe6urk35OjxKaSkhiYmcnDev0jqU37LTiJ6QyhWFUKaBnTc14N6RyWTNnoMxJYWykyeJmPQMZUeO2Bt6lvsfn5oqCmvq1UMxm1FQETl5kr0Tu81W7TNwl0AppaVogoMxbdvm/JyhSUnV1ukp/ecfDNde67J9vLrF2Y4RJMfWfocCLwvfH/6ezvU7E+fX9nSiU3nLui8KSYP688L+lzhVmsftC8+MUFdceBzm503XFmH8XMWanTA/Wa8jxL91TsnOjBkz6NSpEy1atGDIkCG0bNkSgF27drF8+XJKS0t58803azXAu+66i7vuuqtW7ymEp7lURPYPQBMaUmmXTqG5+p1YhZZS2LQCdc+eLsfdjZaUn4rxbtQIlU7n8iVuAbbF6ojdYURrg6wgyKpnIL64AVovbyKnTCbj2ZmYNm9B7eXlXB8TmpSEb7du+LRsib5De3zj4+DJJynetOlMh/K4OCImjCfzf89TvH698z1jVqzAtHOn2/o1UEMdnQo7rVyKKlYYqYqYOBHT9u2Yjx4lfMQIMhX7NTUVOwwZmIg2PLzcvRII9g3jzuIO3H3VjWjziylMXeH29aUpG7l+WCIvAIVm1/pGP+/PZsJH250LjwMN3jz3QDsmfLTdJeHp2iKMeQ+0k8XJQtSCc0p2YmJi2LhxI0899RQTJ07EsbZZpVLRo0cPli1bRoMGDf6TQIW4VLitiNylC1EzZ+JV/0z9FX/vynVxyvPXeEPCMDQ+CoaEOIyn1/ScVYPKctsSToZ4UeBvoePpaavdzdW0HTCGetPmY9yfSln6McqystC3a4e+49VoypWWyPvgAxq9/hon58ytUH04gSYfvI/5+HH7GqEFC10SHXsgkPvqq6fr16hcn0d8PCED+juLCTr43nQj3o0aYTMaiV6yGLW3zrnOyJHMRYwba9+1FRCArbSUshMnKDtylJOzZtMgeQX6du0ISRxgr+VTjfJJo70f2EPkzltAvT69yV2+nODHHqO6dFRjtL/e16vy4uKf92eTXWR2JjJRQXqWPtiB7CIzhSVl+Pt4EeYndXaEqC3nXFSwSZMmfPPNN+Tl5bF//34AmjdvTkiI9HcRoiZVVkT+9VeOT5lC9ILnnSM8IT4hJEQlkHK88k6shMjrCDm8Ca64A+36GUQNHcRxRcGYusntdFLFOjSN3rQX0dveykCDo0ZaHAazBra39eHqNBOREU04dvq1itWKzxVXYP7nAOnDRxDy6KPO0ZjgXr0qVT0G+3qcjFmz7du828dWTnSwT3vpY2M5/swkGq1+A2tOjr0xp1aLYjKhWK3OOj+K0WhvWDpyJBnPzqy0Td1RJycnORmfVi1JHz7CJWFyjDABzqTMsWi7KiqdDm1YGA1ffw21nx9HnkzClpPj7COG2Vzt660GHZ0j48nMc/9rtuLCY8cojxCi9p1X13OwV02+9tprazMWIS551VZE/vVXrDk5zuu8CwtZ2HQ0P0f0YOqOeQAkNelPj4BrCTZr8fL3w4IX2g4D8NKqiR7ZF+vQgdhUvgTddy8nZs5yvlfF9Sm5P/zAlo6+xG4pRqPAyWDICzHQKc2IIS7etReVRgMqFSV79hC94HmOT55C1KyZQM19rxwd2N1xTD1ZsrPJWrKUekOHVG4XkZBA4zXv2ZuQWizuE6tyBRBzkpOdI1vG1FRQqWj42qtY8/IwpW3DZjqzRbzaNUZxcVgyMzGl2dcFGeLiCO7Vi5zkZOfnsreQcF+pWZcQx16ymXDNVL7bbqx0HmThsRAX0nknO0KIc1dTRWTrqXwyTi8GdmjVJYGfZnyATVHInTaTopQVOFaBGBLiqT9pAtYTB+xrf+pHovtpAhz6hchx32I+0d+5kNiRlGSEefPPVyu5+oh9LmtXCzXR6Spa/mPEkBBP+PARHBl8urpwXBzFm37DtGM7+rbtyF39JsEPPkjJrt1ETp2KJTOz2s/j6Ebu9tzpdUSN33mbsvTjVY4QnZwz1zlC5C4xgTOJVcV1PsaUFKwD+rusMXKMSlXXPDVscBK2khJOPjfP5f7lP1fu6tU0/vB9js+eXaEsQDwhM6ZwxKiiz/JdzHugckFBWXgsxIUlyY4QF5DaTX+q8hRzqZsprhSyp84k4LZbKy3kNaakcmLmXGdFYENCHFFDB+F16Bc0B74g94O/MaZuInrJYgC2tfal0aFiIrOhVAt7ejTljgcmQnGxs9Bf1vLlRM2aSd6a9wnu09s5DRQyYAA5ycmEjxlN5vML8GnTGmqoSeq4p7sRFEfhQ8Vmw//m7uc9QnTm4SkuTUMd1H5+hA4dSu6rr1Kyby+RkyeTMWsWxpRU5zqfsCefQOXtjcpbBygUbfjZWWfHefsK2+wVoxGLYuXXx68hrlxZgHXF22luNvLwq5WbJ4MsPBbCEyTZEeIC0oSGYujSBeOvv1Y6Z0hIoHjTb25fZ0xJIWRAf/fnyo06GFM2chwVkc+sx3qqgMgp/ciYNRtrgB9b2nnTfkcxagUyQsHcrQtxx62kP1a5sa5SZiakf3+XAnyOL3tLZibGjRsJ7tuHkt17qp4KirdPhzlHUFQq504plcFAgxUryF61koyp04hevKja51bdCJGDJjCQI489Xqkysq2oCNPWLTRYmYw2JISCtWvRd7iakAED7NvYAwOxmUxoAwM5dP//VXl/x/s7Ro90CXH8UrydfJ2F/0t72nnddZFx5Bm7On9uGGJg3agbZOGxEB6k9nQAQlxOtIGBRM2ciaFLF5fjhi5diJw8qcru21BDheJy54wpqZiPHufwQwM4+EBPsju1IHXK41y93Yxagb+u0GAwaojv8VDV00Ip9vUu5RMHZ7KhsreKUBkMqLy8iHhmIoaEeNfPExdH5JTJ6DteTdTcOaDVEjKgPzErk2m4+g0arX6D7JUrz/SyOosdZI4RIncM8fEU/fxLpUTHkZgYU1LJTk4m/5tvMW7chL71VaSPGk3ee2uwmUyc+vxz1AYDDV9/jejFi2iQnExoUhKq05WZDQnxoCj4dutGSOIAivftoXh0ItN2zKdd2Jlpqusi4+jXdAyv/GyvJN+1RRjh/jqahfvRvmEwzcL9JNERwgNkZEeIC8yrfiTRC54/XWenyNkN25qbW+nLurzqEoKK5xzJz9amKpq+9DpNjVDiBbs6hXL1xhyXa6risvU6IR5tWD0iZz5Lyc5dqAwGtIFBGP/8k5xXXiEkMZGQAQNAUdCGh1OyZy8FX39TqRWEISGB8JEjUKk1LvVwqi9IGI8lM7PqNTYJ8dSfOpWMefNdX1euDxbYE7iQAQPIWbYM1CoarX6Dwh/WcerzL4gYMYITzz7rMk3o2OXlmM7L++BD6o0fx3HjcTb0b0Xy1nGYLCYMWj9e7fEmVouOlH2lDH3zAEazVaarhKhDJNkRopbll+aTW5JLobkQf29/QnxCCNS5FgzUBgZWKiIIVLm7x5CQUOViYHfF90qwsrWNF7F/FaMGToSBuW1r7uk9BPWTeqz5+S7Vj90pP20T0r8/hxMT0XfoQEjiAEKTnqRkz27nWpryfbX07dsTOXWKc3Fv+TgjJk7gUO8+NFhZdYXniouFQxITKflrJ9ELF6BYLISPHmWvxGyxYjMW4xUZycF+DxHcqxfBvXs5E67Cn9ZXavipNhiIXrwItc4HlV6PvuPV6GPbcWLGs5WqLxs3bgS1ymU6z1JqYsOg9s4u5wBFRh1D3jjGo12aEN80hhtbNCJQL9NVQtQlkuwIUYsyijOYljrN2eYBICEqgenx04n0jazmlaD1thE1ehDHFSvG1E3O44b4eMJHDMdWWlopGao4egGQ2aU1h+aPoUO6ffHwjpYauk5cTt7AJ6GPiiMPPwKc3plUTXsHbViYsxO448vemJKCytubiHFjq613Yz5yFJ+WLQnu28e53saSmUnhd98DoKmwULt8heeQgYmo/fzQBAdjy8/Hkp2NvnVrZzLlSF6iFy8iffgIohcvwpaTU6m/lT42tvL6HaOR9OEjTn/GeEL69wet1m2bCTgzGuS4T/nKyGCfttpy0ILRbGX70VM8dG1D6gdVX6xQCHHhSbIjRC3JL82vlOgApBxPYXrqdOZ1nVdphMf5WqMZfcFJvD99gOheQ7A+9RBWqw+KoqV4028cSRpMozdeP9OXymzGKzqakp07XUYvtneJosnmnYSbwOQNu1rp6bRfRYDGlzxcp6acoylqVaXpG8cIjLtpNZ8rr7TvZqqm3g1WS4WKyvbEInPxEhokr8C0fUelRMvRbNO3WzfqPfUUJ+fMcTut5Pi8jpEnd9N7FbeK22NIAEVBZTCcTtxSwaZQb8Rwt/9OnHFVmO5zVEaOj4pn0rXTOFWg5752zWUkR4g6TJIdIWpJ+cadFaUcTyG3JNdtsnP8lInxH21nXmczUeZitL/NRwuU3vURB/qf2eVjPnQIXbNm5L6xGtO2bYQOGoTfDV1ptPoNinKyWPfSeGJ/PQ5AejiYfOyJTvSC51GfXmhbPjFwjKY0Wv0GlgEDUBsM9qJ7NhuK2UzU3Dku01OOxKemQoL1nh4KajUxyStcdjuljx5DxITxZK9ciWlrGo1ef53Mcju0wJ4U1Rs6hMwFC90nU2oVjVavpmTXTkp27qqyfxZUXnMUkjiAvHffc0mYjBs3ohoz2u3rHSomU8Gh0ay5aw1BuiCi/KJo6D5/FULUIZLsCFFLamzc6eZ8vtHM+I+288v+bMwJ0S7nbCbXdgQqrRZrURFhTz0FNhvZyclkL13K4SgfUEqIPWG/bntrL5rttxKjPZ3o+Pqi8vOl4euvoVitNHz9dZdGnYU/rMO0bRuGTp0wdOpE9ukqwQ4VR1RqWthszS9AGxpK+qjR6Du0J2LiRKyHDhG9cAGagACMU6cBYMk/5exT5dxarijYioqr3SVmGTCAgu++IywpCZ+2bUgfMdLttV4xMUQvXoR3o0YUbthA+shRzvjLdzO3njpVbSXl8smULiGOT7J/pJk+liVbllQ7WieEqDsk2RGiltTYuNPN+ewiM7+c7nS99oiNR5rchPbgjwCo9a5TIqa0bfjGx6H29SVzoX3kY0s7A1fsM+JXAkYd7OsYzl0DpoNWi1dEBJmLFhHcqxcZM2a4TgklxNN4zXuYDx9GrdMRcOstmHbvJnvVSvtoh8FASGIi+vax9gXIeoMz4dG4WVjtQgWZixfR5MMPyP/8C5fpsPL1dBzTVs6XGQyEPPoofp2vq/b2SmmpfSs5KgLuvMPtVJshLo7CtT+Qk5xMzIrlKKYS53WVqiFbLIQkDqhUByhiwgR8Wl9FWXo6DZKTKc06SUab+mw5vobibFW1o3VCiLpFkh0hakm1jTujEgjxqdwst6BcM8gXfs0k/sHZXMkktAd/RJP1G4b4zs7FyrmrV+N/c3dsRUXk/raRPa21XH2679LRCLB4+dA+NRMSVZg2byF3m73RZvmeWA7GlFRnG4ac5GR8b7qR8BEjyHhmkkvT0IrrbhqsWIEmKLDqXWPl6tooZjP69rFEtTozHaby8XFeW356SB0aSsMVK8hcvAh9m9bVPmdn76uUFMJHjaw0KuNYH+RctK3V4n9z9zOJm84HdXAQKoMBfWwspq1pGPftRT95FIGWURgLcgkIrU/OrLlkTJ3qvK8+IR5D+8H0ubIPozfYp75qGs0TQtQNkuwIUUsCdYFMj5/O9NTpLgmPYzeWuxGAgHLNII1mK73ePcLILlO4pfM0grVm6j/bnxPTZmBM2YhiNHJ0xEjyEu8gP1Sh/U4LANuu0nLlPis+lhLAPvLhWFcTMjCxxjYMAMU//kRZr95A5aahzutTUskGDNdeR+SUyWSUazQKlXeGWQsLnRWUHV3H/W/u7uxN5aitY9q2jYYrk53rdPRt2531tFJZejr62Fj753Cz5dy3Wze0gUFkvvBCpZGtBskrUMKCsVotfHmtCnPhL2w+uZk4v7bcOPM1SlJd39+Ukor+OTj4eCdMFntDUb1Wdl4JcTGQZEeIWhTpG8m8rvNqrLPjEObnTdcWYfx8eirLaLYy+8cTzAYSmoey7FYj0T2bYR3cD5vJzFffvUGTBatoUArFOth7pcE5uuOg0umc62rOpXAg9sLI1S9ATkklZODDoNUSOekZzEeOONfblN+iDmArLsa0bZtz+su4cSNZy14kctIkMmbOOtP1POs2bEVFZzqyV1tzx3WbvXfDhgB41a+PaZvr+xvi4qpe7JySShawf8Rd7LEcY9X+l1h20zJeTHuRce0foiR1hdvPb0pJJW7YAAA61+/M9qzthBvCZSpLiDpOkh0halmgLvCsv/wCDd4890A7Jny03ZnwAFzfPIynbmxOScFugn+bj9Gi4ds9UbT9ywrAkUhQ1D7ORMexxsY3rjOoVGgi69NozXuovKvfCl1+KsmUtg1DQnyNCZLGoCdj2jT0bdth2rat2hGY8tvRc5KTKV6/Hsvjj2Ho1Inw0aMAFZqAABSr1fnaijV3lNJSvKKjKfxhnWsyEx8PXl7kvbcGQ8eOaOuF2QsPlku+LDk5VS52NqWk0mXcOE4UZ6DX6im12j+3trgUS3Wf31hK5/qdeajVQ4z7eRxXR1wtyY4QdZwkO0J4WFSQnqUPduB4vgmdpZBIbREabGhVJ1AbAvijOIxTv3nTNtOeEKS18eKmQXMoef9jjMc3Vr3GJi6O8FEj8b3pRop//KnS+1acEspdvZomH35QZaVmB8VisU9DbU07qxEY48aNhDw80HlebTDgc1UrlxGXmGTXkZSKi5cbvv5ahfVDCdSfNpWTCxYSMqC/s6VD7hurXWKJWeF+hMbBmp3N/2mv5v9aXoOm1J/1176FTu3N0WpeUy+sEe182jHuZ3u7CFm3I0TdJ8mOEBeYJT//dF+sQtT+AWhCQ/AFmhSdxJZ9DE1IBJrczWh+msCHuVfR9HtvYsxQqIdjd8fS/v1t5E2a5hz50AQHk7Vkidu6NJmLVESMHcdJU0nlRbyJiaSPHOU8po+NxVZSgs1kcq6rqah8Z/byIzDhY8dQdvSo2+kssG+bd1Dr9ZXWBNXUG8tmMlWq21N6+DBF331H8S+/2AsZarXUGzEc1ZjRWE+dQhMUVOMolUqj4cjAh8+8V1wchpEj8b3xRop/qpwg6hLiyNFbWZW6ynmspl14QgjPk2RHiAuo7EQGxydPdl3Ym5BA2JNPcDRpsDNBsMZ3ZHdhOG12pANwOArq972RO+IHUXZjASqVGmNaGumjRhO9cIHbxATsa1PKEjOci3jPtG7IouSvnS7rW0IeeRjFYgGtlojxEzg5b16lOCMnPcPBnr2cxxwjMP49bna2YXBHExTkfB9bQUGlpKbKdTrx8YQPH8Hhhx92LnJ2jBpFzZ3jEkNFMStW2OsHVZVExcc7Ezfn89q4kUyw998qLXUpeKhLiEMZn8S3Ob84j8VHxbvdZSeEqFsk2RHiArHk51dKdMC+hTrbZju9C2o16XdeAz9toE022IAdnXy5Y8JS8l54mYML+ztf5yj2p5jNVEcpKXGbDDT5+CN0zZuh0uko2bcPbWgotsJCFJMJxWoh7MknsA0ciEqjQRsWimn7dszp6W7r2igWS7U7qBznQxIHUJaRUfn15UeJRo+iLD0dTWAgmnr1sObl0fDll1BptZTs2VOpXURVvKLqk7dmjb2GDhU7pScQ0v8hl8XODsaNG7Hk5KBv147w8WMpLMyhwMvKXlUmfoZSXv3jVcC+QHnStc/Ieh0hLgKS7AhxgVhzctzWpoHT61oefYS0w2u58tMN+JRBgQEONDUQr4tFY9Rg2ppW6TWo1USMH+c8VqkYoM4HTViosx9UeTaTCWtRMVqDgYAePTg5bz4+LVuibx9L2ZEjaAKD8G7YAOO2beS8/DLGjRsJHTrU7RSXtaDAfVJxOsFReXujj40lffQYohcucPsMnKNEN3fHu1EjFJsNi8XM++ZU9lmPM6lpEt6NG6OPjcW4cWO1U1/6hHj+Jovo7je5TG/ZiovBpqDS+3DkkUfdJm5wJkH06hpHWlgxMf4x6I02AF7s/iLGMiPZxiwCZeu5EBcFSXaEuEBshVUvZC3Qq/lu0dPE7rSvMTkYrcLb7E37v4wYSSXbpri0OHAwpqRQplVhiI/HlJZWZTHA8u0eHNR6AwVff4W+bTvy3n3XvsC34mvj44mcMpnM+f8jdOhQ/G/shu9118LjT7i0nLBkZFD40/pK02WmtG3krXkfn5Ytnfetdn1OXByFP6zDNz4O8+HDlF7fnriwHnSylrG9LJ2Y6GDCpk1GMRqxlZQQeN+9lev9JMQTMWECZYoFdecYcubOdy7QjklewbGkwfb1P1UkOnBml1qOpoTRG0az4IYFzkKCC25YwMf7PmB656kE+oZXeQ8hRN0hyY4QF4ja3/1C1n1NDAQUGGm9sxQbkNbWi7Z/leGlnFlc666Lt0NO1hFCp0zE8ufWKosBUiFZMiTEY/prh71uzgD7iIzb16amcvJ/z9urGy9aRM6yZc5zhoR4Gr/3LmUnT2LJyiJi7FgyZs2qlGhFjB+P5eRJSEoid/XqarutO9bj6NvHUvDtd4Rd1wHFR8+xsiKKy4o55gWlgfV4YMODAHx58/v43XoLIYkDUOv1YLNRvOk3DvXpa1/jk5BA+PQp+I8ciqqwGI2iBmpOuExp2/BJiCfHYB/N0WnOTJk1DmjEvIRZBPpGuP33IYSoe9SeDkCIy4UmNBRDly4ux/5sr6fRMSOROZDvCzta+3L1jjK8lMqvr2pnka9/CN/k/IK+TZuqG2hu3Ii+fSxwOgGZOJGTz81z3lffPrbK1/pccQWZixe5LNaF0y0n5j6HafMWMp6ZhPnYUfRt2xKzMpmGb66m4euvoW/bjkN9H+To4084CwwC5H3wIWFPPklM8gqiFy8iJnmFc5pLHxt7uuVECidnzGHbkT8J8Q7Hl4ZE+UWxM3snnet3BkBfUELm1GmU/LUT86FD2EpK8GnVkpiFCwlNSsK0dSuZ02fym3En1+94lAwv+2hO7urVhCQOwBAX5/KZHN3RS/btxTg6EZNeYy8emL0dsFfDjvCNlERHiIuMjOwIcYFoAwOpN30GJ6dN5eTWTRyLUeiUZm87cKChmuiBQ4mduaTK17tbkGtIiEe9cz+3t70W85HqqsPY69s0+exTCtb+gPnQIec0TvmKy+5UW1G53IiTY81NaFKS22KDjp8jJk7Au0kT1D56sldU7rBevkZPacpGmg1LZP7mOTzYZCxzf59LWmYaC25YwP9F3oqv4oXPksV4N2qMaZt9d1r5HWaO6bvrDWN45ZZX8LL5oznd16ti4UJNYCBKRBgZRSfYn3QTh8t200pp5SweWF3bDyFE3SbJjhAXSL7RzOh16cS2jKHtbiut94FNBds6BdDuLzPhWWZM1dSaqVjszxAXR8TEiRzq3YfoBQtqrJas8fPDZjKBxVKpcrJvNZ3Gz7blhOOeNSVH4SNHUPTLr/i0a4s+NpaIsWMwV1OjR2MsZWvmVma10zI2vB9e/r0JK6tPzuLXOZIyxeV5lF+bVL56c37eSUr1av40ptNt2gSYMRdjSqozTl1CHMWjExmU2s/Z9+rF7i8S6RuJ2WJmzV1rqm37IYSo2yTZEeICOXnKyJU/T6Trb+l4W+CUHxxu4EuHPwqA6mvNhDz8MNhsNP7oQ2xFRaj9/LEVF1H444/2xEBV8zqUgu/X2jucd+tGwN130fD117Hmn0Kt16OpVw9DfHylqSoATWD1X/Aqnc65zkVlMKAJDiZmZTIqrRZtSCioVSg2G1htFG3YQFlmJj6trzrrGj02g55X2s/H+MxMLCmpBCYlkbPtnSpHjsqvTXKMPJUEBOKjLePrQ18zN2suSYP688D40eTkHMVq0LG+eDvJW8c5Ex2wr9OJMERIgiPEJUCSHSEugIwj+9nx9APctbcMgL8bq/Er0BC7u9h5TflaM6FjR5GZn47NoMcvKBJ1Zj7Zy1e4rYKsMhgwpW2jZN/eard/p48eg8pgILhPb07Omu1yjW+3btSfNpUTz8503dkUH482KgrD6amfigxxcVgyMwlJHMDxyVOIXvC8vZqzm4XHeWveJ/SRR9CEhmA+dMieJCXEo/LxqTLR0iXEUehtI+B/qzGe7kJ+ttNqZx4s6AJCWLV9JptObALghf0v0fnKm+mzsXKdHYcgXbAkOkJcIiTZEeI/9tN7C9EseomrToFVBRs6hdBt8BLUj/avdK1jtCP/xlj+b98YOtePZ27EVLKT51W7yyp39Woar3mPzAULndu/1QYDNqPRZWoodOhQct98q9K9itevJ0OtJnzkCCyJA1BKSpzTSpkLFhI+fDiZKK5JTEICkVMmY9yyxZmkud3RdfpnfWws2cnJBNxxO/q2bTFt307kpElkrUi2NwUdMRxbSQlqX19QoGTfXjQdY1GKMihJPXPPc+rkDnjVj6TMVOJMdBwsVgud63eudBzsBQNVsn9DiEuGJDtC/EesFgsfjL2HVt8fxNsKuf7wfY+becN2G5pcNbfEx1PqZjTDkJCANiyapde/x+YDZZhOFlTdDuL0SIZ+R3sKv1/rLAqolJai8vPjWNJg57UqgwH/G7u5bB8vr/jHHwlNHAA2G8cnPuOybiao5wNEjB+PyVaCpagIvX8w5BeS+cIigu67F31s7FmNuOQkJ9tHXtRqLFnZWE6dIvCuO8lcuLDSaFDY4CTIPkX94EiOlCuKqPLxqfa5l1+PZEiIx7RzJ/oOHdBr9S7TVPnmfB5q9RCAS8Lj6GguyY4Qlw5JdoT4D6Qf3MUfw/oQu98CwP6mGtq/sJonwltya46RwlILYdOfJWfGNErKTQ/p4uPJGzKWZWtPMezmFtzVRk1IxkGKqnszRXEuVC6foDT59BOXy0ISE2vsaG7NzyfvvTWVChgqRiMqqwl1/UAoDSBr4rOEJSa6NOJUGwzV3tsx4qKUlmLJziZ76VKa3H4b2cterLytfeNGsoGA228DlYqQRx8lZ9kyVAYD2tDQKqe9yndyL9/sVN+hA0mD+vPC/pec16ZlpbE3dy/t6rWjf6v+lFpL0Wl0bM/ezod7P2Zq3IxqP48Q4uIh/+siRC1b9/Z8DvR9gCv3W7CoYdvNMdz5eRraiFZM+HgH/V7+jSff3MwWoxff3T8E7VsfEPbGm8R88gmBo8fiq1jpFKYlt8jM89/tReNXfRKhCQx02UoOYIjvjKbkCIaEBOcxfftYUKmqvZdKp3OpyVP+PTSnthF4Kh1dgYmS1I1nkpfTU2+2aioSO+7t+LtSVmavcVNW5jZpAXvCow0PR1uvHn5drwfsCVvWshcJGdDfbY2ciEnP4NOmNTHJKwi49VZns1NjSgo9Aq51uf7NXW/S84qebM/aztAfhzJ6w2iG/jiU7Vl/Mf7aCYT7Blf7eYQQFw8Z2RGillgtFj4YeQet1x1Fa4OcACgZOoC+ic+QbzQz/qPt/LI/23m9sczKy2nZ3Ne7GcZ5L5Bdbhrnlvh4gqZMJ+I6P7RZG932owL7SEbRz7+4JCeG+M5EPf0gXmufov64DzkxT8GYmopSWkrJ7j01Vg4G13Uvhvh4vEINaD+YQP7tz6Eo9i3uFev+nE1VYseCZoCQxAFYsrMrXVueMw6rlcg5s9G3b4//zd2xlZQQ8cxEsFoxHzmCytsbU9q2SqNbMckrnP8cXKomvn5nUk9PWZksJj7e9yEzrptCoaWMYksRfl5+BOlCJNER4hIjyY4QteDI/m1sHdGf2H/s01Z7m2vpuPhdGjRrA0B2kdkl0QEweGn4un9zTM/OdO40cihNTaVw1gyaD+qKdv0E6o/7mhPzcBkFce5y+uBDAu+7m8YvLUCt90aT9Rvabx4GczHen/ek/sSPKct5HLWfH8cnPlNjqwY4k8gYEuKpP/5pvD/9PzAXkxsUjW+J/ddGxeSmyq3z5XZjhQ1OQhMaSuG333HyuXnErFhe7XNV6XSg0aAJDsa7rIyTM2dV7l6eOID0kaPcd2Mvl7R5Uch878bkdu1LodWMv8abkMObCCwrhbAW1cYhhLi4SbIjxL/0/euzMCx/mysKoEwDu3o0otfzX6LRnvnPq6CkrNLrAlRFBOafICe18kgI2Jt82gb3rZS0WPPzzzTZ/OBDwieMwVs5iWrdg2de7O1Lfrdx5DbqTLEtg/qNW+BrLEPfob298/iC5wl73PVejh1bhoQEvOoF0fTtZag1VmxFeZg6L0Ft0KFXh/JD4R8kJMST98EH9p5ZpxMnx9b5iIkTCJ0wlpKCPAz+waBSY7NZMYwZyklVCfXNBfi1a4zu3Tf4x5ZNUDWjVpbMTLwbN8b4xx8UfP2Nmx1pKYD7JqlQPmmLQ5O5icDf5uOymbxZd7juqar/5QohLgmS7AhxnsrMpXw08g5a/3QcrQ2ygsAybBB9+1Wu3RLg41XpWHN9MbbM/Grfw2Yy2/+hOAvvD25D3WEI1nrXYTMZCbzjFtR9+nH1is183S+Sxo4XefuS0ecNpv29htSf33Pe66aYG5g7dTxZM+Y6E56899ZUqt0TOWkiFB5H5RfK8XmLMaZucjkfOzoR09iraFDgRdby5ejbtiNkwABnywVt/XrkK8eZmvsGKbt+c742IfI6prfoiz7zT6jfnoxgFY9/P47XRv8PP1Su9X0cu7HUatS+vmjr1au671e5ZqblOabOdPHxhEyZAikTXc4rzbqjumcp6GXKSohLnSQ7QpyHQ7v/ZPuoh4k9aAVgzxVedF76PvUbtXR7fZifN11bhPFzuaksg7UQq776Fg/q8ufNxWh/m3/mP9qhf1BiCKRTo2DWHrHxaNOb0Bz4kfz4IfZEJ+M3l3v9eGwDE4G5ox5EU/IQtlIzkZPGoNi02ApOoVjsHcMP9uxNSGLi6f5WrjVojCmpGIDCsQPJXvkqxpRUitevd7nGEN+Z6L6tmKdrQm7XBynUB+FfVkrIwV8JtNjg8Eb4aS4h3cZxdVg7Htk6lhFPPs7/jR+HtaAAjcEXdN4opaUY9+zBq2EM1FBbhwqNUw0JCURMmsSRPBM/Nu/Ky2/t58lrx9Kj43i8rUWYNX4E1YsiKDC8+vsKIS4JkuwIcY6+fWkKAas+pEUhmDWw57Zm9Jz3qcu0VUWBBm+ee6AdEz7afibh8fZDk/UThvjOLqMnDoaEBDRlJ9zfsFl38K1HoN5+32mf/UWXznO4kknkNursMqJT3o/HNnCs3VCi8nPxVZehKjuMRR1M+vJVLuuGqquZU5KSSrOxozlSVe2f1E1Yn3qIwC8fIBBQ+r2P6tjv0PIu+GEGHFhvfyapLzK9zxtMB+buXkJBeyuNvMNoXhaGJr8Uq0HHL5HH6OoVRoSbJqjlKfXrEfDRGwSWeeEVEIgmNJSdBTbu/WCX85rZP55gtvOnfD59qhHtQ6u9rRDiEiHJjhBnqcxcykdP30qbn0+iUSAzGJRRT9Gn19Nn9fqoID1LH+xAdpGZwpIy0OWhzdtO1NODOA4VpoviiJoxBa2/N3gvAf8IsJSC1gcKM6B5D+f0S1SQnud7xZJTbCb7tuUU2o5XG8fB/AK8LQb83rsBvH2xPvB9pQXSNVUpprC42tPO6TegwCeKvKi7iVHUaA/8dOYiczGRawYyL34IuV0fpFgfSIB3ILP/nO/cMQVQv8M0QrOUqnd6JSRQFuKPX2AMfuXaO/iVVludCH83U4tCiEuTJDtCnIX921LYO+4JYg/bANjdypsuyz4mPLrZWb3ekp+PNScH78JCYvwD0ISGoPUOhrin8dq4lOhe7bA+9RA2kxl1QCCakGC0oQFgNsLOT+HAj2du1qw7NL/Z5f6BBm8CDd6AH8X5JqrjrTLgbc2z/2AuxpZ5tNI1FbeVV6Tx96/2vGP6zdLkJt7fU8bsH0+QOiCAqIoXmosJXG9fNGwZ+C1ZeVlMbjuOglgbZpURs9XM1sytNG7TmgaNkgAq7caKmDkTn/D6lWJwN3Xo0LVFGGF+1U8hCiEuHRdVsjN37lyeeeYZhg8fzqJFizwdjrhMfLViIiEvf0qzYijVwr47W/LA7A+qnbYqr+xEBscnT3ZdgNulC1EzZ+IV0gyuvAOtfwRaSzEE6KDwbwjoYb/ws6GuiQ7AP+vg86eh5ytuF9eG+ISQEJVAyvHKjTuvi4xjy0ELrZqcGQFRGyp/6VdVM0dlMBAxcTwoFmJWLEelUmNMSyN39Wrn1m9DfGc0Wb9haXITe6+dzQvvHgHArKk+QbLpgzmpicBf40WMnz15yy/NJ1wfRrG5kJLgICKmTcVmNGEpLsZi8ENbrx4+4ZXnovKNZrKLzAzr3oLB3ZqR8k8Or/56EKPZStcWYcx7oN3p5FAIcTm4aJKdP/74g1WrVtGuXTtPhyIuE6UmI588fSttU7JRK5ARAl7jR9L73ifO+h6W/PxKiQ6A8ddfOT7F3iVce9U9UJwFJQXgEwANrrMnMdn7Kic6Dv+ss7/GTbITqAtkevx0pqdOd0l44qPimdBpKoolkCBdiX2E6OgmNKGhGBLiMKacSWycNXNUKmdtH5XBQEzyCnJWriRjyjTntYa4OKIXPE/66DHoO3Qgatoz4GPh+0P3M+bdIxjN9kXcuwq8adSsO6p/1lX+PM264x0YQfsKnyewpIjAz8e4PAelaXfMdy5C0UcS4CZhOX7KVKmA4/Utwvji6S6ogFBfb0l0hLjMXBTJTlFREQ899BAvvfQSs2bN8nQ44jKwd+t6/h4/hNgj9mmrna11dFv2CWH1m5zTfaw5OZUSHQfjr79izclB27Sp++3PJQXV37ya85G+kczrOo/cklwKzYX4e/sT4hNCoHNNix/csxTl7x/Qps4lauggjiuKc92QYjSS98H7RD47A8xmygoKKfP1J+e52ZVq4hg3bgS1miaffmLfeh5of48EPzNfNG5IYUkZ/j5ehPl5o7pyqX1UqnzC06w7uNsCbspzO7KlOrAO3dcj0PV8BXBNWtxVqgb4ZX82Mz7fydIHO0iiI8Rl6KJIdoYMGcKdd97JzTffXGOyU1paSmm5xZUFBTV8YQhRwZdLRxP22tc0NUKpF+y7pw29Z39wXveyFRbWcL6aRbQ+AdXfvIbzgbrAcsmNuwuiURpci+qLYXgd+oXoXkPOrBs6XYlZpTOiibkCHWD65x8yqtqBlZICFosz0YHy64jKi7ZPv5UfyfKt5z7ZK84655Etd5WqHX7en012kVmSHSEuQ3U+2XnvvffYsmULf/zxx1ldP3fuXGbMkG7F4tyZigv4bOjttN2Yixo4EQY+E8fR+85Hzvue6poW8vr7VX3St5591KOKaR986513XM73N5/eVVWxho9D21vP/HNR9bubqk3cytMHn10hv/MY2XJXqbq8whrOCyEuTXW66/nRo0cZPnw4b731Fj4+Pmf1mokTJ5Kfn+/86+jRyjtNhKho52/fs/6ezsSeTnR2tvWhwyc/EP8vEh3Avh6mSxe35wxduqAJrabQiz7YPr3TrLvr8aqmfc7HOYwe/avE7Xycx8iWu0rV5cl2cyEuT3V6ZGfz5s1kZmbSsWNH5zGr1crPP//MsmXLKC0tRaPRuLxGp9Ohq2HbrBDlfbbgaeq/9QONTWDyhn/ua0+vZ9+tlXtrAwOJmjmT41OmYPz1V+dxQ5cuRM2a6TLt41bgOUz7nI9zGD1yJG7lP4dDjYnbfxybg2w3F0K4o1IURan5Ms8oLCzk8OHDLsceeeQRWrZsyfjx42nTpk2N9ygoKCAwMJD8/HwCAmr4P0VxWSkuzOeLobcR+9spANLrQcCUSVx7S/9afy9HnR1bYRFqfz80oaE1JzoXSn561YuGA6NdLi07kVFl4uYVGenR2ByOnzK5VqoG53bz+kH62o9RCPGfqM3v7zqd7LjTrVs32rdvf9Z1diTZEe5sT/2K45PH0ui4/Y//X7EGeiz/kqDQysXpLgumvLMePbrgids5xObgqLNTfieYLEwW4uJSm9/fdXoaS4j/wifzk4h5ZwONSsDoDYd6XkOvqas9HZZnne2iYexTcxd0VOocYnNwvxNMCHG5uuiSnfUVOiwLcbaK8nP56qnbaLfZvh38WISKkKkzeKB7Lw9HJoQQ4r900SU7QpyPrRs+IXPaJNpl2KetdnT047ZlXxEQHO7hyIQQQvzXJNkRl7yP5wyi4ZpUGpZCsQ6O9E2g98SXPR2WEEKIC0SSHXHJKsjL5Nun7qDtVnvhvCP1VUQ8O5f/u/5eD0cmhBDiQpJkR1yS/lz3HnnPPkvbk/Zpq+2dArhrxff4+teR7d5CCCEuGEl2xCXnw2cTafLhH8SYoVAPx/vdQJ+xyZ4OSwghhIdIsiMuGadyTrD2qTtps80EwOEoFTFzFnBf59s9HJkQQghPkmRHXBJ++3Y1RbPn0ibL/vO264K4d/l36H2lkKQQQlzuJNkRF733p/Sl+WfbiDJDgQFODuhB35FLPB2WEEKIOkKSHXHRyj15lB+H3EPbv0oAOBijpsncxVx3zc0ejkwIIURdIsmOuCht/PIVTM89T+tssAE74kO4/8W16PQGT4cmhBCijpFkR1x03p/4AFd8uYugMsg3QPajd9J36POeDksIIUQdJcmOuGhknzjIhiH303ZXKQAHGqppPn8Fndt39XBkQggh6jJJdsRF4ZdPVmD93xKuygWbCnZ0qcf9S76VaSshhBA1kmRH1GlWi4UPJz5Ay2/24W2BU36QO+g++g6e6+nQhBBCXCQk2RF1VsaR/aQO60m7PWYA/m6s5qr/vURc23gPRyaEEOJiIsmOqJPWr1mEatFKWuWBVQU7boik55Jv8fLWeTo0IYQQFxlJdkSdYrVY+GDcvbT67gDeVsjzh8Ine/PgYzM8HZoQQoiLlCQ7os5IP7iL34f1JXZ/GQD7m2hot/B1Grfq5OHIhBBCXMwk2RF1wrq35+O95DVa5oNFDX/dFE3vRd+i0cofUSGEEP+OfJMIj7JaLHww6k5a/3AErQ1yAsD41EM8+PBkT4cmhBDiEiHJjvCYI/u3sWVkf2L/tgCwt7mWq194i4YtYj0cmRBCiEuJJDvCI75/fRaG5W9zZQGUaWDnzY3oveBLmbYSQghR6+SbRVxQVouFD4bfSuufjqO1QXYglA0fxIP9xng6NCGEEJcoSXbEBXNo959sH/UwsQetAOy5wotrF79HdJOrPByZEEKIS5kkO+KC+O7lafitfJ8WhWDWwO5bm9Jr/mcybSWEEOI/J9804j9VZi7lw2G30nbDSTQKZAaDMuJJ+vYZ4enQhBBCXCYk2RH/mX92pLJr7OO0P2QDYHdLb+KXfEhkwxYejkwIIcTlRJId8Z/4asVEQl75lOZFUKqFvXdcSc85H8q0lRBCiAtOvnlErSo1Gfnk6Vtpm5KNWoGMENCOHUaf+wd7OjQhhBCXKUl2RK3Zu3U9f48fQuwR+7TVrtY6blj2CWH1m3g4MiGEEJczSXZErfhy6WjCXvuapkYo9YJ9d7em95wPPR2WEEIIIcmO+HdKTUY+GdKDtqm5qIETYaCfMIbedw3ydGhCCCEEIMmO+Bd2//EDBycOI/aYAsDOtj50X/4lwfWiPRyZEEIIcYYkO+K8fP7CMCLeXEsTI5i84e97Y+k98z1PhyWEEEJUIsmOOCem4gI+e+pWYn87BcDxeuA3aSK9b0v0bGBCCCFEFSTZEWdte+pXHJ88ltjj9mmrv2IN9Fj+JUGh9T0cmRBCCFE1SXbEWfn0f0lEv72BRiVg9IaDPa+h19TVng5LCCGEqJEkO6JaxYX5fDm4B+3+LATgWISK4KlT6dm9r4cjE0IIIc6OJDuiSls3fELmtEm0y7BPW+242o/bXvyKgOBwD0cmhBBCnD1JdoRbH88ZRMM1qTQshWIdHOkTT+9nXvF0WEIIIcQ5k2RHuCjIy+TbIXfSdksRAEciVYTPmM3/3XC/hyMTQgghzo8kO8Jp87oPyH12Gm1P2qettnfy584Xv8UvMMTDkQkhhBDnT+3pAKozd+5crrnmGvz9/QkPD+e+++5j7969ng7rkvTRs4kwcioxJxWKfGDPozfQ563fJdERQghx0avTyc6GDRsYMmQImzZtYu3atVgsFm655RaKi4s9Hdol41TOCT7o05Gr3vkDgxkOR6nQLX+e+8clezo0IYQQolaoFEVRPB3E2crKyiI8PJwNGzbQtWvXs3pNQUEBgYGB5OfnExAQ8B9HeHH5/fu3KJg5m+gs+8/brgvi7mXf4usf6NnAhBBCXPZq8/v7olqzk5+fD0BISNVTK6WlpZSWljp/Ligo+M/juhh9MPVBmn2aRrQZCvWQkdiDviOXeDosIYQQotbV6Wms8hRFYdSoUXTp0oU2bdpUed3cuXMJDAx0/tWgQYMLGGXdl5eVzoc9O9Dm/TT0ZjgYo8J/1VLukURHCCHEJeqimcYaMmQIX331Fb/++isxMTFVXuduZKdBgwYyjQVs/PIVTM89T/1ssAE74kK4d9k36H0v7+cihBCi7rnsprGefvppPv/8c37++edqEx0AnU6HTqe7QJFdPN5/pidXfLGToDLIN0DWw7fTd9hCT4clhBBC/OfqdLKjKApPP/00n3zyCevXr6dJkyaeDumik33iIBuG3k/bnfbRrgMN1TSf9yKdO3TzaFxCCCHEhVKnk50hQ4bwzjvv8Nlnn+Hv709GRgYAgYGB6PV6D0dX9/3yyQos/1vCVblgU8GOhDDuX/odOr3B06EJIYQQF0ydXrOjUqncHn/ttdd4+OGHz+oel+PWc6vFwofP9OTKr/eis8ApP8gddB93Dp7r6dCEEEKIs3LZrNmpw3lYnZVxZD+pw3rSbo8ZgH8aqWn1/EvEtY33cGRCCCGEZ9TpZEecm/UfLEW1cDmt8sCqgh03RNBzyXd4ecuCbSGEEJcvSXYuAVaLhQ/G3Uur7w7gbYU8fyh8sjcPPjbD06EJIYQQHifJzkXuxOE9bHq6N7H7ygDY30RDu4Wv07hVJw9HJoQQQtQNkuxcxH5853m0S16h5SmwqGHnjVH0WvwdGq38axVCCCEc5FvxImS1WHh/9F20/uEwXlbICQDjU/3o+/AUT4cmhBBC1DmS7FxkjuzfxpaR/Wn/twWAfc20dFj0Fg1bxHo4MiGEEKJukmTnIvLD6jn4LHuTKwtOT1vd3JBeC7+SaSshhBCiGvIteRGwWiy8P+I22vyYjtYG2YFgHvYIfR8a5+nQhBBCiDpPkp067sjeLaSNTKT9ASsAe1p4ce2S94hucpWHIxNCCCEuDpLs1GHfvTIdv+Q1tCgEswZ239qUXvM/k2krIYQQ4hzIt2YdVGYu5cNht9F2QwYaBTKDQRnxJH37jPB0aEIIIcRFR5KdOuafHansGvs47Q/ZANjd0pv4JR8S2bCFhyMTQgghLk6S7NQhXyc/Q/DLn9C8CMxa2HP7FfSc+5FMWwkhhBD/gnyL1gGlJiOfDLuNtr9moVbgZAhoxgylz/8N8XRoQgghxEVPkh0P25v2M3+PG0zsEfu01a6rdNzw4ieE1W/i4ciEEEKIS4MkOx705bIxhL36FU2NUOoF++66it5zP/J0WEIIIcQlRZIdDyg1GflkSA/apuaiBk6Egc+4UfS+53FPhyaEEEJcciTZucB2//EDBycOJ/aYfdpqZxsfbnrxc0IiGng4MiGEEOLSJMnOBfT5ouFErP6eJkYwecPf98bSe+Z7ng5LCCGEuKRJsnMBmIoL+OypW4n97RQAx+uB36SJ9L4t0bOBCSGEEJcBSXb+Y39t+oZjk0YTm67Yf47V02P5VwSF1vdwZEIIIcTlQZKd/9Cnzz9F1Ns/0chkn7Y68H8d6TX9LU+HJYQQQlxWJNn5DxQX5vPlU7fQ7o8CAI5FqAieOpWe3ft6ODIhhBDi8iPJTi1L++UzTk6dSLsT9mmrHR18uW351wQEh3s4MiGEEOLyJMlOLfp47mM0fC+FhqVQrIPDvePoPelVT4clhBBCXNYk2akFRfm5fD34VtpuKQLgaKSKejNm88AN93s4MiGEEEJIsvMvbV73AbnPTqPtSfu01fZO/tz54rf4BYZ4ODIhhBBCgCQ7/8pHzybS+MM/iDFDkQ8ce7Arfcav9HRYQgghhChHkp3zcCrnBGufuos224wAHI5SETXrf9wff6eHIxNCCCFERZLsnKPfv3+LglmzaZNp/3nbdUHcvexbfP0DPRuYEEIIIdySZOccfDCtH80+2Uq0GQr1cKL/zfQdvdTTYQkhhBCiGpLsnIW8rHR+eOpu2uwwAXAoWkXDOYu497pbPByZEEIIIWoiyU4NUr96jZK582mTDTZgR1wI9y77Br1vgKdDE0IIIcRZkGSnGu9P6kWLz/8iuAzyDZD18O30HbbQ02EJIYQQ4hxIsuNG9omDbBh6P213lgJwoIGa5vNfpHOHbh6NSwghhBDnTpKdCn79bBVl81/gqhywqWBHQhj3L/0Ond7g6dCEEEIIcR4k2TnNarHw0aReXPHVHnQWOOULuYPuoe9T8zwdmhBCCCH+BUl2gMz0f/h1yP/Rdo8ZgH8aqbly/iriYhM8HJkQQggh/q3LPtlZ/8FSVAuX0yoPrCrYcUMEPZd8h5e3ztOhCSGEEKIWXLbJjtVi4cPx99Hy23/wtkKePxQ80ZMHH5/p6dCEEEIIUYsuy2TnxOE9bBrWm3Z7ywDY30RDmwWvEn/VtR6OTAghhBC17bJLdn58dwHaxS/T8hRY1LCzWxQPLPpapq2EEEKIS5Ta0wGcjeXLl9OkSRN8fHzo2LEjv/zyyznfw2qx8N6I2wib9TL1TkFOAJwY14++y9dJoiOEEEJcwup8srNmzRpGjBjBpEmT2Lp1K9dffz233347R44cOaf7fPNgF2K/PYyXFfY109Dw7fe45eEp/1HUQgghhKgrVIqiKJ4OojrXXXcdV199NStWrHAea9WqFffddx9z586t8fUFBQUEBgbye/MW+Hhp2Nm9Ab1e+BqN9rKbwRNCCCEuGo7v7/z8fAIC/l0/yjr9jW82m9m8eTMTJkxwOX7LLbeQmprq9jWlpaWUlpY6f87PzwfgiJ8V7eB+3NF3FMVG438XtBBCCCH+tYKCAgBqY0ymTic72dnZWK1WIiIiXI5HRESQkZHh9jVz585lxowZlY73TDsAT86w/yWEEEKIi0JOTg6BgYH/6h51OtlxUKlULj8rilLpmMPEiRMZNWqU8+dTp07RqFEjjhw58q8f1qWmoKCABg0acPTo0X89RHipkWdTNXk27slzqZo8m6rJs6lafn4+DRs2JCQk5F/fq04nO2FhYWg0mkqjOJmZmZVGexx0Oh06XeXdVYGBgfIHqQoBAQHybKogz6Zq8mzck+dSNXk2VZNnUzW1+t/vparTu7G8vb3p2LEja9eudTm+du1a4uPjPRSVEEIIIS4mdXpkB2DUqFEMGDCATp06ERcXx6pVqzhy5AhJSUmeDk0IIYQQF4E6n+z06dOHnJwcnn32WU6cOEGbNm34+uuvadSo0Vm9XqfTMW3aNLdTW5c7eTZVk2dTNXk27slzqZo8m6rJs6labT6bOl9nRwghhBDi36jTa3aEEEIIIf4tSXaEEEIIcUmTZEcIIYQQlzRJdoQQQghxSbukk53ly5fTpEkTfHx86NixI7/88ounQ/K4uXPncs011+Dv7094eDj33Xcfe/fu9XRYddLcuXNRqVSMGDHC06HUCenp6fTv35/Q0FAMBgPt27dn8+bNng7L4ywWC5MnT6ZJkybo9XqaNm3Ks88+i81m83RoF9zPP//M3XffTVRUFCqVik8//dTlvKIoTJ8+naioKPR6Pd26dWPnzp2eCfYCq+7ZlJWVMX78eNq2bYuvry9RUVEkJiZy/PhxzwV8AdX056a8J598EpVKxaJFi87pPS7ZZGfNmjWMGDGCSZMmsXXrVq6//npuv/12jhw54unQPGrDhg0MGTKETZs2sXbtWiwWC7fccgvFxcWeDq1O+eOPP1i1ahXt2rXzdCh1Ql5eHgkJCXh5efHNN9+wa9cuFixYQFBQkKdD87h58+aRnJzMsmXL2L17N/Pnz+d///sfS5cu9XRoF1xxcTGxsbEsW7bM7fn58+ezcOFCli1bxh9//EFkZCQ9evSgsLDwAkd64VX3bIxGI1u2bGHKlCls2bKFjz/+mH379nHPPfd4INILr6Y/Nw6ffvopv/32G1FRUef+Jsol6tprr1WSkpJcjrVs2VKZMGGChyKqmzIzMxVA2bBhg6dDqTMKCwuVFi1aKGvXrlVuuOEGZfjw4Z4OyePGjx+vdOnSxdNh1El33nmn8uijj7oc+7//+z+lf//+HoqobgCUTz75xPmzzWZTIiMjleeee855rKSkRAkMDFSSk5M9EKHnVHw27vz+++8KoBw+fPjCBFVHVPVsjh07pkRHRyt//fWX0qhRI+WFF144p/tekiM7ZrOZzZs3c8stt7gcv+WWW0hNTfVQVHVTfn4+QK00WrtUDBkyhDvvvJObb77Z06HUGZ9//jmdOnWiV69ehIeH06FDB1566SVPh1UndOnShXXr1rFv3z4Atm3bxq+//sodd9zh4cjqloMHD5KRkeHye1mn03HDDTfI72U38vPzUalUMnoK2Gw2BgwYwNixY2nduvV53aPOV1A+H9nZ2Vit1krNQiMiIio1Fb2cKYrCqFGj6NKlC23atPF0OHXCe++9x5YtW/jjjz88HUqdcuDAAVasWMGoUaN45pln+P333xk2bBg6nY7ExERPh+dR48ePJz8/n5YtW6LRaLBarcyePZsHH3zQ06HVKY7fve5+Lx8+fNgTIdVZJSUlTJgwgX79+klzUOxTxVqtlmHDhp33PS7JZMdBpVK5/KwoSqVjl7OhQ4eyfft2fv31V0+HUiccPXqU4cOH8/333+Pj4+PpcOoUm81Gp06dmDNnDgAdOnRg586drFix4rJPdtasWcNbb73FO++8Q+vWrUlLS2PEiBFERUUxcOBAT4dX58jv5eqVlZXRt29fbDYby5cv93Q4Hrd582YWL17Mli1b/tWfk0tyGissLAyNRlNpFCczM7PS/1Vcrp5++mk+//xzfvrpJ2JiYjwdTp2wefNmMjMz6dixI1qtFq1Wy4YNG1iyZAlarRar1erpED2mfv36XHXVVS7HWrVqddkv+AcYO3YsEyZMoG/fvrRt25YBAwYwcuRI5s6d6+nQ6pTIyEgA+b1cjbKyMnr37s3BgwdZu3atjOoAv/zyC5mZmTRs2ND5e/nw4cOMHj2axo0bn/V9Lslkx9vbm44dO7J27VqX42vXriU+Pt5DUdUNiqIwdOhQPv74Y3788UeaNGni6ZDqjO7du7Njxw7S0tKcf3Xq1ImHHnqItLQ0NBqNp0P0mISEhEolCvbt23fWDXkvZUajEbXa9VepRqO5LLeeV6dJkyZERka6/F42m81s2LDhsv+9DGcSnf379/PDDz8QGhrq6ZDqhAEDBrB9+3aX38tRUVGMHTuW77777qzvc8lOY40aNYoBAwbQqVMn4uLiWLVqFUeOHCEpKcnToXnUkCFDeOedd/jss8/w9/d3/l9WYGAger3ew9F5lr+/f6W1S76+voSGhl72a5pGjhxJfHw8c+bMoXfv3vz++++sWrWKVatWeTo0j7v77ruZPXs2DRs2pHXr1mzdupWFCxfy6KOPejq0C66oqIi///7b+fPBgwdJS0sjJCSEhg0bMmLECObMmUOLFi1o0aIFc+bMwWAw0K9fPw9GfWFU92yioqLo2bMnW7Zs4csvv8RqtTp/N4eEhODt7e2psC+Imv7cVEz8vLy8iIyM5Morrzz7N/n3G8XqrhdffFFp1KiR4u3trVx99dWyvVqxb+tz99drr73m6dDqJNl6fsYXX3yhtGnTRtHpdErLli2VVatWeTqkOqGgoEAZPny40rBhQ8XHx0dp2rSpMmnSJKW0tNTToV1wP/30k9vfLwMHDlQUxb79fNq0aUpkZKSi0+mUrl27Kjt27PBs0BdIdc/m4MGDVf5u/umnnzwd+n+upj83FZ3P1nOVoijKOaVgQgghhBAXkUtyzY4QQgghhIMkO0IIIYS4pEmyI4QQQohLmiQ7QgghhLikSbIjhBBCiEuaJDtCCCGEuKRJsiOEEEKIS5okO0KIi8b06dNp37698+eHH36Y++6774LHcejQIVQqFWlpaRf8vYUQ506SHSHEv/bwww+jUqlQqVR4eXnRtGlTxowZQ3Fx8X/6vosXL+b1118/q2slQRHi8nXJ9sYSQlxYt912G6+99hplZWX88ssvPPbYYxQXF7NixQqX68rKyvDy8qqV9wwMDKyV+wghLm0ysiOEqBU6nY7IyEgaNGhAv379eOihh/j000+dU0+vvvoqTZs2RafToSgK+fn5PPHEE4SHhxMQEMBNN93Etm3bXO753HPPERERgb+/P4MGDaKkpMTlfMVpLJvNxrx582jevDk6nY6GDRsye/ZswN51G6BDhw6oVCq6devmfN1rr71Gq1at8PHxoWXLlixfvtzlfX7//Xc6dOiAj48PnTp1YuvWrbX45IQQ/zUZ2RFC/Cf0ej1lZWUA/P3337z//vt89NFHaDQaAO68805CQkL4+uuvCQwMZOXKlXTv3p19+/YREhLC+++/z7Rp03jxxRe5/vrrefPNN1myZAlNmzat8j0nTpzISy+9xAsvvECXLl04ceIEe/bsAewJy7XXXssPP/xA69atnZ2kX3rpJaZNm8ayZcvo0KEDW7du5fHHH8fX15eBAwdSXFzMXXfdxU033cRbb73FwYMHGT58+H/89IQQtepfNisVQghl4MCByr333uv8+bffflNCQ0OV3r17K9OmTVO8vLyUzMxM5/l169YpAQEBSklJict9mjVrpqxcuVJRFEWJi4tTkpKSXM5fd911SmxsrNv3LSgoUHQ6nfLSSy+5jdHRWXrr1q0uxxs0aKC88847LsdmzpypxMXFKYqiKCtXrlRCQkKU4uJi5/kVK1a4vZcQom6SaSwhRK348ssv8fPzw8fHh7i4OLp27crSpUsBaNSoEfXq1XNeu3nzZoqKiggNDcXPz8/518GDB/nnn38A2L17N3FxcS7vUfHn8nbv3k1paSndu3c/65izsrI4evQogwYNcolj1qxZLnHExsZiMBjOKg4hRN0j01hCiFpx4403smLFCry8vIiKinJZhOzr6+tyrc1mo379+qxfv77SfYKCgs7r/fV6/Tm/xmazAfaprOuuu87lnGO6TVGU84pHCFF3SLIjhKgVvr6+NG/e/Kyuvfrqq8nIyECr1dK4cWO317Rq1YpNmzaRmJjoPLZp06Yq79miRQv0ej3r1q3jscceq3TesUbHarU6j0VERBAdHc2BAwd46KGH3N73qquu4s0338RkMjkTquriEELUPTKNJYS44G6++Wbi4uK47777+O677zh06BCpqalMnjyZP//8E4Dhw4fz6quv8uqrr7Jv3z6mTZvGzp07q7ynj48P48ePZ9y4caxevZp//vmHTZs28corrwAQHh6OXq/n22+/5eTJk+Tn5wP2QoVz585l8eLF7Nu3jx07dvDaa6+xcOFCAPr164darWbQoEHs2rWLr7/+mueff/4/fkJCiNokyY4Q4oJTqVR8/fXXdO3alUcffZQrrriCvn37cujQISIiIgDo06cPU6dOZfz48XTs2JHDhw8zePDgau87ZcoURo8ezdSpU2nVqhV9+vQhMzMTAK1Wy5IlS1i5ciVRUVHce++9ADz22GO8/PLLvP7667Rt25YbbriB119/3blV3c/Pjy+++IJdu3bRoUMHJk2axLx58/7DpyOEqG0qRSakhRBCCHEJk5EdIYQQQlzSJNkRQgghxCVNkh0hhBBCXNIk2RFCCCHEJU2SHSGEEEJc0iTZEUIIIcQlTZIdIYQQQlzSJNkRQgghxCVNkh0hhBBCXNIk2RFCCCHEJU2SHSGEEEJc0iTZEUIIIcQl7f8BZ8fDnhGKxssAAAAASUVORK5CYII=\n" 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\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ + "# Iterate over targets (adenosine receptors)\n", "for target,accession in adenosine_receptors.items():\n", " # Split dataset in training and test set (leave one target out split)\n", " print('== Leave one target out split ==')\n", - " train_loto, test_loto = split_train_test(ar_pcm_dataset, 0.20, 'loto', accession)\n", + " train_loto, test_loto = split_train_test(ar_pcm_dataset, 0.20, 'loto', target, accession)\n", " # Train and validate PCM model, every time leaving a different target out for validation\n", " train_validate_pcm_model(adenosine_receptors,train_loto,test_loto)" ], @@ -1657,18 +1853,38 @@ } } }, + { + "cell_type": "markdown", + "source": [ + "Immediately we see that the LOTO split method is way more difficult to model than the random split. The PCM metrics show that even though the true and predicted values are somewhat correlated (Pearson's $r$), the PCM model features are not able to explain the variance in the target variable ($R^{2}$). We can also see this in the shape of the predicted vs. observed graph, where the datapoints are not aggregated around the unit line that would define a perfect fit. Rather, the predictions are aggregated around the mean bioactivity values in the training set." + ], + "metadata": { + "collapsed": false + } + }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discussion\n", "\n", - "Compared to purely ligand-based compound activity prediction models, PCM modelling has certain advantages and limitations." + "In this talktorial we created a PCM model for the four adenosine receptors based on a random split of the data. This model performed pretty well on our test set. Compared to independent QSAR models trained for each of the adenosine receptors, the performance of the PCM model is slightly better, which indicates that the PCM model is able to extrapolate between targets. However, there are several elements that could have an effect of the observed results:\n", + "* The four adenosine receptors had enough data on their own to be able to train individual models. The true advantage of a PCM model could be more relevant in a target set where some targets have very little data.\n", + "* The QSAR models are only trained on 22 features compared to 1,300 for the PCM model. QSAR models trained on molecular fingerprints would probably have better chances of achieving better performance.\n", + "* For better comparison, it would be good to train several models of the same type and calculate aggregated metrics, so that statistically significant results could be derived.\n", + "\n", + "Moreover, we trained four PCM models on three adenosine receptors and validated them on the remaining receptor, following a leave one target out (LOTO) split method. We did this to evaluate whether these PCM models could be used to predict bioactivity for a target for which the model has never seen any data in training. We immediately derive some observations:\n", + "* The LOTO split is harder in validation than the random split, since the random split allows data leakage between targets.\n", + "* While the descriptors used in the PCM model trained on random split allowed for a good performance, in order to get a good performance in the LOTO split, we would need to search more carefully to find the optimal descriptors. Similarly, we could opt for a selection of the binding pocket prior to protein descriptor generation. Addtionally, we could optimize the model parameters.\n" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "## Quiz\n", "\n", @@ -1676,30 +1892,6 @@ "2. How many types of training/test set splitting methods commonly used in PCM modelling do you know?\n", "3. Which applications do you know of PCM in drug discovery?" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Useful checks at the end: \n", - " \n", - "
    \n", - "
  • Clear output and rerun your complete notebook. Does it finish without errors?
    • \n", - "
  • Check if your talktorial's runtime is as excepted. If not, try to find out which step(s) take unexpectedly long.
    • \n", - "
  • Flag code cells with # NBVAL_CHECK_OUTPUT that have deterministic output and should be tested within our Continuous Integration (CI) framework.
    • \n", - "
\n", - "\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 11bee4eed4537cb3ed9ec3bc16ea1f15edf73783 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Tue, 18 Oct 2022 15:37:48 +0200 Subject: [PATCH 15/62] Remove contribution cell and update practical list of contents --- .../talktorial.ipynb | 17 +++++------------ 1 file changed, 5 insertions(+), 12 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index 27ece231..c1fcf3a2 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -1,16 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "\n", - "Thank you for contributing to TeachOpenCADD!\n", - "\n", - "
" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -68,7 +57,11 @@ " * Calculate compound descriptors\n", "* Proteochemometrics modelling\n", " * Helper functions\n", - " * Model training and validation" + " * Preprocessing\n", + " * Model training and validation\n", + " * Random split PCM model\n", + " * Random split QSAR models\n", + " * Leave one target out split PCM model" ] }, { From 045b2de6c35d70cb2c7aae710be12b4ab61dff35 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Tue, 18 Oct 2022 15:42:38 +0200 Subject: [PATCH 16/62] Update README file with intro from talktorial --- .../README.md | 92 +++++++------------ 1 file changed, 35 insertions(+), 57 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md index 499f7ef6..05761246 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md @@ -1,80 +1,58 @@ -
- -Thank you for contributing to TeachOpenCADD! - -
- - -
- -Set up your PR: Please check out our issue on how to set up a PR for new talktorials, including standard checks and TODOs. - -
- - -# T000 · Talktorial topic title +# T032 · Compound activity: Proteochemometrics **Note:** This talktorial is a part of TeachOpenCADD, a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects. Authors: -- First and last name, year(s) of contribution, lab, institution -- First and last name, year(s) of contribution, lab, institution - - -*The examples used in this talktorial template are taken from [__Talktorial T001__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T001_query_chembl/talktorial.ipynb) and [__Talktorial T002__](https://github.com/volkamerlab/teachopencadd/blob/master/teachopencadd/talktorials/T002_compound_adme/talktorial.ipynb).* - - -
+- Marina Gorostiola González, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands) +- Olivier J.M. Béquignon, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands) +- Willem Jespers, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands) -Cross-referencing talktorials: If you want to cross-reference to existing talktorials in your notebook, please use the following formatting: Talktorial T000. - -
## Aim of this talktorial -Add a short summary of this talktorial's content. +While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be solved leveraging similarities and differences between proteins. In this talktorial, we use Proteochemometrics modelling (PCM) to enrich our activity models with protein data to predict the activity of novel compounds against the four adenosine receptor isoforms (A1, A2A, A2B, A3). ### Contents in *Theory* -_Add Table of Contents (TOC) for Theory section._ - -* ChEMBL database -* Compound activity measures - +* Data preparation + * Papyrus dataset + * Molecule encoding: molecular descriptors + * Protein encoding: protein descriptors -
- -Sync TOC with section titles: These points should refer to the headlines of your Theory section. - -
+* Proteochemometrics (PCM) + * Machine learning principles: regression + * Splitting methods + * Regression evaluation metrics + * ML algorithm: Random Forest + * Applications of PCM in drug discovery ### Contents in *Practical* -_Add Table of Contents (TOC) for Practical section._ - -* Connect to ChEMBL database -* Load and draw molecules - - -
- -Sync TOC with section titles: These points should refer to the headlines of your Practical section. - -
+* Download Papyrus dataset +* Data preparation + * Filter activity data for targets of interest + * Align target sequences + * Calculate protein descriptors + * Calculate compound descriptors +* Proteochemometrics modelling + * Helper functions + * Preprocessing + * Model training and validation + * Random split PCM model + * Random split QSAR models + * Leave one target out split PCM model ### References -* Paper -* Tutorial links -* Other useful resources - -*We suggest the following citation style:* -* Keyword describing resource: Journal (year), volume, pages (link to resource) - -*Example:* -* ChEMBL web services: [Nucleic Acids Res. (2015), 43, 612-620](https://academic.oup.com/nar/article/43/W1/W612/2467881) +* Papyrus scripts [github](https://github.com/OlivierBeq/Papyrus-scripts) +* Papyrus dataset preprint: [ChemRvix (2021)](https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0) +* Molecular descriptors (Modred): [J. Cheminf., 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y) +* Protein descriptors (ProDEC) [github](https://github.com/OlivierBeq/ProDEC) +* Regression metrics [(Scikit learn)](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics) +* XGBoost [Documentation](https://xgboost.readthedocs.io/en/stable/index.html) +* Proteochemometrics review: [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub) From 284d50e76697c6d51b628828c5a3f182d7252b85 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Tue, 18 Oct 2022 15:49:01 +0200 Subject: [PATCH 17/62] Update data README file --- .../data/README.md | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/README.md index cc6d1e4c..5f4280b7 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/README.md +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/README.md @@ -1,6 +1,13 @@ -# Data +# T032 · Compound activity: Proteochemometrics +## Data This folder stores input and output data for the Jupyter notebook. -- `xxx.csv`: Describe data. -- `xxx.sdf`: Describe data. +Stable: +- `clustalo.py`: ClustalO REST API client. + +Generated within the notebook: +- `papyrus`: Directory with Papyrus bioactivity dataset downloads. +- `sequences.fasta`: Sequences of the targets of interest for PCM modelling, in FASTA format. +- `aligned_sequences.aln-fasta.fasta`: ClustalO multiple sequence alignment output, in FASTA format. +- `aligned_sequences.[...]`: Additional ClustalO output files, not needed for the talktorial. From a09404942783d3e8f45b81e05e264dcbd76c6743 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Tue, 18 Oct 2022 15:50:35 +0200 Subject: [PATCH 18/62] Update figures README file --- .../images/README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/README.md index d4ebaa47..820060ba 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/README.md +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/README.md @@ -1,5 +1,8 @@ -# Talktorial title +# T032 · Compound activity: Proteochemometrics ## Images This folder stores images used in the Jupyter notebook. +- `PCM_model_text-01.png` +- `papyrus_workflow.png` +- `splitting_methods.png` \ No newline at end of file From 59b8d6856713ef13b0dc7bbc88506f24734009fd Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Tue, 18 Oct 2022 15:57:39 +0200 Subject: [PATCH 19/62] Update Papyrus workflow image --- .../images/README.md | 2 +- .../images/papyrus_workflow.svg | 1384 +++++++++++++++++ 2 files changed, 1385 insertions(+), 1 deletion(-) create mode 100644 teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/papyrus_workflow.svg diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/README.md index 820060ba..64e982a8 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/README.md +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/README.md @@ -4,5 +4,5 @@ This folder stores images used in the Jupyter notebook. - `PCM_model_text-01.png` -- `papyrus_workflow.png` +- `papyrus_workflow.svg` - `splitting_methods.png` \ No newline at end of file diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/papyrus_workflow.svg b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/papyrus_workflow.svg new file mode 100644 index 00000000..76a26c46 --- /dev/null +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/papyrus_workflow.svg @@ -0,0 +1,1384 @@ + + + + + + image/svg+xml + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + ChEMBL 30 + + + + + + + + + + + + ExCAPE-DB + + Sharma et al. (2016)Christmann-Francket al. (2016)Klaegeret al. (2017)Mergetet al.(2016) + + + + + + + + + + + + + + + + + + + + + + StandardisationNormalisationData Repair + + + + + + + + + + + + + + apyrus + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From bed66ab2c9a2332ee1ee333faf660c44d398d26c Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Tue, 18 Oct 2022 16:01:44 +0200 Subject: [PATCH 20/62] Update Papyrus workflow image --- .../images/README.md | 2 +- .../images/papyrus_workflow.png | Bin 80810 -> 183410 bytes .../images/papyrus_workflow.svg | 1384 ----------------- .../talktorial.ipynb | 2 +- 4 files changed, 2 insertions(+), 1386 deletions(-) delete mode 100644 teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/papyrus_workflow.svg diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/README.md index 64e982a8..820060ba 100644 --- 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b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/papyrus_workflow.svg deleted file mode 100644 index 76a26c46..00000000 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/papyrus_workflow.svg +++ /dev/null @@ -1,1384 +0,0 @@ - - - - - - image/svg+xml - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - ChEMBL 30 - - - - - - - - - - - - ExCAPE-DB - - Sharma et al. (2016)Christmann-Francket al. (2016)Klaegeret al. (2017)Mergetet al.(2016) - - - - - - - - - - - - - - - - - - - - - - StandardisationNormalisationData Repair - - - - - - - - - - - - - - apyrus - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index c1fcf3a2..f1d20b97 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -143,7 +143,7 @@ "\n", "*Figure 2:*\n", "Papyrus dataset generation scheme.\n", - "Figure taken from: [ChemRvix (2021)](https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0)." + "Figure taken from: Papyrus scripts [github](https://github.com/OlivierBeq/Papyrus-scripts)." ], "metadata": { "collapsed": false From 1616aebb81d3cd07a7a777a0f867cbc870d2707b Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Tue, 18 Oct 2022 17:38:33 +0200 Subject: [PATCH 21/62] Proofread grammar --- .../talktorial.ipynb | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index f1d20b97..16fbab8a 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -21,7 +21,7 @@ "source": [ "## Aim of this talktorial\n", "\n", - "While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be solved leveraging similarities and differences between proteins. In this talktorial, we use Proteochemometrics modelling (PCM) to enrich our activity models with protein data to predict the activity of novel compounds against the four adenosine receptor isoforms (A1, A2A, A2B, A3)." + "While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be solved leveraging similarities and differences between proteins. In this talktorial, we use proteochemometrics (PCM) modelling to enrich our activity models with protein data to predict the activity of novel compounds against the four adenosine receptor isoforms (A1, A2A, A2B, A3)." ] }, { @@ -49,7 +49,7 @@ "source": [ "### Contents in *Practical*\n", "\n", - "* Downlaod Papyrus dataset\n", + "* Download Papyrus dataset\n", "* Data preparation\n", " * Filter activity data for targets of interest\n", " * Align target sequences\n", @@ -93,7 +93,7 @@ "source": [ "To successfully apply PCM modelling, we need a large dataset of molecule-protein pairs with known bioactivity values, a way of describing molecules and proteins, and a ML algorithm to train a model. Then, we can make predictions for new molecule-protein pairs.\n", "\n", - "NOTE: PCM modelling is an extension of ligand-based modelling with ML described in Talktorial T007. Explore that talktorial to know more about the basic principle of activity prediction using ML." + "NOTE: PCM modelling is an extension of ligand-based modelling with ML (also known as Quantitative Structure Activity Relationship or QSAR) described in Talktorial T007. Explore that talktorial to know more about the basic principle of activity prediction using ML." ] }, { @@ -194,7 +194,7 @@ { "cell_type": "markdown", "source": [ - "As done for molecules, the proteins of interest need to be converted to a list of features or protein descriptors. Protein descriptors used in PCM applications are commonly based on the protein sequence and represent physicochemical characteristics of the amino acids that make up the sequence (e.g. Z-scales). Other protein descriptors represent topological (e.g. St-scales) or electrostatic properties (e.g. MS-WHIM) of the protein sequence. Moreover, if structural information is available, protein descriptors can be derived from the 3D structure of the protein (e.g. sPairs) or the ligand-protein interaction in 3D (e.g. interaction fingerprints). Finally, with the widespread use of deep learning, protein embeddings can be obtained after parsing the protein sequence through the network (e.g. UniRep, AlphaFold embeddings).\n", + "As done for molecules, the proteins of interest need to be converted to a list of features or protein descriptors. Protein descriptors used in PCM applications are commonly based on the protein sequence and represent physicochemical characteristics of the amino acids that make up the sequence (e.g. Z-scales). Other protein descriptors represent topological (e.g. ST-scales) or electrostatic properties (e.g. MS-WHIM) of the protein sequence. Moreover, if structural information is available, protein descriptors can be derived from the 3D structure of the protein (e.g. sPairs) or the ligand-protein interaction in 3D (e.g. interaction fingerprints). Finally, with the widespread use of deep learning, protein embeddings can be obtained after parsing the protein sequence through the network (e.g. UniRep, AlphaFold embeddings).\n", "\n", "For protein descriptors based on the protein sequence, an aspect to take into account is that for ML the length of the protein descriptor needs to be the same. However, most proteins do not have the same sequence length. To solve this issue, there are two main approaches:\n", "* Multiple sequence alignment: when the whole protein wants to be incorporated to the model, a multiple sequence alignment can be performed. The final descriptor will have as many features as the number of features per amino acid multiplied by the number of aligned positions. To take into account, gaps in the alignment will receive zeroes in the descriptor.\n", @@ -236,7 +236,7 @@ { "cell_type": "markdown", "source": [ - "Proteochemometrics (PCM) consists in the modelling via supervised machine learning algorithms of a biological endpoint (e.g. compound activity) based on a series of features derived from chemical compounds and target proteins. PCM is an extension of a more widespread bioactivity modelling technique, Quantitative Structure Activity Relationship (QSAR) modelling, which relies solely on chemical features and that was introduced on Talktorial T007. The ML principles for proteochemometric modelling are equivalent to those explained for QSAR modelling. However, in this talktorial we will explore a supervised ML application other than classification, this is regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values." + "Proteochemometrics (PCM) consists in the modelling via supervised ML algorithms of a biological endpoint (e.g. compound activity) based on a series of features derived from chemical compounds and target proteins. PCM is an extension of a more widespread bioactivity modelling technique, Quantitative Structure Activity Relationship (QSAR) modelling, which relies solely on chemical features and that was introduced on Talktorial T007. The ML principles for PCM modelling are equivalent to those explained for QSAR modelling. However, in this talktorial we will explore a supervised ML application other than classification, this is regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values." ], "metadata": { "collapsed": false @@ -258,11 +258,11 @@ "cell_type": "markdown", "source": [ "Similarly to classification tasks, in supervised ML regression applications the model is first fitted to a training set and subsequently the predictive performance is evaluated on a test set. Therefore, the original dataset needs to be split between training and test sets. The split needs to ensure that the fitting process has enough data, and that the test set is representative. Normally, the distribution between train and test set is 80/20 or 70/30. Depending on the applicability domain, the split can be done in multiple ways. In PCM modelling, some of the most common splitting methods are:\n", - "* Random split: This method is not particularly relevant in drug discovery applications as it does not refflect the reality of a drug discovery campaign and it will most likely lead to data leaks between the training and test set. This is, very similar data will be found in both sets, which will lead to an overestimation of the rpedictive performance of the model. This type of split is commonly used, however, as a baseline and point of reference for other splitting methods, or as a starting point for quick model comparisons.\n", + "* Random split: This method is not particularly relevant in drug discovery applications as it does not reflect the reality of a drug discovery campaign and it will most likely lead to data leaks between the training and test set. This is, very similar data will be found in both sets, which will lead to an overestimation of the predictive performance of the model. This type of split is commonly used, however, as a baseline and point of reference for other splitting methods, or as a starting point for quick model comparisons.\n", "* Leave one target out (LOTO) split: To evaluate the ability of the model to extrapolate to targets not previously seen, one of the targets can be completely moved to the test set.\n", "* Leave one compound cluster out (LOCCO) split: This method evaluates the ability of the model to extrapolate to compounds with properties not previously seen by the model. Clustering can be done based on different molecular characteristics, such as physicochemical properties, scaffold, or approval status, for example (see Talktorial T005 to learn more about clustering). One (or several) clusters can then be left out for testing. This methods prevents data leaking in terms of chemistry between training and test sets.\n", "* Temporal split: This method was developed in order to account for the usual timeline of drug discovery campaigns, where chemical series are populated sequentially over time. In this approach, the molecules included in the training set are those released until certain date and the rest (most novel) are included in the test set.\n", - "* Stratified split per target: This method can be applied to any of the splitting methods described above (except LOTO), and aims to include data of all targets in both the training and test set, so that additional target-compound interactions can be extracted by the model." + "* Stratified split per target: This method can be applied to any of the splitting methods described above (except LOTO), and aims to include data of all targets in both the training and test set, so that additional target-compound interactions can be extracted by the model. (NOTE: stratification can be also done in regards to other reference points apart from targets, for example classes in classification tasks, to make sure that the distribution is similar across training and test set)." ], "metadata": { "collapsed": false @@ -1653,7 +1653,7 @@ { "cell_type": "markdown", "source": [ - "Based on the performance metrics of the PCM model, Pearson's $r$ tells us that the true and rpedicted values are highly correlated .Moreover, the $R^{2}$ tells us that almost 50 % of the variance of the target variable is explained by the model features and that the model is predictive. The $MAE$ tells us that the predictions are on average 0.64 p-value units off, which is an acceptable prediction error.\n", + "Based on the performance metrics of the PCM model, Pearson's $r$ tells us that the true and predicted values are highly correlated .Moreover, the $R^{2}$ tells us that almost 50 % of the variance of the target variable is explained by the model features and that the model is predictive. The $MAE$ tells us that the predictions are on average 0.64 p-value units off, which is an acceptable prediction error.\n", "An interesting observation is that the $R^{2}$ score is quite similar if we calculate it independently for the test set datapoint corresponding to each target." ], "metadata": { @@ -1868,7 +1868,7 @@ "\n", "Moreover, we trained four PCM models on three adenosine receptors and validated them on the remaining receptor, following a leave one target out (LOTO) split method. We did this to evaluate whether these PCM models could be used to predict bioactivity for a target for which the model has never seen any data in training. We immediately derive some observations:\n", "* The LOTO split is harder in validation than the random split, since the random split allows data leakage between targets.\n", - "* While the descriptors used in the PCM model trained on random split allowed for a good performance, in order to get a good performance in the LOTO split, we would need to search more carefully to find the optimal descriptors. Similarly, we could opt for a selection of the binding pocket prior to protein descriptor generation. Addtionally, we could optimize the model parameters.\n" + "* While the descriptors used in the PCM model trained on random split allowed for a good performance, in order to get a good performance in the LOTO split, we would need to search more carefully to find the optimal descriptors. Similarly, we could opt for a selection of the binding pocket prior to protein descriptor generation. Additionally, we could optimize the model parameters.\n" ] }, { From d5319f4eb26d3bca1eeae008ccdd87f494ea8b3e Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Thu, 20 Oct 2022 10:26:18 +0200 Subject: [PATCH 22/62] Grammar and code revision by Olivier --- .../README.md | 2 +- .../talktorial.ipynb | 24 +++++++++---------- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md index 05761246..436c598b 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md @@ -24,7 +24,7 @@ While activity data is very abundant for some protein targets, there are still a * Proteochemometrics (PCM) * Machine learning principles: regression - * Splitting methods + * Data splitting methods * Regression evaluation metrics * ML algorithm: Random Forest * Applications of PCM in drug discovery diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index 16fbab8a..083c2c12 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -37,7 +37,7 @@ "\n", "* Proteochemometrics (PCM)\n", " * Machine learning principles: regression\n", - " * Splitting methods\n", + " * Data splitting methods\n", " * Regression evaluation metrics\n", " * ML algorithm: Random Forest\n", " * Applications of PCM in drug discovery" @@ -259,8 +259,8 @@ "source": [ "Similarly to classification tasks, in supervised ML regression applications the model is first fitted to a training set and subsequently the predictive performance is evaluated on a test set. Therefore, the original dataset needs to be split between training and test sets. The split needs to ensure that the fitting process has enough data, and that the test set is representative. Normally, the distribution between train and test set is 80/20 or 70/30. Depending on the applicability domain, the split can be done in multiple ways. In PCM modelling, some of the most common splitting methods are:\n", "* Random split: This method is not particularly relevant in drug discovery applications as it does not reflect the reality of a drug discovery campaign and it will most likely lead to data leaks between the training and test set. This is, very similar data will be found in both sets, which will lead to an overestimation of the predictive performance of the model. This type of split is commonly used, however, as a baseline and point of reference for other splitting methods, or as a starting point for quick model comparisons.\n", - "* Leave one target out (LOTO) split: To evaluate the ability of the model to extrapolate to targets not previously seen, one of the targets can be completely moved to the test set.\n", - "* Leave one compound cluster out (LOCCO) split: This method evaluates the ability of the model to extrapolate to compounds with properties not previously seen by the model. Clustering can be done based on different molecular characteristics, such as physicochemical properties, scaffold, or approval status, for example (see Talktorial T005 to learn more about clustering). One (or several) clusters can then be left out for testing. This methods prevents data leaking in terms of chemistry between training and test sets.\n", + "* Leave one target out (LOTO) split: To evaluate the ability of the model to extrapolate to targets not previously seen, one of the targets can be completely moved to the test set. In a big enough set, instead of one \"some\" targets can be moved to the test set (i.e. Leave some targets out, or LSTO).\n", + "* Leave one compound cluster out (LOCCO) split: This method evaluates the ability of the model to extrapolate to compounds with properties not previously seen by the model. Clustering can be done based on different molecular characteristics, such as physicochemical properties, scaffold, or approval status, for example (see Talktorial T005 to learn more about clustering). One (or several, LSCCO) clusters can then be left out for testing. This methods prevents data leaking in terms of chemistry between training and test sets.\n", "* Temporal split: This method was developed in order to account for the usual timeline of drug discovery campaigns, where chemical series are populated sequentially over time. In this approach, the molecules included in the training set are those released until certain date and the rest (most novel) are included in the test set.\n", "* Stratified split per target: This method can be applied to any of the splitting methods described above (except LOTO), and aims to include data of all targets in both the training and test set, so that additional target-compound interactions can be extracted by the model. (NOTE: stratification can be also done in regards to other reference points apart from targets, for example classes in classification tasks, to make sure that the distribution is similar across training and test set)." ], @@ -304,9 +304,9 @@ "\n", "* Coefficient of determination ($R^{2}$ score): Represents the portion of variance of the target variable that has been explained by the independent variables (features) in the model. $R^{2}$ score varies between 1.0 (best score) and minus infinite, where 0.0 represents a model that always predicts the average target variable. As the variance is dataset dependent, it might not be meaningful metric to compare between datasets. When dealing with linear regression, and model fitting and evaluation are performed on a single dataset, $R^{2}$ is equivalent to the square of the Pearson correlation coefficient, described below, and can be noted as $r^{2}$.\n", "* Pearson's correlation coefficient (Pearson's $r$): Is a measure of the linear correlation between the true and predicted values of the target variable. It is calculated as the covariance of the two variables divided by the product of their standard deviation. Pearson's $r$ can vary between 1.0 (a perfect positive correlation) and -1.0 (a perfect negative correlation), where 1.0 would represent a perfect prediction.\n", - "* Man absolute error (MAE): Measures the average absolute difference between the predicted and the true values. MAE is interpreted based on the scale of the data, and it varies between infinite and 0.0 (best).\n", + "* Mean absolute error (MAE): Measures the average absolute difference between the predicted and the true values. MAE is interpreted based on the scale of the data, and it varies between infinite and 0.0 (best).\n", "* Mean squared error (MSE): Measures the average of the squares of the difference between the predicted and the true values. It varies between 1.0 (worst) and 0.0 (best).\n", - "* Root mean square error (RMSE): Also called root mean square deviation (RMSD), it is the square root of the MSE and represents the standard deviation of the prediction errors respect to the line of best fit. RMSE is a measure of accuracy and it cannot be applied to compare between datasets, as it is scale-dependent. It varies between 1.0 (worst) and 0.0 (best).\n", + "* Root mean square error (RMSE): It is the square root of the MSE and represents the standard deviation of the prediction errors respect to the line of best fit. RMSE is a measure of accuracy and it cannot be applied to compare between datasets, as it is scale-dependent. It varies between 1.0 (worst) and 0.0 (best).\n", "\n", "To learn more about evaluation metrics, you can consult scikit learn's regression metrics [Docs](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics).\n", "\n", @@ -357,7 +357,7 @@ { "cell_type": "markdown", "source": [ - "The possibility to predict bioactivity for multiple targets in one model with PCM is very interesting in drug discovery. Some applications of this technique are listed below and help answer the following questions in drug discovery:\n", + "The possibility to predict bioactivity for multiple targets in one model with PCM is very interesting in drug discovery and expands the applicability domain of QSAR modelling. Some applications of this technique are listed below and help answer the following questions in drug discovery:\n", "\n", "* Poly-pharmacology: Is it possible to target several proteins of interest simultaneously with one single drug?\n", "* Off-target prediction: What other proteins do these compounds target apart from the intended therapeutic target? Are maybe these off-targets responsible for side effects?\n", @@ -411,7 +411,7 @@ "import rich\n", "from rich_msa import RichAlignment\n", "\n", - "from prodec import ProteinDescriptors, Transform\n", + "from prodec import ProteinDescriptors\n", "from rdkit import Chem\n", "from mordred import Calculator, descriptors\n", "\n", @@ -487,7 +487,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "263cdf1d80254e6c86fe1227e2fce90d" + "model_id": "81f4f6bc47f1421288c90fd4973984d9" } }, "metadata": {}, @@ -573,7 +573,7 @@ " filter = keep_accession(data, target_accession_list)\n", "\n", " # Iterate through chunks and apply the filter defined\n", - " filtered_data = consume_chunks(filter, total=round(get_num_rows_in_file('bioactivities', False) / CHUNKSIZE))\n", + " filtered_data = consume_chunks(filter, total=-(-get_num_rows_in_file('bioactivities', False) // CHUNKSIZE))\n", " # Add column named 'Target' for easier data visualization\n", " filtered_data['Target'] = filtered_data['accession'].apply(lambda x: [i for i in targets.keys() if targets[i]==x][0])\n", "\n", @@ -600,11 +600,11 @@ "outputs": [ { "data": { - "text/plain": " 0%| | 0/12 [00:00 Date: Wed, 26 Oct 2022 12:28:45 +0200 Subject: [PATCH 23/62] Theory revision based on Andrea's review. --- .../talktorial.ipynb | 80 ++++++++++--------- 1 file changed, 41 insertions(+), 39 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index 083c2c12..6670afef 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -10,7 +10,7 @@ "\n", "Authors:\n", "\n", - "- Marina Gorostiola González, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands)\n", + "- Marina Gorostiola González, 2022, [Computational Drug Discovery](https://www.universiteitleiden.nl/en/science/drug-research/drug-discovery-and-safety/computational-drug-discovery), Drug Discovery & Safety Leiden University (The Netherlands)\n", "- Olivier J.M. Béquignon, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands)\n", "- Willem Jespers, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands)" ] @@ -21,7 +21,7 @@ "source": [ "## Aim of this talktorial\n", "\n", - "While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be solved leveraging similarities and differences between proteins. In this talktorial, we use proteochemometrics (PCM) modelling to enrich our activity models with protein data to predict the activity of novel compounds against the four adenosine receptor isoforms (A1, A2A, A2B, A3)." + "While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be partially solved leveraging similarities and differences between proteins. In this talktorial, we use proteochemometrics (PCM) modelling to enrich our activity models with protein data to predict the activity of novel compounds against the four [adenosine receptor](https://journals.physiology.org/doi/full/10.1152/physrev.00049.2017) isoforms (A1, A2A, A2B, A3)." ] }, { @@ -29,13 +29,11 @@ "metadata": {}, "source": [ "### Contents in *Theory*\n", - "\n", - "* Data preparation\n", - " * Papyrus dataset\n", - " * Molecule encoding: molecular descriptors\n", - " * Protein encoding: protein descriptors\n", - "\n", - "* Proteochemometrics (PCM)\n", + "* Proteochemometrics (PCM) modelling\n", + " * Data preparation\n", + " * Papyrus dataset\n", + " * Molecule encoding: molecular descriptors\n", + " * Protein encoding: protein descriptors\n", " * Machine learning principles: regression\n", " * Data splitting methods\n", " * Regression evaluation metrics\n", @@ -87,13 +85,25 @@ "## Theory" ] }, + { + "cell_type": "markdown", + "source": [ + "### Proteochemometrics (PCM) modelling" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, { "cell_type": "markdown", "metadata": {}, "source": [ - "To successfully apply PCM modelling, we need a large dataset of molecule-protein pairs with known bioactivity values, a way of describing molecules and proteins, and a ML algorithm to train a model. Then, we can make predictions for new molecule-protein pairs.\n", + "Proteochemometrics (PCM) consists in the modelling via supervised ML algorithms of a biological endpoint (e.g. compound activity) based on a series of features derived from chemical compounds and target proteins. PCM is an extension of a more widespread bioactivity modelling technique, Quantitative Structure Activity Relationship (QSAR) modelling, which relies solely on chemical features and that was introduced on Talktorial T007. Explore that talktorial to know more about the basic principle of activity prediction using ML.\n", "\n", - "NOTE: PCM modelling is an extension of ligand-based modelling with ML (also known as Quantitative Structure Activity Relationship or QSAR) described in Talktorial T007. Explore that talktorial to know more about the basic principle of activity prediction using ML." + "To successfully apply PCM modelling, we need a large dataset of molecule-protein pairs with known bioactivity values, a way of describing molecules and proteins, and a ML algorithm to train a model. Then, we can make predictions for new molecule-protein pairs.\n" ] }, { @@ -110,7 +120,7 @@ { "cell_type": "markdown", "source": [ - "### Data preparation" + "#### Data preparation" ], "metadata": { "collapsed": false @@ -119,7 +129,7 @@ { "cell_type": "markdown", "source": [ - "#### Papyrus dataset" + "##### Papyrus dataset" ], "metadata": { "collapsed": false @@ -128,9 +138,9 @@ { "cell_type": "markdown", "source": [ - "The Papyrus dataset is a highly curated compilation of bioactivity data intended for modelling in drug discovery. Apart from the bioactivity data contained in the ChEMBL database (see also Talktorial T001), the Papyrus dataset contains binary data for classification tasks from the ExCAPE-DB, and bioactivity data from a number of kinase-specific papers (Figure 1).\n", + "The Papyrus dataset is a highly curated compilation of bioactivity data intended for modelling in drug discovery. Apart from the bioactivity data contained in the [ChEMBL database](https://www.ebi.ac.uk/chembl/) (see also Talktorial T001), the Papyrus dataset contains binary data for classification tasks from the [ExCAPE-DB](https://solr.ideaconsult.net/search/excape/), and bioactivity data from a number of kinase-specific papers (Figure 1). The Papyrus dataset consists of almost 60M compound-protein pairs, representing data of around 1.2M unique compounds and 7K proteins across 499 different organisms.\n", "\n", - "The bioactivity data aggregated is standardized, repaired, and normalised to form the Papyrus dataset, which is updated with every new version of ChEMBL released. The Papyrus dataset contains \"high quality\" data associated to pChEMBL values for regression tasks and \"low quality\" data associated to an active/inactive label for classification tasks (read more about ML applications in Talktorial T007)." + "The aggregated bioactivity data is standardized, repaired, and normalised to form the Papyrus dataset, which is updated with every new version of ChEMBL released. The Papyrus dataset contains \"high quality\" data associated to pChEMBL values for regression or classification tasks. pChEMBL value is a canonical activity metric defined as $-log_{10}(molar IC_{50}, XC_{50}, EC_{50}, AC_{50}, Ki, Kd, or potency)$. Moreover, \"low quality\" data that is only associated to an active/inactive label can be used for classification tasks (read more about ML applications in Talktorial T007)." ], "metadata": { "collapsed": false @@ -152,7 +162,7 @@ { "cell_type": "markdown", "source": [ - "#### Molecule encoding: molecular descriptors" + "##### Molecule encoding: molecular descriptors" ], "metadata": { "collapsed": false, @@ -168,12 +178,12 @@ "\n", "Molecular descriptors are the \"final result of a logical and mathematical procedure, which transforms chemical information encoded within a symbolic representation of a molecule into a useful number or the result of some standardized experiment\" ([J. Cheminf., 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y)). These descriptors can be, for example, molecular weight, ring count, Eccentric Connectivity Index (calculated from the 2D structure), or Geometrical Index (calculated from the 3D structure).\n", "\n", - "In this talktorial, we use Modred as a software engine to calculate molecular descriptors. Modred calculates more than 1,800 molecular descriptors, including the ones implemented in RDKit, including an automatic preprocessing step that is common for all descriptors calculated. For simplicity, here we calculate only 4 types of descriptors from the vast list of possibilities from Modred, excluding their 3D representation. These include:\n", + "In this talktorial, we use [Modred](https://github.com/mordred-descriptor/mordred) as a software engine to calculate molecular descriptors. Modred calculates more than 1,800 molecular descriptors, including the ones implemented in RDKit. The algorithm starts with an automatic preprocessing step that is common for all possible descriptors calculated. For simplicity, here we calculate only four types of descriptors from the vast list of possibilities from Modred, excluding their 3D representation. These four descriptors are:\n", "\n", "* ABC Index: 2 descriptors that represent the atom-bond connectivity index or the Graovac-Ghorbani atom-bond connectivity index (see Modred ABCIndex [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.ABCIndex.html))\n", "* Acid-Base: 2 descriptors that count acidic and basic groups, respectively (see Modred AcidBase [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.AcidBase.html?highlight=acidbase))\n", "* Atom count: 16 descriptors that represent a count of different types of atoms (see Modred AtomCount [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.AtomCount.html?highlight=atomcount))\n", - "* Balaban J index: 1 descriptor (included in RDkit), which represents a topological index (see Modred BalabanJ [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.BalabanJ.html?highlight=balaban#module-mordred.BalabanJ))" + "* Balaban J index: 1 descriptor that represents a topological index (see Modred BalabanJ [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.BalabanJ.html?highlight=balaban#module-mordred.BalabanJ))" ], "metadata": { "collapsed": false, @@ -185,7 +195,7 @@ { "cell_type": "markdown", "source": [ - "#### Protein encoding: protein descriptors" + "##### Protein encoding: protein descriptors" ], "metadata": { "collapsed": false @@ -194,16 +204,16 @@ { "cell_type": "markdown", "source": [ - "As done for molecules, the proteins of interest need to be converted to a list of features or protein descriptors. Protein descriptors used in PCM applications are commonly based on the protein sequence and represent physicochemical characteristics of the amino acids that make up the sequence (e.g. Z-scales). Other protein descriptors represent topological (e.g. ST-scales) or electrostatic properties (e.g. MS-WHIM) of the protein sequence. Moreover, if structural information is available, protein descriptors can be derived from the 3D structure of the protein (e.g. sPairs) or the ligand-protein interaction in 3D (e.g. interaction fingerprints). Finally, with the widespread use of deep learning, protein embeddings can be obtained after parsing the protein sequence through the network (e.g. UniRep, AlphaFold embeddings).\n", + "As done for molecules, the proteins of interest need to be converted to a list of features or protein descriptors. Protein descriptors used in PCM applications are commonly based on the protein sequence and represent physicochemical characteristics of the amino acids that make up the sequence (e.g. Z-scales). Other protein descriptors represent topological (e.g. ST-scales) or electrostatic properties (e.g. MS-WHIM) of the protein sequence. Moreover, if structural information is available, protein descriptors can be derived from the 3D structure of the protein (e.g. sPairs) or the ligand-protein interaction in 3D (e.g. interaction fingerprints). Finally, with the widespread use of deep learning, protein embeddings can be obtained after parsing the protein sequence through the network (e.g. UniRep, AlphaFold embeddings). To read more about protein descriptors, check out these selection of articles ([Brief. Bioinform.,18, (2017)](https://pubmed.ncbi.nlm.nih.gov/26873661/), [Int. J. Mol. Sci., 22, (2021)](https://pubmed.ncbi.nlm.nih.gov/34884688/), [Comput. Struct. Biotechnol. J., 20, (2022)](https://pubmed.ncbi.nlm.nih.gov/35222841/)).\n", "\n", "For protein descriptors based on the protein sequence, an aspect to take into account is that for ML the length of the protein descriptor needs to be the same. However, most proteins do not have the same sequence length. To solve this issue, there are two main approaches:\n", - "* Multiple sequence alignment: when the whole protein wants to be incorporated to the model, a multiple sequence alignment can be performed. The final descriptor will have as many features as the number of features per amino acid multiplied by the number of aligned positions. To take into account, gaps in the alignment will receive zeroes in the descriptor.\n", + "* Multiple sequence alignment: If the entire protein is to be included in the model, a multiple sequence alignment can be performed. The final descriptor has as many entries as the number of features per amino acid multiplied by the number of aligned positions. To account for gaps in the alignment, zeros are introduced in the descriptor.\n", "* Binding pocket selection: To avoid unnecessary features, a binding pocket of the same length can be selected for each protein. Normally, the binding pocket selection is preceded by a multiple sequence alignment and driven by known structural or mutagenesis data.\n", "\n", "Other options are available when proteins are not of the same family or do not share a binding pocket (see [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub))\n", "\n", "In this talktorial, we will focus on physicochemical protein descriptors, mainly Z-scales ([J. Med. Chem, 30 (1987)](https://pubs.acs.org/doi/10.1021/jm00390a003)). The Z-scales descriptor assigns three pre-determined values (Z1, Z2, Z3) to each amino acid in the sequence. The Z1, Z2, and Z3 values are the first principal components of a principal component analysis (PCA) including 29 different physicochemical variables to characterize the amino acids.\n", - "Since we are calculating activity for four proteins with very high sequence similarity (Adenosine receptors A1, A2A, A2B, and A3), we will use multiple sequence alignment prior to calculation of the Z-scales. To calculate Z-scales we will use ProDEC, an open source resource that compiles a large number of protein descriptors." + "Since we are calculating activity for four proteins with very high sequence similarity (Adenosine receptors A1, A2A, A2B, and A3), we will use multiple sequence alignment prior to calculation of the Z-scales. To calculate Z-scales we will use [ProDEC](https://github.com/OlivierBeq/ProDEC), an open source resource that compiles a large number of protein descriptors." ], "metadata": { "collapsed": false, @@ -212,15 +222,6 @@ } } }, - { - "cell_type": "markdown", - "source": [ - "### Proteochemometrics (PCM)" - ], - "metadata": { - "collapsed": false - } - }, { "cell_type": "markdown", "source": [ @@ -236,7 +237,8 @@ { "cell_type": "markdown", "source": [ - "Proteochemometrics (PCM) consists in the modelling via supervised ML algorithms of a biological endpoint (e.g. compound activity) based on a series of features derived from chemical compounds and target proteins. PCM is an extension of a more widespread bioactivity modelling technique, Quantitative Structure Activity Relationship (QSAR) modelling, which relies solely on chemical features and that was introduced on Talktorial T007. The ML principles for PCM modelling are equivalent to those explained for QSAR modelling. However, in this talktorial we will explore a supervised ML application other than classification, this is regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values." + "The ML principles for PCM modelling are equivalent to those explained for QSAR modelling. However, in this talktorial we will explore a supervised ML application other than classification, this is regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values.\n", + "NOTE: Target variable is the variable we want to predict in ML. Not to be confused with (protein) target." ], "metadata": { "collapsed": false @@ -245,7 +247,7 @@ { "cell_type": "markdown", "source": [ - "##### Splitting methods" + "##### Data splitting methods" ], "metadata": { "collapsed": false, @@ -260,7 +262,7 @@ "Similarly to classification tasks, in supervised ML regression applications the model is first fitted to a training set and subsequently the predictive performance is evaluated on a test set. Therefore, the original dataset needs to be split between training and test sets. The split needs to ensure that the fitting process has enough data, and that the test set is representative. Normally, the distribution between train and test set is 80/20 or 70/30. Depending on the applicability domain, the split can be done in multiple ways. In PCM modelling, some of the most common splitting methods are:\n", "* Random split: This method is not particularly relevant in drug discovery applications as it does not reflect the reality of a drug discovery campaign and it will most likely lead to data leaks between the training and test set. This is, very similar data will be found in both sets, which will lead to an overestimation of the predictive performance of the model. This type of split is commonly used, however, as a baseline and point of reference for other splitting methods, or as a starting point for quick model comparisons.\n", "* Leave one target out (LOTO) split: To evaluate the ability of the model to extrapolate to targets not previously seen, one of the targets can be completely moved to the test set. In a big enough set, instead of one \"some\" targets can be moved to the test set (i.e. Leave some targets out, or LSTO).\n", - "* Leave one compound cluster out (LOCCO) split: This method evaluates the ability of the model to extrapolate to compounds with properties not previously seen by the model. Clustering can be done based on different molecular characteristics, such as physicochemical properties, scaffold, or approval status, for example (see Talktorial T005 to learn more about clustering). One (or several, LSCCO) clusters can then be left out for testing. This methods prevents data leaking in terms of chemistry between training and test sets.\n", + "* Leave one compound cluster out (LOCCO) split: This method evaluates the ability of the model to extrapolate to compounds with properties not previously seen by the model. Clustering can be done based on different molecular characteristics, such as physicochemical properties or scaffold, for example (see Talktorial T005 to learn more about clustering). One (or several, LSCCO) clusters can then be left out for testing. This methods prevents data leaking in terms of chemistry between training and test sets.\n", "* Temporal split: This method was developed in order to account for the usual timeline of drug discovery campaigns, where chemical series are populated sequentially over time. In this approach, the molecules included in the training set are those released until certain date and the rest (most novel) are included in the test set.\n", "* Stratified split per target: This method can be applied to any of the splitting methods described above (except LOTO), and aims to include data of all targets in both the training and test set, so that additional target-compound interactions can be extracted by the model. (NOTE: stratification can be also done in regards to other reference points apart from targets, for example classes in classification tasks, to make sure that the distribution is similar across training and test set)." ], @@ -305,8 +307,8 @@ "* Coefficient of determination ($R^{2}$ score): Represents the portion of variance of the target variable that has been explained by the independent variables (features) in the model. $R^{2}$ score varies between 1.0 (best score) and minus infinite, where 0.0 represents a model that always predicts the average target variable. As the variance is dataset dependent, it might not be meaningful metric to compare between datasets. When dealing with linear regression, and model fitting and evaluation are performed on a single dataset, $R^{2}$ is equivalent to the square of the Pearson correlation coefficient, described below, and can be noted as $r^{2}$.\n", "* Pearson's correlation coefficient (Pearson's $r$): Is a measure of the linear correlation between the true and predicted values of the target variable. It is calculated as the covariance of the two variables divided by the product of their standard deviation. Pearson's $r$ can vary between 1.0 (a perfect positive correlation) and -1.0 (a perfect negative correlation), where 1.0 would represent a perfect prediction.\n", "* Mean absolute error (MAE): Measures the average absolute difference between the predicted and the true values. MAE is interpreted based on the scale of the data, and it varies between infinite and 0.0 (best).\n", - "* Mean squared error (MSE): Measures the average of the squares of the difference between the predicted and the true values. It varies between 1.0 (worst) and 0.0 (best).\n", - "* Root mean square error (RMSE): It is the square root of the MSE and represents the standard deviation of the prediction errors respect to the line of best fit. RMSE is a measure of accuracy and it cannot be applied to compare between datasets, as it is scale-dependent. It varies between 1.0 (worst) and 0.0 (best).\n", + "* Mean squared error (MSE): Measures the average of the squares of the difference between the predicted and the true values. It varies between infinite and 0.0 (best).\n", + "* Root mean square error (RMSE): It is the square root of the MSE and represents the standard deviation of the prediction errors respect to the line of best fit. RMSE is a measure of accuracy and it cannot be applied to compare between datasets, as it is scale-dependent. It varies between infinite and 0.0 (best).\n", "\n", "To learn more about evaluation metrics, you can consult scikit learn's regression metrics [Docs](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics).\n", "\n", @@ -332,8 +334,8 @@ { "cell_type": "markdown", "source": [ - "Different ML algorithms can be used to train PCM models. Some of them include support vector machines (SVM), random forest (RF), and neural networks (NN), that were described in Talktorial T007. In this talktorial, we will use RF. RF is a decision tree-based algorithm, more in detail a bagging ensemble method. This means that there are multiple decision trees trained independently with subsets of features and data and the final prediction is made from a consensus between the independent predictions.\n", - "RF models have been used extensively in PCM applications due to their efficiency in large datasets and resistance to overfitting with more features." + "Different ML algorithms can be used to train PCM models. Some of them include support vector machines (SVM), random forest (RF), and neural networks (NN), that were described in Talktorial T007. RF models have been used extensively in PCM applications due to their efficiency in large datasets and resistance to overfitting with more features. However, deep learning applications are also gaining momentum. See [J. Cheminform., 45, (2017)](https://pubmed.ncbi.nlm.nih.gov/29086168/) for a comparative use of ML methods in PCM modelling.\n", + "In this talktorial, we will use RF. RF is a decision tree-based algorithm, more in detail a bagging ensemble method. This means that there are multiple decision trees trained independently with subsets of features and data and the final prediction is made from a consensus between the independent predictions.\n" ], "metadata": { "collapsed": false, @@ -527,7 +529,7 @@ { "cell_type": "markdown", "source": [ - "From the Papyrus dataset, we want to extract continuous activity data for all compound-protein pairs for the four human Adenosine receptors. In the Papyrus set, unique compound-target activity values are reported by aggregating data from different assays available. To account for the different types of bioactivity units available, a canonical activity metric defined by ChEMBL is used, pchembl value, which is defined as $-log_{10}(molar IC_{50}, XC_{50}, EC_{50}, AC_{50}, Ki, Kd, or potency)$. From the activity aggregation strategies available with the Papyrus set, we will use the Mean (pchembl_value_Mean variable). \n", + "From the Papyrus dataset, we want to extract continuous activity data for all compound-protein pairs for the four human Adenosine receptors. In the Papyrus set, unique compound-target activity values are reported by aggregating data from different assays available. To account for the different types of bioactivity units available, a canonical activity metric defined by ChEMBL is used, pChEMBL value. From the activity aggregation strategies available with the Papyrus set, we will use the Mean (pchembl_value_Mean variable).\n", "\n", "|Receptor|Uniprot accession|\n", "|---|---|\n", From 494c8feef719d8df3f810c700e8cb0e0c0308453 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Wed, 26 Oct 2022 12:29:34 +0200 Subject: [PATCH 24/62] Theory contents revision based on Andrea's review. --- .../README.md | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md index 436c598b..f0ecc6fb 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md @@ -17,12 +17,11 @@ While activity data is very abundant for some protein targets, there are still a ### Contents in *Theory* -* Data preparation - * Papyrus dataset - * Molecule encoding: molecular descriptors - * Protein encoding: protein descriptors - -* Proteochemometrics (PCM) +* Proteochemometrics (PCM) modelling + * Data preparation + * Papyrus dataset + * Molecule encoding: molecular descriptors + * Protein encoding: protein descriptors * Machine learning principles: regression * Data splitting methods * Regression evaluation metrics From 45cf83da4a3ea8c2bb8a335941cf20bd4b8172f4 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Thu, 27 Oct 2022 08:24:33 +0100 Subject: [PATCH 25/62] Run CI unit tests on T032 only (temporarily) --- .github/workflows/ci.yml | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index b60fa0a2..4318e097 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -45,7 +45,7 @@ jobs: #miniconda-version: "latest" activate-environment: teachopencadd channel-priority: true - environment-file: devtools/test_env.yml + environment-file: teachopencadd/talktorials/T032_compound_activity_proteochemometrics/T032_env.yml auto-activate-base: false - name: Additional info about the build @@ -76,11 +76,12 @@ jobs: shell: bash -l {0} run: | PYTEST_ARGS="--nbval-lax --current-env --dist loadscope --numprocesses 2" - if [ "$RUNNER_OS" != "Windows" ]; then - pytest $PYTEST_ARGS teachopencadd/talktorials/T*/talktorial.ipynb - else - pytest $PYTEST_ARGS teachopencadd/talktorials/ --ignore=teachopencadd/talktorials/T008_md_simulation/talktorial.ipynb --ignore=teachopencadd/talktorials/T019_md_simulation/talktorial.ipynb - fi + pytest $PYTEST_ARGS teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb + # if [ "$RUNNER_OS" != "Windows" ]; then + # pytest $PYTEST_ARGS teachopencadd/talktorials/T*/talktorial.ipynb + # else + # pytest $PYTEST_ARGS teachopencadd/talktorials/ --ignore=teachopencadd/talktorials/T008_md_simulation/talktorial.ipynb --ignore=teachopencadd/talktorials/T019_md_simulation/talktorial.ipynb + # fi format: name: Black From 101233d76a21b353e786321b60ea03d5e9e27619 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Thu, 27 Oct 2022 12:13:05 +0100 Subject: [PATCH 26/62] Add T032 to docs --- docs/all_talktorials.rst | 1 + docs/talktorials.rst | 1 + 2 files changed, 2 insertions(+) diff --git a/docs/all_talktorials.rst b/docs/all_talktorials.rst index 13a14810..96a9e5be 100644 --- a/docs/all_talktorials.rst +++ b/docs/all_talktorials.rst @@ -34,3 +34,4 @@ This is the complete list of talktorials available for online reading. Take into talktorials/T026_kinase_similarity_ifp.nblink talktorials/T027_kinase_similarity_ligand_profile.nblink talktorials/T028_kinase_similarity_compare_perspectives.nblink + talktorials/T032_compound_activity_proteochemometrics.nblink diff --git a/docs/talktorials.rst b/docs/talktorials.rst index 4c2ea220..3a4a05e7 100644 --- a/docs/talktorials.rst +++ b/docs/talktorials.rst @@ -63,6 +63,7 @@ The basis for computer-aided drug discovery talktorials/T013_query_pubchem.nblink talktorials/T021_one_hot_encoding.nblink talktorials/T022_ligand_based_screening_neural_network.nblink + talktorials/T032_compound_activity_proteochemometrics.nblink Structural biology ------------------ From 8af34f8ed50f5547805e81c9c4c3e3ba9630f634 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Thu, 27 Oct 2022 12:13:16 +0100 Subject: [PATCH 27/62] Add pytest to tmp env --- .../T032_compound_activity_proteochemometrics/T032_env.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/T032_env.yml b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/T032_env.yml index f6005bcf..047a1676 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/T032_env.yml +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/T032_env.yml @@ -17,6 +17,7 @@ dependencies: - seaborn # Dependencies for PCM and papyrus scripts - mordred + - pytest - pip: - https://github.com/OlivierBeq/Papyrus-scripts/tarball/master - prodec From fdd76f642edd1152315cc473c2228662239ed7e8 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Thu, 27 Oct 2022 12:13:44 +0100 Subject: [PATCH 28/62] Update README --- .../T032_compound_activity_proteochemometrics/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md index f0ecc6fb..247c1606 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md @@ -4,19 +4,17 @@ Authors: -- Marina Gorostiola González, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands) +- Marina Gorostiola González, 2022, [Computational Drug Discovery](https://www.universiteitleiden.nl/en/science/drug-research/drug-discovery-and-safety/computational-drug-discovery), Drug Discovery & Safety Leiden University (The Netherlands) - Olivier J.M. Béquignon, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands) - Willem Jespers, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands) - ## Aim of this talktorial -While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be solved leveraging similarities and differences between proteins. In this talktorial, we use Proteochemometrics modelling (PCM) to enrich our activity models with protein data to predict the activity of novel compounds against the four adenosine receptor isoforms (A1, A2A, A2B, A3). +While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be partially solved leveraging similarities and differences between proteins. In this talktorial, we use proteochemometrics (PCM) modelling to enrich our activity models with protein data to predict the activity of novel compounds against the four [adenosine receptor](https://journals.physiology.org/doi/full/10.1152/physrev.00049.2017) isoforms (A1, A2A, A2B, A3). ### Contents in *Theory* - * Proteochemometrics (PCM) modelling * Data preparation * Papyrus dataset @@ -55,3 +53,5 @@ While activity data is very abundant for some protein targets, there are still a * Regression metrics [(Scikit learn)](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics) * XGBoost [Documentation](https://xgboost.readthedocs.io/en/stable/index.html) * Proteochemometrics review: [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub) + + From a14321ed6f199b5917d96096be4035e4419d9df4 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Thu, 27 Oct 2022 12:40:57 +0100 Subject: [PATCH 29/62] CI: Temporarily remove CLI test --- .github/workflows/ci.yml | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 4318e097..52ace99e 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -66,11 +66,11 @@ jobs: conda info --all conda list - - name: Test CLI - shell: bash -l {0} - run: | - teachopencadd -h - pytest -v --cov=${PACKAGE} --cov-report=xml --color=yes ${PACKAGE}/tests/ + # - name: Test CLI + # shell: bash -l {0} + # run: | + # teachopencadd -h + # pytest -v --cov=${PACKAGE} --cov-report=xml --color=yes ${PACKAGE}/tests/ - name: Run tests shell: bash -l {0} From 88acd016e253d6fb404ea39f210debf994808bba Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Thu, 27 Oct 2022 18:37:44 +0200 Subject: [PATCH 30/62] Add output directory argument --- .../data/clustalo.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/clustalo.py b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/clustalo.py index 78746b73..a3daf138 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/clustalo.py +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/clustalo.py @@ -104,6 +104,7 @@ parser.add_option('-h', '--help', action='store_true', help='Show this help message and exit.') parser.add_option('--email', help='E-mail address.') parser.add_option('--title', help='Job title.') +parser.add_option('--outdir', help='Relative directory for results.') parser.add_option('--outfile', help='File name for results.') parser.add_option('--outformat', help='Output format for results.') parser.add_option('--asyncjob', action='store_true', help='Asynchronous mode.') @@ -388,6 +389,10 @@ def getResult(jobId): else: filename = (jobId + u'.' + unicode(resultType[u'identifier']) + u'.' + unicode(resultType[u'fileSuffix'])) + if options.outdir: + filepath = os.path.join(options.outdir, filename) + else: + filepath = filename # Write a result file outformat_parm = str(options.outformat).split(',') @@ -410,16 +415,16 @@ def getResult(jobId): fmode = 'w' try: - fh = open(filename, fmode) + fh = open(filepath, fmode) fh.write(result) fh.close() except TypeError: fh.close() - fh = open(filename, "wb") + fh = open(filepath, "wb") fh.write(result) fh.close() if outputLevel > 0: - print("Creating result file: " + filename) + print("Creating result file: " + filepath) printDebugMessage(u'getResult', u'End', 1) From 34e813e440c4024a99cde2f514284821d9fa1eb8 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Thu, 27 Oct 2022 18:42:47 +0200 Subject: [PATCH 31/62] Add Dominique's comments --- .../README.md | 28 +- .../talktorial.ipynb | 330 +++++++++--------- 2 files changed, 177 insertions(+), 181 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md index f0ecc6fb..335219a9 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md @@ -12,21 +12,21 @@ Authors: ## Aim of this talktorial -While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be solved leveraging similarities and differences between proteins. In this talktorial, we use Proteochemometrics modelling (PCM) to enrich our activity models with protein data to predict the activity of novel compounds against the four adenosine receptor isoforms (A1, A2A, A2B, A3). +While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be solved leveraging similarities and differences between proteins. In this talktorial, we use Proteochemometrics modeling (PCM) to enrich our activity models with protein data to predict the activity of novel compounds against the four adenosine receptor isoforms (A1, A2A, A2B, A3). ### Contents in *Theory* -* Proteochemometrics (PCM) modelling - * Data preparation - * Papyrus dataset - * Molecule encoding: molecular descriptors - * Protein encoding: protein descriptors - * Machine learning principles: regression - * Data splitting methods - * Regression evaluation metrics - * ML algorithm: Random Forest - * Applications of PCM in drug discovery +* Proteochemometrics (PCM) modeling +* Data preparation + * Papyrus dataset + * Molecule encoding: molecular descriptors + * Protein encoding: protein descriptors +* Machine learning principles: regression + * Data splitting methods + * Regression evaluation metrics + * ML algorithm: Random Forest +* Applications of PCM in drug discovery ### Contents in *Practical* @@ -37,7 +37,7 @@ While activity data is very abundant for some protein targets, there are still a * Align target sequences * Calculate protein descriptors * Calculate compound descriptors -* Proteochemometrics modelling +* Proteochemometrics modeling * Helper functions * Preprocessing * Model training and validation @@ -48,10 +48,10 @@ While activity data is very abundant for some protein targets, there are still a ### References -* Papyrus scripts [github](https://github.com/OlivierBeq/Papyrus-scripts) +* Papyrus scripts [GitHub](https://github.com/OlivierBeq/Papyrus-scripts) * Papyrus dataset preprint: [ChemRvix (2021)](https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0) * Molecular descriptors (Modred): [J. Cheminf., 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y) -* Protein descriptors (ProDEC) [github](https://github.com/OlivierBeq/ProDEC) +* Protein descriptors (ProDEC) [GitHub](https://github.com/OlivierBeq/ProDEC) * Regression metrics [(Scikit learn)](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics) * XGBoost [Documentation](https://xgboost.readthedocs.io/en/stable/index.html) * Proteochemometrics review: [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index 6670afef..a07a23a0 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -21,7 +21,7 @@ "source": [ "## Aim of this talktorial\n", "\n", - "While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be partially solved leveraging similarities and differences between proteins. In this talktorial, we use proteochemometrics (PCM) modelling to enrich our activity models with protein data to predict the activity of novel compounds against the four [adenosine receptor](https://journals.physiology.org/doi/full/10.1152/physrev.00049.2017) isoforms (A1, A2A, A2B, A3)." + "While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be partially solved by leveraging similarities and differences between proteins. In this talktorial, we use proteochemometrics (PCM) modeling to enrich our activity models with protein data to predict the activity of novel compounds against the four [adenosine receptor](https://journals.physiology.org/doi/full/10.1152/physrev.00049.2017) isoforms (A1, A2A, A2B, A3)." ] }, { @@ -29,16 +29,16 @@ "metadata": {}, "source": [ "### Contents in *Theory*\n", - "* Proteochemometrics (PCM) modelling\n", - " * Data preparation\n", - " * Papyrus dataset\n", - " * Molecule encoding: molecular descriptors\n", - " * Protein encoding: protein descriptors\n", - " * Machine learning principles: regression\n", - " * Data splitting methods\n", - " * Regression evaluation metrics\n", - " * ML algorithm: Random Forest\n", - " * Applications of PCM in drug discovery" + "* Proteochemometrics (PCM) modeling\n", + "* Data preparation\n", + " * Papyrus dataset\n", + " * Molecule encoding: molecular descriptors\n", + " * Protein encoding: protein descriptors\n", + "* Machine learning principles: regression\n", + " * Data splitting methods\n", + " * Regression evaluation metrics\n", + " * ML algorithm: Random Forest\n", + "* Applications of PCM in drug discovery" ] }, { @@ -53,7 +53,7 @@ " * Align target sequences\n", " * Calculate protein descriptors\n", " * Calculate compound descriptors\n", - "* Proteochemometrics modelling\n", + "* Proteochemometrics modeling\n", " * Helper functions\n", " * Preprocessing\n", " * Model training and validation\n", @@ -68,10 +68,10 @@ "source": [ "### References\n", "\n", - "* Papyrus scripts [github](https://github.com/OlivierBeq/Papyrus-scripts)\n", + "* Papyrus scripts [GitHub](https://github.com/OlivierBeq/Papyrus-scripts)\n", "* Papyrus dataset preprint: [ChemRvix (2021)](https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0)\n", "* Molecular descriptors (Modred): [J. Cheminf., 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y)\n", - "* Protein descriptors (ProDEC) [github](https://github.com/OlivierBeq/ProDEC)\n", + "* Protein descriptors (ProDEC) [GitHub](https://github.com/OlivierBeq/ProDEC)\n", "* Regression metrics [(Scikit learn)](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics)\n", "* XGBoost [Documentation](https://xgboost.readthedocs.io/en/stable/index.html)\n", "* Proteochemometrics review: [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub)\n", @@ -88,7 +88,7 @@ { "cell_type": "markdown", "source": [ - "### Proteochemometrics (PCM) modelling" + "### Proteochemometrics (PCM) modeling" ], "metadata": { "collapsed": false, @@ -101,9 +101,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Proteochemometrics (PCM) consists in the modelling via supervised ML algorithms of a biological endpoint (e.g. compound activity) based on a series of features derived from chemical compounds and target proteins. PCM is an extension of a more widespread bioactivity modelling technique, Quantitative Structure Activity Relationship (QSAR) modelling, which relies solely on chemical features and that was introduced on Talktorial T007. Explore that talktorial to know more about the basic principle of activity prediction using ML.\n", + "Proteochemometrics (PCM) models a biological endpoint (e.g. compound activity) via supervised ML algorithms based on a series of features derived from chemical compounds and target proteins. PCM is an extension of a more widespread bioactivity modeling technique, Quantitative Structure Activity Relationship (QSAR) modeling, which relies solely on chemical features and that was introduced on Talktorial T007. Explore that talktorial to know more about the basic principle of activity prediction using ML.\n", "\n", - "To successfully apply PCM modelling, we need a large dataset of molecule-protein pairs with known bioactivity values, a way of describing molecules and proteins, and a ML algorithm to train a model. Then, we can make predictions for new molecule-protein pairs.\n" + "To successfully apply PCM modeling, we need a large dataset of molecule-protein pairs with known bioactivity values, a way of describing molecules and proteins, and a ML algorithm to train a model. Then, we can make predictions for new molecule-protein pairs.\n" ] }, { @@ -113,14 +113,14 @@ "\n", "\n", "*Figure 1:*\n", - "Proteochemometrics modelling construction from protein and molecular descriptors for which protein-compound pair bioactivity data is known.\n", + "Proteochemometrics modeling construction from protein and molecular descriptors for which protein-compound pair bioactivity data is known.\n", "Figure made by Marina Gorostiola González." ] }, { "cell_type": "markdown", "source": [ - "#### Data preparation" + "### Data preparation" ], "metadata": { "collapsed": false @@ -129,7 +129,7 @@ { "cell_type": "markdown", "source": [ - "##### Papyrus dataset" + "#### Papyrus dataset" ], "metadata": { "collapsed": false @@ -138,7 +138,7 @@ { "cell_type": "markdown", "source": [ - "The Papyrus dataset is a highly curated compilation of bioactivity data intended for modelling in drug discovery. Apart from the bioactivity data contained in the [ChEMBL database](https://www.ebi.ac.uk/chembl/) (see also Talktorial T001), the Papyrus dataset contains binary data for classification tasks from the [ExCAPE-DB](https://solr.ideaconsult.net/search/excape/), and bioactivity data from a number of kinase-specific papers (Figure 1). The Papyrus dataset consists of almost 60M compound-protein pairs, representing data of around 1.2M unique compounds and 7K proteins across 499 different organisms.\n", + "The Papyrus dataset is a highly curated compilation of bioactivity data intended for modeling in drug discovery. Apart from the bioactivity data contained in the [ChEMBL database](https://www.ebi.ac.uk/chembl/) (see also Talktorial T001), the Papyrus dataset contains binary data for classification tasks from the [ExCAPE-DB](https://solr.ideaconsult.net/search/excape/), and bioactivity data from a number of kinase-specific papers (Figure 1). The Papyrus dataset consists of almost 60M compound-protein pairs, representing data of around 1.2M unique compounds and 7K proteins across 499 different organisms.\n", "\n", "The aggregated bioactivity data is standardized, repaired, and normalised to form the Papyrus dataset, which is updated with every new version of ChEMBL released. The Papyrus dataset contains \"high quality\" data associated to pChEMBL values for regression or classification tasks. pChEMBL value is a canonical activity metric defined as $-log_{10}(molar IC_{50}, XC_{50}, EC_{50}, AC_{50}, Ki, Kd, or potency)$. Moreover, \"low quality\" data that is only associated to an active/inactive label can be used for classification tasks (read more about ML applications in Talktorial T007)." ], @@ -153,7 +153,7 @@ "\n", "*Figure 2:*\n", "Papyrus dataset generation scheme.\n", - "Figure taken from: Papyrus scripts [github](https://github.com/OlivierBeq/Papyrus-scripts)." + "Figure taken from: Papyrus scripts [GitHub](https://github.com/OlivierBeq/Papyrus-scripts)." ], "metadata": { "collapsed": false @@ -162,7 +162,7 @@ { "cell_type": "markdown", "source": [ - "##### Molecule encoding: molecular descriptors" + "#### Molecule encoding: molecular descriptors" ], "metadata": { "collapsed": false, @@ -195,7 +195,7 @@ { "cell_type": "markdown", "source": [ - "##### Protein encoding: protein descriptors" + "#### Protein encoding: protein descriptors" ], "metadata": { "collapsed": false @@ -207,7 +207,7 @@ "As done for molecules, the proteins of interest need to be converted to a list of features or protein descriptors. Protein descriptors used in PCM applications are commonly based on the protein sequence and represent physicochemical characteristics of the amino acids that make up the sequence (e.g. Z-scales). Other protein descriptors represent topological (e.g. ST-scales) or electrostatic properties (e.g. MS-WHIM) of the protein sequence. Moreover, if structural information is available, protein descriptors can be derived from the 3D structure of the protein (e.g. sPairs) or the ligand-protein interaction in 3D (e.g. interaction fingerprints). Finally, with the widespread use of deep learning, protein embeddings can be obtained after parsing the protein sequence through the network (e.g. UniRep, AlphaFold embeddings). To read more about protein descriptors, check out these selection of articles ([Brief. Bioinform.,18, (2017)](https://pubmed.ncbi.nlm.nih.gov/26873661/), [Int. J. Mol. Sci., 22, (2021)](https://pubmed.ncbi.nlm.nih.gov/34884688/), [Comput. Struct. Biotechnol. J., 20, (2022)](https://pubmed.ncbi.nlm.nih.gov/35222841/)).\n", "\n", "For protein descriptors based on the protein sequence, an aspect to take into account is that for ML the length of the protein descriptor needs to be the same. However, most proteins do not have the same sequence length. To solve this issue, there are two main approaches:\n", - "* Multiple sequence alignment: If the entire protein is to be included in the model, a multiple sequence alignment can be performed. The final descriptor has as many entries as the number of features per amino acid multiplied by the number of aligned positions. To account for gaps in the alignment, zeros are introduced in the descriptor.\n", + "* Multiple sequence alignment (MSA): If the entire protein is to be included in the model, a MSA can be performed. The final descriptor has as many entries as the number of features per amino acid multiplied by the number of aligned positions. To account for gaps in the alignment, zeros are introduced in the descriptor. A MSA is a tool to identify common patterns between three or more biological sequences, usually DNA, RNA, or protein. One of the most common tools to perform MSA is Clustal Omega (or ClustalO), available as a [webtool](https://www.ebi.ac.uk/Tools/msa/clustalo/).\n", "* Binding pocket selection: To avoid unnecessary features, a binding pocket of the same length can be selected for each protein. Normally, the binding pocket selection is preceded by a multiple sequence alignment and driven by known structural or mutagenesis data.\n", "\n", "Other options are available when proteins are not of the same family or do not share a binding pocket (see [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub))\n", @@ -225,7 +225,7 @@ { "cell_type": "markdown", "source": [ - "#### Machine learning principles: regression" + "### Machine learning principles: regression" ], "metadata": { "collapsed": false, @@ -237,7 +237,7 @@ { "cell_type": "markdown", "source": [ - "The ML principles for PCM modelling are equivalent to those explained for QSAR modelling. However, in this talktorial we will explore a supervised ML application other than classification, this is regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values.\n", + "The ML principles for PCM modeling are equivalent to those explained for QSAR modeling. However, in this talktorial we will explore a supervised ML application other than classification, this is regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values.\n", "NOTE: Target variable is the variable we want to predict in ML. Not to be confused with (protein) target." ], "metadata": { @@ -247,7 +247,7 @@ { "cell_type": "markdown", "source": [ - "##### Data splitting methods" + "#### Data splitting methods" ], "metadata": { "collapsed": false, @@ -259,7 +259,7 @@ { "cell_type": "markdown", "source": [ - "Similarly to classification tasks, in supervised ML regression applications the model is first fitted to a training set and subsequently the predictive performance is evaluated on a test set. Therefore, the original dataset needs to be split between training and test sets. The split needs to ensure that the fitting process has enough data, and that the test set is representative. Normally, the distribution between train and test set is 80/20 or 70/30. Depending on the applicability domain, the split can be done in multiple ways. In PCM modelling, some of the most common splitting methods are:\n", + "Similarly to classification tasks, in supervised ML regression applications the model is first fitted to a training set and subsequently the predictive performance is evaluated on a test set. Therefore, the original dataset needs to be split between training and test sets. The split needs to ensure that the fitting process has enough data, and that the test set is representative. Normally, the distribution between train and test set is 80/20 or 70/30. Depending on the applicability domain, the split can be done in multiple ways. In PCM modeling, some of the most common splitting methods are:\n", "* Random split: This method is not particularly relevant in drug discovery applications as it does not reflect the reality of a drug discovery campaign and it will most likely lead to data leaks between the training and test set. This is, very similar data will be found in both sets, which will lead to an overestimation of the predictive performance of the model. This type of split is commonly used, however, as a baseline and point of reference for other splitting methods, or as a starting point for quick model comparisons.\n", "* Leave one target out (LOTO) split: To evaluate the ability of the model to extrapolate to targets not previously seen, one of the targets can be completely moved to the test set. In a big enough set, instead of one \"some\" targets can be moved to the test set (i.e. Leave some targets out, or LSTO).\n", "* Leave one compound cluster out (LOCCO) split: This method evaluates the ability of the model to extrapolate to compounds with properties not previously seen by the model. Clustering can be done based on different molecular characteristics, such as physicochemical properties or scaffold, for example (see Talktorial T005 to learn more about clustering). One (or several, LSCCO) clusters can then be left out for testing. This methods prevents data leaking in terms of chemistry between training and test sets.\n", @@ -289,7 +289,7 @@ { "cell_type": "markdown", "source": [ - "##### Regression evaluation metrics" + "#### Regression evaluation metrics" ], "metadata": { "collapsed": false, @@ -322,7 +322,7 @@ { "cell_type": "markdown", "source": [ - "##### ML algorithm: Random Forest" + "#### ML algorithm: Random Forest" ], "metadata": { "collapsed": false, @@ -334,7 +334,7 @@ { "cell_type": "markdown", "source": [ - "Different ML algorithms can be used to train PCM models. Some of them include support vector machines (SVM), random forest (RF), and neural networks (NN), that were described in Talktorial T007. RF models have been used extensively in PCM applications due to their efficiency in large datasets and resistance to overfitting with more features. However, deep learning applications are also gaining momentum. See [J. Cheminform., 45, (2017)](https://pubmed.ncbi.nlm.nih.gov/29086168/) for a comparative use of ML methods in PCM modelling.\n", + "Different ML algorithms can be used to train PCM models. Some of them include support vector machines (SVM), random forest (RF), and neural networks (NN), that were described in Talktorial T007. RF models have been used extensively in PCM applications due to their efficiency in large datasets and resistance to overfitting with more features. However, deep learning applications are also gaining momentum. See [J. Cheminform., 45, (2017)](https://pubmed.ncbi.nlm.nih.gov/29086168/) for a comparative use of ML methods in PCM modeling.\n", "In this talktorial, we will use RF. RF is a decision tree-based algorithm, more in detail a bagging ensemble method. This means that there are multiple decision trees trained independently with subsets of features and data and the final prediction is made from a consensus between the independent predictions.\n" ], "metadata": { @@ -347,7 +347,7 @@ { "cell_type": "markdown", "source": [ - "#### Applications of PCM in drug discovery" + "### Applications of PCM in drug discovery" ], "metadata": { "collapsed": false, @@ -359,7 +359,7 @@ { "cell_type": "markdown", "source": [ - "The possibility to predict bioactivity for multiple targets in one model with PCM is very interesting in drug discovery and expands the applicability domain of QSAR modelling. Some applications of this technique are listed below and help answer the following questions in drug discovery:\n", + "The possibility to predict bioactivity for multiple targets in one model with PCM is very interesting in drug discovery and expands the applicability domain of QSAR modeling. Some applications of this technique are listed below and help answer the following questions in drug discovery:\n", "\n", "* Poly-pharmacology: Is it possible to target several proteins of interest simultaneously with one single drug?\n", "* Off-target prediction: What other proteins do these compounds target apart from the intended therapeutic target? Are maybe these off-targets responsible for side effects?\n", @@ -394,28 +394,14 @@ "metadata": {}, "outputs": [], "source": [ - "import os.path\n", + "import json\n", "from pathlib import Path\n", - "import numpy as np\n", - "import pandas as pd\n", "import re\n", - "import json\n", - "\n", - "from papyrus_scripts.download import download_papyrus\n", - "from papyrus_scripts.reader import read_papyrus, read_protein_set\n", - "from papyrus_scripts.preprocess import *\n", - "from papyrus_scripts.utils.IO import get_num_rows_in_file\n", "\n", - "from Bio.Seq import Seq\n", - "from Bio.SeqIO import SeqRecord, write as SeqIO_write, parse as SeqIO_parse\n", - "from Bio.Align.Applications import ClustalOmegaCommandline\n", - "import Bio.AlignIO\n", - "import rich\n", - "from rich_msa import RichAlignment\n", - "\n", - "from prodec import ProteinDescriptors\n", - "from rdkit import Chem\n", - "from mordred import Calculator, descriptors\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", "\n", "from sklearn.preprocessing import RobustScaler\n", "from sklearn.model_selection import train_test_split\n", @@ -423,8 +409,15 @@ "from sklearn.metrics import r2_score,mean_absolute_error\n", "from scipy.stats import pearsonr\n", "\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt" + "import Bio\n", + "import Bio.SeqIO as Bio_SeqIO\n", + "import papyrus_scripts\n", + "from rdkit import Chem\n", + "import rich\n", + "import rich_msa\n", + "import mordred\n", + "import mordred.descriptors as mordred_descriptors\n", + "import prodec" ] }, { @@ -441,7 +434,7 @@ { "cell_type": "markdown", "source": [ - "### Download Payrus dataset" + "### Download Papyrus dataset" ], "metadata": { "collapsed": false @@ -450,7 +443,7 @@ { "cell_type": "markdown", "source": [ - "By default, the download_papyrus function retrieves bioactivity, target and other information for the latest version of the Papyrus dataset. The data retrieved consists of the highest quality continuous bioactivity data (Papyrus++) without stereochemistry annotated." + "To work with the Papyrus dataset, we use the papyrus_scripts [library](https://github.com/OlivierBeq/Papyrus-scripts). This library allows us to download, read, and explore the dataset. Many other features, including bioactivity modeling, are possible using the papyrus_scripts. If you want to dive into them, feel free to follow the [notebook with simple examples](https://github.com/OlivierBeq/Papyrus-scripts/blob/master/notebook_examples/simple_examples.ipynb). By default, the download_papyrus function retrieves bioactivity, target and other information for the latest version of the Papyrus dataset. The data retrieved consists of the highest quality continuous bioactivity data (Papyrus++) without stereochemistry annotated (i.e. nostereo=True and stereo=False). Check out the [documentation](https://github.com/OlivierBeq/Papyrus-scripts/blob/master/src/papyrus_scripts/download.py) to learn more about the options available." ], "metadata": { "collapsed": false @@ -489,7 +482,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "81f4f6bc47f1421288c90fd4973984d9" + "model_id": "88820ff244424ef6b8f8e2c5054d7c26" } }, "metadata": {}, @@ -497,7 +490,7 @@ } ], "source": [ - "download_papyrus(outdir=DATA, version=PAPYRUS_VERSION, nostereo=True, stereo=False, descriptors=None)\n", + "papyrus_scripts.download_papyrus(outdir=DATA, version=PAPYRUS_VERSION, nostereo=True, stereo=False, descriptors=None)\n", "# If you want to download the latest version of the Papyrus dataset, change 'PAPYRUS_VERSION' to 'latest'" ], "metadata": { @@ -529,7 +522,7 @@ { "cell_type": "markdown", "source": [ - "From the Papyrus dataset, we want to extract continuous activity data for all compound-protein pairs for the four human Adenosine receptors. In the Papyrus set, unique compound-target activity values are reported by aggregating data from different assays available. To account for the different types of bioactivity units available, a canonical activity metric defined by ChEMBL is used, pChEMBL value. From the activity aggregation strategies available with the Papyrus set, we will use the Mean (pchembl_value_Mean variable).\n", + "From the Papyrus dataset, we want to extract continuous activity data for all compound-protein pairs for the four human Adenosine receptors; unique compound-target activity values are reported by aggregating data from different assays available. To account for the different types of bioactivity units available, a canonical activity metric defined by ChEMBL is used, pChEMBL value. From the activity aggregation strategies available with the Papyrus set, we will use the Mean (pchembl_value_Mean variable).\n", "\n", "|Receptor|Uniprot accession|\n", "|---|---|\n", @@ -547,7 +540,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "outputs": [], "source": [ "def filter_explore_activity_data(papyrus_version, targets):\n", @@ -568,14 +561,15 @@ " \"\"\"\n", " # Read downloaded Papyrus dataset in chunks, as it does not fit in memory\n", " CHUNKSIZE = 100000\n", - " data = read_papyrus(version=papyrus_version, chunksize=CHUNKSIZE, source_path=DATA)\n", + " data = papyrus_scripts.read_papyrus(version=papyrus_version, chunksize=CHUNKSIZE, source_path=DATA)\n", "\n", " # Create filter for targets of interest\n", " target_accession_list = targets.values()\n", - " filter = keep_accession(data, target_accession_list)\n", + " filter = papyrus_scripts.keep_accession(data, target_accession_list)\n", "\n", " # Iterate through chunks and apply the filter defined\n", - " filtered_data = consume_chunks(filter, total=-(-get_num_rows_in_file('bioactivities', False) // CHUNKSIZE))\n", + " filtered_data = papyrus_scripts.preprocess.consume_chunks(filter,\n", + " total=-(-papyrus_scripts.utils.IO.get_num_rows_in_file('bioactivities', False) // CHUNKSIZE))\n", " # Add column named 'Target' for easier data visualization\n", " filtered_data['Target'] = filtered_data['accession'].apply(lambda x: [i for i in targets.keys() if targets[i]==x][0])\n", "\n", @@ -598,7 +592,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "outputs": [ { "data": { @@ -606,7 +600,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "72efbb6b6f524ad182e8483b68fd4d3a" + "model_id": "42617588611b43feba9e4d115bfb000f" } }, "metadata": {}, @@ -643,7 +637,7 @@ " 'A3': 'P0DMS8'}\n", "\n", "# Filter the Papyrus bioactivity dataset and plot the distribution of activity values for the targets of interest\n", - "ar_data = filter_explore_activity_data(PAPYRUS_VERSION, adenosine_receptors)" + "ar_dataset = filter_explore_activity_data(PAPYRUS_VERSION, adenosine_receptors)" ], "metadata": { "collapsed": false, @@ -655,10 +649,10 @@ { "cell_type": "markdown", "source": [ - "For PCM modelling, we keep from our bioactivity dataset three variables:\n", + "For PCM modeling, we keep from our bioactivity dataset three variables:\n", "* Bioactivity (pchembl_value_mean), which is our target variable to predict\n", - "* Target IDs (target_id), to link the protein descriptors\n", - "* Compound IDs (SMILES), to link the compound descriptors" + "* Target IDs (accession), which is the Uniprot code to link the protein descriptors that we will calculate with ProDEC\n", + "* Compound IDs (SMILES), to link the compound descriptors that we will calculate with Mordred" ], "metadata": { "collapsed": false, @@ -669,20 +663,20 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "outputs": [ { "data": { "text/plain": " SMILES accession \\\n222 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... 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" }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "ar_dataset = ar_data[['SMILES', 'accession', 'pchembl_value_Mean']]\n", + "ar_dataset = ar_dataset[['SMILES', 'accession', 'pchembl_value_Mean']]\n", "ar_dataset" ], "metadata": { @@ -707,7 +701,8 @@ { "cell_type": "markdown", "source": [ - "In order to ensure protein descriptors are of the same length, we first need to align the target sequences. We do this by using the software ClustalO. To begin with, we extract the protein sequences from the target files in Papyrus. The sequences could also be obtained from Uniprot, but tis way we ensure we are always retrieving the canonical isoform sequence." + "In order to ensure protein descriptors are of the same length, we first need to align the target sequences. We do this by creating a MSA with the software Clustal Omega (ClustalO). To begin with, we extract the protein sequences from the target files in Papyrus. The sequences could also be obtained from Uniprot, but this way we ensure we are always retrieving the canonical isoform sequence.\n", + "Since Papyrus also contains bioactivity data for different mutants and species, the main protein identifier (target_id variable) consists of the Uniprot accession code and the mutant ('WT' for wild type). Even though we are interested in the wild type, to map our targets of interest we calculate a new variable called accession to be consistent with the rest of the talktorial." ], "metadata": { "collapsed": false @@ -715,22 +710,24 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "outputs": [ { "data": { - "text/plain": " target_id HGNC_symbol UniProtID Status Organism \\\n80 P30542_WT ADORA1 AA1R_HUMAN reviewed Homo sapiens (Human) \n81 P29274_WT ADORA2A AA2AR_HUMAN reviewed Homo sapiens (Human) \n47 P29275_WT ADORA2B AA2BR_HUMAN reviewed Homo sapiens (Human) \n82 P0DMS8_WT ADORA3 AA3R_HUMAN reviewed Homo sapiens (Human) \n\n Classification Length \\\n80 Membrane receptor->Family A G protein-coupled ... 326 \n81 Membrane receptor->Family A G protein-coupled ... 412 \n47 Membrane receptor->Family A G protein-coupled ... 332 \n82 Membrane receptor->Family A G protein-coupled ... 318 \n\n Sequence accession \n80 MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC... P30542 \n81 MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV... P29274 \n47 MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL... P29275 \n82 MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT... P0DMS8 ", - "text/html": "
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82P0DMS8_WTADORA3AA3R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...318MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT...P0DMS8
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" }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "protein_data = read_protein_set(version=PAPYRUS_VERSION)\n", + "protein_data = papyrus_scripts.read_protein_set(version=PAPYRUS_VERSION)\n", + "# Create new variable 'accession' with the Uniprot accession codes by splitting target_id and keeping the first part\n", "protein_data['accession'] = protein_data['target_id'].apply(lambda x: x.split('_')[0])\n", - "targets = pd.concat(protein_data[protein_data.target_id.str.startswith(x)] for x in adenosine_receptors.values())\n", + "# Filter protein data for our targets of interest based on accession code\n", + "targets = protein_data[protein_data.accession.isin(adenosine_receptors.values())]\n", "targets" ], "metadata": { @@ -743,7 +740,7 @@ { "cell_type": "markdown", "source": [ - "In order to align the sequences with Clustal Omega, we first need to write them into a FASTA file." + "In order to align the sequences with ClustalO, we first need to write them into a FASTA file." ], "metadata": { "collapsed": false @@ -751,17 +748,19 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "outputs": [], "source": [ + "# Create object with sequences and descriptions\n", "records = []\n", "for index, row in targets.reset_index(drop=True).iterrows():\n", - " records.append(SeqRecord(seq=Seq(row.Sequence),\n", + " records.append(Bio_SeqIO.SeqRecord(seq=Bio.Seq.Seq(row['Sequence']),\n", " id=str(index),\n", - " name=row.accession,\n", - " description=' '.join([row.UniProtID, row.Organism, row.Classification])))\n", - "sequences_path = os.path.join(DATA, 'sequences.fasta')\n", - "_ = SeqIO_write(records, sequences_path, 'fasta')" + " name=row['accession'],\n", + " description=' '.join([row['UniProtID'], row['Organism'], row['Classification']])))\n", + "sequences_path = Path(DATA / 'sequences.fasta')\n", + "# Write sequences as .fasta file\n", + "_ = Bio_SeqIO.write(records, sequences_path, 'fasta')" ], "metadata": { "collapsed": false, @@ -790,31 +789,28 @@ "name": "stdout", "output_type": "stream", "text": [ - "RUNNING\n", "RUNNING\n", "FINISHED\n", - "Creating result file: aligned_sequences.out.txt\n", - "Creating result file: aligned_sequences.sequence.txt\n", - "Creating result file: aligned_sequences.aln-fasta.fasta\n", - "Creating result file: aligned_sequences.tree.dnd\n", - "Creating result file: aligned_sequences.phylotree.ph\n", - "Creating result file: aligned_sequences.pim.pim\n", - "Creating result file: aligned_sequences.submission.params\n" + "Creating result file: data\\aligned_sequences.out.txt\n", + "Creating result file: data\\aligned_sequences.sequence.txt\n", + "Creating result file: data\\aligned_sequences.aln-fasta.fasta\n", + "Creating result file: data\\aligned_sequences.tree.dnd\n", + "Creating result file: data\\aligned_sequences.phylotree.ph\n", + "Creating result file: data\\aligned_sequences.pim.pim\n", + "Creating result file: data\\aligned_sequences.submission.params\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "JobId: clustalo-R20221018-141634-0284-84527467-p1m\n" + "JobId: clustalo-R20221027-170800-0191-18047492-p1m\n" ] } ], "source": [ - "os.chdir(DATA) # Move to data folder to generate ClustalO results there\n", "# Query ClustalO webservice from command line\n", - "!python clustalo.py --email m.gorostiola.gonzalez@lacdr.leidenuniv.nl --stype protein --sequence sequences.fasta --outfmt fa --outfile aligned_sequences --order input\n", - "os.chdir(HERE) # Move back to main notebook directory" + "!python data/clustalo.py --email m.gorostiola.gonzalez@lacdr.leidenuniv.nl --stype protein --sequence data/sequences.fasta --outfmt fa --outdir data --outfile aligned_sequences --order input" ], "metadata": { "collapsed": false, @@ -840,8 +836,8 @@ "execution_count": 11, "outputs": [], "source": [ - "alignment_file = os.path.join(DATA, 'aligned_sequences.aln-fasta.fasta')\n", - "aligned_sequences = [str(seq.seq) for seq in SeqIO_parse(alignment_file, 'fasta')]" + "alignment_file = Path(DATA / 'aligned_sequences.aln-fasta.fasta')\n", + "aligned_sequences = [str(seq.seq) for seq in Bio.SeqIO.parse(alignment_file, 'fasta')]" ], "metadata": { "collapsed": false, @@ -868,8 +864,8 @@ "outputs": [ { "data": { - "text/plain": "╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA1R_HU… \u001B[1;36m 1\u001B[0m 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╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA1R_HU…     1  ---MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFCFIVSLAVADVAVGALVIPLAILINIGPQTYFHTCLMVACPVLI  │\n│ 1 AA2AR_H…     1  ------MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLV  │\n│ 2 AA2BR_H…     1  -----MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVAVGLFAIPFAITISLGFCTDFYGCLFLACFVLV  │\n│ 3 AA3R_HU…     1  MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTTTFYFIVSLALADIAVGVLVMPLAIVVSLGITIHFYSCLFMTCLLLI  │\n│                                                                                                                 │\n│ 0 AA1R_HU…    90  LTQSSILALLAIAVDRYLRVKIPLRYKMVVTPRRAAVAIAGCWILSFVVGLTPMFGWNNLSAVER----AWA---ANGSMGEPVIKCEFEKV  │\n│ 1 AA2AR_H…    87  LTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWNN-------CGQPKEGKNHSQGCGEGQVACLFEDV  │\n│ 2 AA2BR_H…    88  LTQSSIFSLLAVAVDRYLAICVPLRYKSLVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCC---LVKCLFENV  │\n│ 3 AA3R_HU…    93  FTHASIMSLLAIAVDRYLRVKLTVRYKRVTTHRRIWLALGLCWLVSFLVGLTPMFGWNMKLTSEYH-------------RNVTFLSCQFVSV  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   175  ISMEYMVYFNFFVWVLPPLLLMVLIYLEVFYLIRKQLNKKVSAS--SGDPQKYYGKELKIAKSLALILFLFALSWLPLHILNCITLFCPSC-  │\n│ 1 AA2AR_H…   172  VPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDC-  │\n│ 2 AA2BR_H…   177  VPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTEL----MDHSRTTLQREIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQG  │\n│ 3 AA3R_HU…   172  MRMDYMVYFSFLTWIFIPLVVMCAIYLDIFYIIRNKLSLNLSN---SKETGAFYGREFKTAKSLFLVLFLFALSWLPLSIINCIIYFNG---  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   264  -HKPSILTYIAIFLTHGNSAMNPIVYAFRIQKFRVTFLKIWNDHFRCQPAPPIDEDLPEE--------------------------------  │\n│ 1 AA2AR_H…   263  SHAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHP  │\n│ 2 AA2BR_H…   265  KNKPKWAMNMAILLSHANSVVNPIVYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQ----------AGVQPALGVGL--------------  │\n│ 3 AA3R_HU…   258  -EVPQLVLYMGILLSHANSMMNPIVYAYKIKKFKETYLLILKACVVCHPSDSLDTSIEKNSE------------------------------  │\n│                                                                                                                 │\n│ 0 AA1R_HU…   323  --RPDD----------------------------------------------------                                    │\n│ 1 AA2AR_H…   355  ERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS                                    │\n│ 2 AA2BR_H…   333  ----------------------------------------------------------                                    │\n│ 3 AA3R_HU…   319  ----------------------------------------------------------                                    │\n╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n
\n" + "text/plain": "╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA2BR_H… \u001B[1;36m 1\u001B[0m \u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;31mM\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;34mE\u001B[0m\u001B[1;32mT\u001B[0m\u001B[1;32mQ\u001B[0m\u001B[1;34mD\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;32mY\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;34mE\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;32mS\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;32mG\u001B[0m\u001B[1;32mN\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;32mC\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;32mG\u001B[0m\u001B[1;32mT\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;32mN\u001B[0m\u001B[1;32mT\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;32mQ\u001B[0m\u001B[1;32mT\u001B[0m\u001B[1;31mP\u001B[0m\u001B[1;32mT\u001B[0m\u001B[1;32mN\u001B[0m\u001B[1;32mY\u001B[0m\u001B[1;31mF\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;32mS\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;34mD\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;32mG\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mF\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;31mP\u001B[0m\u001B[1;31mF\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;32mT\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;32mS\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;32mG\u001B[0m\u001B[1;31mF\u001B[0m\u001B[1;32mC\u001B[0m\u001B[1;32mT\u001B[0m\u001B[1;34mD\u001B[0m\u001B[1;31mF\u001B[0m\u001B[1;32mY\u001B[0m\u001B[1;32mG\u001B[0m\u001B[1;32mC\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mF\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;32mC\u001B[0m\u001B[1;31mF\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mV\u001B[0m │\n│ 1 AA1R_HU… \u001B[1;36m 1\u001B[0m \u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;38;5;239m-\u001B[0m\u001B[1;31mM\u001B[0m\u001B[1;31mP\u001B[0m\u001B[1;31mP\u001B[0m\u001B[1;32mS\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;32mS\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mF\u001B[0m\u001B[1;32mQ\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;32mY\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;32mG\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;34mE\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;32mS\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mP\u001B[0m\u001B[1;32mG\u001B[0m\u001B[1;32mN\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;31mW\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;38;5;129mK\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;32mN\u001B[0m\u001B[1;32mQ\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;38;5;129mR\u001B[0m\u001B[1;34mD\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;32mT\u001B[0m\u001B[1;31mF\u001B[0m\u001B[1;32mC\u001B[0m\u001B[1;31mF\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;32mS\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;34mD\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;32mG\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;31mP\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;32mN\u001B[0m\u001B[1;31mI\u001B[0m\u001B[1;32mG\u001B[0m\u001B[1;31mP\u001B[0m\u001B[1;32mQ\u001B[0m\u001B[1;32mT\u001B[0m\u001B[1;32mY\u001B[0m\u001B[1;31mF\u001B[0m\u001B[1;32mH\u001B[0m\u001B[1;32mT\u001B[0m\u001B[1;32mC\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mM\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mA\u001B[0m\u001B[1;32mC\u001B[0m\u001B[1;31mP\u001B[0m\u001B[1;31mV\u001B[0m\u001B[1;31mL\u001B[0m\u001B[1;31mI\u001B[0m │\n│ 2 AA2AR_H… \u001B[1;36m 1\u001B[0m 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╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA2BR_H…     1  -----MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVAVGLFAIPFAITISLGFCTDFYGCLFLACFVLV  │\n│ 1 AA1R_HU…     1  ---MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFCFIVSLAVADVAVGALVIPLAILINIGPQTYFHTCLMVACPVLI  │\n│ 2 AA2AR_H…     1  ------MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLV  │\n│ 3 AA3R_HU…     1  MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTTTFYFIVSLALADIAVGVLVMPLAIVVSLGITIHFYSCLFMTCLLLI  │\n│                                                                                                                 │\n│ 0 AA2BR_H…    88  LTQSSIFSLLAVAVDRYLAICVPLRYKSLVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCC---LVKCLFENV  │\n│ 1 AA1R_HU…    90  LTQSSILALLAIAVDRYLRVKIPLRYKMVVTPRRAAVAIAGCWILSFVVGLTPMFGWNNLSAVER----AWA---ANGSMGEPVIKCEFEKV  │\n│ 2 AA2AR_H…    87  LTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWNN-------CGQPKEGKNHSQGCGEGQVACLFEDV  │\n│ 3 AA3R_HU…    93  FTHASIMSLLAIAVDRYLRVKLTVRYKRVTTHRRIWLALGLCWLVSFLVGLTPMFGWNMKLTSEYH-------------RNVTFLSCQFVSV  │\n│                                                                                                                 │\n│ 0 AA2BR_H…   177  VPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTEL----MDHSRTTLQREIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQG  │\n│ 1 AA1R_HU…   175  ISMEYMVYFNFFVWVLPPLLLMVLIYLEVFYLIRKQLNKKVSAS--SGDPQKYYGKELKIAKSLALILFLFALSWLPLHILNCITLFCPSC-  │\n│ 2 AA2AR_H…   172  VPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDC-  │\n│ 3 AA3R_HU…   172  MRMDYMVYFSFLTWIFIPLVVMCAIYLDIFYIIRNKLSLNLSN---SKETGAFYGREFKTAKSLFLVLFLFALSWLPLSIINCIIYFNG---  │\n│                                                                                                                 │\n│ 0 AA2BR_H…   265  KNKPKWAMNMAILLSHANSVVNPIVYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQ----------AGVQPALGVGL--------------  │\n│ 1 AA1R_HU…   264  -HKPSILTYIAIFLTHGNSAMNPIVYAFRIQKFRVTFLKIWNDHFRCQPAPPIDEDLPEE--------------------------------  │\n│ 2 AA2AR_H…   263  SHAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHP  │\n│ 3 AA3R_HU…   258  -EVPQLVLYMGILLSHANSMMNPIVYAYKIKKFKETYLLILKACVVCHPSDSLDTSIEKNSE------------------------------  │\n│                                                                                                                 │\n│ 0 AA2BR_H…   333  ----------------------------------------------------------                                    │\n│ 1 AA1R_HU…   323  --RPDD----------------------------------------------------                                    │\n│ 2 AA2AR_H…   355  ERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS                                    │\n│ 3 AA3R_HU…   319  ----------------------------------------------------------                                    │\n╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n
\n" }, "metadata": {}, "output_type": "display_data" @@ -878,7 +874,7 @@ "source": [ "# Read MSA\n", "msa = Bio.AlignIO.read(alignment_file, \"fasta\")\n", - "viewer = RichAlignment(\n", + "viewer = rich_msa.RichAlignment(\n", " names=[record.description for record in msa],\n", " sequences=[str(record.seq) for record in msa],\n", ")\n", @@ -938,7 +934,7 @@ ], "source": [ "# Parse ProDEC descriptors\n", - "desc_factory = ProteinDescriptors()\n", + "desc_factory = prodec.ProteinDescriptors()\n", "# Print available descriptors\n", "print('Available ProDEC descriptors: ', desc_factory.available_descriptors)" ], @@ -1032,7 +1028,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "94ff9603ffa241c293c00e3d0e79265f" + "model_id": "ab1f1f6a65bc4b46ae0bf3cbe2ee7ccb" } }, "metadata": {}, @@ -1040,8 +1036,8 @@ }, { "data": { - "text/plain": " accession Zscale_1 Zscale_2 Zscale_3 Zscale_4 Zscale_5 Zscale_6 \\\n0 P30542 0.00 0.00 0.00 0.00 0.00 0.00 \n1 P29274 0.00 0.00 0.00 0.00 0.00 0.00 \n2 P29275 0.00 0.00 0.00 0.00 0.00 0.00 \n3 P0DMS8 -2.49 -0.27 -0.41 -1.22 0.88 2.23 \n\n Zscale_7 Zscale_8 Zscale_9 ... Zscale_1269 Zscale_1270 Zscale_1271 \\\n0 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n1 0.00 0.00 0.00 ... 0.09 2.23 -5.36 \n2 0.00 0.00 0.00 ... 0.00 0.00 0.00 \n3 3.22 1.45 0.84 ... 0.00 0.00 0.00 \n\n Zscale_1272 Zscale_1273 Zscale_1274 Zscale_1275 Zscale_1276 \\\n0 0.0 0.00 0.00 0.00 0.00 \n1 0.3 -2.69 -2.53 -1.29 1.96 \n2 0.0 0.00 0.00 0.00 0.00 \n3 0.0 0.00 0.00 0.00 0.00 \n\n Zscale_1277 Zscale_1278 \n0 0.00 0.00 \n1 -1.63 0.57 \n2 0.00 0.00 \n3 0.00 0.00 \n\n[4 rows x 1279 columns]", - "text/html": "
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" }, "execution_count": 16, "metadata": {}, @@ -1085,10 +1081,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 32, "outputs": [], "source": [ - "def calculate_molecular_descriptors(bioactivity_dataset, moldred_descriptors):\n", + "def calculate_molecular_descriptors(bioactivity_dataset, user_descriptors):\n", " \"\"\"\n", " Calculate compound molecular descriptors of choice for unique molecules in the bioactivity dataset\n", "\n", @@ -1096,8 +1092,8 @@ " ----------\n", " bioactivity_dataset : pandas.Dataframe\n", " Pandas dataframe with bioactivity dataset for PCM\n", - " moldred_descriptors : list\n", - " List of descriptors from Moldred to calculate\n", + " user_descriptors : list\n", + " List of descriptors from Mordred to calculate\n", " Use ['all'] for calculate all possible descriptors\n", "\n", " Returns\n", @@ -1108,18 +1104,18 @@ " # Extract unique molecules from the bioactivity dataset\n", " molecules = [Chem.MolFromSmiles(x) for x in bioactivity_dataset.SMILES.unique()]\n", "\n", - " # Use Moldred to calculate molecular descriptors of interest\n", - " if moldred_descriptors == ['all']:\n", - " molecular_descriptor = Calculator(descriptors, ignore_3D=True).pandas(molecules, pynb=False)\n", + " # Use Mordred to calculate molecular descriptors of interest\n", + " if user_descriptors == ['all']:\n", + " molecular_descriptor = mordred.Calculator(mordred_descriptors, ignore_3D=True).pandas(molecules, pynb=False)\n", " else:\n", - " moldred_list = [descriptors.__dict__[descriptor] for descriptor in moldred_descriptors]\n", - " molecular_descriptor = Calculator(moldred_list, ignore_3D=True).pandas(molecules,ipynb=False)\n", + " mordred_list = [mordred_descriptors.__dict__[descriptor] for descriptor in user_descriptors]\n", + " molecular_descriptor = mordred.Calculator(mordred_list, ignore_3D=True).pandas(molecules,ipynb=False)\n", "\n", " # Clean descriptors by: renaming duplicated columns; replacing values bigger than 2,147,483,647 by 0;\n", " # rounding values to 3 decimals; converting to minimal memory footprint; inserting SMILES in first column\n", " mordred_descs_names = {\n", " str(x): re.sub(r'(.*F?)A(H?Ring)$', r'\\1aliph\\2', re.sub(r'(.*F?)a(H?Ring)$', r'\\1arom\\2', str(x))) for x in\n", - " Calculator(descriptors, ignore_3D=True).descriptors}\n", + " mordred.Calculator(mordred.descriptors, ignore_3D=True).descriptors}\n", "\n", " molecular_descriptor = pd.DataFrame(molecular_descriptor.fill_missing(np.NAN).rename(mordred_descs_names)).\\\n", " astype(np.float32).replace([np.inf, -np.inf], np.NAN).round(3)\n", @@ -1138,13 +1134,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 33, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 6898/6898 [00:10<00:00, 636.77it/s]\n" + "100%|██████████| 6898/6898 [00:12<00:00, 574.14it/s]\n" ] }, { @@ -1152,7 +1148,7 @@ "text/plain": " SMILES ABC ABCGG \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... 21.041 17.684 \n1 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... 20.701 15.635 \n2 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 23.23 17.455999 \n3 CCNC(=O)C1OC(n2cnc3c2ncnc3Nc2ccc(OCC(=O)Nc3ccc... 31.336 22.212999 \n4 NC(=NC(=O)Cn1c(O)c2CCCCc2c1O)Nc1nc2c(cccc2)s1 21.408001 17.066 \n... ... ... ... \n6893 CC(C)(C)C1(O)CCN2CC3c4c(cccc4)CCc4cccc(c43)C2C1 22.177999 16.375 \n6894 CNC(=O)C12CC1C(n1cnc3c(NC(C)C)nc(C#Cc4ccc(Cl)s... 26.351 22.591999 \n6895 CCCOc1ccc(C=Cc2cc3c(c(=O)n(C)c(=O)n3C)n2C)cc1 20.021999 15.893 \n6896 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... 23.736 18.441999 \n6897 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 18.511 15.661 \n\n nAcid nBase nAtom nHeavyAtom nSpiro nBridgehead nHetero ... nN \\\n0 0 1 51 27 0 0 8 ... 6 \n1 0 0 42 26 0 0 8 ... 4 \n2 0 0 43 29 0 0 8 ... 6 \n3 0 0 66 40 0 0 14 ... 7 \n4 0 3 46 27 0 0 9 ... 5 \n... ... ... ... ... ... ... ... ... .. \n6893 0 1 58 27 0 0 2 ... 1 \n6894 0 0 56 33 0 0 11 ... 6 \n6895 0 0 49 26 0 0 6 ... 3 \n6896 0 0 52 30 0 0 9 ... 4 \n6897 0 0 43 24 0 0 8 ... 5 \n\n nO nS nP nF nCl nBr nI nX BalabanJ \n0 2 0 0 0 0 0 0 0 1.631 \n1 3 1 0 0 0 0 0 0 1.307 \n2 2 0 0 0 0 0 0 0 1.328 \n3 6 0 0 0 1 0 0 1 1.043 \n4 3 1 0 0 0 0 0 0 1.234 \n... .. .. .. .. ... ... .. .. ... \n6893 1 0 0 0 0 0 0 0 1.46 \n6894 3 1 0 0 1 0 0 1 1.303 \n6895 3 0 0 0 0 0 0 0 1.479 \n6896 3 0 0 2 0 0 0 2 1.318 \n6897 3 0 0 0 0 0 0 0 1.68 \n\n[6898 rows x 23 columns]", "text/html": "
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SMILESABCABCGGnAcidnBasenAtomnHeavyAtomnSpironBridgeheadnHetero...nNnOnSnPnFnClnBrnInXBalabanJ
0Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...21.04117.684015127008...6200000001.631
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4NC(=NC(=O)Cn1c(O)c2CCCCc2c1O)Nc1nc2c(cccc2)s121.40800117.066034627009...5310000001.234
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6893CC(C)(C)C1(O)CCN2CC3c4c(cccc4)CCc4cccc(c43)C2C122.17799916.375015827002...1100000001.46
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6895CCCOc1ccc(C=Cc2cc3c(c(=O)n(C)c(=O)n3C)n2C)cc120.02199915.893004926006...3300000001.479
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6897CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc1218.51115.661004324008...5300000001.68
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6898 rows × 23 columns

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" }, - "execution_count": 18, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1171,7 +1167,7 @@ { "cell_type": "markdown", "source": [ - "### Proteochemometrics modelling" + "### Proteochemometrics modeling" ], "metadata": { "collapsed": false, @@ -1183,7 +1179,7 @@ { "cell_type": "markdown", "source": [ - "When our dataset is complete with all the descriptors for proteins and compounds, we can start with the modelling part. Here, we will use a Random Forest (RF) ML regression model to predict the bioactivity of our compound-target pairs.\n", + "When our dataset is complete with all the descriptors for proteins and compounds, we can start with the modeling part. Here, we will use a Random Forest (RF) ML regression model to predict the bioactivity of our compound-target pairs.\n", "\n", "We will try two methods to split our dataset between training and test set:\n", "* Random split\n", @@ -1228,12 +1224,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 34, "outputs": [], "source": [ "def split_train_test(pcm_dataset, test_size, split_method, loto_target=None, loto_accession='None'):\n", " \"\"\"\n", - " Split a dataset for PCM modelling in train and test set based on the split method of choice\n", + " Split a dataset for PCM modeling in train and test set based on the split method of choice\n", "\n", " Parameters\n", " ----------\n", @@ -1300,7 +1296,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 35, "outputs": [], "source": [ "def train_validate_pcm_model(targets_dict, train, test):\n", @@ -1399,7 +1395,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 36, "outputs": [], "source": [ "def train_validate_qsar_model(qsar_dataset, target, accession, test_size):\n", @@ -1495,14 +1491,14 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 37, "outputs": [ { "data": { "text/plain": " SMILES accession \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n1 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n2 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n3 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n4 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n... ... ... \n12714 Cn1cc(Nc2nc(-c3ccco3)c(-c3ncncc3)cn2)ccc1=O P29275 \n12715 N#Cc1c(-c2ccc(OCC3CC3)cc2)c(C#N)c(SCC(N)=O)nc1N P29275 \n12716 O=C(Cc1cccc2c1cccc2)Nc1nc2nn(CCc3ccccc3)cc2c2n... P29275 \n12717 COc1c(OCC(=O)O)ccc(-c2cc3c([nH]2)c(=O)n(C)c(=O... P29275 \n12718 CCCn1c(=O)c2c([nH]c(-c3cc(OCC4CC(=O)N(c5ccc(F)... P29275 \n\n pchembl_value_Mean Zscale_1 Zscale_2 Zscale_3 Zscale_4 Zscale_5 \\\n0 8.6800 0.00 0.00 0.00 0.00 0.00 \n1 6.6800 0.00 0.00 0.00 0.00 0.00 \n2 4.8200 0.00 0.00 0.00 0.00 0.00 \n3 5.6500 0.00 0.00 0.00 0.00 0.00 \n4 7.1515 -2.49 -0.27 -0.41 -1.22 0.88 \n... ... ... ... ... ... ... \n12714 7.5515 0.00 0.00 0.00 0.00 0.00 \n12715 7.5100 0.00 0.00 0.00 0.00 0.00 \n12716 7.3672 0.00 0.00 0.00 0.00 0.00 \n12717 6.5700 0.00 0.00 0.00 0.00 0.00 \n12718 6.6800 0.00 0.00 0.00 0.00 0.00 \n\n Zscale_6 Zscale_7 ... nN nO nS nP nF nCl nBr nI nX BalabanJ \n0 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.631 \n1 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.631 \n2 0.00 0.00 ... 4 3 1 0 0 0 0 0 0 1.307 \n3 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.328 \n4 2.23 3.22 ... 6 2 0 0 0 0 0 0 0 1.328 \n... ... ... ... .. .. .. .. .. ... ... .. .. ... \n12714 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.368 \n12715 0.00 0.00 ... 5 2 1 0 0 0 0 0 0 1.613 \n12716 0.00 0.00 ... 7 2 0 0 0 0 0 0 0 0.998 \n12717 0.00 0.00 ... 3 6 0 0 0 0 0 0 0 1.608 \n12718 0.00 0.00 ... 6 5 0 0 1 0 0 0 1 1.103 \n\n[12719 rows x 1303 columns]", "text/html": "
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SMILESaccessionpchembl_value_MeanZscale_1Zscale_2Zscale_3Zscale_4Zscale_5Zscale_6Zscale_7...nNnOnSnPnFnClnBrnInXBalabanJ
0Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.68000.000.000.000.000.000.000.00...6200000001.631
1Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.68000.000.000.000.000.000.000.00...6200000001.631
2Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.82000.000.000.000.000.000.000.00...4310000001.307
3O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.65000.000.000.000.000.000.000.00...6200000001.328
4O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.1515-2.49-0.27-0.41-1.220.882.233.22...6200000001.328
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12714Cn1cc(Nc2nc(-c3ccco3)c(-c3ncncc3)cn2)ccc1=OP292757.55150.000.000.000.000.000.000.00...6200000001.368
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" }, - "execution_count": 22, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1524,7 +1520,7 @@ { "cell_type": "markdown", "source": [ - "For QSAR modelling, we do the same but we do not include the protein descriptors. This results on a dataset for modelling with a significantly reduced number of features." + "For QSAR modeling, we do the same but we do not include the protein descriptors. This results on a dataset for modeling with a significantly reduced number of features." ], "metadata": { "collapsed": false, @@ -1535,14 +1531,14 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 38, "outputs": [ { "data": { "text/plain": " SMILES accession \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n1 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n2 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n3 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n4 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n... ... ... \n12714 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... P30542 \n12715 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P0DMS8 \n12716 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29274 \n12717 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29275 \n12718 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P30542 \n\n pchembl_value_Mean ABC ABCGG nAcid nBase nAtom nHeavyAtom \\\n0 8.6800 21.041 17.684 0 1 51 27 \n1 6.6800 21.041 17.684 0 1 51 27 \n2 4.8200 20.701 15.635 0 0 42 26 \n3 7.1515 23.23 17.455999 0 0 43 29 \n4 5.6500 23.23 17.455999 0 0 43 29 \n... ... ... ... ... ... ... ... \n12714 5.1000 23.736 18.441999 0 0 52 30 \n12715 7.6100 18.511 15.661 0 0 43 24 \n12716 7.3500 18.511 15.661 0 0 43 24 \n12717 5.1500 18.511 15.661 0 0 43 24 \n12718 7.3400 18.511 15.661 0 0 43 24 \n\n nSpiro ... nN nO nS nP nF nCl nBr nI nX BalabanJ \n0 0 ... 6 2 0 0 0 0 0 0 0 1.631 \n1 0 ... 6 2 0 0 0 0 0 0 0 1.631 \n2 0 ... 4 3 1 0 0 0 0 0 0 1.307 \n3 0 ... 6 2 0 0 0 0 0 0 0 1.328 \n4 0 ... 6 2 0 0 0 0 0 0 0 1.328 \n... ... ... .. .. .. .. .. ... ... .. .. ... \n12714 0 ... 4 3 0 0 2 0 0 0 2 1.318 \n12715 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n12716 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n12717 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n12718 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n\n[12719 rows x 25 columns]", "text/html": "
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SMILESaccessionpchembl_value_MeanABCABCGGnAcidnBasenAtomnHeavyAtomnSpiro...nNnOnSnPnFnClnBrnInXBalabanJ
0Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.680021.04117.6840151270...6200000001.631
1Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.680021.04117.6840151270...6200000001.631
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3O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.151523.2317.4559990043290...6200000001.328
4O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.650023.2317.4559990043290...6200000001.328
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12714Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc...P305425.100023.73618.4419990052300...4300200021.318
12715CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P0DMS87.610018.51115.6610043240...5300000001.68
12716CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292747.350018.51115.6610043240...5300000001.68
12717CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292755.150018.51115.6610043240...5300000001.68
12718CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P305427.340018.51115.6610043240...5300000001.68
\n

12719 rows × 25 columns

\n
" }, - "execution_count": 23, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -1597,7 +1593,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 39, "outputs": [ { "name": "stdout", @@ -1623,7 +1619,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -1632,16 +1628,16 @@ "text": [ "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.6852262686527153,\n", - " \"R2 score\": 0.4645718547018661,\n", - " \"MAE\": 0.6407882396824323\n", + " \"Pearson r\": 0.6849536131301579,\n", + " \"R2 score\": 0.4643494527962835,\n", + " \"MAE\": 0.6418489491748536\n", "}\n" ] }, { "data": { "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": {}, "output_type": "display_data" @@ -1688,7 +1684,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 41, "outputs": [ { "name": "stdout", @@ -1696,34 +1692,34 @@ "text": [ "=== QSAR model performance A1 ===\n", "{\n", - " \"Pearson r\": 0.595672468880893,\n", - " \"R2 score\": 0.3527408234746222,\n", - " \"MAE\": 0.5948338517708422\n", + " \"Pearson r\": 0.6038154800912579,\n", + " \"R2 score\": 0.3629339568560689,\n", + " \"MAE\": 0.5951719649890358\n", "}\n", "=== QSAR model performance A2A ===\n", "{\n", - " \"Pearson r\": 0.642402046425395,\n", - " \"R2 score\": 0.4114591741751973,\n", - " \"MAE\": 0.6908901896785428\n", + " \"Pearson r\": 0.6366940512586494,\n", + " \"R2 score\": 0.40404170942848616,\n", + " \"MAE\": 0.697996378368688\n", "}\n", "=== QSAR model performance A2B ===\n", "{\n", - " \"Pearson r\": 0.7109948284816284,\n", - " \"R2 score\": 0.4962178778442754,\n", - " \"MAE\": 0.5510204294516342\n", + " \"Pearson r\": 0.7073059813985801,\n", + " \"R2 score\": 0.49122750443919383,\n", + " \"MAE\": 0.5538678217807245\n", "}\n", "=== QSAR model performance A3 ===\n", "{\n", - " \"Pearson r\": 0.6591265363449496,\n", - " \"R2 score\": 0.432561408555847,\n", - " \"MAE\": 0.6934180112934113\n", + " \"Pearson r\": 0.6634620887237606,\n", + " \"R2 score\": 0.4378357788382091,\n", + " \"MAE\": 0.6887102919977854\n", "}\n" ] }, { "data": { "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": {}, "output_type": "display_data" @@ -1775,7 +1771,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 42, "outputs": [ { "name": "stdout", @@ -1787,9 +1783,9 @@ "Test set has 3519 datapoints (27.667 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.23569780962301712,\n", - " \"R2 score\": -0.13386952750922565,\n", - " \"MAE\": 0.8689090617261386\n", + " \"Pearson r\": 0.2536376583808103,\n", + " \"R2 score\": -0.0975202887751574,\n", + " \"MAE\": 0.8507882684261455\n", "}\n", "== Leave one target out split ==\n", "Target left out for testing is A2A\n", @@ -1797,9 +1793,9 @@ "Test set has 3991 datapoints (31.378 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.18495789114122146,\n", - " \"R2 score\": -0.057385950238118655,\n", - " \"MAE\": 0.9729507524765646\n", + " \"Pearson r\": 0.19066687766825757,\n", + " \"R2 score\": -0.0616928437864086,\n", + " \"MAE\": 0.9748460591657273\n", "}\n", "== Leave one target out split ==\n", "Target left out for testing is A2B\n", @@ -1807,9 +1803,9 @@ "Test set has 1988 datapoints (15.63 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.005277079367292801,\n", - " \"R2 score\": -0.25410344457937306,\n", - " \"MAE\": 0.9783984679304268\n", + " \"Pearson r\": 0.010846572454369874,\n", + " \"R2 score\": -0.252401814599831,\n", + " \"MAE\": 0.9748361950597918\n", "}\n", "== Leave one target out split ==\n", "Target left out for testing is A3\n", @@ -1817,16 +1813,16 @@ "Test set has 3221 datapoints (25.324 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.08667633542325252,\n", - " \"R2 score\": -0.27063646454770796,\n", - " \"MAE\": 1.053553854194796\n", + " \"Pearson r\": 0.11818194406647817,\n", + " \"R2 score\": -0.2752711043423113,\n", + " \"MAE\": 1.0569179680805063\n", "}\n" ] }, { "data": { "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": {}, "output_type": "display_data" @@ -1884,7 +1880,7 @@ "## Quiz\n", "\n", "1. What types of features are needed for PCM?\n", - "2. How many types of training/test set splitting methods commonly used in PCM modelling do you know?\n", + "2. How many types of training/test set splitting methods commonly used in PCM modeling do you know?\n", "3. Which applications do you know of PCM in drug discovery?" ] } From dea919bf1155b93c9392f6d51c6a6b0eefbddbe1 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Fri, 28 Oct 2022 11:43:53 +0200 Subject: [PATCH 32/62] Add more Dominique's comments --- .../talktorial.ipynb | 145 +++++++++--------- 1 file changed, 76 insertions(+), 69 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index a07a23a0..90e9b747 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -140,7 +140,7 @@ "source": [ "The Papyrus dataset is a highly curated compilation of bioactivity data intended for modeling in drug discovery. Apart from the bioactivity data contained in the [ChEMBL database](https://www.ebi.ac.uk/chembl/) (see also Talktorial T001), the Papyrus dataset contains binary data for classification tasks from the [ExCAPE-DB](https://solr.ideaconsult.net/search/excape/), and bioactivity data from a number of kinase-specific papers (Figure 1). The Papyrus dataset consists of almost 60M compound-protein pairs, representing data of around 1.2M unique compounds and 7K proteins across 499 different organisms.\n", "\n", - "The aggregated bioactivity data is standardized, repaired, and normalised to form the Papyrus dataset, which is updated with every new version of ChEMBL released. The Papyrus dataset contains \"high quality\" data associated to pChEMBL values for regression or classification tasks. pChEMBL value is a canonical activity metric defined as $-log_{10}(molar IC_{50}, XC_{50}, EC_{50}, AC_{50}, Ki, Kd, or potency)$. Moreover, \"low quality\" data that is only associated to an active/inactive label can be used for classification tasks (read more about ML applications in Talktorial T007)." + "The aggregated bioactivity data is standardized, repaired, and normalized to form the Papyrus dataset, which is updated with every new version of ChEMBL released. The Papyrus dataset contains \"high quality\" data associated with pChEMBL values for regression or classification tasks. pChEMBL value is a canonical activity metric defined as $-log_{10}(molar IC_{50}, XC_{50}, EC_{50}, AC_{50}, Ki, Kd, or potency)$. Moreover, \"low quality\" data that is only associated with an active/inactive label can be used for classification tasks (read more about ML applications in Talktorial T007)." ], "metadata": { "collapsed": false @@ -262,8 +262,8 @@ "Similarly to classification tasks, in supervised ML regression applications the model is first fitted to a training set and subsequently the predictive performance is evaluated on a test set. Therefore, the original dataset needs to be split between training and test sets. The split needs to ensure that the fitting process has enough data, and that the test set is representative. Normally, the distribution between train and test set is 80/20 or 70/30. Depending on the applicability domain, the split can be done in multiple ways. In PCM modeling, some of the most common splitting methods are:\n", "* Random split: This method is not particularly relevant in drug discovery applications as it does not reflect the reality of a drug discovery campaign and it will most likely lead to data leaks between the training and test set. This is, very similar data will be found in both sets, which will lead to an overestimation of the predictive performance of the model. This type of split is commonly used, however, as a baseline and point of reference for other splitting methods, or as a starting point for quick model comparisons.\n", "* Leave one target out (LOTO) split: To evaluate the ability of the model to extrapolate to targets not previously seen, one of the targets can be completely moved to the test set. In a big enough set, instead of one \"some\" targets can be moved to the test set (i.e. Leave some targets out, or LSTO).\n", - "* Leave one compound cluster out (LOCCO) split: This method evaluates the ability of the model to extrapolate to compounds with properties not previously seen by the model. Clustering can be done based on different molecular characteristics, such as physicochemical properties or scaffold, for example (see Talktorial T005 to learn more about clustering). One (or several, LSCCO) clusters can then be left out for testing. This methods prevents data leaking in terms of chemistry between training and test sets.\n", - "* Temporal split: This method was developed in order to account for the usual timeline of drug discovery campaigns, where chemical series are populated sequentially over time. In this approach, the molecules included in the training set are those released until certain date and the rest (most novel) are included in the test set.\n", + "* Leave one compound cluster out (LOCCO) split: This method evaluates the ability of the model to extrapolate to compounds with properties not previously seen by the model. Clustering can be done based on different molecular characteristics, such as physicochemical properties or scaffold, for example (see Talktorial T005 to learn more about clustering). One (or several, LSCCO) cluster(s) can then be left out for testing. This method prevents data leaking in terms of chemistry between training and test sets.\n", + "* Temporal split: This method was developed in order to account for the usual timeline of drug discovery campaigns, where chemical series are populated sequentially over time. In this approach, the molecules included in the training set are those released until a certain date and the rest (most novel) are included in the test set.\n", "* Stratified split per target: This method can be applied to any of the splitting methods described above (except LOTO), and aims to include data of all targets in both the training and test set, so that additional target-compound interactions can be extracted by the model. (NOTE: stratification can be also done in regards to other reference points apart from targets, for example classes in classification tasks, to make sure that the distribution is similar across training and test set)." ], "metadata": { @@ -304,11 +304,11 @@ "\n", "To evaluate the predictive performance of a regression model, there are several metrics that in simple terms measure the differences between the true target values and the predictions made by the model. These metrics can be used in cross-validation on the training set (see Talktorial T007) or in the test set. The most commonly used metrics include:\n", "\n", - "* Coefficient of determination ($R^{2}$ score): Represents the portion of variance of the target variable that has been explained by the independent variables (features) in the model. $R^{2}$ score varies between 1.0 (best score) and minus infinite, where 0.0 represents a model that always predicts the average target variable. As the variance is dataset dependent, it might not be meaningful metric to compare between datasets. When dealing with linear regression, and model fitting and evaluation are performed on a single dataset, $R^{2}$ is equivalent to the square of the Pearson correlation coefficient, described below, and can be noted as $r^{2}$.\n", + "* Coefficient of determination ($R^{2}$ score): Represents the portion of variance of the target variable that has been explained by the independent variables (features) in the model. $R^{2}$ score varies between 1.0 (best score) and minus infinite, where 0.0 represents a model that always predicts the average target variable. As the variance is dataset-dependent, it might not be a meaningful metric to compare between datasets. When dealing with linear regression, and model fitting and evaluation are performed on a single dataset, $R^{2}$ is equivalent to the square of the Pearson correlation coefficient, described below, and can be noted as $r^{2}$.\n", "* Pearson's correlation coefficient (Pearson's $r$): Is a measure of the linear correlation between the true and predicted values of the target variable. It is calculated as the covariance of the two variables divided by the product of their standard deviation. Pearson's $r$ can vary between 1.0 (a perfect positive correlation) and -1.0 (a perfect negative correlation), where 1.0 would represent a perfect prediction.\n", "* Mean absolute error (MAE): Measures the average absolute difference between the predicted and the true values. MAE is interpreted based on the scale of the data, and it varies between infinite and 0.0 (best).\n", "* Mean squared error (MSE): Measures the average of the squares of the difference between the predicted and the true values. It varies between infinite and 0.0 (best).\n", - "* Root mean square error (RMSE): It is the square root of the MSE and represents the standard deviation of the prediction errors respect to the line of best fit. RMSE is a measure of accuracy and it cannot be applied to compare between datasets, as it is scale-dependent. It varies between infinite and 0.0 (best).\n", + "* Root mean square error (RMSE): It is the square root of the MSE and represents the standard deviation of the prediction errors with respect to the line of best fit. RMSE is a measure of accuracy and it cannot be applied to compare between datasets, as it is scale-dependent. It varies between infinite and 0.0 (best).\n", "\n", "To learn more about evaluation metrics, you can consult scikit learn's regression metrics [Docs](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics).\n", "\n", @@ -334,7 +334,7 @@ { "cell_type": "markdown", "source": [ - "Different ML algorithms can be used to train PCM models. Some of them include support vector machines (SVM), random forest (RF), and neural networks (NN), that were described in Talktorial T007. RF models have been used extensively in PCM applications due to their efficiency in large datasets and resistance to overfitting with more features. However, deep learning applications are also gaining momentum. See [J. Cheminform., 45, (2017)](https://pubmed.ncbi.nlm.nih.gov/29086168/) for a comparative use of ML methods in PCM modeling.\n", + "Different ML algorithms can be used to train PCM models. Some of them include support vector machines (SVM), random forest (RF), and neural networks (NN), which were described in Talktorial T007. RF models have been used extensively in PCM applications due to their efficiency in large datasets and resistance to overfitting with more features. However, deep learning applications are also gaining momentum. See [J. Cheminform., 45, (2017)](https://pubmed.ncbi.nlm.nih.gov/29086168/) for a comparative use of ML methods in PCM modeling.\n", "In this talktorial, we will use RF. RF is a decision tree-based algorithm, more in detail a bagging ensemble method. This means that there are multiple decision trees trained independently with subsets of features and data and the final prediction is made from a consensus between the independent predictions.\n" ], "metadata": { @@ -600,7 +600,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "42617588611b43feba9e4d115bfb000f" + "model_id": "ef25c37586c140b3af0aeb0a539e022e" } }, "metadata": {}, @@ -922,13 +922,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Available ProDEC descriptors: ['ADFQ', 'BLOSUM', 'c-scales', 'CBFQ', 'CDFQ', 'Combined descriptors', 'Contact energies', 'CUFQ', 'DPPS', 'E-scale', 'FASGAI', 'G-scales', 'GH-scale', 'GRID tscore', 'HESH', 'HPI', 'HSEHPCSV', 'Independent descriptors', 'ISA-ECI', 'Kidera', 'MS-WHIM', 'P-scale', 'PhysChem', 'ProtFP hash', 'ProtFP PCA', 'PSM', 'QCP', 'Raychaudhury', 'Sneath', 'SSIA AM1', 'SSIA DFT', 'SSIA HF', 'SSIA PM3', 'STscale', 'SVEEVA', 'SVGER', 'SVHEHS', 'SVMW', 'SVRDF', 'SVRG', 'SVWG', 'SZOTT', 'Tscale', 'V-scale', 'VARIMAX', 'VHSE', 'VHSEH', 'VSGETAWAY', 'VSTPV', 'VSTV', 'VSW', 'VTSA', 'Zscale binary', 'Zscale Hellberg', 'Zscale Jonsson', 'Zscale Sandberg', 'Zscale Sjöström', 'Zscale van Westen']\n" + "Available ProDEC descriptors: ['ADFQ', 'BLOSUM', 'c-scales', 'CBFQ', 'CDFQ', 'Combined descriptors', 'Contact energies', 'CUFQ', 'DPPS', 'E-scale', 'FASGAI', 'G-scales', 'GH-scale', 'GRID tscore', 'HESH', 'HPI', 'HSEHPCSV', 'Independent descriptors', 'ISA-ECI', 'Kidera', 'MS-WHIM', 'P-scale', 'PhysChem', 'ProtFP hash', 'ProtFP PCA', 'PSM', 'QCP', 'Raychaudhury', 'Sneath', 'SSIA AM1', 'SSIA DFT', 'SSIA HF', 'SSIA PM3', 'STscale', 'SVEEVA', 'SVGER', 'SVHEHS', 'SVMW', 'SVRDF', 'SVRG', 'SVWG', 'SZOTT', 'Tscale', 'V-scale', 'VARIMAX', 'VHSE', 'VHSEH', 'VSGETAWAY', 'VSTPV', 'VSTV', 'VSW', 'VTSA', 'Zscale binary', 'Zscale Hellberg', 'Zscale Jonsson', 'Zscale Sandberg', 'Zscale Sjöström', 'Zscale van Westen']\n" ] } ], @@ -936,7 +936,7 @@ "# Parse ProDEC descriptors\n", "desc_factory = prodec.ProteinDescriptors()\n", "# Print available descriptors\n", - "print('Available ProDEC descriptors: ', desc_factory.available_descriptors)" + "print(f'Available ProDEC descriptors: {desc_factory.available_descriptors}')" ], "metadata": { "collapsed": false, @@ -947,7 +947,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 9, "outputs": [ { "name": "stdout", @@ -960,7 +960,7 @@ "data": { "text/plain": "{'Authors': 'Hellberg, Sjöström, Skagerberg, Wold',\n 'Year': 1987,\n 'Journal': 'Journal of Medicinal Chemistry',\n 'DOI': '10.1021/jm00390a003',\n 'PMID': None,\n 'Patent': None}" }, - "execution_count": 14, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -979,7 +979,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "outputs": [], "source": [ "def calculate_protein_descriptor(targets, aligned_sequences, protein_descriptor):\n", @@ -988,7 +988,7 @@ "\n", " Parameters\n", " ----------\n", - " targets : pandas.Dataframe\n", + " targets : pandas.DataFrame\n", " Pandas dataframe with information about targets of interest\n", " aligned_sequences : list\n", " List of aligned sequences read from fasta file produced with Clustal Omega\n", @@ -1020,28 +1020,18 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 11, "outputs": [ { - "data": { - "text/plain": " 0%| | 0/4 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
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\n" - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" + "ename": "NameError", + "evalue": "name 'aligned_sequences' is not defined", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[1;32mIn [11], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m protein_features \u001B[38;5;241m=\u001B[39m calculate_protein_descriptor(targets, \u001B[43maligned_sequences\u001B[49m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mZscale Hellberg\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m 2\u001B[0m protein_features\n", + "\u001B[1;31mNameError\u001B[0m: name 'aligned_sequences' is not defined" + ] } ], "source": [ @@ -1081,7 +1071,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 21, "outputs": [], "source": [ "def calculate_molecular_descriptors(bioactivity_dataset, user_descriptors):\n", @@ -1090,7 +1080,7 @@ "\n", " Parameters\n", " ----------\n", - " bioactivity_dataset : pandas.Dataframe\n", + " bioactivity_dataset : pandas.DataFrame\n", " Pandas dataframe with bioactivity dataset for PCM\n", " user_descriptors : list\n", " List of descriptors from Mordred to calculate\n", @@ -1102,25 +1092,40 @@ " Dataset with SMILES and features for the compound descriptors of interest for molecules in the bioactivity dataset\n", " \"\"\"\n", " # Extract unique molecules from the bioactivity dataset\n", - " molecules = [Chem.MolFromSmiles(x) for x in bioactivity_dataset.SMILES.unique()]\n", + " molecules = [Chem.MolFromSmiles(x) for x in bioactivity_dataset['SMILES'].unique()]\n", "\n", " # Use Mordred to calculate molecular descriptors of interest\n", " if user_descriptors == ['all']:\n", - " molecular_descriptor = mordred.Calculator(mordred_descriptors, ignore_3D=True).pandas(molecules, pynb=False)\n", + " mordred_calculator = mordred.Calculator(mordred.descriptors, ignore_3D=True)\n", + " molecular_descriptor = mordred_calculator.pandas(molecules, pynb=False)\n", + "\n", " else:\n", " mordred_list = [mordred_descriptors.__dict__[descriptor] for descriptor in user_descriptors]\n", - " molecular_descriptor = mordred.Calculator(mordred_list, ignore_3D=True).pandas(molecules,ipynb=False)\n", + " mordred_calculator = mordred.Calculator(mordred_list, ignore_3D=True)\n", + " molecular_descriptor = mordred_calculator.pandas(molecules,ipynb=False)\n", "\n", - " # Clean descriptors by: renaming duplicated columns; replacing values bigger than 2,147,483,647 by 0;\n", - " # rounding values to 3 decimals; converting to minimal memory footprint; inserting SMILES in first column\n", - " mordred_descs_names = {\n", - " str(x): re.sub(r'(.*F?)A(H?Ring)$', r'\\1aliph\\2', re.sub(r'(.*F?)a(H?Ring)$', r'\\1arom\\2', str(x))) for x in\n", - " mordred.Calculator(mordred.descriptors, ignore_3D=True).descriptors}\n", + " # Clean descriptors step 1: replace mordred format to pandas format\n", + " molecular_descriptor = pd.DataFrame(molecular_descriptor.fill_missing(np.NAN))\n", "\n", - " molecular_descriptor = pd.DataFrame(molecular_descriptor.fill_missing(np.NAN).rename(mordred_descs_names)).\\\n", - " astype(np.float32).replace([np.inf, -np.inf], np.NAN).round(3)\n", - " molecular_descriptor.fillna(value=0, inplace=True)\n", + " # Clean descriptors step 2: replace ambiguous names (relevant for writing and reading to/from a file)\n", + " mordred_descs_names = {}\n", + " for desc_name in mordred_calculator.descriptors:\n", + " # First we replace the ambiguous names with aliphatic rings\n", + " mordred_descs_names[str(desc_name)] = re.sub(r'(.*F?)A(H?Ring)$', r'\\1aliph\\2', str(desc_name))\n", + " # Then we replace the ambiguous names with aromatic rings\n", + " mordred_descs_names[str(desc_name)] = re.sub(r'(.*F?)a(H?Ring)$', r'\\1arom\\2', mordred_descs_names[str(desc_name)])\n", + "\n", + " molecular_descriptor = molecular_descriptor.rename(mordred_descs_names)\n", + "\n", + " # Clean descriptors step 3: remove absurdly big and small values (e.g. topological indexes of molecules containing fragments)\n", + " molecular_descriptor = molecular_descriptor.astype(np.float32).replace([np.inf, -np.inf], np.NAN)\n", + " molecular_descriptor = molecular_descriptor.fillna(value=0)\n", + "\n", + " # Clean descriptors step 4: round values to 3 decimals to reduce memory footprint\n", + " molecular_descriptor = molecular_descriptor.astype(float).round(3)\n", " molecular_descriptor = molecular_descriptor.convert_dtypes()\n", + "\n", + " # Insert SMILES in first column for mapping\n", " molecular_descriptor.insert(0, 'SMILES', bioactivity_dataset.SMILES.unique())\n", "\n", " return molecular_descriptor" @@ -1134,21 +1139,21 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 22, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 6898/6898 [00:12<00:00, 574.14it/s]\n" + "100%|██████████| 6898/6898 [00:15<00:00, 439.31it/s]\n" ] }, { "data": { - "text/plain": " SMILES ABC ABCGG \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... 21.041 17.684 \n1 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... 20.701 15.635 \n2 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 23.23 17.455999 \n3 CCNC(=O)C1OC(n2cnc3c2ncnc3Nc2ccc(OCC(=O)Nc3ccc... 31.336 22.212999 \n4 NC(=NC(=O)Cn1c(O)c2CCCCc2c1O)Nc1nc2c(cccc2)s1 21.408001 17.066 \n... ... ... ... \n6893 CC(C)(C)C1(O)CCN2CC3c4c(cccc4)CCc4cccc(c43)C2C1 22.177999 16.375 \n6894 CNC(=O)C12CC1C(n1cnc3c(NC(C)C)nc(C#Cc4ccc(Cl)s... 26.351 22.591999 \n6895 CCCOc1ccc(C=Cc2cc3c(c(=O)n(C)c(=O)n3C)n2C)cc1 20.021999 15.893 \n6896 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... 23.736 18.441999 \n6897 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 18.511 15.661 \n\n nAcid nBase nAtom nHeavyAtom nSpiro nBridgehead nHetero ... nN \\\n0 0 1 51 27 0 0 8 ... 6 \n1 0 0 42 26 0 0 8 ... 4 \n2 0 0 43 29 0 0 8 ... 6 \n3 0 0 66 40 0 0 14 ... 7 \n4 0 3 46 27 0 0 9 ... 5 \n... ... ... ... ... ... ... ... ... .. \n6893 0 1 58 27 0 0 2 ... 1 \n6894 0 0 56 33 0 0 11 ... 6 \n6895 0 0 49 26 0 0 6 ... 3 \n6896 0 0 52 30 0 0 9 ... 4 \n6897 0 0 43 24 0 0 8 ... 5 \n\n nO nS nP nF nCl nBr nI nX BalabanJ \n0 2 0 0 0 0 0 0 0 1.631 \n1 3 1 0 0 0 0 0 0 1.307 \n2 2 0 0 0 0 0 0 0 1.328 \n3 6 0 0 0 1 0 0 1 1.043 \n4 3 1 0 0 0 0 0 0 1.234 \n... .. .. .. .. ... ... .. .. ... \n6893 1 0 0 0 0 0 0 0 1.46 \n6894 3 1 0 0 1 0 0 1 1.303 \n6895 3 0 0 0 0 0 0 0 1.479 \n6896 3 0 0 2 0 0 0 2 1.318 \n6897 3 0 0 0 0 0 0 0 1.68 \n\n[6898 rows x 23 columns]", - "text/html": "
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6898 rows × 23 columns

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" }, - "execution_count": 33, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1233,7 +1238,7 @@ "\n", " Parameters\n", " ----------\n", - " pcm_dataset : pandas.Dataframe\n", + " pcm_dataset : pandas.DataFrame\n", " Pandas dataframe with bioactivity dataset for PCM including compound and protein descriptors\n", " test_size : float\n", " Ratio of the data to include in the test set\n", @@ -1265,10 +1270,13 @@ " else:\n", " raise ValueError(\"loto_accession needs to be defined\")\n", "\n", - " # Move data associated to target to test set and rest to training set\n", + " # Move data associated with target to test set and rest to training set\n", " train = pcm_dataset[pcm_dataset['accession'] != test_target]\n", " test = pcm_dataset[pcm_dataset['accession'] == test_target]\n", "\n", + " else:\n", + " raise ValueError(f\"Split method {split_method} undefined; use random or loto.\")\n", + "\n", " # Print statistics of training and test sets\n", " print(f'Training set has {train.shape[0]} datapoints')\n", " print(f'Test set has {test.shape[0]} datapoints ({round(100*test.shape[0]/pcm_dataset.shape[0], 3)} %)')\n", @@ -1285,7 +1293,7 @@ { "cell_type": "markdown", "source": [ - "Function to train a PCM RF model on a training set and validate it on a test set. Performance metrics are calculated for the whole test set, and also separately" + "Function to train a PCM RF model on a training set and validate it on a test set. Performance metrics are calculated for the whole test set, and also separately." ], "metadata": { "collapsed": false, @@ -1319,9 +1327,8 @@ " fig:\n", " Figure with true vs. predicted values colored by target, with r2_score calculated per target\n", " \"\"\"\n", - " # Store keys of training and test sets\n", - " train_keys = train[['SMILES', 'accession']]\n", - " test_keys = test[['SMILES', 'accession', 'pchembl_value_Mean']].reset_index(drop=True)\n", + " # Store compound-target pairs in test set to allow comparative performance evaluation\n", + " test_pairs = test[['SMILES', 'accession', 'pchembl_value_Mean']].reset_index(drop=True)\n", "\n", " # Remove identifiers\n", " train = train.drop(columns=['SMILES', 'accession'])\n", @@ -1332,13 +1339,13 @@ " \"n_estimators\": 100, # number of trees to grows\n", " \"criterion\": \"squared_error\", # cost function to be optimized for a split\n", " }\n", - " model_RF = RandomForestRegressor(**param)\n", + " model_rf = RandomForestRegressor(**param)\n", "\n", " # Fit model\n", - " model_RF.fit(train.iloc[:, 1:], train.iloc[:, 0])\n", + " model_rf.fit(train.iloc[:, 1:], train.iloc[:, 0])\n", "\n", " # Make predictions on test set\n", - " predictions = model_RF.predict(test.iloc[:, 1:])\n", + " predictions = model_rf.predict(test.iloc[:, 1:])\n", "\n", " # Calculate model performance with regression metrics\n", " model_performance = {}\n", @@ -1349,15 +1356,15 @@ " print(json.dumps(model_performance, indent=4))\n", "\n", " # Add column named 'Target' for easier data visualization\n", - " test_keys['Target'] = test_keys['accession'].apply(lambda x: [i for i in targets_dict.keys() if targets_dict[i]==x][0])\n", + " test_pairs['Target'] = test_pairs['accession'].apply(lambda x: [i for i in targets_dict.keys() if targets_dict[i]==x][0])\n", "\n", " # Calculate model performance per target\n", - " test_keys['prediction'] = pd.Series(predictions)\n", + " test_pairs['prediction'] = pd.Series(predictions)\n", "\n", " for target,accession in targets_dict.items():\n", " # Define true values and predictions per target\n", - " true_target = test_keys[test_keys['accession'] == accession]['pchembl_value_Mean']\n", - " prediction_target = test_keys[test_keys['accession'] == accession]['prediction']\n", + " true_target = test_pairs[test_pairs['accession'] == accession]['pchembl_value_Mean']\n", + " prediction_target = test_pairs[test_pairs['accession'] == accession]['prediction']\n", "\n", " try:\n", " # Calculate r2 score\n", @@ -1404,7 +1411,7 @@ "\n", " Parameters\n", " ----------\n", - " qsar_dataset : pandas.Dataframe\n", + " qsar_dataset : pandas.DataFrame\n", " Pandas dataframe with bioactivity dataset for QSAR including compound descriptors\n", " target : str\n", " Target label for QSAR model\n", @@ -1434,13 +1441,13 @@ " \"n_estimators\": 100, # number of trees to grows\n", " \"criterion\": \"squared_error\", # cost function to be optimized for a split\n", " }\n", - " model_RF = RandomForestRegressor(**param)\n", + " model_rf = RandomForestRegressor(**param)\n", "\n", " # Fit model\n", - " model_RF.fit(train.iloc[:, 1:], train.iloc[:, 0])\n", + " model_rf.fit(train.iloc[:, 1:], train.iloc[:, 0])\n", "\n", " # Make predictions on test set\n", - " predictions = model_RF.predict(test.iloc[:, 1:])\n", + " predictions = model_rf.predict(test.iloc[:, 1:])\n", "\n", " # Calculate model performance with regression metrics\n", " model_performance = {}\n", From 89e1b10b73961e6b9ec22aa57efa34f30ed2c5d9 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Fri, 28 Oct 2022 11:53:40 +0200 Subject: [PATCH 33/62] Move clustalo.py script to /scripts folder and create README --- .../data/README.md | 4 ---- .../scripts/README.md | 6 ++++++ .../{data => scripts}/clustalo.py | 0 3 files changed, 6 insertions(+), 4 deletions(-) create mode 100644 teachopencadd/talktorials/T032_compound_activity_proteochemometrics/scripts/README.md rename teachopencadd/talktorials/T032_compound_activity_proteochemometrics/{data => scripts}/clustalo.py (100%) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/README.md index 5f4280b7..cba851e3 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/README.md +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/README.md @@ -3,10 +3,6 @@ This folder stores input and output data for the Jupyter notebook. -Stable: -- `clustalo.py`: ClustalO REST API client. - -Generated within the notebook: - `papyrus`: Directory with Papyrus bioactivity dataset downloads. - `sequences.fasta`: Sequences of the targets of interest for PCM modelling, in FASTA format. - `aligned_sequences.aln-fasta.fasta`: ClustalO multiple sequence alignment output, in FASTA format. diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/scripts/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/scripts/README.md new file mode 100644 index 00000000..06372122 --- /dev/null +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/scripts/README.md @@ -0,0 +1,6 @@ +# T032 · Compound activity: Proteochemometrics +## Scripts + +This folder stores scripts needed for the Jupyter notebook. + +- `clustalo.py`: ClustalO REST API client. diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/clustalo.py b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/scripts/clustalo.py similarity index 100% rename from teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/clustalo.py rename to teachopencadd/talktorials/T032_compound_activity_proteochemometrics/scripts/clustalo.py From 85506a93216e185f47084b5c2fd2500612fdb3a2 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Fri, 28 Oct 2022 11:55:20 +0200 Subject: [PATCH 34/62] Resize figures --- .../talktorial.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index 90e9b747..55a0580a 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -110,7 +110,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n", + "\n", "\n", "*Figure 1:*\n", "Proteochemometrics modeling construction from protein and molecular descriptors for which protein-compound pair bioactivity data is known.\n", @@ -149,7 +149,7 @@ { "cell_type": "markdown", "source": [ - "\n", + "\n", "\n", "*Figure 2:*\n", "Papyrus dataset generation scheme.\n", @@ -273,7 +273,7 @@ { "cell_type": "markdown", "source": [ - "\n", + "\n", "\n", "*Figure 3:*\n", "Overview of splitting methods, including target-stratified random and temporal splits and leave one target out approach.\n", From e8951b072603b3e49f1ddf021b222c30e0f9a025 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Fri, 28 Oct 2022 11:39:40 +0100 Subject: [PATCH 35/62] CI: Revert back to test_env.yml and CLI tests --- .github/workflows/ci.yml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 52ace99e..e3b935a7 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -45,7 +45,7 @@ jobs: #miniconda-version: "latest" activate-environment: teachopencadd channel-priority: true - environment-file: teachopencadd/talktorials/T032_compound_activity_proteochemometrics/T032_env.yml + environment-file: devtools/test_env.yml auto-activate-base: false - name: Additional info about the build @@ -66,11 +66,11 @@ jobs: conda info --all conda list - # - name: Test CLI - # shell: bash -l {0} - # run: | - # teachopencadd -h - # pytest -v --cov=${PACKAGE} --cov-report=xml --color=yes ${PACKAGE}/tests/ + - name: Test CLI + shell: bash -l {0} + run: | + teachopencadd -h + pytest -v --cov=${PACKAGE} --cov-report=xml --color=yes ${PACKAGE}/tests/ - name: Run tests shell: bash -l {0} From 3b0b60b9e9d60983cd4c2c2464502d2151d71118 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Fri, 28 Oct 2022 11:40:09 +0100 Subject: [PATCH 36/62] Env: Add T032 packages + tmp. comment other talktorial packages --- devtools/test_env.yml | 52 +++++++++++++++++++++++++------------------ 1 file changed, 30 insertions(+), 22 deletions(-) diff --git a/devtools/test_env.yml b/devtools/test_env.yml index 7d43b2dc..d7eb70ba 100644 --- a/devtools/test_env.yml +++ b/devtools/test_env.yml @@ -13,37 +13,39 @@ dependencies: # Explicitly add numpy because of https://github.com/volkamerlab/teachopencadd/issues/150 - numpy - scikit-learn + - scipy # API changed after v2.6, see https://github.com/volkamerlab/teachopencadd/issues/265 - - tensorflow<=2.6 + #- tensorflow<=2.6 - seaborn - - matplotlib-venn + #- matplotlib-venn # Remove jsonschema once this issue is fixed: https://github.com/Yelp/bravado/issues/478 - jsonschema<4.0.0 - - bravado - - requests - - requests-cache + #- bravado + #- requests + #- requests-cache - redo - - suds-community - - beautifulsoup4 - - chembl_webresource_client - - pypdb + #- suds-community + #- beautifulsoup4 + #- chembl_webresource_client + #- pypdb - biopython<=1.77 - - biopandas + #- biopandas - rdkit==2021.09.5 - - openbabel - - opencadd - - biotite>=0.34.0 - - smina - - mdanalysis>=1.0.0 - - mdtraj - - plip - - openmm + #- openbabel + #- opencadd + #- biotite>=0.34.0 + #- smina + #- mdanalysis>=1.0.0 + #- mdtraj + #- plip + #- openmm # depends on openff-toolkit->ambertools -> not available on Windows yet! # - openmmforcefields - - pdbfixer - - tqdm - - lxml - - kissim + #- pdbfixer + #- tqdm + #- lxml + #- kissim + - mordred ## CI tests # Workaround for https://github.com/computationalmodelling/nbval/issues/153 - pytest 5.* @@ -68,5 +70,11 @@ dependencies: - black-nb - nbsphinx-link - sphinxext-opengraph + # T032 - we might move some of this to conda-forge + - https://github.com/OlivierBeq/Papyrus-scripts/tarball/master + - prodec + - rich-msa + # Dependency for ClustalO webservice (also conda installable via -c bioconda) + - xmltramp2 # TeachOpenCADD itself - ../ From 8aa6c67d0d1178facca5aacbe906531a92453999 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Fri, 28 Oct 2022 16:57:17 +0200 Subject: [PATCH 37/62] Add latest Dominique comments --- .../talktorial.ipynb | 319 ++++++++++++------ 1 file changed, 213 insertions(+), 106 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index 55a0580a..0d8353dc 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -390,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -422,7 +422,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -451,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "outputs": [], "source": [ "# Let's specify the Papyrus version for the rest of the work\n", @@ -540,7 +540,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "outputs": [], "source": [ "def filter_explore_activity_data(papyrus_version, targets):\n", @@ -592,7 +592,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "outputs": [ { "data": { @@ -600,7 +600,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "ef25c37586c140b3af0aeb0a539e022e" + "model_id": "93844585c41d400793354c0424cfed30" } }, "metadata": {}, @@ -663,21 +663,21 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "outputs": [ { "data": { "text/plain": " SMILES accession \\\n222 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n223 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n383 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n462 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n464 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n... ... ... \n1238255 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... P30542 \n1238605 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P0DMS8 \n1238606 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29274 \n1238607 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29275 \n1238608 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P30542 \n\n pchembl_value_Mean \n222 8.6800 \n223 6.6800 \n383 4.8200 \n462 7.1515 \n464 5.6500 \n... ... \n1238255 5.1000 \n1238605 7.6100 \n1238606 7.3500 \n1238607 5.1500 \n1238608 7.3400 \n\n[12719 rows x 3 columns]", "text/html": "
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" }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ar_dataset = ar_dataset[['SMILES', 'accession', 'pchembl_value_Mean']]\n", - "ar_dataset" + "ar_dataset.head()" ], "metadata": { "collapsed": false, @@ -710,14 +710,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "outputs": [ { "data": { "text/plain": " target_id HGNC_symbol UniProtID Status Organism \\\n47 P29275_WT ADORA2B AA2BR_HUMAN reviewed Homo sapiens (Human) \n80 P30542_WT ADORA1 AA1R_HUMAN reviewed Homo sapiens (Human) \n81 P29274_WT ADORA2A AA2AR_HUMAN reviewed Homo sapiens (Human) \n82 P0DMS8_WT ADORA3 AA3R_HUMAN reviewed Homo sapiens (Human) \n\n Classification Length \\\n47 Membrane receptor->Family A G protein-coupled ... 332 \n80 Membrane receptor->Family A G protein-coupled ... 326 \n81 Membrane receptor->Family A G protein-coupled ... 412 \n82 Membrane receptor->Family A G protein-coupled ... 318 \n\n Sequence accession \n47 MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL... P29275 \n80 MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC... P30542 \n81 MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV... P29274 \n82 MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT... P0DMS8 ", "text/html": "
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" }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -922,7 +922,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "outputs": [ { "name": "stdout", @@ -947,7 +947,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "outputs": [ { "name": "stdout", @@ -960,7 +960,7 @@ "data": { "text/plain": "{'Authors': 'Hellberg, Sjöström, Skagerberg, Wold',\n 'Year': 1987,\n 'Journal': 'Journal of Medicinal Chemistry',\n 'DOI': '10.1021/jm00390a003',\n 'PMID': None,\n 'Patent': None}" }, - "execution_count": 9, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -979,7 +979,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 15, "outputs": [], "source": [ "def calculate_protein_descriptor(targets, aligned_sequences, protein_descriptor):\n", @@ -1020,18 +1020,28 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 16, "outputs": [ { - "ename": "NameError", - "evalue": "name 'aligned_sequences' is not defined", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mNameError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn [11], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m protein_features \u001B[38;5;241m=\u001B[39m calculate_protein_descriptor(targets, \u001B[43maligned_sequences\u001B[49m, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mZscale Hellberg\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m 2\u001B[0m protein_features\n", - "\u001B[1;31mNameError\u001B[0m: name 'aligned_sequences' is not defined" - ] + "data": { + "text/plain": " 0%| | 0/4 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
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\n" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -1071,7 +1081,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 17, "outputs": [], "source": [ "def calculate_molecular_descriptors(bioactivity_dataset, user_descriptors):\n", @@ -1139,13 +1149,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 18, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 6898/6898 [00:15<00:00, 439.31it/s]\n" + "100%|██████████| 6898/6898 [00:12<00:00, 539.76it/s]\n" ] }, { @@ -1153,14 +1163,14 @@ "text/plain": " SMILES ABC ABCGG \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... 21.041 17.684 \n1 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... 20.701 15.635 \n2 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 23.23 17.456 \n3 CCNC(=O)C1OC(n2cnc3c2ncnc3Nc2ccc(OCC(=O)Nc3ccc... 31.336 22.213 \n4 NC(=NC(=O)Cn1c(O)c2CCCCc2c1O)Nc1nc2c(cccc2)s1 21.408 17.066 \n... ... ... ... \n6893 CC(C)(C)C1(O)CCN2CC3c4c(cccc4)CCc4cccc(c43)C2C1 22.178 16.375 \n6894 CNC(=O)C12CC1C(n1cnc3c(NC(C)C)nc(C#Cc4ccc(Cl)s... 26.351 22.592 \n6895 CCCOc1ccc(C=Cc2cc3c(c(=O)n(C)c(=O)n3C)n2C)cc1 20.022 15.893 \n6896 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... 23.736 18.442 \n6897 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 18.511 15.661 \n\n nAcid nBase nAtom nHeavyAtom nSpiro nBridgehead nHetero ... nN \\\n0 0 1 51 27 0 0 8 ... 6 \n1 0 0 42 26 0 0 8 ... 4 \n2 0 0 43 29 0 0 8 ... 6 \n3 0 0 66 40 0 0 14 ... 7 \n4 0 3 46 27 0 0 9 ... 5 \n... ... ... ... ... ... ... ... ... .. \n6893 0 1 58 27 0 0 2 ... 1 \n6894 0 0 56 33 0 0 11 ... 6 \n6895 0 0 49 26 0 0 6 ... 3 \n6896 0 0 52 30 0 0 9 ... 4 \n6897 0 0 43 24 0 0 8 ... 5 \n\n nO nS nP nF nCl nBr nI nX BalabanJ \n0 2 0 0 0 0 0 0 0 1.631 \n1 3 1 0 0 0 0 0 0 1.307 \n2 2 0 0 0 0 0 0 0 1.328 \n3 6 0 0 0 1 0 0 1 1.043 \n4 3 1 0 0 0 0 0 0 1.234 \n... .. .. .. .. ... ... .. .. ... \n6893 1 0 0 0 0 0 0 0 1.46 \n6894 3 1 0 0 1 0 0 1 1.303 \n6895 3 0 0 0 0 0 0 0 1.479 \n6896 3 0 0 2 0 0 0 2 1.318 \n6897 3 0 0 0 0 0 0 0 1.68 \n\n[6898 rows x 23 columns]", "text/html": "
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" }, - "execution_count": 22, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "molecular_features = calculate_molecular_descriptors(ar_dataset, ['ABCIndex', 'AcidBase', 'AtomCount', 'BalabanJ'])\n", - "molecular_features" + "molecular_features.head()" ], "metadata": { "collapsed": false, @@ -1229,7 +1239,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 19, "outputs": [], "source": [ "def split_train_test(pcm_dataset, test_size, split_method, loto_target=None, loto_accession='None'):\n", @@ -1290,6 +1300,108 @@ } } }, + { + "cell_type": "markdown", + "source": [ + "Function to report performance metrics for a regression model based on the true and predicted values of the target variable." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 38, + "outputs": [], + "source": [ + "def _performance_metrics(y_true, y_predicted):\n", + " \"\"\"\n", + " Calculate regression performance metrics\n", + "\n", + " Parameters\n", + " ----------\n", + " y_true: pandas.Series\n", + " True values of the target variable\n", + " y_predicted: list\n", + " Predicted values of the target variable\n", + "\n", + " Returns\n", + " -------\n", + " dict:\n", + " Pearson's r, R2 score and MAE on test set\n", + " \"\"\"\n", + " model_performance = {}\n", + " model_performance['Pearson r'] = pearsonr(y_true, y_predicted)[0]\n", + " model_performance['R2 score'] = r2_score(y_true, y_predicted)\n", + " model_performance['MAE'] = mean_absolute_error(y_true, y_predicted)\n", + "\n", + " print(json.dumps(model_performance, indent=4))\n", + "\n", + " return model_performance" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Function to plot the goodness of fit of true and predicted values for a regression model." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 31, + "outputs": [], + "source": [ + "def _performance_plot(target, y_true, y_predicted, r2_score):\n", + " \"\"\"\n", + " Plot fit of true values vs. predicted values for the target variable\n", + "\n", + " Parameters\n", + " ----------\n", + " target: str\n", + " Protein target to generate plot for\n", + " y_true: pandas.Series\n", + " True values of the target variable\n", + " y_predicted: list\n", + " Predicted values of the target variable\n", + " r2_score: float\n", + " R2 score value to add to plot legend's\n", + "\n", + " Returns\n", + " -------\n", + " fig:\n", + " Figure with true vs. predicted values colored by target, with r2_score calculated per target\n", + " \"\"\"\n", + " ax = sns.scatterplot(y=y_true, x=y_predicted, label=(f'{target} R2 = {r2_score:.2f}'))\n", + " _ = sns.lineplot(x=(0, 14), y=(0, 14))\n", + " _ = ax.set_xlim((0,14))\n", + " _ = ax.set_ylim((0,14))\n", + " _ = ax.set_xlabel('Predicted')\n", + " _ = ax.set_ylabel('Observed')\n", + "\n", + " return ax" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, { "cell_type": "markdown", "source": [ @@ -1304,7 +1416,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 39, "outputs": [], "source": [ "def train_validate_pcm_model(targets_dict, train, test):\n", @@ -1348,18 +1460,15 @@ " predictions = model_rf.predict(test.iloc[:, 1:])\n", "\n", " # Calculate model performance with regression metrics\n", - " model_performance = {}\n", - " model_performance['Pearson r'] = pearsonr(test.iloc[:, 0], predictions)[0]\n", - " model_performance['R2 score'] = r2_score(test.iloc[:, 0], predictions)\n", - " model_performance['MAE'] = mean_absolute_error(test.iloc[:, 0], predictions)\n", " print('=== PCM model performance ===')\n", - " print(json.dumps(model_performance, indent=4))\n", + " model_performance = _performance_metrics(test.iloc[:, 0], predictions)\n", "\n", " # Add column named 'Target' for easier data visualization\n", - " test_pairs['Target'] = test_pairs['accession'].apply(lambda x: [i for i in targets_dict.keys() if targets_dict[i]==x][0])\n", + " _targets_dict_reversed = {uniprot: target for target, uniprot in targets_dict.items()}\n", + " test_pairs['Target'] = test_pairs['accession'].apply(lambda x: _targets_dict_reversed[x])\n", "\n", " # Calculate model performance per target\n", - " test_pairs['prediction'] = pd.Series(predictions)\n", + " test_pairs['prediction'] = predictions\n", "\n", " for target,accession in targets_dict.items():\n", " # Define true values and predictions per target\n", @@ -1371,15 +1480,10 @@ " r2_target = r2_score(true_target, prediction_target)\n", "\n", " # Plot correlation between true and predicted values\n", - " ax = sns.scatterplot(y=true_target, x=prediction_target, label=(f'{target} R2 = {r2_target:.2f}'))\n", - " _ = sns.lineplot(x=(0, 14), y=(0, 14))\n", - " _ = ax.set_xlim((0,14))\n", - " _ = ax.set_ylim((0,14))\n", - " _ = ax.set_xlabel('Predicted')\n", - " _ = ax.set_ylabel('Observed')\n", + " _performance_plot(target, true_target, prediction_target, r2_target)\n", + "\n", " except ValueError:\n", - " # Performance can only be plotted for the left out target in LOTO split\n", - " pass\n" + " print(f'Not plotting {target}. Performance can only be plotted for the left out target in LOTO split')" ], "metadata": { "collapsed": false, @@ -1402,7 +1506,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 40, "outputs": [], "source": [ "def train_validate_qsar_model(qsar_dataset, target, accession, test_size):\n", @@ -1450,20 +1554,11 @@ " predictions = model_rf.predict(test.iloc[:, 1:])\n", "\n", " # Calculate model performance with regression metrics\n", - " model_performance = {}\n", - " model_performance['Pearson r'] = pearsonr(test.iloc[:, 0], predictions)[0]\n", - " model_performance['R2 score'] = r2_score(test.iloc[:, 0], predictions)\n", - " model_performance['MAE'] = mean_absolute_error(test.iloc[:, 0], predictions)\n", " print(f'=== QSAR model performance {target} ===')\n", - " print(json.dumps(model_performance, indent=4))\n", + " model_performance = _performance_metrics(test.iloc[:, 0], predictions)\n", "\n", " # Plot correlation between true and predicted values\n", - " ax = sns.scatterplot(y=test.iloc[:, 0], x=predictions, label=(f'{target} R2 = {model_performance[\"R2 score\"]:.2f}'))\n", - " _ = sns.lineplot(x=(0, 14), y=(0, 14))\n", - " _ = ax.set_xlim((0,14))\n", - " _ = ax.set_ylim((0,14))\n", - " _ = ax.set_xlabel('Predicted')\n", - " _ = ax.set_ylabel('Observed')" + " _performance_plot(target, test.iloc[:, 0], predictions, model_performance[\"R2 score\"])" ], "metadata": { "collapsed": false, @@ -1498,14 +1593,14 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 21, "outputs": [ { "data": { "text/plain": " SMILES accession \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... 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12714Cn1cc(Nc2nc(-c3ccco3)c(-c3ncncc3)cn2)ccc1=OP292757.55150.000.000.000.000.000.000.00...6200000001.368
12715N#Cc1c(-c2ccc(OCC3CC3)cc2)c(C#N)c(SCC(N)=O)nc1NP292757.51000.000.000.000.000.000.000.00...5210000001.613
12716O=C(Cc1cccc2c1cccc2)Nc1nc2nn(CCc3ccccc3)cc2c2n...P292757.36720.000.000.000.000.000.000.00...7200000000.998
12717COc1c(OCC(=O)O)ccc(-c2cc3c([nH]2)c(=O)n(C)c(=O...P292756.57000.000.000.000.000.000.000.00...3600000001.608
12718CCCn1c(=O)c2c([nH]c(-c3cc(OCC4CC(=O)N(c5ccc(F)...P292756.68000.000.000.000.000.000.000.00...6500100011.103
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12719 rows × 1303 columns

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" }, - "execution_count": 37, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1515,7 +1610,7 @@ "ar_pcm_dataset = ar_dataset.merge(protein_features, on='accession')\n", "ar_pcm_dataset = ar_pcm_dataset.merge(molecular_features, on='SMILES')\n", "\n", - "ar_pcm_dataset" + "ar_pcm_dataset.head()" ], "metadata": { "collapsed": false, @@ -1538,14 +1633,14 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 22, "outputs": [ { "data": { - "text/plain": " SMILES accession \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n1 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n2 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n3 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n4 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n... ... ... \n12714 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... P30542 \n12715 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P0DMS8 \n12716 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29274 \n12717 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29275 \n12718 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P30542 \n\n pchembl_value_Mean ABC ABCGG nAcid nBase nAtom nHeavyAtom \\\n0 8.6800 21.041 17.684 0 1 51 27 \n1 6.6800 21.041 17.684 0 1 51 27 \n2 4.8200 20.701 15.635 0 0 42 26 \n3 7.1515 23.23 17.455999 0 0 43 29 \n4 5.6500 23.23 17.455999 0 0 43 29 \n... ... ... ... ... ... ... ... \n12714 5.1000 23.736 18.441999 0 0 52 30 \n12715 7.6100 18.511 15.661 0 0 43 24 \n12716 7.3500 18.511 15.661 0 0 43 24 \n12717 5.1500 18.511 15.661 0 0 43 24 \n12718 7.3400 18.511 15.661 0 0 43 24 \n\n nSpiro ... nN nO nS nP nF nCl nBr nI nX BalabanJ \n0 0 ... 6 2 0 0 0 0 0 0 0 1.631 \n1 0 ... 6 2 0 0 0 0 0 0 0 1.631 \n2 0 ... 4 3 1 0 0 0 0 0 0 1.307 \n3 0 ... 6 2 0 0 0 0 0 0 0 1.328 \n4 0 ... 6 2 0 0 0 0 0 0 0 1.328 \n... ... ... .. .. .. .. .. ... ... .. .. ... \n12714 0 ... 4 3 0 0 2 0 0 0 2 1.318 \n12715 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n12716 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n12717 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n12718 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n\n[12719 rows x 25 columns]", - "text/html": "
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SMILESaccessionpchembl_value_MeanABCABCGGnAcidnBasenAtomnHeavyAtomnSpiro...nNnOnSnPnFnClnBrnInXBalabanJ
0Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.680021.04117.6840151270...6200000001.631
1Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.680021.04117.6840151270...6200000001.631
2Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.820020.70115.6350042260...4310000001.307
3O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.151523.2317.4559990043290...6200000001.328
4O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.650023.2317.4559990043290...6200000001.328
..................................................................
12714Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc...P305425.100023.73618.4419990052300...4300200021.318
12715CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P0DMS87.610018.51115.6610043240...5300000001.68
12716CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292747.350018.51115.6610043240...5300000001.68
12717CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292755.150018.51115.6610043240...5300000001.68
12718CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P305427.340018.51115.6610043240...5300000001.68
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12719 rows × 25 columns

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" + "text/plain": " SMILES accession \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n1 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n2 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n3 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n4 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n... ... ... \n12714 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... P30542 \n12715 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P0DMS8 \n12716 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29274 \n12717 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29275 \n12718 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P30542 \n\n pchembl_value_Mean ABC ABCGG nAcid nBase nAtom nHeavyAtom \\\n0 8.6800 21.041 17.684 0 1 51 27 \n1 6.6800 21.041 17.684 0 1 51 27 \n2 4.8200 20.701 15.635 0 0 42 26 \n3 7.1515 23.23 17.456 0 0 43 29 \n4 5.6500 23.23 17.456 0 0 43 29 \n... ... ... ... ... ... ... ... \n12714 5.1000 23.736 18.442 0 0 52 30 \n12715 7.6100 18.511 15.661 0 0 43 24 \n12716 7.3500 18.511 15.661 0 0 43 24 \n12717 5.1500 18.511 15.661 0 0 43 24 \n12718 7.3400 18.511 15.661 0 0 43 24 \n\n nSpiro ... nN nO nS nP nF nCl nBr nI nX BalabanJ \n0 0 ... 6 2 0 0 0 0 0 0 0 1.631 \n1 0 ... 6 2 0 0 0 0 0 0 0 1.631 \n2 0 ... 4 3 1 0 0 0 0 0 0 1.307 \n3 0 ... 6 2 0 0 0 0 0 0 0 1.328 \n4 0 ... 6 2 0 0 0 0 0 0 0 1.328 \n... ... ... .. .. .. .. .. ... ... .. .. ... \n12714 0 ... 4 3 0 0 2 0 0 0 2 1.318 \n12715 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n12716 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n12717 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n12718 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n\n[12719 rows x 25 columns]", + "text/html": "
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SMILESaccessionpchembl_value_MeanABCABCGGnAcidnBasenAtomnHeavyAtomnSpiro...nNnOnSnPnFnClnBrnInXBalabanJ
0Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.680021.04117.6840151270...6200000001.631
1Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.680021.04117.6840151270...6200000001.631
2Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.820020.70115.6350042260...4310000001.307
3O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.151523.2317.4560043290...6200000001.328
4O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.650023.2317.4560043290...6200000001.328
..................................................................
12714Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc...P305425.100023.73618.4420052300...4300200021.318
12715CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P0DMS87.610018.51115.6610043240...5300000001.68
12716CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292747.350018.51115.6610043240...5300000001.68
12717CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292755.150018.51115.6610043240...5300000001.68
12718CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P305427.340018.51115.6610043240...5300000001.68
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12719 rows × 25 columns

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" }, - "execution_count": 38, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -1554,7 +1649,7 @@ "# Add molecular features to bioactivity dataset to generate QSAR dataset\n", "ar_qsar_dataset = ar_dataset.merge(molecular_features, on='SMILES')\n", "\n", - "ar_qsar_dataset" + "ar_qsar_dataset.head()" ], "metadata": { "collapsed": false, @@ -1600,7 +1695,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 23, "outputs": [ { "name": "stdout", @@ -1626,7 +1721,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1635,16 +1730,16 @@ "text": [ "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.6849536131301579,\n", - " \"R2 score\": 0.4643494527962835,\n", - " \"MAE\": 0.6418489491748536\n", + " \"Pearson r\": 0.6855434084419538,\n", + " \"R2 score\": 0.4647699109179838,\n", + " \"MAE\": 0.6409635421984158\n", "}\n" ] }, { "data": { "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": {}, "output_type": "display_data" @@ -1658,7 +1753,7 @@ { "cell_type": "markdown", "source": [ - "Based on the performance metrics of the PCM model, Pearson's $r$ tells us that the true and predicted values are highly correlated .Moreover, the $R^{2}$ tells us that almost 50 % of the variance of the target variable is explained by the model features and that the model is predictive. The $MAE$ tells us that the predictions are on average 0.64 p-value units off, which is an acceptable prediction error.\n", + "Based on the performance metrics of the PCM model, Pearson's $r$ tells us that the true and predicted values are highly correlated. Moreover, the $R^{2}$ tells us that almost 50 % of the variance of the target variable is explained by the model features and that the model is predictive. The $MAE$ tells us that the predictions are on average 0.64 p-value units off, which is an acceptable prediction error.\n", "An interesting observation is that the $R^{2}$ score is quite similar if we calculate it independently for the test set datapoint corresponding to each target." ], "metadata": { @@ -1691,7 +1786,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "outputs": [ { "name": "stdout", @@ -1699,34 +1794,34 @@ "text": [ "=== QSAR model performance A1 ===\n", "{\n", - " \"Pearson r\": 0.6038154800912579,\n", - " \"R2 score\": 0.3629339568560689,\n", - " \"MAE\": 0.5951719649890358\n", + " \"Pearson r\": 0.5903576322919979,\n", + " \"R2 score\": 0.34623779841739744,\n", + " \"MAE\": 0.5990705889267219\n", "}\n", "=== QSAR model performance A2A ===\n", "{\n", - " \"Pearson r\": 0.6366940512586494,\n", - " \"R2 score\": 0.40404170942848616,\n", - " \"MAE\": 0.697996378368688\n", + " \"Pearson r\": 0.6264855012916158,\n", + " \"R2 score\": 0.39084261119128527,\n", + " \"MAE\": 0.7022387102541356\n", "}\n", "=== QSAR model performance A2B ===\n", "{\n", - " \"Pearson r\": 0.7073059813985801,\n", - " \"R2 score\": 0.49122750443919383,\n", - " \"MAE\": 0.5538678217807245\n", + " \"Pearson r\": 0.6997350798604474,\n", + " \"R2 score\": 0.48131630633807854,\n", + " \"MAE\": 0.5612815187795313\n", "}\n", "=== QSAR model performance A3 ===\n", "{\n", - " \"Pearson r\": 0.6634620887237606,\n", - " \"R2 score\": 0.4378357788382091,\n", - " \"MAE\": 0.6887102919977854\n", + " \"Pearson r\": 0.661668025151235,\n", + " \"R2 score\": 0.43458547383101476,\n", + " \"MAE\": 0.6927533399906751\n", "}\n" ] }, { "data": { "text/plain": "
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\n" 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\n" }, "metadata": {}, "output_type": "display_data" @@ -1769,7 +1864,7 @@ { "cell_type": "markdown", "source": [ - "The random split PCM model works pretty well. However, a model trained and validated on a random split might overestimate the performance compared to a real life drug discovery scenario.\n", + "The random split PCM model works pretty well. However, a model trained and validated on a random split might overestimate the performance compared to a real life drug discovery scenario since some compounds can be tested in several targets.\n", "Finally, to test whether our PCM model could be used to predict bioactivity data on a target for which we have no previously known bioactivity data, we can train and validate PCM models following the \"leave one target out\" (LOTO) split method. We can do this process for each of the adenosine receptors." ], "metadata": { @@ -1778,7 +1873,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "outputs": [ { "name": "stdout", @@ -1790,46 +1885,58 @@ "Test set has 3519 datapoints (27.667 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.2536376583808103,\n", - " \"R2 score\": -0.0975202887751574,\n", - " \"MAE\": 0.8507882684261455\n", + " \"Pearson r\": 0.24619690865122593,\n", + " \"R2 score\": -0.10444150019112697,\n", + " \"MAE\": 0.8544105189289312\n", "}\n", + "Performance can only be plotted for the left out target in LOTO split\n", + "Performance can only be plotted for the left out target in LOTO split\n", + "Performance can only be plotted for the left out target in LOTO split\n", "== Leave one target out split ==\n", "Target left out for testing is A2A\n", "Training set has 8728 datapoints\n", "Test set has 3991 datapoints (31.378 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.19066687766825757,\n", - " \"R2 score\": -0.0616928437864086,\n", - " \"MAE\": 0.9748460591657273\n", + " \"Pearson r\": 0.20825285841494767,\n", + " \"R2 score\": -0.042945249712776024,\n", + " \"MAE\": 0.9629048242470047\n", "}\n", + "Performance can only be plotted for the left out target in LOTO split\n", + "Performance can only be plotted for the left out target in LOTO split\n", + "Performance can only be plotted for the left out target in LOTO split\n", "== Leave one target out split ==\n", "Target left out for testing is A2B\n", "Training set has 10731 datapoints\n", "Test set has 1988 datapoints (15.63 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.010846572454369874,\n", - " \"R2 score\": -0.252401814599831,\n", - " \"MAE\": 0.9748361950597918\n", + " \"Pearson r\": -0.015321923240914772,\n", + " \"R2 score\": -0.2867236006293721,\n", + " \"MAE\": 0.9895603553174414\n", "}\n", + "Performance can only be plotted for the left out target in LOTO split\n", + "Performance can only be plotted for the left out target in LOTO split\n", + "Performance can only be plotted for the left out target in LOTO split\n", "== Leave one target out split ==\n", "Target left out for testing is A3\n", "Training set has 9498 datapoints\n", "Test set has 3221 datapoints (25.324 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.11818194406647817,\n", - " \"R2 score\": -0.2752711043423113,\n", - " \"MAE\": 1.0569179680805063\n", - "}\n" + " \"Pearson r\": 0.1027567408073104,\n", + " \"R2 score\": -0.2629762096426478,\n", + " \"MAE\": 1.0540418854090967\n", + "}\n", + "Performance can only be plotted for the left out target in LOTO split\n", + "Performance can only be plotted for the left out target in LOTO split\n", + "Performance can only be plotted for the left out target in LOTO split\n" ] }, { "data": { "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": {}, "output_type": "display_data" @@ -1872,7 +1979,7 @@ "* For better comparison, it would be good to train several models of the same type and calculate aggregated metrics, so that statistically significant results could be derived.\n", "\n", "Moreover, we trained four PCM models on three adenosine receptors and validated them on the remaining receptor, following a leave one target out (LOTO) split method. We did this to evaluate whether these PCM models could be used to predict bioactivity for a target for which the model has never seen any data in training. We immediately derive some observations:\n", - "* The LOTO split is harder in validation than the random split, since the random split allows data leakage between targets.\n", + "* The LOTO split is a more challenging form of validation than the random split, since the random split allows data leakage between targets.\n", "* While the descriptors used in the PCM model trained on random split allowed for a good performance, in order to get a good performance in the LOTO split, we would need to search more carefully to find the optimal descriptors. Similarly, we could opt for a selection of the binding pocket prior to protein descriptor generation. Additionally, we could optimize the model parameters.\n" ] }, @@ -1887,7 +1994,7 @@ "## Quiz\n", "\n", "1. What types of features are needed for PCM?\n", - "2. How many types of training/test set splitting methods commonly used in PCM modeling do you know?\n", + "2. What types of training/test set splitting methods commonly used in PCM modeling do you know?\n", "3. Which applications do you know of PCM in drug discovery?" ] } From 441fb2bfd6e61fc2ff944429723dc82ea1098d0b Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Fri, 28 Oct 2022 17:02:17 +0200 Subject: [PATCH 38/62] Run black-nb --- .../talktorial.ipynb | 2617 +++++++++++++---- 1 file changed, 2088 insertions(+), 529 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index 0d8353dc..fe41d3e3 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -87,15 +87,15 @@ }, { "cell_type": "markdown", - "source": [ - "### Proteochemometrics (PCM) modeling" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Proteochemometrics (PCM) modeling" + ] }, { "cell_type": "markdown", @@ -119,60 +119,66 @@ }, { "cell_type": "markdown", - "source": [ - "### Data preparation" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "### Data preparation" + ] }, { "cell_type": "markdown", - "source": [ - "#### Papyrus dataset" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "#### Papyrus dataset" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "The Papyrus dataset is a highly curated compilation of bioactivity data intended for modeling in drug discovery. Apart from the bioactivity data contained in the [ChEMBL database](https://www.ebi.ac.uk/chembl/) (see also Talktorial T001), the Papyrus dataset contains binary data for classification tasks from the [ExCAPE-DB](https://solr.ideaconsult.net/search/excape/), and bioactivity data from a number of kinase-specific papers (Figure 1). The Papyrus dataset consists of almost 60M compound-protein pairs, representing data of around 1.2M unique compounds and 7K proteins across 499 different organisms.\n", "\n", "The aggregated bioactivity data is standardized, repaired, and normalized to form the Papyrus dataset, which is updated with every new version of ChEMBL released. The Papyrus dataset contains \"high quality\" data associated with pChEMBL values for regression or classification tasks. pChEMBL value is a canonical activity metric defined as $-log_{10}(molar IC_{50}, XC_{50}, EC_{50}, AC_{50}, Ki, Kd, or potency)$. Moreover, \"low quality\" data that is only associated with an active/inactive label can be used for classification tasks (read more about ML applications in Talktorial T007)." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "\n", "\n", "*Figure 2:*\n", "Papyrus dataset generation scheme.\n", "Figure taken from: Papyrus scripts [GitHub](https://github.com/OlivierBeq/Papyrus-scripts)." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", - "source": [ - "#### Molecule encoding: molecular descriptors" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Molecule encoding: molecular descriptors" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "For the ML models used in PCM, molecules need to be converted into a list of features. In Talktorial T007, molecular fingerprints were introduced. In this talktorial, we will use a different type of representation that is often used on its own or in combination with fingerprints: molecular descriptors.\n", "\n", @@ -184,25 +190,25 @@ "* Acid-Base: 2 descriptors that count acidic and basic groups, respectively (see Modred AcidBase [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.AcidBase.html?highlight=acidbase))\n", "* Atom count: 16 descriptors that represent a count of different types of atoms (see Modred AtomCount [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.AtomCount.html?highlight=atomcount))\n", "* Balaban J index: 1 descriptor that represents a topological index (see Modred BalabanJ [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.BalabanJ.html?highlight=balaban#module-mordred.BalabanJ))" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "#### Protein encoding: protein descriptors" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "#### Protein encoding: protein descriptors" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "As done for molecules, the proteins of interest need to be converted to a list of features or protein descriptors. Protein descriptors used in PCM applications are commonly based on the protein sequence and represent physicochemical characteristics of the amino acids that make up the sequence (e.g. Z-scales). Other protein descriptors represent topological (e.g. ST-scales) or electrostatic properties (e.g. MS-WHIM) of the protein sequence. Moreover, if structural information is available, protein descriptors can be derived from the 3D structure of the protein (e.g. sPairs) or the ligand-protein interaction in 3D (e.g. interaction fingerprints). Finally, with the widespread use of deep learning, protein embeddings can be obtained after parsing the protein sequence through the network (e.g. UniRep, AlphaFold embeddings). To read more about protein descriptors, check out these selection of articles ([Brief. Bioinform.,18, (2017)](https://pubmed.ncbi.nlm.nih.gov/26873661/), [Int. J. Mol. Sci., 22, (2021)](https://pubmed.ncbi.nlm.nih.gov/34884688/), [Comput. Struct. Biotechnol. J., 20, (2022)](https://pubmed.ncbi.nlm.nih.gov/35222841/)).\n", "\n", @@ -214,50 +220,47 @@ "\n", "In this talktorial, we will focus on physicochemical protein descriptors, mainly Z-scales ([J. Med. Chem, 30 (1987)](https://pubs.acs.org/doi/10.1021/jm00390a003)). The Z-scales descriptor assigns three pre-determined values (Z1, Z2, Z3) to each amino acid in the sequence. The Z1, Z2, and Z3 values are the first principal components of a principal component analysis (PCA) including 29 different physicochemical variables to characterize the amino acids.\n", "Since we are calculating activity for four proteins with very high sequence similarity (Adenosine receptors A1, A2A, A2B, and A3), we will use multiple sequence alignment prior to calculation of the Z-scales. To calculate Z-scales we will use [ProDEC](https://github.com/OlivierBeq/ProDEC), an open source resource that compiles a large number of protein descriptors." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "### Machine learning principles: regression" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Machine learning principles: regression" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "The ML principles for PCM modeling are equivalent to those explained for QSAR modeling. However, in this talktorial we will explore a supervised ML application other than classification, this is regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values.\n", "NOTE: Target variable is the variable we want to predict in ML. Not to be confused with (protein) target." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", - "source": [ - "#### Data splitting methods" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Data splitting methods" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "Similarly to classification tasks, in supervised ML regression applications the model is first fitted to a training set and subsequently the predictive performance is evaluated on a test set. Therefore, the original dataset needs to be split between training and test sets. The split needs to ensure that the fitting process has enough data, and that the test set is representative. Normally, the distribution between train and test set is 80/20 or 70/30. Depending on the applicability domain, the split can be done in multiple ways. In PCM modeling, some of the most common splitting methods are:\n", "* Random split: This method is not particularly relevant in drug discovery applications as it does not reflect the reality of a drug discovery campaign and it will most likely lead to data leaks between the training and test set. This is, very similar data will be found in both sets, which will lead to an overestimation of the predictive performance of the model. This type of split is commonly used, however, as a baseline and point of reference for other splitting methods, or as a starting point for quick model comparisons.\n", @@ -265,41 +268,41 @@ "* Leave one compound cluster out (LOCCO) split: This method evaluates the ability of the model to extrapolate to compounds with properties not previously seen by the model. Clustering can be done based on different molecular characteristics, such as physicochemical properties or scaffold, for example (see Talktorial T005 to learn more about clustering). One (or several, LSCCO) cluster(s) can then be left out for testing. This method prevents data leaking in terms of chemistry between training and test sets.\n", "* Temporal split: This method was developed in order to account for the usual timeline of drug discovery campaigns, where chemical series are populated sequentially over time. In this approach, the molecules included in the training set are those released until a certain date and the rest (most novel) are included in the test set.\n", "* Stratified split per target: This method can be applied to any of the splitting methods described above (except LOTO), and aims to include data of all targets in both the training and test set, so that additional target-compound interactions can be extracted by the model. (NOTE: stratification can be also done in regards to other reference points apart from targets, for example classes in classification tasks, to make sure that the distribution is similar across training and test set)." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "\n", "\n", "*Figure 3:*\n", "Overview of splitting methods, including target-stratified random and temporal splits and leave one target out approach.\n", "Figure made by Marina Gorostiola González." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "#### Regression evaluation metrics" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Regression evaluation metrics" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "\n", "To evaluate the predictive performance of a regression model, there are several metrics that in simple terms measure the differences between the true target values and the predictions made by the model. These metrics can be used in cross-validation on the training set (see Talktorial T007) or in the test set. The most commonly used metrics include:\n", @@ -314,50 +317,53 @@ "\n", "\n", "\n" - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "markdown", - "source": [ - "#### ML algorithm: Random Forest" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### ML algorithm: Random Forest" + ] }, { "cell_type": "markdown", - "source": [ - "Different ML algorithms can be used to train PCM models. Some of them include support vector machines (SVM), random forest (RF), and neural networks (NN), which were described in Talktorial T007. RF models have been used extensively in PCM applications due to their efficiency in large datasets and resistance to overfitting with more features. However, deep learning applications are also gaining momentum. See [J. Cheminform., 45, (2017)](https://pubmed.ncbi.nlm.nih.gov/29086168/) for a comparative use of ML methods in PCM modeling.\n", - "In this talktorial, we will use RF. RF is a decision tree-based algorithm, more in detail a bagging ensemble method. This means that there are multiple decision trees trained independently with subsets of features and data and the final prediction is made from a consensus between the independent predictions.\n" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Different ML algorithms can be used to train PCM models. Some of them include support vector machines (SVM), random forest (RF), and neural networks (NN), which were described in Talktorial T007. RF models have been used extensively in PCM applications due to their efficiency in large datasets and resistance to overfitting with more features. However, deep learning applications are also gaining momentum. See [J. Cheminform., 45, (2017)](https://pubmed.ncbi.nlm.nih.gov/29086168/) for a comparative use of ML methods in PCM modeling.\n", + "In this talktorial, we will use RF. RF is a decision tree-based algorithm, more in detail a bagging ensemble method. This means that there are multiple decision trees trained independently with subsets of features and data and the final prediction is made from a consensus between the independent predictions.\n" + ] }, { "cell_type": "markdown", - "source": [ - "### Applications of PCM in drug discovery" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "### Applications of PCM in drug discovery" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + }, "source": [ "The possibility to predict bioactivity for multiple targets in one model with PCM is very interesting in drug discovery and expands the applicability domain of QSAR modeling. Some applications of this technique are listed below and help answer the following questions in drug discovery:\n", "\n", @@ -366,13 +372,7 @@ "* Selectivity prediction: Do certain novel compounds target one protein isoform while avoiding others (off-targets) known to cause adverse effects?\n", "\n", "To know more about applications of PCM in drug discovery, have a look at this review [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub)." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "markdown", @@ -406,7 +406,7 @@ "from sklearn.preprocessing import RobustScaler\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.ensemble import RandomForestRegressor\n", - "from sklearn.metrics import r2_score,mean_absolute_error\n", + "from sklearn.metrics import r2_score, mean_absolute_error\n", "from scipy.stats import pearsonr\n", "\n", "import Bio\n", @@ -433,40 +433,46 @@ }, { "cell_type": "markdown", - "source": [ - "### Download Papyrus dataset" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "### Download Papyrus dataset" + ] }, { "cell_type": "markdown", - "source": [ - "To work with the Papyrus dataset, we use the papyrus_scripts [library](https://github.com/OlivierBeq/Papyrus-scripts). This library allows us to download, read, and explore the dataset. Many other features, including bioactivity modeling, are possible using the papyrus_scripts. If you want to dive into them, feel free to follow the [notebook with simple examples](https://github.com/OlivierBeq/Papyrus-scripts/blob/master/notebook_examples/simple_examples.ipynb). By default, the download_papyrus function retrieves bioactivity, target and other information for the latest version of the Papyrus dataset. The data retrieved consists of the highest quality continuous bioactivity data (Papyrus++) without stereochemistry annotated (i.e. nostereo=True and stereo=False). Check out the [documentation](https://github.com/OlivierBeq/Papyrus-scripts/blob/master/src/papyrus_scripts/download.py) to learn more about the options available." - ], "metadata": { "collapsed": false - } + }, + "source": [ + "To work with the Papyrus dataset, we use the papyrus_scripts [library](https://github.com/OlivierBeq/Papyrus-scripts). This library allows us to download, read, and explore the dataset. Many other features, including bioactivity modeling, are possible using the papyrus_scripts. If you want to dive into them, feel free to follow the [notebook with simple examples](https://github.com/OlivierBeq/Papyrus-scripts/blob/master/notebook_examples/simple_examples.ipynb). By default, the download_papyrus function retrieves bioactivity, target and other information for the latest version of the Papyrus dataset. The data retrieved consists of the highest quality continuous bioactivity data (Papyrus++) without stereochemistry annotated (i.e. nostereo=True and stereo=False). Check out the [documentation](https://github.com/OlivierBeq/Papyrus-scripts/blob/master/src/papyrus_scripts/download.py) to learn more about the options available." + ] }, { "cell_type": "code", "execution_count": 6, - "outputs": [], - "source": [ - "# Let's specify the Papyrus version for the rest of the work\n", - "PAPYRUS_VERSION = '05.5'" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# Let's specify the Papyrus version for the rest of the work\n", + "PAPYRUS_VERSION = \"05.5\"" + ] }, { "cell_type": "code", "execution_count": 4, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -478,27 +484,25 @@ }, { "data": { - "text/plain": "Donwloading version 05.5: 0%| | 0.00/118M [00:00pchembl_value_Mean variable).\n", "\n", @@ -530,17 +540,17 @@ "|A2A|P29274|\n", "|A2B|P29275|\n", "|A3|P0DMS8|" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "code", "execution_count": 7, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "def filter_explore_activity_data(papyrus_version, targets):\n", @@ -561,47 +571,65 @@ " \"\"\"\n", " # Read downloaded Papyrus dataset in chunks, as it does not fit in memory\n", " CHUNKSIZE = 100000\n", - " data = papyrus_scripts.read_papyrus(version=papyrus_version, chunksize=CHUNKSIZE, source_path=DATA)\n", + " data = papyrus_scripts.read_papyrus(\n", + " version=papyrus_version, chunksize=CHUNKSIZE, source_path=DATA\n", + " )\n", "\n", " # Create filter for targets of interest\n", " target_accession_list = targets.values()\n", " filter = papyrus_scripts.keep_accession(data, target_accession_list)\n", "\n", " # Iterate through chunks and apply the filter defined\n", - " filtered_data = papyrus_scripts.preprocess.consume_chunks(filter,\n", - " total=-(-papyrus_scripts.utils.IO.get_num_rows_in_file('bioactivities', False) // CHUNKSIZE))\n", + " filtered_data = papyrus_scripts.preprocess.consume_chunks(\n", + " filter,\n", + " total=-(\n", + " -papyrus_scripts.utils.IO.get_num_rows_in_file(\"bioactivities\", False) // CHUNKSIZE\n", + " ),\n", + " )\n", " # Add column named 'Target' for easier data visualization\n", - " filtered_data['Target'] = filtered_data['accession'].apply(lambda x: [i for i in targets.keys() if targets[i]==x][0])\n", + " filtered_data[\"Target\"] = filtered_data[\"accession\"].apply(\n", + " lambda x: [i for i in targets.keys() if targets[i] == x][0]\n", + " )\n", "\n", " # Print number of bioactivity datapoints per target\n", - " for target,accession in zip(targets.keys(), targets.values()):\n", - " print('Number of bioactivity datapoints')\n", - " print(f'{target} ({accession}) : {filtered_data[filtered_data[\"accession\"]==accession].shape[0]}')\n", + " for target, accession in zip(targets.keys(), targets.values()):\n", + " print(\"Number of bioactivity datapoints\")\n", + " print(\n", + " f'{target} ({accession}) : {filtered_data[filtered_data[\"accession\"]==accession].shape[0]}'\n", + " )\n", "\n", " # Plot distribution of activity values (pchembl_value_Mean) per target\n", - " g = sns.displot(filtered_data, x='pchembl_value_Mean', hue='Target', element='step', hue_order=targets.keys())\n", + " g = sns.displot(\n", + " filtered_data,\n", + " x=\"pchembl_value_Mean\",\n", + " hue=\"Target\",\n", + " element=\"step\",\n", + " hue_order=targets.keys(),\n", + " )\n", "\n", " return filtered_data" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 8, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 8, + }, "outputs": [ { "data": { - "text/plain": " 0%| | 0/13 [00:00", - "image/png": 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\n" 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" + ] }, "metadata": {}, "output_type": "display_data" @@ -631,44 +661,164 @@ ], "source": [ "# Define the set of receptors of interest with a label and their Uniprot accession\n", - "adenosine_receptors = {'A1': 'P30542',\n", - " 'A2A': 'P29274',\n", - " 'A2B': 'P29275',\n", - " 'A3': 'P0DMS8'}\n", + "adenosine_receptors = {\"A1\": \"P30542\", \"A2A\": \"P29274\", \"A2B\": \"P29275\", \"A3\": \"P0DMS8\"}\n", "\n", "# Filter the Papyrus bioactivity dataset and plot the distribution of activity values for the targets of interest\n", "ar_dataset = filter_explore_activity_data(PAPYRUS_VERSION, adenosine_receptors)" - ], + ] + }, + { + "cell_type": "markdown", "metadata": { "collapsed": false, "pycharm": { - "name": "#%%\n" + "name": "#%% md\n" } - } - }, - { - "cell_type": "markdown", + }, "source": [ "For PCM modeling, we keep from our bioactivity dataset three variables:\n", "* Bioactivity (pchembl_value_mean), which is our target variable to predict\n", "* Target IDs (accession), which is the Uniprot code to link the protein descriptors that we will calculate with ProDEC\n", "* Compound IDs (SMILES), to link the compound descriptors that we will calculate with Mordred" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "code", "execution_count": 9, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": " SMILES accession \\\n222 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n223 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n383 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n462 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n464 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n... ... ... \n1238255 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... P30542 \n1238605 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P0DMS8 \n1238606 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29274 \n1238607 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29275 \n1238608 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P30542 \n\n pchembl_value_Mean \n222 8.6800 \n223 6.6800 \n383 4.8200 \n462 7.1515 \n464 5.6500 \n... ... \n1238255 5.1000 \n1238605 7.6100 \n1238606 7.3500 \n1238607 5.1500 \n1238608 7.3400 \n\n[12719 rows x 3 columns]", - "text/html": "
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SMILESaccessionpchembl_value_Mean
222Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.6800
223Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.6800
383Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.8200
462O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.1515
464O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.6500
............
1238255Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc...P305425.1000
1238605CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P0DMS87.6100
1238606CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292747.3500
1238607CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292755.1500
1238608CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P305427.3400
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12719 rows × 3 columns

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SMILESaccessionpchembl_value_Mean
222Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.6800
223Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.6800
383Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.8200
462O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.1515
464O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.6500
............
1238255Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc...P305425.1000
1238605CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P0DMS87.6100
1238606CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292747.3500
1238607CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292755.1500
1238608CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P305427.3400
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" + ], + "text/plain": [ + " SMILES accession \\\n", + "222 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n", + "223 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n", + "383 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n", + "462 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n", + "464 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n", + "... ... ... \n", + "1238255 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... P30542 \n", + "1238605 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P0DMS8 \n", + "1238606 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29274 \n", + "1238607 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29275 \n", + "1238608 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P30542 \n", + "\n", + " pchembl_value_Mean \n", + "222 8.6800 \n", + "223 6.6800 \n", + "383 4.8200 \n", + "462 7.1515 \n", + "464 5.6500 \n", + "... ... \n", + "1238255 5.1000 \n", + "1238605 7.6100 \n", + "1238606 7.3500 \n", + "1238607 5.1500 \n", + "1238608 7.3400 \n", + "\n", + "[12719 rows x 3 columns]" + ] }, "execution_count": 9, "metadata": {}, @@ -676,46 +826,146 @@ } ], "source": [ - "ar_dataset = ar_dataset[['SMILES', 'accession', 'pchembl_value_Mean']]\n", + "ar_dataset = ar_dataset[[\"SMILES\", \"accession\", \"pchembl_value_Mean\"]]\n", "ar_dataset.head()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "#### Align target sequences" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Align target sequences" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "In order to ensure protein descriptors are of the same length, we first need to align the target sequences. We do this by creating a MSA with the software Clustal Omega (ClustalO). To begin with, we extract the protein sequences from the target files in Papyrus. The sequences could also be obtained from Uniprot, but this way we ensure we are always retrieving the canonical isoform sequence.\n", "Since Papyrus also contains bioactivity data for different mutants and species, the main protein identifier (target_id variable) consists of the Uniprot accession code and the mutant ('WT' for wild type). Even though we are interested in the wild type, to map our targets of interest we calculate a new variable called accession to be consistent with the rest of the talktorial." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 10, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": " target_id HGNC_symbol UniProtID Status Organism \\\n47 P29275_WT ADORA2B AA2BR_HUMAN reviewed Homo sapiens (Human) \n80 P30542_WT ADORA1 AA1R_HUMAN reviewed Homo sapiens (Human) \n81 P29274_WT ADORA2A AA2AR_HUMAN reviewed Homo sapiens (Human) \n82 P0DMS8_WT ADORA3 AA3R_HUMAN reviewed Homo sapiens (Human) \n\n Classification Length \\\n47 Membrane receptor->Family A G protein-coupled ... 332 \n80 Membrane receptor->Family A G protein-coupled ... 326 \n81 Membrane receptor->Family A G protein-coupled ... 412 \n82 Membrane receptor->Family A G protein-coupled ... 318 \n\n Sequence accession \n47 MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL... P29275 \n80 MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC... P30542 \n81 MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV... P29274 \n82 MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT... P0DMS8 ", - "text/html": "
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target_idHGNC_symbolUniProtIDStatusOrganismClassificationLengthSequenceaccession
47P29275_WTADORA2BAA2BR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...332MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL...P29275
80P30542_WTADORA1AA1R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...326MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...P30542
81P29274_WTADORA2AAA2AR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...412MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV...P29274
82P0DMS8_WTADORA3AA3R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...318MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT...P0DMS8
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target_idHGNC_symbolUniProtIDStatusOrganismClassificationLengthSequenceaccession
47P29275_WTADORA2BAA2BR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...332MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL...P29275
80P30542_WTADORA1AA1R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...326MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...P30542
81P29274_WTADORA2AAA2AR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...412MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV...P29274
82P0DMS8_WTADORA3AA3R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...318MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT...P0DMS8
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" + ], + "text/plain": [ + " target_id HGNC_symbol UniProtID Status Organism \\\n", + "47 P29275_WT ADORA2B AA2BR_HUMAN reviewed Homo sapiens (Human) \n", + "80 P30542_WT ADORA1 AA1R_HUMAN reviewed Homo sapiens (Human) \n", + "81 P29274_WT ADORA2A AA2AR_HUMAN reviewed Homo sapiens (Human) \n", + "82 P0DMS8_WT ADORA3 AA3R_HUMAN reviewed Homo sapiens (Human) \n", + "\n", + " Classification Length \\\n", + "47 Membrane receptor->Family A G protein-coupled ... 332 \n", + "80 Membrane receptor->Family A G protein-coupled ... 326 \n", + "81 Membrane receptor->Family A G protein-coupled ... 412 \n", + "82 Membrane receptor->Family A G protein-coupled ... 318 \n", + "\n", + " Sequence accession \n", + "47 MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL... P29275 \n", + "80 MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC... P30542 \n", + "81 MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV... P29274 \n", + "82 MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT... P0DMS8 " + ] }, "execution_count": 10, "metadata": {}, @@ -725,65 +975,69 @@ "source": [ "protein_data = papyrus_scripts.read_protein_set(version=PAPYRUS_VERSION)\n", "# Create new variable 'accession' with the Uniprot accession codes by splitting target_id and keeping the first part\n", - "protein_data['accession'] = protein_data['target_id'].apply(lambda x: x.split('_')[0])\n", + "protein_data[\"accession\"] = protein_data[\"target_id\"].apply(lambda x: x.split(\"_\")[0])\n", "# Filter protein data for our targets of interest based on accession code\n", "targets = protein_data[protein_data.accession.isin(adenosine_receptors.values())]\n", "targets" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "In order to align the sequences with ClustalO, we first need to write them into a FASTA file." - ], "metadata": { "collapsed": false - } + }, + "source": [ + "In order to align the sequences with ClustalO, we first need to write them into a FASTA file." + ] }, { "cell_type": "code", "execution_count": 8, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "# Create object with sequences and descriptions\n", "records = []\n", "for index, row in targets.reset_index(drop=True).iterrows():\n", - " records.append(Bio_SeqIO.SeqRecord(seq=Bio.Seq.Seq(row['Sequence']),\n", - " id=str(index),\n", - " name=row['accession'],\n", - " description=' '.join([row['UniProtID'], row['Organism'], row['Classification']])))\n", - "sequences_path = Path(DATA / 'sequences.fasta')\n", + " records.append(\n", + " Bio_SeqIO.SeqRecord(\n", + " seq=Bio.Seq.Seq(row[\"Sequence\"]),\n", + " id=str(index),\n", + " name=row[\"accession\"],\n", + " description=\" \".join([row[\"UniProtID\"], row[\"Organism\"], row[\"Classification\"]]),\n", + " )\n", + " )\n", + "sequences_path = Path(DATA / \"sequences.fasta\")\n", "# Write sequences as .fasta file\n", - "_ = Bio_SeqIO.write(records, sequences_path, 'fasta')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + "_ = Bio_SeqIO.write(records, sequences_path, \"fasta\")" + ] }, { "cell_type": "markdown", - "source": [ - "Now, we use ClustalO to align the sequences and write out the alignment file. We do this by calling the ClustalO webservice from the command line." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Now, we use ClustalO to align the sequences and write out the alignment file. We do this by calling the ClustalO webservice from the command line." + ] }, { "cell_type": "code", "execution_count": 10, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -811,61 +1065,116 @@ "source": [ "# Query ClustalO webservice from command line\n", "!python data/clustalo.py --email m.gorostiola.gonzalez@lacdr.leidenuniv.nl --stype protein --sequence data/sequences.fasta --outfmt fa --outdir data --outfile aligned_sequences --order input" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "Finally we parse the aligned sequences." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Finally we parse the aligned sequences." + ] }, { "cell_type": "code", "execution_count": 11, - "outputs": [], - "source": [ - "alignment_file = Path(DATA / 'aligned_sequences.aln-fasta.fasta')\n", - "aligned_sequences = [str(seq.seq) for seq in Bio.SeqIO.parse(alignment_file, 'fasta')]" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "alignment_file = Path(DATA / \"aligned_sequences.aln-fasta.fasta\")\n", + "aligned_sequences = [str(seq.seq) for seq in Bio.SeqIO.parse(alignment_file, \"fasta\")]" + ] }, { "cell_type": "markdown", - "source": [ - "And we visualize the MSA." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "And we visualize the MSA." + ] }, { "cell_type": "code", "execution_count": 12, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": "╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA2BR_H… \u001B[1;36m 1\u001B[0m 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│\n│ 2 AA2AR_H… \u001B[1;36m355\u001B[0m 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│\n│ 3 AA3R_HU… \u001B[1;36m319\u001B[0m 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│\n╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n", - "text/html": "
╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA2BR_H…     1  -----MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVAVGLFAIPFAITISLGFCTDFYGCLFLACFVLV  │\n│ 1 AA1R_HU…     1  ---MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFCFIVSLAVADVAVGALVIPLAILINIGPQTYFHTCLMVACPVLI  │\n│ 2 AA2AR_H…     1  ------MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLV  │\n│ 3 AA3R_HU…     1  MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTTTFYFIVSLALADIAVGVLVMPLAIVVSLGITIHFYSCLFMTCLLLI  │\n│                                                                                                                 │\n│ 0 AA2BR_H…    88  LTQSSIFSLLAVAVDRYLAICVPLRYKSLVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCC---LVKCLFENV  │\n│ 1 AA1R_HU…    90  LTQSSILALLAIAVDRYLRVKIPLRYKMVVTPRRAAVAIAGCWILSFVVGLTPMFGWNNLSAVER----AWA---ANGSMGEPVIKCEFEKV  │\n│ 2 AA2AR_H…    87  LTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWNN-------CGQPKEGKNHSQGCGEGQVACLFEDV  │\n│ 3 AA3R_HU…    93  FTHASIMSLLAIAVDRYLRVKLTVRYKRVTTHRRIWLALGLCWLVSFLVGLTPMFGWNMKLTSEYH-------------RNVTFLSCQFVSV  │\n│                                                                                                                 │\n│ 0 AA2BR_H…   177  VPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTEL----MDHSRTTLQREIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQG  │\n│ 1 AA1R_HU…   175  ISMEYMVYFNFFVWVLPPLLLMVLIYLEVFYLIRKQLNKKVSAS--SGDPQKYYGKELKIAKSLALILFLFALSWLPLHILNCITLFCPSC-  │\n│ 2 AA2AR_H…   172  VPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDC-  │\n│ 3 AA3R_HU…   172  MRMDYMVYFSFLTWIFIPLVVMCAIYLDIFYIIRNKLSLNLSN---SKETGAFYGREFKTAKSLFLVLFLFALSWLPLSIINCIIYFNG---  │\n│                                                                                                                 │\n│ 0 AA2BR_H…   265  KNKPKWAMNMAILLSHANSVVNPIVYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQ----------AGVQPALGVGL--------------  │\n│ 1 AA1R_HU…   264  -HKPSILTYIAIFLTHGNSAMNPIVYAFRIQKFRVTFLKIWNDHFRCQPAPPIDEDLPEE--------------------------------  │\n│ 2 AA2AR_H…   263  SHAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHP  │\n│ 3 AA3R_HU…   258  -EVPQLVLYMGILLSHANSMMNPIVYAYKIKKFKETYLLILKACVVCHPSDSLDTSIEKNSE------------------------------  │\n│                                                                                                                 │\n│ 0 AA2BR_H…   333  ----------------------------------------------------------                                    │\n│ 1 AA1R_HU…   323  --RPDD----------------------------------------------------                                    │\n│ 2 AA2AR_H…   355  ERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS                                    │\n│ 3 AA3R_HU…   319  ----------------------------------------------------------                                    │\n╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n
\n" + "text/html": [ + "
╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n",
+       "│ 0 AA2BR_H…     1  -----MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVAVGLFAIPFAITISLGFCTDFYGCLFLACFVLV  │\n",
+       "│ 1 AA1R_HU…     1  ---MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFCFIVSLAVADVAVGALVIPLAILINIGPQTYFHTCLMVACPVLI  │\n",
+       "│ 2 AA2AR_H…     1  ------MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLV  │\n",
+       "│ 3 AA3R_HU…     1  MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTTTFYFIVSLALADIAVGVLVMPLAIVVSLGITIHFYSCLFMTCLLLI  │\n",
+       "│                                                                                                                 │\n",
+       "│ 0 AA2BR_H…    88  LTQSSIFSLLAVAVDRYLAICVPLRYKSLVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCC---LVKCLFENV  │\n",
+       "│ 1 AA1R_HU…    90  LTQSSILALLAIAVDRYLRVKIPLRYKMVVTPRRAAVAIAGCWILSFVVGLTPMFGWNNLSAVER----AWA---ANGSMGEPVIKCEFEKV  │\n",
+       "│ 2 AA2AR_H…    87  LTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWNN-------CGQPKEGKNHSQGCGEGQVACLFEDV  │\n",
+       "│ 3 AA3R_HU…    93  FTHASIMSLLAIAVDRYLRVKLTVRYKRVTTHRRIWLALGLCWLVSFLVGLTPMFGWNMKLTSEYH-------------RNVTFLSCQFVSV  │\n",
+       "│                                                                                                                 │\n",
+       "│ 0 AA2BR_H…   177  VPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTEL----MDHSRTTLQREIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQG  │\n",
+       "│ 1 AA1R_HU…   175  ISMEYMVYFNFFVWVLPPLLLMVLIYLEVFYLIRKQLNKKVSAS--SGDPQKYYGKELKIAKSLALILFLFALSWLPLHILNCITLFCPSC-  │\n",
+       "│ 2 AA2AR_H…   172  VPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDC-  │\n",
+       "│ 3 AA3R_HU…   172  MRMDYMVYFSFLTWIFIPLVVMCAIYLDIFYIIRNKLSLNLSN---SKETGAFYGREFKTAKSLFLVLFLFALSWLPLSIINCIIYFNG---  │\n",
+       "│                                                                                                                 │\n",
+       "│ 0 AA2BR_H…   265  KNKPKWAMNMAILLSHANSVVNPIVYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQ----------AGVQPALGVGL--------------  │\n",
+       "│ 1 AA1R_HU…   264  -HKPSILTYIAIFLTHGNSAMNPIVYAFRIQKFRVTFLKIWNDHFRCQPAPPIDEDLPEE--------------------------------  │\n",
+       "│ 2 AA2AR_H…   263  SHAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHP  │\n",
+       "│ 3 AA3R_HU…   258  -EVPQLVLYMGILLSHANSMMNPIVYAYKIKKFKETYLLILKACVVCHPSDSLDTSIEKNSE------------------------------  │\n",
+       "│                                                                                                                 │\n",
+       "│ 0 AA2BR_H…   333  ----------------------------------------------------------                                    │\n",
+       "│ 1 AA1R_HU…   323  --RPDD----------------------------------------------------                                    │\n",
+       "│ 2 AA2AR_H…   355  ERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS                                    │\n",
+       "│ 3 AA3R_HU…   319  ----------------------------------------------------------                                    │\n",
+       "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+       "
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│\n", + "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n" + ] }, "metadata": {}, "output_type": "display_data" @@ -881,25 +1190,19 @@ "# Visualize MSA\n", "panel = rich.panel.Panel(viewer, title=\"Multiple sequence alignment\")\n", "rich.print(panel)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "In the MSA we can observe that the adenosine A2A receptor has a longer C terminus than the rest of the adenosine receptors. Moreover, there are clear parts of the proteins that are very similar in all the receptors (i.e. transmembrane domains), and parts that vary in amino acid composition and length between receptors (i.e. mostly loops)." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "In the MSA we can observe that the adenosine A2A receptor has a longer C terminus than the rest of the adenosine receptors. Moreover, there are clear parts of the proteins that are very similar in all the receptors (i.e. transmembrane domains), and parts that vary in amino acid composition and length between receptors (i.e. mostly loops)." + ] }, { "cell_type": "markdown", @@ -910,19 +1213,25 @@ }, { "cell_type": "markdown", - "source": [ - "Now that our protein sequences are aligned, we can calculate protein descriptors using ProDEC. For that, let's parse all default descriptors available. Since we are focusing on Z-scale descriptors in this talktorial, we can explore the details about this descriptor, and in case we want some extra information we can look at the article where it is first described." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Now that our protein sequences are aligned, we can calculate protein descriptors using ProDEC. For that, let's parse all default descriptors available. Since we are focusing on Z-scale descriptors in this talktorial, we can explore the details about this descriptor, and in case we want some extra information we can look at the article where it is first described." + ] }, { "cell_type": "code", "execution_count": 13, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -936,18 +1245,18 @@ "# Parse ProDEC descriptors\n", "desc_factory = prodec.ProteinDescriptors()\n", "# Print available descriptors\n", - "print(f'Available ProDEC descriptors: {desc_factory.available_descriptors}')" - ], + "print(f\"Available ProDEC descriptors: {desc_factory.available_descriptors}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 14, + }, "outputs": [ { "name": "stdout", @@ -958,7 +1267,14 @@ }, { "data": { - "text/plain": "{'Authors': 'Hellberg, Sjöström, Skagerberg, Wold',\n 'Year': 1987,\n 'Journal': 'Journal of Medicinal Chemistry',\n 'DOI': '10.1021/jm00390a003',\n 'PMID': None,\n 'Patent': None}" + "text/plain": [ + "{'Authors': 'Hellberg, Sjöström, Skagerberg, Wold',\n", + " 'Year': 1987,\n", + " 'Journal': 'Journal of Medicinal Chemistry',\n", + " 'DOI': '10.1021/jm00390a003',\n", + " 'PMID': None,\n", + " 'Patent': None}" + ] }, "execution_count": 14, "metadata": {}, @@ -967,19 +1283,19 @@ ], "source": [ "# Print information about Z-scales\n", - "print('More about Z-Scales:')\n", - "desc_factory.get_descriptor('Zscale Hellberg').Info" - ], + "print(\"More about Z-Scales:\")\n", + "desc_factory.get_descriptor(\"Zscale Hellberg\").Info" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 15, + }, "outputs": [], "source": [ "def calculate_protein_descriptor(targets, aligned_sequences, protein_descriptor):\n", @@ -1007,37 +1323,208 @@ " protein_features = prodec_descriptor.pandas_get(aligned_sequences)\n", "\n", " # Insert protein labels in the obtained features\n", - " protein_features.insert(0, 'accession', targets.accession.reset_index(drop=True))\n", + " protein_features.insert(0, \"accession\", targets.accession.reset_index(drop=True))\n", "\n", " return protein_features" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 16, + }, "outputs": [ { "data": { - "text/plain": " 0%| | 0/4 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
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For this, we first have to convert the molecules in our dataset to chemical entities from their text representation (SMILES). Afterwards, we use Mordred to calculate 2D molecular descriptors." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "The final step to prepare our dataset for PCM is to calculate compound descriptors. For this, we first have to convert the molecules in our dataset to chemical entities from their text representation (SMILES). Afterwards, we use Mordred to calculate 2D molecular descriptors." + ] }, { "cell_type": "code", "execution_count": 17, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "def calculate_molecular_descriptors(bioactivity_dataset, user_descriptors):\n", @@ -1102,17 +1589,19 @@ " Dataset with SMILES and features for the compound descriptors of interest for molecules in the bioactivity dataset\n", " \"\"\"\n", " # Extract unique molecules from the bioactivity dataset\n", - " molecules = [Chem.MolFromSmiles(x) for x in bioactivity_dataset['SMILES'].unique()]\n", + " molecules = [Chem.MolFromSmiles(x) for x in bioactivity_dataset[\"SMILES\"].unique()]\n", "\n", " # Use Mordred to calculate molecular descriptors of interest\n", - " if user_descriptors == ['all']:\n", + " if user_descriptors == [\"all\"]:\n", " mordred_calculator = mordred.Calculator(mordred.descriptors, ignore_3D=True)\n", " molecular_descriptor = mordred_calculator.pandas(molecules, pynb=False)\n", "\n", " else:\n", - " mordred_list = [mordred_descriptors.__dict__[descriptor] for descriptor in user_descriptors]\n", + " mordred_list = [\n", + " mordred_descriptors.__dict__[descriptor] for descriptor in user_descriptors\n", + " ]\n", " mordred_calculator = mordred.Calculator(mordred_list, ignore_3D=True)\n", - " molecular_descriptor = mordred_calculator.pandas(molecules,ipynb=False)\n", + " molecular_descriptor = mordred_calculator.pandas(molecules, ipynb=False)\n", "\n", " # Clean descriptors step 1: replace mordred format to pandas format\n", " molecular_descriptor = pd.DataFrame(molecular_descriptor.fill_missing(np.NAN))\n", @@ -1121,14 +1610,20 @@ " mordred_descs_names = {}\n", " for desc_name in mordred_calculator.descriptors:\n", " # First we replace the ambiguous names with aliphatic rings\n", - " mordred_descs_names[str(desc_name)] = re.sub(r'(.*F?)A(H?Ring)$', r'\\1aliph\\2', str(desc_name))\n", + " mordred_descs_names[str(desc_name)] = re.sub(\n", + " r\"(.*F?)A(H?Ring)$\", r\"\\1aliph\\2\", str(desc_name)\n", + " )\n", " # Then we replace the ambiguous names with aromatic rings\n", - " mordred_descs_names[str(desc_name)] = re.sub(r'(.*F?)a(H?Ring)$', r'\\1arom\\2', mordred_descs_names[str(desc_name)])\n", + " mordred_descs_names[str(desc_name)] = re.sub(\n", + " r\"(.*F?)a(H?Ring)$\", r\"\\1arom\\2\", mordred_descs_names[str(desc_name)]\n", + " )\n", "\n", " molecular_descriptor = molecular_descriptor.rename(mordred_descs_names)\n", "\n", " # Clean descriptors step 3: remove absurdly big and small values (e.g. topological indexes of molecules containing fragments)\n", - " molecular_descriptor = molecular_descriptor.astype(np.float32).replace([np.inf, -np.inf], np.NAN)\n", + " molecular_descriptor = molecular_descriptor.astype(np.float32).replace(\n", + " [np.inf, -np.inf], np.NAN\n", + " )\n", " molecular_descriptor = molecular_descriptor.fillna(value=0)\n", "\n", " # Clean descriptors step 4: round values to 3 decimals to reduce memory footprint\n", @@ -1136,20 +1631,20 @@ " molecular_descriptor = molecular_descriptor.convert_dtypes()\n", "\n", " # Insert SMILES in first column for mapping\n", - " molecular_descriptor.insert(0, 'SMILES', bioactivity_dataset.SMILES.unique())\n", + " molecular_descriptor.insert(0, \"SMILES\", bioactivity_dataset.SMILES.unique())\n", "\n", " return molecular_descriptor" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 18, "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } - }, - { - "cell_type": "code", - "execution_count": 18, + }, "outputs": [ { "name": "stderr", @@ -1160,8 +1655,360 @@ }, { "data": { - "text/plain": " SMILES ABC ABCGG \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... 21.041 17.684 \n1 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... 20.701 15.635 \n2 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 23.23 17.456 \n3 CCNC(=O)C1OC(n2cnc3c2ncnc3Nc2ccc(OCC(=O)Nc3ccc... 31.336 22.213 \n4 NC(=NC(=O)Cn1c(O)c2CCCCc2c1O)Nc1nc2c(cccc2)s1 21.408 17.066 \n... ... ... ... \n6893 CC(C)(C)C1(O)CCN2CC3c4c(cccc4)CCc4cccc(c43)C2C1 22.178 16.375 \n6894 CNC(=O)C12CC1C(n1cnc3c(NC(C)C)nc(C#Cc4ccc(Cl)s... 26.351 22.592 \n6895 CCCOc1ccc(C=Cc2cc3c(c(=O)n(C)c(=O)n3C)n2C)cc1 20.022 15.893 \n6896 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... 23.736 18.442 \n6897 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 18.511 15.661 \n\n nAcid nBase nAtom nHeavyAtom nSpiro nBridgehead nHetero ... nN \\\n0 0 1 51 27 0 0 8 ... 6 \n1 0 0 42 26 0 0 8 ... 4 \n2 0 0 43 29 0 0 8 ... 6 \n3 0 0 66 40 0 0 14 ... 7 \n4 0 3 46 27 0 0 9 ... 5 \n... ... ... ... ... ... ... ... ... .. \n6893 0 1 58 27 0 0 2 ... 1 \n6894 0 0 56 33 0 0 11 ... 6 \n6895 0 0 49 26 0 0 6 ... 3 \n6896 0 0 52 30 0 0 9 ... 4 \n6897 0 0 43 24 0 0 8 ... 5 \n\n nO nS nP nF nCl nBr nI nX BalabanJ \n0 2 0 0 0 0 0 0 0 1.631 \n1 3 1 0 0 0 0 0 0 1.307 \n2 2 0 0 0 0 0 0 0 1.328 \n3 6 0 0 0 1 0 0 1 1.043 \n4 3 1 0 0 0 0 0 0 1.234 \n... .. .. .. .. ... ... .. .. ... \n6893 1 0 0 0 0 0 0 0 1.46 \n6894 3 1 0 0 1 0 0 1 1.303 \n6895 3 0 0 0 0 0 0 0 1.479 \n6896 3 0 0 2 0 0 0 2 1.318 \n6897 3 0 0 0 0 0 0 0 1.68 \n\n[6898 rows x 23 columns]", - 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Here, we will use a Random Forest (RF) ML regression model to predict the bioactivity of our compound-target pairs.\n", "\n", @@ -1205,44 +2054,46 @@ "Ultimately, we want a model that can predict compound activity data towards a target of interest for compound-target pairs that it has never seen before. By combining several targets in one model, we expect the model to be able to learn the similarities and differences between targets and use the additional data to make better predictions.\n", "\n", "We start by defining a few functions that will help us split the data (split_train_test) and train and validate a PCM regression model (train_validate_pcm_model). The validation will be done on the test set and the performance will be assessed using regression metrics such as Person's $r$, $R^{2}$ and $MAE$. This function will also plot the correlation between true and predicted values, making a distinction between the different targets in the test set to assess whether the PCM model has a different performance per protein. Finally, we will define a function (train_validate_qsar_model) to train a QSAR model for a single target based on a random split. The output of this function will be comparable to that of the PCM model for comparison purposes." - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%% md\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "#### Helper functions" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Helper functions" + ] }, { "cell_type": "markdown", - "source": [ - "Function to split the data using one of these two methods described in theory: random split or leave one target out (LOTO) split." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Function to split the data using one of these two methods described in theory: random split or leave one target out (LOTO) split." + ] }, { "cell_type": "code", "execution_count": 19, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ - "def split_train_test(pcm_dataset, test_size, split_method, loto_target=None, loto_accession='None'):\n", + "def split_train_test(\n", + " pcm_dataset, test_size, split_method, loto_target=None, loto_accession=\"None\"\n", + "):\n", " \"\"\"\n", " Split a dataset for PCM modeling in train and test set based on the split method of choice\n", "\n", @@ -1268,53 +2119,55 @@ " Testing dataset\n", " \"\"\"\n", " # Random split\n", - " if split_method == 'random':\n", + " if split_method == \"random\":\n", " train, test = train_test_split(pcm_dataset, test_size=test_size, random_state=1234)\n", "\n", " # Leave one target out\n", - " elif split_method == 'loto':\n", + " elif split_method == \"loto\":\n", " if loto_accession != None:\n", " # Leave out defined accession\n", " test_target = loto_accession\n", - " print(f'Target left out for testing is {loto_target}')\n", + " print(f\"Target left out for testing is {loto_target}\")\n", " else:\n", " raise ValueError(\"loto_accession needs to be defined\")\n", "\n", " # Move data associated with target to test set and rest to training set\n", - " train = pcm_dataset[pcm_dataset['accession'] != test_target]\n", - " test = pcm_dataset[pcm_dataset['accession'] == test_target]\n", + " train = pcm_dataset[pcm_dataset[\"accession\"] != test_target]\n", + " test = pcm_dataset[pcm_dataset[\"accession\"] == test_target]\n", "\n", " else:\n", " raise ValueError(f\"Split method {split_method} undefined; use random or loto.\")\n", "\n", " # Print statistics of training and test sets\n", - " print(f'Training set has {train.shape[0]} datapoints')\n", - " print(f'Test set has {test.shape[0]} datapoints ({round(100*test.shape[0]/pcm_dataset.shape[0], 3)} %)')\n", + " print(f\"Training set has {train.shape[0]} datapoints\")\n", + " print(\n", + " f\"Test set has {test.shape[0]} datapoints ({round(100*test.shape[0]/pcm_dataset.shape[0], 3)} %)\"\n", + " )\n", "\n", - " return train,test" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + " return train, test" + ] }, { "cell_type": "markdown", - "source": [ - "Function to report performance metrics for a regression model based on the true and predicted values of the target variable." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Function to report performance metrics for a regression model based on the true and predicted values of the target variable." + ] }, { "cell_type": "code", "execution_count": 38, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "def _performance_metrics(y_true, y_predicted):\n", @@ -1334,36 +2187,36 @@ " Pearson's r, R2 score and MAE on test set\n", " \"\"\"\n", " model_performance = {}\n", - " model_performance['Pearson r'] = pearsonr(y_true, y_predicted)[0]\n", - " model_performance['R2 score'] = r2_score(y_true, y_predicted)\n", - " model_performance['MAE'] = mean_absolute_error(y_true, y_predicted)\n", + " model_performance[\"Pearson r\"] = pearsonr(y_true, y_predicted)[0]\n", + " model_performance[\"R2 score\"] = r2_score(y_true, y_predicted)\n", + " model_performance[\"MAE\"] = mean_absolute_error(y_true, y_predicted)\n", "\n", " print(json.dumps(model_performance, indent=4))\n", "\n", " return model_performance" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "Function to plot the goodness of fit of true and predicted values for a regression model." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Function to plot the goodness of fit of true and predicted values for a regression model." + ] }, { "cell_type": "code", "execution_count": 31, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "def _performance_plot(target, y_true, y_predicted, r2_score):\n", @@ -1386,37 +2239,37 @@ " fig:\n", " Figure with true vs. predicted values colored by target, with r2_score calculated per target\n", " \"\"\"\n", - " ax = sns.scatterplot(y=y_true, x=y_predicted, label=(f'{target} R2 = {r2_score:.2f}'))\n", + " ax = sns.scatterplot(y=y_true, x=y_predicted, label=(f\"{target} R2 = {r2_score:.2f}\"))\n", " _ = sns.lineplot(x=(0, 14), y=(0, 14))\n", - " _ = ax.set_xlim((0,14))\n", - " _ = ax.set_ylim((0,14))\n", - " _ = ax.set_xlabel('Predicted')\n", - " _ = ax.set_ylabel('Observed')\n", + " _ = ax.set_xlim((0, 14))\n", + " _ = ax.set_ylim((0, 14))\n", + " _ = ax.set_xlabel(\"Predicted\")\n", + " _ = ax.set_ylabel(\"Observed\")\n", "\n", " return ax" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "Function to train a PCM RF model on a training set and validate it on a test set. Performance metrics are calculated for the whole test set, and also separately." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Function to train a PCM RF model on a training set and validate it on a test set. Performance metrics are calculated for the whole test set, and also separately." + ] }, { "cell_type": "code", "execution_count": 39, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "def train_validate_pcm_model(targets_dict, train, test):\n", @@ -1440,11 +2293,11 @@ " Figure with true vs. predicted values colored by target, with r2_score calculated per target\n", " \"\"\"\n", " # Store compound-target pairs in test set to allow comparative performance evaluation\n", - " test_pairs = test[['SMILES', 'accession', 'pchembl_value_Mean']].reset_index(drop=True)\n", + " test_pairs = test[[\"SMILES\", \"accession\", \"pchembl_value_Mean\"]].reset_index(drop=True)\n", "\n", " # Remove identifiers\n", - " train = train.drop(columns=['SMILES', 'accession'])\n", - " test = test.drop(columns=['SMILES', 'accession'])\n", + " train = train.drop(columns=[\"SMILES\", \"accession\"])\n", + " test = test.drop(columns=[\"SMILES\", \"accession\"])\n", "\n", " # Set model parameter for random forest\n", " param = {\n", @@ -1460,20 +2313,20 @@ " predictions = model_rf.predict(test.iloc[:, 1:])\n", "\n", " # Calculate model performance with regression metrics\n", - " print('=== PCM model performance ===')\n", + " print(\"=== PCM model performance ===\")\n", " model_performance = _performance_metrics(test.iloc[:, 0], predictions)\n", "\n", " # Add column named 'Target' for easier data visualization\n", " _targets_dict_reversed = {uniprot: target for target, uniprot in targets_dict.items()}\n", - " test_pairs['Target'] = test_pairs['accession'].apply(lambda x: _targets_dict_reversed[x])\n", + " test_pairs[\"Target\"] = test_pairs[\"accession\"].apply(lambda x: _targets_dict_reversed[x])\n", "\n", " # Calculate model performance per target\n", - " test_pairs['prediction'] = predictions\n", + " test_pairs[\"prediction\"] = predictions\n", "\n", - " for target,accession in targets_dict.items():\n", + " for target, accession in targets_dict.items():\n", " # Define true values and predictions per target\n", - " true_target = test_pairs[test_pairs['accession'] == accession]['pchembl_value_Mean']\n", - " prediction_target = test_pairs[test_pairs['accession'] == accession]['prediction']\n", + " true_target = test_pairs[test_pairs[\"accession\"] == accession][\"pchembl_value_Mean\"]\n", + " prediction_target = test_pairs[test_pairs[\"accession\"] == accession][\"prediction\"]\n", "\n", " try:\n", " # Calculate r2 score\n", @@ -1483,30 +2336,32 @@ " _performance_plot(target, true_target, prediction_target, r2_target)\n", "\n", " except ValueError:\n", - " print(f'Not plotting {target}. Performance can only be plotted for the left out target in LOTO split')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + " print(\n", + " f\"Not plotting {target}. Performance can only be plotted for the left out target in LOTO split\"\n", + " )" + ] }, { "cell_type": "markdown", - "source": [ - "Function to split a QSAR dataset randomly and train and validate a RF QSAR model for a target of interest." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Function to split a QSAR dataset randomly and train and validate a RF QSAR model for a target of interest." + ] }, { "cell_type": "code", "execution_count": 40, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "def train_validate_qsar_model(qsar_dataset, target, accession, test_size):\n", @@ -1532,10 +2387,10 @@ " Figure with true vs. predicted values, with r2_score calculated\n", " \"\"\"\n", " # Extract target-specific dataset\n", - " target_dataset = qsar_dataset[qsar_dataset['accession'] == accession]\n", + " target_dataset = qsar_dataset[qsar_dataset[\"accession\"] == accession]\n", "\n", " # Remove identifiers\n", - " target_dataset = target_dataset.drop(columns=['SMILES', 'accession'])\n", + " target_dataset = target_dataset.drop(columns=[\"SMILES\", \"accession\"])\n", "\n", " # Random-split in training and test set\n", " train, test = train_test_split(target_dataset, test_size=test_size, random_state=1234)\n", @@ -1554,51 +2409,403 @@ " predictions = model_rf.predict(test.iloc[:, 1:])\n", "\n", " # Calculate model performance with regression metrics\n", - " print(f'=== QSAR model performance {target} ===')\n", + " print(f\"=== QSAR model performance {target} ===\")\n", " model_performance = _performance_metrics(test.iloc[:, 0], predictions)\n", "\n", " # Plot correlation between true and predicted values\n", " _performance_plot(target, test.iloc[:, 0], predictions, model_performance[\"R2 score\"])" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "##### Preprocessing" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "##### Preprocessing" + ] }, { "cell_type": "markdown", - "source": [ - "For each compound-target pair in our bioactivity dataset, we need to add the protein and molecular features previously calculated. We join the protein features based on Uniprot accession and the molecular features based on SMILES." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "For each compound-target pair in our bioactivity dataset, we need to add the protein and molecular features previously calculated. We join the protein features based on Uniprot accession and the molecular features based on SMILES." + ] }, { "cell_type": "code", "execution_count": 21, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": " SMILES accession \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n1 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n2 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... 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" + ], + "text/plain": [ + " SMILES accession \\\n", + "0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n", + "1 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n", + "2 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n", + "3 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n", + "4 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n", + "... ... ... \n", + "12714 Cn1cc(Nc2nc(-c3ccco3)c(-c3ncncc3)cn2)ccc1=O P29275 \n", + "12715 N#Cc1c(-c2ccc(OCC3CC3)cc2)c(C#N)c(SCC(N)=O)nc1N P29275 \n", + "12716 O=C(Cc1cccc2c1cccc2)Nc1nc2nn(CCc3ccccc3)cc2c2n... P29275 \n", + "12717 COc1c(OCC(=O)O)ccc(-c2cc3c([nH]2)c(=O)n(C)c(=O... P29275 \n", + "12718 CCCn1c(=O)c2c([nH]c(-c3cc(OCC4CC(=O)N(c5ccc(F)... P29275 \n", + "\n", + " pchembl_value_Mean Zscale_1 Zscale_2 Zscale_3 Zscale_4 Zscale_5 \\\n", + "0 8.6800 0.00 0.00 0.00 0.00 0.00 \n", + "1 6.6800 0.00 0.00 0.00 0.00 0.00 \n", + "2 4.8200 0.00 0.00 0.00 0.00 0.00 \n", + "3 5.6500 0.00 0.00 0.00 0.00 0.00 \n", + "4 7.1515 -2.49 -0.27 -0.41 -1.22 0.88 \n", + "... ... ... ... ... ... ... \n", + "12714 7.5515 0.00 0.00 0.00 0.00 0.00 \n", + "12715 7.5100 0.00 0.00 0.00 0.00 0.00 \n", + "12716 7.3672 0.00 0.00 0.00 0.00 0.00 \n", + "12717 6.5700 0.00 0.00 0.00 0.00 0.00 \n", + "12718 6.6800 0.00 0.00 0.00 0.00 0.00 \n", + "\n", + " Zscale_6 Zscale_7 ... nN nO nS nP nF nCl nBr nI nX BalabanJ \n", + "0 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.631 \n", + "1 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.631 \n", + "2 0.00 0.00 ... 4 3 1 0 0 0 0 0 0 1.307 \n", + "3 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.328 \n", + "4 2.23 3.22 ... 6 2 0 0 0 0 0 0 0 1.328 \n", + "... ... ... ... .. .. .. .. .. ... ... .. .. ... \n", + "12714 0.00 0.00 ... 6 2 0 0 0 0 0 0 0 1.368 \n", + "12715 0.00 0.00 ... 5 2 1 0 0 0 0 0 0 1.613 \n", + "12716 0.00 0.00 ... 7 2 0 0 0 0 0 0 0 0.998 \n", + "12717 0.00 0.00 ... 3 6 0 0 0 0 0 0 0 1.608 \n", + "12718 0.00 0.00 ... 6 5 0 0 1 0 0 0 1 1.103 \n", + "\n", + "[12719 rows x 1303 columns]" + ] }, "execution_count": 21, "metadata": {}, @@ -1607,38 +2814,390 @@ ], "source": [ "# Add protein and molecular features to bioactivity dataset to generate PCM dataset\n", - "ar_pcm_dataset = ar_dataset.merge(protein_features, on='accession')\n", - "ar_pcm_dataset = ar_pcm_dataset.merge(molecular_features, on='SMILES')\n", + "ar_pcm_dataset = ar_dataset.merge(protein_features, on=\"accession\")\n", + "ar_pcm_dataset = ar_pcm_dataset.merge(molecular_features, on=\"SMILES\")\n", "\n", "ar_pcm_dataset.head()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "For QSAR modeling, we do the same but we do not include the protein descriptors. This results on a dataset for modeling with a significantly reduced number of features." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "For QSAR modeling, we do the same but we do not include the protein descriptors. This results on a dataset for modeling with a significantly reduced number of features." + ] }, { "cell_type": "code", "execution_count": 22, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "data": { - "text/plain": " SMILES accession \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n1 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n2 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n3 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n4 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n... ... ... \n12714 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... P30542 \n12715 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P0DMS8 \n12716 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29274 \n12717 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29275 \n12718 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P30542 \n\n pchembl_value_Mean ABC ABCGG nAcid nBase nAtom nHeavyAtom \\\n0 8.6800 21.041 17.684 0 1 51 27 \n1 6.6800 21.041 17.684 0 1 51 27 \n2 4.8200 20.701 15.635 0 0 42 26 \n3 7.1515 23.23 17.456 0 0 43 29 \n4 5.6500 23.23 17.456 0 0 43 29 \n... ... ... ... ... ... ... ... \n12714 5.1000 23.736 18.442 0 0 52 30 \n12715 7.6100 18.511 15.661 0 0 43 24 \n12716 7.3500 18.511 15.661 0 0 43 24 \n12717 5.1500 18.511 15.661 0 0 43 24 \n12718 7.3400 18.511 15.661 0 0 43 24 \n\n nSpiro ... nN nO nS nP nF nCl nBr nI nX BalabanJ \n0 0 ... 6 2 0 0 0 0 0 0 0 1.631 \n1 0 ... 6 2 0 0 0 0 0 0 0 1.631 \n2 0 ... 4 3 1 0 0 0 0 0 0 1.307 \n3 0 ... 6 2 0 0 0 0 0 0 0 1.328 \n4 0 ... 6 2 0 0 0 0 0 0 0 1.328 \n... ... ... .. .. .. .. .. ... ... .. .. ... \n12714 0 ... 4 3 0 0 2 0 0 0 2 1.318 \n12715 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n12716 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n12717 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n12718 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n\n[12719 rows x 25 columns]", - "text/html": "
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SMILESaccessionpchembl_value_MeanABCABCGGnAcidnBasenAtomnHeavyAtomnSpiro...nNnOnSnPnFnClnBrnInXBalabanJ
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0Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.680021.04117.6840151270...6200000001.631
1Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.680021.04117.6840151270...6200000001.631
2Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.820020.70115.6350042260...4310000001.307
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4O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.650023.2317.4560043290...6200000001.328
..................................................................
12714Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc...P305425.100023.73618.4420052300...4300200021.318
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12719 rows × 25 columns

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" + ], + "text/plain": [ + " SMILES accession \\\n", + "0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n", + "1 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n", + "2 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n", + "3 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n", + "4 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n", + "... ... ... \n", + "12714 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... P30542 \n", + "12715 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P0DMS8 \n", + "12716 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29274 \n", + "12717 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29275 \n", + "12718 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P30542 \n", + "\n", + " pchembl_value_Mean ABC ABCGG nAcid nBase nAtom nHeavyAtom \\\n", + "0 8.6800 21.041 17.684 0 1 51 27 \n", + "1 6.6800 21.041 17.684 0 1 51 27 \n", + "2 4.8200 20.701 15.635 0 0 42 26 \n", + "3 7.1515 23.23 17.456 0 0 43 29 \n", + "4 5.6500 23.23 17.456 0 0 43 29 \n", + "... ... ... ... ... ... ... ... \n", + "12714 5.1000 23.736 18.442 0 0 52 30 \n", + "12715 7.6100 18.511 15.661 0 0 43 24 \n", + "12716 7.3500 18.511 15.661 0 0 43 24 \n", + "12717 5.1500 18.511 15.661 0 0 43 24 \n", + "12718 7.3400 18.511 15.661 0 0 43 24 \n", + "\n", + " nSpiro ... nN nO nS nP nF nCl nBr nI nX BalabanJ \n", + "0 0 ... 6 2 0 0 0 0 0 0 0 1.631 \n", + "1 0 ... 6 2 0 0 0 0 0 0 0 1.631 \n", + "2 0 ... 4 3 1 0 0 0 0 0 0 1.307 \n", + "3 0 ... 6 2 0 0 0 0 0 0 0 1.328 \n", + "4 0 ... 6 2 0 0 0 0 0 0 0 1.328 \n", + "... ... ... .. .. .. .. .. ... ... .. .. ... \n", + "12714 0 ... 4 3 0 0 2 0 0 0 2 1.318 \n", + "12715 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n", + "12716 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n", + "12717 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n", + "12718 0 ... 5 3 0 0 0 0 0 0 0 1.68 \n", + "\n", + "[12719 rows x 25 columns]" + ] }, "execution_count": 22, "metadata": {}, @@ -1647,28 +3206,22 @@ ], "source": [ "# Add molecular features to bioactivity dataset to generate QSAR dataset\n", - "ar_qsar_dataset = ar_dataset.merge(molecular_features, on='SMILES')\n", + "ar_qsar_dataset = ar_dataset.merge(molecular_features, on=\"SMILES\")\n", "\n", "ar_qsar_dataset.head()" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "#### Model training and validation" - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "#### Model training and validation" + ] }, { "cell_type": "markdown", @@ -1683,19 +3236,25 @@ }, { "cell_type": "markdown", - "source": [ - "The first PCM model that we train for the four adenosine receptors is based on a random split, where 20 % of the data (2,544 datapoints) is part of the test set for validation." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "The first PCM model that we train for the four adenosine receptors is based on a random split, where 20 % of the data (2,544 datapoints) is part of the test set for validation." + ] }, { "cell_type": "code", "execution_count": 23, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -1709,15 +3268,9 @@ ], "source": [ "# Split dataset in training and test set (random split)\n", - "print('== Random split ==')\n", - "train_random,test_random = split_train_test(ar_pcm_dataset, 0.20, 'random')" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + "print(\"== Random split ==\")\n", + "train_random, test_random = split_train_test(ar_pcm_dataset, 0.20, \"random\")" + ] }, { "cell_type": "code", @@ -1738,8 +3291,10 @@ }, { "data": { - "text/plain": "
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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -1747,46 +3302,52 @@ ], "source": [ "# Train and validate PCM model\n", - "train_validate_pcm_model(adenosine_receptors,train_random,test_random)" + "train_validate_pcm_model(adenosine_receptors, train_random, test_random)" ] }, { "cell_type": "markdown", - "source": [ - "Based on the performance metrics of the PCM model, Pearson's $r$ tells us that the true and predicted values are highly correlated. Moreover, the $R^{2}$ tells us that almost 50 % of the variance of the target variable is explained by the model features and that the model is predictive. The $MAE$ tells us that the predictions are on average 0.64 p-value units off, which is an acceptable prediction error.\n", - "An interesting observation is that the $R^{2}$ score is quite similar if we calculate it independently for the test set datapoint corresponding to each target." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Based on the performance metrics of the PCM model, Pearson's $r$ tells us that the true and predicted values are highly correlated. Moreover, the $R^{2}$ tells us that almost 50 % of the variance of the target variable is explained by the model features and that the model is predictive. The $MAE$ tells us that the predictions are on average 0.64 p-value units off, which is an acceptable prediction error.\n", + "An interesting observation is that the $R^{2}$ score is quite similar if we calculate it independently for the test set datapoint corresponding to each target." + ] }, { "cell_type": "markdown", - "source": [ - "##### Random split QSAR models" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "##### Random split QSAR models" + ] }, { "cell_type": "markdown", - "source": [ - "Now, we want to compare the use of a single PCM model for four targets vs. four independent QSAR models trained for each target solely on chemical compound features." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Now, we want to compare the use of a single PCM model for four targets vs. four independent QSAR models trained for each target solely on chemical compound features." + ] }, { "cell_type": "code", "execution_count": 42, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -1820,8 +3381,10 @@ }, { "data": { - "text/plain": "
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+ "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -1829,51 +3392,51 @@ ], "source": [ "# Iterate over targets (adenosine receptors)\n", - "for target,accession in adenosine_receptors.items():\n", + "for target, accession in adenosine_receptors.items():\n", " # Train and validate QSAR models\n", - " train_validate_qsar_model(ar_qsar_dataset,target,accession,0.20)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + " train_validate_qsar_model(ar_qsar_dataset, target, accession, 0.20)" + ] }, { "cell_type": "markdown", - "source": [ - "The four QSAR models trained have quite good performance, with high correlation between the observed and predicted values. Compared to the PCM model, the $R^{2}$ score is less homogeneous between targets and, in general, lower. These results seem to indicate that the PCM model is able to extrapolate certain properties between targets." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "The four QSAR models trained have quite good performance, with high correlation between the observed and predicted values. Compared to the PCM model, the $R^{2}$ score is less homogeneous between targets and, in general, lower. These results seem to indicate that the PCM model is able to extrapolate certain properties between targets." + ] }, { "cell_type": "markdown", - "source": [ - "##### Leave one target out split PCM model" - ], "metadata": { "collapsed": false - } + }, + "source": [ + "##### Leave one target out split PCM model" + ] }, { "cell_type": "markdown", + "metadata": { + "collapsed": false + }, "source": [ "The random split PCM model works pretty well. However, a model trained and validated on a random split might overestimate the performance compared to a real life drug discovery scenario since some compounds can be tested in several targets.\n", "Finally, to test whether our PCM model could be used to predict bioactivity data on a target for which we have no previously known bioactivity data, we can train and validate PCM models following the \"leave one target out\" (LOTO) split method. We can do this process for each of the adenosine receptors." - ], - "metadata": { - "collapsed": false - } + ] }, { "cell_type": "code", "execution_count": 43, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -1935,8 +3498,10 @@ }, { "data": { - "text/plain": "
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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -1944,28 +3509,22 @@ ], "source": [ "# Iterate over targets (adenosine receptors)\n", - "for target,accession in adenosine_receptors.items():\n", + "for target, accession in adenosine_receptors.items():\n", " # Split dataset in training and test set (leave one target out split)\n", - " print('== Leave one target out split ==')\n", - " train_loto, test_loto = split_train_test(ar_pcm_dataset, 0.20, 'loto', target, accession)\n", + " print(\"== Leave one target out split ==\")\n", + " train_loto, test_loto = split_train_test(ar_pcm_dataset, 0.20, \"loto\", target, accession)\n", " # Train and validate PCM model, every time leaving a different target out for validation\n", - " train_validate_pcm_model(adenosine_receptors,train_loto,test_loto)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + " train_validate_pcm_model(adenosine_receptors, train_loto, test_loto)" + ] }, { "cell_type": "markdown", - "source": [ - "Immediately we see that the LOTO split method is way more difficult to model than the random split. The PCM metrics show that even though the true and predicted values are somewhat correlated (Pearson's $r$), the PCM model features are not able to explain the variance in the target variable ($R^{2}$). We can also see this in the shape of the predicted vs. observed graph, where the datapoints are not aggregated around the unit line that would define a perfect fit. Rather, the predictions are aggregated around the mean bioactivity values in the training set." - ], "metadata": { "collapsed": false - } + }, + "source": [ + "Immediately we see that the LOTO split method is way more difficult to model than the random split. The PCM metrics show that even though the true and predicted values are somewhat correlated (Pearson's $r$), the PCM model features are not able to explain the variance in the target variable ($R^{2}$). We can also see this in the shape of the predicted vs. observed graph, where the datapoints are not aggregated around the unit line that would define a perfect fit. Rather, the predictions are aggregated around the mean bioactivity values in the training set." + ] }, { "cell_type": "markdown", From a3ca4eecf8eecfac40de25ce30b8f2015748f380 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Fri, 28 Oct 2022 17:03:19 +0200 Subject: [PATCH 39/62] Automatically generate README file (and correct accents in author's surnames --- .../T032_compound_activity_proteochemometrics/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md index 335219a9..50cbf5c3 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md @@ -4,19 +4,17 @@ Authors: -- Marina Gorostiola González, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands) +- Marina Gorostiola González, 2022, [Computational Drug Discovery](https://www.universiteitleiden.nl/en/science/drug-research/drug-discovery-and-safety/computational-drug-discovery), Drug Discovery & Safety Leiden University (The Netherlands) - Olivier J.M. Béquignon, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands) - Willem Jespers, 2022, Computational Drug Discovery, Drug Discovery & Safety Leiden University (The Netherlands) - ## Aim of this talktorial -While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be solved leveraging similarities and differences between proteins. In this talktorial, we use Proteochemometrics modeling (PCM) to enrich our activity models with protein data to predict the activity of novel compounds against the four adenosine receptor isoforms (A1, A2A, A2B, A3). +While activity data is very abundant for some protein targets, there are still a number of underexplored proteins where the use of machine learning (ML) for activity prediction is very difficult due to the lack of data. This issue can be partially solved by leveraging similarities and differences between proteins. In this talktorial, we use proteochemometrics (PCM) modeling to enrich our activity models with protein data to predict the activity of novel compounds against the four [adenosine receptor](https://journals.physiology.org/doi/full/10.1152/physrev.00049.2017) isoforms (A1, A2A, A2B, A3). ### Contents in *Theory* - * Proteochemometrics (PCM) modeling * Data preparation * Papyrus dataset @@ -55,3 +53,5 @@ While activity data is very abundant for some protein targets, there are still a * Regression metrics [(Scikit learn)](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics) * XGBoost [Documentation](https://xgboost.readthedocs.io/en/stable/index.html) * Proteochemometrics review: [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub) + + From 982adc9379916913bb9f78e3f5a4016cbb4b2d62 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Fri, 28 Oct 2022 17:25:12 +0200 Subject: [PATCH 40/62] Improve aesthetics of outputs --- .../talktorial.ipynb | 1740 +---------------- 1 file changed, 99 insertions(+), 1641 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index fe41d3e3..4f02e82d 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -390,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -422,7 +422,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -451,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": { "collapsed": false, "pycharm": { @@ -484,14 +484,12 @@ }, { "data": { + "text/plain": "Donwloading version 05.5: 0%| | 0.00/118M [00:00" - ] + "text/plain": "
", + "image/png": 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SMILESaccessionpchembl_value_Mean
222Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.6800
223Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.6800
383Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.8200
462O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.1515
464O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.6500
............
1238255Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc...P305425.1000
1238605CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P0DMS87.6100
1238606CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292747.3500
1238607CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P292755.1500
1238608CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12P305427.3400
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12719 rows × 3 columns

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" - ], - "text/plain": [ - " SMILES accession \\\n", - "222 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n", - "223 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n", - "383 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n", - "462 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n", - "464 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n", - "... ... ... \n", - "1238255 Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc... P30542 \n", - "1238605 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P0DMS8 \n", - "1238606 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29274 \n", - "1238607 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P29275 \n", - "1238608 CCOC(=O)c1cnc(NCC(C)C)n2nc(-c3ccco3)nc12 P30542 \n", - "\n", - " pchembl_value_Mean \n", - "222 8.6800 \n", - "223 6.6800 \n", - "383 4.8200 \n", - "462 7.1515 \n", - "464 5.6500 \n", - "... ... \n", - "1238255 5.1000 \n", - "1238605 7.6100 \n", - "1238606 7.3500 \n", - "1238607 5.1500 \n", - "1238608 7.3400 \n", - "\n", - "[12719 rows x 3 columns]" - ] + "text/plain": " SMILES accession \\\n222 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n223 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n383 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n462 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n464 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n\n pchembl_value_Mean \n222 8.6800 \n223 6.6800 \n383 4.8200 \n462 7.1515 \n464 5.6500 ", + "text/html": "
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SMILESaccessionpchembl_value_Mean
222Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.6800
223Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.6800
383Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.8200
462O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.1515
464O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.6500
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" }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -854,7 +722,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": { "collapsed": false, "pycharm": { @@ -864,110 +732,10 @@ "outputs": [ { "data": { - "text/html": [ - "
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target_idHGNC_symbolUniProtIDStatusOrganismClassificationLengthSequenceaccession
47P29275_WTADORA2BAA2BR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...332MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL...P29275
80P30542_WTADORA1AA1R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...326MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...P30542
81P29274_WTADORA2AAA2AR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...412MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV...P29274
82P0DMS8_WTADORA3AA3R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...318MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT...P0DMS8
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" - ], - "text/plain": [ - " target_id HGNC_symbol UniProtID Status Organism \\\n", - "47 P29275_WT ADORA2B AA2BR_HUMAN reviewed Homo sapiens (Human) \n", - "80 P30542_WT ADORA1 AA1R_HUMAN reviewed Homo sapiens (Human) \n", - "81 P29274_WT ADORA2A AA2AR_HUMAN reviewed Homo sapiens (Human) \n", - "82 P0DMS8_WT ADORA3 AA3R_HUMAN reviewed Homo sapiens (Human) \n", - "\n", - " Classification Length \\\n", - "47 Membrane receptor->Family A G protein-coupled ... 332 \n", - "80 Membrane receptor->Family A G protein-coupled ... 326 \n", - "81 Membrane receptor->Family A G protein-coupled ... 412 \n", - "82 Membrane receptor->Family A G protein-coupled ... 318 \n", - "\n", - " Sequence accession \n", - "47 MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL... P29275 \n", - "80 MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC... P30542 \n", - "81 MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV... P29274 \n", - "82 MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT... P0DMS8 " - ] + "text/plain": " target_id HGNC_symbol UniProtID Status Organism \\\n47 P29275_WT ADORA2B AA2BR_HUMAN reviewed Homo sapiens (Human) \n80 P30542_WT ADORA1 AA1R_HUMAN reviewed Homo sapiens (Human) \n81 P29274_WT ADORA2A AA2AR_HUMAN reviewed Homo sapiens (Human) \n82 P0DMS8_WT ADORA3 AA3R_HUMAN reviewed Homo sapiens (Human) \n\n Classification Length \\\n47 Membrane receptor->Family A G protein-coupled ... 332 \n80 Membrane receptor->Family A G protein-coupled ... 326 \n81 Membrane receptor->Family A G protein-coupled ... 412 \n82 Membrane receptor->Family A G protein-coupled ... 318 \n\n Sequence accession \n47 MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL... P29275 \n80 MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC... P30542 \n81 MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV... P29274 \n82 MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT... P0DMS8 ", + "text/html": "
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target_idHGNC_symbolUniProtIDStatusOrganismClassificationLengthSequenceaccession
47P29275_WTADORA2BAA2BR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...332MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL...P29275
80P30542_WTADORA1AA1R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...326MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...P30542
81P29274_WTADORA2AAA2AR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...412MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV...P29274
82P0DMS8_WTADORA3AA3R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...318MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT...P0DMS8
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" }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -992,7 +760,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "collapsed": false, "pycharm": { @@ -1039,26 +807,11 @@ } }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RUNNING\n", - "FINISHED\n", - "Creating result file: data\\aligned_sequences.out.txt\n", - "Creating result file: data\\aligned_sequences.sequence.txt\n", - "Creating result file: data\\aligned_sequences.aln-fasta.fasta\n", - "Creating result file: data\\aligned_sequences.tree.dnd\n", - "Creating result file: data\\aligned_sequences.phylotree.ph\n", - "Creating result file: data\\aligned_sequences.pim.pim\n", - "Creating result file: data\\aligned_sequences.submission.params\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "JobId: clustalo-R20221027-170800-0191-18047492-p1m\n" + "python: can't open file 'data/clustalo.py': [Errno 2] No such file or directory\n" ] } ], @@ -1118,63 +871,8 @@ "outputs": [ { "data": { - "text/html": [ - "
╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n",
-       "│ 0 AA2BR_H…     1  -----MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVAVGLFAIPFAITISLGFCTDFYGCLFLACFVLV  │\n",
-       "│ 1 AA1R_HU…     1  ---MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFCFIVSLAVADVAVGALVIPLAILINIGPQTYFHTCLMVACPVLI  │\n",
-       "│ 2 AA2AR_H…     1  ------MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLV  │\n",
-       "│ 3 AA3R_HU…     1  MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTTTFYFIVSLALADIAVGVLVMPLAIVVSLGITIHFYSCLFMTCLLLI  │\n",
-       "│                                                                                                                 │\n",
-       "│ 0 AA2BR_H…    88  LTQSSIFSLLAVAVDRYLAICVPLRYKSLVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCC---LVKCLFENV  │\n",
-       "│ 1 AA1R_HU…    90  LTQSSILALLAIAVDRYLRVKIPLRYKMVVTPRRAAVAIAGCWILSFVVGLTPMFGWNNLSAVER----AWA---ANGSMGEPVIKCEFEKV  │\n",
-       "│ 2 AA2AR_H…    87  LTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWNN-------CGQPKEGKNHSQGCGEGQVACLFEDV  │\n",
-       "│ 3 AA3R_HU…    93  FTHASIMSLLAIAVDRYLRVKLTVRYKRVTTHRRIWLALGLCWLVSFLVGLTPMFGWNMKLTSEYH-------------RNVTFLSCQFVSV  │\n",
-       "│                                                                                                                 │\n",
-       "│ 0 AA2BR_H…   177  VPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTEL----MDHSRTTLQREIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQG  │\n",
-       "│ 1 AA1R_HU…   175  ISMEYMVYFNFFVWVLPPLLLMVLIYLEVFYLIRKQLNKKVSAS--SGDPQKYYGKELKIAKSLALILFLFALSWLPLHILNCITLFCPSC-  │\n",
-       "│ 2 AA2AR_H…   172  VPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDC-  │\n",
-       "│ 3 AA3R_HU…   172  MRMDYMVYFSFLTWIFIPLVVMCAIYLDIFYIIRNKLSLNLSN---SKETGAFYGREFKTAKSLFLVLFLFALSWLPLSIINCIIYFNG---  │\n",
-       "│                                                                                                                 │\n",
-       "│ 0 AA2BR_H…   265  KNKPKWAMNMAILLSHANSVVNPIVYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQ----------AGVQPALGVGL--------------  │\n",
-       "│ 1 AA1R_HU…   264  -HKPSILTYIAIFLTHGNSAMNPIVYAFRIQKFRVTFLKIWNDHFRCQPAPPIDEDLPEE--------------------------------  │\n",
-       "│ 2 AA2AR_H…   263  SHAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHP  │\n",
-       "│ 3 AA3R_HU…   258  -EVPQLVLYMGILLSHANSMMNPIVYAYKIKKFKETYLLILKACVVCHPSDSLDTSIEKNSE------------------------------  │\n",
-       "│                                                                                                                 │\n",
-       "│ 0 AA2BR_H…   333  ----------------------------------------------------------                                    │\n",
-       "│ 1 AA1R_HU…   323  --RPDD----------------------------------------------------                                    │\n",
-       "│ 2 AA2AR_H…   355  ERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS                                    │\n",
-       "│ 3 AA3R_HU…   319  ----------------------------------------------------------                                    │\n",
-       "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
-       "
\n" - ], - "text/plain": [ - "╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n", - "│ 0 AA2BR_H… \u001B[1;36m 1\u001B[0m 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│\n", - "│ 1 AA1R_HU… \u001B[1;36m 1\u001B[0m 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│\n", - "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n" - ] + "text/plain": "╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA2BR_H… \u001B[1;36m 1\u001B[0m 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╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA2BR_H…     1  -----MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVAVGLFAIPFAITISLGFCTDFYGCLFLACFVLV  │\n│ 1 AA1R_HU…     1  ---MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFCFIVSLAVADVAVGALVIPLAILINIGPQTYFHTCLMVACPVLI  │\n│ 2 AA2AR_H…     1  ------MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLV  │\n│ 3 AA3R_HU…     1  MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTTTFYFIVSLALADIAVGVLVMPLAIVVSLGITIHFYSCLFMTCLLLI  │\n│                                                                                                                 │\n│ 0 AA2BR_H…    88  LTQSSIFSLLAVAVDRYLAICVPLRYKSLVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCC---LVKCLFENV  │\n│ 1 AA1R_HU…    90  LTQSSILALLAIAVDRYLRVKIPLRYKMVVTPRRAAVAIAGCWILSFVVGLTPMFGWNNLSAVER----AWA---ANGSMGEPVIKCEFEKV  │\n│ 2 AA2AR_H…    87  LTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWNN-------CGQPKEGKNHSQGCGEGQVACLFEDV  │\n│ 3 AA3R_HU…    93  FTHASIMSLLAIAVDRYLRVKLTVRYKRVTTHRRIWLALGLCWLVSFLVGLTPMFGWNMKLTSEYH-------------RNVTFLSCQFVSV  │\n│                                                                                                                 │\n│ 0 AA2BR_H…   177  VPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTEL----MDHSRTTLQREIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQG  │\n│ 1 AA1R_HU…   175  ISMEYMVYFNFFVWVLPPLLLMVLIYLEVFYLIRKQLNKKVSAS--SGDPQKYYGKELKIAKSLALILFLFALSWLPLHILNCITLFCPSC-  │\n│ 2 AA2AR_H…   172  VPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDC-  │\n│ 3 AA3R_HU…   172  MRMDYMVYFSFLTWIFIPLVVMCAIYLDIFYIIRNKLSLNLSN---SKETGAFYGREFKTAKSLFLVLFLFALSWLPLSIINCIIYFNG---  │\n│                                                                                                                 │\n│ 0 AA2BR_H…   265  KNKPKWAMNMAILLSHANSVVNPIVYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQ----------AGVQPALGVGL--------------  │\n│ 1 AA1R_HU…   264  -HKPSILTYIAIFLTHGNSAMNPIVYAFRIQKFRVTFLKIWNDHFRCQPAPPIDEDLPEE--------------------------------  │\n│ 2 AA2AR_H…   263  SHAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHP  │\n│ 3 AA3R_HU…   258  -EVPQLVLYMGILLSHANSMMNPIVYAYKIKKFKETYLLILKACVVCHPSDSLDTSIEKNSE------------------------------  │\n│                                                                                                                 │\n│ 0 AA2BR_H…   333  ----------------------------------------------------------                                    │\n│ 1 AA1R_HU…   323  --RPDD----------------------------------------------------                                    │\n│ 2 AA2AR_H…   355  ERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS                                    │\n│ 3 AA3R_HU…   319  ----------------------------------------------------------                                    │\n╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n
\n" }, "metadata": {}, "output_type": "display_data" @@ -1267,14 +965,7 @@ }, { "data": { - "text/plain": [ - "{'Authors': 'Hellberg, Sjöström, Skagerberg, Wold',\n", - " 'Year': 1987,\n", - " 'Journal': 'Journal of Medicinal Chemistry',\n", - " 'DOI': '10.1021/jm00390a003',\n", - " 'PMID': None,\n", - " 'Patent': None}" - ] + "text/plain": "{'Authors': 'Hellberg, Sjöström, Skagerberg, Wold',\n 'Year': 1987,\n 'Journal': 'Journal of Medicinal Chemistry',\n 'DOI': '10.1021/jm00390a003',\n 'PMID': None,\n 'Patent': None}" }, "execution_count": 14, "metadata": {}, @@ -1340,191 +1031,20 @@ "outputs": [ { "data": { + "text/plain": " 0%| | 0/4 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
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SMILESaccessionpchembl_value_MeanZscale_1Zscale_2Zscale_3Zscale_4Zscale_5Zscale_6Zscale_7...nNnOnSnPnFnClnBrnInXBalabanJ
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" }, - "execution_count": 21, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -2834,7 +1650,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "metadata": { "collapsed": false, "pycharm": { @@ -2844,362 +1660,10 @@ "outputs": [ { "data": { - "text/html": [ - "
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0Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.680021.04117.6840151270...6200000001.631
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4O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.650023.2317.4560043290...6200000001.328
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12714Cc1c(C)c(C(=O)N2CCC(Nc3cc(=O)n(C)c4cc(F)c(F)cc...P305425.100023.73618.4420052300...4300200021.318
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SMILESaccessionpchembl_value_MeanABCABCGGnAcidnBasenAtomnHeavyAtomnSpiro...nNnOnSnPnFnClnBrnInXBalabanJ
0Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.680021.04117.6840151270...6200000001.631
1Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.680021.04117.6840151270...6200000001.631
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5 rows × 25 columns

\n
" }, - "execution_count": 22, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -3248,7 +1712,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "metadata": { "collapsed": false, "pycharm": { @@ -3274,7 +1738,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -3283,18 +1747,16 @@ "text": [ "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.6855434084419538,\n", - " \"R2 score\": 0.4647699109179838,\n", - " \"MAE\": 0.6409635421984158\n", + " \"Pearson r\": 0.6858217378640958,\n", + " \"R2 score\": 0.46550829516212433,\n", + " \"MAE\": 0.6407963506676366\n", "}\n" ] }, { "data": { - "image/png": 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\n", 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\n" }, "metadata": {}, "output_type": "display_data" @@ -3341,7 +1803,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 28, "metadata": { "collapsed": false, "pycharm": { @@ -3355,36 +1817,34 @@ "text": [ "=== QSAR model performance A1 ===\n", "{\n", - " \"Pearson r\": 0.5903576322919979,\n", - " \"R2 score\": 0.34623779841739744,\n", - " \"MAE\": 0.5990705889267219\n", + " \"Pearson r\": 0.6029925425551398,\n", + " \"R2 score\": 0.36192570952238345,\n", + " \"MAE\": 0.5973210044267906\n", "}\n", "=== QSAR model performance A2A ===\n", "{\n", - " \"Pearson r\": 0.6264855012916158,\n", - " \"R2 score\": 0.39084261119128527,\n", - " \"MAE\": 0.7022387102541356\n", + " \"Pearson r\": 0.6391547405926432,\n", + " \"R2 score\": 0.4073561135234154,\n", + " \"MAE\": 0.6936822842294988\n", "}\n", "=== QSAR model performance A2B ===\n", "{\n", - " \"Pearson r\": 0.6997350798604474,\n", - " \"R2 score\": 0.48131630633807854,\n", - " \"MAE\": 0.5612815187795313\n", + " \"Pearson r\": 0.7084601022622155,\n", + " \"R2 score\": 0.49053947942778353,\n", + " \"MAE\": 0.5524308526205923\n", "}\n", "=== QSAR model performance A3 ===\n", "{\n", - " \"Pearson r\": 0.661668025151235,\n", - " \"R2 score\": 0.43458547383101476,\n", - " \"MAE\": 0.6927533399906751\n", + " \"Pearson r\": 0.6585745793937321,\n", + " \"R2 score\": 0.43160610100190355,\n", + " \"MAE\": 0.6896203135686225\n", "}\n" ] }, { "data": { - "image/png": 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F/1Xk1W2AoPIkJCSgUCjo3bu3w15SUhKDBw+mdu3aqNVq7r77btauXVvuWU2aNMHd3Z2UlJQKn7tlyxZkMpntJyQkhIcffpjk5GS765YuXcq9996Lt7c3wcHBPProow7TxW8Ehw8fpnPnzqjVasLDw1mwYAFXOxXFaDTSunVrZDKZbXBqCS+99BJt27ZFpVLRunXrqjdcIBAIqogvVo2hcORL1EuR0LvD34Pa8MT2v+5IoQNC7Pyn2bRpE2PGjGHPnj2cO3fObu/AgQMEBQXx3nvvkZyczMyZM5k+fTrr1693OGfPnj0YDAaeeOIJtmzZclXP9vHx4eLFi6SmpvL1119TWFjIgw8+SFFRke2aXbt28eKLL7Jv3z5+/PFHTCYTPXv2pLCw8Lretyvy8vLo0aMHYWFh/PHHH6xbt46VK1eyevXqq7p/ypQphIWFOd2TJIlnn32WQYMGVaXJAoFAUGUU5mv5aGgkd731E956SAkC08qZPDH/g+o2rXq57lGitzg3euq5JEmSpMuWpMxjknT+D0nKPG59fYMpKCiQvL29paNHj0qDBg2S5s+fX+E9L7zwgtS1a1eH9WHDhknTpk2Tvv32W6lBgwaSxWJxec7mzZslX19fu7Uvv/xSAqRDhw6Ve19GRoYESLt27arQ1sry+uuvS76+vpLBYLCtLV26VAoLC6vwfX3zzTdS06ZNpeTkZAmQDh486PS6uXPnShERERXaIqaeCwSCm0lS/FfSt13vlo40sU4r3zboHiknK7W6zao0VTn1XHh2rhdtCmx/FtbfC28/AOvbwSfPWddvINu2baNJkyY0adKEmJgYNm/eXGGoRqvV4u/vb7eWn5/P9u3biYmJoUePHhQWFrJz585rsiU3N5cPPrD+q8HNzc3l8wEHG0qze/duvLy8XP4sWbKk3Pv37t1L586dUalUtrVevXqRmprKmTNnyr0vPT2d4cOH8+6776LRaMq9TiAQCG5FPlsxEuMLk6ibKqFzhyNP3cvAjw5QI6BmdZt2SyASlK8HfQ58MRpO/WK/fvJn+HIMDNgIar8b8uiNGzcSExMDQO/evSkoKODnn3+me/fuTq/fu3cvH3/8MV9//bXd+kcffUTjxo1p3rw5AE8++SQbN26ka9euLp+v1Wrx8vJCkiR0Oh0AjzzyCE2bOp+OK0kSEyZMoGPHjrRo0aLcc9u1a+eQK1MWV2IpLS2NevXq2a2FhITY9urXd+wnIUkSw4YNY+TIkbRr186lKBIIBIJbiQJtNl+/0JtWB/IBuBAiw3/OfPo/8EQ1W3ZrIcTO9VCY6Sh0Sjj5s3X/BoidY8eO8fvvv/Ppp58CoFQqGTRoEJs2bXIqdpKTk+nXrx9z5syhR48ednulRRNATEwMnTp1Ijc3lxo1apRrg7e3N3/99Rcmk4ldu3bx8ssvExcXV+71o0eP5tChQ+zZs8fle1Or1TRq1MjlNRUhk8nsXpd4vMqul7Bu3Try8vKYPn36dT1XIBAIbiYHd31GxtyZtEqz/h13uK0Xvdd/jY9fcDVbdushxM71YMi7vv1KsnHjRkwmE+HhV7LqJUnCzc2NnJwc/PyuCKwjR47QrVs3hg8fzqxZs+zOOXLkCPv37+ePP/5g6tSptnWz2cyHH37IqFGjyrVBLpfbREnTpk1JS0tj0KBB/Pbbbw7Xjhkzhi+//JLffvuNWrVquXxvu3fvpk+fPi6vmTFjBjNmzHC6FxoaSlpamt1aRkYGcMXDU5ZffvmFffv22YW+wOplevrpp3nnnXdc2iMQCAQ3mx2Ln6Xux3upY4RCFZx7MpqB09+ubrNuWYTYuR48fK5vvxKYTCa2bt3KqlWr6Nmzp91e//79ef/99xk9ejRg9eh069aNoUOHsnjxYoezNm7cSKdOnXjttdfs1t999102btzoUuyUZfz48axevZrPPvuMxx57DLAKsDFjxvDZZ5+xc+dOpyGkslxvGCsyMpIZM2ZQVFSEu7s7AD/88ANhYWEO4a0SXn31VRYtWmR7nZqaSq9evdi2bRvt27ev0GaBQCC4WeTlZPDdC31pedBa1XqupoyQBUt5/P5+1WzZLc51pzjf4tzQaixdtiRtfUyS5vo4/mx97IZUZX322WeSu7u7lJub67A3Y8YMqXXr1pIkSdLff/8tBQUFSU8//bR08eJF209GRoYkSZJUVFQkBQUFSRs2bHA45/jx4xIgJSYmOrXBWTWWJEnShAkTpJYtW9qqnkaNGiX5+vpKO3futLNBp9NV9u1XSG5urhQSEiINHjxYOnz4sPTpp59KPj4+0sqVK23X7N+/X2rSpIl04cIFp2ecPn3aaTXWv//+Kx08eFAaMWKEdNddd0kHDx6UDh48KBmNRqfniGosgUBQlfzx04fSD52uVFt99PR9UkGe43fB7UJVVmMJsXO9X0a5FxwFz9bHrOs3gIceekjq27ev070DBw5IgHTgwAFp7ty5EuDwU7duXUmSJOmTTz6R5HK5lJaW5vSsli1bSmPGjHG6V57YOXv2rKRUKqVt27ZJkiQ5fT4gbd68+Zrf97Vw6NAh6f7775dUKpUUGhoqzZs3z67s/Ndff5UA6fTp007vL0/sdO7c2en7Ke8cIXYEAkFVsX3+EOnPllaRs791U+mzFSOq26QbTlWKHZkkXWVr2f8oeXl5+Pr6otVq8fGxDysZDAZOnz5N/fr18fDwqPxD9DnWZGRDnjV05Rl0w6qwBP8dquzPl0AguGPJvXSRH194kBZJegDOhsuotXgVLTq4zm28HXD1/X2tiJydqkDtJ8SNQCAQCKqU/d9tpWDxUlpkWl8nta9Bv9e/R+1Z9fmgtztC7AgEAoFAcIvx8ewnafRFEmFFkKeB9CE9eHL8q9Vt1n8WIXYEAoFAILhFyE4/zy8vPkLLvw0AnK4lp/7StbS/13nDWMHVUa3jIn777TcefvhhwsLCkMlkfP7557a94uJipk6dSsuWLfH09CQsLIzY2FhSU1Orz2CBQCAQCG4Qe7/aSGL/njT/24AFSIry54H//cHdQuhcN9UqdgoLC4mIiHA6iVun0/HXX38xe/Zs/vrrLz799FOOHz/OI488Ug2WCgQCgUBw4/h4en/U01dSMwu0Gjg9+kGe3BSPSi1m9VUF1RrG6tOnT7ndcn19ffnxxx/t1tatW8d9993HuXPnqFOnzs0wUSAQCASCG0bWxdPsevExWh4xAnCqjpxGKzbQoXWnarbs9uI/lbOj1WqRyWQuZzYZjUaMRqPtdV7ejRnZIBAIBALB9bD709cwr1xPs2ywyOBwxyAee/U74c25AVRrGOtaMBgMTJs2jaeeesplvf3SpUvx9fW1/dSuXfsmWikQCAQCgWvMJhPbJvfDZ856QrIh1wvOjH2UJ9/6TQidG8R/QuwUFxfz5JNPYrFYeP31111eO336dLRare3n/PnzN8lKgUAgEAhck3buX74Y0JZW/zuOuwlO1JMTvHkjD45aWt2m3dbc8mGs4uJiBg4cyOnTp/nll18q7KKoUqkcplcLBAKBQFDd7Nz2CrJX3uDuHDDL4HDnUAa8+h1u7uI760ZzS3t2SoTOv//+y08//URAQEB1m3RLkZCQgEKhoHfv3g57SUlJDB48mNq1a6NWq7n77rtZu3at3TU7d+5EJpPZftRqNc2bN+fNN990+dyy9wUEBNCtWzfi4+Ptrnvrrbe4//778fPzw8/Pj+7du/P7779f/xuvgHPnzvHwww/j6elJYGAgY8eOpaio6KrulSSJPn36OLRCAPjrr7/o0aMHNWrUICAggOeff56CgoIb8A4EAsHthNlk4qMJD+K34A2CcyDbG1ImDWJw3K9C6NwkqlXsFBQUkJiYSGJiIgCnT58mMTGRc+fOYTKZGDBgAH/++Sfvv/8+ZrOZtLQ00tLSrvqL63Zn06ZNjBkzhj179nDu3Dm7vQMHDhAUFMR7771HcnIyM2fOZPr06U7L/I8dO8bFixc5cuQII0aMYNSoUfz8888VPr/kvp07dxIUFMSDDz5IRkaGbX/nzp0MHjyYX3/9lb1791KnTh169uxJSkrK9b/5cjCbzTz44IMUFhayZ88ePvroI3bs2MHEiROv6v5XXnkFmUzmsJ6amkr37t1p1KgR+/fv57vvviM5OZlhw4ZV8TsQCAS3Eymnj/DlY/cQ8c0p3M3wbwMF4Vvfpddz86rbtDuL6x4leh2UTJ8u+zN06FDb5GlnP7/++utVP+OGTz2XJCnXkCudyj0lJWUkSadyT0m5htzrOu9qKCgokLy9vaWjR49KgwYNkubPn1/hPS+88ILUtWtX2+uSzz8nJ8fuugYNGkgrVqwo9xxn9x06dEgCpC+//LLc+0wmk+Tt7S298847FdpaWb755htJLpdLKSkptrUPP/xQUqlUFU7OTUxMlGrVqiVdvHhRAqTPPvvMtvfGG29IwcHBktlstq0dPHhQAqR///3X6Xli6rlAcGfz03vLpd/us04qP3R3U+mDFx+QTMXF1W3Wf4aqnHperTk7Xbp0QXIxdN3V3q1CWmEacxPmkpCaYFuLDotmXtQ8Qj1Db9hzt23bRpMmTWjSpAkxMTGMGTOG2bNnO/VKlKDVavH39y93X5Ikvv/+e86fP0/79u2v2hadTsfmzZsBcHNzc3ldcXGxSxvOnTtHs2bNXD4vJiaGuLg4p3t79+6lRYsWhIWF2dZ69eqF0WjkwIEDdO3atVzbBg8ezPr16wkNdfy9GY1G3N3dkcuvOEPVajUAe/bsoVGjRi5tFggEdw5mk4ntEx6k+U/nUFrgkg/oXniawcNmVbdpdyy3fILyrYzWqHUQOgDxqfHMS5jH8k7L8VX53pBnb9y4kZiYGAB69+5NQUEBP//8M927O28rvnfvXj7++GO+/vprh71atWoB1i90i8XCggUL6NSp4oZWJffpdDokSaJt27Y88MAD5V4/bdo0wsPDy7URICwszBbWLA9XSeppaWmEhITYrfn5+eHu7k5aWlq5940fP56oqCj69evndL9bt25MmDCBl19+mZdeeonCwkJmzJgBwMWLF13aKxAI7hzO/ZvEX+NjiDhhAuBYIyX3rHmPOo0jqtmyOxshdq6DbEO2g9ApIT41nmxD9g0RO8eOHeP333/n008/BUCpVDJo0CA2bdrkVEgkJyfTr18/5syZQ48ePRz2d+/ejbe3N0ajkd9//53Ro0fj7+/PqFGjXNqxe/duPD09OXjwIFOnTmXLli3lenZWrFjBhx9+yM6dO/Hw8Cj3TKVSed1eEmfeLUmSyvV6ffnll/zyyy8cPHiw3DObN2/OO++8w4QJE5g+fToKhYKxY8cSEhKCQqG4LnsFAsHtwQ9bFqF5/X2a5EGxApK712Xgqq9QKMVXbXUjfgPXQX5R/nXtV5aNGzdiMpkIDw+3rUmShJubGzk5Ofj5+dnWjxw5Qrdu3Rg+fDizZjl3odavX9/Wlbp58+bs37+fxYsXVyh2Su676667MBgMPPbYY/z9998Opf8rV65kyZIl/PTTT7Rq1crlmdcbxgoNDWX//v12azk5ORQXFzt4fEr45ZdfOHnypENn7v79+3P//fezc+dOAJ566imeeuop0tPT8fT0RCaTsXr1aurXr+/SXoFAcHtjNpnY/lIvmv+aitICWb5Q/NJzDH5qUnWbJriMEDvXgbe793XtVwaTycTWrVtZtWoVPXv2tNvr378/77//PqNHjwasHp1u3boxdOhQFi9efNXPUCgU6PX6a7JryJAhLFiwgNdff53x48fb1l9++WUWLVrE999/T7t27So853rDWJGRkSxevJiLFy9Ss2ZNAH744QdUKhVt27Z1es+0adP4v//7P7u1li1bsmbNGh5++GGH60tE06ZNm/Dw8HDqLRMIBHcGZ/75k0MThhFx2gzA0bvcuG/tR4TXd/2PNsHNRYid68Dfw5/osGjiU+Md9qLDovH3KD8Rt7J89dVX5OTk8Nxzz+Hrax8iGzBgABs3bmT06NEkJyfTtWtXevbsyYQJE2z5KgqFgqCgILv7MjIyMBgMtjDWu+++y4ABA67JLrlczrhx41i0aBEjRoxAo9GwYsUKZs+ezQcffEC9evVsNnh5eeHl5eX0nOsNY/Xs2ZNmzZoxZMgQXn75ZbKzs5k0aRLDhw+3iaSUlBQeeOABtm7dyn333UdoaKjTpOQ6derYeW3Wr19PVFQUXl5e/Pjjj0yePJlly5a5nNUmEAhuX75/ey7eb3xM43woUsA/vRrwxIovRNjqVuS667lucW506fnFgovSiB9GSC22tLD9jPhhhHSx4OL1mF0uDz30kNS3b1+newcOHJAA6cCBA9LcuXOdlu3XrVvXdn3Z0n+lUinVr19fmjRpklRQUFCuDeWVrBcUFEh+fn7S8uXLJUmSpLp16zq1Ye7cudf7Mbjk7Nmz0oMPPiip1WrJ399fGj16tGQwGGz7JW0NXLUwoEzpuSRJ0pAhQyR/f3/J3d1datWqlbR161aXdojSc4Hg9qTIaJA+GNFZOtzUWla+s0NT6dePX61us247qrL0XCZJ/4H67usgLy8PX19ftFqtQ/jDYDBw+vRp6tev7zJptiK0Ri3Zhmzyi/LxdvfG38P/hlVhCf47VNWfL4FAcOtw8nACRyYPp9EZCwD/NHUn6tVPCK3TuJotu/1w9f19rQhfWxXgq/IV4kYgEAhuc77eMB3/jZ/TqACMSjjWtwkDlnwiwlb/AcRvSCAQCAQCFxj1Oj4b04uW8VnIJUjzB+XksQx6zHXFquDWQYgdgUAgEAjK4djBnZyY+iIR56xhqyPNVXRe/xmBNUXLif8SQuwIBAKBQOCEr9ZNJHDzNzTQgdENjj/cnIFLPqluswSVQIgdgUAgEAhKYdTr+OzFHrRMyEYOXAwE9bRJDHzoueo2TVBJhNgRCAQCgeAy//zxE6enjyXigrVQObmlBw+8/hV+QeEV3Cm4lRFiRyAQCAQC4Ms1Ywnd+iP19aB3hxP9Ihi48KPqNktQBQixIxAIBII7Gn1hHl+80IuI/bkApAaB18zpDOwdW72GCaoMIXYEAoHgJmPSajFfuoQlPx+5tw+KAH+UvqJXV3VwKOFrUmdNJiLVGrb6O0JDj9e/okZAzWq2TFCVCLEjEAgEN5Hii2mkzpqFLv7KTD1Nx46ELVyIW03HGW2CG8fnL48k/P1d1DWAzh1OD7iXJ+ZsrW6zBDcAeXUbIKg8CQkJKBQKevfu7bB36dIlevfuTVhYGCqVitq1azN69Gjy8vJcnlmvXj1kMhkymQy1Wk3Tpk15+eWXKT1VJCkpicGDB1O7dm3UajV33303a9eurfL3VxZJkpg3bx5hYWGo1Wq6dOlCcnLyVd//0UcfIZPJePTRR+3WN2zYQKtWrfDx8cHHx4fIyEi+/fbbKrZeILB6dMoKHQDdnj2kzp6NSautJsvuLArztWyLuY8mG3fhZYALITKkNXMZIITObYsQO/9hNm3axJgxY9izZw/nzp2z25PL5fTr148vv/yS48ePs2XLFn766SdGjhxZ4bkLFizg4sWL/PPPP0yaNIkZM2bw5ptv2vYPHDhAUFAQ7733HsnJycycOZPp06ezfv36Kn+PpVmxYgWrV69m/fr1/PHHH4SGhtKjRw/y8/MrvPfs2bNMmjSJ+++/32GvVq1aLFu2jD///JM///yTbt260a9fv2sSUgLB1WC+dMlB6JSg27MH86VLN9miO4+Duz5jz8ORtPrT+vfG4Xu8aP/5Tto98GQ1Wya4kYhBoFUwqLE64u+FhYXUrFmTP/74g7lz59KsWTPmzJnj8p5XX32Vl19+mfPnz5d7Tb169Rg3bhzjxo2zrbVt25Z69eqxY8eOcu978cUX+eeff/jll1+u+b1cDZIkERYWxrhx45g6dSoARqORkJAQli9fzogRI8q912w207lzZ5555hl2795Nbm4un3/+ucvn+fv78/LLL/Pcc5XvqyEGgQrKok9K4syg8r9U623bhjqi1U206M7i0yXPUWdbAp5GKFTBuUFRPD5jY3WbJSiHqhwEKjw710nxxTRSJkzkVN8HOTPoSU717UvKxEkUX0y7oc/dtm0bTZo0oUmTJsTExLB582Zc6dbU1FQ+/fRTOnfufNXPkCSJnTt38s8//+Dm5ubyWq1Wi7+/v8tr+vTpg5eXl8uf8jh9+jRpaWn07NnTtqZSqejcuTMJCQkun7tgwQKCgoKuSriYzWY++ugjCgsLiYyMrPB6geBakHt7V7Bf/v8DgsqTl5PBx0/dy91brULnXKgMxatLhNC5gxAJytdBRfH38FUrb5iHZ+PGjcTExADQu3dvCgoK+Pnnn+nevbvddYMHD+aLL75Ar9fz8MMP8/bbb1d49tSpU5k1axZFRUUUFxfj4eHB2LFjy71+7969fPzxx3z99dcuz3377bfR6/VX8e4cSUuziseQkBC79ZCQEM6ePVvuffHx8WzcuJHExESX5x8+fJjIyEgMBgNeXl589tlnNGvWrFK2CgTloQgIQNOxI7o9exz2NB07oggIqPgQfQ4UZoIhDzx8wTMQ1H43wNrbgz9//oicBQtomW79x+Chdt48+Np3ePm6/seZ4PZCiJ3r4Gri7zdC7Bw7dozff/+dTz/9FAClUsmgQYPYtGmTg9hZs2YNc+fO5dixY8yYMYMJEybw+uuvuzx/8uTJDBs2jMzMTGbOnEm3bt2Iiopyem1ycjL9+vVjzpw59OjRw+W54eHX34FUJpPZvZYkyWGthPz8fGJiYnjrrbcIDAx0eW6TJk1ITEwkNzeXHTt2MHToUHbt2iUEj6BKUfr6ErZwIamzZ9sJHk3HjoQtWljx3xfaFPhiNJwqFS5u+AA8sg58RYffsnyyIJb6n/xBrSIo8IALT3Vm0JS46jZLUA0IsXMdWCpIjLXkF9yQ527cuBGTyWQnHiRJws3NjZycHPz8rvwrLzQ0lNDQUJo2bUpAQAD3338/s2fPpmbN8ntIBAYG0qhRIxo1asSOHTto1KgRHTp0cBBSR44coVu3bgwfPpxZs2ZVaHefPn3YvXu3y2sKCpx/ZqGh1pLctLQ0O9szMjIcvD0lnDx5kjNnzvDwww/b1iwW6+RipVLJsWPHaNiwIQDu7u40atQIgHbt2vHHH3+wdu1a3njjjQrfl0BwLbjVDCV81crLeX4FyL29UAQEVCx09DmOQgfg5M/w5RgYsFF4eC6Te+kiP77wIC2SrJ7ks2Eywha9zGNRD1azZYLqQoid66A64u8mk4mtW7eyatUqu/wVgP79+/P+++8zevRop/eW5PQYjcarfp6fnx9jxoxh0qRJHDx40OZFSU5Oplu3bgwdOpTFixdf1VnXE8aqX78+oaGh/Pjjj7Rp0waAoqIidu3axfLly53e07RpUw4fPmy3NmvWLPLz81m7di21a9cu93mSJF3T5yQQXAtKX99r9/oWZjoKnRJO/mzdF2KH3394j7yFi2mRaX2d1L4GD6//Dk9v0bTxTkaIneugSuLv18hXX31FTk4Ozz33HL5l/rIcMGAAGzduZPTo0XzzzTekp6dz77334uXlxZEjR5gyZQrR0dHUq1fvmp754osvsnz5cnbs2MGAAQNITk6ma9eu9OzZkwkTJtjyaRQKBUFBQeWecz1hLJlMxrhx41iyZAmNGzemcePGLFmyBI1Gw1NPPWW7LjY2lvDwcJYuXYqHhwctWrSwO6dGjRoAduszZsygT58+1K5dm/z8fD766CN27tzJd999V2l7BYIqx+C6R1aF+3cA2+cMpuHniYQXQZ4G0of04Mnxr1a3WYJbACF2roPrjr9Xgo0bN9K9e3cHoQNWz86SJUv466+/UKvVvPXWW4wfPx6j0Ujt2rV5/PHHmTZt2jU/MygoiCFDhjBv3jwef/xxtm/fTmZmJu+//z7vv/++7bq6dety5syZ63l7LpkyZQp6vZ4XXniBnJwc2rdvzw8//IB3KQ/buXPnkMuvrcgwPT2dIUOGcPHiRXx9fWnVqhXfffddhTlIgjuU6koQ9qig9Lai/duYnMwUfh71EC3+NgBwupaM+ktfpf293Su4U3CnIPrsVGmfnWuIvwtue0SfnduQ6kwQ1ufAJ89ZQ1ZlafjAHZuzs/erjeiXraRmFliAw1H+9Fv3LWrPO1f83S6IPju3GEpfX1QNGqCOaIWqQQMhdASC25GKEoT1OTf2+Wo/q6hq+ID9eonYugOFzsczBqCebhU6Wg2cfrEvT26KF0JH4IAIYwkEAsHVcCskCPuGWz04tjCaD3gG3XFCJ+viaXaNfoyWydYiglN15DRa/hod2nSpVrsEty5C7AgEAsHVcKskCKv97jhxU5rdn23A9PKrNMsGiwwORwfy2LrvUak11W2a4BZGiB2BQCC4GipKAFaqIOtf0dH4BmE2mfhkxgCafHMMlQlyvSD7uUd5ctTS6jZN8B9A5OyAy5lSAkFlEX+ubjM8gxzzZUpo0AWOfA7r21mTiLUpN9Oy2560c//yxYC2tPrSKnRO1pUTvHkjDwqhI7hK7mixUzLcUqfTVbMlgtuRoqIiwNp/SHAbUF6CcIMu0H4k7NtgfX2zEpbvEHZuX8exQY9w99EizDJI7BJCr//9RcOWzkfYCATOuKPDWAqFgho1apCRkQGARqMpd86SQHAtWCwWMjMz0Wg0KJV39P9mtxelE4R12WDIhQt/wI7noKjwynWio/F1YzaZ2D6lH3d/fwp3M+R4Q/6IgQz+v/nVbZrgP8gd/7dwycylEsEjEFQVcrmcOnXqCAF9mSv9qPKRe/ugCPD/b7ZpKEkQvvAnfDCo/OtER+NKk3L6CL+/9CQRx4sB+Le+glart1Dv7nbVbJngv8odL3ZkMhk1a9YkODiY4uLi6jZHcBvh7u5+zd2cb1eKL6aROmsWuvh425qmY0fCFi7ErWZoNVp2HYiOxjeEXz5YifLVjTTNBZMc/u4WzsBXvkMhPKSC60D86bmMQqEQuRUCwQ3ApNU6CB0A3Z49pM6eTfiqlf9ND09JwnJ5HY09y58TJ3DEbDLx8cSHaP7TWdzMcMkHdC88zeBhs6rbNMFtgPhnp0AguKGYL11yEDol6PbswXzp0k22qIoQHY2rjHP/JvG/R9vQ+nur0DneUEmd9z+ipxA6gipCeHYEAsENxZKfX8F+wU2y5AYgOhpfNz9tXYLH+ndpkmcNWyV3r8MTq78WYStBlSL+NAkEghuKvNRUeuf7XjfJkhvEHd7RuLKYTSY+HtebFr+koLRAli8UjX2GJ5+eUt2mCW5DhNgRCAQ3FEVAAJqOHdHt2eOwp+nYEUVAQDVYdQPR55Ty9PiKjspOOHfsLxLHx9L6lBmAo43duO/Vjwiv36yaLRPcrlRrzs5vv/3Gww8/TFhYGDKZjM8//9xuX5Ik5s2bR1hYGGq1mi5dupCcnFw9xgoEgkqh9PUlbOFCNB072q1rOnYkbNFCx+RkfQ5kHbeWdmf9+99qzqdNge3Pwvp74e0HREdlJ3y/cR7nY56m8SkzRQpI6tuARz77SwgdwQ2lWj07hYWFRERE8Mwzz9C/f3+H/RUrVrB69Wq2bNnCXXfdxaJFi+jRowfHjh3DuwLXuEAguHVwqxlK+KqVl/vsFCD39kIREOAodLQp8MVo++niJQm/vuE31+hrRZ/jaDtc6ag8YOMd7eEpLjLyydjetNyVhkKCDD+Qxo3gyUHjqts0wR2ATLpFBvjIZDI+++wzHn30UcDq1QkLC2PcuHFMnToVAKPRSEhICMuXL2fEiBFXdW5eXh6+vr5otVp8fETfC4HglkWfY/WKlBULYBU8t7pYyDpu9eiUx+g/IPCum2fPLcTJwwkcmTycRmcsAPzT1J2oVz8htE7jarZMcCtTld/ft2zp+enTp0lLS6Nnz562NZVKRefOnUlISCj3PqPRSF5ent2PQCD4D1CY6VzowJXxC7cyFXVMvkM7Kn8TN4OMZ56j0RkLRUo49EgT+n1yQAgdwU3llk1QTktLAyAkJMRuPSQkhLNnz5Z739KlS5k/X8xOEQj+c1SRWKi2sRSio7IdRr2Oz8b2puWeTOQSpPuDYvJYBj02qrpNE9yB3LJip4Syc4UkSXI5a2j69OlMmDDB9jovL4/atWvfMPsEAkEVUQVioVrHUoiOyjaOJf7GiSmjiDhnDVsdaaai82ufEVizfjVbJrhTuWXDWCUDOks8PCVkZGQ4eHtKo1Kp8PHxsfsRCAT/AUrEgjOuQixUNJbCpNVWlaXOER2VAfhq/SRynh1Bg3MWjG5w+LFm9P80UQgdQbVyy3p26tevT2hoKD/++CNt2rQBoKioiF27drF8+fJqtk4gEFQ1piI55shlWBpdQO4uocjYh/Lga1C7w1WJhasZS+G0zL0qe+LcwR2VjXodn73Yg5YJ2ciBi4HgMWUCAx8ZXt2mCQTVK3YKCgo4ceKE7fXp06dJTEzE39+fOnXqMG7cOJYsWULjxo1p3LgxS5YsQaPR8NRTT1Wj1QKBoKpxGn6KjiZsfgJuAb5XJRaueSzFjSpzL6+j8m3cbPCfP37i9PSXiLhgDVslt/Cg22tf4h8iUggEtwbVKnb+/PNPunbtantdkmszdOhQtmzZwpQpU9Dr9bzwwgvk5OTQvn17fvjhB9FjRyC4jSg3/BQfT+q8xdap6OqKz7mmsRQ3uyfOf7l/UAV8+cpLhGz9gfo60LvDiX4RDFz4UXWbJRDYccv02blRiD47AsGtjfHUKU71fbDc/QbffI2qQYMKzzFptaRMnFTuWIrwVSuvhLHK64nj7gkdRkHzx6FYXzUemFukf1BVV6npC/P44oVeROzPBSA1CLxmTqd979gqslhwp1OV39+3bM6OQCC4M6iqqeglYylSZ8+2EzxOx1I4K2N394T+G2F/HPy28sr6VXpgyhUTV9M/6AaLnaquUvt737dcmDmRiBTrv5X/jlDT4/WvqRFQs8psFgiqEiF2BAJBtVKVU9GveiyFszL2DqOsQufUTvv1qwhtuRQTlgrEmj7X6mmqilyekrwgY4H1DJMRU5GM1Lkr0MXbN2MtqVKz83hdBZ+/PJKwD3ZRV28NW516vC1PzHuvcvYKBDeJW7b0XCAQ3BmUTEV3RmWmoit9fVE1aIA6ohWqBg2cf5E7K3Ovda+j0CnBRQfnCkveFf6uDS7WV83g0JIhpG92gYJ0+GocvN4ec9p5B6FT2kbzpUtXdXxhvpZtMe1psnEX3nq4ECLDsmYuA4TQEfwHEGJHIBBUK9c8Fb0qcNYTx2R0fU85HZwrLHkvKIIGXZyf2aALnPnNfq3Ek3Qt095LJ1yX8VBZ9EUub72aMGHi7i/Y80gkrf60fgaH23jS/vOdtHvgyau3USCoRkQYSyAQVDtZal8ODZvIXSNews2gw6uGN24eKozpGVh0ehS+GpRSHhi0VVe2Xbonji4blB6ury+ng3OFOUeZF6D9SOuL0p6jBt2g/fOw4znHm641l6d0XlCte+1yjuRqd5e3VhQm/HTp/1Hno3jqGKFQBWcHRjJw5qars0sguEUQYkcgEFQrWl0RU3YcYve/WWjcFXz4aH1ka1ZgKDXwVxMdSdjoJ3H7dhgUFVZd2XZJT5wLf8LR/1k9Lc5CWQ26ldvBuaKcI1mNEPQZ55C3nYOi0wKU+vOgUIImELY+AoCp/RTMQe2x6IuQa1TWhorGq0vMBqx5PyWU8VApMvejieqALmGfw22uwoQF2my+GdWLln9Z7TgfKiNo/mL6d37s6u0SCG4RhNgRCASVQqsrIqugiDxDMT5qNwI93fHVuPYiOCOroIjd/2YBMPa+EHzW2wsdAF38XlIlifAnXkS5f0XV98Px8IF9G6zVWFDGA9MFHlpV7nNKco6clrxHRZH3/U9ciou7/LoDYWMGW0XbAKt3pLjPFlLXfYgu4b1S93UgrKU/bldjuzbFmvdTglJlt608+BphY7aQCnaCx1WY8MDP28leMJeW6dZqq0PtvHnwte/w8q0g/0gguEURfXYEAsE1k5qrZ+plb0wJnRoHsqx/K8JquOgA6KSLcFIW9HvNKm6+718Xy5Anyr29wXvrUH3V/8rC6D8g8K6r7k5crkDT51gTg8/vs+a81LrX6iFRqiA/HZo94lJUFV9Mcyx5j4rCf0gMKRMnIel0pdY7EP5EI5QqCyZ1XVLe+rUcr0s04atWuc5ZKunhU+seq3fq1E7oNOnKf5fg7ompzYuYw7pgMVqQq+QoatZDGezoGduxIJZ6n/yBpggKPODC4E48NvWN8m0QCG4Qos+OQCCoNrS6IgehA/Dbv1lM23GIdYPbOPfwlNNFuGnfV9C4K9AVmVHqC3GVTuuQbGvIu+ruxK4F2uWE5S/HOO+xU0boOOupU7rkXeahIu/b7xyEDli9K+YXnkb57ybM9w5Cl7DU6XvV7Yl3Ps+rNCW5Ohf2X/FKOfNQFRWizPwTZcN28PUzV0KBpTxjuZcu8uMLD9EiyWrv2TAZYYte5rGo8hs+CgT/FYTYEQgE10TpsFNZfvs3i6yCIkexU7paqKRL8WXviXveGb56pjEPbf4Xk9rT5bMdkm3dva5q7MNVCbSrHOLpqqdOSadnfVKSLXTlDEuxHPquxHI63eX7rbBSqqRCrKjQmujcYZT1x2yC7vNAroTs06B0v1w6L7OGz0xGa0K2PgfUfvz+w3vkLVpMiwzrcUnta/Dw+u/w9L4BlXACQTUgxI5AILgm8gzFLvfzne2XeCCcdCmWAfUbduPbZ5bz0z96ukZFYUxw7AujieqAInP/lYUGXaxf2Bf2O1wL2FU0XbVAK2+I52Uq6qlja9Dn5brCSR5cB5CQy3Sur7tcKVVu+K10hVhRob1XCuCpbfDxkCuf+77X4dSvV/YbdGP7MQUN/5dMeBHkq+FiTHeenLjOpV0CwX8NIXYEAsE14ePhOm3W29l+iQeiwyg4sAVT0H2Ym42xqz6qs28e/XuuRd5iPjnz55WZgG6txlJ+O8y60KCLtZz7u+nWM8t+yZd5rraCXjP5huKryvupqKeOISOLozlmQpSeqKOj0Tu5VtOxI4oa3vDFSBRB7SqslCoJvx04m8P4jsH0qCPHlGOg2DsUS6EMS6/PkLtL1gqug69ZRQ/AXb3RymRkD/2UfDcV3ijwr3Mvvhd+h6JCckxKfv78OC2OWCeVnwmXUWfJK/Rr39PlZyUQ/BcRYkcgEFwTgV7udGocyG9OPCWdGgcS6OUkX6fEA1G7A8V+7Z1XH40ZRg0pF+o0RrN61ZWRDxoVCsMZlB4yeHSDNWn4wh/WsE1RIbQbWr6xl5+rcS//r7pAL3fudi/EePwsljztZfH1PcqCE5g6L8JcYLTl5pi1uS4/m/SLWQz45igadwVfjp+OhqVlRFs0YQvmoZTy4dQvKNMPEzbnG1IXLkMXv7fUdZGEzZ9NoZuaxL9PsKijirBuviilAmQpf1Ac2p3UWQvtOiNbP8Mt1kqveveT1nshc/9YQULaFc9XdGh75g16h1NvjsMQb6F5lgULcLhDDfq99j1qT1HEIbg9EWJHIBBcE74ad5b1b8W0HYfsBE+nxoEs79/KeXLy5fEMJkUAqevWOXgydAn7SAXCF0xDibWrsi0x98KfsP0ZazVRSS8a3ygUbeRWTwYK531qcpNtvXHkchnRjQKIP2E/GkHjruD7wY1ImzEHXUIpsRHVgZqzppM2dxGFv+60rdfZshmZRoN/bCzq1hFIRiNylQe6xESyt269nHOUha7IzJOfn+KHubMIzUmxiii1O4rM/Sh3TYYe8622PbIOt4Q5hA9oiXnUU1b7S677bRqevZbR658ZKE5fDj25e2KK+YnUeSvt7LV9hjIF4XN/pNB4wUHoAMSn7efjr/+h068W/IpBq4HMKAVPTlsHQugIbmOE2BEIBNdMWA016wa3IaugiHxDMd4ebgR6Oe+zo9UVcanQjdDea1BkFzgN2cDlKiX9lb+UbBVPWhmyx39EfyiZ9JlTbdVNmqgOhI3dCuqGpG4v4ymKjiRs/jLcLoehlHIZz0TXB7ATPMt71qNgySKnwuHiouWoW7WyEzu6A39RO24DWRvi7BKQNZGR1I7bwIdZZtvaiPsC8P113BWhUhqTHp7aDsY8OP4tSr51+pexov0IZKXv7zAKc06Og702++LjMRsmkO1f20Ho+OhkjPnBQsQ/2QCcqiWj0b0FdFBpy+0OLRDcLgixIxAIKoWvpuImgqXzTSZ3DmWQv9nl9ZZCa3M8pxVPkZGEr1ppK+e2ejLk+PTu7ShW4veSOncB4autfWoCPN05cfY863t6o35AhtndhyzJBw9tAdnlDclMSMA/doidJ0fm7g6ShDoiAn1Skk146fbuJUsup92YaXzfvy5KfSGhIRok872Q8Tfae58hu24H8s1FeCvc8T+7D9+iAjDkuvw8ZGX3a92LJV3r+jNMP0O+XGa31va8iqHfFhJ6CSwyOBhZgyfCj6BSYC1BL6c7tEBwuyDEjkAguCGUlHsfOJvD9sF1aPr7DEw1xpR7vUyjQe7vj/HcOdLmz3eY1K3baxU0/rGxNq+KLj4B/yFDnJ6ni7f2qSl0U+OhT6PHkRnIvrpSoq6q3w19i1lku3gPci8v6m7ZQsaaNQ6enNLCq+R5jYZncX7YMxQB57B6n4LmfsPsf9fy62/jbPdHh7Znns8AQk0GF0/HoRsyJiPyqyjP91ZYxY5ktjD0DxU9dheiMkGuJxwd3IGOwyZj+PszVBeToe/LVdOFWiC4hRFTzwUCwQ2hpNx7fMdgmv5uzTtRZOxDE9XB4VqZRkPtuA2kzV9A0alTDkKnBN3evahbR9itScbyp5WbcnLJOH8B5XdTkJXuxePuCcHtcPNUE772FWrHxREwciQyjcbufrmHBxlr1tiEVmk7sre+i39srN26Jc9+MrouYR/pC5bTWtPEbj0+bT/z/lqFFovzieieQRQP+g6DpS66vj9gGJRA8aPbwd3bNuvKGZroKBSZ+/G/cIDpjV5g/pcKHvpVj8oEJ+rImD9EzeqgP3n860FMLT5H2kMvX/98MYHgP4AQOwKB4IZQ0o+nRx25LW/FOqdpsMOXdcj06WS98Qa6+HiX4gUcxY1MpSrnSrDk5+G+YgmWZs9aBQ6AuyfFfbaQsv0Epx/tT8pL4zg/ciT6pCTCV620CR5NZCRSUZGD0CnBmfBSBgY6XGeIT+B+z1YO6/Gpe8l2c7OW0JcWPJ5BFA34ntQVb3H60cc5GzuM048NIPXVbRS51YegpoRMnkitDa/biTRNdCRhs6eiPLaNhGMZhE96lWZHizHLYE+UJzOflJPiV1zq+QnM27cQrdF1WEwguB0QYSyBQHBDKOnH427Ov7JYVIjbt8MIf+JFzC88ba0+CqmPJHcjbfZswLV4KbuviY7ClJHh9DrPLl1AkvAfEoMhV8L8yFcoU3+iKPweMld/6FgRVhIme/ZZ3IKD8GjeHEtBoUtbSgsvTXSUNaentK2X8300boF82XglZk8PEvRHAIhU343/JTVGGSjavIAy6iVM8hqY5X6kLVzk6E1KSODigsX49O5F2uwn7J7bYMc25HIdsvj5fHgmlGY/fo27GXK84cTTnXjVLwFr+0Z74lPjyTZk46sSnZIFtzdC7AgEgkrjavJ5ST+eIkUZ8VJUiHL/CttfPmlDduOVd0U06BOT0ERGOvWoaCIj0ScmWf87OpqwMU+CTIZn1854NLnbVg6uqFEDZUgI6cuWU7hz55X7o6MImPKwyzBZyLSppC9fQdqcudSK2+Dy/ZcIL01kJCHTp1N05syVPY2G8FUryd76rl2+T+foKAJHjOD8M6NILaksi44mdPYsis6dQxmgKt+++HiCx4+jdlycrdxdF5/AxUVLkfcK5c8PEml9wtok8Hg9Gev7qJjcrz/scn4eQH5Rfrl7AsHtghA7AoGgUpQerKlxVzD2vhD61lKhkIpw8/XFM8Cf5f1bcfD4aWrV74by9C8OZ5jqd+N/J030CvawrWVv3Ur4KmtH5NKCRxMdReis6eRdykMe/QB+wWrc3r8f3DSEjvuEiyvW2ScRX546rvv991JJxAn4p6a5fF/FqRfRXR5X4VJ4RUeh8POjVtwGTBkZ5H//A56RHagVtwHJaMQtvBYZq1c7emjiE8iySGUSreNJm78AdUQEHnc3dW1fSgopL42zS5L+MyORoNU6muaCSQ6/tVfxxv0mJIUJVVmxWQZvd2+X+wLB7YAQOwKB4JopPVhT467gw0fr47N+BYUJCZQEfjQdOxIwdx61aoZiargWxTfjkJ382XaGqX43jt23mDUfnsN8Xwh9oqOtOTs6HSkTJ+EfG4v/0FiQwC0sFLklh1NFJh7/MgNdkZmZ3Wryf7U7YA7pwMUV6xyrtxISrGGsUqICAJljOMeOUtvlCq/ISPxjYjj3zLOoIyLwjx1C/s5dKIMCSZszF4BacRtsoqksur17re+t7NqwoSi8XYuPEm+Sbu9ezJKZA23UtNx3CTczXPKBjGf7Eqf+wfZGDmUdokPNDuy76NjfKDosGn8Pf9efh0BwGyDEjkAguGZKD9Yce18IPutXYCjzxa7bswfz3DkkPD6aJ39PZ+VDi+jywGK0OZcwKrz48ZyFNR+eA0CllhE6dxZp8xfZBM+luDirqIgdwpnBT6Nu0wbL2KlM7hxK13BQSYVIbVdiyjSgi9/s1E5nokKfmITmsrAqiyY6yhYmK8m3QS4n4P+eI3jSJFAqkGQgt0gUXbhA+OpV6BOTyNm+nYBhwzg/cpTtrGtNtAaQq9UYT5ywhuuSkhw6NRdnpGNItub8XPJRkJXxB/eckgA43lBBmz51aNnUnx+M7Ym/3FTw3SPvsqLTCgA7wRMdFs28qHkiX0dwRyDEjkAguGq0Ri3ZhmxyLVq2PF+PA6eKeaCGEmNiIgEjRzododB1lBuvAucyjGS5q1AZ1RTJlZiLitG4K3inf7i1NH3/74SP24xp4hiK07NBJkP/dzKGI/8QvnoVktFIoCGbRmEW3D/tD0WFmKJmUux2n0uby4qK7K1bqb/jE9IWLHTw1oRMn86ZgYPKzbfRREcTNGcmUn4hCh8f5J6e+DzYF9/+j1N06hRhS5cgV3mgT07GrVYtW0ir9OdRElJzmohtsZC+bDnhr6xBrvIgK65Mp+boKILHjeNP3TG8t/9Ak1PWsNWhdj4MGD4S1c/j4I/jrBj4KdpwHaY8rTUpOuUID9brw5R7xqEvzMDbpxb+mmAhdAR3DELsCASCqyKtMI25CXNJSL3iwWkfGsmzilEonQmDyzklWUUGp2GunlFRPDptJkF/XJn9pFRJmPMyuTBqjJ3gyFq37sq50VGEjX0XWfoBTLUfRGmWXNpdVlS4t4kg36K3hp+Gxl7Orwkn/6efyf/+B9QREagjIsje+q6TfJt4MucvQt2qle29aqKjCRzxPCnjJyDpdNaeQRs2WPN1Sg/qLJVjo46IsHmQrryvaAr/PICk06E/dBj9X385PL8gPp7dxsNE/JWP0gJZvpDdpjFPzHwNKeME+gc+QhZUB/P+ZAqXLbcJq87R0YRMG43H5n7Wye4NH4ABG11+bgLB7YTosyMQCCpEa9Q6CB2A/Wl7kfmonQuDy433AkMDnIa5jAkJGJcvQfJveWXRZMSiLwKsnZKdnas/mEiR0ZuUbcc4/dgA8n/6GU1kpFO7NVFRdqJCEx0FU0fyTeYu9IcPcWHkKFJeGocpK4tLcXFc2rgR/9gheHZoX35/nYQEu/46uvh4sjbE2RoM+sfGkhUX57QDdPbWdwmZPo3AUSPJ3rr1il2RkQSOeB7Z5XwidYvmDvk+mb5KTtST0/ZPq9A52lCGQuVFt0HjuThnLqeefoEzwydw+tEB5H37nV3PIF18POlL12Jq8Yz1sJM/W0WPQHCHIDw7AoGgQrIN2Q5Cp4Rio95l472g4iIHoWPbj4/HPOrJK38RKVXI1dZX6tYR9onFl/F/9lmy4t6wiQGX1Vtz5mLR6/C6vyNyT09kHu5IRUaeCOiGbOoDFKekIpPJkCQLnl26ULhzJ6mzZlNrzWqXn0fZ0Fjp3KDy7LZ9HmPHUJiw1xaak6lU6BOTOD9yFOGrVzk9/3BTDeEpOu46A0UKSO5Uk4hfUwkaGVOu0IQyozUS9mF+4ekrn7XBvtuzQHA7I8SOQCCoEFe9WAq1rj0E5jzXX6olnhwALvyBQl0XTVQHpwm8Mo0G765duLR+vW2tbPWWXKNB7umJITmZ0489dmVKenQUgSNHYdEVkr3lnTLCKJqac2aTrvagxiOPgMXi0uaS0FjpIaFytZracXHg5vqvVVNGhl1Yzg7J/nwTkBThTutDOhQSZPjBpQANDw+aw4VfR1UorMomZ9t91mLSueAOQogdgUBQIa56sRRXMPmcCkqp5T6lkmT3bUA5YAthk4ZTlGeNsttNHXdzw3JZvJSmpHoLoPbGt8lYucppf5uivn3J++Ybp7k4F+fNtzYUXLECdctWFTY2LC+Juc4W55VhJbjqEO0WHmYLveVGtyTrwt+0TbIKlH8ayQlNk9E26Eq+z7VWfMnVl39XYtK54A5D5OwIBIIK8ffwJzos2uneCbJQRzvf00RHY/H2ReNiXxEcDg27WReKCuGTYSiLz+FWO5w672yhdtwGPDt0QJ+YxIUxY7EUuh7hoAwIsBMpMo2GgJEjqfXWm6hbtHDZPRlJQhefQPbWrfjHDnHIBdJER+MfO+TyvvOcosJ9+9FERZXzfssfb6GJjASZDHWrVuwtTIakwzQ6K1GkhD8jPGhywkx4q/a258O1j9ZQZO63Cp1H1olJ54I7CuHZEQgEFeKr8mVe1DzmJcwjPvVKf5rosGha1o+maEIbVNISjKVyczTRHQlbvBC30FA0ixaROns2uj17Su1HEjZ6IMr3ukPboRA9DpQqJI8amHRyLs6Z57SayZB8xEVX42i7poGlvS8ARDgO5CyNWWsNuZUNjZXk1rjVrEnGmleQdLpyQ0i2HCKZzK6Xj7VnUCzIZA72a6KjCJk2Dd25M/y4dzOtDxmRS5DuD9KwwTzW/AEkswX3enVJX7bMFprTJyahiYpy2rxQEx2F/u9k639HdSBs3iyUahN4viCEjuCOQyZJkuu6zf84eXl5+Pr6otVq8fERMWqB4Hoo6bOTX5SPt7s3/h7++Kp80eqKMGm1uOVrkRUWoPTxxi0wEKXvlRCVSavFfOkSljwtcpkORepOlAdfs3pz3D2hwyio3xmTMpiUeS879cBoIiNRt22LunkzB6+KJjKS0FkzKUpN5cLw5wEIGDkSw9GjeDRtineP7lh0eqTCQoeeNyXU/+xTzjwdY9/MT61GMptBqUShVoNCicxNicVg5FxMjNPPSabRUGfLZsw5OcjVahReXuT98KPNI1P6fJlKhVtYGAdWzyfnVCINz1nzhZLvklPrggxfndnWXFFWMxSjhxyPtBzMWi3KkBDkKhXpK152+CwCR41E4e2BrDADReZ+lK36QPg9lfzNCwQ3n6r8/hZiRyAQ3Fwy/sF04FPMQe2tU889PVD4+6NMWArHv8P40A5OxYwp9/ZacRtImTAR/9hYvLs/QHFKCjKVClNmJqaMTKTiYvRJSej27qXWxrdR+vpiKSjArNXamvsZjh7Fb9BAUiZOsktgDpk5E3NmlrV0vLR4uDxnq+R6TWQkoXNmc7r/AAfBVNrOCyNHXfEuvfd+uV2bExspCfnkN3wLwegGh5uraZeot78uKorgebNJm7cAQ8Jeh8+itHjSJyZZmye+uRzVV/2tB4z+AwLvuqZflUBQnVTl97cIYwkEghuLPgfy08GQCypvios0XPzyPB5NvKxf0JlGFMVGLPfOxD3lgH3FkBPskm5lMuReXij9/XELCwNJojgjA3Xbe5CpVLiHhTntlOwfO4Sc7dupu3Ur+T/9hOH4MYInTMCi15P15huOCcxl5mzp9u4lbdFiQqZPI232HAcbS09nl3Q6crZ9TPBLL5FhsdidrYhqT4L5CK3eyUUOpAWAqWtHHu3+tEPnZV1CApLeYBM6JZ9F6eTsstg+ywbdQO5m/V2IEJbgDkSIHYFAcOPQpsAXo+GUdeK5qf0ULn55Ab+Bg5yMYogidNYPyApzXB4p8/Cwekrefc+hY7N/7BByPt5O8MQJ+MU8TdqiReX2oFFHRGDKykR/+BChs2eT98sveEdHu0xgLl3KrYuPJ3jCeDTRUfa5RdFRhEyfTtGZM9SOi6M4IwNlaAjnRo3C74knbDlAp3POkPL2WlpfsDrX/26iIHrG6xTHbeFCqRlbpTsvly3jryhBWa52hwZdoP3zEBcNtTtYk5N9w13eJxDcbgixIxAIqgytroisgiLyDMXU9yzG9+vRyC4LHQBzUHs8mniVM4ohgbQFiwiZNrX8QZ2RkWCxuGykp46IIH3JUkKmTK5QuEhGo/W5CxfhHzuE4tSLLt9f2VJuyWBAExVF0IsvIpnMKHx90B86xJmBg0qFx6IJHDkCSa+3ibPDHcOo+1cq9XVgcIOjHcPp3fV58t58x2WDQJm3p92ePjHJRbJ2FIrAIKjVDnY8Z82NOvkzfDnGOipCeHgEdxBC7AgEgkphSzjOz0fu7YPBy4eJ357ip3+spdU7h4VTo5TQAWtYxVUjPH2SNfQTMm0q6cuWlfGYRBM6YzqSyeSyY7P/UGuoqaIS9ZL8FrjipTFlum6QWNaTIhUX492xI2cGP4V/bKwtV8jOpvh4spCou/Ud8k79y68fL6XlnlTkQGoQSKOe4+FGnVD6+pI2Z2657ytgxPOclDLxjo7EGG99hqvu0WGjB6H8oI9V5JSmZFSEEDuCOwghdgQCwTVTfDGN1Fmz7LwvHtHRjHlxMgknL6ErMuNuduy6LFe7I11y3givJJE3fcUK9AcTrWXfQ4aAJOEWEkrxpSx0SUmo6tZxaVuJ90V+eS5UeSh8fSnct9/22qLTu/aUlMrDKXlduG8/nh3aU3frOwDldzOOT+BItzZkbHyNiFTr2uFmbtSdPJ8p515hvWc76l10nuhcgtlNzthDc9k8dSWa5XJ08fG2EvmQaVMJmTIBKes8crWbNeH7o4cchU7Je9VrRZM1wR2FEDsCgeCaMGm1DkIHwBAfj7ckMfbx0Szbk0qR4nLn5JKy8lrtUahCUJicfwGXbdJXNh8ndO4csrdssQqgUpTusCwZjbjVqk3AyJEglzvk09jOi4pC7uVlN4xT4akp31NSqhqrxB7/2CGkTJyEx91NsRgM5Y638I+NZXfmPmqvfI16etC7w7+9mtL9vsF8LUsj25DNc4lT+LrNG+V+5gByHx+29N6Cv4c/nqtXXfaqFVir2TL3o/ysp03cSLH/K1foAJjcvKmg77VAcFshxI5AILgmzJcuOc2nAesk866jxrMM+DVFYthdDyJrNwxtYRrZbgoKTRdpFH6X00Z4Fc15shQWYjh6DGVgoLWHjVaLXKNB4VuDjDVr7MVRVBTePXsSOmcOposX7cvOjx8jaOQozo0YaZdXU5yejjoiwqGZoFutWtaux0olYUuX2Eq7S8rQlcHByNVqKNPFQ6bR4Ld4Pr9snkXEIasQuhAM0l1N6Nt3LCkTJxG52Tr4U2/S87/c3XQpFaIqjaZjR9TBNalf0rdIhV0PI8KDoHF763BPDx90aFDV74by9C8OZ5nqdyNXVoNgp5+0QHB7ckuLHZPJxLx583j//fdJS0ujZs2aDBs2jFmzZiGXCyesQFAdWPLLHwoKoNRbPQp5eJL/wGJ0RWnMPf0xCYf2o1aqeaftGupPnkT6ylX2oqmCjl+WQh11t2wmfclSm9clYORIp3ky+sRELPn5ZKxa6ZD3EzJpImf/bziWS5cur0URMm0qZ4c9Q9iihWRvtfcqhS6YT973P5TbI8d44gRps+cQMHKkXQgs85GOnF0+mYg067WHmilpdMKMZs8xss3v4h8bS4Huijco7vR73DthBV7IMJS2uWNHwhYttBc3ZVH72eXgpGcWoL9vMU2YaSd4TPW7cey+xajlXkLsCO4orlrsvPrqq1d96NixYytlTFmWL19OXFwc77zzDs2bN+fPP//kmWeewdfXl5deeqlKniEQCK4Ni6eXy32T2hPIokWYL1pLHouObCQhzZobM7J+DB4rNnImMQn/Z5+1JgVnZIBMhjLI9WBKhY+3ndCB8r1B/rGxDo0BwZosnA7U3bQRyWRC5uaG/u+/KU5Lw3LpktMREYbkI4TOmknawkV23ihNdBSBI0Zw/nKZeOkQ2J7CJJp8/gOBBtCp4GgTDfccupKTU5JIbfT2sK3pTXrevridhfMn41ZoxlRYiOTtTbGvJzpfNS6kjgMBnu5M+qaQ+2rOpkf7ubibCyhSePHjOQu//17IyidEEEtwZ3HVYmfNmjV2rzMzM9HpdNSoUQOA3NxcNBoNwcHBVSZ29u7dS79+/XjwwQcBqFevHh9++CF//vlnlZwvEAiuDa2uiPQiebml4R7R0XyfXgyA0WTBoCi2CR2A+z1bYUjYAMCl9evJ3rTpSr6N2Vx+yXl0NDIPDwfxUt7Ub5chsfh4ii/GcGHkKDTRUfjHxGA4dNjmlSl7nyYyEtyUBE8Yj+X54Zi1WmQqFcrAQM7GDrWFwiSdjpNTJnGsvoV7DhkAOB8CZjcPO6Fjd3ZwGNsf/Iii3HN4K9zxz0tDr1EwOXml3ecWHRbNvKh5hHqGOj2nLL4ad+b3a8G0HYdY/EvW5VUtnRoHsrx/K3wrmlQvENxmXLXYOX36tO2/P/jgA15//XU2btxIkyZNADh27BjDhw9nxIgRVWZcx44diYuL4/jx49x1110kJSWxZ88eXnnllSp7hkAguHoUlzJxX/cy/jFPQ5luwKqoKILnz6enypcOrerh4aagwJxqd7+y0Iip1OvS3X9lGg31d3xibQRYpkmff+wQis6dd7CnvKZ65Ymgsvu6+ASwSKjbtsU/1pr4XLaE2z/Gmph8Ke4NQmbNRN26NZLBgKWwkFqrV9s6HJ/2t6Ao1tHqkPXeQ/d4ctchHR4mg1Mb3MLC2JH5C51DO9DULANLMVpNIHP+WG4ndADiU+OZlzCP5Z2W46u6Oh9PWA016wa3IaugiHxDMd4ebgR6uQuhI7gjqVTOzuzZs/nkk09sQgegSZMmrFmzhgEDBvD0009XiXFTp05Fq9XStGlTFAoFZrOZxYsXM3jw4HLvMRqNGEv9RZdXpuOoQCCoHCatlkvz5qKPjydl/367cI/c15fjqgAKJA11Pd2RJDBLEm7Yh7xMnuV3/JV0OiwGA+qWrfAfMsRuzlPK+AmEr17lcE95peIVdRYuvV8SUiqZMWU35TwsjOL0dGq/EYfFYEBZowbpixc7TGM/1L0uDb//B40RCj3g2F0aHp+xhYw1r5TbHFGflETkvS2wFOs45OmFt4cfCjcNB38/5NTm+NR4sg3ZVy12wOrhEeJGIKik2Ll48SLFxcUO62azmfT09Os2qoRt27bx3nvv8cEHH9C8eXMSExMZN24cYWFhDB061Ok9S5cuZf78+VVmg0AgsFK6CsvZPCbPd7eDm4LRHx5k979Z1PZT87+hTfim7VuY8rSYPT0odJfsmuKVINNoCJk2FUthYfkNBxOTHErJbXkyMpldPo0pM7P8svMy/XLA+YwpTWQk6ghrOCx87SsY/jmK/tAhu+cUuss5lPsHrfZa/VVnawIyDzp6RmA4fpzQObNJmzff6WyulImT8Nz6Gn13DbftRYVFsaLTCqb8NgW9yX4QKEB+kevkcIFA4JxKiZ0HHniA4cOHs3HjRtq2bYtMJuPPP/9kxIgRdO/evcqMmzx5MtOmTePJJ58EoGXLlpw9e5alS5eWK3amT5/OhAkTbK/z8vKoXbt2ldkkENypVFSFFYQRN9MFVkSZ0PRtgtrkxsUZs9GVGlzp060LAbOmc2nRMlvFkUyjoXbcBrLeeMOhh05pcr/6irqbN2FKSbErJc/94ktC586h6Ow5JL3OOgE9O5vQWbPKHQJa0i+nBEWZSqey18lUKoc8oFN1PFAZDLT6xwJAUlsvmiQW4H9fGwJHjUTh74/x7FnUERH4DxuKTKlEUaMGksmEJS8P/9hYCgsL7J6bkJqARbIwpNkQ3jz0psNn4O3u7fJ3IBAInFMpsbNp0yaGDh3Kfffdh5ubG2AtE+/Vqxdvv/12lRmn0+kcSswVCgUWi6Xce1QqFaoKXNgCgeDakXu7/qL1dDeg2vow3v03YsqXkbLsbTuhA2D4ZSe5yFDNmYCfcQKFuZmo/APJWroGXXwC6patnIal5AEB1H0jjrS588pUREVTc85szr44GtOJE/b2dOuKf+wQ/IfGInNzQ6ZQULhvv60/TukzFEFB1NvxCZLBgLmgAP3BRLvr9H8no27e3HbPgQg1zf7Roy6CfDWcaOhJv3GvI1erkXt6UnThArqff8GrYzS0aI57WDhpS5fa2x4ZSVC/vqhPqO28OPsu7iPm7hiHzzc6LBp/D3+XvwOBQOCcSjWrCQoK4ptvvuHo0aNs376djz/+mH/++YdvvvmG4OCq697w8MMPs3jxYr7++mvOnDnDZ599xurVq3nssceq7BkCgeDqUAQEoOnY0emeJjoKhbcahn0D++MwSz42oSPTaAgYOZJab8RRe+PbBL04Gj+DkkKLgSxv8DDJ8Bs4kNpvvoH6njaEzJyBJjrK7vxaa1aTtmixQyNCXXw8F+cvIHTiBMpS+MuvIJNxYeQoLowZC3I5+sOH7IVOlDX5Of+77zFlZmLOy0cZGAhK5RXbR4/G+4FuuIWFUeAh43BTBW2TrELnTBhofVW0+bsQS2EhZwY8gUWnA5MJ3b59nOk/AMPfyaQtWeJo+969ZC1axsj6jsKmLCXVWNeSryMQCK5wXU0F69WrhyRJNGzYEKWy6vsTrlu3jtmzZ/PCCy+QkZFBWFgYI0aMYM6cOVX+LIFA4Bqlry9hCxeSOns2uj17bOua6Ejr0MmPHoIBm+DUTizNrI0FS+ZdZW991yEfJmTGdMyr3ub8zp1X1qOiCJ4wgeAJE2H8eCx6vTXEZLaUP/wzIYFgJ2IHrlRdqdu0tiY/t7kH/9hY5Go1Ci8vLJd77SiD0kmZMNGuo3Ldd7Zg0mrJ3rSZS+vXkzFmIGYviZZHzViApJZuNE8uxt1itOUBaaKjwduT7JVXxl64HHwan8D9Y4ewpsx6Le9afPnol+QX5ePt7o2/h78QOgLBdVAphaLT6RgzZgzvvGMdfnf8+HEaNGjA2LFjCQsLY9q0aVVinLe3N6+88oooNRcIbhHcaoYSvmolRZlZGHJy8XQ3oEjdifLbYdZZTKYiAOTenoDjvKsSdHv3kr50GepWrSgsJXZ0CQlkAOpWrWwCQRMVReCokS7tshRcEVe2vj0mE6qGDan/+WeYCwqQdDpkbkqUAYFkrFltl7zs2a0r9T/9FHNONqbMTOTuKgz/HEUdEYE+MZEDEWqax32MRzHkaeBUI2+6Rz2N+sUIkCSUwcEYjh1D88SjGAry7d5vRWXwCp39fnRYNIHqwKoTN/oc65RzQx54+IJnoJh4LrjjqJTYmT59OklJSezcuZPevXvb1rt3787cuXOrTOwIBIJbD6WvLyd0MjxM+fh/1Nd+0ysEAEVRCp7duuDV6f7ym/slJNh62zhbtwmXtvegqCBfSO7laedFKqnSckhQjo5G07oN+oOJwGVx9OyzeHftQtHpU8hkcgxH/iF761bUERHo6obyd91i2iZZq09P15JR85nRPB7dl7SFC+3emzo6Cr97W2Mqk3RcURm8WXNlPzosmtmRs8kx5HA27+z1e3W0KfDFaDhVakZWwwfgkXXgG165MwWC/yCVytn5/PPPWb9+PR07dkQmk9nWmzVrxsmTJ6vMOIFAcGuSZzDhbnZSnaXyhgZdUP48mdCpUzFrtS7PKc/rIZlMhK9aieHoUTCZMOfloYmKcnqtJioKuacn/s8+a/MiletRio8nKy4O/9hYmzjS//UXZ/oP4MKoFzh/edZW+KqVJF5K5viE52n+TzEWGfzVyp1aKRLhmcWkzV/gUNauj08gd94SPGoE2K9f7gXk1PaO0fjXbMD7fd/n836fM/2+6ZzKPcW5/HPsurCLQV8NYupvU0krTHP5OTpFn+ModABO/gxfjrHuCwR3CJXy7GRmZjpNRC4sLLQTPwKB4PbEx0NJkdGJt8WQC+1HYsrO5OL8+fjHOm8RUUJpr0fpEJTCywvJZMZv0EBytn2M4cQJ6r79Fqa0NPsJ5seOETxhPBlr1xI0YgSX1q8Hys+TkWk0qCMi8O7RHa+uXch89VWHxOGCvQnslo7S4lQeKhPkesLZep7cc6jQ5dlgFVNBxWZCF8xHGRxsbbio0eDdrRsZlO3OHE3YokW4BYdSXOjO3IS5JKResaVDzQ62njvX2j0ZsIauygqdEk7+bN0X4SzBHUKlxM69997L119/zZgxYwBsAuett94ispx/wQgEgtsHP093Dpx3p1b9bnZTtXFTw/sDMPf7H7qEpahbtXZaSg5Wj4w+MckulGTKyECGjII98dZQUpvW+A8dCpJk9aaUEQuhc2Zz9rn/w3zhAv6lOquX9RiVfYY5MwtFYIAtnFWCVqMgJVyi7T6r1+NkHRn1np9CxKzlV84uKnL52UgFOvK+/94+J6hLF0KmTkUymzDrdMj9fHEPCkHp64vWqHUQOmAtQQdsPXeutXsyhgq6x1e0LxDcRlRK7CxdupTevXtz5MgRTCYTa9euJTk5mb1797Jr166qtlEgENwKlCS6GgsIUPjT1c8NY8vZmCMXo1AaUJqzwU0Dte7FUmjtG1N6EnjZmVMh06aR+drr1jybd9+zeWXAWq0VvmolKRMnYerVi7zvvncakkpbsIAaDz3Epbg45BqNba+sx8jpM6KjbM+QdDqONtTgl62j2b9glsGhDn603JtDw7vu4Uyp57qFuh7GKXdTOoS4CnfuJL24CHWbe/Du3QN1o7usn2fWcbJlFgehU0LpnjvX3D3Zw+f69gWC24hKiZ2oqCji4+NZuXIlDRs25IcffuCee+5h7969tGzZsqptFAgEN4vyKndKEl3TD2N49BPSly20nw8V1YGwMYNxO7gU+ryM7HI6iKTTkTJxkuPMqbp1MZiN+E8aT9bc+U570IC1mksZHOywb7suPsGu67ImKgpdQoLdzKzy83esQ0B9n36an/54l1aHdbibIccLztfW8PDQJWTL3wOlkno7diBZzLZEaVejKJA7T4XUxScQOHw4bpn7sfi7IftuBrLj35Af85HLX4nRbPVSXXP3ZM8gazLyyZ8d9xo+YN0XCO4QKt0cp2XLlrbSc4FAcBtQXuXOw6+iPf0rOZ1ewltdj/wZC9CXmW2lS9hHKhD+RGOUF35H5n2PTWw4mznlOXMCXeMH82nrdZiciAa4MqDzaiaYa6KjkXt5UXPuHC7OX2DnUXKVY3Ph799Jv/QH7Y5bZ1udqCvDO19JhxoRmDIy8Y95mrMxVjFVO24Daa+8gv5govVsi+R0FEVxWvnJxDKlHOUPU5H+vRdZrXZwZheeXq4bsaoUqsp1T1b7WauuvhxjL3hKqrFEvo7gDqJSYqdr167ExMQwYMAAfH1FoyuB4D9PeZU75/eRZjEwN2MXLYjg0QIPB6FTgi5hH+YXY1AGeGI5f95WVu5MEJBnYHzj4bjpijC5MKvEE+QKt5o1CZkymeKUFGRKJf5DYuCZYUhGI8ETJ2DRG5zed6SxhqBMHXcfB5McklqqiEgy4hN9L6GzZpP3zTekL1uGpNMRMHIkWRvibO+ltLcKSULh60vBb7tJmTjJ6XT2EhSyfCgqRHZqJ0SOJq1uBxK1p+hQs4MtR6c0HWp2IEufVfnuyb7hMGBjKW+dj9WjI4SO4A6jUmKnZcuWzJo1i9GjR9O3b1+GDBlC3759cXd3r2r7BALBzaCcyh1t1IvM/XMFCWn7earZEEznXZeSW/ACfQ5ylZKUMY7hK31ikk0QdHk3kYDJD3DBxXkylQpTRkb5YaPoaAxHj5FWqqu6Z5cuBI1+EdOlSxSnpOBWqzYBI0eSvXUrkk6HBYm/WquJOKzDzQzZPqAd0INHowchFRejT0zCePIEWevW2c4s6x0q662qFbeBS3FxaKKjMWVmOX0vmqgOKDKuCBqtZwBzD7/OwaxDrOi0AsBO8ESFRTGz/Ux8Vb7X12BQ7SfEjeCOp1Ji59VXX+WVV17hp59+4oMPPmDo0KEoFAoGDBjA008/TefOnavaToFAcCMppzInu24HEn6z5pQYzUZMnq69LHKlCWSgyNyPuk0bp+GjktEKxvi9yEYWusx/MWVkoAwNxT821i5sJNNoCJk+DXXzFhRdOE/tuDh0iYnkbN+O36CBZKxabe9RirImIx+aO5VMrwLaJVq9Pcfry2n06HO0692f0489bhsXUStug50tVxNK84iOImTWRGTuXmiiI9HFl36+NadJ+e0w21q2m4qEtP0ATPltCkOaDSHm7hiMZiMqhYoQdR18laH4qsQ/IgWC66XSOTtyuZyePXvSs2dP4uLi+N///sfixYvZuHEjZrO5Km0UCAQ3mnIqc/LNV8qsVQoVuwuT6BIdibFMKKtEfEgKD/S5FuTh3QieH07akqUYftlpu64kjJUycRIAksFI6KxZpC1cVGaaeRQh06eT//0PpC+zln37x8YSPGkiFoMRpb8faYsWkTZ7jt3ZdTZsIPP11x2TkRMSOKhII8CYT5OMy2Grtt48FLuYtKnTkDr1IWzpEuQqD4ozMzAkH7F/fxWF0urWJWDueDy2PYwl9n+ED2iIecKLWPJykbtJKDL3XxmpAUgNupBvzEGtVDOyfgz3e7ZCWWjEnA2/FR4l7vR7LGy/HjdLEL4aIXYEguvluqd3pqWl8dFHH/Hee+9x6NAh7r333qqwSyAQ3EzKqdzxVlz5oj2UdYhj+uPcOyEWT7AJHplGQ+24DWS98QZps0sLlmjU018iaMwYLOfO24WxSjwokk6HWatF3aqVdUSEmxsyhYLCffs5M3CQ3YRyfVKS1abuDziMgQBrblCGXIa6pf28LQsSf0V40Dr+FEoLZPlCQf8e9HRvaBU6Oh3m/HxSXhpnsztw5AhkGo3t+aWru8qiiYzEcPgwXo39oFiHTJ+Dcv8KlAdfg/4bYX8cnLpij9SgG4ZeK/E2ZbGx9Qq8Vm3FkLDBlrvUJTqSeyesIEfuRb6huOLfnUAgqJBKjYvIy8tj8+bN9OjRg9q1a7NhwwYefvhhjh8/zv79+6vaRoFAcKMpqdxp+IDdsn9uKtFh1jEN7x55lwF3DeCNtE/Y+VxrlNs2oNr8CqHb3yfrjTccQlG6+Hh0S9eARoVMpbJWTbVubRvVAFdyci7FxXFh5CgujBmLXOOJ/tAhO6FT4hEyHD2KVFRU/gT0+ATUrSNsrzN9lZyoJ6ddkhGlBY41UiCTFNTb9COX4uJsz5AplaXOiCcr7g1Cpl+Z8Ze9dSuBI0eiibYfWaGJjCR4/HiUoaEYchQYHvkKs3souHtavTg7noNa7eCpbTBwKzy1jbwHlnP/W6fwsNTEe/VWDAn278UYvxev1e+iKnDD28PN9e9NIBBcFTJJkqRrvUmtVuPn58fAgQN5+umnb2lvTl5eHr6+vmi1Wnx8RBMtgcAldn12rJU7aRYjS/cvJcKjIZ08W+NbrETjG4DMzR2TNgelSsPZRx8r98g6WzZzbtgzttelQ1lB06fiUbuO3b48IIA6b8RhKSjArNXaPEKGo0fxGzQQqbiYlLEvlfu88LWvkPLSOP5uoiEsVYd/PhQr4FALDx4a/jIXR4+xu14THU3whAnk//ijLYkZoO4H72POy0MyGlH4+iL38aFg9240EREgl9vCaZnrX7PzJGmiowmb+Bxun/e3ha1KsDR4gDNdX+V0gRstLLlk93+03Pfhte1TfBo3FGEswR1LVX5/X3MYS5Ik1q5dS0xMDJpSHUsFAsFtQNnKHX0OofoClt81nvS5C9HFb6Ck1qhEtJiKnVcflVB2GGiJVyZo+lTSWtSkvk8YoQsWoAwOss6SUnmQv3MXMpkMry6dKb5wweatqai0G8Ci8eBAhIqIwzqUFsisAZlBGu73ao2UYz/8UhMdhX/sEM4OGYI6IsKuo7IpM9MW2iq5NmTGDPL+9xXZW7fiHxuLPinJaWfnVCD8/5aj/H60bd1UvxvH7l3EE2/9ja7IzM99A12+Dz+pCC8hdASCKqFSYmf06NF07dqVxo0b3wibBALB9VJeJ+Rr4XKTQVNQO9K3n0CXYN8HxiZaxox2drcNZ8m9ur17CZ4+lVzPLMwWM3nff2ffkTkyksBRI5G5udkJDnCdP5PbMYITS16g7WkLAP80khOSLqNdYAT+sbEY/k62emy0WpDJrDlE4ycg6XR2XZsvxcU52K2LT0AyGGwVZhUNBDXPmIZy9B9gyKNI6cU7h3Ss+fAcuiJrAYdJ7enyc3PzucaOyQKBoFyuWezI5XIaN27MpUuXhNgRCG5FyuuE/Mg6a5O5q6FUk0FzsxfRJbzn9DLd3r3IJk5wmbyrT0xyem9xQT7eFjPZLy9yzPfZu5csuQz/2FiHs0u6I8s8PPBo0gR16wgko5Fdf31OwI6dNCqAIiUceaAuDw6cg7ufH3k//GATNR7Nm3Fh1Avlvh//obHl2m3Ozb3SGbqCcnRLgQ4atgJAryti9/mDNqED8Gt6MT2jojA6GYWh6dgRRUCAy/MFAsHVU6lqrBUrVjB58mQ2bNhAixYtqtomgUBQWcrrhHzyZ+vYgAEbr87DU6rJoEXvesp3cVqatZJKpcKjaVOb+FDUqIHc05NzI0Y63CPTaFD5BdAw3cS58sZFxCcQPH4CoTNmkLZsqU0QSToduZ9/QciUyaQtWkR6XBxJrdxpfbgIhQTpfpAb5s8DjR9GqfbAoitE07o1YBVKFYkUJMmuPF6m0eAfG4u6dQQyNzeCJ03EkJxsS7IuD7m3l+2/fTXuLOvfimk7DvHbv9aw36u/p9N94gw0smXo4vfYrtVER1Nz9iyKz53D7OmFIsAfpehULxBcF5USOzExMeh0OiIiInB3d0etVtvtZ2dnV4lxAoHgGimnEzJgFTyFmVcndko1GZSrXeeNyJRKUmfOos6GDWSsfcV+DlZUFGGLFtqVmwOETJtK2qJF+A0c6PLs4pQLpE6fQcismYTOno2loACLTofC35+0RYs4eexPdLVltD1kFWRHGstpENyGyNmLSFu40GHKed0tWzBpc10+061mTUyZmfjHxpKzfTthixaSvfVd+/cVHU3onNl4dutG4S+On3eJZ8ak1WK+dAlLfj4B3j7E9WtMmtScPH0x3h5uBHq547l65eVrCpCp1eiTEjlVqsGhpmNHwhYuxK2m62nrAoGgfColdl555ZUqNkMgEFQJ5XRCvup9sH5B672xPPAhco0KucKMJqqDQ84OXAlT+T3xBBlr1jht5ocM/J991iY8NJGReLRoSdqcuXYTy51Rkjej9PMjbf4Ca9hMo6Huu1vZm5tEPX0RNbPAqIS/W6hpk6hD/u8BTGlpTkrhE8iQIGTmjPLDbtHR5H33vXX0Q2Qkdd6Ic+jGbD0rnrT5CwiZPw9T7BAsWi1ylQe6xEQMx49Tc+ZUpIJ8UufMQxcff+X8jh2pvXAhbnVKCxd3lL6+mLRaUiZMtLseQLdnD6mzZxO+aqXw8AgElaRSYmfo0KFVbYdAIKgKyumEfLX7xRfTSJ01y+4L17NrZ2rOms7FRUvRJeyzhXU8O7QHuRzJYEBRo4aLZN0EQqZMwTMqErlKhWQ2I+l11Pt0BzIPDzTR0Q5f8HBFSPnHxpK99V2b4PB86kk+X/F/tDpSiFyCtADI89HQNlEHyACQzGa7poA2WxISMGdlETpnNmmLFjkkRYfOnMHpAU9Yr927F0tBQfk9fRISMKekcL50WX10NDVnz4TsE6SueQ9dmR46roSL+dIlp59DyX3mS5eE2BEIKkmlOyifPHmSzZs3c/LkSdauXUtwcDDfffcdtWvXpnnz5lVpo0AguFrK6YQMWNc9g8q91aTVOggdgMJfd5EGhI0djGX88+AZQtrixfaDMDdsoCylc13MWi2KGjXQ//036YsW20SIV69ehM6eZe2IXHpcRFQUoXPnYDx1CrfgYNuzUoJUFH2/mdbnre3B/m6ioM45idBL9qJGplDYlZGXxpybi6WoCHXLVvgPGWIbUmrKzCTvx5/sri9bNl8Wh7L6+Hguzl9I8PhxDkLHdk05wsWSn+/yWZb8Apf7AoGgfCrVQXnXrl20bNmS/fv38+mnn1JQYP2f8NChQ8ydO7dKDRQIBNdAOZ2QbdVYLvJ1XHkWCn/dhUUdhoI80hYvdhzcKSvzUqMhfNVK9ElJXBg5inOxQzn9SD/yvv6a8DWrbcm9qvr1SV+2HHWrVtSK20D42leoFbcBdatWpC9dhiHpEMUXrHPRE1to8C40Uv+8hMEN/ooMoMUxMz56i92zNZGRFO7bT/Z77+H/7LMO70WmUoHJbOvanPLSOLLffRdlcDCX3njD8VoXlFdWbzEYXN7nTLjIvV2XmpdOeBYIBNdGpTw706ZNY9GiRUyYMAHvUv+Ddu3albVr11aZcQKBoBL4hlurrsp0Qq4oMbnEs1DaI1PS5E+XmIglKwVUCqcTyvWJSXbTy8uGnkoovX8pLs7Wq6Z0B+LS+D05CL1Rx8EWbkT8rUMOpAaCroYPA+a8S9qixfY5MaW6M0s6HcETJpC9adOVZN/LoTGvTvdTb8cnFF+4gHvduuR9972tNN3V+yqNq7J6+TVUapWgCAhA07Ejuj17HPZEKbpAcH1USuwcPnyYDz74wGE9KCiIS5cuXbdRAoHgClqjlmxDNvlF+Xi7e+Pv4Y+vqoLcjbKdkK8Cube3zSPjUH0UGUmNR/pSnJ3h9N6c7dup9967tgGdrhvuJdgSkysqAz96/Heytm2hTYo1bHW4qYI2UYNRnbqAxWAgaOwYzENibKGosoNGTRkZl4XXVkKmT8OjWTNMmZnI3NwwZWWR+8WXhEyfjv7wYQACRo60E3nF2ZfwfqAbGRbJTrhpoqPxj3naVp7ujHKToMsRLkpfX8IWLiR19mw7waPp2JGwRQtFvo5AcB1USuzUqFGDixcvUr9+fbv1gwcPEh5+lU3LBAJBhaQVpjE3YS4JqVc8C9Fh0cyLmkeoZ9WWIisCAgiZPs25R2bvXi4uXo7vDOdf7n5PPIHu4EF8+vTGf2gs8jLtKMpSInJchYkOtvCk0YbN1NOD3h2OtguiTUImlsxvCd6y2Zp7U1jIhZGjyn+QTIZPr174PNiX9GXLSJs9x7aliY4idPZszo0aRejUKchHPE/WhrgyJeZRKIcNQ922Lf5DY22iyq12bTJWrXLwBIFV5BTs+g3/2CEgl9knQUdHuxQubjVDCV91pRRd7u2FIiBACB2B4DqpVM7OU089xdSpU0lLS0Mmk2GxWIiPj2fSpEnExsZWtY0CwR2J1qh1EDoA8anxzEuYh9boOnn2WkmT5ePWqoWLieLxFBv1qKIjHfbUrSNIX7QYZVAQ2e++i0Wvd/msEpGjT0xCE2U/SdyggMQWbrT5uxBvPVwIhrTaPnRv1Z/6n31KnTfiSF+2DJlCYRsd4QxNdJQ1zCSTkb5kqdNS9LSFiwidOAGlf4B1cruTsFv2ps1gMtnyey6MHAVmM0HTJqN2MgXdP3YIlzZuJGfbx/jHxNhykep/9gnhi2fhFupapCp9fVE1aIA6ohWqBg2E0BEIqoBKiZ3FixdTp04dwsPDKSgooFmzZnTq1ImoqChmzZpV1TYKBHck2YZsB6FTQnxqPNmG62zeqc+BrONw4U8y8s4zf998srLPu7ylQJtJ4YRYNGW+5JEkJJ2OlImT8I+JwS00FE10tNMzbCIEMBw/RujcObZrz4Z5kBEMrf8uBuBQMyX+uTLqn8xDn2QVLqasLHTxCRQk7MVw/Bj+sUMcBI8mKorgl8aRvXUrILkUcHK1GlNmhtO8HMAWlrtifzRKdwPp7rn8NrwdtT7/hFobNlgTqyMiSJk4CXVEBH6DBpIycRIXRo4i5+OPUSq0QrgIBNVEpcJYbm5uvP/++yxcuJC//voLi8VCmzZtxKwsgaAKyS9yXYpc0b5LyszPyh2dwL6L+ygOedrlbSaNO88lTuHbSW/ZlW0rfHxsic24uyNTqwl8/nmyLBaHXJfQWbMwZWbg06c3+kOHOPPU04QtWsgfoQXU+zoJTwPoVHC0mRf3HCy4fF8UgSNGYNbpkLm5AZC9aRPha1aTs+1j1BERtjCTwtcXuZcX50aMRN26NeY8140UKyovhythN01UB8JGD0T5QW/8+iwjIf8g609u5pv2m1ClXMIzKhLPDu0p3LffljukiYwkdPYsirW5mC/mogiQC9EjENxkKt1nB6BBgwY0aNAAs9nM4cOHycnJwc/vGicrCwQCp3i7uy5Frmi/XJzMz8ovLgRgd+EhukRHYox39ISooiPZWXgIADMWu1yZwMmTqffhB5hzcpCp1aQtWID+YCL+sbF2uS6mjEwseh2FCXvRJyWh27sXg1LG1+vHEpFsAuB8CHh0eYBHug4ACdzCalpt3LUL727dMBdZR0NIOh0p4ycQ8OILeHe8n+LUVGQqlbXsfOtW1K1bEzJ9GsUX01x+HMqgIMwV9Lhxr12LBu+tQ5G5H+W3w6CoEN9vpzFvyA7mSZvIz0ghc8Q4u0q2sLuX2JKmjSdOkjJ6DCDGPwgE1UGlxM64ceNo2bIlzz33HGazmc6dO5OQkIBGo+Grr76iS5cuVWymQHDn4e/hT3RYNPGpjr1vosOi8ffwv/ZD9TmQlwoX9mNqPwVzUHss+iKaFPoyvvFw3jm/g3snzMcT7ASPZ7dueE4ZS5e8DB6/uwNKhQfen39G8YULyFUq3GrVIm3hInQJCdSK22ALCTmryKr3yXZbtdaZWh4oig02oXMowoPOw+bjExSOubAAfWIiKBVcGP48ASNHkr5kKepWrewqnTwaNCDz9dep8Wg/lEHBeEZG4tOzB6hUnBsxkhoPPeRyKrvxxAk8mjVzMUIiCuXZz1HuX2G/UVRI6Lv9Wf7styjylJzHKsCcvedacVeaLorxDwLBzadSYueTTz4hJiYGgP/973+cOnWKo0ePsnXrVmbOnEl8OY3JBALB1eOr8mVe1DzmJcyzEzwl1VgVlp+XpSR0de9zFPfZQuq6D9ElvGfb7hodxb0T5jP677kMfa4/94+NRaEz4u7rR6B3KOnz5mOMT+Di5etLknH1hw5zadNmm1CoqJzcotMhmUwkda9H491n0Bih0ANOdmpAxJ40csZPxXPD6+iTDnEpLo7wta8A2ASSPjGR8FUrrWsREbbqsYLvv7d7jiYqihoPPUT21q3W62Uyhy7NwePGcXbYMDT33Ufw+PFkgEPYLWzeTJSntll7FbUdCrXuBZMRlB5w4Xd8c86jM7vjER2F4Sr78YjxDwLBzaVSYicrK4vQyxUF33zzDQMHDuSuu+7iueee49VXX61SAwWCO5lQz1CWd1p+7X12ylIqdGXqNI/Ul19xGOxpiE/ACxj6XH/W/PsWa4AONTuwrOFsLk6fhyHesRwdIHjiBLLWrbOtV9R1WCcz8+tbU4hIslZsnasJksyDDvkh+K+aZO1do1Ti3f0B1K0jkGs01I6LQ+5XwzbvKmXiJPxjY/Hu0Z1LcXHlNkL07tUTj7ubglKJ/5AYAp9/HslsQiouRp+YhEmbi6TTUbhzJ35PP2XL/UGScAsPR3nmc5TvRlsFztD/wU/z4LeVV95Mgy7QYgAZxXkUTBiCJ5KdR6x0k8Oy3OzxD1pdEVkFReQZivFRuxHo6Y6vxvVEe4HgdqFSYickJIQjR45Qs2ZNvvvuO15//XUAdDodCoWiSg0UCO50fFW+1y5uylKYacvRMReanE4wB6vgeWLaZFo06ICPuw8FxgK0aWed5vAA6JOSkGk01NuxA5lCjqWgALm3N6ELF5C+dJlDH5rz9zflwsRnaXm5N2FiCzfuPqsk7Omh1oonSaLuO1uQaTRkrFpF4S+/2u7VREfZzbu6FBeHx91NXTZC9Onbh9TpM2yJwqW7KwM2rxFcCUFpojoQNmYwbn/OhdBmUFQIp3bCd9OgVjs4/t2VN3RqJ3w/k7xes/m/xDGMfC7G5hEL9quF7rufnM7ngps7/iE1V8/UHYfY/W+Wba1T40CW9W9FWA3XPZEEgtuBSomdZ555hoEDB1KzZk1kMhk9evQAYP/+/TRt2rRKDRQIBFWA4UpFkqWcLsglZGad5bmESazvtp7Rv4zmy8YrnV4n02gIf2UN5kuXyIqLc2ieVztuA+dHjrJ90R+MCqLp/qOoiyBfDScaabjnJM6FSlQU/kNi0O3bj6TTIdNoULdshVytpta6V6HYhC4x0ebRKa8RYvryFdTZshmZXI5UVIQpK4taa19BMplALkeuVlM7Lg5dYiLuderQ4P31KDL22ZKQaTcU3D3RRr1Idr1ItB6+eLbqj9xcjEKfg9+ZeHwTXsNbqUFv0ts8Ymqlmi8jN2E8dMh548GbOP5BqytyEDoAv/2bxbQdh1g3uI3w8AhueyoldubNm0eLFi04f/48TzzxBKrLbmuFQsG0adOq1ECBQFAFePjY/lOudnN5qVlj/f/ZaLbm3pg8nYel/GNjMaWlkffd93a5MGDtX5OFRN2t75B5/G/2f7CcNgmZAJwJlyE3q2hzWIf/yJEOpeMlIaic7dupu/UditPSUNWtR9rSpQ6eG+9u3fCMinQxmiIe+cSJpL/8Mrq9e+28QGVzc3z79UPSNIDcQ6U+DBNpg95h7oltJOz6yLbcoWYHnr77adaYUpk+6B38LXK7ZPKR9WMwvPwaAUNiQCozaiIqiprz5920fJ2sgiIHoVPCb/9mkVVQJMSO4Lan0qXnAwYMcFgbOnTodRkjEAhuEJ5B1snnJ39GkbkfTVQHp6Gs0uXlKoVV5JRXjl7SaK+s0ClBF5/A723rYPrgI1pkgQU4fJ8vvSZtIH3Y/yEB6rb3oI5o5TQE5R87BFNODoa/k8l5/wOnnpsMuZyQKZNdvvXi9HTbveUPKI0nbcECfPr0xr1eNNKDrXD7eghav9rMTVpHQtp+u+v3XbR+dq2CWjHvxDaW3TeTue2mMP/P5cSnJnC/Zyv0v24gZf/vDuX3+sQkpMvl8zeDPEOxy/38CvYFgtuBSnVQBvj555956KGHaNiwIY0aNeKhhx7ip59+qkrbBAJBVaH2g0fWQcMHUB58jbAxgx06HKuiIymcGEvcaWuFVo4hh5fbzKWTdxtCpk9z7IgsSS4rr/6MUOOz4SPCsiBPA4daaIj4XUve2vXU3rABmUaDwsen3BBU9tZ3Ufj6om4d4bIDsmQyuX7vslIfg6uz9u5FGRxM1oY4Co5dwtR1GdmSyUHogDVMFenVkiGe3Zjq/QTKLANBf3zCco+GfNntDYLNntaP6HIeUOlRE5fi4rDkXUdDyGtAqytC7eY6j9Lbw7WnTyC4HaiUZ2f9+vWMHz+eAQMG8NJLLwGwb98++vbty+rVqxk9enSVGikQCKoA33AYsBEKM3EzFhC+aAbmPB3FhXpy3M38mPc7cQenoDfp6Va7C73UbchYtgBdfALnL4d/AocPRzKbUNSogUyhoDjNsWFfnlrOuToy2l2utjpdS4a70Z3Wf1tzV3TxCWQBIdOmIlMqXYoP+dQpmIpdex4kvR5NVJRTD1Pp0RRQcVm8ZDSi27sX/6GxmP0CydddcLhGrVSzsfUKvFZt5WKCtX/OebAlNvu+OwDjg9tdPudmJCeXJCVH1K5BdKMA4k9ccrimU+NAAr1ECEtw+1MpsbN06VLWrFljJ2rGjh1LdHQ0ixcvFmJHIKhGTFrt5anZ+ci9fVAE+KP09b28noMlX4/c2w+FGlRJK1CFNMPcpA9dQ7rRrn4HfNx9CSgsJmPuAlvSsaTTgVKJRa+zeWICRo7ELawmmugo23XH62vwydPR4pg1bJXUzosWBwpwk+xFhi4+gaAXX0SqYGCoOTcX99q1XV4jmcwEjxtn7ZFTuo9OZCQh06dzZuAg21pFZfEl+5LRiKVQh3edWg7XjKwfg9eqrRgSynijEvaRCoQ/8SKK1J12n0tpbkZycumk5ANnc3h1cBsAO8HTqXEgy/u3Evk6gjuCSomdvLw8evfu7bDes2dPpk6det1GCQSCylF8MY3UWbPQlWrs6dmtGyHTppI2f4HduiY6mpqz5yNP34vvpj74FhWCuydpI3eSm1fg8EWt8PEh85W1Nk9M9tathL+yhsCRI0mXzCToDtHibx0qE2g9IWNQV+75aD+onffAMWVn2+ZclYfFaEQB5QuH6CgK9+2z5v60aoV/7BC73Jj8739AHXEldFUyJb28bsolXiCZSoVcysM/+UeiQ9sTXyqUdb9nKwwJGxzuB6vgMb8Yg+LSAUKmTqE45SIymQxdYqJ1hMU99xC2aOENT04unZSsKzIz9sODPNuxPs9G18dostAg0JOavh5C6AjuGColdh555BE+++wzJk+2Twz84osvePjhh6vEMIFAcJXoc6AwE5NBInXuCgdR4HHXXaTNnec0Kffi/IX49O2DImY32WojGoU7SxLXMt7jIYfHyNzdHaumDh3GEHEXB3P+ou0/1nDTyTpywh4fSo9OD2GKHIxbSAjpy5c79sB5sC953/9QvpCJjER/MBGpRXNCpk0jfclSh6qmkKlTOfPkYMLubuq0Iquk+gq5DF18QvndlEv14NFERmLKzKSoQVOUejfmNR7MPLAJHmWhEVdZQmbJm7SP/kGXsPnK+dHRNPjsU+Q1atyUKqyyScm6IjPrfzlhe/35C1FC6AjuKK5a7JTujHz33XezePFidu7cSWRkJGDN2YmPj2fixIlVamBKSgpTp07l22+/Ra/Xc9ddd7Fx40batm1bpc8RCP6TlJpebn5oh1PRUDJmwRkl+SmZ8xez87nW7C9MZnDTwZiz7WsX5AEByN3d0Scl2Z114v5GeH+wnmbZYJHBwZbutLmgoUFUHzJWrULdshXZlwd+ln1u+rJlaO5p61zIREbiP2wohr+TcatZE3NuLiFTpwBQdP48Mnd33Os3wJR20dqHp5zwVEm35fqf7iA17wLeRhkKtS+BL4xCPnECpoxMkFk9PikTJ6GOiCBw1EjOeBt5Zvdz3BPYivm0Y7n6LrIfnkKeSU9QngfnXfxKJJPFodJNFx/PxYWLbGMuHLgsWDHkgYcveAZak8oriU8FScciKVlwp3HVYmfNmjV2r/38/Dhy5AhHjhyxrf1/e/cd3mS9/nH8ndGkSUegLZ1QLFBllwoCbRAHKipupjJEcTBkK0P2rCCiIjJVODh+4kI5LkRUxJbhAcqWWSirlNLSlTRtxu+P0NDQAchIKffruriukydPntx54CQfv7NatWp89NFHjB079qoUl5WVhdFo5J577uGnn34iODiYgwcPUq1atatyfSFuaBfsXm43lz2d+VIG5VoS13PnoF68vX8xdoedl6O6E3BuurlCrydy4QLSpk8/v/+VTktyCwON1h9AY4UsXzh1d1Na/HaAgF7dSH/7bWeQ6tmzgjVwkgjo2RPzjh34P/yQ+/TsnbucrS//+5/bVhR6Yzyho0aT88svOOx2FDhbTSrqntLFNsO0ZQum+oGsL9jHk15tOdKpM8rAQMKnT8MrOBilMR7/B9uDlxd5ikKeW/8KZquZxLSNTABmBN9FlK4G6Kpj1Wajb9MG019/lXovZ7da6dlbUMF+WCUCq0vdds7Zc4aICv/uyhPkq6FtdBB/lrG+jgxKFjejSw47KSkppY5lZGSgUCgIvEaD7WbMmEGtWrVYsuR8c/Att9xyTd5LiBtOiS0gAJS6sn/ALnVQbkiRnpXRs7D5eOOlDyZv+LP4okDXpCn2vPNjeLKCfEkLKCB2nXMl5gO1Ffjlqrn9jA8Bb80CtdoVcC4WtJR6PdZdu/Fr/4Czdedc11Jg375kLllaRtdbEmnTpzvXw6lZkyO9nyN86hSyvviSgF49nedc2ELUowfHh7+Kb2wz2rdvT1G1Iyj0esKnTiFz6X9KnR84xn1h1MS0jWS2HInhXEuL2mAgfMoUTowb5xZ49EYjoWNeJ6VT53I/b6n9sC4IrC4H18DKgc7Zc/+ihceg1/BGx6aM+nq7W+CRQcniZnXZY3bOnj3LmDFjWL58OVlZWYCzladbt25MnTr1qra6rFy5kvbt29O5c2fWrl1LREQE/fv358UXXyz3NRaLBUuJL9icnJxyzxXiRmY3Z7stlFXeYoHm5G3lT80uOSg3z0RB3yEAeBmNhE0YSf6U1/DPVVB4+DAAu6P11DidR4N9YFNAclMtMdssqClyhYYaQwa7rn+xoGU3mch47z28m8UQPGwo9pdeAqUCla/vRbveTr3xBtU7d3ZtCqoODSVk1EgcViu2s2dxWK2Ytya79qYqbkmCChYXXL8eEmbQt08P3t6/2HU8p8jMwfS885toGgKIeGvWuVlveSj9fFEFBmLLzCxze4hipaacXxBY3Rxc43z+X3ZnhVfT8d7TsWTkFZJbUISftxdBvrL5p7g5XdaigpmZmbRq1Yr//Oc/dOzYkbfeeotZs2bx1FNPsXTpUuLi4lwB6Go4dOgQ8+fPJzo6mlWrVtG3b18GDRrEsmXLyn1NQkICBoPB9afWRaatCnGjsnr5uT12LRYY39rtuHnfPsLGvY7eGO92vHhQbuayZW6hB5xjTM5OSmD14V/IzE7DrtGwpU0Q0YdM1DgLmX6w51Y9zbdZ3P6LybR+PcoSAae4e6ksJd/TkZfHkV7PYjebyFi4kMKjFY2KObceTmKSc4bXuYX7rGlpFB4+zOGOnTja5wWOvdyXMwsWuIUPh8WCOXkbPq1bVbBQoXMFZLd7aPGi3ey1PDkviXZvrWXg/20l3aFBW6cOupimaOvUQW0woAoIQN+mTdmft6wp5wUX+Y+xiz1/EQa9hrrBvjSLrE7dYF8JOuKmdVktO5MnT0aj0XDw4EFCQkJKPffAAw8wefLkUuN7/i273U6LFi2YPn06ALGxsezatYv58+fTq1evMl8zevRohg0b5nqck5MjgUdUSVmKagRG3Ys65VzLQGE+Xj/1JqLzAGyDXyDHouWs0htdoA9eyjQiXrwX66tDKUo7DQqF26Dc4plIJRWP4zmafoITc6Zx+34bAPvqqKh+RkHjvWW3YFjPnHGNn3HNfoJSM6kCevZwvafKYHCGEoXCrQWmPCXXwymmDg7GnlvxysQKrZbMZcvwiS87gBVTmc5ft3VoPJsOFDGqTTj3hHjhZTHjVyMA1fEj5B+2oPY/v5ZRuV1cbdqUPeW8xJ5lZbrY80KIS3JZYefbb79l4cKFpYIOQGhoKDNnzqRv375XLeyEhYXRsGFDt2MNGjTg66+/Lvc1Wq3WtTGpEFVZvtKXjJbTuI0xboGH9P+R3fRpTDo/FI6zpOWmkO9QEBAYhEGRjrp2NWw2PQo/PT4P3Ef+L7+6unoutGvN1wQu/5X62WBVQnITLY++OIuTrwwsty6H1eocP3NuendxN1PQyy+BUom9wIJXRDhFJ04QnjAdhVaL3WJBb4x3hZfLWQ+n+Fju73+ga9QQvdHotp7Qha9zmEzYL7aY4bnNUOPD43km6lUCch34fjwDS3IyNd6aReYb893DW5s2hE+ZgldYKF5hoWV2cZU55bzEnmWl1G3nfF4IccUuK+ycPHmSRo0alft848aNSStj+fh/y2g0snfvXrdj+/bto3bt2lftPYS4UQX6aHj1x3xaho3j/lYT0Nry0PsZQO1NobeDhM1jSDpx/gfZGNqKifW6Evr5I6ifWIB2VV/Mj/+3zLExdhxsbaYj5oNfUdvhjD+khfrQYls+hTt3lR9EjPEU/PMP6ho1CBk7xhksTCaU3t5Ys7I4PnQYuthYdE2auL2vQq8n4u3Zri6wcluEjPGuAcfF4UVvNBIyepRzXJFaTfDgwaTb7aVeF/Tyyxzt2w8A89bkCj+DNiCQlY9+jbfCj1/+Pk3k5+9SkJTkHDhd1lifv/7ixLhx+E6ZTmqR2jmuJ7QmhjoX6TYq3rNs5UD3wFM8G+sKpp8LIc67rLATFBTE4cOHqVmz9BLq4JyxdTVnZg0dOpT4+HimT59Oly5d2LRpE4sWLWLRokVX7T2EuFEZ9BomPd6YCd/tRK1S8EyDamhyj5FjCGPK/+aSdNJ9oHJi2kYmAjPiB2BQa5zdXqk/lxrUfNqgJivARvPkAgD21lURdBoa7csHKg4iIaNHg8OBeWsyhzt1drUW6ePiCHl9NPqWLQkZOYJTM2a61eYwmTg+dBi1Fsx3tcwUtwgVT0lXGQzYzWZn11tsM0JGjsR29iy6mKYc7tLV9V4Kvf7863z1nPIysda8h44RwehiYzElJpa/uKAxnvBXuuL1cXuoeQeORk/xVGQrjp07RxfbrPyB03/9RcGxUzz59RHAOfPpjY5NCa+mu8hf5Pk9y5zr7Pg7W3Qk6Ahx1SgcDofjUk/u06cPBw4cYPXq1Wg07v/FYrFYaN++PXXr1uXDDz+8agV+//33jB49mv379xMVFcWwYcMqnI11oZycHAwGA9nZ2fj7S/+3qHpsWUdR/ncQinOzelKe/YbH/hxS7vlfd/icgoIs/BVeBJ/NRakMoygtHYVCyW8/LiTw960E5EKRCna0MPDYgDkc6/Ws2zWKA4WuWQwqXz8c1iLyN2wkc9kyHCaT24rErsBjNBLQozsF+/ejj2lGxoIFpaZ9Bw3oj1KrJX3226WCVOjYsRQePYrK15e8P9eRv+8f9MMHUPDWPEy//V7qc2qNcfzRp5lrVtX/3T2XIK9Qck+fRGWyYPfVk6+24VOoRGkyY9Nr8atWjdBPH3Z2BwI8sxzTGS+OPPcKCr2eWgvmk3rBvShJs2gp7X50n+r93tOxMjBYiH/hav5+X1bLzqRJk2jRogXR0dEMGDCA+vXrA7B7927mzZuHxWLh448/vqKCLvTII4/wyCOll64Xoqorb0NPN+YsVP8d5DZ9OddW9uKCxQ7nHWds4lg+bDYTZn+GOXE9VmBbjJaYHRbUdjhdDcxdOvBYbAeshw+X6vIpngGlN8bj3749aeMnuL1H8bkBvXq5WkJMiYkEvfgCZxYsxPudt/F/6EG3hQSt6aexm0wcGzyE6p07u8b4KH18KNi1i/ytW9HENuVU+mGs98SwvrWGut7ZhA3pjdZSgCXxfH3e996N7tUBtM3N5J5zawdVVwSQ62XjqeTyxxutbPvO+aADoPFF5a9zfRbs9grvrVXnA5wPO3/uzyAjr1DCjhAedllhp2bNmqxfv57+/fszevRoihuFFAoF999/P3PnzpWZT0JcBWVt6FlyEKxLGeu0+Kkq/mHVqrSunbvNSes5FeBFjp+V5tucg4P31FMRO3I2If4hnH7nXczbtlXYbVVyV/GSitfDKUnh5YWuWTOODxnqXBsnLAyviAgUajVKvR6lry+15r2PLSeH/A0bKfjnH/za3Yu+ZUuO2zN5blMfMgsyAXip6Uss3bWUbae30bdPDx55dSBFudmYdSp0umqYp86mKGk9xbtE5RnjCZ04lnsj2vLb8T9L1WsMbUXAEfeuPwrzUOUcQm80omsWQ/6GjRWM9THy06miUsdzC0ofE0JcX5e9qGBUVBQ//fQTWVlZ7N+/H4B69eoREBBw1YsT4mZkzc4uFXTg/CDYiLdmnW/hKWMdloAjG0rt1F2sdVhrtmds58FzO3dvb6Cn1lET0UegUAXbm3hze7KZ0OBIsDtcP+oXjp9RaLWog4IoPHy4wkX0Sq2grFIR0LMH6uHDsJvMqIMCSZs61W1Pr+IusIJ9ewl+dTi5q34h5amO6GJjWTLyTbr97dzKoWlQUxZtd47fW5DyCYU+Gu6Lvo/9R5OpP30JBUmlV18+NXEqUya9isVmcbs/xtBWTIp+Bp8D/8PyyNfYzYUo/auhsuSi/msy4cM/puCUpfzxSnFxBI4ezQefH2LMvWHcH6lEY8ulUO1HNR8JO0J42r/a9Rycqya3bNnyatYihABsZ86UOXUaythfydsfND5YYwdgq9EKu7kQbx9vZgY+yjjVQrcWjNZhreneoDsj/hxB+5pj2dxUQ7MdJlQOOFUdsgL0tEg2AQqsJ9LQRNZCodfjMJlc3VYl1Zw/H4W64q+Qkiso6+PiUKjVHOvXH3BuCWEuZ5NQlEoCenR3G3hsSkxEPwN+nLacXUWp6L30AOjUOma2ncmnez7lo50f8f3tCzmTVLrlxXmNJELzbcwIbkvmrU+Tay/Cz78WASe2oc+xc/zC3crjWxM+cB5eP/TE9sSPro1FLwx+5uRtpOVY+E/HCOpveh1V0vkxRI4r3OdKCHHl/nXYEUJcGxdbGM9tfyWfGhQ98TUnZi3ClPSJ67A+vjUzxr5GWkxfcqwFaLX+/HLkF0b8OYKA01b2/Wc0zVOd4092RyuJOK6g/sESLTQKSJue4Dbm5kJeYaGYt2+/pPVw9EYjwYMHU1RiaQqf1q0q2CQ0keBhQ89t67DMLfCEnjzCHV6F2LUhrIyehU9AMJuzDrLt9DbMVjP5WekV37/0oxiKjmDwCcbhgCKrAmuNOzkxbmLp3cqTNnACiOj8HGqdw7UB6IV1641tcDz0KPU3DEOV4j5YWnGF+1wJIa6chB0hKhmln1/Fz/vqIWMfFORgVdfgxFsflPkjfWrabCJGvcAtXz7FwZd/Z2/mXnqn1CZ2xU6q5YNFDTsb64hNNqFE4XptcUgxJSYSPHiQc0sGiwWl1htTcjKZy5aha9aMolOnsJ7OIHT8ONKmTnMfX3RuPE/h4cPUXDAfr7AwitLTUdWMoNanH6MosuKw2Sr8nEXHjrnGCxXP6lLo9eAXzunpCa7PXADcZozjw2Ez6ZM8AqvPRTY+DYniRJEfhXZffj/uoF1dL0KyU9260i68l7b+3dF6q6gxaTynxk+koMS53sZ4akwej1ZTUCrouFzhPldCiCsjYUeISkYVGOhqQbiQ3mhE5WWB7d/AhvnYHlhW/o90YiL27G5QmI/NZML46Raars9C6YC0QHB0fZI7t53CROnxMsXbOFjPZHKsXz+352vNn4/dUoDS29u5iWfTJoSMHEHRiRNu3Tolu6Bqf/YpSm9vcrUOvHanogkIQB0UVOF9UGi157q0FK7AE9CrF2kJM0qFO0vienyAvn16sC5/O3cb49xmZxXTxseTqauOuVpNcguKeLJZEYYfXqYgquLlLOxFCrK1esZtf4NmfWK4c1BPVCYLNr2W3/O3s23vLCY3HUAZaySfd4X7XAkh/j0JO0JUMuXurxTfmvBXuqD+5H6oeQd0/BB7etmDX4vXwXHoQtkePZGjz3en2VHn7Mmdt6qIPAqGpasIXvYfrD17uoUUt60jFO7X1MXEgMOOSq9H6efHLV9/jcNuw7xzJzk//Fj2Ng3GePKS1qNr3AjLgoX4vfQSR/v1I6BXr0vqAjMlJhE8eDC1P3ZuAFxe11fxXl7dd47gjmEz8dVq8bm1/vmWKUM1cgNCKPD2oW6Nc7uPZ+yDQ7+hbDigwr8TZfUQMovy+P3o7/zO75S1Ic6QZv0rDjuyz5UQHiNhR4hKKENnYHvv4bQeMgyf3BOovByoTm9E/VNv5zowh/4AQNl8fKnXKvR6It6aReayj1nz18fUOZRPHRMUeMHednVo9mcaDrMJByby1v6JukYQ6uBgHBYL+mbNAOcqybqYmPN7UJW4ZsmwoTcaCRk5Al2TJuhiY0l/803ySyzwpzfGE9CrF1n/9zlYrZgSE8mw211jccqb2XThxqTWM2c41q8/Ee++U+F9U5ksmK1mXtk5geXD5mJOeKdUvWGTJ4O5yNmtlO9cE0d1emOplaRLfgZV6o/kVru3wvfOtRbIPldCVFISdoSoZLJNhYz4ejvr9mfwR+8Iaqx6quwTj/2N8h4DkUuXYMvOdo2pQa3mxLKlbM7dSszOIpTAySDI89URlx2CrtcDnFmwAIVej3fjRmQuW1Zq6netBfOxFxRwfMhQgHPhpIw9oRITOZXwhjMY7dhO6IQJWHv3xpaV5Wopylq+nOBXh1N4+DC1Fi3EYbOhDg3Fu0F9FBoNwcOHoVCrnZ/B1w9r+ilOjB3nPqVd4WxiUlxkk9/iDTyfrdWRgoS3MSeWUe/48YS8OhiFKR1VYBBqjQ/qre8TPnApJ8At8BRvH6H+qTd+LR6v8L39tP6yz5UQlZSEHSEqmYy8Qtbtd7Y4aGzlzMzS+FD00FJOTEwoFVQyH2tD2opNxB53HttRX0XUYTthGWZMh88v9BfQqxeZS/9T5tTvDKUSXWysK3DomsWUP3Pq3OKBZxYsIG3SJLJfe5Zov0iKjh7F7/778AoPcxu/o4+PJ6BnD05OnkL41CmkvzW7VMtO+NQpru60kl1aFe6GbjTiHRLJR+0/on6uH8cS55ddb2IiRT17cKzvK84wM/wrvL7thNdPvYnoPABb/+7n1tkxoFLmof5vb6jVmgBtNYzhRhJPlO6qM4YbCfAOAK1B9rkSohKSsCNEJZNTYsXdQlXZM7OssQM48d7/lep2SczdRr3J67nFDGYN7G6go/k2s9s5xQv9VRhgird2uOA15Sl+3pSYhG5QTyw6O4W795D1f5+XDlNJSeBwED51StmtRSW2mjDv2O7a5RzK34TU2xhPzvAedF7TDbPVzM/137vkek8AES+8gXrVQNQbZzq/FOvcDa36wtd9oFZreOw9DD4hTIyfyMSkiW6BxxhuZGL8RAzacyN2dNUl3AhRyUjYEaKS8ff2cv3v1al2nou6F3XKuS0hzi0gaL3lCUxJnVznFahgTwMvYnc6W0+OB4PZu3TQgfNdQRcLMAovDXpjPKbEpIt2H5V8PrDIm8IQvWsdnZKbhpacwq4ODS2zhQacQSb41eH43deOI72edbUKXbion8rPjzPaIlbnbGLB1hGYrc7Pa/KusFy3ek2JSdiG9Uf9zHJsXv7kqfxQe2nRWnNRv/SHW8tMqE8oM9rOILMgk9zCXPw0fgR4B5wPOkKISknp6QKEEO6CfDW0jXZOy377r3T2t56Orc69rq6r418eoDD1mOv8I+HenAqG2J3OFqHtTbSE1WtJvdTSQUdvjEcdFETEu+/gVbNmhXXYfbTomjSl9mefog4KQm80lnleyW4mAK0VzmQdw6ZWugY2m7dt41jffhwfPISj51ZOVmo0znVzylF07Bi5v65xzgAroXg158yPPyEvojoPb36Rt/cvdgUdgHX529Ea4y6pXgBbgQNQkKcJIu6D4zR6dz/P/2IlW39LqVYag9ZAlCGKhprahJ9xoPnnMJZDKVizs8v9LEIIz5KwI0QlY9BreKNjU1fgSXME8nP9aWQ+vZYTc5djStrgapnY0lRPYGYBtU+CSQubY/TEHFRRb/wk9MZ4t+vq4+IIGTWK03Pf5/jgIeSu/hV9XNmBQGuM46DjNHk7tnF04CAU3t4EvfRSqfOLZ05lLnNOC9cb48nfsBHtrCXg61P+wOb1610rNJfHq2ZNvBs3IuT10aWClj4ujsCxo9hhSSnztQtSPiF/WK8y70HJeosp9Tr4rAt+a0by5dOR6DUq147lZSk6mcbxYcM59HAHDnftxqGHH+b48FcpOplW5vlCCM9SOIq3Lq+icnJyMBgMZGdn4+8v61yIG0e2qZCzpiLGfruDdQfOsKpjbew9OwPg3acXGzctJ2aHsyvqaAhYvbyJOlaAtzGedS+2wGov4k6fpugtEGAIJfenVWR9+SXVO3d2dilZrWhq1+bUG2+4DXLWGuPwHjOUFG0ODWzBaE+dJWPBAsxbk53dUbHNUOp0YLeTv2GjazsHfVwcIWNedw1GvuWnH7CfSie193PlfsbIpUvKfF4fF4cuxjmmyOfuu6netQsoFK71gApPp/NDrTPUjmjIK7+9Uua1dWodX9y1hKIzpwm3+kKR1a3eku8V8uogvL+8CwBr1L0sCRvHtN9O8m3/eJpFurfsWLOzOT5seNlrCrVp475RqxDiX7uav98yZkeISsqg1zhnZh1wDhNWm/MpBA7X9Eb1/TJiTjnP29ZQzW37bHhbnUEnb3hP5p4bv1K8+N3M2Ak02LEd+5kzboOSFXo9IaNGUmPka6SdOYJdr0MZGMCsAx/RzDuamJD7wWZzhaHi15Ych1MrfgEKtQqlXo/19Glqzp6NKTkZ+9lsUF6k8VipRB8f7xy0fE7JdXb0RiMBzz/Hsb79XAFFa4wjf3gv5m6dRU9tT1qHtWbDydLr48TUiOG/p39j0fZFrLh9HiE5VszbtpUKOgG9eqKwZLmOqVN+4/5WE5gG+Ou8wJxVYnaVAVu24tI3ahVCVAoSdoSoxErOzLLqfNjSVE/9vSb0FsjXwsG2dejQ8TUcFgtetWvzf5m/uA3ULTZhx0zWTFrOmQlTMZdoxdHFNkMX34oT+kKO6rwxaP1JObufPsGPE7rzFGcWTqd6t66l6iq5C3rtTz7m9HtzS00fNzzxmNvYorLYzWZ0TZsS9NKLOGw2lDodCo2GorQ0as57H0vNGnxz7BfilryFymShemAEKzJ+c33Gj3d/zMy2MwHcAk/JHd4BkvJ3cveXO9HFxJTarTzryy/xGdWPgrtHYEh6Hwrz0djyuL9BMLXVWfDlIDj02/ma26+o+DOV3KhVCFEpSNgR4nq7oKUAn6BypyoXz8zS23PZ8mYvbt/uDDGpoeBQetN09SGOre6HPr412gmDeXv/4rLf0mpmk+0QR0rs61QjIAKHt50R/8zk9+N/us6dGTsB33eXoenZ07WGTkWUPj4EPvssupgYVxeRaf160qZMJahv34q3hNiazJkFCzizYAE1F8znSJ8X3M4JW/kVb+6d53o89965bp/RbDUz4s8R9GzYkx4NeqD30mMqMrE9Yzsj/nQGIp1aR1TNxliHNCdvxoILVlSOJ294Lzqt68PtQU2Z2PU/hC5/FqW3P28/HoZq5UtuQQdA6WWv+H74+Vb4vBDi+pOwI8T1lH0cvnvF/Qe0eIVdQ0Sp04N8NXT03cWDvyyhZrrz2LamWm7bZcHbVgCAzz13ETqsP0UWFSujZ2Hz8ebP/G0sSPnErYVHrVTz9v7Frq6tHx75mmn/m0lS2kbAOcalZ8OexHk34GTiOKp36QJcZCG/uDhyVv3CmQUL0MfFue1QbkpMwj7iVYL69SVDqSi1+OGFW0KUNRW+MOesWzfV9oztpbqtzFYzi7YvIj4sjgdueYCJ6ye5XaNnw54s272Mbae30bdPD+4c1Mu1ieduRQZHivZgtppJTNvIRGDGQzOoViMc78LMUkEHLrK1RJs2qAIDSx0XQniWhB0hrhdzVumgo/HBGtQc2+HD2B3pKP2rYfM3kObQkG0uYveiAXRfuRVdIeTq4Lv4pnR6eQoNHaex52Sj9NGh8KnOyYS33X587zbGccewmfRJdrZutA5rzfaM7a7njaGtKLIVuAWdmW1n8umeT8ky3wqcX4um3D2sjPFuC/6VXAywuPUkI/Mo61VHeGrsWBwmE0XHjpW94SjO2Vc1Fy7AUVDgWotH4etLr1Bny9KGkxvK7bYyhscxsekAFKYsjKGtSDz3uQBahrZk0fZFAG5hr9jce+e6/ndi2kYyW44kyj8Ijh0u86/RtbWEQuU2dkffpg3hU6fIeB0hKiEJO0JcL/mnSwWdooeWnlsJ+RPXYW+jkZM9nuXw3IHcvsvZ2nE4QoH3yOl0rn8XPt4qVCovND/MxFajJce/PFCqlcGSuB5/FCx+dSaLT3xFp1s7ucavGENbMfnWntis3nzT7D3U+RZ8AoLZnHWQbae3YQ3pDri36JRcyM9hsaAyGLCbzaUCy4XdXla9hoTkORRYzTx5PIycH38qZ6uHeAp27SJt/ITzx+Lj8X28PQqFguYhzenRoAcWmwUfLx8mxwykoOHz5Hpp8TOfJeDwegwfPgTAjPgBZLZ9mlxbIX4Bdci2ll5vyO1e2dxblHKLzy9vl/LCfOfWEtOSsJns2HPzUPr5ogoMlKAjRCUlYUeI66Ugx+1heVs+7Dy2Bf24RG7PADuwPtaXdXfPZE4bIwa9BrKP48g8hqJmC2wRj2NK6lzm25kSk6j34ovMvG0Ep21H+KDVJPxUGgKObUZrq8apcTOwJiZhBQqA24xxfDhsJutz99DGGFeqRae4taZki47bZp3nFHdHaY1x/JHvbE3aYt7H0807oImsXbpLyxhP0Msvc7RvP/f6k5KwTZ1J7uCHKbIX8cpvr2AMj2diixGEZKeh8NJD2j+w7Qu3EGn4YyYGcHYPdvqQlMKzFf61aFXuq0P7ac5t0eFTo/xdzGu1Rm0woA6VbSGEuBFI2BHiermgpcBWo5Vbiw7A5hgdjXab8S6CHD182fp2vvJ/BlJMZOQVYlDkw3evoGjRG/6chf2+Oyp8S1t2NlmLPyCiUxS1Nzq7f6ytRnA8YY5b4ABna5APwIt3kD+sFz6zl51v0en9LAqtFnW1aqDVcrhjpzKDDji7v4qnhy/YOoLWYa3pdGsnTpw9if//thMyZgxFqamuGVHqoCC3LSEurKneoF5E1WrAI9UaUt1Qh31ZfqBREbJmKIpjG6HzMmj0OPiFgtUCam/ITYN694OuOgFKZbkbeJbq3ive0BOcg8ZlF3MhqgQJO0JcLxe0FNjN51fnzdEpSY1UuPaySolQ4PPcYL7aeX5Lh9yCIlCe6wpr/TL41EARHFXhWyq0WkyJidj6dXP9n91WoxWmxE/KPN+SuJ64Qb3onjzCOZjXpxd5Jgv6oFAU3lrSE2bifett6GJi3Lqjitfd8WndCoWXhtBRo8j2hbfueotdZ3aRenwPjfxb492wAbaMDI6VaMWJePedcoMTgMpkwXw2lfqfdMPx3Cq6LNrNH70jUBz6DTQ+4LDBrhVw6I/zL6rbDurdBzi3dyhrA88Lp6eX2tATnIPGZRdzIW54EnaEuF4uaClQ6jQA7IvS459jovFeZ7dVchMvmuwsQnubEXYecb3cz9vrfFfYsb+xPr7sojOliveAshWdX9yvZMgqi8pkcS5IeG4w710172KG7ygyxk7AlJiEacNGt+6t4v2vMpd9XGpad81R/QjQNEL/7hIykuYAUHPBfLf3u9gmo36GYJTqc/tOafTAGTS2XOfj1v1gwzz3oAPOQLlyoDOo6Kq7beCZY8lFp/ZBq9RTaDez+P4P8NdWsKGn7GIuxA1Pwo4Q11OJlgJVgYMtrQNotDkTbRFk+8DhW3y4fUc+2vh4fjl1fkHBttFBBPlqwHSuK2zDfGydOnPqjYHO4HGRqd3KoAgcde5Fceg3V8gqj01/PnzcXetuZtw6HMeRY67rl9p5vHp1Ts+ZU3r/q8Qk9G8ouKXvSxwtsfHmhQHtYoFNuesAoc2i4dYHsXk517ApVJ0bV1PzDvhzVtkf5OAaZ4vMuaBi0Bpkd3IhblISdoS43nTVSc/MZN2gjty+2zmY92AtBXqzFzG78vE2Gske8BpzvnVuctk2OogZHZs6BycrzneF2XOyXcEj4q1ZBL34Irbs7FJTu/XxrVGnfIei5u042gxF5RWM3mgsc8sDnTEeAmvwYfsPMReZaaqpzenRk6jetZvbeSVXUK65YH6p8T/FTImJ8OILbuvvXDjw2fVYoSh324i82BgiJk7AqvGnbXQQq1PtPBd1L2pr6bV53FwwKPx6yDYVkpFXSE5BEf46L4J8NM6/OyGEx0jYEeI6W/fN+9hmzaVhJtgVsL1NDR59dSGqgiKUfr7Y/Kvhb7WxoYcKRX4ean9/vBSFgM7VFeZYORClnw+AW+DJ+ny5+1o48a0JH/g06p96Q2E+ij9noW70FEGTJnBqwmQsiefP1Rrj8B49hLysTKKLtORrvdHpCjmVvJ2AXs+W+3nKWgywJFt2NllffEHkko+wZ53FlJzMibHjqN65M8EjXiMz5xSn9CpCJ4wh4PBRHAUFpQKbKXE9Nosab/8g3ujow4TvdhLfchoNtadRVPTm5U0fv0ZOnDUz8uvtrNuf4TrWNjqINzo2Jbya7rrWIoQ4T8KOENeJzWrlq1FPUf/n/WiscNYXsl54kqf7Tnc7r+hkGqfHji21YF3IpEmc0Phjc/iz/7ap3O2rR2+Mw5S4vlTXEg4HXmE1UKd85wo6LoF1sDrS2Df4YeqeW03Yrteh9wug4M33Kfr9D9KKazbGE/HWLMw7d5Xb1aS6yNoyzkHSSdh69uRY337o4+IInzqF48NfxXJfKx7+ZyAAK6NnUfDykHKvY893rhgdXk3HrM4xnMkvxKTwR1/3XhQHS690TN12zsHE10m2qbBU0AH4c38Go77ezntPx0oLjxAeImFHiOsgLXU/SQM70nSvcxzOgVuUNHxzMXFN4t3Os2Znc+KCoAPO3bRPjh/PL08OILpuOP2/SSHIV8PvY8fB1CmuwHNmwQL0xjjCJo5H8/l9zjErGh+srUZgq9EKu7kQZXBtfA78RtPYR5n093SSTqxnaPSL3PNuMgVJpcfdYHegb9GCgF49ncfcVlE2oqpVC2+jkYIyusVKDpIubgEqfn3I6FGsyNl0/rP7VDxQueSeUwZ9cdeQLzw2t1JMD8/IKywVdIr9uT/DuXSAhB0hPELCjhDX2O+fz0b1zmIanAWbAnbeHUbHd3/CS1P6x9125kyZY2kAChITuafvEA5anRtRZuQV8vAXKXw+diJRRSbsebkoff0o9PPnVF4mtc4FnbJWadYb47FHtCY2+Haeqd+dpuZA0pKcs6SKp5HrmsXgsFhQar1RBQWS+nJfqnfufH4V5erV8YoIx5qfQ9iY1zk1bbp7a9QFg6RLzroyrV9P8OiRLPj7TdexdfnbudsY59a15rpWRXtOVZLp4SV3qC9L7kWeF0JcOxJ2hLhGbFYrX772GA1+SUFjg0w/yOvblW59Jpb7GntuboXXVJvz0fo6u2b0GhUTHmvEiDUpJB44c+6MdO6sF8SMDpE46rbDFtS8zFWaTYlJaGc40PRpxivJr7Ay2jlguKJp5MVdT44FJnzuvYfQ14Zzctw4TEkbXAEp6MUXQKnEbjZj3pp8fpB0iRaeYgW5eW4blS5I+YQ7hs3EB9wCzyXtOVXB9HBrdja2M2ew5+ai9PNHFRhwTbZ1KN6hvjx+F3leCHHtSNgR4ho4nrKbvwd1JWa/FYD9dVQ0e3sZkbfdXuHrlH5+FT4fGuxLgD6D95+K4kCumiWJJYOO07oDGUz4RcmCx97FlppaapXmYpbE9dw5qBdvc74LKaBXLzKXfVzmNHKAWz7+EIVSgRITJ6ZMdYWo4i60MwsWoI+PR9e06fntJcrY4RzArvchPjyepBPOa5utZvqcW8zw/qEvE2DTovbzv6I9p4pOppXqFtS3aUP4lCl4hYX+q2uWJ8hXQ9voIP4soyvLtXSAEMIjlBc/RQhxOdZ8OpND3Tpy234rViVsu68mHVYmXzToAKgCA9G3aVPmc/r41mhTvqX6R/E89M9Y+jTRsjX1bJnnrt6TzhFrdewOfcXvZ3KOo1mXvx2tMQ5ds5gyByGDM/Ao1Gq0Pmbs+fmlWotc5yUl4d/+Pmr/ZzGRS5egi4kptY+Wt9GIV2ANJsVPwhhudB03W81syt+NV1RtfGKaoa1T518HnYrGP50YNw5rdva/um55DHoNb3RsStvoILfjbksHCCE8Qlp2hLhKbFYrXw59mEZrjqK2wxl/KHilJ916vX7J11AbDIRPmcKJceMw/fWX67jbFHJAeWgN+p+HMrTNWKb9drLMa+WYi4jwrzgoFC8gWNyFVCPdUeH59vRUCCzCbq64S8aRcQT9uhcoemgpGduT3YKO3mgkZMoUvIMD8QfXysa5hbn4aSpYyfgyVTT+yfTXX9jOnLnq3Vnh1XS893QsGXmF5BYU4eftRZCvrLMjhKdJ2BHiKkjdv42tQ3oQc9DZbbW3nprm7/4fteo2vuxreYWFEvHWLIoyMrCdzcLLcRbV6Y2lppArD63h/pbjmVbOdfy8vVBpnS1FJYNTsZK7khd3Ia1q/kGFtSl1GlArUeoq/upQ6jRQmI/XT72J6DwAW//uzplgIVGoQsLdQsa1Wtn4YuOf7Ll5V/09oeRMMSFEZSHdWEJcoV+WTiW1ezduPWilSAXbHqzNo99u/VdBp5jaYOCEXwg53ma033dEvXGm+1o55/gpyt5As3iMSHFL0YVdY3qjEUb2Y0HK+fE8MTViyPZVojXGlXlNfXxrVKc3Yss5haroBPr41mWfZ4xHdXqj80FhPuqNM9F+3xHdmqfRBqiuyeDgslxs/FPJqexCiKpNWnaE+JeKCi18PfRhGv1+ArUdTlcD66A+dHvm1Yu+9lLkFBShUlX8g22oFkjbaJXboNgLx4gUtxQ5ZyTlofTzRRUYSL43LA9Z7uw+UuvwycvAbMsiaPwosibPcN9ry2gkbMJorBorBV4B+CkthL8azYlZuI3d0RuNhA1/AfW3T5Wq1VbnXoo0AXhf6Y25RMXjn8pq1apwKrsQospROByOijvpb3A5OTkYDAays7Px97++S8eLquvwnv+xfVhvolNsAPxzqxet3/uCsNr1r9p7HEzP47fkfTx3cgrqlNIrBDvqtkPR6UOyHT5XZ4yIOcu5Vo0lD6sqAFu+FXueyRWOSrXImLOc07pzzNjzTSj9DeTpDaRnZxC98XW3mq1R97K35TR0QZHUqXH9WlSKTqaVHv90biq7V+jVnY0lhLi6rubvt4QdIS7Tz4vH4b/oK6rnQqEK/nmwLp1mfItKfXUbSrNNhbz65TaGtPThtk1j3MKDrc698Oh7qKrXvKrveaW2pmbR/YONDG0TzP2RSjS2PApVvqxOtfP2X+l89kIrmkVe38X+zq+zk1d+cBNCVDoSdi6DhB1xtRQVWvh6YHsa/3kKlQPSq4NjWH/u7jzwmr3nibNmJny3k5ZhKld4UHr74xsYhm+167fv06U6mJ5Hu9lry31+zbC7qBssY2WEEBd3NX+/ZcyOEJdg/7ZE9o54iZgjzq0a9jTQ0GbuNwRH1L2m71u86WVGXiFnS3RT+VbS2T6ysJ4QojK6oWZjJSQkoFAoGDJkiKdLETeRH+aPJuP5F6h7xI5FDTser8/jX26+5kGnmEGvoW6wL80iq1M32LdST2uWhfWEEJXRDdOy8/fff7No0SKaNm3q6VLETcJiNrFiYHuaJGagdEBaAHiNHEqXx1/ydGmVmiysJ4SobG6IsJOXl0f37t1ZvHgxU6dO9XQ54iawd+sfHBg5gJhUZ7fVrkZa7p67gqCwKA9XdmOQhfWEEJXJDdGNNWDAADp06MB999130XMtFgs5OTluf4S4HN+/N5ysPv2ok2rH4gU7Ojam09fJEnSEEOIGVelbdj7//HO2bNnC33//fUnnJyQkMGnSpGtclaiKzPk5fPfKQzRZn4kSOBkE3qNH0KXDc54uTQghxBWo1C07R48eZfDgwXzyySd4e1/auqujR48mOzvb9efo0aPXuEpRFeza+At/PNaamHNBZ1cTb2JX/Eq8BB0hhLjhVep1dr799luefPJJVCqV65jNZkOhUKBUKrFYLG7PlUXW2REX891bAwn75Ff8zGDWwMEnmtF58v95uiwhhLip3TTr7LRr144dO3a4HXvuueeoX78+I0eOvGjQEaIi+bnZ/PeVB4nZeBaA4zXAf9wYOj/Qw7OFCSGEuKoqddjx8/OjcWP3naN9fHwIDAwsdVyIy7E96QdOjH2NmBPOhs2dMXrun/c91QLDPFyZEEKIq61Shx0hroUVM/tS87O11C4AkwYOd7qDzuOXebosIYQQ18gNF3b++OMPT5cgblB52Zn80P9Bmm7OBeBYiIKA8ZPo2K6zhysTQghxLd1wYUeIf2Pr2hWkTxhD0zRnt9WO5r48OPcH/KsHe7gyIYQQ15qEHVHlfTO9D5HLk4i0QL4WUrsZ6TL6A0+XJYQQ4jqRsCOqrJysdH7u/zBNtuYDkBqmIGRyAk/d+biHKxNCCHE9SdgRVdL/1nxO1uTJNDnl7Lba3sKfR+b/go+fwcOVCSGEuN4k7Igq56vJvYj66m9qFkKuDk48cxddX1vg6bKEEEJ4iIQdUWWcPXOS1f070HibGYAj4QpqTn+LJ1o/5OHKhBBCeJKEHVElbPx5GXnTEmh82vl4W6tqPD5vFTof2SJECCFudhJ2xA3vi3HdqPfdNsILIUcPp3reT7ehczxdlhBCiEpCwo64YWWeOspvAx6jyc4CAFJqKolKeJdWd9zn4cqEEEJUJhJ2xA1p/fcfYn5jFo0ywA7siA/gyfdXo9XpPV2aEEKISkbCjrjhfDG6I7d+v5tqRZCth4znO9DtlVmeLksIIUQlJWFH3DAyTqawdsCTNNltAeBQpJJ6M+fTullbD1cmhBCiMpOwI24I61bMx/bmHBpmgl0BO9rU4Mk5P0u3lRBCiIuSsCMqNZvVylejO1L/p31orHDWFzL7PEG3fgmeLk0IIcQNQsKOqLTSUveTNKgTTf8pBODALUoavrmYuCbxHq5MCCHEjUTCjqiU/lj+Dop3FtIgC2wK2HFXKJ3m/IyXRuvp0oQQQtxgJOyISsVmtfLliMdpsOoQGhtk+UHuy114+oVJni5NCCHEDUrCjqg0jqfsZtOgbsTsLwJgf5SKprOXckuDFh6uTAghxI1Mwo6oFNZ8OhPNnCXUzwarEnbeG0GXd35GpZZ/okIIIa6M/JIIj7JZrXw5rAONfk1FbYcz/mDq352ne4/1dGlCCCGqCAk7wmNS929jy9AexBywArC3nprb3/6EyOgYD1cmhBCiKpGwIzzil6VT0c/7lNtyoEgFu+6rTZe3vpduKyGEEFed/LKI68pmtfLl4PY0+v0EajtkGKBocB+efuZVT5cmhBCiipKwI66bw3v+x/ZhvYlJsQHwz61etHz3cyKiGnq4MiGEEFWZhB1xXaz6YAK+C78gOhcKVbCnfR06z/xOuq2EEEJcc/JLI66pokILXw1qT5O1p1A5IL06OIa8TLeuQzxdmhBCiJuEhB1xzRzckcTu116k2WE7AHvqa4if8xWhkdEerkwIIcTNRMKOuCZ+mD+agA+/pV4eWNSw9+Hb6DT9K+m2EkIIcd3JL4+4qixmEysGtqdJYgZKB6QFgPq1QXR9sp+nSxNCCHGTkrAjrpq9W//gwMgBxKQ6u612N9Jy19wVBIVFebgyIYQQNzMJO+Kq+P694QQt+ZE6JrB4wb5HG9Fl+leeLksIIYSQsCOujMVsYsWA+2mSlIkSOBkEulGv0uWRPp4uTQghhAAk7IgrsOfvX0kZPYiYYw4AdjXxpt2876leI8LDlQkhhBDnSdgR/8rKtwcR8vFqokxg1sCBx2PoMuVzT5clhBBClCJhR1wWc34O3/VvT8zGswCcqAG+Y0bT5cFeni1MCCGEKIeEHXHJtif9wImxrxFzwtlttTNGz/3zvqdaYJiHKxNCCCHKJ2FHXJJv3+xLxKdrqV0AJg2kdLqDzuOXebosIYQQ4qIk7IgK5edm832/+2n6v1wAjoUoqD5+PJ3adfNwZUIIIcSlkbAjyrV17QrSJ4yhaZqz22rH7b48+P4P+FcP9nBlQgghxKWTsCPK9M30PkQuTyLSAvlaSO0aT5fXP/R0WUIIIcRlk7Aj3ORkpfPzgA402ZIHQGqoguBJ03jqric9XJkQQgjx70jYES6b13xJ5uQJNDnl7Lba3sKPDu//jK8hwMOVCSGEEP+e0tMFVCQhIYE77rgDPz8/goODeeKJJ9i7d6+ny6qSvp7cC4aOp+YpB3ne8M/zd9H1k00SdIQQQtzwKnXYWbt2LQMGDGDDhg2sXr0aq9XKAw88QH5+vqdLqzLOnjnJl12b0/Czv9EXwpFwBdp5s3hyxAJPlyaEEEJcFQqHw+HwdBGX6vTp0wQHB7N27Vratm17Sa/JycnBYDCQnZ2Nv7//Na7wxrLpl0/ImTKNiNPOx9taVePRuT/j42fwbGFCCCFuelfz9/uGGrOTnZ0NQEBA+V0rFosFi8XiepyTk3PN67oRfTn+aep+m0xEIeTqIK3X/XQbOsfTZQkhhBBXXaXuxirJ4XAwbNgw2rRpQ+PGjcs9LyEhAYPB4PpTq1at61hl5Zd1+jhfdYql8RfJ6AohpaYCv0Xv8ZgEHSGEEFXUDdONNWDAAH744Qf++usvatasWe55ZbXs1KpVS7qxgPXff4j5jVmEZYAd2BEXwONzf0Lnc3PfFyGEEJXPTdeNNXDgQFauXMmff/5ZYdAB0Gq1aLXa61TZjeOL1ztx6393Ua0IsvVwuvdDdBs029NlCSGEENdcpQ47DoeDgQMHsmLFCv744w+ioqI8XdINJ+NkCmtfeZImu5ytXYcildSb8T6tY+/2aF1CCCHE9VKpw86AAQP47LPP+O677/Dz8yMtLQ0Ag8GATqfzcHWV37oV87G+OYeGmWBXwA5jEE++twqtTu/p0oQQQojrplKP2VEoFGUeX7JkCb17976ka9yMU89tVitfvd6J237ci9YKZ30hs88TdOiX4OnShBBCiEty04zZqcQ5rNJKS91P0qBONP2nEICDtZU0mLWYuCbxHq5MCCGE8IxKHXbE5fnjy/dQzJ5HgyywKWDHXSF0mrMKL40M2BZCCHHzkrBTBdisVr4c8TgNVh1CY4MsP8h9uQtPvzDJ06UJIYQQHidh5wZ38sg/bBjYhZh9RQDsj1LRdPZSbmnQwsOVCSGEEJWDhJ0b2G+fzUI950PqnwWrEnbdE07nd1ehUstfqxBCCFFMfhVvQDarlS+GP0KjX4/gZYMz/mDq/wzdeo/zdGlCCCFEpSNh5waTun8bW4b2oNkBKwD76qqJfecTIqNjPFyZEEIIUTlJ2LmB/LpsOt5zP+a2nHPdVvdF0nn2D9JtJYQQQlRAfiVvADarlS+GPEjj346jtkOGAQoHPUe37iM8XZoQQghR6UnYqeRS924heWgvmh2yAfBPtBct53xORFRDD1cmhBBC3Bgk7FRiqz6ciO+C5UTnQqEK9rSvQ+eZ30m3lRBCCHEZ5FezEioqtPDVoAdpsjYNlQPSq4NjyMt06zrE06UJIYQQNxwJO5XMwR1J7H7tRZodtgOwp76G+DlfERoZ7eHKhBBCiBuThJ1K5McFr1P9gxXUy4NCNfzz0K10Svhauq2EEEKIKyC/opWAxWxixaAHafLXaZQOOBUAqldfoetTAzxdmhBCCHHDk7DjYXuT/+TAiH7EpDq7rXY31HLX+ysICovycGVCCCFE1SBhx4O+n/sqQR/9QB0TWLxg3yMN6ZLwtafLEkIIIaoUCTseYDGbWDHgfpokZaIETgaB94hhdHnsRU+XJoQQQlQ5Enausz1//0rK6MHEHHN2W+1q7M29768kIKSWhysTQgghqiYJO9fRyncGE7LsF6JMYNbAgcdj6DLlc0+XJYQQQlRpEnauA3N+Dt/1b0/MxrMAnKgBvmNG0+XBXp4tTAghhLgJSNi5xnZu+IljY4YTc9zhfByj4/55P1AtMMzDlQkhhBA3Bwk719C3s/oT/unv1DY7u60OPdWczhM/8XRZQgghxE1Fws41kJ+bzff9H6Dp3zkAHAtRUH38eDq16+bhyoQQQoibj4Sdqyx53XecGj+apied3VY7Yn14cN6P+FcP9nBlQgghxM1Jws5V9E3CC0R+nkikBfK1cKRLHF3GfOTpsoQQQoibmoSdqyAvO5Mf+7WnyZY8AI6GKqgxaRod73rSw5UJIYQQQsLOFdq85ksyJ0+gySlnt9X2Fn50eP9nfA0BHq5MCCGEECBh54p8PbkXt3z1NzULIc8bjj3dlq4jF3q6LCGEEEKUIGHnXzh75iSr+z9C420mAI6EKwif+iZPxnfwcGVCCCGEuJCEncu06ZdPyJk6jcbpzsfbWlXj0bk/4+Nn8GxhQgghhCiThJ3L8OWEZ6i7YisRhZCrg5M97qPb8Pc8XZYQQgghKiBh5xJknT7Or/0fpfEOMwCHIxRETn+Hx1s94OHKhBBCCHExEnYuIumHJRQkzKRxBtiBHXEBPD73J3Q+/p4uTQghhBCXQMJOBb4Y05nolTupXgTZejjd+yG6DZrt6bKEEEIIcRkk7JQh42QKa195kia7LAAcqqWk3sz3aR17t0frEkIIIcTlk7Bzgb++W0TRzLdpeAbsCthhDOLJ91ah1ek9XZoQQggh/gUJO+fYrFa+HtOZW3/4B60VzvpAZp/H6NZ/hqdLE0IIIcQVkLADpB8/yF8DnqLJP4UAHKyt5LaZi4iLMXq4MiGEEEJcqZs+7Pzx5XsoZs+jQRbYFLDjrhA6zVmFl0br6dKEEEIIcRXctGHHZrXy1cgnqP/zQTQ2yPKDnJc68fSLUzxdmhBCCCGuopsy7Jw88g8bBnWh6d4iAPZHqWj81kfEN2zp4cqEEEIIcbXddGHnt/97C/W7H1D/LFiVsOvucDq+86N0WwkhhBBVlNLTBVyKefPmERUVhbe3N82bN2fdunWXfQ2b1crnQx4kaOoH1DgLZ/zh5Ihn6DZvjQQdIYQQogqr9GFn+fLlDBkyhDFjxrB161buvPNOHnroIVJTUy/rOj893YaYn4/gZYN9dVVEfvo5D/Qed42qFkIIIURloXA4HA5PF1GRVq1acfvttzN//nzXsQYNGvDEE0+QkJBw0dfn5ORgMBjYVC8aby8Vu9rVovPbP6JS33Q9eEIIIcQNo/j3Ozs7G3//K9uPslL/4hcWFrJ582ZGjRrldvyBBx4gKSmpzNdYLBYsFovrcXZ2NgCpvjbU/Z7h4W7DyDeZrl3RQgghhLhiOTk5AFyNNplKHXYyMjKw2WyEhIS4HQ8JCSEtLa3M1yQkJDBp0qRSxzslH4KXJzn/CCGEEOKGcObMGQwGwxVdo1KHnWIKhcLtscPhKHWs2OjRoxk2bJjr8dmzZ6lduzapqalXfLOqmpycHGrVqsXRo0evuImwqpF7Uz65N2WT+1I+uTflk3tTvuzsbCIjIwkICLjia1XqsBMUFIRKpSrVipOenl6qtaeYVqtFqy09u8pgMMg/pHL4+/vLvSmH3Jvyyb0pm9yX8sm9KZ/cm/IplVc+l6pSz8bSaDQ0b96c1atXux1fvXo18fHxHqpKCCGEEDeSSt2yAzBs2DB69uxJixYtiIuLY9GiRaSmptK3b19PlyaEEEKIG0ClDztdu3blzJkzTJ48mZMnT9K4cWN+/PFHateufUmv12q1TJgwocyurZud3Jvyyb0pn9ybssl9KZ/cm/LJvSnf1bw3lX6dHSGEEEKIK1Gpx+wIIYQQQlwpCTtCCCGEqNIk7AghhBCiSpOwI4QQQogqrUqHnXnz5hEVFYW3tzfNmzdn3bp1ni7J4xISErjjjjvw8/MjODiYJ554gr1793q6rEopISEBhULBkCFDPF1KpXD8+HF69OhBYGAger2eZs2asXnzZk+X5XFWq5WxY8cSFRWFTqejTp06TJ48Gbvd7unSrrs///yTRx99lPDwcBQKBd9++63b8w6Hg4kTJxIeHo5Op+Puu+9m165dnin2Oqvo3hQVFTFy5EiaNGmCj48P4eHh9OrVixMnTniu4OvoYv9uSnr55ZdRKBS88847l/UeVTbsLF++nCFDhjBmzBi2bt3KnXfeyUMPPURqaqqnS/OotWvXMmDAADZs2MDq1auxWq088MAD5Ofne7q0SuXvv/9m0aJFNG3a1NOlVApZWVkYjUa8vLz46aef2L17N2+99RbVqlXzdGkeN2PGDBYsWMDcuXPZs2cPM2fO5M033+S9997zdGnXXX5+PjExMcydO7fM52fOnMns2bOZO3cuf//9N6Ghodx///3k5uZe50qvv4rujclkYsuWLYwbN44tW7bwzTffsG/fPh577DEPVHr9XezfTbFvv/2WjRs3Eh4efvlv4qiiWrZs6ejbt6/bsfr16ztGjRrloYoqp/T0dAfgWLt2radLqTRyc3Md0dHRjtWrVzvuuusux+DBgz1dkseNHDnS0aZNG0+XUSl16NDB8fzzz7sde+qppxw9evTwUEWVA+BYsWKF67HdbneEhoY63njjDdexgoICh8FgcCxYsMADFXrOhfemLJs2bXIAjiNHjlyfoiqJ8u7NsWPHHBEREY6dO3c6ateu7Xj77bcv67pVsmWnsLCQzZs388ADD7gdf+CBB0hKSvJQVZVTdnY2wFXZaK2qGDBgAB06dOC+++7zdCmVxsqVK2nRogWdO3cmODiY2NhYFi9e7OmyKoU2bdqwZs0a9u3bB8C2bdv466+/ePjhhz1cWeWSkpJCWlqa2/eyVqvlrrvuku/lMmRnZ6NQKKT1FLDb7fTs2ZPXXnuNRo0a/atrVPoVlP+NjIwMbDZbqc1CQ0JCSm0qejNzOBwMGzaMNm3a0LhxY0+XUyl8/vnnbNmyhb///tvTpVQqhw4dYv78+QwbNozXX3+dTZs2MWjQILRaLb169fJ0eR41cuRIsrOzqV+/PiqVCpvNxrRp03j66ac9XVqlUvzdW9b38pEjRzxRUqVVUFDAqFGjeOaZZ2RzUJxdxWq1mkGDBv3ra1TJsFNMoVC4PXY4HKWO3cxeeeUVtm/fzl9//eXpUiqFo0ePMnjwYH755Re8vb09XU6lYrfbadGiBdOnTwcgNjaWXbt2MX/+/Js+7CxfvpxPPvmEzz77jEaNGpGcnMyQIUMIDw/n2Wef9XR5lY58L1esqKiIbt26YbfbmTdvnqfL8bjNmzfz7rvvsmXLliv6d1Ilu7GCgoJQqVSlWnHS09NL/VfFzWrgwIGsXLmS33//nZo1a3q6nEph8+bNpKen07x5c9RqNWq1mrVr1zJnzhzUajU2m83TJXpMWFgYDRs2dDvWoEGDm37AP8Brr73GqFGj6NatG02aNKFnz54MHTqUhIQET5dWqYSGhgLI93IFioqK6NKlCykpKaxevVpadYB169aRnp5OZGSk63v5yJEjDB8+nFtuueWSr1Mlw45Go6F58+asXr3a7fjq1auJj4/3UFWVg8Ph4JVXXuGbb77ht99+IyoqytMlVRrt2rVjx44dJCcnu/60aNGC7t27k5ycjEql8nSJHmM0GkstUbBv375L3pC3KjOZTCiV7l+lKpXqppx6XpGoqChCQ0PdvpcLCwtZu3btTf+9DOeDzv79+/n1118JDAz0dEmVQs+ePdm+fbvb93J4eDivvfYaq1atuuTrVNlurGHDhtGzZ09atGhBXFwcixYtIjU1lb59+3q6NI8aMGAAn332Gd999x1+fn6u/8oyGAzodDoPV+dZfn5+pcYu+fj4EBgYeNOPaRo6dCjx8fFMnz6dLl26sGnTJhYtWsSiRYs8XZrHPfroo0ybNo3IyEgaNWrE1q1bmT17Ns8//7ynS7vu8vLyOHDggOtxSkoKycnJBAQEEBkZyZAhQ5g+fTrR0dFER0czffp09Ho9zzzzjAervj4qujfh4eF06tSJLVu28P3332Oz2VzfzQEBAWg0Gk+VfV1c7N/NhcHPy8uL0NBQbrvttkt/kyufKFZ5vf/++47atWs7NBqN4/bbb5fp1Q7ntL6y/ixZssTTpVVKMvX8vP/+97+Oxo0bO7RaraN+/fqORYsWebqkSiEnJ8cxePBgR2RkpMPb29tRp04dx5gxYxwWi8XTpV13v//+e5nfL88++6zD4XBOP58wYYIjNDTUodVqHW3btnXs2LHDs0VfJxXdm5SUlHK/m3///XdPl37NXezfzYX+zdRzhcPhcFxWBBNCCCGEuIFUyTE7QgghhBDFJOwIIYQQokqTsCOEEEKIKk3CjhBCCCGqNAk7QgghhKjSJOwIIYQQokqTsCOEEEKIKk3CjhDihjFx4kSaNWvmety7d2+eeOKJ617H4cOHUSgUJCcnX/f3FkJcPgk7Qogr1rt3bxQKBQqFAi8vL+rUqcOrr75Kfn7+NX3fd999l6VLl17SuRJQhLh5Vdm9sYQQ19eDDz7IkiVLKCoqYt26dbzwwgvk5+czf/58t/OKiorw8vK6Ku9pMBiuynWEEFWbtOwIIa4KrVZLaGgotWrV4plnnqF79+58++23rq6njz76iDp16qDVanE4HGRnZ/PSSy8RHByMv78/9957L9u2bXO75htvvEFISAh+fn706dOHgoICt+cv7May2+3MmDGDevXqodVqiYyMZNq0aYBz122A2NhYFAoFd999t+t1S5YsoUGDBnh7e1O/fn3mzZvn9j6bNm0iNjYWb29vWrRowdatW6/inRNCXGvSsiOEuCZ0Oh1FRUUAHDhwgC+++IKvv/4alUoFQIcOHQgICODHH3/EYDCwcOFC2rVrx759+wgICOCLL75gwoQJvP/++9x55518/PHHzJkzhzp16pT7nqNHj2bx4sW8/fbbtGnThpMnT/LPP/8AzsDSsmVLfv31Vxo1auTaSXrx4sVMmDCBuXPnEhsby9atW3nxxRfx8fHh2WefJT8/n0ceeYR7772XTz75hJSUFAYPHnyN754Q4qq6ws1KhRDC8eyzzzoef/xx1+ONGzc6AgMDHV26dHFMmDDB4eXl5UhPT3c9v2bNGoe/v7+joKDA7Tp169Z1LFy40OFwOBxxcXGOvn37uj3fqlUrR0xMTJnvm5OT49BqtY7FixeXWWPxztJbt251O16rVi3HZ5995nZsypQpjri4OIfD4XAsXLjQERAQ4MjPz3c9P3/+/DKvJYSonKQbSwhxVXz//ff4+vri7e1NXFwcbdu25b333gOgdu3a1KhRw3Xu5s2bycvLIzAwEF9fX9eflJQUDh48CMCePXuIi4tze48LH5e0Z88eLBYL7dq1u+SaT58+zdGjR+nTp49bHVOnTnWrIyYmBr1ef0l1CCEqH+nGEkJcFffccw/z58/Hy8uL8PBwt0HIPj4+bufa7XbCwsL4448/Sl2nWrVq/+r9dTrdZb/GbrcDzq6sVq1auT1X3N3mcDj+VT1CiMpDwo4Q4qrw8fGhXr16l3Tu7bffTlpaGmq1mltuuaXMcxo0aMCGDRvo1auX69iGDRvKvWZ0dDQ6nY41a9bwwgsvlHq+eIyOzWZzHQsJCSEiIoJDhw7RvXv3Mq/bsGFDPv74Y8xmsytQVVSHEKLykW4sIcR1d9999xEXF8cTTzzBqlWrOHz4MElJSYwdO5b//e9/AAwePJiPPvqIjz76iH379jFhwgR27dpV7jW9vb0ZOXIkI0aMYNmyZRw8eJANGzbw4YcfAhAcHIxOp+Pnn3/m1KlTZGdnA86FChMSEnj33XfZt28fO3bsYMmSJcyePRuAZ555BqVSSZ8+fdi9ezc//vgjs2bNusZ3SAhxNUnYEUJcdwqFgh9//JG2bdvy/PPPc+utt9KtWzcOHz5MSEgIAF27dmX8+PGMHDmS5s2bc+TIEfr161fhdceNG8fw4cMZP348DRo0oGvXrqSnpwOgVquZM2cOCxcuJDw8nMcffxyAF154gQ8++IClS5fSpEkT7rrrLpYuXeqaqu7r68t///tfdu/eTWxsLGPGjGHGjBnX8O4IIa42hUM6pIUQQghRhUnLjhBCCCGqNAk7QgghhKjSJOwIIYQQokqTsCOEEEKIKk3CjhBCCCGqNAk7QgghhKjSJOwIIYQQokqTsCOEEEKIKk3CjhBCCCGqNAk7QgghhKjSJOwIIYQQokqTsCOEEEKIKu3/ARGEvQhXQ4uXAAAAAElFTkSuQmCC\n" }, "metadata": {}, "output_type": "display_data" @@ -3430,7 +1890,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 29, "metadata": { "collapsed": false, "pycharm": { @@ -3448,60 +1908,58 @@ "Test set has 3519 datapoints (27.667 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.24619690865122593,\n", - " \"R2 score\": -0.10444150019112697,\n", - " \"MAE\": 0.8544105189289312\n", + " \"Pearson r\": 0.23776732765837533,\n", + " \"R2 score\": -0.11679644568418523,\n", + " \"MAE\": 0.8612702259972462\n", "}\n", - "Performance can only be plotted for the left out target in LOTO split\n", - "Performance can only be plotted for the left out target in LOTO split\n", - "Performance can only be plotted for the left out target in LOTO split\n", + "Not plotting A2A. Performance can only be plotted for the left out target in LOTO split\n", + "Not plotting A2B. Performance can only be plotted for the left out target in LOTO split\n", + "Not plotting A3. Performance can only be plotted for the left out target in LOTO split\n", "== Leave one target out split ==\n", "Target left out for testing is A2A\n", "Training set has 8728 datapoints\n", "Test set has 3991 datapoints (31.378 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.20825285841494767,\n", - " \"R2 score\": -0.042945249712776024,\n", - " \"MAE\": 0.9629048242470047\n", + " \"Pearson r\": 0.18866090915121556,\n", + " \"R2 score\": -0.0545495596016794,\n", + " \"MAE\": 0.9709822341330826\n", "}\n", - "Performance can only be plotted for the left out target in LOTO split\n", - "Performance can only be plotted for the left out target in LOTO split\n", - "Performance can only be plotted for the left out target in LOTO split\n", + "Not plotting A1. Performance can only be plotted for the left out target in LOTO split\n", + "Not plotting A2B. Performance can only be plotted for the left out target in LOTO split\n", + "Not plotting A3. Performance can only be plotted for the left out target in LOTO split\n", "== Leave one target out split ==\n", "Target left out for testing is A2B\n", "Training set has 10731 datapoints\n", "Test set has 1988 datapoints (15.63 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": -0.015321923240914772,\n", - " \"R2 score\": -0.2867236006293721,\n", - " \"MAE\": 0.9895603553174414\n", + " \"Pearson r\": 0.004563441926494551,\n", + " \"R2 score\": -0.2723172120957098,\n", + " \"MAE\": 0.987973315685161\n", "}\n", - "Performance can only be plotted for the left out target in LOTO split\n", - "Performance can only be plotted for the left out target in LOTO split\n", - "Performance can only be plotted for the left out target in LOTO split\n", + "Not plotting A1. Performance can only be plotted for the left out target in LOTO split\n", + "Not plotting A2A. Performance can only be plotted for the left out target in LOTO split\n", + "Not plotting A3. Performance can only be plotted for the left out target in LOTO split\n", "== Leave one target out split ==\n", "Target left out for testing is A3\n", "Training set has 9498 datapoints\n", "Test set has 3221 datapoints (25.324 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.1027567408073104,\n", - " \"R2 score\": -0.2629762096426478,\n", - " \"MAE\": 1.0540418854090967\n", + " \"Pearson r\": 0.10516136983832487,\n", + " \"R2 score\": -0.2645648633512194,\n", + " \"MAE\": 1.0521379074235313\n", "}\n", - "Performance can only be plotted for the left out target in LOTO split\n", - "Performance can only be plotted for the left out target in LOTO split\n", - "Performance can only be plotted for the left out target in LOTO split\n" + "Not plotting A1. Performance can only be plotted for the left out target in LOTO split\n", + "Not plotting A2A. Performance can only be plotted for the left out target in LOTO split\n", + "Not plotting A2B. Performance can only be plotted for the left out target in LOTO split\n" ] }, { "data": { - "image/png": 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\n", 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\n" }, "metadata": {}, "output_type": "display_data" From 5ee3305f4ef513576c17186601a10e27cdfdf202 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Fri, 28 Oct 2022 18:07:18 +0200 Subject: [PATCH 41/62] Update code for new location of ClustalO client --- .../talktorial.ipynb | 154 +++++++++++++----- 1 file changed, 116 insertions(+), 38 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index 4f02e82d..a8d71ac0 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -488,7 +488,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "3759cd800e6c408aa1eb3aa499da5edb" + "model_id": "b4bc29a1342a43d89ce727d231e31674" } }, "metadata": {}, @@ -542,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 5, "metadata": { "collapsed": false, "pycharm": { @@ -610,7 +610,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 6, "metadata": { "collapsed": false, "pycharm": { @@ -624,7 +624,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "0a582f9dc8fe4d1c83f6151a0336d09e" + "model_id": "63bcedd602dc4d73b4b49a2b829efbd3" } }, "metadata": {}, @@ -807,17 +807,95 @@ } }, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "RUNNING\n", + "FINISHED\n", + "Creating result file: data\\aligned_sequences.out.txt\n", + "Creating result file: data\\aligned_sequences.sequence.txt\n", + "Creating result file: data\\aligned_sequences.aln-fasta.fasta\n", + "Creating result file: data\\aligned_sequences.tree.dnd\n", + "Creating result file: data\\aligned_sequences.phylotree.ph\n", + "Creating result file: data\\aligned_sequences.pim.pim\n", + "Creating result file: data\\aligned_sequences.submission.params\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ - "python: can't open file 'data/clustalo.py': [Errno 2] No such file or directory\n" + "JobId: clustalo-R20221028-164629-0996-9023833-p2m\n" ] } ], "source": [ "# Query ClustalO webservice from command line\n", - "!python data/clustalo.py --email m.gorostiola.gonzalez@lacdr.leidenuniv.nl --stype protein --sequence data/sequences.fasta --outfmt fa --outdir data --outfile aligned_sequences --order input" + "!python scripts/clustalo.py --email m.gorostiola.gonzalez@lacdr.leidenuniv.nl --stype protein --sequence data/sequences.fasta --outfmt fa --outdir data --outfile aligned_sequences --order input" ] }, { @@ -1035,7 +1113,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "a68ee50cb7c946c69520128922a670c1" + "model_id": "0146b40423da48068676dee298871f29" } }, "metadata": {}, @@ -1170,7 +1248,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 6898/6898 [00:11<00:00, 591.59it/s]\n" + "100%|██████████| 6898/6898 [00:11<00:00, 618.20it/s]\n" ] }, { @@ -1747,16 +1825,16 @@ "text": [ "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.6858217378640958,\n", - " \"R2 score\": 0.46550829516212433,\n", - " \"MAE\": 0.6407963506676366\n", + " \"Pearson r\": 0.6879571893126766,\n", + " \"R2 score\": 0.4674360461331021,\n", + " \"MAE\": 0.6391679642401793\n", "}\n" ] }, { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" @@ -1817,34 +1895,34 @@ "text": [ "=== QSAR model performance A1 ===\n", "{\n", - " \"Pearson r\": 0.6029925425551398,\n", - " \"R2 score\": 0.36192570952238345,\n", - " \"MAE\": 0.5973210044267906\n", + " \"Pearson r\": 0.5979378336321199,\n", + " \"R2 score\": 0.3554456390456112,\n", + " \"MAE\": 0.5959370946837163\n", "}\n", "=== QSAR model performance A2A ===\n", "{\n", - " \"Pearson r\": 0.6391547405926432,\n", - " \"R2 score\": 0.4073561135234154,\n", - " \"MAE\": 0.6936822842294988\n", + " \"Pearson r\": 0.6338700554374328,\n", + " \"R2 score\": 0.40068418541881656,\n", + " \"MAE\": 0.6963380462215866\n", "}\n", "=== QSAR model performance A2B ===\n", "{\n", - " \"Pearson r\": 0.7084601022622155,\n", - " \"R2 score\": 0.49053947942778353,\n", - " \"MAE\": 0.5524308526205923\n", + " \"Pearson r\": 0.7065249948153149,\n", + " \"R2 score\": 0.4899178130584151,\n", + " \"MAE\": 0.5532495807492329\n", "}\n", "=== QSAR model performance A3 ===\n", "{\n", - " \"Pearson r\": 0.6585745793937321,\n", - " \"R2 score\": 0.43160610100190355,\n", - " \"MAE\": 0.6896203135686225\n", + " \"Pearson r\": 0.6694961299383835,\n", + " \"R2 score\": 0.44575765980842574,\n", + " \"MAE\": 0.6786831601566242\n", "}\n" ] }, { "data": { "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": {}, "output_type": "display_data" @@ -1908,9 +1986,9 @@ "Test set has 3519 datapoints (27.667 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.23776732765837533,\n", - " \"R2 score\": -0.11679644568418523,\n", - " \"MAE\": 0.8612702259972462\n", + " \"Pearson r\": 0.2409832494512499,\n", + " \"R2 score\": -0.10391599922077033,\n", + " \"MAE\": 0.8573939533239808\n", "}\n", "Not plotting A2A. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2B. Performance can only be plotted for the left out target in LOTO split\n", @@ -1921,9 +1999,9 @@ "Test set has 3991 datapoints (31.378 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.18866090915121556,\n", - " \"R2 score\": -0.0545495596016794,\n", - " \"MAE\": 0.9709822341330826\n", + " \"Pearson r\": 0.18895331681438773,\n", + " \"R2 score\": -0.05573206624512794,\n", + " \"MAE\": 0.972199067173692\n", "}\n", "Not plotting A1. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2B. Performance can only be plotted for the left out target in LOTO split\n", @@ -1934,9 +2012,9 @@ "Test set has 1988 datapoints (15.63 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.004563441926494551,\n", - " \"R2 score\": -0.2723172120957098,\n", - " \"MAE\": 0.987973315685161\n", + " \"Pearson r\": -0.0014721522748623993,\n", + " \"R2 score\": -0.2696333253550791,\n", + " \"MAE\": 0.9818040885882379\n", "}\n", "Not plotting A1. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2A. Performance can only be plotted for the left out target in LOTO split\n", @@ -1947,9 +2025,9 @@ "Test set has 3221 datapoints (25.324 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.10516136983832487,\n", - " \"R2 score\": -0.2645648633512194,\n", - " \"MAE\": 1.0521379074235313\n", + " \"Pearson r\": 0.09869989160334747,\n", + " \"R2 score\": -0.2698470611471262,\n", + " \"MAE\": 1.0537603895377747\n", "}\n", "Not plotting A1. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2A. Performance can only be plotted for the left out target in LOTO split\n", @@ -1959,7 +2037,7 @@ { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" From e7566aab04b387fec971883049b9ee9146dccd98 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Fri, 28 Oct 2022 18:30:41 +0200 Subject: [PATCH 42/62] Update ClustalO client check time --- .../talktorial.ipynb | 129 +++++------------- 1 file changed, 36 insertions(+), 93 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index a8d71ac0..c5d48a26 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -488,7 +488,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "b4bc29a1342a43d89ce727d231e31674" + "model_id": "11d16cd81f4c4b7295cd91143d6025e4" } }, "metadata": {}, @@ -624,7 +624,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "63bcedd602dc4d73b4b49a2b829efbd3" + "model_id": "77bc07768ca941cfbb1097da96b5e752" } }, "metadata": {}, @@ -811,63 +811,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", "RUNNING\n", "RUNNING\n", "RUNNING\n", @@ -889,13 +832,13 @@ "name": "stderr", "output_type": "stream", "text": [ - "JobId: clustalo-R20221028-164629-0996-9023833-p2m\n" + "JobId: clustalo-R20221028-171535-0775-74892409-p2m\n" ] } ], "source": [ "# Query ClustalO webservice from command line\n", - "!python scripts/clustalo.py --email m.gorostiola.gonzalez@lacdr.leidenuniv.nl --stype protein --sequence data/sequences.fasta --outfmt fa --outdir data --outfile aligned_sequences --order input" + "!python scripts/clustalo.py --email m.gorostiola.gonzalez@lacdr.leidenuniv.nl --stype protein --sequence data/sequences.fasta --outfmt fa --outdir data --outfile aligned_sequences --order input --pollFreq 20" ] }, { @@ -1113,7 +1056,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "0146b40423da48068676dee298871f29" + "model_id": "b25d3617450043a1b0b98967753e483b" } }, "metadata": {}, @@ -1248,7 +1191,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 6898/6898 [00:11<00:00, 618.20it/s]\n" + "100%|██████████| 6898/6898 [00:10<00:00, 655.83it/s]\n" ] }, { @@ -1825,16 +1768,16 @@ "text": [ "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.6879571893126766,\n", - " \"R2 score\": 0.4674360461331021,\n", - " \"MAE\": 0.6391679642401793\n", + " \"Pearson r\": 0.6857487308961996,\n", + " \"R2 score\": 0.46467514184684344,\n", + " \"MAE\": 0.6424885455055979\n", "}\n" ] }, { "data": { "text/plain": "
", - "image/png": 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\n" 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\n" }, "metadata": {}, "output_type": "display_data" @@ -1895,34 +1838,34 @@ "text": [ "=== QSAR model performance A1 ===\n", "{\n", - " \"Pearson r\": 0.5979378336321199,\n", - " \"R2 score\": 0.3554456390456112,\n", - " \"MAE\": 0.5959370946837163\n", + " \"Pearson r\": 0.5985317411966065,\n", + " \"R2 score\": 0.3558927132291356,\n", + " \"MAE\": 0.5958968533575324\n", "}\n", "=== QSAR model performance A2A ===\n", "{\n", - " \"Pearson r\": 0.6338700554374328,\n", - " \"R2 score\": 0.40068418541881656,\n", - " \"MAE\": 0.6963380462215866\n", + " \"Pearson r\": 0.6306127424850827,\n", + " \"R2 score\": 0.3959873598820135,\n", + " \"MAE\": 0.6994656717509705\n", "}\n", "=== QSAR model performance A2B ===\n", "{\n", - " \"Pearson r\": 0.7065249948153149,\n", - " \"R2 score\": 0.4899178130584151,\n", - " \"MAE\": 0.5532495807492329\n", + " \"Pearson r\": 0.7074095194000876,\n", + " \"R2 score\": 0.48947563102683467,\n", + " \"MAE\": 0.5534574461539086\n", "}\n", "=== QSAR model performance A3 ===\n", "{\n", - " \"Pearson r\": 0.6694961299383835,\n", - " \"R2 score\": 0.44575765980842574,\n", - " \"MAE\": 0.6786831601566242\n", + " \"Pearson r\": 0.6644154212320273,\n", + " \"R2 score\": 0.4388718113230262,\n", + " \"MAE\": 0.6880455126649877\n", "}\n" ] }, { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" @@ -1986,9 +1929,9 @@ "Test set has 3519 datapoints (27.667 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.2409832494512499,\n", - " \"R2 score\": -0.10391599922077033,\n", - " \"MAE\": 0.8573939533239808\n", + " \"Pearson r\": 0.24856918182631366,\n", + " \"R2 score\": -0.09625591525773713,\n", + " \"MAE\": 0.8533690296313972\n", "}\n", "Not plotting A2A. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2B. Performance can only be plotted for the left out target in LOTO split\n", @@ -1999,9 +1942,9 @@ "Test set has 3991 datapoints (31.378 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.18895331681438773,\n", - " \"R2 score\": -0.05573206624512794,\n", - " \"MAE\": 0.972199067173692\n", + " \"Pearson r\": 0.20870773382438598,\n", + " \"R2 score\": -0.04865874342423271,\n", + " \"MAE\": 0.9630896623958625\n", "}\n", "Not plotting A1. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2B. Performance can only be plotted for the left out target in LOTO split\n", @@ -2012,9 +1955,9 @@ "Test set has 1988 datapoints (15.63 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": -0.0014721522748623993,\n", - " \"R2 score\": -0.2696333253550791,\n", - " \"MAE\": 0.9818040885882379\n", + " \"Pearson r\": 0.02522068165865813,\n", + " \"R2 score\": -0.22872057130974377,\n", + " \"MAE\": 0.9666788970661387\n", "}\n", "Not plotting A1. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2A. Performance can only be plotted for the left out target in LOTO split\n", @@ -2025,9 +1968,9 @@ "Test set has 3221 datapoints (25.324 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.09869989160334747,\n", - " \"R2 score\": -0.2698470611471262,\n", - " \"MAE\": 1.0537603895377747\n", + " \"Pearson r\": 0.10119697676223516,\n", + " \"R2 score\": -0.25986962080223663,\n", + " \"MAE\": 1.0528712212109281\n", "}\n", "Not plotting A1. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2A. Performance can only be plotted for the left out target in LOTO split\n", @@ -2037,7 +1980,7 @@ { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" From c84f5dd75173c064b941df5e5ce5ba4185324716 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 7 Nov 2022 20:32:19 +0000 Subject: [PATCH 43/62] Docs config: Set language to "en" (not None) --- docs/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/conf.py b/docs/conf.py index 39588075..350f7494 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -86,7 +86,7 @@ # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. -language = None +language = "en" # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. From feacb89cb14e0de33565a8cf8efc45741ff66397 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 7 Nov 2022 20:32:40 +0000 Subject: [PATCH 44/62] README: Fix broken conda-forge badge --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bf7021c1..55a3acc4 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ A teaching platform for computer-aided drug design (CADD) using open source pack ![GitHub tag (latest by date)](https://img.shields.io/github/v/tag/volkamerlab/teachopencadd) [![GH Actions CI ](https://github.com/volkamerlab/teachopencadd/workflows/CI/badge.svg)](https://github.com/volkamerlab/teachopencadd/actions?query=branch%3Amaster+workflow%3ACI) [![GH Actions Docs](https://github.com/volkamerlab/teachopencadd/workflows/Docs/badge.svg)](https://projects.volkamerlab.org/teachopencadd/) -[![Anaconda-Server Badge](https://anaconda.org/conda-forge/teachopencadd/badges/installer/conda.svg)](https://anaconda.org/conda-forge/teachopencadd) +[![Conda Version](https://img.shields.io/conda/vn/conda-forge/teachopencadd.svg)](https://anaconda.org/conda-forge/teachopencadd) Open source programming packages for cheminformatics and structural bioinformatics are powerful tools to build modular, reproducible, and reusable pipelines for computer-aided drug design (CADD). While documentation for such tools is available, only few freely accessible examples teach underlying concepts focused on CADD applications, addressing especially users new to the field. From 56d1398e127e0ee74b1754ea1bd397d88f0686dd Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 7 Nov 2022 21:12:58 +0000 Subject: [PATCH 45/62] Docs: Add T032 nblink file --- .../talktorials/T032_compound_activity_proteochemometrics.nblink | 1 + 1 file changed, 1 insertion(+) create mode 100644 docs/talktorials/T032_compound_activity_proteochemometrics.nblink diff --git a/docs/talktorials/T032_compound_activity_proteochemometrics.nblink b/docs/talktorials/T032_compound_activity_proteochemometrics.nblink new file mode 100644 index 00000000..4928e682 --- /dev/null +++ b/docs/talktorials/T032_compound_activity_proteochemometrics.nblink @@ -0,0 +1 @@ +{"path": "../../teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb", "extra-media": ["../../teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images"]} \ No newline at end of file From 17177493e1b028b9ca4a104c25e5af2bb0be4ebd Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 7 Nov 2022 22:03:55 +0000 Subject: [PATCH 46/62] T032: Add pre-calculated alignments (ClustalO) --- .gitignore | 10 +++++- .../data/aligned_sequences.aln-fasta.fasta | 36 +++++++++++++++++++ 2 files changed, 45 insertions(+), 1 deletion(-) create mode 100644 teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/aligned_sequences.aln-fasta.fasta diff --git a/.gitignore b/.gitignore index 9ccf82c7..6e9b51d6 100644 --- a/.gitignore +++ b/.gitignore @@ -155,4 +155,12 @@ node_modules/ # Talktorial outputs # T018 teachopencadd/talktorials/T018_automated_cadd_pipeline/data/Outputs -teachopencadd/talktorials/T018_automated_cadd_pipeline/data/PipelineInputData_Project2.csv \ No newline at end of file +teachopencadd/talktorials/T018_automated_cadd_pipeline/data/PipelineInputData_Project2.csv + +# Talktorial outputs +# T032 +teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/papyrus +teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/sequences.fasta +teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/aligned* +# Keep this file for CI purposes (ClustalO w/o email) +!teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/aligned_sequences.aln-fasta.fasta \ No newline at end of file diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/aligned_sequences.aln-fasta.fasta b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/aligned_sequences.aln-fasta.fasta new file mode 100644 index 00000000..b470f755 --- /dev/null +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/aligned_sequences.aln-fasta.fasta @@ -0,0 +1,36 @@ +>0 AA2BR_HUMAN Homo sapiens (Human) Membrane receptor->Family A G protein-coupled receptor->Small molecule receptor (family A GPCR +-----MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVA +VGLFAIPFAITISLGFCTDFYGCLFLACFVLVLTQSSIFSLLAVAVDRYLAICVPLRYKS +LVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCC---LVKCL +FENVVPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTEL----MDHSRTTLQR +EIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQGKNKPKWAMNMAILLSHANSVVNPI +VYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQ----------AGVQPALGVGL------ +------------------------------------------------------------ +------ +>1 AA1R_HUMAN Homo sapiens (Human) Membrane receptor->Family A G protein-coupled receptor->Small molecule receptor (family A GPCR) +---MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFCFIVSLAVADVA +VGALVIPLAILINIGPQTYFHTCLMVACPVLILTQSSILALLAIAVDRYLRVKIPLRYKM +VVTPRRAAVAIAGCWILSFVVGLTPMFGWNNLSAVER----AWA---ANGSMGEPVIKCE +FEKVISMEYMVYFNFFVWVLPPLLLMVLIYLEVFYLIRKQLNKKVSAS--SGDPQKYYGK +ELKIAKSLALILFLFALSWLPLHILNCITLFCPSC--HKPSILTYIAIFLTHGNSAMNPI +VYAFRIQKFRVTFLKIWNDHFRCQPAPPIDEDLPEE------------------------ +----------RPDD---------------------------------------------- +------ +>2 AA2AR_HUMAN Homo sapiens (Human) Membrane receptor->Family A G protein-coupled receptor->Small molecule receptor (family A GPCR +------MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIA +VGVLAIPFAITISTGFCAACHGCLFIACFVLVLTQSSIFSLLAIAIDRYIAIRIPLRYNG +LVTGTRAKGIIAICWVLSFAIGLTPMLGWNN-------CGQPKEGKNHSQGCGEGQVACL +FEDVVPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQK +EVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDC-SHAPLWLMYLAIVLSHTNSVVNPF +IYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVW +ANGSAPHPERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQ +DGAGVS +>3 AA3R_HUMAN Homo sapiens (Human) Membrane receptor->Family A G protein-coupled receptor->Small molecule receptor (family A GPCR) +MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTTTFYFIVSLALADIA +VGVLVMPLAIVVSLGITIHFYSCLFMTCLLLIFTHASIMSLLAIAVDRYLRVKLTVRYKR +VTTHRRIWLALGLCWLVSFLVGLTPMFGWNMKLTSEYH-------------RNVTFLSCQ +FVSVMRMDYMVYFSFLTWIFIPLVVMCAIYLDIFYIIRNKLSLNLSN---SKETGAFYGR +EFKTAKSLFLVLFLFALSWLPLSIINCIIYFNG----EVPQLVLYMGILLSHANSMMNPI +VYAYKIKKFKETYLLILKACVVCHPSDSLDTSIEKNSE---------------------- +------------------------------------------------------------ +------ From c76c5554645121f229c89817c6b0490e52a77744 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 7 Nov 2022 22:04:22 +0000 Subject: [PATCH 47/62] T032: Set email to None; more formatting --- .../talktorial.ipynb | 1512 ++++++++++++++--- 1 file changed, 1281 insertions(+), 231 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index c5d48a26..77f5468d 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -55,7 +55,7 @@ " * Calculate compound descriptors\n", "* Proteochemometrics modeling\n", " * Helper functions\n", - " * Preprocessing\n", + " * Preprocessing\n", " * Model training and validation\n", " * Random split PCM model\n", " * Random split QSAR models\n", @@ -88,7 +88,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -119,27 +118,21 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "### Data preparation" ] }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "#### Papyrus dataset" ] }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "The Papyrus dataset is a highly curated compilation of bioactivity data intended for modeling in drug discovery. Apart from the bioactivity data contained in the [ChEMBL database](https://www.ebi.ac.uk/chembl/) (see also Talktorial T001), the Papyrus dataset contains binary data for classification tasks from the [ExCAPE-DB](https://solr.ideaconsult.net/search/excape/), and bioactivity data from a number of kinase-specific papers (Figure 1). The Papyrus dataset consists of almost 60M compound-protein pairs, representing data of around 1.2M unique compounds and 7K proteins across 499 different organisms.\n", "\n", @@ -148,9 +141,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "\n", "\n", @@ -162,7 +153,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -174,7 +164,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -194,9 +183,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "#### Protein encoding: protein descriptors" ] @@ -204,7 +191,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -213,6 +199,7 @@ "As done for molecules, the proteins of interest need to be converted to a list of features or protein descriptors. Protein descriptors used in PCM applications are commonly based on the protein sequence and represent physicochemical characteristics of the amino acids that make up the sequence (e.g. Z-scales). Other protein descriptors represent topological (e.g. ST-scales) or electrostatic properties (e.g. MS-WHIM) of the protein sequence. Moreover, if structural information is available, protein descriptors can be derived from the 3D structure of the protein (e.g. sPairs) or the ligand-protein interaction in 3D (e.g. interaction fingerprints). Finally, with the widespread use of deep learning, protein embeddings can be obtained after parsing the protein sequence through the network (e.g. UniRep, AlphaFold embeddings). To read more about protein descriptors, check out these selection of articles ([Brief. Bioinform.,18, (2017)](https://pubmed.ncbi.nlm.nih.gov/26873661/), [Int. J. Mol. Sci., 22, (2021)](https://pubmed.ncbi.nlm.nih.gov/34884688/), [Comput. Struct. Biotechnol. J., 20, (2022)](https://pubmed.ncbi.nlm.nih.gov/35222841/)).\n", "\n", "For protein descriptors based on the protein sequence, an aspect to take into account is that for ML the length of the protein descriptor needs to be the same. However, most proteins do not have the same sequence length. To solve this issue, there are two main approaches:\n", + "\n", "* Multiple sequence alignment (MSA): If the entire protein is to be included in the model, a MSA can be performed. The final descriptor has as many entries as the number of features per amino acid multiplied by the number of aligned positions. To account for gaps in the alignment, zeros are introduced in the descriptor. A MSA is a tool to identify common patterns between three or more biological sequences, usually DNA, RNA, or protein. One of the most common tools to perform MSA is Clustal Omega (or ClustalO), available as a [webtool](https://www.ebi.ac.uk/Tools/msa/clustalo/).\n", "* Binding pocket selection: To avoid unnecessary features, a binding pocket of the same length can be selected for each protein. Normally, the binding pocket selection is preceded by a multiple sequence alignment and driven by known structural or mutagenesis data.\n", "\n", @@ -225,7 +212,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -236,18 +222,16 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "The ML principles for PCM modeling are equivalent to those explained for QSAR modeling. However, in this talktorial we will explore a supervised ML application other than classification, this is regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values.\n", - "NOTE: Target variable is the variable we want to predict in ML. Not to be confused with (protein) target." + "\n", + "**Note**: Target variable is the variable we want to predict in ML. Not to be confused with (protein) target." ] }, { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -258,11 +242,10 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "Similarly to classification tasks, in supervised ML regression applications the model is first fitted to a training set and subsequently the predictive performance is evaluated on a test set. Therefore, the original dataset needs to be split between training and test sets. The split needs to ensure that the fitting process has enough data, and that the test set is representative. Normally, the distribution between train and test set is 80/20 or 70/30. Depending on the applicability domain, the split can be done in multiple ways. In PCM modeling, some of the most common splitting methods are:\n", + "\n", "* Random split: This method is not particularly relevant in drug discovery applications as it does not reflect the reality of a drug discovery campaign and it will most likely lead to data leaks between the training and test set. This is, very similar data will be found in both sets, which will lead to an overestimation of the predictive performance of the model. This type of split is commonly used, however, as a baseline and point of reference for other splitting methods, or as a starting point for quick model comparisons.\n", "* Leave one target out (LOTO) split: To evaluate the ability of the model to extrapolate to targets not previously seen, one of the targets can be completely moved to the test set. In a big enough set, instead of one \"some\" targets can be moved to the test set (i.e. Leave some targets out, or LSTO).\n", "* Leave one compound cluster out (LOCCO) split: This method evaluates the ability of the model to extrapolate to compounds with properties not previously seen by the model. Clustering can be done based on different molecular characteristics, such as physicochemical properties or scaffold, for example (see Talktorial T005 to learn more about clustering). One (or several, LSCCO) cluster(s) can then be left out for testing. This method prevents data leaking in terms of chemistry between training and test sets.\n", @@ -273,7 +256,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -289,7 +271,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -300,9 +281,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "\n", "To evaluate the predictive performance of a regression model, there are several metrics that in simple terms measure the differences between the true target values and the predictions made by the model. These metrics can be used in cross-validation on the training set (see Talktorial T007) or in the test set. The most commonly used metrics include:\n", @@ -313,16 +292,12 @@ "* Mean squared error (MSE): Measures the average of the squares of the difference between the predicted and the true values. It varies between infinite and 0.0 (best).\n", "* Root mean square error (RMSE): It is the square root of the MSE and represents the standard deviation of the prediction errors with respect to the line of best fit. RMSE is a measure of accuracy and it cannot be applied to compare between datasets, as it is scale-dependent. It varies between infinite and 0.0 (best).\n", "\n", - "To learn more about evaluation metrics, you can consult scikit learn's regression metrics [Docs](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics).\n", - "\n", - "\n", - "\n" + "To learn more about evaluation metrics, you can consult scikit learn's regression metrics [Docs](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics)." ] }, { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -334,7 +309,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -347,7 +321,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -359,7 +332,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -402,19 +374,17 @@ "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", - "\n", "from sklearn.preprocessing import RobustScaler\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.metrics import r2_score, mean_absolute_error\n", "from scipy.stats import pearsonr\n", - "\n", "import Bio\n", "import Bio.SeqIO as Bio_SeqIO\n", - "import papyrus_scripts\n", "from rdkit import Chem\n", "import rich\n", "import rich_msa\n", + "import papyrus_scripts\n", "import mordred\n", "import mordred.descriptors as mordred_descriptors\n", "import prodec" @@ -433,27 +403,45 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, + "source": [ + "**Note**: We will lateron use the ClustalO web service to align multiple sequences. In order to use the service, we need to provide an email address (see the [docs](https://www.ebi.ac.uk/seqdb/confluence/display/JDSAT/Clustal+Omega+Help+and+Documentation)).\n", + "Please set your email address here; for the purpose of this template talktorial, we set the email to `None` and use pre-calculated data (see \"Practical\" section of this talktoria and [this discussion](https://github.com/volkamerlab/teachopencadd/discussions/283))." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Set your email for the ClustalO service\n", + "# Replace None with your email address\n", + "MY_EMAIL = None" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, "source": [ "### Download Papyrus dataset" ] }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "To work with the Papyrus dataset, we use the papyrus_scripts [library](https://github.com/OlivierBeq/Papyrus-scripts). This library allows us to download, read, and explore the dataset. Many other features, including bioactivity modeling, are possible using the papyrus_scripts. If you want to dive into them, feel free to follow the [notebook with simple examples](https://github.com/OlivierBeq/Papyrus-scripts/blob/master/notebook_examples/simple_examples.ipynb). By default, the download_papyrus function retrieves bioactivity, target and other information for the latest version of the Papyrus dataset. The data retrieved consists of the highest quality continuous bioactivity data (Papyrus++) without stereochemistry annotated (i.e. nostereo=True and stereo=False). Check out the [documentation](https://github.com/OlivierBeq/Papyrus-scripts/blob/master/src/papyrus_scripts/download.py) to learn more about the options available." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -466,9 +454,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -484,18 +475,29 @@ }, { "data": { - "text/plain": "Donwloading version 05.5: 0%| | 0.00/118M [00:00pchembl_value_Mean variable).\n", "\n", - "|Receptor|Uniprot accession|\n", + "|Receptor|UniProt accession|\n", "|---|---|\n", "|A1|P30542|\n", "|A2A|P29274|\n", @@ -542,9 +542,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -560,7 +563,7 @@ " papyrus_version : str\n", " Version of the Papyrus dataset to read\n", " targets : dict\n", - " Dictionary with target labels as keys and Uniprot accession codes as values\n", + " Dictionary with target labels as keys and UniProt accession codes as values\n", "\n", " Returns\n", " -------\n", @@ -610,9 +613,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -620,12 +626,14 @@ "outputs": [ { "data": { - "text/plain": " 0%| | 0/13 [00:00", - "image/png": 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\n" 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+ "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ - "# Define the set of receptors of interest with a label and their Uniprot accession\n", + "# Define the set of receptors of interest with a label and their UniProt accession\n", "adenosine_receptors = {\"A1\": \"P30542\", \"A2A\": \"P29274\", \"A2B\": \"P29275\", \"A3\": \"P0DMS8\"}\n", "\n", "# Filter the Papyrus bioactivity dataset and plot the distribution of activity values for the targets of interest\n", @@ -661,23 +671,26 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } }, "source": [ "For PCM modeling, we keep from our bioactivity dataset three variables:\n", + "\n", "* Bioactivity (pchembl_value_mean), which is our target variable to predict\n", - "* Target IDs (accession), which is the Uniprot code to link the protein descriptors that we will calculate with ProDEC\n", + "* Target IDs (accession), which is the UniProt code to link the protein descriptors that we will calculate with ProDEC\n", "* Compound IDs (SMILES), to link the compound descriptors that we will calculate with Mordred" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -685,10 +698,82 @@ "outputs": [ { "data": { - "text/plain": " SMILES accession \\\n222 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n223 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n383 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n462 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n464 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n\n pchembl_value_Mean \n222 8.6800 \n223 6.6800 \n383 4.8200 \n462 7.1515 \n464 5.6500 ", - "text/html": "
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SMILESaccessionpchembl_value_Mean
222Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.6800
223Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.6800
383Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.8200
462O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.1515
464O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.6500
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SMILESaccessionpchembl_value_Mean
222Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.6800
223Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.6800
383Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.8200
462O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.1515
464O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.6500
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" + ], + "text/plain": [ + " SMILES accession \\\n", + "222 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n", + "223 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n", + "383 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n", + "462 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n", + "464 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n", + "\n", + " pchembl_value_Mean \n", + "222 8.6800 \n", + "223 6.6800 \n", + "383 4.8200 \n", + "462 7.1515 \n", + "464 5.6500 " + ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -701,7 +786,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -712,19 +796,20 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ - "In order to ensure protein descriptors are of the same length, we first need to align the target sequences. We do this by creating a MSA with the software Clustal Omega (ClustalO). To begin with, we extract the protein sequences from the target files in Papyrus. The sequences could also be obtained from Uniprot, but this way we ensure we are always retrieving the canonical isoform sequence.\n", - "Since Papyrus also contains bioactivity data for different mutants and species, the main protein identifier (target_id variable) consists of the Uniprot accession code and the mutant ('WT' for wild type). Even though we are interested in the wild type, to map our targets of interest we calculate a new variable called accession to be consistent with the rest of the talktorial." + "In order to ensure protein descriptors are of the same length, we first need to align the target sequences. We do this by creating a MSA with the software Clustal Omega (ClustalO). To begin with, we extract the protein sequences from the target files in Papyrus. The sequences could also be obtained from UniProt, but this way we ensure we are always retrieving the canonical isoform sequence.\n", + "Since Papyrus also contains bioactivity data for different mutants and species, the main protein identifier (target_id variable) consists of the UniProt accession code and the mutant ('WT' for wild type). Even though we are interested in the wild type, to map our targets of interest we calculate a new variable called accession to be consistent with the rest of the talktorial." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -732,17 +817,117 @@ "outputs": [ { "data": { - "text/plain": " target_id HGNC_symbol UniProtID Status Organism \\\n47 P29275_WT ADORA2B AA2BR_HUMAN reviewed Homo sapiens (Human) \n80 P30542_WT ADORA1 AA1R_HUMAN reviewed Homo sapiens (Human) \n81 P29274_WT ADORA2A AA2AR_HUMAN reviewed Homo sapiens (Human) \n82 P0DMS8_WT ADORA3 AA3R_HUMAN reviewed Homo sapiens (Human) \n\n Classification Length \\\n47 Membrane receptor->Family A G protein-coupled ... 332 \n80 Membrane receptor->Family A G protein-coupled ... 326 \n81 Membrane receptor->Family A G protein-coupled ... 412 \n82 Membrane receptor->Family A G protein-coupled ... 318 \n\n Sequence accession \n47 MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL... P29275 \n80 MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC... P30542 \n81 MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV... P29274 \n82 MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT... P0DMS8 ", - "text/html": "
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target_idHGNC_symbolUniProtIDStatusOrganismClassificationLengthSequenceaccession
47P29275_WTADORA2BAA2BR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...332MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL...P29275
80P30542_WTADORA1AA1R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...326MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...P30542
81P29274_WTADORA2AAA2AR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...412MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV...P29274
82P0DMS8_WTADORA3AA3R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...318MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT...P0DMS8
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target_idHGNC_symbolUniProtIDStatusOrganismClassificationLengthSequenceaccession
47P29275_WTADORA2BAA2BR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...332MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL...P29275
80P30542_WTADORA1AA1R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...326MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC...P30542
81P29274_WTADORA2AAA2AR_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...412MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV...P29274
82P0DMS8_WTADORA3AA3R_HUMANreviewedHomo sapiens (Human)Membrane receptor->Family A G protein-coupled ...318MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT...P0DMS8
\n", + "
" + ], + "text/plain": [ + " target_id HGNC_symbol UniProtID Status Organism \\\n", + "47 P29275_WT ADORA2B AA2BR_HUMAN reviewed Homo sapiens (Human) \n", + "80 P30542_WT ADORA1 AA1R_HUMAN reviewed Homo sapiens (Human) \n", + "81 P29274_WT ADORA2A AA2AR_HUMAN reviewed Homo sapiens (Human) \n", + "82 P0DMS8_WT ADORA3 AA3R_HUMAN reviewed Homo sapiens (Human) \n", + "\n", + " Classification Length \\\n", + "47 Membrane receptor->Family A G protein-coupled ... 332 \n", + "80 Membrane receptor->Family A G protein-coupled ... 326 \n", + "81 Membrane receptor->Family A G protein-coupled ... 412 \n", + "82 Membrane receptor->Family A G protein-coupled ... 318 \n", + "\n", + " Sequence accession \n", + "47 MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFL... P29275 \n", + "80 MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFC... P30542 \n", + "81 MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVV... P29274 \n", + "82 MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT... P0DMS8 " + ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "protein_data = papyrus_scripts.read_protein_set(version=PAPYRUS_VERSION)\n", - "# Create new variable 'accession' with the Uniprot accession codes by splitting target_id and keeping the first part\n", + "# Create new variable 'accession' with the UniProt accession codes by splitting target_id and keeping the first part\n", "protein_data[\"accession\"] = protein_data[\"target_id\"].apply(lambda x: x.split(\"_\")[0])\n", "# Filter protein data for our targets of interest based on accession code\n", "targets = protein_data[protein_data.accession.isin(adenosine_receptors.values())]\n", @@ -751,18 +936,19 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "In order to align the sequences with ClustalO, we first need to write them into a FASTA file." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -788,7 +974,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -799,9 +984,51 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "def align(entry_name):\n", + " \"\"\"\n", + " From the GPCRdb, get protein annotations associated with the input UniProt entry name.\n", + "\n", + " Parameters\n", + " ----------\n", + " entry_name : str\n", + " UniProt entry name for GPCR of interest.\n", + "\n", + " Returns\n", + " -------\n", + " dict\n", + " Protein annotations deposited in the GPCRdb.\n", + " \"\"\"\n", + " \n", + " url = f\"https://www.ebi.ac.uk/Tools/common/tools/help/index.html?tool=clustalo#!/Submit32job/post_run\"\n", + " url = f\"https://gpcrdb.org/services/protein/{entry_name}/\"\n", + " response = requests.get(url)\n", + " data = response.json()\n", + " return data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "If you are running this notebook on your own data, please set your email address in the `MY_EMAIL` variable.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -811,40 +1038,26 @@ "name": "stdout", "output_type": "stream", "text": [ - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "RUNNING\n", - "FINISHED\n", - "Creating result file: data\\aligned_sequences.out.txt\n", - "Creating result file: data\\aligned_sequences.sequence.txt\n", - "Creating result file: data\\aligned_sequences.aln-fasta.fasta\n", - "Creating result file: data\\aligned_sequences.tree.dnd\n", - "Creating result file: data\\aligned_sequences.phylotree.ph\n", - "Creating result file: data\\aligned_sequences.pim.pim\n", - "Creating result file: data\\aligned_sequences.submission.params\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "JobId: clustalo-R20221028-171535-0775-74892409-p2m\n" + "Email (MY_EMAIL) was not set; use pre-calculated alignments.\n" ] } ], "source": [ - "# Query ClustalO webservice from command line\n", - "!python scripts/clustalo.py --email m.gorostiola.gonzalez@lacdr.leidenuniv.nl --stype protein --sequence data/sequences.fasta --outfmt fa --outdir data --outfile aligned_sequences --order input --pollFreq 20" + "%%bash -s \"$MY_EMAIL\"\n", + "# ClustalO service requires an email address\n", + "# If email was set, run ClustalO service, else use pre-calculated alignments\n", + "if [ $1 != \"None\" ]; then\n", + " echo \"Email (MY_EMAIL) was set to \"$1\n", + " # Query ClustalO webservice from command line\n", + " python scripts/clustalo.py --email $1 --stype protein --sequence data/sequences.fasta --outfmt fa --outdir data --outfile aligned_sequences --order input --pollFreq 20\n", + "else\n", + " echo \"Email (MY_EMAIL) was not set; use pre-calculated alignments.\"\n", + "fi" ] }, { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -855,9 +1068,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -871,7 +1087,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -882,9 +1097,12 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -892,8 +1110,63 @@ "outputs": [ { "data": { - "text/plain": "╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA2BR_H… \u001B[1;36m 1\u001B[0m 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│\n│ 3 AA3R_HU… \u001B[1;36m319\u001B[0m 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│\n╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n", - "text/html": "
╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n│ 0 AA2BR_H…     1  -----MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVAVGLFAIPFAITISLGFCTDFYGCLFLACFVLV  │\n│ 1 AA1R_HU…     1  ---MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFCFIVSLAVADVAVGALVIPLAILINIGPQTYFHTCLMVACPVLI  │\n│ 2 AA2AR_H…     1  ------MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLV  │\n│ 3 AA3R_HU…     1  MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTTTFYFIVSLALADIAVGVLVMPLAIVVSLGITIHFYSCLFMTCLLLI  │\n│                                                                                                                 │\n│ 0 AA2BR_H…    88  LTQSSIFSLLAVAVDRYLAICVPLRYKSLVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCC---LVKCLFENV  │\n│ 1 AA1R_HU…    90  LTQSSILALLAIAVDRYLRVKIPLRYKMVVTPRRAAVAIAGCWILSFVVGLTPMFGWNNLSAVER----AWA---ANGSMGEPVIKCEFEKV  │\n│ 2 AA2AR_H…    87  LTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWNN-------CGQPKEGKNHSQGCGEGQVACLFEDV  │\n│ 3 AA3R_HU…    93  FTHASIMSLLAIAVDRYLRVKLTVRYKRVTTHRRIWLALGLCWLVSFLVGLTPMFGWNMKLTSEYH-------------RNVTFLSCQFVSV  │\n│                                                                                                                 │\n│ 0 AA2BR_H…   177  VPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTEL----MDHSRTTLQREIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQG  │\n│ 1 AA1R_HU…   175  ISMEYMVYFNFFVWVLPPLLLMVLIYLEVFYLIRKQLNKKVSAS--SGDPQKYYGKELKIAKSLALILFLFALSWLPLHILNCITLFCPSC-  │\n│ 2 AA2AR_H…   172  VPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDC-  │\n│ 3 AA3R_HU…   172  MRMDYMVYFSFLTWIFIPLVVMCAIYLDIFYIIRNKLSLNLSN---SKETGAFYGREFKTAKSLFLVLFLFALSWLPLSIINCIIYFNG---  │\n│                                                                                                                 │\n│ 0 AA2BR_H…   265  KNKPKWAMNMAILLSHANSVVNPIVYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQ----------AGVQPALGVGL--------------  │\n│ 1 AA1R_HU…   264  -HKPSILTYIAIFLTHGNSAMNPIVYAFRIQKFRVTFLKIWNDHFRCQPAPPIDEDLPEE--------------------------------  │\n│ 2 AA2AR_H…   263  SHAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHP  │\n│ 3 AA3R_HU…   258  -EVPQLVLYMGILLSHANSMMNPIVYAYKIKKFKETYLLILKACVVCHPSDSLDTSIEKNSE------------------------------  │\n│                                                                                                                 │\n│ 0 AA2BR_H…   333  ----------------------------------------------------------                                    │\n│ 1 AA1R_HU…   323  --RPDD----------------------------------------------------                                    │\n│ 2 AA2AR_H…   355  ERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS                                    │\n│ 3 AA3R_HU…   319  ----------------------------------------------------------                                    │\n╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n
\n" + "text/html": [ + "
╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n",
+       "│ 0 AA2BR_H…     1  -----MLLETQDALYVALELVIAALSVAGNVLVCAAVGTANTLQTPTNYFLVSLAAADVAVGLFAIPFAITISLGFCTDFYGCLFLACFVLV  │\n",
+       "│ 1 AA1R_HU…     1  ---MPPSISAFQAAYIGIEVLIALVSVPGNVLVIWAVKVNQALRDATFCFIVSLAVADVAVGALVIPLAILINIGPQTYFHTCLMVACPVLI  │\n",
+       "│ 2 AA2AR_H…     1  ------MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLV  │\n",
+       "│ 3 AA3R_HU…     1  MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTTTFYFIVSLALADIAVGVLVMPLAIVVSLGITIHFYSCLFMTCLLLI  │\n",
+       "│                                                                                                                 │\n",
+       "│ 0 AA2BR_H…    88  LTQSSIFSLLAVAVDRYLAICVPLRYKSLVTGTRARGVIAVLWVLAFGIGLTPFLGWNSKDSATNNCTEPWDGTTNESCC---LVKCLFENV  │\n",
+       "│ 1 AA1R_HU…    90  LTQSSILALLAIAVDRYLRVKIPLRYKMVVTPRRAAVAIAGCWILSFVVGLTPMFGWNNLSAVER----AWA---ANGSMGEPVIKCEFEKV  │\n",
+       "│ 2 AA2AR_H…    87  LTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWNN-------CGQPKEGKNHSQGCGEGQVACLFEDV  │\n",
+       "│ 3 AA3R_HU…    93  FTHASIMSLLAIAVDRYLRVKLTVRYKRVTTHRRIWLALGLCWLVSFLVGLTPMFGWNMKLTSEYH-------------RNVTFLSCQFVSV  │\n",
+       "│                                                                                                                 │\n",
+       "│ 0 AA2BR_H…   177  VPMSYMVYFNFFGCVLPPLLIMLVIYIKIFLVACRQLQRTEL----MDHSRTTLQREIHAAKSLAMIVGIFALCWLPVHAVNCVTLFQPAQG  │\n",
+       "│ 1 AA1R_HU…   175  ISMEYMVYFNFFVWVLPPLLLMVLIYLEVFYLIRKQLNKKVSAS--SGDPQKYYGKELKIAKSLALILFLFALSWLPLHILNCITLFCPSC-  │\n",
+       "│ 2 AA2AR_H…   172  VPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDC-  │\n",
+       "│ 3 AA3R_HU…   172  MRMDYMVYFSFLTWIFIPLVVMCAIYLDIFYIIRNKLSLNLSN---SKETGAFYGREFKTAKSLFLVLFLFALSWLPLSIINCIIYFNG---  │\n",
+       "│                                                                                                                 │\n",
+       "│ 0 AA2BR_H…   265  KNKPKWAMNMAILLSHANSVVNPIVYAYRNRDFRYTFHKIISRYLLCQADVKSGNGQ----------AGVQPALGVGL--------------  │\n",
+       "│ 1 AA1R_HU…   264  -HKPSILTYIAIFLTHGNSAMNPIVYAFRIQKFRVTFLKIWNDHFRCQPAPPIDEDLPEE--------------------------------  │\n",
+       "│ 2 AA2AR_H…   263  SHAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHP  │\n",
+       "│ 3 AA3R_HU…   258  -EVPQLVLYMGILLSHANSMMNPIVYAYKIKKFKETYLLILKACVVCHPSDSLDTSIEKNSE------------------------------  │\n",
+       "│                                                                                                                 │\n",
+       "│ 0 AA2BR_H…   333  ----------------------------------------------------------                                    │\n",
+       "│ 1 AA1R_HU…   323  --RPDD----------------------------------------------------                                    │\n",
+       "│ 2 AA2AR_H…   355  ERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS                                    │\n",
+       "│ 3 AA3R_HU…   319  ----------------------------------------------------------                                    │\n",
+       "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n",
+       "
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│\n", + "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n" + ] }, "metadata": {}, "output_type": "display_data" @@ -914,7 +1187,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -933,7 +1205,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -944,9 +1215,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -969,9 +1243,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -986,9 +1263,16 @@ }, { "data": { - "text/plain": "{'Authors': 'Hellberg, Sjöström, Skagerberg, Wold',\n 'Year': 1987,\n 'Journal': 'Journal of Medicinal Chemistry',\n 'DOI': '10.1021/jm00390a003',\n 'PMID': None,\n 'Patent': None}" + "text/plain": [ + "{'Authors': 'Hellberg, Sjöström, Skagerberg, Wold',\n", + " 'Year': 1987,\n", + " 'Journal': 'Journal of Medicinal Chemistry',\n", + " 'DOI': '10.1021/jm00390a003',\n", + " 'PMID': None,\n", + " 'Patent': None}" + ] }, - "execution_count": 14, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1001,9 +1285,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -1042,9 +1329,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -1052,22 +1342,193 @@ "outputs": [ { "data": { - "text/plain": " 0%| | 0/4 [00:00\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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" + ], + "text/plain": [ + " SMILES ABC ABCGG nAcid \\\n", + "0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... 21.041 17.684 0 \n", + "1 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... 20.701 15.635 0 \n", + "2 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 23.23 17.456 0 \n", + "3 CCNC(=O)C1OC(n2cnc3c2ncnc3Nc2ccc(OCC(=O)Nc3ccc... 31.336 22.213 0 \n", + "4 NC(=NC(=O)Cn1c(O)c2CCCCc2c1O)Nc1nc2c(cccc2)s1 21.408 17.066 0 \n", + "\n", + " nBase nAtom nHeavyAtom nSpiro nBridgehead nHetero ... nN nO nS \\\n", + "0 1 51 27 0 0 8 ... 6 2 0 \n", + "1 0 42 26 0 0 8 ... 4 3 1 \n", + "2 0 43 29 0 0 8 ... 6 2 0 \n", + "3 0 66 40 0 0 14 ... 7 6 0 \n", + "4 3 46 27 0 0 9 ... 5 3 1 \n", + "\n", + " nP nF nCl nBr nI nX BalabanJ \n", + "0 0 0 0 0 0 0 1.631 \n", + "1 0 0 0 0 0 0 1.307 \n", + "2 0 0 0 0 0 0 1.328 \n", + "3 0 0 1 0 0 1 1.043 \n", + "4 0 0 0 0 0 0 1.234 \n", + "\n", + "[5 rows x 23 columns]" + ] }, - "execution_count": 18, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -1214,7 +1869,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -1226,7 +1880,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -1235,6 +1888,7 @@ "When our dataset is complete with all the descriptors for proteins and compounds, we can start with the modeling part. Here, we will use a Random Forest (RF) ML regression model to predict the bioactivity of our compound-target pairs.\n", "\n", "We will try two methods to split our dataset between training and test set:\n", + "\n", "* Random split\n", "* Leave one target out (LOTO) split\n", "\n", @@ -1248,7 +1902,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -1260,7 +1913,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -1271,9 +1923,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -1298,7 +1953,7 @@ " loto_target : str\n", " Target label to leave out for testing in 'loto' split method\n", " loto_accession : str\n", - " Target Uniprot accession to leave out for testing in 'loto' split method\n", + " Target UniProt accession to leave out for testing in 'loto' split method\n", "\n", " Returns\n", " -------\n", @@ -1339,7 +1994,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -1350,9 +2004,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -1388,7 +2045,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -1399,9 +2055,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -1441,7 +2100,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -1452,9 +2110,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -1533,7 +2194,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -1544,9 +2204,12 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -1564,7 +2227,7 @@ " target : str\n", " Target label for QSAR model\n", " accession: str\n", - " Target Uniprot accession for QSAR model\n", + " Target UniProt accession for QSAR model\n", " test_size: float\n", " Ratio of the data to include in the test set upon random split\n", "\n", @@ -1608,32 +2271,33 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } }, "source": [ - "##### Preprocessing" + "#### Preprocessing" ] }, { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } }, "source": [ - "For each compound-target pair in our bioactivity dataset, we need to add the protein and molecular features previously calculated. We join the protein features based on Uniprot accession and the molecular features based on SMILES." + "For each compound-target pair in our bioactivity dataset, we need to add the protein and molecular features previously calculated. We join the protein features based on UniProt accession and the molecular features based on SMILES." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 26, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -1641,10 +2305,200 @@ "outputs": [ { "data": { - "text/plain": " SMILES accession \\\n0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n1 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n2 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... 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SMILESaccessionpchembl_value_MeanABCABCGGnAcidnBasenAtomnHeavyAtomnSpiro...nNnOnSnPnFnClnBrnInXBalabanJ
0Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.680021.04117.6840151270...6200000001.631
1Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.680021.04117.6840151270...6200000001.631
2Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.820020.70115.6350042260...4310000001.307
3O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.151523.2317.4560043290...6200000001.328
4O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.650023.2317.4560043290...6200000001.328
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SMILESaccessionpchembl_value_MeanABCABCGGnAcidnBasenAtomnHeavyAtomnSpiro...nNnOnSnPnFnClnBrnInXBalabanJ
0Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P292748.680021.04117.6840151270...6200000001.631
1Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(...P305426.680021.04117.6840151270...6200000001.631
2Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC...P292744.820020.70115.6350042260...4310000001.307
3O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P0DMS87.151523.2317.4560043290...6200000001.328
4O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1P292745.650023.2317.4560043290...6200000001.328
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5 rows × 25 columns

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" + ], + "text/plain": [ + " SMILES accession \\\n", + "0 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P29274 \n", + "1 Cc1nn(-c2cc(NC(=O)CCN(C)C)nc(-c3ccc(C)o3)n2)c(... P30542 \n", + "2 Nc1c(C(=O)Nc2ccc([N+](=O)[O-])cc2)sc2c1cc1CCCC... P29274 \n", + "3 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P0DMS8 \n", + "4 O=C(Nc1nc2ncccc2n2c(=O)n(-c3ccccc3)nc12)c1ccccc1 P29274 \n", + "\n", + " pchembl_value_Mean ABC ABCGG nAcid nBase nAtom nHeavyAtom \\\n", + "0 8.6800 21.041 17.684 0 1 51 27 \n", + "1 6.6800 21.041 17.684 0 1 51 27 \n", + "2 4.8200 20.701 15.635 0 0 42 26 \n", + "3 7.1515 23.23 17.456 0 0 43 29 \n", + "4 5.6500 23.23 17.456 0 0 43 29 \n", + "\n", + " nSpiro ... nN nO nS nP nF nCl nBr nI nX BalabanJ \n", + "0 0 ... 6 2 0 0 0 0 0 0 0 1.631 \n", + "1 0 ... 6 2 0 0 0 0 0 0 0 1.631 \n", + "2 0 ... 4 3 1 0 0 0 0 0 0 1.307 \n", + "3 0 ... 6 2 0 0 0 0 0 0 0 1.328 \n", + "4 0 ... 6 2 0 0 0 0 0 0 0 1.328 \n", + "\n", + "[5 rows x 25 columns]" + ] }, - "execution_count": 25, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -1699,7 +2745,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -1722,7 +2767,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -1733,9 +2777,12 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 28, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -1759,7 +2806,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1768,16 +2815,18 @@ "text": [ "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.6857487308961996,\n", - " \"R2 score\": 0.46467514184684344,\n", - " \"MAE\": 0.6424885455055979\n", + " \"Pearson r\": 0.6838750730061094,\n", + " \"R2 score\": 0.4629920934701346,\n", + " \"MAE\": 0.6424857789562605\n", "}\n" ] }, { "data": { - "text/plain": "
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\n" 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\n", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -1791,7 +2840,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -1803,9 +2851,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "##### Random split QSAR models" ] @@ -1813,7 +2859,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -1824,9 +2869,12 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -1838,34 +2886,36 @@ "text": [ "=== QSAR model performance A1 ===\n", "{\n", - " \"Pearson r\": 0.5985317411966065,\n", - " \"R2 score\": 0.3558927132291356,\n", - " \"MAE\": 0.5958968533575324\n", + " \"Pearson r\": 0.6061543418780471,\n", + " \"R2 score\": 0.3663100021241771,\n", + " \"MAE\": 0.5906718239123281\n", "}\n", "=== QSAR model performance A2A ===\n", "{\n", - " \"Pearson r\": 0.6306127424850827,\n", - " \"R2 score\": 0.3959873598820135,\n", - " \"MAE\": 0.6994656717509705\n", + " \"Pearson r\": 0.6362163694962808,\n", + " \"R2 score\": 0.40386774292895244,\n", + " \"MAE\": 0.6906853759160689\n", "}\n", "=== QSAR model performance A2B ===\n", "{\n", - " \"Pearson r\": 0.7074095194000876,\n", - " \"R2 score\": 0.48947563102683467,\n", - " \"MAE\": 0.5534574461539086\n", + " \"Pearson r\": 0.7015289644139334,\n", + " \"R2 score\": 0.48148295104576777,\n", + " \"MAE\": 0.5557563859787753\n", "}\n", "=== QSAR model performance A3 ===\n", "{\n", - " \"Pearson r\": 0.6644154212320273,\n", - " \"R2 score\": 0.4388718113230262,\n", - " \"MAE\": 0.6880455126649877\n", + " \"Pearson r\": 0.6627839368015453,\n", + " \"R2 score\": 0.43602890319497334,\n", + " \"MAE\": 0.6918106133161609\n", "}\n" ] }, { "data": { - "text/plain": "
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\n" + "image/png": 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\n", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -1881,7 +2931,6 @@ { "cell_type": "markdown", "metadata": { - "collapsed": false, "pycharm": { "name": "#%% md\n" } @@ -1892,18 +2941,14 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "##### Leave one target out split PCM model" ] }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "The random split PCM model works pretty well. However, a model trained and validated on a random split might overestimate the performance compared to a real life drug discovery scenario since some compounds can be tested in several targets.\n", "Finally, to test whether our PCM model could be used to predict bioactivity data on a target for which we have no previously known bioactivity data, we can train and validate PCM models following the \"leave one target out\" (LOTO) split method. We can do this process for each of the adenosine receptors." @@ -1911,9 +2956,12 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 31, "metadata": { "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, "pycharm": { "name": "#%%\n" } @@ -1929,9 +2977,9 @@ "Test set has 3519 datapoints (27.667 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.24856918182631366,\n", - " \"R2 score\": -0.09625591525773713,\n", - " \"MAE\": 0.8533690296313972\n", + " \"Pearson r\": 0.24073383293582623,\n", + " \"R2 score\": -0.11296092040813255,\n", + " \"MAE\": 0.8604964690588454\n", "}\n", "Not plotting A2A. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2B. Performance can only be plotted for the left out target in LOTO split\n", @@ -1942,9 +2990,9 @@ "Test set has 3991 datapoints (31.378 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.20870773382438598,\n", - " \"R2 score\": -0.04865874342423271,\n", - " \"MAE\": 0.9630896623958625\n", + " \"Pearson r\": 0.18760181006210028,\n", + " \"R2 score\": -0.05928985099256723,\n", + " \"MAE\": 0.9722787268838569\n", "}\n", "Not plotting A1. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2B. Performance can only be plotted for the left out target in LOTO split\n", @@ -1955,9 +3003,9 @@ "Test set has 1988 datapoints (15.63 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.02522068165865813,\n", - " \"R2 score\": -0.22872057130974377,\n", - " \"MAE\": 0.9666788970661387\n", + " \"Pearson r\": 0.014694410599454713,\n", + " \"R2 score\": -0.2494809399878013,\n", + " \"MAE\": 0.9773176706991492\n", "}\n", "Not plotting A1. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2A. Performance can only be plotted for the left out target in LOTO split\n", @@ -1968,9 +3016,9 @@ "Test set has 3221 datapoints (25.324 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.10119697676223516,\n", - " \"R2 score\": -0.25986962080223663,\n", - " \"MAE\": 1.0528712212109281\n", + " \"Pearson r\": 0.10940003095698604,\n", + " \"R2 score\": -0.260020559620582,\n", + " \"MAE\": 1.0512621250471534\n", "}\n", "Not plotting A1. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2A. Performance can only be plotted for the left out target in LOTO split\n", @@ -1979,8 +3027,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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NxYsXefzxx/H09CQwMJDx48ej1+ut58+fP2/zOVhe33333e18FIFAIKiQ/64aR+HIl2iQLKFzg78HteOp7QfvSqEDQuzc0bz77ruMGzeOvXv3cvHiRZtzBw4cICgoiA8++ICjR48ya9YsZsyYwYYNG+z22bt3L0VFRTz11FNs3br1uu7t4+NDamoqKSkpfP311xQWFtKnTx8bMbB7927GjBnD77//zo8//ojBYKBnz54UFhbe0nPfKElJSTz66KM88MADHDp0iJkzZzJ+/Hh27tzp9Bqj0UifPn0oLCxk7969fPLJJ+zcuZPJkyfbrf3pp59ITU21vrp37347H0cgEAicUpifyyfDI7n37Z/w1kFyEBhWzuKpBR9VtWlVyy2PEq3m3O6p55IkSZI2W5KunJSkS39K0pVT5ve3mYKCAsnb21s6ceKENGjQIGnBggUVXjN69GipW7dudsefffZZafr06dK3334rNWrUSDKZTC732bJli+Tr62tz7Msvv5QA6fDhw06vy8jIkABp9+7dFdpamUydOlVq1qyZzbERI0ZInTp1cnrNN998I8nlcik5Odl67OOPP5bc3d2tP0tJSUkSIB06dOi6bRFTzwUCwe0iMe4r6dtuzaVjTc3TyrcNai/lZKZUtVk3TWVOPReenVslNxm2Pw8b7oN3esCGjrDjBfPx28i2bdto2rQpTZs2ZejQoWzZsgWpggH2ubm5+Pv72xzLz89n+/btDB06lIcffpjCwkJ27dp1Q7ZcvXqVjz4y/9WgUqlc3h+ws6Esv/32G15eXi5fS5YsuSH79u3bR8+ePW2O9erVi7/++ouSkhKn17Rq1YqwsDCba4qLizlw4IDN2ieeeILg4GCio6PZsWPHDdkmEAgElcHnK0ZSPHoK9VMktG5w7On7GPjJAWoF1K5q06oFIkH5VtDlwH/HwrlfbI+f/Rm+HAcDNoPa77bcevPmzQwdOhSA3r17U1BQwM8//8xDDz3kcP2+ffv49NNP+frrr22Of/LJJzRp0oSWLVsCMHjwYDZv3ky3bt1c3j83NxcvLy8kSUKr1QLmL/1mzRxPx5UkiUmTJtG5c2datWrldN+OHTuSkJDg8t6uxJIj0tLSCAkJsTkWEhKCwWAgMzOT2rXtfxk4usbPzw83NzfS0tIA8PLyYvXq1URHRyOXy/nyyy8ZNGgQ7733nvXfRiAQCG4nBbnZfD26N20O5ANwOUSG/9wF9O/xVBVbVr0QYudWKLxiL3QsnP3ZfP42iJ2TJ0+yf/9+PvvsMwCUSiWDBg3i3XffdSh2jh49St++fZk7dy4PP/ywzbmyoglg6NChdOnShatXr1KrVi2nNnh7e3Pw4EEMBgO7d+/m1VdfdZn0O3bsWA4fPszevXtdPptaraZx48Yu17jCy8vL+t9Dhw612iSTyWzWWbxg5Y+XxdE5SZKsxwMDA5k4caL1XMeOHcnJyWHFihVC7AgEgtvOod2fkzFvFm3SzL/PjnTwoveGr/HxC65iy6ofQuzcCkV5t3b+Jtm8eTMGg4Hw8GtZ9ZIkoVKpyMnJwc/vmsA6duwY3bt358UXX2T27Nk2+xw7dow//viDP//8k2nTplmPG41GPv74Y0aNGuXUBrlcbhUlzZo1Iy0tjUGDBrFnzx67tePGjePLL79kz5491KlTx+Wz/fbbbzzyyCMu18ycOZOZM2c6PFfWK+Tj4wNAaGio1RtjISMjA6VSSUBAgMN9QkND+eOPP2yO5eTkUFJSYufxKUunTp145513XNovEAgEt8rOxc9T/9N91CuGQne4ODiagTPE7x5nCLFzK3j43Nr5m8BgMBAbG8uqVavs8lD69+/Phx9+yNixYwGzR6d79+4MHz6cxYsX2+21efNmunTpwuuvv25z/P3332fz5s0uxU55Jk6cyOrVq/n888/p168fYBZg48aN4/PPP2fXrl00bFhxS/JbDWM58gpFRkbyv//9z+bYDz/8QMeOHZ3mGEVGRrJ48WJSU1OtYa4ffvgBd3d3OnTo4PT+hw4dchgWEwgEgsogLyeD70Y/SutD5qrWi7VlhCxcyr8f6FvFllVzbjnFuZpzW6uxtNmSFNtPkub52L9i+92WqqzPP/9ccnNzk65evWp3bubMmVLbtm0lSZKkv//+WwoKCpKeeeYZKTU11frKyMiQJEmS9Hq9FBQUJG3cuNFun1OnTkmAlJCQ4NAGR9VYkiRJkyZNklq3bm2t5ho1apTk6+sr7dq1y8YGrVZ7s49/U5w7d07SaDTSxIkTpWPHjkmbN2+WVCqVtGPHDuuazz77TGratKn1vcFgkFq1aiX16NFDOnjwoPTTTz9JderUkcaOHWtds3XrVunDDz+Ujh07Jp04cUJ69dVXJZVKJa1evdqpLaIaSyAQ3Cx//vSx9EOXa9VWnzzzL6kgz/67oKZQmdVYQuzc6hfP1cv2gie2n/n4beCxxx6THn30UYfnDhw4IAHSgQMHpHnz5kmA3at+/fqSJEnSjh07JLlcLqWlpTncq3Xr1tK4ceMcnnMmdi5cuCAplUpp27ZtkiRJDu8PSFu2bLnh575Vdu3aJbVr105yc3OTGjRoYCfytmzZIpXX/hcuXJD69OkjqdVqyd/fXxo7dqxUVFRkPb9161apefPmkkajkby9vaUOHTpI77//vks7hNi5c7haWCydSc+XDl7Ils5k5EtXC4ur2iTBXcz2BcOkv1qbRc4fbZtJn68YUdUm3XYqU+zIJKmCeuU7nLy8PHx9fcnNzbXmcFgoKioiKSmJhg0b4uHhcfM30eWYk5GL8syhK8+g21aFJbizqbSfOcFtJeWqjmk7D/Pb6UzrsS5NAlnWvw1htdRVaJlzcrV6Mgv05BWV4KNWEejphq/GrarNEtwiV7NS+XF0H1ol6gC4EC6jzuJVtOrkOrexJuDq+/tGETk7lYHaT4gbgaCGkKvV2wkdgD2nM5m+8zCvDWlX7UTEnSjOBBXzx3exFCxeSqsr5veJ99ei7xvfo/as/HzQmo5oKigQCARlyCzQ2wkdC3tOZ5JZoHd4rqqoSJzlaquXvYLr49M5g1FNXUrYFcjTwOkRDzP4vX1C6NwkwrMjEAgEZcgrctxV20J+Bef/aa5HnFU3T5TAOdnpl/hlzBO0/rsIgKQ6chouXcf99zluGCu4PqrUs7Nnzx4ef/xxwsLCkMlkfPHFF9ZzJSUlTJs2jdatW+Pp6UlYWBgxMTGkpKRUncECgaDG4+PhfOQJgHcF5/9p7jRxJnDOvq82k9C/Jy3/LsIEJEb50+N/f9JcCJ1bpkrFTmFhIREREQ4ncWu1Wg4ePMicOXM4ePAgn332GadOneKJJ56oAksFAsHdQqCXG12aBDo816VJIIFe1ctLcqeJM4FjPp3RH/WMldTOhFwNJI3tw+B343BXa6ratBpBlYaxHnnkEafdcn19ffnxxx9tjr322mv861//4uLFi9SrV++fMFEgENxl+GrcWNa/DdN3HmZPuYTf5f3bVLuQkEWc7XEQyqqO4kxgS2ZqErvH9KP1sWIAztWT03jFRjq17VLFltUs7qicndzcXGQymcuZTcXFxRQXF1vf5+XdnpENAoGg5hJWS81rQ9qRWaAnv6gEbw8VgV7Vs5T7ThNngmv89tnrGFduoEU2mGRwpHMQ/dZ/J7w5t4E7RuwUFRUxffp0nn76aZf19kuXLmXBggX/oGUCgaAm4qupnuLGEXeSOBOA0WBgx4z+NPv2FG4GuOoF2S88yeBRS6vatBrLHSF2SkpKGDx4MCaTiTfeeMPl2hkzZjBp0iTr+7y8POrWrXu7TRQIBIIq5U4SZ3czaRdPEz9+AG1OmFsCnGkgp8WrbxPZOqqKLavZVHuxU1JSwsCBA0lKSuKXX36psIuiu7s77u7u/5B1AoFAIBBcH7u2rUW29k2a54BRBkceDGXA+u9QuYnvrNtNtW4qaBE6p0+f5qeffiIgIKCqTapWxMfHo1Ao6N27t925xMREhgwZQt26dVGr1TRv3px169bZrNm1axcymcz6UqvVtGzZkrfeesvlfctfFxAQQPfu3YmLi7NZ9/bbb/PAAw/g5+eHn58fDz30EPv377/1B78Jjhw5woMPPoharSY8PJyFCxfialLK+fPneeGFF2jYsCFqtZp77rmHefPmoddfa9CWlZVF7969CQsLw93dnbp16zJ27FiRJyYQCGwwGgx8MqkPfgvfJDgHsr0hecoghmz6VQidf4gq9ewUFBRw5swZ6/ukpCQSEhLw9/cnLCyMAQMGcPDgQb766iuMRiNpaWkA+Pv74+Ym3LXvvvsu48aN45133rGrUDtw4ABBQUF88MEH1K1bl/j4eP7zn/+gUCgYO3aszT4nT57Ex8cHnU7H//73P0aNGsU999xDjx49XN7fct2VK1dYtGgRffr04dSpUwQHBwNmUTRkyBCioqLw8PBgxYoV9OzZk6NHjxIeHl75H4gT8vLyePjhh+nWrRt//vknp06d4tlnn8XT05PJkyc7vObEiROYTCbefPNNGjduzN9//82LL75IYWEhK1euBEAul9O3b18WLVpEUFAQZ86cYcyYMWRnZ/PRRx/9Y88nEAiqL8lJx9g/fjARp839jk43UtBm1VYaNO9YxZbdZdzyKNFb4Ndff3U4FXv48OFSUlKS06nZv/7663Xf47ZPPZck6WrRVenc1XNSYkaidO7qOelq0dVb2u96KCgokLy9vaUTJ05IgwYNkhYsWFDhNaNHj5a6detmfW/5/HNycmzWNWrUSFqxYoXTfRxdd/jwYQmQvvzyS6fXGQwGydvbW3rvvfcqtLUyeeONNyRfX1+bieVLly6VwsLCJJPJdN37rFixQmrYsKHLNevWrZPq1Knj9LyYei4Q3D389MFyac+/zJPKDzdvJn00podkKCmparPuGCpz6nmVena6du3qMpTg6lx1Ia0wjXnx84hPibceiw6LZn7UfEI9Q2/bfbdt20bTpk1p2rQpQ4cOZdy4ccyZMweZTOb0mtzcXPz9/Z2elySJ77//nkuXLnH//fdfty1arZYtW7YAoFI5b2Cm1WopKSlxacPFixdp0aKFy/sNHTqUTZs2Xbd9+/bt48EHH7TJ5erVqxczZszg/PnzNGzY8Lr2qejzS0lJ4bPPPuPBBx+8btsEAkHNw2gwsH1SH1r+dBGlCbJ8QDv6GYY8O7uqTbtrqfYJytWZ3OJcO6EDEJcSx/z4+Szvshxfd9/bcu/NmzczdOhQAHr37k1BQQE///wzDz3kuK34vn37+PTTT/n666/tztWpUwcw9ygymUwsXLiQLl0qbmhluU6r1SJJEh06dHAZ+po+fTrh4eFObQQICwsjISHB5X0rSlIvT1paGg0aNLA5FhISYj13PWLn7NmzvPbaa6xatcru3JAhQ/jvf/+LTqfj8ccf55133rkh+wQCQc3h4ulEDk4cSsQZAwAnGytpv+YD6jWJqGLL7m6E2LkFsouy7YSOhbiUOLKLsm+L2Dl58iT79+/ns88+A0CpVDJo0CDeffddh0Li6NGj9O3bl7lz5/Lwww/bnf/tt9/w9vamuLiY/fv3M3bsWPz9/Rk1apRLO3777Tc8PT05dOgQ06ZNY+vWrU49OytWrODjjz9m165deHh4ON1TqVTSuHFjl/d1RcuWLblw4QIADzzwAN9++y2AncfL4jV05QmzkJKSQu/evXnqqaf4v//7P7vza9asYd68eZw8eZKZM2cyadKkClskCASCmscPWxeheeNDmuZBiQKOPlSfgau+QqEUX7VVjfgXuAXy9fm3dP5m2bx5MwaDwSbJV5IkVCoVOTk5+Pn5WY8fO3aM7t278+KLLzJ7tmMXasOGDa1dqVu2bMkff/zB4sWLKxQ7luvuvfdeioqK6NevH3///bdd6f/KlStZsmQJP/30E23atHG5562Gsb755htKSsyJgGq1GoDQ0FBrcruFjIwM4JqHxxkpKSl069aNyMhIp1VqoaGhhIaG0qxZMwICAnjggQeYM2cOtWvXdrm3QCCoGRgNBra/1IuWv6agNEGmL5S89AJDnp5S1aYJShFi5xbwdvO+pfM3g8FgIDY2llWrVtGzZ0+bc/379+fDDz+0VlsdPXqU7t27M3z4cBYvXnzd91AoFOh0uhuya9iwYSxcuJA33niDiRMnWo+/+uqrLFq0iO+//56OHSuuPrjVMFb9+vXtjkVGRjJz5kz0er21iu+HH34gLCzMLrxVluTkZLp160aHDh3YsmULcnnFnRosHqOyI0sEAkHN5fzxvzg86VkikowAnLhXxb/WfUJ4Q9d/tAn+WYTYuQX8PfyJDosmLiXO7lx0WDT+Hs6TWW+Wr776ipycHF544QV8fW1DZAMGDGDz5s2MHTuWo0eP0q1bN3r27MmkSZOsng2FQkFQUJDNdRkZGRQVFVnDWO+//z4DBgy4IbvkcjkTJkxg0aJFjBgxAo1Gw4oVK5gzZw4fffQRDRo0sNrg5eWFl5eXw31uNYzliKeffpoFCxbw7LPPMnPmTE6fPs2SJUuYO3euNYy1f/9+YmJi+PnnnwkPDyclJYWuXbtSr149Vq5cyZUrV6z7hYaaE8+/+eYb0tPTue+++/Dy8uLYsWNMnTqV6OholyJKIBDUDL5/Zx7eb35Kk3zQK+B4r0Y8teK/ImxVHbnleq5qzu0uPU8tSJVG/DBCarW1lfU14ocRUmpB6q2Y7ZTHHntMevTRRx2eO3DggARIBw4ckObNm+ewbL9+/frW9eVL/5VKpdSwYUNpypQpUkFBgVMbnJWsFxQUSH5+ftLy5cslSZKk+vXrO7Rh3rx5t/ox3DCHDx+WHnjgAcnd3V0KDQ2V5s+fb1N2bnmmpKQkSZIkacuWLU5bH1j45ZdfpMjISMnX11fy8PCQmjRpIk2bNs3ucymLKD0XCO589MVF0kcjHpSONDOXle/q1Ez69dP1VW1WjaMyS89lknQH1HffAnl5efj6+pKbm2sX/igqKiIpKYmGDRu6TJqtiNziXLKLssnX5+Pt5o2/h/9tq8IS3NlU1s+cQCCoGs4eiefYyy/S+LwJgOPN3Ihav4PQek2q2LKah6vv7xtF+NoqAV93XyFuBAKBoIbz9cYZ+G/+gsYFUKyEk482ZcCSHSJsdQcg/oUEAoFAIHBBsU7L5+N60TouE7kEaf6gfHk8g/q5rlgVVB+E2BEIBAKBwAknD+3izLQxRFw0h62OtXTnwQ2fE1j7+jqvC6oHQuwIBAKBQOCAr16bTOCWb2ikhWIVnHq8JQOX7KhqswQ3gRA7AoFAIBCUoVin5fMxD9M6Phs5kBoI6ulTGPjYC1VtmuAmEWJHIBAIBIJSjv/5E0kzxhNx2VyofLS1Bz3e+Aq/oPAKrhRUZ4TYEQgEAoEA+HLNeEJjf6ShDnRucKZvBANf+aSqzRJUAkLsCAQCgeCuRleYx39H9yLij6sApASB16wZDOwdU7WGCSoNIXYEAsENY8jNxZiVhSk/H7m3D4oAf5S+otfUP4ouBwqvQFEeePiCZyCo/Sq+TmDD4fivSZn9MhEp5rDV3xEaHn7jK2oFiEG+NQkhdgQCwQ1RkppGyuzZaOOuzYTTdO5M2CuvoKodWoWW3UXkJsN/x8K5X64du6cHPPEa+Irckuvli1dHEv7hbuoXgdYNkgbcx1NzY6vaLMFtoOIxzoJqS3x8PAqFgt69e9udy8rKonfv3oSFheHu7k7dunUZO3YseXl5Lvds0KABMpkMmUyGWq2mWbNmvPrqq5SdKpKYmMiQIUOoW7cuarWa5s2bs27dukp/vuuhuLiYcePGERgYiKenJ0888QSXL192ec3SpUu577778Pb2Jjg4mCeffJKTJ0/arTt+/DhPPPEEvr6+eHt706lTJy5evHi7HuWOwJCbayd0ALR795IyZw6G3NwqsuwuQpdjL3QAzv4MX44znxe4pDA/l21D/0XTzbvxKoLLITKkNfMYIIROjUWInTuYd999l3HjxrF37167L2G5XE7fvn358ssvOXXqFFu3buWnn35i5MiRFe67cOFCUlNTOX78OFOmTGHmzJm89dZb1vMHDhwgKCiIDz74gKNHjzJr1ixmzJjBhg0bKv0ZK2LChAl8/vnnfPLJJ+zdu5eCggIee+wxjEaj02t2797NmDFj+P333/nxxx8xGAz07NmTwsJC65qzZ8/SuXNnmjVrxq5du0hMTGTOnDl3/TwrY1aWndCxoN27F2NW1j9s0V1I4RV7oWPh7M/m8wKnHNr9OXsfj6TNX/kAHGnvxf1f7KJjj8FVbJngdiIGgVbCUMaqyF8oLCykdu3a/Pnnn8ybN48WLVowd+5cl9esX7+eV199lUuXLjld06BBAyZMmMCECROsxzp06ECDBg3YuXOn0+vGjBnD8ePH+eUXJ7+EbwO5ubkEBQXx/vvvM2jQIABSUlKoW7cu33zzDb169bqufa5cuUJwcDC7d++mS5cuAAwePBiVSsX7779fqTbf6YNAdYmJnB/k/EuhwbZtqCPa/IMW3YVc/gve6eH8/P/9DHU6/nP23EF8tuQF6m2Lx7MYCt3h4qAo/j1zc1WbJXBCZQ4CFZ6dW6QkNY3kSZM592gfzg8azLlHHyV58hRKUtNu6323bdtG06ZNadq0KUOHDmXLli240q0pKSl89tlnPPjgg9d9D0mS2LVrF8ePH0elUrlcm5ubi7+/v8s1jzzyCF5eXi5fN8KBAwcoKSmhZ8+e1mNhYWG0atWK+Pj4694ntzT0YrHfZDLx9ddfc++999KrVy+Cg4O5//77+eKLL27IvpqI3Nu7gvM39m8ouAk8KvilX9H5u5C8nAw+ffo+mseahc7FUBmK9UuE0LmLEGLnFqjK/IXNmzczdOhQAHr37k1BQQE///yz3bohQ4ag0WgIDw/Hx8eHd955p8K9p02bhpeXF+7u7nTr1g1Jkhg/frzT9fv27ePTTz9lxIgRLvd95513SEhIcPm6EdLS0nBzc8PPz7YCJSQkhLS06xObkiQxadIkOnfuTKtWrQDIyMigoKCAZcuW0bt3b3744Qf69evHv//9b3bv3n1DNtY0FAEBaDp3dnhO07kzioCAf9ii24guBzJPmT0pmaerTy6MZ5A5GdkR9/QwnxdY+evnT/jjya60PlgAwOGO3kT/dy/tHuxXxZYJ/kmE2LkFqip/4eTJk+zfv5/Bg83hBKVSyaBBg3j33Xft1q5Zs4aDBw/yxRdfcPbsWSZNmlTh/i+//DIJCQns3r2bbt26MWvWLKKiohyuPXr0KH379mXu3Lk8/PDDLvcNDw+ncePGLl/OWLJkiY0HyFWisCRJyGSyCp8TYOzYsRw+fJiPP/7YesxkMg/869u3LxMnTqRt27ZMnz6dxx57jE2bNl3XvjUVpa8vYa+8Yid4NJ07E7bolZpTfp6bDNufhw33mUNGGzrCjhfMx6satZ+56qq84LFUY4nycys7FsYgm7iAOukSBR5w4vkHGfTBfrx8XXuhBTUPUXp+C5jy8ys4X3Bb7rt582YMBgPh4ddKTCVJQqVSkZOTY+PpCA0NJTQ0lGbNmhEQEMADDzzAnDlzqF3beQ+JwMBAq/jYuXMnjRs3plOnTjz00EM2644dO0b37t158cUXmT17doV2P/LII/z2228u1xQUOP7MRo4cycCBA63vw8LCCA0NRa/X2z1zRkaGU3FWlnHjxvHll1+yZ88e6tSpYz0eGBiIUqmkRYsWNuubN2/O3r17K9y3pqOqHUr4qpWleWoFyL29UAQE1ByhU1G104DNVS8ofMPNdlj77PiYPTpVbVc14WpWKj+O7kOrRB0AF8JkhC16lX5RfarYMkFVIcTOLVAV+QsGg4HY2FhWrVplk6sC0L9/fz788EPGjh3r8FpLTk9xcfF138/Pz49x48YxZcoUDh06ZPWYHD16lO7duzN8+HAWL158XXu988476HS66753Wfz9/e1ygjp06IBKpeLHH3+0CqHU1FT+/vtvVqxY4XQvSZIYN24cn3/+Obt27aJhw4Y2593c3LjvvvvsytFPnTpF/fr1b8r+mobS17fmiJvyXE+1U3UQFWq/6mFHNWP/Dx+Q98piWpUWpSXeX4vHN3yHp3cN/XkVXBdC7NwClvwFrYO/9m9X/sJXX31FTk4OL7zwAr7lvmwGDBjA5s2bGTt2LN988w3p6encd999eHl5cezYMaZOnUp0dDQNGjS4oXuOGTOG5cuXs3PnTgYMGMDRo0fp1q0bPXv2ZNKkSdb8GIVCQVCQ83yBsp6oysDX15cXXniByZMnExAQgL+/P1OmTKF169Y2XqgePXrQr18/qwgcM2YMH330Ef/973/x9va22u/r64tarQbMobxBgwbRpUsXunXrxnfffcf//vc/du3aVanPIKiGFLnuRVXheUGVsX3uEO75IoFwPeRpIH3YwwyeuL6qzRJUA0TOzi1QFfkLmzdv5qGHHrITOmD27CQkJHDw4EHUajVvv/02nTt3pnnz5kyYMIHHHnuMr7766obvGRQUxLBhw5g/fz4mk4nt27dz5coVPvzwQ2rXrm193XfffZXxiDfEmjVrePLJJxk4cCDR0dFoNBr+97//oVAorGvOnj1LZmam9f3GjRvJzc2la9euNvZv27bNuqZfv35s2rSJFStW0Lp1a9555x127txJZyfJuYI7hOtJOhbVTnccOVeS2TGgHa0+TUCth6Q6MnzefI0nhNARlCL67FRqn50amL8gqFTu9D471ZLrnRF1vSMWdDnmZOSz9tWN3NOjeuTsCKzs+2ozumUrqZ0JJuBIlD99X/sWtacQpXc6ldlnR4SxKoEanb8gEFQlFQmZGxEwTpKOpS/HISsrYCzVTl+OsxU8otqp2vHpzAHc+7+j1CqBXA1kPvcog8etqmqzBNUQIXYEAkH1pCIhcyNVUy6SjmVnf8aQl4GyrIgR1U7VmszUJHaP7Ufro+Zii3P15DRe/jqd2nWtUrsE1RchdgQCQfXjeoTMjVRNVZBUnHs1C6W3Hl+N27WDzqqdrjdsJrgt/Pb5RgyvrqdFNphkcCQ6kH6vfY+7WlPVpgmqMULsCASC6sf1CJkbqZqqIKm4AA2GgnJixxG5KXDmR/AOAUMxaLPgQjw0fhh8w1xfK7gljAYDO2YOoOk3J3E3wFUvyH7hSQaPWlrVpgnuAITYAZczpQSCyuSu+Vm7Ve/H9QiZG6ma8gxCatQD2Tn7pGOpUTfC/T0xaY9Bpp9zW3U5kH0Ojn4G53ZdO96oK/jfA25q++uEF6hSSLt4mvjxA2hzQg/A2fpymq98m8jWFTcPFQjgLhc7luGWWq3W2l9FILid6PXmX9ZlS+NrHNebNFyOa1WN+cg9vVHcPxXloddBX2i/2JJDc08P51VTZWdEqf0wPrYaxVcvIbMRKt2RPTAJ5dsPXruPM1t1OfDbq7ZCB669f2zNzSVPC1yya/tryFa/QfMcMMrgyIMhDFj/PSo396o2TXAHcVeXnoO54+7Vq1cJDg5Go9Fc90wlgeBGMZlMpKSkoFKpqFevXs38WdPlmGdKOQpBuSjbLklNsxuqq4mOJGzsYFTfPmsreMruk5vsvGrKIihKvSuS9ioypRsYiqAgA7yC4coJ+H6mvaByZGvaEdjkos/SyL0Q2vqWPgfBNYwGA9un9qX59+dwM0KON+SPGEiv/1tQ1aYJ/iFE6XklEhoaCpjnKQkEtxu5XF5zhQ7c1KgFQ26ujdCRaTT4x8SgbhtB0VUJ4xNfobz0rdnLU7eTbfl3RVVTZbwr1k+8UVe4f6R5/f9eun5bHXmYylL2/J0ycqKakpx0jP0vDSbiVAkApxsqaLN6Kw2ad6xiywR3Kne92JHJZNSuXZvg4GBKSkqq2hxBDcfNzQ25vAY3Lr+JUQvGrCwboRO+aiXZse+TVWbCvCY6mrAF8agCfO1FgquqKUcVXZawU5QToePMVo9arteXPS9GTtw0v3y0EuX6zTS7CgY5/N09nIFrv0OhvOu/rgS3gPjpKUWhUNTsPAqB4J/gJkYtmPLzrf/tHxNDduz7aPfts1mjjYsjZf5iwletRHm96XWuvCvndsFDFYRDVGqzYLIIKe8Q1zlC3iHX3ouREzeM0WDg08mP0fKnC6iMkOUD2tHPMOTZ2VVtmqAGUIP/xBQIBP84lqRhR5RPGi5F7u1t/W912wg7oWNBu3cvxqys67NDlwPabNdrTAZzSMsRjbqaq652vGAOhcG1zsrln89RZ+Wb+BzuZi6eTuR/T7aj7fdmoXPqHiX1PvyEnkLoCCoJIXYEAkHlcSOCoBRFQIB1mK5UXOxye1N+QcU25Cabk4OLrrpep1JDn9X2tlpyen7feK2JoWVgqCVHaOyf8H8/m/93wGb76qqb+BzuVn6KXcLFZwbT9IwBgxwSe9bjsf8eol6TiKo2TVCDEGEsgUBQudzgqAWlry9hr7xCypw5yNxdlxPLvb1c37tsnk6d9mbhUr5UHMyiwyfMbNOAzZCXAllnQOkOl/+EnS9cSzgun1DsLEeoPGLkhEuMBgOfTuhNq1+SUZog0xf0459j8DNTq9o0QQ1EiB2BQFD5XK8gKEVVO5TwVSsx5uaiiY62KUG3oOncGUVAgOuNLHk6bp6gUEHvZfDdTJvcHemeHugfXcuJTPDyKCDQ0xPfEh18GuN835tNKL7Bz+Fu4eLJgyRMjKHtOSMAJ5qo+Nf6Twhv2KKKLRPUVKo0jLVnzx4ef/xxwsLCkMlkfPHFFzbnJUli/vz5hIWFoVar6dq1K0ePHq0aYwUCgT26HMg8BZf/gszT18I9N4HS1xf3evUIW7TIGtayoOncmbBFr6D09XW9SVGeWej03wwX9sE7Pcwenqe3wZBtSKPi+bH5YtqtO0bf1+PpsWo34z4+hEHl7Xrf8gnFLp67KC8TfdoJ9Of3U5J+kqK8zBv5GGo832+ez6Whz9DknBG9AhIfbcQTnx8UQkdwW6lSz05hYSERERE899xz9O/f3+78ihUrWL16NVu3buXee+9l0aJFPPzww5w8eRJv7wp+OQkEgttL+Q7Bbp4Yui3HGHQ/pkIdcm8fFAH+FQuUcli9PFlZmPILkHt7oQgIuL59PHyg0yj4Y9O18NWeldfON+rOhdA5aPVG66E9pzP54YI3j9zTA9n1dGN22hl5AyVGI6qvxqE496v1lLxRd/SPrcfNv+51fgI1kxJ9MTvG96b17jQUEmT4gTRhBIMHTahq0wR3AdWmg7JMJuPzzz/nySefBMxenbCwMCZMmMC0adMAKC4uJiQkhOXLlzNixIjr2rcyOzAKBIJSyncIdvOk5JGtpLz2Mdr4363LNJ07E/bKK6hqh97Y3hXNk3K2RpcDqUcg9nGn258fvJuuW5NtjmncFBx6qQXu30youBuzk87I0uPrkY5+gdzBOWOj7kh9X0dZUnBXzsk6eySeYy+/SOPzJgCON3Mjav0OQus1qWLLBNWZu6KDclJSEmlpafTs2dN6zN3dnQcffJD4+HinYqe4uJjiMhUdeXmieZdAUOmU7WHj5onh8S2krN1mI3TAXC6eMmeOuT/O9XhmrmeeVEVrlK6TnN2M9hVdWr2Rk1pv2lSUUOyid4/MOwSZk3OKc78gZZ2G2CecP1cN5ZtNM/F753MaF4BeCScebcqAJTtEk0DBP0q1/WlLS0sDICQkxOZ4SEgIFy5ccHrd0qVLWbBAzE4RCG4rloTd0vwYY5EX2rh4h0u1e/dizDb3vDFcuYIpNxe5pycyjQaFr+81EeSs47Gl/HvAZvP7itZo/Mx2dRoFde4DQzEoPeDyfvh9I3qFF5BrZ6enuwrUXq69LY4SlS338gqBgbE29yo7QkJWvhS+rM010MNTrNPy+fjetN57BbkE6f6geHk8g/qNqmrTBHch1VbsWCg/Q0iSJJdzhWbMmMGkSZOs7/Py8qhb9+6OlQsElY4lYbc0P8bU0NbTWna+laTXI5WUkP/DD6QvXYak1QKgiYwkcNRIpHr1UYWGXN88Kah4jWcQDPnUPKHcJl+nK9KQT/n1nP2lXZoEEujldv3PbcGSDP3HJrt70X+zbQm7I49TDZ2TdTJhD2emjiLiojlsdayFOw++/jmBtRtWsWWCu5VqK3YsAzrT0tKoXbu29XhGRoadt6cs7u7uuFfQq0MgENwilg7Bde6DPSuRtxxvPeV0vlVkJOGrVpI8eQqSVot23z4yAZ9HH8W758MoK2OeVFGe2bbfVtn31zm3C1SeDOmyiIHhHmhzC7iqUHOySEHHVvXx1VyH2LE8tyWvp3wytJsnuVFjyK7fiXyZDO/nv8L/xLf4phw29++52ee6g/hqwxQC3/2aRlooVsGpx1owcOnOqjZLcJdTbcVOw4YNCQ0N5ccff6Rdu3YA6PV6du/ezfLly6vYOoHgLsfSITj9CABGdyMe0VEUxcU7n29V+t4/JsYqgrT79uE/PAZjVhbK8gmI5UNRKrX55ebpfAK5h49zD5GbJ4Zmz5I+d7FNyK11dDRe8xeQ6xZUseCxPPeX48yCp1TsWfZPG/Qe885sI37PJ9ZLomt3Yn7vVwjd/Khzm2sAxTotn495mNbx2ciB1EDwmDqJgU+8WNWmCQRVK3YKCgo4c+aM9X1SUhIJCQn4+/tTr149JkyYwJIlS2jSpAlNmjRhyZIlaDQann766Sq0WiAQAObE2uI8DPdPRXILx3/UCBT/+Q9ylcrGo1MWi7gpG+aSq9VIej0GyQflvb3h1HfOw0P3dIfnv4f3+10La1nPlZaIZ552eG9DuzF21WJgHjJqnDeXY8+/TPtW9QmrVcGk0dLOyMb8DOTaLCxB9dyoMWahk/aHzfK41N+Zf2Aly598Hd/tz9kKtRoyJ+v4nz+RNOMlIi6bw1ZHW3nQ/fUv8Q8RKQSC6kGVip2//vqLbt26Wd9bcm2GDx/O1q1bmTp1KjqdjtGjR5OTk8P999/PDz/8IHrsCATVAV0OJQUSKdvPoo0fYD1cZ+NGl5dJer3jMFd0NGHzl6MCCG1lGx6ycPYXkEww7At4t+c14VDa58ZYVIC8RIejrD5j0P1o4z9waFNxfDxNRhmYvvMwrw1pV6GHJ6XYg2n/u8or0RoalB7Lrt/JxqNTlriUfWQ3exbfsnk8NWRO1pdrXyIk9gcaakHnBmf6RjDwFcefg0BQVVSp2OnatSuu2vzIZDLmz5/P/Pnz/zmjBIIaiiE3t7RRX/5NN/wrv1/KolftPCUOlUYZlIGBXHltg32YKy6OlPmLCJk6D5lcjqJYjvLyn/Yhq3O7QJsFz30PBh2oa4FnEAXFBtT/G42sTgeHM7FMOr1ru3SF7DmdSWaB3qXYydXqmbbzMAcu5JAX1RCpUXdk534h3+h6//ziXKT9byH7v19ALr/j52TpCvP47+heRPxxFYCUIPCaNYOBvV2M3RAIqohqm7MjEAgqj5LUNFJmz7aZOXVTDf/KYMzT2QsdQPf3UTTRUQ5L0TWRkcjc3OyEjgVtXBwlqUO5PHIUmqhOhI2PRZZxEKN/e0w6PXKNO4qM31EW55uFTq0GAJgyz+ChVJs7F1/ebw6BgY3gkfu4FnYGtSeQSX5Rict1mQV6DlzIYfuQejQ7OA/Z/f8BTHgrXHuDvBVuyM7+gkGSoQy81+Xa6s7fv3/L5VmTiUg2/7H6d4Sah9/4mloBtSu4UiCoGoTYEQhqOIbcXDuhAzfR8K8cpkKtw+MymYzA/4wg0yTZiBrP7t0ImTYNSaslfN1a5O4eaBMSyI6NtZajA0ilTUG18b+TIlMQPHECxswstAlHyY6NRd22DWFzn0JlTIWrl+CjAcgB+cBY8wb6QnOoqNMo88tQDEp3FJpANFGdHAo0TXQ0QWFeJI6uhyenIdPPtitzmW7NXpI3Lz8YSrP9M1Ek/QoX9kCnUfirA4kOiyQuxV7IRYfej/+lA9BlCjJjMaZLfyJX17ojuyh/8epIwj7aTX2dOWx17t8deGq+4/CgQFBdEGJHIKjhGLOyHE4Rh9KGf1lZNyV25OVy5yxJx15dH6QkOZmg8eOQTZ5ESVoaMnd3lL61SJu/wEYAWcrRU2bPwe+pp8wJyxoNdTdtsgohQ2am2dNTpnQ95ZXlhM+bhNJbfq06q2wfG32hbWIzoHxmB2HjhpACtiMtojoRNn8Wqt3TzMnRFpr2gV6L4atJNtVdwff04JleK1AoO0HkSGvTQt+T3zL/nkHMN5mIK5OkHB16P/PbjsNX7g7fTkdhk3B953RRLszP5atRPWnzl7lU/nKIDL+5cxnQY3AVWyYQVIwQOwJBDUcymaizaSNScbFDb4op3358QoXoclBk/oVn9+543Hsv6g7tUYWEkL58uV1vHf+YYegSD6M7cMBpOXq9NzeRsWq1w748ksFgs9ZSum7MH41y1ytm782eleY+Ng5ydQBo1A3yUlB9P4Pwp8ZgHP2MOSymdkNRkoIyM85W6ACENIevJtjtJzv7M6rvX4YWfeGjQWXu0ZXQ2u1Zrr6X7Ecnkp97EW+lO/7uAfimHIEj2x0kXN8ZXZQTfvsv6XNn0CbVHLY60s6T3m98g49fcBVbJhBcH0LsCAQ1mJLUNNKXLrXJnynf3E/u7XXjGxdeQfnbXEKn/UTq/IUAZCcmOhUzQePGkrVhg8OttPv2YSoocHpt8ORJNsf8h5sTYE3ZGXDuV4gcg+H+qRh9O2Hy7Yk8YhqKlF9RHnrd7OFp1NUsiPzqQ537UP6x4tovvkZdofcyeKeHvWFle+iUQ3b2F7i/3Hy+UiHjW6cj3iYZpri3UNZtD5f/Mt/fkQiDat9F+bOl/0e9T+KoVwyF7nBhYCQDZ71b1WYJBDeEEDsCQQ3lWq6ObaJwWQ+J7u+/UQQE3PjmRXkYIv5D6sJXrALEVW8dxox2uZ0x135WleVamUJhk+Nj8fTI1SrztHVlPVK2f2RTVq6Jjqb23D246ZPg0u+w4zno9xbU6WiTx0NqAobCIow9Y20ToA+9bl7jCkOxwxlckncYBaog9jZfzMN1jKj2rISOz7veqxp2US7IzeabUb1ofdDs+bsUKiNowWL6P9ivii0TCG4cIXYEghqKy1ydffsIHPEf/AYPuql8HaO7D8b6j6KN2wJcSyp2hszDw/V5FyNe9BcvkvzSBMDslfLp8yie3buhyD5obhS4aIXDRoGpC5cRPqAhyj9KvTMKpa2nxs2Tkke2krJog63nK6oTYeO2olJVVEOvdjIXqzupnZYSGBRCXv5JAqDCSezVrYvygZ+3k71wHq3TzWGrwx296fP6d3j5+lexZQLBzSHEjkBQQzHl57s8L/PwQBVqX3aeq9WTWaAnr6gEH7WKQE83c9+Z0qokgzYXSeWJMe/a/q7EigVNZKTDknNNdBS6hETndpbZW7tvH+nLlhE4fy4XPYupnWVCG+fY06CNi8Pw8iRKvNqZPTYlJpR9XzdXQBmKMWgakPLKenvPV/zvpADhk55G0agbsnO/2m/eqCtIRoeND2XnfuEeaQbv15lL86a1zAdd5RNVsy7KOxfG0GDHn9TRQ4EHXB7ShUHT3qxqswSCW0KIHYGghlK+Wqo8ivKzqICUqzqm7TzMb6czrccebh7M64+H4vbVeDj3i/WXhqnfN9Y1uoRE52ImMpKC3XvwjxkGYFuNFR1F8IQJXHn9DYc2aiIj7YSQNi6e1OwL9PtlDN81e83lM+ovXCL5pcnWe4VNfgHVjgGgL8T42E6HvYDALHiM019G8cAUQLIVKY26Q5cpUKJ1moejTPqFbvfP45NjRfzfPT2Q/b7RYe+f6tRF+WpWKj+OfoxWiebE9QthMsIWvUq/qD5VbJlAcOsIsSMQ1FAUAQFoOndGu3ev3TlN5852uTqWzsBlhQ7Av2orUHw1Di7vJ7frVPNEb6OecDcfa/PA7NhYwleZQznlS8v9Y4aRPHkKYM4T8h8eg1ytwaTTYsjIwJCXR9DYMUglJbZND8tdWxa5Vme+l+vomK1XKC6eFMlE+FNjUP6xosKOyqasNGS/DrXr10N+Grh5QbFrz5mbsYA1ewt4ZtwaNN9NtO39A1CrHnjXrhZCZ/8PH5C3aDGtMszvE++vxeMbvsPT++Y7bAsE1QkhdgSCO4AbHfVgyM3FmJ1N6KyZpC1abN85edErdtdnFeqJDnFjdpv6KHWFGNWe/JJeQq8GKgrUUeQ+9iqL/nqVfaXzn9RKNZ9M24BmuQxtXBzJk6fgHxND4Ij/IFOpMObno0tItFZ9AdYk5jqbNpI8aTL+MTG41a+PpC8hZOYMpMJCStLSUIWHk//jTzbXlsXHNxi1Us1vhYfpGh1JcZxjj5KdVyj+d4yjn0EJyNWuOx7LVSaH/XoAeHq7eUyFC0qUXjzfOYhLRj9q9XwDP66i1OcjV/tWq1ER2+cO4Z4vEgjXQ74aUoc+xODJrj1mAsGdhhA7AkE150ZHPZRdb2n0F/ji/yFzc0dRyxdFQIBDoRSgu0qvzzZQFB+PxefROzoa/wWzudT8cdYceJV9qddEhc6g47lDL/PZnLcITPs/jLm5yDw8kHt5UXTyJHlffe00R6fo5EnHw0BLvTkFu3ajS0y0ETqWZ/HsdD8yncTu9u+hdQfDzO4olqyxK68PnjgRQ+5Vu27NFo+O4sofLjoqR6G48ofdcQuSuzeSmzeye7qby9DLYWjYHTyDOHTxEht+OWM93qVJIMv61yVMXcFk9X+AnCvJ/Dz6MVodKQLgfLiMekvW0vf+nlVsmUBQ+cgkV5M4awB5eXn4+vqSm5uLj4McBYGgOmPIzSV50mSHVVWazp3tRj3c6Prruc49OhL5gsk8/rN9p9yJTV6k2zsJFCck4h8Tg/dDPchYswbdoQTC16wmOzbWToQEjhqJ3MuLjFdXOs3xUXfogLplC7Jj3zeXn2s0VnFUPkwWOHYMypBQpIJ8jPn55lwkpZIr61+j4PvvrWs9u3YlaOwYZAoZUuYF5J4aZIENSFu6gsJfryUha6KiqD1/Dm7bHjL3v3HE09tgx/PQfzPSH5uQlcnDMTTsTlaPlcz4KYtfTthf36VJ4HVNVr+dxH+9haKlK6idCSbgSKQ/fTd8i9pT/I4UVB8q8/tbeHYEgmrMjY56uNnREK6uK47bR6jW8d9ED3i2oTjhPasQUbeNsIqbor+P4tOnD8ETJwJg0mqRqVQUxMXjef+/nA8DLe3bYwlz+Q+PQeHnx5X16x02HsyUy/Dp1Yu0ufOsxzVRUfgPG0rhb78habXINBr8Bg0kY9VqW7EUZU6Q9nt6iHmduzu6hETSlq0gbNhclN+PszewUVdzdVXpDC5Zp1FInSehl7ljdPclzeiNTu7FLydOOHy+65msfjv5dOYA7v3fUfxKIFcDV559hMHjV1eJLQLBP4UQOwJBNaai8vHyox5udP31XkeB46GfysJifGNiyNn2KeqICJSBgdawkSI4CMOVK/YCIzIS30d6o6hTh9qzZqIMDsZUUIDcyxtDRjops+cgFRcjabXWEFe9rVucV07FxeM/bJjtsfh4kCTraAn/mBg7r5BlXYZMRsi0qejPn0eGubeO9vc/ML40GmX5cvFGXeH+keZkY7Dm9Mj2rCR18G66vnkOgM3DO7r8OMtOVnda6l/JZKYmsXtsP1ofNfdEOldXTuMVr9OpXddKv5dAUN0QYkcgqMZUVD5eftSD3M/P5RwsZ6MhKrqPm5cP33R4G0NeLkZPD/YUJrIp6QMMnu6oO7RHHdHGLv8m9JWF5H33nUNvTPqq1dR/+y3SSjswW9BERVF/6xYko5GAkSPNU87btQO53KV9jpoalh0toW7X1nmH57g4SlJSSB7/ktmG0nEahiI97hFDoOsMKEgHj1rmcvOdL5hFTjnCNSV8MToKbw8VpgqyA7w9VIDjUn9zXk8bwmpVXl7P3v++RcmKNbTIApMMjkQH0u+173FXayrtHgJBdUaIHYGgGnMj5eMlqWmkLVhoV75tmYOlbtcOlEp0iYl2FV0u7xMdTXHiEQrmzAXMicJ9Z0zjqQ5bkMsVyBUGO+8NgDIoyKk3xuPee0lbtMihpyV92TKCJ01Gd+QwDXfuwFhYiDEry+Xn5KypoVRcjEyjQV5BQnBZsWSxyXf2VNIC7if07Yev5e48vc2h0AFQaXxpG2iusMrV6unSJJA9ZUSMxk3BC11CiW7qTj7nOJvjxZ/n9By4kGOzz57TmUzfebhS8nqMBgM7Zz3FvV+fwN0AVz0h+4UnGDx6+S3tKxDcaQixIxBUY5S+voS98gopc+bYCJHy5ePX5mDZ5t1YvrhDZkzHrX59kp7sZ/XylK3oUvr6ErRgLulz51NUNqE4OprAEf/h0khzbxibROE585BpNDT46EOH+TeuRkio20a48LTEI5s6Dc2/7seQno5MqUTh40PowgWkL1tuV4ruqgOzzN0d/5gYMJmc2mJZZ/1vjQZ1RARuJRKFJy+ge/RTVBe/Nc/LcuawKdcF2VfjxrL+bZi+8zB7TmeicVOwYVgjPj63kq2/XPus7g+N5JPRs8i+qkZbYsRDpeDgxRze3Zt0y3k9Gcln2Tvm37Q+Ya4+O1tfTtMVbxEZEX3TewoEdypC7AgE1RxV7VDCV60s7bNTgNzby658vKI5WMETJ3Dh2edshIJ2715S5swhfNVKCj1gzslX6fhiR/q9PAlSM0AmQxkYyIWY4dbrAl54AcOVK/gPj8Fv8CDk7h6YCh17OlyNkKhollZJSgpKPz8uPvuc9ZgmOoq6GzdyadSoa4ItKoqQKS9z/pln7PbQRJlFkLptBIW//3Fd4yrKijmLGMujzLwsn1D7sQ+NukMf+148YbXUvDakHZkFepAXsvzQXH5Ps73/H2n7eI0lNFOM4rWfkgGIbhzA+iHtKCwusdvzetm1/TVkq9+geQ4YZXDkwRAGrP8elVvFYz0EgpqIEDsCwR2A0tfXZRPBihKMS1JTHTbns1RoZQfI+PXSr7Rt0pjMFasoijd/KYevW2u9TqbR4N3zYdKXLEWXaC41V7dri1zjOO/D1QgJRUXDR2WgDLadF6WNiycTqB/7HiXJySh8fZF7+yDTqFG3a2cXvgueMAFD7lXkbm4uOzyHTJvG+cFDAFwkMv9OikxG+JIFKPu9BbosyLkIMsyVWZs6Q91O5tEPvuHW63w15mTj09np7EuJR61UM6zFMNoEtqHYWIyH0oPEK4lE+F7z4MSdMYfsljzZ2vVn5ACjwcCOaU/S7LuzuBkhxxvy/jOAIS++csN7CQQ1CSF2BIIaQEUJxq68LKb8AvJLL+/i3Q7PNhKBzz2HwscHubs7dTe/A0YjktFI+rJl6BITzT103osla9MmGuzY7lDUZMfGUnfTRjLlsnJTxaNQhdexjpooj6XzsUfzZrbPoNGgbt0GmVKJMjAQFApAImPtWtStW+M/bChScTEKX1+UtWuT9/33ZG16k/DVq5C0WmuHZ//hMeZcntIy85L0dKugcx1e24cx7TLKRg3hu1lwrlwzwbM/w5fjYMBmm+7IuVo9yXk5qJVqVnRZwYfHP+Stw29Zz3eq3Yle9fugcVOg1RsBs+DRG12H3sqTeuEEv48bSJtTZo/Q6YYKWq16l6gW/7qhfQSCmogQOwJBDcB1grF9ToulG7G6bQSSoYTGud782nkbXjIPMk5+jDqiDVfWrrMRMJby78CJEzFkZOD/3LMEvTQemUpF6OxZpC1ZauNdUbdrC3I56nbt8R82zEZgZGzYQOicOeaEaieztMJXr7Kx12HH5dJ+OsmTpyBt0trs4/PII4SvWonu76NWMVZeyGiiovDp3RuZRoOk1VYYXjPl5YDWx17oWDj7szmZuYzYScsrQjJ6MKzFMD48/iG/p9p2bP499XdWHljOC12uhbIACosNLm0pyy8fr0K57h2aXQWDHI52DaP/2m9E2EogKEWIHYGgBuA0kTk6muCXXiJz82YCRo40ixu9HlV4OEVHj5I8abLVqxG6eBHp33yDunUbm1CORRjJvbwI3/Aa7o0bk75sOX6DBloFkc1YCqUKY0G+Xb5PeQKeHY7Po4/YeVqSJ09BHRFhI9Bc9ckp20/Hery07Dz7/Q8ImjgB3z6PkvbKIvN6y2cTGYn/sKFkrF9Pg08+Rn/+PKrwcFwhV7tBUa7rf4yiPGvvnFydnhKjxMEkA51b/svGo1OWfSnxDOls28DQUp7uCqPBwPYpj9HixwuojJDlA9rRTzP42TkVXisQ3E0IsSMQ1BAcJTKjVHLh+Reot/EN0pcstZtDZSlLl6nVaDp0wKNFC2QKBT5PPI5UVISpoACFjw+6o0e5EDMc/5gYcj7+2E4QWRoAZm3aROjCBeR9/z1+Awfa5PtYPUmlPYAwSShDQsjestVGMNV943VQKJB0OrD02nEVXirTT6csUnGxeT7YpInoL19G3aYN/jHD7ISVpNVSMvApkse/ROC4cYQuXIAyONiuV5GmUyekkIYU6YyY+vxoHjehlFCc/xblX2utJekGlTdjPz5k7Z2zeXhHNu9Jo0vLYJf/fnqpNOnaTcG8vg1AlcHhK+fwdvPG38MfX3fbPKdLZ//mwEtDiDhj9gCdukdJu7UfUK9JRAU/KQLB3YcQOwJBDaJ8IrMhN5egESNIX7LUYXM/gIAR/8G7e3ezt+bpIcjdPcjasgWPZs1Qt43AmJODW5061H8/FkN2trUjsTPxkb5sOQ22fWLtjeM0BBUdTeDIEWg6dsT/+edwq12btKWOBZlkcB3ScRR+UtWpQ/i6teb/DgoiOTbWJtRV/nqZRoNHq5YO53nVeetNpIBapM9f4nDWl9SnA6qvh2Gscz+ntWp+O31t+OehS1dpV68WhTrXISU3mQaNm4It/9eEzSdXsOjva/eJDotmftR8Qj3Ng19/il2Cx4b3aZpXGrZ6qB5Prf4ahVL8ShcIHCEGgQoENZyiU6dJeuIJp+cb7NxBxspVqCMiUIXVJv+XX/EbNNBmCKd12rhKhamwEJmHBxdjhjvds85bb6EKq0360qWoW7dBl5jotOw7eNJk5J4au/ydsmtCZs0i6dE+zu+3aSOXS3sBgTkXR92mDdmxsVbbkcuRiorQHjxk01UazPlIhb//4dTOkFcWkv/dd04Tqn0e6Y1X3SJy6nUmQ67mijYLvaTFXe5J4gUDbWrXJq0gm5+zVvNHmv3+3ep2Y27zGagLC7ialYzOQ2btUq0z6ACz4FkStZgfpg2m5S+XUZog0xf045+jxzNTnX42AsGdihgEKhAI7DDk5paGsPJtOiRLOsfeDAuSrsgmFOTRrJnDaeNZmzZZhY9P7142e5QPU6lCgpEplQRPnISpIN9lA0FpzBgM+XnOB4PGxSPpdM775ETZJmBbkpZTZs9x7FEqE76TtFo0kZHIvbzwjIoEsOYQlQ1hqVx0g7Z8dld9QsnzcGP9gXnsS71mZ6faUfQJnoHGW09Uk1ks2b+E+JRre3UJ78rSe6eQOXsOV8okeHeNjuS+SSt4IWEqOoOOM8f/4Lc1DxJxzlyxdbKJkvvWbyO8YQuHdgkEgmsIsSMQ1ABKUtPsOihbOiTLK/iLSK4xj1KwhILK5seUTQwuK3wAq/hwHqaKInTmTIz5edeSox3M7JIMBoy5rpN+DVeulFZ8LbErYw+dOwfJYEDdNgJlUBD5P/5kLTN3mNRc+t4/JgZdYiLBEydiyM3FLTQUXWKi4zCaXu/SPqm4GGWRlhUHVtsIHYDfU+NZ/tdi2gS14f1d7/Nyx6lMaD+ZAr2WQp0KdYEbmfNsx3zINBq8WkcQku/Bz83f4Jfd7xP40Q/454NeAX8/1JDBq74UYSuB4DoR/08RCO5wnI6KKO2QHLZksfOy9MhI8PAArvXiKZv/4kz46BITrU361BERjkVFXDzpq1YTMmWyUxGRPHkKCh8fZKqKK4/SV60mZOZMKC7GWFCASadDdyiB9BUr8O7aFbeGDZF0Out9KkpqDpk2Fe+HH+LKaxvM3qwjh50Ko6BxY13apgoPR6VS0am4FQnKBGvoycLvqb8ztPlQdAYdC39fQKfaUbRSjWbN9+fZM7ghheWEjkU8pm/aRGKEG20P61FIkOEHsZ0eoaTVIB7Tm/AVv8EFguvC9ShhgUBQbTDk5lJ87hy6xER0Z8+RlXqFxEs5FGVkOh8VsXcvxrw8wl55BU3nzjbnzLkwM0GSrKEgQ0aGTXfj8sKnbPWVpUTc++GHnIagPO69l7TFix2KiOzY9wmZPo28n35GrtagiYpyuIelyWDhL78gFRUhSRKF+/8keeIksjZtovCXX/Fo2RK5Wo1UZgZWhSMpUtMoOnqUwl27zM/mIkwl12jQRDueKaWJjCT/p5853/dJum5OYHPbFaiV9oNHi43X7Pk9NZ429RR0aRKIv2TrNbKIynMn/+J8fRkdEs1C53hjOb4tO3Jfj2HsOZ1pHkMhEAiuCyF2BII7gJLUNJInTebco304P2gw5/v0IXfWdLiSQUZqputrL18GmYzwVStp+MXnhK9bS51NG1G3a0/+9z9QkpJC6KyZFJ06hTI0FEUtP6vwKNt5ubx4sJSbl1y+7PTeFYkIj5YtkWnUGAvyCZk2lTobN1J30yYCRo5EptFYmwxmx8Zan+V8/wHoDhwgfNVKZKWjKkpSUyk6fhyFjw91Npn3UAYFObyvFRkog4MdPlt5inUFFE4Zbid4yttXHLcPz9WxjGw41G4Pd4VtNZavl5HXhrRD5WsbZlS3jWDf1cN46EtofEFCr4S/IjxoesaI+96/6BZi9oLlF9387CyB4G5DiB2BoJrjLExVHB+P94YVePlVXKWQMns2ADI3N5JfmkDypMmoW7ZA+9dfXH7xPyQ9NRCPe+9F7uEBSgW1581FEx1tnW8FzkdO3NLAz9RUfLp3J/P1N0jq+ySXR43i0siR6I4cpsG2T9B07GhNJC57L4tnyD8mBplGg1v9+uR98y1JT/bj8kjzHsVnzqCJdu0tstjn6hkArsi1PHtwMrtf7EDoF9uo/+EHZsEYEWFjH5gFzwOebWyu71S7E4czD9sc8/PwwVfjZu1+DWAA/vfeTFqfKKRWAaT7Q1J9DR0Ti5AjA0CpM/fzuZ6mgwKBwIwQOwJBNcfVRPPi+Hhkbm64VxACsgz8LPLyxSM62i551+KluRgznPTFS8j96mvUrVvjGRVJ6JzZdsKnLLqERKchqIoGfqpq13ZYcq6Niyd9yVKkkpJrE85Ln8W6Zt8+1G0jCJkxnfSly+z2SF+2nMARIxx6Y4InTkTdoT0KPz/qbtoEkoRn164ObXSPjmRPoTkP59WTb9Dlj2colhm5PHIUWZs2OewQrdBeE3mdanfimebP8P6x963HosOi8ffwB651v85+oC0X6spoty8HuQRH75XjUaSg6Vnb/Q1qT7o0CSTQyw2BQHB9iPQ2gaCaU9FEc/eCPAzTZqFYscR+8vfEiRhyr5qnl+v1xJ3JpP20WXhmp1fYkdjyZV7nrbcInjgBQ04Ovo8/ZjcDq+jECbPgKNe4UBMdjTIsDE10tEOxpomMRKZWOy85L1MOX3Zmlg0SeLRoQdqcuXbXS1otl0aOon7sexisQ0JroQwNIX3Zcgp37bKxJWTmDDLA9nh0NAWTh7Hp0Mu2m3s5nvRuIdi/Dh92fhW1b10OZx1j6p6pNv1y5kfNt+mI/N22ZQQdTOCeQihWwdEO/rT/PdtuX3V0NH8XK1nevw2+GiF2BILrRYgdgaCaYumbI6tgmKMsP5eid94mdO5cDOeTkIqKkHl4oAwI4MqG122+vJtFReE1ezaOeomW7ZUjV6up+9abSEYjyqBASi5eJPmlCQSOG0fIy1MoGTbUZsbWxVGjqT1rJsFTJmMqLETh64sxN5cLz79A7VkzwWRyOPDTmG3/hV4WuacndTZttBntUBaFXy1KkpOdXG0WPCXJySS/NAGAgJEjHTYO1O7bR/qSpfjHDCPg2eEgl4NJQlLIKJDZh+JyNTLcoyMpjnPcBNHjwte0iV8Mbp4ER42hfdRS8tW+eKsDbEY/FOu0fD76IVrvy0EOpAVAfoAvT45eQ6biTdsy++hoAhYs4AH/ICF0BIIbRIgdgaAaUrZvTsDIkc4b6pWGdop+/ZUMvR7v3r1InzOXgLFj0R08aDP4Esxhr6zFiwmZZttx12HzwOefx7vLA0haLW4NGtDwm68xpKeDJKFLSCQ7NhaZWk3Y0iXUf/st0hYtshulUHvWTFIXL6Hu2jWYCgow5uVZBVLK7DnUf3ezy89BplCQ/V6sk87K0chUqgrzbcqer6gcPXjKZPJ//ImiEyfwGzTQXHHWLoKRLwxlzem3AXNY6rus34ieFIMn2Age9+hIjNNGkBXoR0jdFqBQ4Xt5P74fD4P/7ALfhta1Jw78wrlpY4m4bBaefzdVUO+iRGhWLpdGjiJkxnSCpk3FqC1E6e2DKjDIZhSIQCC4foTYEQiqGeUTkrNjY609bRx5RyyhHW1cHP5TXsajWze8u3Ula8MGh/tr4+KQiopsBJRd88A1q8mOjbXZw3K/888MRR0RQfia1SCTYUhLsw7ztLlP6fvac2ZTdOyYufJJkjBmZ6MMCqLeW2+iO3zYuZCLjqLwzz/xjxlm/+zRUeb+PadOofTxqVAMWqgoYdpUUIi6rXmQpsWTVBy3jwfGx7CGa/k3U/dM5V1g5AtDeWB8DAptMf4Bdfgs82c2/TmW9oFtWO7eEN9dK6BRVxjyKXheqw77cu1LhMT+QEMtFKngaAs1HRKv9eaRtFrS5syl0Tdfo2nb3qXNAoGgYoTYEQiqGeUTki09bfxjYgiePImS5GS7qd0WrmTmcvU/EwnMcB7aAShJS7MREXbNA9+LtfMKlRVGWZs2gVyGT69eKIODHQoNmUZjnrcVGgqShAwZuuMnzFPMIyIInjKZ9KXLnAq50Jkz0ScnkzJjJn5PPYX/8BjkGg0mrRZdQiIZ69YTPHkSksFAyMwZpC9bZudZKp/nU5EXyKTT2szYshBo1BD7SCxnc84ydY/ZKzb2nud4xKcT7toSFLWCMalUKOXmCqm4tD/IeXAonvdPxRh0P6Y0PXJTFnqNlq9nDKD171eRAylBoFPbCp2yn59kkig+dw5jbh5yTw3I5cgUShT+fsLLIxDcAELsCATVDEcJyZZqKXXbCGv+iSOC/b0IlhuRZK7voQwMxFhYSNCEl5BNmYypjGCqKNRjSRrWxsXjP2yYw1EKTkdIlOmcbMjIsBFylplUFiFXnJTE1R07CVv0CshkZsGkUllDaJJWS3qJnqDx47nwzFDzHsOGIVdrkKmUFCclUXTsOOGrV1nHVEiSCc9u3Sj89Vc7m8t7gco+i4dfIL5pyfQw1KNT+9dRuakpWrKGzLj1Ntf/e9RIOrRvxejDs/GXhZG8/Qza+A8AuBjqgUlRTERyadgqQk2XqW+Q/cxzTj+/9KX2U9b9Y4aRs3IltWfPRlU71OG/k0AgsEWIHYGgmiH39nZ6TpeQiCY6yun0be2PP+LRvBlFx0+4CA9FU3zmDGlz51m/VOXqax1/Kwr1lD0vFRc7bN53PXOpkJkVmUXIlafO22+Zp69/8IHdF75FMGnj4pG/PJU669aiPXCQ5EmTCV+9ipRJs6m/dQvpXy0l87XXrl1bOksrXQaFv1wTPJroKAJHjOBSOa+OTKOh7pubyFi0mOK4eNJwneQMEPpob9a3WUDOwuVo438H4FBrDY3PaPHWgc4NTj4ewb8mvIO3TE+Rg1EeFX1+6ogIUuaYB50KD49AUDGiz45AUM0o22SuPEUnT1J73jyXnXxl7u5kx8biHzPMri+OOTw0g/Rly4FrX6qFv/9hbcBXYcJv6Swty1qZm5vdfcqOliiPJWxmFm7RyDQaAkaOpM6mjYSvW0vdTZsIXbgAJMn8hR9nH06zNBQEKElNQTIYKDpxgrqbNoJcjt+QIXal8ADa+HjSFr5CyLTp1P/oQ8LXr6PBju2o27XHVFSEOiLCZn3IrJlkbtpkY0NFz+YWFExjWTDauHiKFJDQSkW7I2ahczkYUkM9aP/kbHqs2s1LX58laP4Cu39vz073V/j5WXonCQSCiqnWnh2DwcD8+fP58MMPSUtLo3bt2jz77LPMnj0buVzoNEHNROnrS+0F80mdM9cmb0YTGYnfwKdIX7uWsKVLzNVNOVcx5ufZ5O/oEhKtnX3Lh4cMV66Q9/PP10rMNRo0bduiO3XSXLqemopMrXbeGycqCmVAgDkfp11bDFeuIBkM5vwfucwqCq7HO5QdG0vDzz7DkJFO5sZNdhPTQ2bMQJdoH1YC23AaMhnZ75nzgDLffAufRx9xnaAdH4/+fBLZW9/D/4XnkWk0eHfriqmgkJDp05Cp1RiyslCo1aBQUHLpsjlcVxoKQ+X616ZUXAyFOjIH90S7+0fa/m0e63C4tTtNLshRZ+isXZB/PJ7BS8C6ZcshN4ucrGS07qArNlZ8D8CUX+BynUAgMHPdYmf9+vUVLypl/PjxN2VMeZYvX86mTZt47733aNmyJX/99RfPPfccvr6+vPTSS5VyD4GgOiLp9ajbtME/ZphNHotF0JjGjsW9USN0iYl2CbWW6q3sWGwFRFQnQmfNRH85heyt71nPyTQa6m7cSNrCheawUEAADT54n7RXFtmJLf9hQ7my4XVCpk9D3b49xpyrIJnQ/vkXIbNmmW3TalG4CMWB2SOk6XQ/SCYy33zTcQflpUuvJUM7+oyKi691iC4VP1mbNuE/bCiGjAzX91cqKb50Cbfatc3PWTY5OiqKkBnTuThqNPXf3GQ3sb3e1i2u9/bw4Nsv19DgvwcILAKtO5xoqqGzZwT+y80J0wa1J2Ceafbj8Qwyn2jI4nMriU8xf96ftX3NxR2ued/k3l4u1wkEAjPXLXbWrFlj8/7KlStotVpq1aoFwNWrV9FoNAQHB1ea2Nm3bx99+/alT58+ADRo0ICPP/6Yv/76q1L2FwiqK6a8PKdf8nDtL3pH+T021VvTpmEsyENFLoorf2A0FpO99T2bL3f/mBgyN5m/1APGjjV7OYqKXIqtoHFjyVi5Ct2RI9TfugXtX3/Z5MaELlzgMrfIkJFByLRpGFJTnQ8KLU2AdobC19em2sri7bDY6wqZQkG9jW+Q/upKh6Gu9KXLqLt2DWlLltidL/z9DzRRUXbVagB06sg3GyfRJsGc8H0pBIwqD9of1qLFvE/Q9OlsT7cd4qk15VqFDsBvhYfp6qxpYanA03TujCIgwOVzCgQCM9cdC0pKSrK+Fi9eTNu2bTl+/DjZ2dlkZ2dz/Phx2rdvzyuvvFJpxnXu3Jmff/6ZU6dOAZCYmMjevXt59NFHK+0eAkF1xFWSsvm8+S96Z/k9klZLweHDpLn7kOmlwv2r/ij/WIGkL7b20rHkyXg/1ANdYiLha1ajO3SQ8/0HUHLxIlmbNnF55CiSX5pgHR0B5gRdmUxOrX5PUu/NN53PpfrPCLtBnJYEYcOVTEz5BRhzc11/EA46PYM5zGXS6RwOCbUIs/J5TdZrIyMp/P0P0pctx2/QQOvk9LJo4+ORKZUOhVh2bCz+w4ba7Z/SuRmXkg5YhU5CSyVBWTIaXC66tu++fagj2vBOgu2keq3BNhy1KekDCifF4B5tn3PlHzOMotOnCVv0ikhOFgiuk5vK2ZkzZw47duygadOm1mNNmzZlzZo1DBgwgGeeeaZSjJs2bRq5ubk0a9YMhUKB0Whk8eLFDBkyxOk1xcXFFJfJF8jLy6sUWwSCfxKLiClfpQPY/EV/RXKjZOJ03E1LKC7jaXCPiiJv7FR2n8unsbcbdRp2R5n0C1JOhl1ZePi6tXa9dRx5RhyVk9fZtNGhh0PSark0ahQNdmzHdPUqhsxMZG5u6BIS0V++TOZrr6G5r2OFHhhlcLBdVZkmMhL/oUNthI7V21E6sDQ7NpaGO7aTtnix0947klZL4Iv/Z63sKj+KwuhkJpnFc9bg449A9jLG/AK++Ww5jb46gqYYCj3g5L0a2h+2HxAKQHYy7/UP56mPL6LVG3mgSSC+brbiVmfQ8ULCVEa+MJQu458l1OSFUuMJCiUyhZywJYuF0BEIboCbEjupqamUlJTYHTcajaSnp9+yURa2bdvGBx98wEcffUTLli1JSEhgwoQJhIWFMXz4cIfXLF26lAULFlSaDQJBVWCZhJ0yZ46N4NF07mz9iz5Xq2fqzsMcuJDD+H+PpduoiQTLS8gwqfghvYT1XyQB8PrT7bn8wHLqMA25WmVX1ixzd7frrWOZcF4+3FW+HNpVIrKk1aI/fdquL1D4urXm+5b2zHFVIl+wdy/qiAhzkrVlFtexY3ZCxz9mGDnbPiVk2lTODx6COiICU1ERQePHYxzmOBQHYMzNJeeTbQ5zg1zlHUlaLSWSgYNSMpeWTabDEQMAF2qD79gJtJ+11um1CpWJpvtnMbHzHHZd1DOmW2O83eREh0UTl3ItKVxn0LHm9NvsD4tmeZfleLkLcSMQ3Cw3JXZ69OjBiy++yObNm+nQoQMymYy//vqLESNG8NBDD1WacS+//DLTp09n8ODBALRu3ZoLFy6wdOlSp2JnxowZTJo0yfo+Ly+PunXrVppNAsE/hap2KOGrVmLMysKUX4Dc2wtFQID1L/rMAj2/nTaHQ5btTWG9m4LXhrTDQ6WgnkcJGxqHc/BiDi/vSGRYZH0G9HydYKkYz05edsJGHdHG5t6ORlQ4ajZ4I3OpLChK7Tfm5VF08qTjcRBRUaXJ1JdJnjARaVNpqEqjod6Wdwlfsxq5uzuKWn6Y9MUYc3PxaNaMkvR0szh67lkkgwGZUmmtotImJFibEZa1z6ayq8z9JYPBqRBTR0fx4d43aRz7Ix1Kc6H3tVexobuJxU0CqB0dhc5RvlJUJxRX/kCZ9AvPPLyEXDx5fuuffDWuM/Oj5jM/fr6N4HE0Id0ZuVo9mQV68opK8FGrCPR0EwNDBYJSbkrsvPvuuwwfPpx//etfqFTm9ugGg4FevXrxzjvvVJpxWq3WrsRcoVBgMpmcXuPu7o57Bb+ABYI7BaWvr9NwRV7RNe+qxk3B+iHteDcuibgz13qvRDcOYFn/Nnyy/yJ9I8KRS8nISv8/ayE7Npb678faHLNJcp4ymZLLl5E7yG1x6Zlx0JFYExmJSaejzqaNKIOC8Bv4FDnbPr3mvSkuRuHri9zLC31qKnI3N+pv3Yoh9yqSVovc3QOZQkHyxElIWi2a6CjUrduQtWkTmugovB/qgaZjR5Akrqxb57QZoaTV2thX1kOliYoiZOYM8nfvIXT2LNKWLLUpw3ePjuS3wEz+tT4etR7y1fBFD0/+17oYkDHvyAq+n/cRpgVLbBKMNVGdCBs3BOW3zwJwNSeLDb+Yc5bydCU0CgpleZflZBdlk6/Px9vN22ZCuitSruqYtvOwVfwCdGkSyLL+bQirpXZxpUBwd3BTYicoKIhvvvmGU6dOceLECSRJonnz5tx7772Vatzjjz/O4sWLqVevHi1btuTQoUOsXr2a559/vlLvIxDcifh4XBMtz3duyJZyQgcg7kwWcpmMlU9FEOLjgelSrk23ZDALm/xfd9n11rGOqIhoQ/JLE6izaaOdDU6HlDroSKyJjsZ/6DNWsRG+bi0pM2Zae/5YQk2Fv/9BdmwsYcuWIgsJwaQtRCaXIyv1zhSdOmnTQdl/2DBrHs+FmOHmcFu5irOy9vnHxKBLTLSp5HKrX59677+PwtsL3NzAaEIbH0fW66+XjqEYCpLEVY2M+FdfomOceUTG+XAZGx9VkxR4TSzpDDoOShe48EJb65DQQP9wNOf+axY6enOPHb3CCzCLHQ+VgkMXc0o9MuE09L1+j0yuVm8ndAD2nM5k+s7DvDaknfDwCO56bqmpYIMGDZAkiXvuuQelsvL7E7722mvMmTOH0aNHk5GRQVhYGCNGjGDu3LmVfi+B4E7Dy0PJR/93P1d1JdT117DhlzMO1/12OpOCIgMhPmB080GmKLbzxmS/+y4NPvmY9OXL7bwhyuBgwLEXx+IBCpkxnZDp0zDm5iL39MSQmYn2UIJ1LpXFW3Nlw+s21VPORkUAqMLDyVi92vFsqG2fWvNsFN7eBI4aiVRcjH9MDJ6dOrmc7RU8ZTKArYfnyBHc6tfn/NPPWMWQ5Tkte51uoMarQEebTDABiZ18CZ29kIfzT/L+sffRGa4N81TKlaw5/TaWhh1fdlmLzx8rrOcNDbvz40Wzh7pz4wC+OpJq/fe7UY9M2XBmefacziSzQC/EjuCu56YUilarZdy4cbz33nsAnDp1ikaNGjF+/HjCwsKYPn16pRjn7e3N2rVrWbt2baXsJxDUFMqHLd54pr3L9TlaPel5RRy4YKSn9DeBo0eRWbbjsVaL8epVgidOxFAuoTf/111oIiOdenHU7dqZhcKQp61CxrNrV4LGjsGQlYXMzR2Fvz8mvZ6Q6dNIK9GjjYt3PecrOoqio0ftzukSEzFc6U3gf14059S0bYvM3Z2LMcORtFo8u3fH59FHqLNpo9NcnZLkZKuAKVudpY6IsHqZyoulAxFqWh7T4VECeRq4NCCSqG6DMRS6U6yozaoHVzF592R0Bh2danficOZh67XRtTvhf/kAhtIJ6Ea9DGNAHeRni+jRLIgh99dn/MeHrOtv1CNTNpzpiPwKzgsEdwM3JXZmzJhBYmIiu3btonfv3tbjDz30EPPmzas0sSMQCOxxFLZwV7pumZWrK0GfUcCUry6SMO4+VIUKfHr1so5BkLm7g1xO0fHj5H33nY3IsJacvy+zGUGBZE6i1h09yqWRo6xiQh4QQNDo0WSsWWPrlYmOInT2bPyfe46Ql80l2779niRt4Ss24TNNdBShM2eS9NRAm2coW/pePmQWvmolKbPn4PfUABvvlEyjIWT6NOq/H0tJcjJyN3dUDerT8PvvkJlMmHQ6TAUF1Nv8DgVx8agj2tgkMOepFVysCx0SzV6bpDoywl+cQM9a9ZEXu6P9I4EWp05imvg8z7d6ngPpB3im+TNM3TMVgPtDI3kpYiZKnUTyK4vQxn1g3fuh6Gh6T5tN308Po9Xbjoe4EY9M2XCmI7wrOC8Q3A3IJMlJ1y4X1K9fn23bttGpUye8vb1JTEykUaNGnDlzhvbt21er3jZ5eXn4+vqSm5uLj49PVZsjENwyZzMK6LF6t82xsd0bc+hijl3ODsADjQOZ0uteQmQlyK7m4FVcAAYDhft+JzvWnJjsHxODukN73MLDkYxGuyGaZT01UlERMnd3VPXqUZKcTPJLE67l3RgMuDduYh494aD/jiYqipCpUzn/9NMANPh0G7qDB1HWro3CxweZUokpPx+Fby3yvv/exiPjbNq4RdB4tGyJISMDmUyONiGBnO3bCVuy2KZ/EJhzh0JnziT91Vcp3LWrzPEoQqZPRyopwZCRwZ+HvkW240tCs8Akg4TWbrQ+oqfhxo3WER3WsNr27WjmvozJXSLXaOCKroBivRsHkwzU0svovmM9RQ4+D/eoKH7491iW7U2xO/fF6Cja1vOzO16eXK2ecR8fYo+DUFaXJoEiZ0dwx1KZ39835dm5cuUKwaVx/LIUFhYik8luySCBQOAaR2GLd/cmsX5IOwCr4NG4KVj5WD261lMgL75K+sJFaOPiyS69xrN7Nxp+/hmSTkdJaioyo4ncb77F68EuqCMiCJ48iZLkZGs460JpqMhCvVhzGLtso8GAkSNReHk5HqWAuTOx8WqOdWJ5+tKl6A4lEL5qJVfWrrNrHli2esph6XsZb0/a3Hk219bbtJErG+2bHmrj4khbsgR1u3Y2YkcbF0/6suV49niI775cQau/dbgb4KonZPSNosMXCag7dbCpMLOW5UdEoMkrxsP9KrpiDeGaEB557whavZHv+9d3KHQAiuPj6TZqIsscnLtej4yvxo1l/dswfedhG8HTpUkgy/u3EUJHIOAmxc59993H119/zbhx4wCsAuftt98mMjLS1aUCgeAWcRS20OqNjP/4EM93bsjsPi3IKiymrY8WzU/TMfjNIHXJchshIQ8IIGjkKOvwTwuayEi8u3Sh6NRJ1G0j7BoC2mCScKtTxxyGKtOLx9EICJlGY/X+IJPh/fDDIJnMoxccNCuEMtVTzz8PBgPKwEDC1621ycNxdW3GWjnq1q0p/OVX+88rLo6gMaPtJqOnHvqd5Kz9dDhhFpRn68nw1KpomyThM30ayqAgawVX2Xv5D49Byi9E7uVB/S0PYGrUg+1DFvHUxxdR6grRO/8UrRPQy9KlSSCBXtcvUsJqqXltSDsyC/TkF5Xg7aEi0Ev02REILNyU2Fm6dCm9e/fm2LFjGAwG1q1bx9GjR9m3bx+7d++ueAOBQHDTBHq50aVJoF3YQqs3cvjSVZ5qXweNhx71ty9hCr6PkiytjRiQaTTUe3MTGatWOxYJQOj8eWj373feQycqCu2BA3g/1MO2Okuvt2sk6GjMBFzLtUEur7B6KmPlKttrS70+Lq+NizOXjDtBMhhs3p+4R4NftpYWJ4wYS8NWEYf1qDRKNB07ornvPkyFhdTdtBGZSkVBXDzZ776LpNWak6FDQkClAEB+7meaKtx565mFBGAk1akVoPLxxjIBHW7eI+OrEeJGIHDGTYmdqKgo4uLiWLlyJffccw8//PAD7du3Z9++fbRu3bqybRQIBGWoKGyhcVOg0uehSPqV4pZjMWbZelr8Y2IwFRQ4FDFgFhiG1DQMeXmEzptL2iuv2Cb8zpiOunVrTIWFSAYjAWPHInNT4RUVhUylIu/b72yqrJx6X+LiwSQR8H8vOLTD4g0C8Bs8iIDhw60eHctezq614GqchaVJogmJg23VtDmixc0IOV6Q1a8LT3T8N3KNBmV4OMasLBsPFpSKtTWrSZ44ydwVWu0BhaW5N26eZHZ6ntizr9LJq5XzCeadOxNYJ5SfJ9UTHhmB4DZy081xWrdubS09FwgE/ywVhS2M+cDT2zDleSNz19lc6yzUVBZjXi4ejRtjyMhA3boN/sOGmZOSg4NJW7KEtDnXel1poqMInTUL7aFDqFu2RN02Ap/evdD1OkL6smUOc20saPftI2Tqy3bHnXqDyuTxlO2Z4wyFby0CRo60Ni20hMCKTpwAINtbQXqIRMcE82TyM/Xl1Ov3LH5r3yX5/T0AhC5cSN733zkWa0DIjOnI/fzIkxegVnoCkBs1hnlntrEv7Q8SlAncN2kFnmDbUbl0zpkq0J97XD6FQCC4VW5K7HTr1o2hQ4cyYMAAfMXkXYGgSnAWtjBkJGNMy0afZ0IW6AX5Wjy7drUm41pLzV3gVq8e6a++it/AgVaxETB2LLpDB+173xxKwJCZSd7X35A2a7b1uCY6mobbP8WQnY0rJKPRrt9OhXk8pQ0FZUqVi1490Shrh6JLTLQVTKVzt379dA0ahZHmZ8Agh8PtvXhi2jskD3+esiWqyuAgh/uDWfCETJ1GknQFtVwB+TkAZNfvRPyeT8yfT5kJ5paOysFBDfEIChGTywWCfwjXzTmc0Lp1a2bPnk1oaCj9+/fniy++QK93lYInEAj+CfSXL5E8Yy7nBv0f51+cTFK/AWS//wHBkyfh2bUrgLW6SuOkmEATbR6CqTuUgCo8nDqbNhK+bi3e3bo6/NL3j4khc+MmJ1VPS5FXIKwMubmEzpyJJirKekzdNsJlmE3dNgIAU5GOwBEj7J5FExlJyMtTzD13yu1TEB/HjgXPEPbBzwRdhWxvuPTsQ/x7xlbIK7CpOAPXoTAAQ14um5I/xs8zBL40F23kG21/H1ommP87YRx9T03hrE8RF4rNIyLOXikgVyt+fwoEt5Ob8uysX7+etWvX8tNPP/HRRx8xfPhwFAoFAwYM4JlnnuHBBx+sbDsFAkEF6FNSSJ0zz94bEh9P+tJl+A8bit/gQSj8/Cg6dcrptPHglyZQcuUK4atWkrF6jVXEhK9b6/C+LsNUcXEwcYLzROfoKNDrSX91Jeo2bfAfHmOe3VVBCwupuBhNdDQypRK5lxc+j/S2DhKVubtjyMhAkiS7SqwsHwWZgSba7zeH8U41lHPv8MkErHidjOOFBE14ye5eFU529/ZkRuOJ+L7XFwqvAOBVwfBOXbGKvq9fK+YQQzsFgtvLTXl2AORyOT179mTr1q2kp6fz5ptvsn//frp3716Z9gkEguvAkJtLycWLzr0h8fEog4ORSj2wIdOmkrN9O+qICKvnpt7WLQRPmsjFUaNQBQWZw0hlvDWOqqwCxo5F7unl0raStDT8Y4Y59r5MmwZyOYW7dpG1aRO6QwlkbtyEqdC+HLssCl9fQqZN5crrr4PBQEmKbb1TSUoqJZcu2Rw7eq8nksxI03MSBjkc6hJM4yQjmu/34h8Tg3bfPmQOZvy59oJFI/f1JdTDH/3gT0l56mvOD95NijaI+0MdX9MpNIq4k7beIsuICOHhEQhuD7c8vTMtLY1PPvmEDz74gMOHD3PfffdVhl0CgeAGMGZlVZh0XHL5MskvTSBg5EiKTpzAo1kz1G0jkHt6YgLrtHFJq0XS6+2EU9lBoDKNhvA1q8mOjUXdqqXL+8qUSpInTbaOmSg7d6skPR2pqMi61uIlUkdEuPQGyb28wGQi4NlnwWTC+6GHKNi9m6zNm61hKMuUdhMSByM8aHukEKUJMn0hPcSTx59ewOU9o2wSnU1lbLGQHRtL3U0bbWaJme2IJnTuHBQaL1D7opM8mfd9Pk1rq+lYz5epHeaw8tAi9qVcuyYqLIpBDSYz9v1zts/kpqBN3Vqk5hZxLrOwdPq5OR8rs0BPXlGJ9Zio1BIIbpybEjt5eXns3LmTjz76iF27dtGoUSOefvppPvnkExo3blzZNgoEggow5edXHG4pPW8RFJaE5fqffIwuIRF12wjCmi9B7u6BSaezuz47NpbwtWvweaQ37o0bY8zNI2BYDJJkskmALosmOtocUnIw3dziLbHk38C1/BhnQ0ctIx0yVq22G/UQOnMmmvv/haTVoj14CN3fR8nv3JbUy4l0TDTve7KRjMBMOfcFRaD7+6j1+pLLl9ElJuL7ZF88u3ezCX9p7r8fRWAgIbNmQVERJp0OuZcX+Xv3ktTv36jbtSN0wXx869RhzmMtmP8/874d8WN0i3lMbKelyFhILQ8fDHoNfV87ZDMLS+OmYP2Qdnz0xwW89Fq6hahAV0iurw8yPz8GxR4hs8Ds8RHhLoHg5rgpsRMSEoKfnx8DBw5kyZIlwpsjEPwDGHJzMWZlYcrPR+7tgyLA31rNI/f2Rlc6ndziebHOqyouRlGrFiatFplGY5NwK9NokCkUdhVL9bZucWyEJJH33fdo421HM4TMnEEG2AqQyEhCpk+jJDXVzktTdtp4SPh0NNHRaOPirIJM0mptho5avEFu9euTsXadnbDSxsWTtmgx6gizkNNERnK0hRe+hxO4Nw9KFHC4lQftEnV4RXYicMQITEVF5s9Dq0Xm7o523z7SXllE6Ly56AcONN9To0EVGkr64sV2naatOU9xcaTNnUfQwnlkyHRM7l2PC+dyqZ2fgTKjEKPak8vF7gS3CEHCZDf08/nODfnojwtMjKiF94bl6OLi0QN6zGLxxznzePiDE2QW6G94IrpAIDBzw4NAJUni7bffZujQoWhKm3JVZ8QgUEFNoCQ1jZTZs22ng3fuTNgrr6CqHYohN5eUmbPwG9CfnG2f4jdooP10cMsXtFLJ5Rf/AzgfrhkwciS6w4fRxsdbhZNXlwcw5uZaB22WHdJp3VsmQyouxq1uPfJ++AF1h/a2g0L1epSBgcjc3ChJS8OtTh1wc8Nw+TJZ772HunUbh/ZY7uHz6COUJKc4TYius2kj50eOIjHCnYgjxShNcKUW6J56lMhWPa3hs+zYWNQREagjItAlJlpFEkDDLz5Hf+ECcncPJMlEzrZPHXutIiNtrqu7dQufcYjO/h1QLH/TRhy5R0VhmjwTn7rhzPr8CM3CVbRvqEQvaanr609huozgNUucltBLs1/h0S2JVqH086QHuSfYda6UQHCnU5nf3zcsdkwmEx4eHhw9epQmTZrc0s3/CYTYEdzpGHJzSZ402UboWNB07kz4qpUofX0pSU0jddEi/Ab0J/uDDxx/cZaKEosQqvPmJi6PGGm3ztLUL2f7DvyeGuBUOFmGdIJZaFimgTfYuYPz/Qc4FVOWPULnzUWfdJ6iU6dQ+vmhrB2KW1gYaUuWOPSkJE+eQvjqVdb7lMc4czxJH22g8XkTAMfvVdI06t8oPv3KrqQczB4sk05n8xzh69ZaZ4JpoqPwHzrU5nxZyj5z+Lq1FNcNpmDlaxTF2z+ve1QUvouXofUsYeG++fyedm3Nrvs/JOPJQQ6fCSDo88/ZeknGhl/OANc/EV0guJOp0qnncrmcJk2akJWVdUeIHYHgTseYleVQ6ABo9+7FmJWF0tcXVe1QwpYsxpCW5rwJ3r59BE99mYC5M+DVNeYybwdYwkgNP9tpHhdRQXM/uJZvo4mMxKTVoomOcpp749m9G8EvvYQpLw+5pwbvB7uQ9GQ/AMI3voH/0KEET5qEqaAAuZc3hox0UmbPsc6hcsSR5hrqrF9P4wLQK+FwSw/aJ+rwCriEf5np6TbPaTTaHS+b+2QZaVH2OW2uLxcS9FRo8IiJQRo0yGZgqaTVUhwfj3vBVWb8vdJG6ADItfaJ0WVRaAvoem8j2tWtRbHBhIebglytXoSyBILr5KZydlasWMHLL7/Mxo0badWqVWXbJBAIymDKz6/gfIH1v5W+vpScP+9yfbGugJ4Hx/D2uBUE5mM3TkF39CiSJKFu1RJJp3MpnPyHx1jfy9zd0URGEjhqJIqAAHMXZJNkk3uDBMrQEGQyGekrVlj3tvTwkWk0uNWpQ/qSpXaepLBFr5A8eYp5DlUZDEBiGzfaHtGikCDdD3L8NXRM1AIyh8LMglRSYiN0NJGR6BISXT5nWSzCyLNrV5S+tcgo80yW/cLLCC1Dfi5Dmz9D68BWvH/sfXSG0kRwrwpSArw8ydWV8MJ7f1kPiWRlgeD6uSmxM3ToULRaLREREbi5uaEu99dhdgXt4QUCwfUj9/au4LxXufeu16t8fIkIisDLpELh72+TnCzTaKi7cSNZ720FgwF5ma7GjrB6c6KjUdWtS/DkSRSdOgWXL5O+aLGNyFGFhSFTq8FotBkuCtdEg39MDOlLlzn1JIVMn44iKMia8Jwa6IZWXUKHw+ZqpWP3ygm/LKPZWa3d9eUFiyYqykbYlA1ZOXtOm+ujo1AGBlJn4xuo6te3S2Iua7dFaF1RFDDm53F0qt2JFV1WMHXPVHQGHbkaGe5OhoW6R0eS6+EG5aJoIllZILh+bkrsrF27tpLNEAgEzlAEBKDp3Bnt3r125zSdO6MICLj+9dHRmNQSUzpOITzfg/QFC22EhX9MDFlbtuA3aCBXv/gvPo/0dmmbzN29VCQ8w/mnBqJu2xb/4TEU/X2U8NWrrCLBkJONW8MGpM2dh//wGDtRUHT0GKELF+DRsqXLoaHBUyZTkpaGOiKChIYQ8t991M6EYiUc716fNj+cR47j7stlBYsmMpLgCRNApcSjeTNk7u4oAwO5EDPcYW5OeW+SJjqawBH/sa6vs2ljhR4wj+5dyXODz9q+hrKwGGWemjf+tYLR+6fyXdZv9Jw2EvfltsNC3aMjkaaN5K8sA+czr9rtved0JpkFIpwlEFTETYmd4cOHV7YdAoHACUpfX8JeeYWUOXNsBIxlanb5YZJKX19qz51L6vz5Nh2QNVFRhM6dzaxT62mjbkId2tnNs7L0vMmOfR//mGHoEhNdNPeLRhkUROi8eUhaLfXefgvkcgri4sl+910b0RAwciRpX3+Ndt8+/J552jZ0plajCAjgyuuv24mK8hhzczF4uPHzoU9os/8qcgnSAkD27DP0i/43ST/0d3qtW/361P/gA+RenkhGEwW7duHe9F6SX5qAJjoanz6POhQ6mshITDoddTZtRK7RIFMqKT57lksjR1nXVzQ/CyDg5UnwylKK4vdhKD0WFB3F5kkrGPv3PDp2vY/Mlx7lntJhoUaNOyfIJEgdSBvfQOZ+4VhM5ReVVHhvgeBu56Y7KJ89e5YtW7Zw9uxZ1q1bR3BwMN999x1169alZUvXHVUFAsGNoaodSviqlaV9dgqQe3uhCAhwODXbkJtL2rJl5llTMcNsOhanL19Bi6HNiFQ3x5hm33FZKi62Nh0MnjLZXP3kqLlfZCShs2ehv3SJ7NWrXeapwLVGhjKNBrf69cn58CO7SeT+w4aC3PUEm7MXj5C8eR1tL5mLSP9uqqBZQFua9orBpNc7n4AeGUned99be/D4xwxDd+QI3j0fxrNrVwKefw5VaCie3btT+MsvNteVrTqrF/seppIS0ubMtdm/ooaOUmgQWaVCpyzauHg8kZj+0jgK8304l3Mvwfe6Y5R0aJSeNJW3ILdQSXZhCRuebs/Bizm8uzfJplePt4fK5b0FAsFNip3du3fzyCOPEB0dzZ49e1i8eDHBwcEcPnyYd955hx07dlS2nQLBXY/S19ehuCmPMSuLwl9+sfnSLsvjY1/EXVuCzMH3s8zd3eqlMBUUOG3up0tIxJCTQ/aWrY7za+RyGny6DamoiJK0NOSlPbmc5uTEx4MkWWdoOfIkHe4cRoMVa2mohSIV/N1CTcdEHXCA1PkLCJ48Cf9nn8WnVy/zHLDShOuSjAw8WrTgQkzMNfsAdUQE6cuW4z/0GS6NHGXuhDx7FvqBT9k8p021lskEKntxUXaURnnU0VGo3NUEDBqEPGa4XY+i4rh9dJ42g4lxV/npeAZ8e62r8pa4c8SdybLuFd04gPVD2jH+Y3MX5i5NAgn0EiEsgaAibkrsTJ8+nUWLFjFp0iS8yyRDduvWjXXr1lWacQKB4MapqHrLIz0XZI6/oHUJiXhGRRIwcqQ1pORo1AOAT+9ezgePxsUhFRdzZcPr+A0aiKxUILickL5vH/7PP2duTlhmDpVeDsfaeNBmbwpyICUQlE8PouP6bdeujY/HNHIEylq1zAKsbPguOgp1+3b29xpuThr2HzbUPGIiLg5jTg7Z73/guKdRZCSFfx3A++GH7DpUyzUavLt3J4Py4y1Kc3v6P2XTgLG858uQX8DsPi3QG0zsOZ3J850bsiUuyUboANb3z3duyOFLV1nev43I1xEIroObEjtHjhzho48+sjseFBREVlaWgysEAsE/RUXVWBahU3TixLWRB6Vf0Dnbt+P7ZN/SrsJt0ERF2eX1gDnsVBGGK1fwaNaM7Nj3CZkxHU10VIW5LZJWS8qMmdTb8i7GYcM4cWo/mdu20jbB3IfmSDMFDc+baHpPJMlss7lW4eNL+qsr7OzVxsWTvnQZ9ba8iynn6jXPSqktZW0yZGYSOnMGaYsW2zdRfHY4yGQUHT1K3Y0bydy0yUa4eXbtSsj0aYCEKTcXua8vusNHbHJ7wHGPIpmnF34aFa8NaUdmgZ5ig9HaQLA8cWeymNOnBS92biiEjkBwndyU2KlVqxapqak0bNjQ5vihQ4cIDw+vFMMEAsHN4bIaq7SPjKXZX862T1FHRJirnC5fRlWnjrWJYNGpU9TfusUcdiqf6DxvLiYHybxlUdWujcLHB++HHwbMZePGCv4Ykrm7I2m1GHNy+N+GKTQ+W0gDHejc4FhzNR0SddZ15ZGMBucVUXFxGIcN5fLIUVbPCkql3V4yNzdMJSX4xwwjeMpkm6aGRSdPot33O+oOHcgrTbYuS+GuXaSXlKBu146sDRuos2mjXW6P1Z4ypfDuUVGU+NQiqFS4+GrcOHQxx+XnVFRiFEJHILgBXGcDOuHpp59m2rRppKWlIZPJMJlMxMXFMWXKFGJiHDffEggE/wyW6i1N5842xzXRUaWjImKtuTgezZqZQzEGAzmfbkcqLrYKBlNWFheefQ7/YUNpsHMH9d57j4ZffE7IrJmcf/oZMP5/e/cd3nS9PXD8ndGmSRcdtKUtZYmCjFJApQ0q7oFe9SqIQguKCsjeIHsWUIaAgBMujuvCdd24UFvWjy1DUPbohq6kSZN8f3+EhqZNy7CQAuf1PDzPzfrmNHKbw+dzPufYXZPLKzIkJmLetg17fj6HkpPJnD0HVCocFgsGo+dVobK+NyUa+OrV4ST8UUygGY5GwIkoP1eiU7Hxn8pgIGrqFBymypPayytbwTGtXUve2++AzYbBaEQTGkr9ZcuImjYV26lTqDQa8la+zcFHH+Nwz14cfPRR8la+TeBttzlXvFq2qDap0rds4fZ+1cVjMBoJmTyOQn0R+ZYzBeNBZyk6lqJkIc7PBa3szJgxg169ehETE4OiKFx//fXY7XaefPJJxo8fX9MxCiHOU44+mO29hnPX8GHYCwpxmE2V+siUr8VRGQw0/PAD7CfdVxQcubkc7fe863bMywso2b0Hv2uvpTQjo1J9DbifYNLHx7u2azJTUwnq3JmIwUPI9tXhd911blPZtfXqsWPj9+RGqIjf4oxxewsfrtlnw2B1bmOV1cAcOT2PqqwJYs5rrxKanFztZ+I2BiI9nfA+zxExeDCHevVCMZkwGI1EjR9P5uzZHouuM2fNJvTpp88pian4fp5oGtTnx96tWfZbN8w2M8ZoI5OTJhPlH0V4gC+3NA3n1305lV4nRclCnL8LSnZ8fHx49913mTZtGps3b8bhcJCQkCCzsoSoBfJNVkat2s5v+3LY2asRDrOJo337OXvbxMd7PjGU0AbFWorGv/pJ2iqdzrUFptbrOfJ8fxqs/A+25GSPJ5jKb9eY0tKJHD4Cu6mYuv36kfXyAreal63GKJpuyiCuBEw62HN9AHfd1AP9wHhQFHwioyjNzcG08f+ImTcXtd6A2t9A1rz5mNLT0bdqXXVPIA9jIFQaLYf7namnMaWlkTF9OvpWrTxOOTelpRExbCi2nMoJSMXPCKo/oeVnTOKDvB+Yv+91131px9OYnD6Z2bfMJtgQzKxHWzNm1Xa3hOeWpuFSlCzEBbjgPjsAjRs3pnHjxtjtdnbs2MHJkycJCZFJvEJ4U06Rld9Of0FadD6osrIwJCa6khSVTndm+8pqxadePVT+/lgPHEDXuHG1vWrMW7e5tsDqv7oMfUICpceOuaaEe+JWAHzyJNrwMDJnnVk9KdGq+PM6DW3SMgA4Egl2Hz/abikid0u5XjzGJPQJbcldvBiVwUCDlf8BlcpVT1TV0FFDUhIRQ4ZwqFcvt7jsxUU4KtQQmdLSnP1+qmDLygaUszZaDOvbl5MffUT09GmV4zEaCR87ilvNmdD0WZYdeMc1IyvteBp5JXkE64KJrqN3FSwXlpQS6OdDeICvJDpCXIALSnaGDBlCq1at6N27N3a7nVtvvZX09HQMBgNffvklnTp1quEwhRDnqshSypiO0dwW6YM9p5CgG9vh27AhOUuXcXz8BOKWLq20qlK29XSwRzKx8+eRA+5bU6eb/pXNjdLHx+Mwm4kYOgRHUVHFENyU385RSq0oNpvry/9grB+a0hLidzp7Cm9roaXTMzM4OXR0peuY0tIJTU5GZTAQM/clsubNJ+TJJ9y6Mas0GkJTkgl9+ikUkwmfmBgKf/gRW/6pygM/t2x1j/P0cXJteDgxLy+oNLXc+SRQGwIqnWJzfYY9unMoOQV9fDzR06dxfPwEIoYMJnzsGGwFhWhtpRSvW8+Rrk+gmEx0MiZyw7A59N46ypXwFFrPtA4INkhyI0RNuKBk5+OPP6ZHD+e/fv73v/+xf/9+9uzZw8qVKxk3bhxpHnpUCCEujSaYCPpkMSXp6eQAuQYDkePHETVxPAoqsl58EX2r1oSe3noq+1I/+cGHhHTpwpG+/YgcM4a6gwah9vMDux17URGaoCAarPwPis2GSqulNCMDRVHQRkdjMBqr7E1Ttn1kMCZh/mMnAR1uAmBzawPN/jRhsECxH/zZ1EDbHSYMah1VnUVSLBbnNPWVb2Peto3IsWMqd2M+nbgdH/sCMfPmkrtsmWuqujMOo7OmaOgw131lCVTeyrcrXausJ44+Ph7z1m0EdDSCry91hwxGPWok9vx8HBYL5i1b3bbvAMKHDMbcrhkbTTu5dumXmCusmFnS1uIP9O3dw7WlFeh7ltYBQojzdkHJTk5ODlFRUQB8/fXXdO3alWuvvZbevXuzcOHCGg1QCHHubPn55E6eREmFXjOlh49gi45GU6cOIV26ePxSD01JBq0WZdkyMmfNotGnn5AxeUqFLZgkwp/rw+Gne6OYTM4C4TffJGrCeDKmTXdLeMoXKhsSE4kcPZrC1T9QkJ/N9uu1tN3uXC05XA8UlR9tdzhvq/z83GdnlSVjH32Eb8OG+DZogH9SIprwcOek8SonpI92JVo+sbHEvLwATXAwmpAQzHv2uK30lCVQ1V1LW7cux4aPwL/DTZg3/h+5y5YRu2wpR08XS1dkWruW8LGj+PfGPrzRbEqlRKeMJW0tNw9KYT5gjDYS6hfq8XlCiAt3QclOZGQku3btol69enz77bcsWbIEAJPJhEajqdEAhRBnZ8vPx56bi/3kScKSk9EnJKBSqfBr2QK1Xg8OheJ16wi4/bZqv9QjRo5wbedkTJ1a+XlpztWisgZ9iuLAemA/ua+uJmLoEGzJPVCsVrTh4ah8fSnNyCBm3lzMW7dRmpnJnz4nMU9ZROss5/W2tvSh2e5S/OxnTlv5REaRt21bpWSs4cr/kDl3LsU//Qxw1knjESOGkzlrNobERApX/+C6niExkcgJ493qbs7W2bnuwAEcfuZZ9PHxFK9b7xqYeraTWeb8PPJK8tAWW1zDPz3RmCwkRScxOWkywbqzjwQRQpyfC0p2nnrqKbp27Uq9evVQqVTcdbpp2Pr162nWrFmNBiiEqF7piQyOjx/vvqpyegXmSLnTRobExOpHPKxdiwqVq/DXr3kzwpJTKtWtmNLSsScnOxv0GZOIHDOGrLnzsGVnk/fOO5i3bHWNUsBuR6X1wScmmm++Xsg1X/1BmBUK9bC/bZSrKLksvqgJ48mcM8djMpYxYyb61q1dyc7ZEo3SY8fQJ7QhtMeZWqOya1FiIWL4MBSrFcVuB0Wp9lq27OzTx+idK1XRzWcCZz9e7hfoTFxs/tU/LzQslrvD7kav1Vf7PCHEhbmgZGfy5Mm0bNmSI0eO0KVLF3Sn/w+v0WgYM2ZMjQYohKiaLT+/UqIDp1dgHIrbSALT2rXYT52q9noOUzHZryypVHhbcZaT2mAgrG9f8lauJGv+fOKWLiV7yRJCe/ZE/Vwft1EKRX4qDjTW0mpXKQAHo0GjDeL2Vg+jT453O7KuWCyuZKYiU3q6qzAYzp5o+MbFETF8OEU//0LMvLluW2J2UzGHezivFfPygrNeyyc2FkP79ph37nIeezcYqL9sGSp/Q6VJ6eU/N4tiI9QvlNA69fD/ZBX2wkJXR+bj4yfgyM1FZ0zkk5wfmb/vddpGtpWVHSEuggs+ev7YY49Vuq9nz57/KBghxPmxZWd7LAwG95EEZRxna4hX7qRU+euA+ywntb8/PtH1iFkwH/P2HWS9vABTWjp+LVti3rLZ9Zq/GugxFJtptasUB7DjxiASNM1wrN1A7rJlbgM19fGtz3qyq/xqTnV9bAyJiZh37sTQvj2mzZvIWbTI7bHghx9CHRaGIzfXlWhVd62iX9bgd31zZ63T4sVnHjMmETl2LFkOh1tvnrJ6pWxMfHXTW+ROnkFWhePwDVYsJ2PJKxQ++zDLtowC3E9iCSFqzgWNiwD48ccfeeCBB2jSpAnXXHMNDzzwAD/88ENNxiaEqEbpiQxKjxyt9jkVt3rMW7ZWPa7BmETxuvUeHzOtXeuqUymrgSn45lvUOj8COhoxb9lKWN++BN7WiZCuXam/bBlbb4sm9riZ6BwoMMD2lgYeGruC6JReGBITXSegSvbscRYSq1Qodjv1ly0jrG9fVAZDpTjKr8DkrVxJaEpypZEVBqOR0F49sWVkOuuOKtT1mNauJWPaNKKnT3ObFebxWknOERuKoniudUpLJ3PGTEIe70rssqXEvLyAuBXLCU1J5uRHHxMZGEPuFA9F1OnO4aS6Qc/Qe8uZY+dyEkuIi+OCVnYWL17M0KFDeeyxxxg8eDAA69at4/7772fevHkMGDCgRoMUQrgr276qrgEeVN7qyVu5koYfvE/mrFmVRjxEjh7NwW5PVHktxWJxO2GlmEzkAJFjx7gd2y7Qqzkcp6LNn3YADsSq8LX40uYPE4rVemb46PBhZC9ZQsjjXas98l22deZ/+2341I+jwXvvYsvJQe2rw7xzJ0EPdCZi6BDshYUopaXYsrOdNUetW7mt6JRnSksncuRIwvv1RSlxFkcfHz+BkC5dCO2ZgmKx4BNbH0dxEergYIJiYtxWdNyudXoF7Wjffq6VqqC776LO0z1RF5mqrpFKTyfMMsyV6MhJLCEungtKdlJTU5k/f75bUjNo0CCMRiMzZsyQZEeIi8yem+scOtmq1XmNSNDHx1P4/WrC+/SBZ5/Fnp/v2sYpzcx0O45dkU9sLPr4eLcExLR2LSofH9eqx95GBoIKTLT8ExzAtgQDdz81HZ2v3lmb4+ND3QH9yV78Cvo28fhde121p8PKts78O3UiYuhQMqdPr9QdOWrcOBxWC4rN5kxS6kZQmpGBrnnz6j/D/AIOn956NyQmEj19mvNnW2Zydl0eNpRjL4yjwavLsB4+XO21FIvFY6+euP+sqP51hcUAchJLiIvsgpKdgoIC7r333kr333333YweXbnzqRCiZriOmOflAdWMSKgwMBPA//bbiRw1EkdxMfaCAnzq1iXn9dddKzxhfftiSEpyjV8or+Lx7fK1NkpJCUVr09nSxkDLP0zobJDvDwcb+pOwpRj94BAO93qqXGzOBEWx2UCl8nzN08XE2rj66Nu1BZuNzJmpHreDMmbOJOieu8mYOMkt3qgbbkBlMFSZwKkD/M9cp1xyZd62jdDkHmQvWUqD5cvJmDixUu1TRSqdzmOvHnVA9bPGtEFBLL59MbGBsUT5R1X7XCHEhbugZOdf//oXn376KSNHjnS7//PPP+fBBx+skcCEEO7KHzGPfdWZIJTNqQpNSXFtv6h0OnwbxIHWl/pLXnGu3hgMaOvUIWPaNFdyozIYiBwzmsgRI3CYTNhNJgJvv50sRamUOIX26O46vl1xBcNv6lj2NNXQbqtzO+bv+ioMZh/idzpXLez5+W4/hyktnYxp0wm6/z58GzTweM0z751EaI8eoNVWvR3kYZ6Vae1aMqbPIHLMaLckqPzPpPLzc0uGynrzAK7VK2XQQGe9Unz8WVfQPPXqsWVlVZ1AJiWx15HBgJ8G8sXDX3j82YQQNeOck53ynZGbN2/OjBkz+OWXX0g8XdC3bt060tLSGD58eI0GeOzYMUaPHs0333yD2Wzm2muv5c0336Rdu3Y1+j5C1Gblj5irDAa0desSNXUq2oi6bkeq81auRB8fjz4+nryVK4kcMxq/Fi1Q7HayFixwq9NRTCYyJk7CYDQSNX4c2Ytfca5qVEic1AEB5L613JUUlF/B2NPEQMiCVK7PA4cKtrTyJX67FS1W1/t4OtbtOinmcFS6ptvz0tLBoVB3yOBqPx9PPXfKppRXTFIMSUlEDB5M1tx5hD79tFstTunRo24Ji73QeTqquhW0stETZb13yjs+fgINViwnM3WWW8JjSEoiZMJYUjb0llodIS6Bc0525s+f73Y7JCSEXbt2sWvXLtd9derU4a233mL8+PE1EtzJkycxGo3cdtttfPPNN0RERPD3339Tp06dGrm+EJeLshodgLDevXEUF1Pw3beViozrL12Kw1LCsSFDzyQziYnUHTig6m7DaWnY8/MJ79uXnNdec19ZSUoitFdPQrs9jmIpwbxlK4F33oFvyxak6w9z/a/H8LXByQA4Wt9Au+3uW0Zlqx6etqjUIXUo+u13DEbjWTsYq0ZU/4+oqvrkOAoLnc0ATydvmuBg1AEBHO7TF0duLuHP9HZLdipeRxPoPB1V5QpaXByFSgm+r8xEHRJb+f1zcznU6ymip08jctRISgsLUAX687eSTcqG3jQPbS61OkJcAuec7Bw4cKDSfTk5OahUKsLCwmo0qDKzZ8+mfv36LF++3HVfw4YNL8p7CVGbOQrP9F8JuPUWsubO81jUm6NWoU9o61anYlq7FqVf3+qvX1yMJjSUqPHjsB465Pwy9/NDGxZGzutvoGvalLoDB4LdwYlDe9i6bDJt9jlPW/3VQE3TEdOJee9/mKi8/XV8/ISqt6h69iT43nuxHjlSbXz2U6fOqxC7jKZOnTMT0XU6itetd+sGrdhsVV6n7Hh82TaUYjJVTgSTe3AyVMW/945gaNNn6WRMxJLmHqMjN5fsd97mr8H/4vpmN1LqsKCyhrLi3hWE+oVKoiPEJXDefXZOnTpF//79CQ8PJzIykoiICMLDwxkwYACnztKd9Xx98cUXtG/fni5duhAREUFCQgKvv/56ta+xWCwUFBS4/RHicqcOdO+/UnX9Sjr6li0q3a8JrL5/i0qj4eAj/6b0+HGO9u3HscFDONqnL4f79CW8d2/MWzZzqNsTfDP5WY5Om0DzfXbsKtjUxo+GhxwYVn1PaEoyscuWErfyP8QuW0poj+6uSepVbVHlLV+BolGjjahbbXyKzVZlT53wfs5OzhUZEhNBrcEnOhrf+nFoAgIIuvdeYubPQ336H2jq0718yvrplF2n7Ii95eBBoiZOwJDk3pvIkJRE5NgxHB8/AY3JuYW27MA7FA9LqdTHyGA0Ejl1MvGNbsZaEsSp/Dr405hQnxhJdIS4RM6rQDkvL4/ExESOHTtG9+7dad68OYqisHv3blasWMGPP/5Ieno6ISEhNRLc/v37Wbp0KcOGDeOFF15gw4YNDBo0CJ1OR0qK59MRqampTJkypUbeX4jaQhMWhqFjR0y//46jmuPh4Ll+RbHbqy6UNRpxmM3OYl2b+7jKkC5dyJo/33naKl5P6z9M+NghLxAy2zTg7hb3kbt1GcW//EJIt8c5+f4HhDze1dUgMHL4cGy5OVVuUZm3bQObDZVGg8Fo9NgN2mBMQhsWhsNcQsTwYeBwoNhszgGnajWlmZno4+MrjbgI7dUTlVbjnMZeoc6mwX9WkP3KK6gMBhquWoUtKxO0WqJTZ7qO4h8bPoKYeXOxHjtGaHIPIoYPw1FUjDrAH1tWFod6PYUjNxe7wbn1ZbaZWZ79BaOmv4A6N5vSwnzsBh0/Fm9n658v0f2awTz1xj5MVueK2C1Nw5n1aGui68g8LCEutvNKdqZOnYqvry9///03kZGRlR67++67mTp1aqX6ngvlcDho3749M2c6C/8SEhLYuXMnS5curTLZGTt2LMOGDXPdLigooH79+jUSjxDeog0OJnraNI5PmIBKW/3/bcvqTsrqZPw73IS9sJCoF8aSkTrLfWBoYqJrBabBihWg0WAwJrnqe/Rt4vnr3TfIbKKm3TZn8729DVWEnNTQ/LdD6LvHn3ljtYbIkSPImH6mY7DKYKD+q54TnbITWJmzZmHestVZAOxwVE5aevTgUEpPGry9ElteHthsoNHiMJvR1q2LLScHQ/v2brU05q3bsOXmkveflR5WlNLITE0lavJkTBs3om/Vyq13UHnmrdvwiYmm4OtvPK6m6Y1J/FS8HYAO9TrwdMunmbB5FutOrKv0XFNpCb1veZ5FPxwD4Nd9OYxZtZ1FTyQQbPD1+BkJIWrGeSU7n332Ga+++mqlRAcgKiqKOXPm0Ldv3xpLdurVq8f111/vdl/z5s1ZtWpVla/R6XSuwaRCXCnyLfmcNJTgN20MKmt1qyBGUBTUYWFET5/mVicTPnAg4X2eg2efcWsm6DpmbbViaNfOedTb4Tx+/vvajwlQ22j2N9jUsLWVjrbbSlDjnBJefhXJN66+82h7uaRAMZlcW0UVVTyBVb4AGAU0dYIpWvMrx4aPQJ/QBrW/P75+fmTMnOlemG00Ev7cc+4T3o1GAu+6k4wXxnl8b1NaOkpxMZnTZ6BPaFPlEfWSvXsJ/vcj+DZoQA6VT2KFTRnPzYZSOlx3J6dKTqFWqT0mOgDrM9bSveMgt/t+3ZdDTpFVkh0hLrLzSnZOnDhBixaV6wHKtGzZkoyMjH8cVBmj0ciff/7pdt/evXtpcLo3hxBXg2OFJ5iybjJrj59ebdHq+WDMEgyz8LxK89HHNHznbTJffNHty9mvxfU4TCaOlms0WEZlMKBv3ZqATrdSeuwYoc/3I113gJZv/4jWAblBkBHlT/ttxYDqzOtO/8PCYDTiKCjweOJLsdk8FhdXPIFVsQC44aqPnUfpE9oQ2qMH+V9+hXnTJo8rNTkoNFj5H0qPHUOl0+ETF4f1r7+q/VzthYU0ePttFFspaj89UdOmkpk660zClJhISJfHOPjEk4Q++aRzC43Tw1QD/Sk1KDiUYvyOF6ItyMfX3w9VqA29Vu8aAVGRVam8elRYUlptnEKIf+68kp3w8HAOHjxIbGzlI5bgPLFVkyezhg4dSlJSEjNnzqRr165s2LCB1157jddee63G3kOI2ux4QS6T105iXcaZL3izzczjG57nvy8sIS7b8ypNhqUEfavWFP/0s+t1nmp5oHJDv+xgLSdD7bQ54Fy9+bOJhmbdh9Amuqmrl49iMrlOLxmMSYT3eY7SKv6hYy8oIDQlGahQWK0o1f7stqxsGqz8DwCHUnoSM29u1fOp0tKxpaRwbPAQABq88w4q3+pXSxzFxRxOTnEVIxf+/AsNP3gfe34+jqIit88zZ9Ei15ytsE/fo8uWPnxl/A85k6diTiu/2pPEm8Pm0HvrKI8Jj07tD2S73Rfo51NtnEKIf+68kp17772XcePGsXr1anwr/CKxWCxMmDDB4xiJC3XDDTfw6aefMnbsWKZOnUqjRo1YsGAB3bt3r7H3EKK2yjdZOZyf5ZbolDHbzDiKijjcq/IqDTi//COGDcOveTNXw0GVn5/HBKP8dtKO6wzEHDdx7QEo1cCOdsHcHt+VU1PncoQzAzpPfvQxkcOHodhsBN1/Hwe7PUHMvLkeYzFv2kzJnj1u/W5UOh2aoKDqPwAV2HJyUCwW5zZbFclaGbVef6YjskaNLTv7nI6rlz2uj48nc2aqcwXHZsOveTNi581zS/B0xkS+PPkbPes/Su6kaW6JTtnnHgD07d2D+fvcT452qNeBcIM/Bl+NW5FyeIBsYQlxsZ1XsjNlyhTat29P06ZN6d+/P82aNQNg165dLFmyBIvFwttvv12jAT7wwAM88MADNXpNIWqLsllXjsJC1IFBaMJC0QY7jyPnFFnJtxRW+VptsQVblY86uwGXrXQYEhMJvPMOSnbtcitABud2UuayZWyL1xG/w4TWAdl1ILuugbYb8gl4ui2nTn9vm9auBbWasOeeRVEUzNu3ow0Pdz6oKMStWI49P9+to3PeypXUX7q0UsPCqKlTqq49Op2M+DVvdqbg+my1eA6Hc7bVju2oAwLQt23rudam3OT2MmUdnXOXLTudRLrP8mr4/n+x5+dTGKbnwy0j+G+LOWSkLfUYRklaOncNfZb5nEl2OtTrQPfm3Vn6x1x639KbRT8c45am4cx+tLXU6whxCZxXshMbG8vatWt5/vnnGTt2LMrpfyWqVCruuusuFi9eLCefhDhH5WddlTF07Ej0tGn41IuioKQUX5Xn4l4Am3/1X/7lkwPT2rXkvP4GdQcPwnDDDWRMn+5KeA4d3s2BBirabXOunOy+Rk1kporr951uvFdhRcWUlkbkyBEc7Po49d94HU1goGsbrGJS4VwF+gi1vz9B999HaEoyKl8dPlGRKFYruibXoDz7LMXr1rltj4WmJDtHLbz5Bg6TiZiXF6ANr1ttclS8bj3+HW4i6N57UUpLcRQUoigOoiZOQLFYsRcW4ih2354qz/VzqtTUX7bMlayZ0tLJTJ2FPj4e845tfDzhFZSD1TdB1JXY+eCBDzhaeBSdRsf2nO2M+tW5tTXwgSE83PpWwgN8JdER4hI570GgjRo14ptvvuHkyZPs27cPgGuuuYbQUJntIsS5Kj/rqjzT779zfIKz43CQnw8/7bVxU1Qi6z1sZf1NNs0rrNKUqdgNWGUwUOfhh8icMQPzlq3OU0/JyfyS/l/CFi6kaRFYNbC9lR9tt5pReyhCLs9RXEzkmNFoAgJQ6XSemwaeXgWKGDoE28k8MsaNd9UHZUydVuFkk3P1pDQzE/OmzRwfP4HY+fPIfPFFV7wqPz/C+/aB556leG3l5OjY8BEYbrgBe2EBOcuWVTixlUTEsGEcTk6u8r9J2c/pMDuLuMuStWPDR7it/OROnUndAf2rvA6AxU/Dj4d/5LXtlesLrY5imkVUPw1dCFGzLmjqOThnY9144401GYsQV43ys64qMv3+O/bcXPxCoth11EpKx5H8O2obTQlHW2zB7u/HX6ps2sW0IXjyDZQey3DbOirZs4eQx7u6bdNUPOaduWwZ21r70maHFY0CmSFwMtRA+60myp+2qmoUg9pgoOC77zDceCNKcXG1E8ltKckoJSUe4zjzvDOrJ7nLlhE1dQo5r77q6r9TedSE0S05KlupUev9yF602OP1S+65p9IW3pnrJTmLrT3U8oSmOJOcspUfU3o6Kg8DRsv4GZOw1wng7TWet/QDfavvZi2EqHkXnOwIIS5c+VlXntgKCpn0Wy49OjQk1lKIduE3zhNHpx9vbkwieEI7MubMcTtxZTAaiRgymMN9+7lt05Q/5p0ZE0CRpph2252TyXc18+GGwbNR3lmF6e9yW2qnZz+VT5rg9MwovZ7Q5GSsBw6iCar+y1spKXGtmpxt4GfE8GHo28SjCQrCNHESYX37VpEcpbklR664fH2rTLwyZ82m4YcfkJmaWnnVZ8gQsl9ZUmUtD7ivcJVmZHg8YWYwGgmdMp7Jfy/2eBpLJpwL4R2S7AjhBRVnXVWk+Afww+49JNb1pdGqhZVWI0xp6ZyYOh19a/fj5aa0NLKAkCeecDumXbYqsa1VIA32FxJZDBYt/NFST8JWE3zwBVGTJ2Hdv9+ZnJweApq9+BW3pMmQmEh4v76YNm92Nexr8N671f4sZcfiDYmJZz1RVXrsGMcGDyHm5QXA2ZOjskSkbCurquPv4OzjY8/PJ3LkSBz9TdhyclD5+mLLznb2Gaqik7JisVTeFtRqOTZseKUp6JbYcHZTwoD4oeSXFLhtPxqjjTLhXAgvkWRHCC8oP+uqIkPHjhT6OWs6OtbVYPaw7QLO7ZSy1QW3+9PSiBg8yC3ZsRl0bG7tS5sdhagVyAiDgmA97baaARXFv/xCRmkp+latXMlF2biJkG6Po1gs+MTEULJ7N6jVZE6f4XqOvbCw2rlWtqws8lauJGbuS6j1VRdcA87j8ZxZRTlbcqQJDKTxN1+jKArWQ4fwqVev2uc7Cgs53L3HmTqfYcNddT/6+HiPIyM0wcFuKz6GJOeWV6Up6MYkdg26j6Z1biHl1T94uN3zdO84CKtiolFoGJH+4ZLoCOEl5z31XAjxz5XNujJ07Oh2v6FjR6KnT8Ps5+98nrm42utUlQwopaU0+uxTGn3yCdZpw1k79XnabreiVuCPazUYTBqu3e++zWJKS0Pf5sysq7Iv87Ip6KhUGNq358hzfVBMJmex8YL52HJziRw7xjmqovzPkphIaEoKtuwcYubNRbHb0cbEVJog7np+UhI+sbHlTl4lnfW4ub2wkIyp07Bn56DSaCj6ZU3V169Qj5O38m1CT8/YM61d6/azu15zekjqseEjUOn1xL29ksjRo/BPSqThqlXELl2COiwMg9GIYeIo6kfdSLdl2zhy0syiH47x1OuHeHeNQqRffUl0hPAiWdkRwkt86kURM/el0312ilAHBqAJC0MbHExAQQlpgxMIKyzgQDXXqCoZUAcEkDl7DukF22i8v5jGJijxgT/vaEz8t/urvmAVTY0NiYmU7NqF7tprXasfYb17o9b5UfC/L8mcPuP0Ca8eoIA2oi6FP//CsaHD3MYv1B086PRzlMq9b5J7UHr8OMcGD3EOEF22FOuhQ2dtDGhau5YcIOj++/C/uSNBd99NxqwKdTnV9Nap6mf3MyYSMWkClJRQ/8038AkN5cTkKe5xJyXR8L13SCveTvPQEF7+5BA5RVbX49JLR4jaQZIdIWpQviWfvJI8Cq2FBPoGEuoXWu2/6LXBwa4mgmVOnDJjzTtC7G+jUCJuwJDUAVN65eGSZdsple43Gjn5fxtJK9hI/B+lqIET4VAcEsxDT6dy8NvHq4zHJzKyUnJhMCYR3rcvmqAgSrOyAOf2VeC992DPziak2+OE9eyJaevWM9tCSUnoW7cuN5gzifA+fbBlZXF8zNhKtS5lvW+iU2cCzlWlI337ETl+HFETxpMxY0a1yUtZ4mLPy+PwsKeIHDOayFGjsRcWVBr9UF75lTFNcBANP1lFcfFJTvna0QeGkj1rNsU//Uzs0iWcWLiocqF0ejoZU6YRNjKZEkcxL3WJJ6fISmFJKYF+PtJLR4haQpIdIWpIRnEGk9InkX78zJdyWVFqlH/UOV0j32Rly94D3LP7BTQHfoZjG4getJLjKrX7l31SEpFjx5A1d57b6w1JSZz6920cnzedhGPO+3Y009DooIN6OfkU/vRz1fU1SUkU/PRTpbEOtuxsHCUlqHx80IaE4H/7bYR06UJmxenj5fvSpKcTOWoU+vjWoFJh/mMnpo3/R0CnWyvVupRXfqVKMZnIeGEcDT/9hLqDBqEeORKHxYJKq8V+6hTY7aePsq90GyehmExkTJyEwZhE1KRJHDi97Vbd+xmMSRSlr8Wv6TWcjNLxW/F2bl+41TUOQhsRUfXx+vR0mqiGsd9aSKNgSW6EqI0k2RGiBuRb8islOgBpx9OYnD6Z2bfMrnaFJ99kJafIisVm5/ogqzPRAbAWo8raTNC99xKanOwsFI6NBUUhe/Er+DVr5iog1gQH8933rxI7fjoNzWD2hV3N9bTbdqY2J2/lShq+/18yZ89x79xsTCK0Rw+Pqx8AscuWYj95imPDh9Ng5X/ImjvPcxNBzvSlcRQXcbTf827PUUpLz2leVXmlhw9zfOwLNFr1Mdmz53js0nxs+IhKW3qmtHRsx48TOXYMGRMmVvl+hsREwvv0wbTx/5wzu0wW7gq6kaJy4yAcRUWVXu/2cxUWE1ivbrXPEUJ4jyQ7QtSAvJK8SolOmbTjaeSV5FWZ7Bw/ZWb0qu38ti+HJd3b0kbt3oPHHtqWzDGjCU1JQd8mHvupU6h8fKnz8EOgVqOUlGBWbPz8cn/itzgTlWMRYPZzT3TAuepRmplJ1ITxWA8eRG0woPb3R7HZOPzU067C47L3UiwW1Do/1CF1UPvqaPD2SlCoepVj7VrqDhzgLPZVFLexC4rJRN7KlTRYsYIslQpTevU1NWVUOh2hKSnOERceuzSraPjeu9gLCjBt/D/3zy4/H7/rr/e4NRc5ejSlmZkAHOnbj/pLXqF43Xrqdr4HR1ER5dMbdUD1HY/VgQHSP0eIWkySHSFqQKG1+iaBVT2eb7K6Eh0AnVaNVeXeg8dhsXvuIny66d/a+ZNQLPnEn3Dev72VjpaBrVGnb6z0fobERMybNuNTvz627Gx0TZrgKCxEExR05oRVFR2Lo8aPo3D1agytW1f7s9rz891WdMqvvigmE7b8U+hbtyY0JRm1wYBKq8Xy118eV5XKOhv7d7ip6n47aemUJmeQ97bzdJVr8jnORKn02DHX1hyKgiY4mKJff+Ngtyfc30+tpnjvHlRPPESJ5ZTbe9iysjAkJbklaOX/O6iCg8kt0LDffJIgvQ/h/rKdJURtIkfPhagBZxsBUNXjOUVWV6IDsOXIKXYW+GJrdLvrPnV4Pc9dhNPT+erdqYRm5NPgBJh0sDkhgPi/NTQeMrLSEeyy1RPLgQMohYUUfPsth57szuFeT1Hw/Wpn8uRhnENZwz1bZib+bduiCQqq/sNQqdxumrdtw5adTdwbrzuPlZ+eo3ds2HDsJ09y+Jln0datiz7e/ei3wZhE1PjxWA4dQuVTfeKgWCyY0tLJW/Ef13Hysm0qla+v6wj90X7PYy8ocI5/OJ3chfXtS+yypaBSET54IIqi8HPRFnTGRNf1j4+f4DxeX/EzTUoiavIkxv+axe1z1/DIknTumLuGgf/dwvFTlTsoCyG8Q1Z2hKgBoX6hGKONpB2vXPhb3YiAgpJSt9tv/X6AV55sy9GbZxPLaLQHfkIpLa2U6JRoVey5TkOb350dg49EqlA3bUbb33ejAIf79aPBW29iP3kSe36+68TTyQ8+pO6A/pVqbs40/dO7raB4WukJ69u36lUODwNIPU5ENybR8IP3sefnEztvHuY//yRixHAchYVu8Wa++BJRo0fhOD1bqypl9Tplp7LKEruTH31U6bllhczVrWK1G56MZURLDKgwpaXjyM3lUK+niFkwj4hRI7EVFqAODIDgIHYqJu5pV8q/bmjIpv2lvPlrBr/uy2HMqu0seiJBVniEqAUk2RGiBgTrgpmcNJnJ6ZPdEp6zjQgI8vNxu22y2un/3mb63NqYh+9YTKSmEMdx9y2wg7F+aEpLaLPTOSlrexsDzY7rqNfyVvQ9BrlOUal0Oor/bxP6li1QGwyupnm2vJOVkifFZOLY8BHELlrodr+nlZ6yxAhwr7sxGgnt0b3aAaSunzMtncyZqa7ZVgZjEoY2bTjyfP9KW1knSkoITe5xzoXNaoMBfXw8Jz/4kIhhQznY7Qm356vr1DlLbGnocXBqbC8ODr6PdiMGUFqYj0kHHxavY9mmd1xzrzrU60Druq1d081vikpkcfIIBry9n1/35ZBTZJVkR4haQJIdIWpIlH8Us2+ZfV59dsIDfLmlaTi/ltvKMlntzF+9jw0HwkiIC6FrhL/rsc2tDTT704TBAsU6+PM6Aw8PcRbWll+d0BkTiZw6GfOWLeQuXkzMywvONOtLSsQTxWRCKXVfafI0m6osMQpNSSFi+DBsWVlogoPRhIRw4NHHqhxAWlH5pn6mtHRyHIrrJJfb89LTCX2ql+fBmx4Km8sndqWZme6zvZKSKImqQ9in72FQfKqMzZK2lsaO4fTc0p/Fty9mwIaBHp+37sQ6ejTv4brtnIX1Er1veZ5FPxyjsMLKnRDCOyTZEaIGBeuCz2ssQLDBl1mPtmbMqu1uCY/xmjCeMjbivfWHsDWtD7fdzPacDbTd7vziPlxPheHWu7g5w0rxuvWuL3eVwUDk2DHoW7fGlpFF3dEjsWy7B5XBOZMqNCUFHI4q4zFv3YbBmOTqn1PlOIrTvXL8mjfj2OAhxC5bitrfH31CgtuR9rPNtiq/paSPjyfwrjvxa94Mtc7P7RSXYjJxfOwLhKakUHfgAOz5+c7+PRWaBRoSEyn84UfnapGnmqXkHtitDu7e3IcPG0+vPrYi56gOi736n6Hi4+sz1tK94yAAAius3AkhvEOSHSG8LLqOnkVPJKCYT+FnzUNdWoTaEIpiy+Wmjg7Wr3sfZedvtHY2L2ZrSx+a7S4l9HAREWPHcKjXUwQYE2nw33fRBAWT+dJcSo8dR98mHltOLr5xcWijowm47170beIpXre+yi0hy6GDRI2fQMa0aZjS0886m8r1uAIqf3/Cn3uOHIfDde1zeX2VtTMVeuiUJVhl22h5b79dZVflsqaLpRkZxLy8wK1Ls9/SOQxo8hSRdeIofXlBpcSqjDbQWYit01T/M3h63KqYuKVpOOEBsoUlRG0gyY4QXmbLz8cvOxP7yVwICMKhCUDJz0Jz9Ee+++IjGqdZ0FuhUA/7rvGn7Q7nioMp3Vn3EtKlC2q9BvOuvyj86WdCHu/q8Zh61MQJOMxmVD4+hPbq6bxGhTlP4c88Q+aLc1xHwzUhIW4rPeWVr5XRRtRFKSziSL9+rlEQar0Btb/hrK+vsnbm9O3IMaPdanLKttEarfoYh8mE/VQ+aoMelY+PM7mZNxfz1m2UZmRw9Nnn3K6pMhioG9GQW6e/x8G0M/VJFY/HG4xGzIE6OtTrwPac7XSo14F1JyqP7Ch7vKJgXSCzH20o9TpC1BKS7AjhRaUnMjg+frx7N+PERNSPdGbtig9otctZ83EwGtQOP1eiU8a0di3hfZ5zjnKIjMSvWbMqj6lnTJuGvlVrzNu24deqJfp27c6MhdAb8ImJBouV4p9+pvgnZwfnslUXHB4Gd5atohiTKPr9d/zbt3cbBRG7bClH+vU76+tj5s2ttq4nYsRwMmfNdrtfHx+PvaCAQ493q/KzjXl5QaX7IseOIWf6zErJV/nuz+Zt2wjv8xz7io/xXOvn+M/O/9C9eXcAt4QnMTqRJ5s9yahfR7ldKyk6icahUUT466uMTQhxaUmyI4SX2PLzKyU6ANuPb8UwZy2tcsAB7LghkOabCvF1VHH82teXEnMpGq22+oLgtHQihg7F0KYNpRkZqFQqjvbt50poMqfPIKSb+5DQ8sXIoT1TUBsMOEwm15aQPj7eWQekUqHyca9PMW/dhj4+3u31itWKT0wMJTt3uVZRzlbXY8vOdi8yPp0one11mgoDVg3GJPQtWnocHQFnEitwdlQOWD6fE7oSOsV2QqPSMLDNQIa2G0qhtZAwvzD8ffyZtWGW62QWnDl9F+EfUm1sQohLS5IdIbzEnptbKdHZFK+nxS4zfqVQYIBDbaPp3GMCRzf2q/I6WVoTNsVGbJ26lB496nHcg2nrVmfPGbUaTd1wVAY9+vh4AjrdStG6da7VoLLtrfLKr9Y0+vwz1P7++CclEnTvPdgLCzFt2YJp7Tr07dq5bVm5amtW4paA+XfqRN0B/YlZsABNgD/q08XTVdEEBdHos0+xHj6MytcX89Zt5H30EeFjR+NnTKQkzfNxdIfZTOyypSgWC9q6dbH89RfWo0eqfa/So0ddsQaVauid9gKPXfsYdQ11yTBlEKwLplFwIyIMEQBMNU5lSMmQcz59J4TwDkl2hPASR+GZ/jkFejWH41SuWVYHYlUENmhJq993YG65reoeM0lJhIfE8vmJ1cRqwtGEhHgs9vXv1IkGK5aTOWtWpenpURMnkPvKktNBOSo1DCxLnvw73IT95EmUEouroFefkEDUhPHkLnsV87ZtxMx3TmE3paW7VoUix44hYsRwSo8dcyUrh1J6ophMxLy7knWlu7muqknsiYkU/fobATd3RNe0KaWlJRSEtSW9gy/t/YqpN+kFCqbMqjDU1Eh43z4c6dPXtSJUf8VyMiZOcnZKrkb5gmqLn4YpSVMY9esozDbzmVWb04kOnP/pOyGEd0iyI4SXqAOdIyT2NjIQVGCi5Z/ObautrXzoPPYtTjzp7Cvj1sSvYt1Lcg+yU+fQbFg3bHYNPmFh5CxdVikx8mvWjMyZqVXW8rj622i1hCb3AMVZY1PdSakGK1Zgyz+F9cBB4t58g6I1v3L8hXGEdOniNqG9cPUPZKbO8jhNXVNqJzw4nLrjx5I9dUaVdT36NvFkL36FsH59KQ2ERrEtWbpjKX3rdSX63nsITe7haqZoy8pCrTc4a3ZsdtQx9VzHyMumnJ+tOaHOmMjqgg2sP7GT/3b+L2qVWlZthLiMSbIjhBfkm6zY9IFs7hBKi0156Eoh3x8ONnSetvKxK67nVqybUSwWfOvHUfD99666l+aDB7FPyaJJrp/HL/Kz1fKEJie73qusn01ozxQ0ISFkL1zo8aRUlkqFvnVr13UNxiQarFiO9dAhVKgw795Dyb6/MO/Y4THRMRiTUAcEEgd8lv0T991/35mC6XJHxfXx8Zi3bjtzOuv++zh4nYoEv6YYXlxO5ukTXWXbdj4RkZTs2oktO4ecRYuo98XHFPpagWoSR2MSoT16cGz4CHTGRIqHp7Bsi3NFR61S0yi40bn+pxVC1EKS7AhxiR0/ZWbaf1dzx/cTaLvb+SX8d30VBrMP8TuLMRiT0AS6Dw4tXzcD0OC9d91ulx48RODnn8Ezz3p8z7MV83I6tyrfzwacJ6o8HRuH052NT3c1Bg8jIBITCe//PIG33kJWud474Nw+ixg8hEMpKegTEogf0RN1h2jyJs+ssncOnO66/PRT3GAIwc+3OY5uzfAZPpySnTs5Nmy4W3PByBfGkvvmmxSfyma1bTudjIlY0tZWShw1wcEo9eqSnXEAzfK5/FK83ZXoAJwsOYlGCSTYN0iOkgtxmZJkR4hLKN9kZeGCmTz+/cdE5oFDBdvaB3FvylS0DmeyYcvOxmbQYaiqjsWYRFG6+0qLSqej5KdfoNdTHt/3bM39fGKiXds459JBuUzFx91GQKxdS45aheHGm5yntiqs2tjyT6GYTK5ZVNaZE1jzbHseGzEc2+Ejbqs7ZUmMymDAJzKSvNTZZFdIisr3yTGtXUvmrNmEPv00+TpYtu8dbhg2B3+coyDOrEYZCZo0hnt+e8LtVFV5hdZClmx9gWebjyYuKJp6deRIuRCXG0l2hLhE7DYb/xv3KE+s/gtfG5wKgEP1/UnYWEDmxiFuz/X56DUsw5PxR6Fky1bXNg0KaCMjKPzpZ1QGg7MBXrlak+L0tR6TJPPWbVVPKjcaUekNGNq3x691K4IffJCMmTMxpaWdewdlnIlIWO/e+MTF0eizT3EUFaEyGFD5+VH4zbduKy8AcSv/4/oZLGlrCS+w8OKfS7itXQJFg4d4eDdnH5zM2bOr7ZNTlsiY0tKIHD6cbwt+wWwz03vrKPr27sHNg1LQmCzYDTq0devxcc7PxNeNr7ZpoHPm1WzuCh/Gvc0bywqPEJcZtbcDEOJqkHF4H58/2pZ23zgTnb8aqinx9SF+d7HH56uLTDy1ZSS/Ptue+qs+xLxjO0f79uNov34c/PejmDdtImbuS/h36kRoSjJ5K1cCzpqUehMnVJoLVbJnD5Fjx3ieF9WjO5mpqehbtaRk925seXkE3X8fDd57F5969TAYjR5jLJ9kqQwGYhbMx3DjDWTOmMGBhx/hUI9kDv77UTKnTcfQvj0xC+a7ZnQB4HA4e/ScZi8spEO9DjjqBFX5nv4dbqp6W23tWteMsDKlmZmu/222mZm/73X+vXUgD+0dwb+3DuSo6hR2xU735t1JjHb/bDrU60D35t15e9fbgHPmVUQdGzlFVo/vL4SovWRlR4iL7Of356FZ8DrNT4FdBWvah9Kp30LUT/eo8jV2gw6zzUxdQ92qO/6qVa6i2rIVE8VkwlFS4hr3oFgsqPz8wOGgNCuLyHEvYM/Oxp6fX2mbSLFYiBz3ApnTpp+ZbeXqoFy55iY0uYerliY0JQVbRgYF335XafXItHYtOUDQffe6Vl4MiYmuAaZlR9v9/AOZYe+FpshGwITx2DIzsZ865eoTVLL3z0qNCyuqtO2mgtuD2mFr+iw3+7dGW2zB7u/Hr8Xb2Gzey4aMDbQOb83IX0fy1p3vMzjBwrGiI+g0OrbnbHcdOy9jVUwyyVyIy5AkO0JcJHabjY9G/ovm3x/A1w55gXDy2S6s13RGk6fm7qQkLFVsK9nq1GNhx/9yvVlLXtoEj9cvO0Xl1l04KQlHUbFrK6f80XHT2rXELlvK0b6eGxSa0tLAanVLaiqeBNMEBuLw90NlMJA76yXXe5etqHjaJoMztTzaiAi3ouPolrPdjraXxZuxckGl01IRg4dgO3Wqmk/cfVvNYExCW7cudQMCuGPxbkw/LcV2+rHbjEk8MmkMD699hutCrqN1eBu+21FA20Zahv8+vMrr+6oMMslciMuQbGMJcREcO7CL/z2SQPw3zkRnX2MN9d95lweem8qsR1vzR74D3ZgJlbZr/IxGTvYfyYNvHuCp1w9RlJdf7fuUX8ko67uj9vd33VdxyObZCo7thUWV3+P06ayjffthU+wkru/G2ye/Y2ff26j/2cfUf/NNNAGB51TMrA4IcI2QUEwmfKKi3OKrcihoWjpZ8+eD3YYhMdHj9ctvqzm353pwKDmFjMlTCe3S1W0LrSQtnZNTUulZ/1GCdcE82XgEb/6aweYDNm6K8nz9m6ISyTqllUnmQlyGJNkRoob9+O4c9nd7lOv22bCpYdudsXT+Yitx17UFILqOnjH3NWdSehbfPNIf9dsfoXv9P8R98QUBo8YSZCrgswdjGdMxGsU/oNr38omNJeblBcQuW+pKIsCOIakD4FxxKZ84nK3gWBNY/ftZDM5VjeahzRm1ZQrZeUc5OnAgKr3fORUzq/V6cpctOz1ZPAmVVusWX8V4yzOtXetsepiSXCnhMRiTiJowHn3bBLfPQjGZMKWnk7fybbf6IHCeyror6EZUpREMeHs/JqudN3/N4Nnmo0mqUL9zU1QizzYfw61NGkhxshCXIdnGEqKG2G02Php6Py1+PILWAblBUDIgmW4pL7g9L99kZeynO/htXw7pvhp0t0bRtUk4OdNmuNXm3J2URL3Jk8gyJmKqYv5T4eofKjULVJmziB74BMepvJJTbQdhYxJ2g87t6HnFx78pcJ5Ystid17X5O/vyFP78Cz6REVW/NjERW1YWmpAQ1+3QHj2wHj7s9ryzrg6Va3oYMXw4SokZlV6P2t/fWZ+k0Xh8Xfkj8eWF2HwJCo/hfwPqUlhSSqCfD+EBvsyJnEOOKZd8ayEGbQAGdTDBOumzI8TlSlZ2hKgBh/dt48uHEohf7Ux0/rxGS4P/fsSdFRIdgJwiK7/ty8Hgq+GjJ+Lood9OztRplZIES3o62bNmUW/ieAzGCqeokpLcTmGdub8Dmow0fL7pRUyXa/CNi3V7PG/lSs8rI4mJRL3wAp9n/EDR8BT0Fd5Pb0yiaHgKi/9eDuAam/Bb8XZ0xkTy3noLbWQk4X37Vo41MZHwfn3RRkWh+PpS/5OPXCsvKl/35OFcVocUk4m8lStR6XzJeestbFlZZEyZyoGHHuZI72c48lwf55yuuS+5bV15SqR8/P0JNvjSJCKANnEhNIkIINjgS7AumCYhjWkbGU+zsCbEhYRLoiPEZUxWdoT4h75fMR3Dkne5tgBKNbDrrgZ0eelLNFrP//cqOH2aZ2jHCJpteAFbiwGY0iv3eAEo/ulnlGe6EPNYY+yDnsZh06LGhCoojIz5r1YoTu5A9MAn0H7TC6zFaNfPcd5fru9O+YLj8D7PodjtKKWl2LKysfv58uqBd+jdoBv3jx1KXetQMFuw+vvwv1NpLN4yEoAX20ykZVEw3zZbRIlBg2HsbahS53NsyFDCevcmYthwVCNH4iguRqXXg6JQ9MsazDt3UGfSaPKzMyg6vRpVcaXpXGdXRY4ZTWbqLPStW3uu8fHQd6diImUwJqIJrePxcxdCXFkk2RHiApVaLawaej8tfj6O1gHZdcA2qDfdnhxR7euCTp/muStOjSb9Z6yNn6v2+Y5iM7r1c9AyBxp3gtj28N1SQkf9iPmpfkSrS/BVTqHJXu9KdMpo/1hO5MQfyZw6zS3hMW/bhj6+tXP2VEIb6o0dzjE/K4tbTiFg7kqy0xe6rqE3JvHgpLEkNOpAY2sdciZP53DaRNfj6ts7oR83DHthHqUWBZWPFvO2rWSmzj4zvuF0IuazZiLajhNxnE7AKs6qct1WqdxOdpU/wWVITMSvRQsyJk4iNCW56plf5bauDElJrkQJnIlO9NTJaMMiq/3shRBXBkl2hLgAB3f/H9uH9SL+gB2APdf60GHRh9Rr0Oysrw0P8OWWpuH42gsBUOur3x5xe3z/L9ChH1hfIqfEwj2rjjHu9no8deIVtAd+cn+hrz/W+98ma/Zs9K1aOSeDW61oo6JQBQaQmXUA/5WvEKpTIFhPxKlsVPNWYq4wisKclo4yJZWmE0Y4e/BU2G4r+ekX1BYrIZOGoM3dii64LtqAfRhem43DbEWt93VLxAw39iF6wiiOT03FlL7OtdIU1uc57D5qsnytFI1Mxt/aE63ZQlRoAxSHg9KjR4mZN9c5aiI7Gzi3cRaGjh2pN3kiSomZwFuMqAMD0YTWkURHiKuIJDtCnKdvX59A0Gsf07QQrBrYc28THpv9WZXbVhUFG3yZ9Whr1Ln7ANBkr8eQ1MHjVpYhqQOa7PXud9osKI3vYPVhBwDzf88i6YkZXMc4t4THdttsTsx7A1NaOsU//ex2CZ0xkV96t2H+ptdZ1XkVTb9/AeW6ZzB7KIQG51FtSlRVdy9OSyeqZCS+9RJQLIVo18+p8peLotagtRwlZuIQ7MU2bMVm8gxavizYwLI977g18etQrwP3h95PQkBTbHV9UZvMhN9/D6rT5YZnq/HxbdiQmLkvoQ0OrvZ5QogrmxQoC3GOSq0W3u/Tidh5HxNSCFkhcHLy8zw+t+r6nKpE19FTp240SpM70G55heiBT7iOi5cxGJOcNThbXnF/cVAs1s4vM//3LABMVjtd/nuY5fUmcLDbGo53+Qpr3/XYIxKrTE4saWu52b81ACX2IlR7v8Zhqn6VxFFQfc8fR+YBiosKsPpUn1jYdSFYQ69B89sUdB/djv8PXfHRF7KhaIdbopMYncjoG0aTac6k65qneWRrf+YUfER+oIYP/jJjMBpdNT6eGDp2RBsVJYmOEEJWdoQ4F/u2pfHnqOeIP+RcTdnd3JeOiz8hIqbJeV/Llp+PPTcXpbAQa+JsNNesw+eH54np8hT257vjKFWjDotCc/DLSjU4NO4EmX+gbXw7xiZhrN59JuGZ8dMJZgC3NA1n0RON8c3cXW0cmtPJTaDWeWLprNtpZ0ka1HofMm0GDJo6aBvdXnlbDZzJXVAEWn0IJQ+9xqns4zhKCii1BdGjwSi6XWOljp8Zf18923N38eTXT7oSIGPUTUy+5nGUU9msy/ah+9SpZM2cSWhKsvMzKN9xuWNHoqdPk0RHCAFcZslOamoqL7zwAoMHD2bBggXeDkdcJb5aOpbQNz6jSTFYtLC3czMenfHRea/mAJSeyOD4+PFuU8kNHY1ET/4BH1UeWl0A+NcFqxnWr6uc6NzUF1b1RlO/A/MfepXnbQ5+3ZfjesotTcOZ/Whrgg2+WAIDq43FbtBhjDYSqvEDX380Ortbn5yymVVl09atiprIaVPJSp3ldgoMnNttautxdhZE0zwsgKDOL6P5Zgiqv38886Qmd6D61yLQO3vt+PloCA/QkW9TY8XB7kOFzP89i596RhO14i4ikvrTNimVQruVQI0voYfWEfxBTzIe/5apDzXFr46e6JkzKM3NJeKFseBwYC82oQkOwic8XBIdIYSLSlEUxdtBnIuNGzfStWtXgoKCuO2228452SkoKCA4OJj8/HyCgoIubpDiimIxm/h04D20SstBrUBGKPiMHkrHh6o/PVUVW34+x4YNd0t0yhg6dqxcW5K3H3L2gs0CWh0c3Qjrlp5JgAZsJN/QkJwiq1tDvLJ+MLb8fI4NH4Hp998rvZ/OmMjewZ1p08RIlFoHGTth7SJKm/fm+KL/Yt663W2mlitOo5HwPs9xpG8/t5NW9UY8xwl9OJmEcl1koDMG80kozoaSAvALciZxpxMd8o/B5wNg/5nVH6XxHVg7L0DxMeD3+XNQPlEqe06TO7A89Bp+QeEAHD9lZvSq7fxWIeGb9Whrouvoz/G/jBCiNqrJ7+/LItkpKiqibdu2LFmyhOnTp9OmTRtJdsRF9eeWX/hrdH8any4C3tlCR6fFnxJer9EFX9Oyfz/77+9c5eONv/4KXePGZ+44+n/wxh1VX/CZH53H0KtReiKD4xMmuCU8BqOR8KkTsYUHO5sDmk/CR087Ew9ff2wJ/bE3fpCMOS97rPnRG42EjRuH+lQemgAD+PuRhY71J+zcfE04UWdLMsq/X0VN7oDH3gSrCb4Y6J7wNLkD/rUIgmMAZyfqAf/d4pbolHFu5SVII0AhLmM1+f19WWxj9e/fn86dO3PnnXcyffr0ap9rsViwlDuOWlBQcLHDE1eYLxcNJ3z51zQ2gcUH9v6rJV1nfPSPr+soLDzL4xWGcPqd5f/cZ3sc8KkXRczcl7Dn5uIoLEIdGIAmLMx9Bak4+0zicboZob3uTVUWN5vT0jieW8yM7TamPRTHSZOVQD8f7r7e99ySi/LvV9HfPzofD7/WmfRUtTLEmU7Unvy6L4ecIqskO0II4DJIdt5//302b97Mxo0bz+n5qampTJky5SJHJa5E5uICPh9wH63W5qEGToSD39hRdO38VI1cX32WGhp1xSGc/nWdqxketnNocofz8XOgDQ6uvn6lpPI/CBxma7XX9C81M/vR1tSro6cB/tU+91zez+Pj+hC35Kaisk7UVSk8y+NCiKtHrT56fuTIEQYPHsw777yDn5/fOb1m7Nix5Ofnu/4cOXLkIkcprgQ713/PL//qQPzpRGdnKz8SPv2BpBpKdAA0YWEYOnb0+JihY0c0YWHud+pDnNs2TSpsZZVt51STCJwXDytEZzuZFRYZSr0LrYmpgRUrONOJuiqBZ3lcCHH1qNUrO5s2bSIrK4t27dq57rPb7fz6668sXrwYi8WCpsKUY51Oh+4sjcaEKO/zuQOp984PNDSD2Rf+frgNXab+t8bfRxscTPS0aZVraKo7Jh0cc9btnH/MwwpStY0OO3bEJzy8Rt/P5TxWrMo6Uf9aRc1OeIBsYQkhnGp1gXJhYSGHDh1yu++pp56iWbNmjB49mpYtW571GlKgLKpSXJjP/wbcS/z6UwAcqwtBE8Zx4909Lur7lvXZqbKGxhvyj7kXBPv6U/rwKo7PfbPCMXlnYuYTFVWz7weVCpDPxfFTZsas2u7x+P0FrzwJIWqFq+40VnmdOnWS01jiH9ue/hXHx4+kwXHnX/8/4g3cteRL6oTV83JkXuThqLjNqr54iVl1R9PPQ77JWuXxeyHE5euqO40lRE36dE5fYt9bQ4MSMPnCwcduoMvEld4Oy/s8FARr9Vy8VaezFCCfq2CDJDdCiOpddsnOL7/84u0QxGWqKD+Pr56/l9abnEfAj0aqCJ04hUfv6OLlyIQQQlxMl12yI8SF2LLmU7ImjaN1hnPbake7AO5d/BVBIRFejkwIIcTFJsmOuOJ9MrM3cR+kE2eBYh0c7mak69g3vB2WEEKIS0SSHXHFKjiZxbfP30+rLc5ZUofrqYicmsq/b37Iy5EJIYS4lCTZEVek//vxfU5OnUqrTOe21fb2QTyw9Hv8A2USthBCXG0k2RFXnI+nptDo443EWqFQD8efvJXHRy7zdlhCCCG8RJIdccU4lXuC1c93puU2MwCHolXEzpzLwx3u83JkQgghvEmSHXFFWP/tSopmpNIy23l72011eGjJd+j9pZGkEEJc7STZEZe9Dyd045rPtxFthQIDZCbfRbehC70dlhBCiFpCkh1x2crLPMJP/f9Fqz9KADgQq6ZR6svcdMOdXo5MCCFEbSLJjrgsrf3yTcyzXqJFDjiAHUmhPPLKanR6g7dDE0IIUctIsiMuOx+OfZRrv9xFnVLIN0DO053pNuAlb4clhBCilpJkR1w2ck4cYE3/R2i1ywLA/jg118xZSoc2t3g5MiGEELWZJDvisvDbp0uxv7iQ6/PAoYIdHevyyMJvZdtKCCHEWUmyI2o1u83Gx2Mfpdk3e/G1wakAyOv9MN36pXo7NCGEEJcJSXZErZVxeB/pgx6j9R4rAH81VHP9i6+T2CrJy5EJIYS4nEiyI2qlXz5YgGrBqzQ/CXYV7Lg1iscWfouPr87boQkhhLjMSLIjahW7zcZHox6i+Xf78bXDyUAo7NOVJ56Z4u3QhBBCXKYk2RG1xrEDu9gwqBvx+0oB2NdIQ+t5K2jYvL2XIxNCCHE5k2RH1Ao/vjsH34XLaZYPNjX8cXsMXRd8i0Yrf0WFEEL8M/JNIrzKbrPx0bDOtPjhMFoH5AaB6fnuPNFrvLdDE0IIcYWQZEd4zeF929g8tAfxf9kA+PMaLW3nv0Nc03gvRyaEEOJKIsmO8IrvV0zHsORdriuAUg3svLMBXed+KdtWQgghapx8s4hLym6z8dHge2jx83G0DsgJhtLBvXniyRHeDk0IIcQVSpIdcckc3P1/bB/Wi/gDdgD2XOvDjS+/T0yj670cmRBCiCuZJDvikvjujUkEvPohTQvBqoHd9zSmy5zPZdtKCCHERSffNOKiKrVa+HjQPbRak4lGgawQUIb0odvjQ7wdmhBCiKuEJDviovl7Rzq7Rj5Lm4MOAHY38yVp4cdExTX1cmRCCCGuJpLsiIviq6VjCX3zM64pAosW/rz/Oh6b+bFsWwkhhLjk5JtH1CiL2cSnA++hVVoOagUyQkE7chCPP9LP26EJIYS4SkmyI2rMn1t+4a/R/Yk/7Ny22tVCx62LPyW8XiMvRyaEEOJqJsmOqBFfLhpO+PKvaWwCiw/sfbAFXWd+7O2whBBCCEl2xD9jMZv4tP9dtErPQw2cCAf9mBF0faC3t0MTQgghAEl2xD+we+MPHBg7iPijCgA7W/lxx5IvCakb4+XIhBBCiDMk2REX5Iv5g4h8ezWNTGD2hb8eiqfrtPe9HZYQQghRiSQ74ryYiwv4/Pl7iF9/CoDjdSFg3Fi63pvi3cCEEEKIKkiyI87Z9vSvOD5+JPHHndtWf8QbuGvJl9QJq+flyIQQQoiqSbIjzslnL/Yl5t01NCgBky8ceOwGukxc6e2whBBCiLOSZEdUq7gwny/73UXr/ysE4GikipCJE3nsjm5ejkwIIYQ4N5LsiCptWfMpWZPG0TrDuW21o20A977yFUEhEV6OTAghhDh3kuwIjz6Z2Zu4D9KJs0CxDg4/nkTXF970dlhCCCHEeZNkR7gpOJnFt/0702pzEQCHo1RETJnBv299xMuRCSGEEBdGkh3hsunHj8ibOolWmc5tq+3tA+n8yrcEBId6OTIhhBDiwqm9HUB1UlNTueGGGwgMDCQiIoKHH36YP//809thXZFWTU2BoROJzVQo8oM9T9/K4+9skERHCCHEZa9WJztr1qyhf//+rFu3jtWrV2Oz2bj77rspLi72dmhXjFO5J/jo8XZc/95GDFY4FK1Ct+QlHhm1zNuhCSGEEDVCpSiK4u0gzlV2djYRERGsWbOGW2655ZxeU1BQQHBwMPn5+QQFBV3kCC8vG75/h4JpM4jJdt7edlMdHlz8Lf6Bwd4NTAghxFWvJr+/L6uanfz8fABCQ6veWrFYLFgsFtftgoKCix7X5eijiU/Q5LOtxFihUA8ZKXfRbehCb4clhBBC1LhavY1VnqIoDBs2jI4dO9KyZcsqn5eamkpwcLDrT/369S9hlLXfyexjfPxYAi0/3IreCgdiVQS+toh/SaIjhBDiCnXZbGP179+fr776it9//53Y2Ngqn+dpZad+/fqyjQWs/fJNzLNeol4OOIAdiaE8tPgb9P5X9+cihBCi9rnqtrEGDhzIF198wa+//lptogOg0+nQ6XSXKLLLx4cvPMa1/9tJnVLIN0B2r/voNmiet8MSQgghLrpanewoisLAgQP59NNP+eWXX2jUqJG3Q7rs5Jw4wJoBj9Bqp3O1a3+cmmtmv0KHhE5ejUsIIYS4VGp1stO/f3/ee+89Pv/8cwIDA8nIyAAgODgYvV7v5ehqv98+XYrtxYVcnwcOFewwhvPIou/Q6Q3eDk0IIYS4ZGp1zY5KpfJ4//Lly+nVq9c5XeNqPHput9n4+IXHuO7rP9HZ4FQA5PV+mM79Ur0dmhBCCHFOrpqanVqch9VaGYf3kT7oMVrvsQLwdwM1zV96ncRWSV6OTAghhPCOWp3siPPzy0eLUM1bQvOTYFfBjlsjeWzhd/j4SsG2EEKIq5ckO1cAu83GR6Meovl3+/G1w8lAKOzTlSeemeLt0IQQQgivk2TnMnfi0B7WDexK/N5SAPY10tB63goaNm/v5ciEEEKI2kGSncvYT++9hHbhmzQ7BTY17Lwtmi4vf4dGK/9ZhRBCiDLyrXgZsttsfDj8AVr8cAgfO+QGgen5J+nWa4K3QxNCCCFqHUl2LjOH921j89AetPnLBsDeJloSFrxDXNN4L0cmhBBC1E6S7FxGflg5E7/Fb3Ndweltqzvj6DLvK9m2EkIIIaoh35KXAbvNxodD7qXlT8fQOiAnGKyDnqJb91HeDk0IIYSo9STZqeUO/7mZrUNTaLPfDsCepj7cuPB9Yhpd7+XIhBBCiMuDJDu12HdvTiZg2Qc0LQSrBnbf05gucz6XbSshhBDiPMi3Zi1UarXw8aB7abUmA40CWSGgDOlDt8eHeDs0IYQQ4rIjyU4t8/eOdHaNfJY2Bx0A7G7mS9LCj4mKa+rlyIQQQojLkyQ7tcjXy14g5I1PuaYIrFrYc9+1PJa6SrathBBCiH9AvkVrAYvZxKeD7qXV79moFcgMBc2IATz+7/7eDk0IIYS47Emy42V/bv2Vv0b1I/6wc9tq1/U6bn3lU8LrNfJyZEIIIcSVQZIdL/py8QjC3/qKxiaw+MDeB66na+oqb4clhBBCXFEk2fECi9nEp/3volV6HmrgRDj4jRpG13896+3QhBBCiCuOJDuX2O6NP3Bg7GDijzq3rXa29OP2V74gNLK+lyMTQgghrkyS7FxCXywYTOTK72lkArMv/PVQPF2nve/tsIQQQogrmiQ7l4C5uIDPn7+H+PWnADheFwLGjaXrvSneDUwIIYS4Ckiyc5H9se4bjo4bTvwxxXk7Xs9dS76iTlg9L0cmhBBCXB0k2bmIPnvpeaLf/ZkGZue21f5/t6PL5He8HZYQQghxVZFk5yIoLszny+fvpvXGAgCORqoImTiRx+7o5uXIhBBCiKuPJDs1bOtvn5M5cSytTzi3rXYk+HPvkq8JConwcmRCCCHE1UmSnRr0SeozxL2fRpwFinVwqGsiXce95e2whBBCiKuaJDs1oCg/j6/73UOrzUUAHIlSUXfKDB699REvRyaEEEIISXb+oU0/fkTe1Em0ynRuW21vH0jnV74lIDjUy5EJIYQQAiTZ+UdWTU2h4ccbibVCkR8cfeIWHh/9qrfDEkIIIUQ5kuxcgFO5J1j9/AO03GYC4FC0iujpL/JIUmcvRyaEEEKIiiTZOU8bvn+HgukzaJnlvL3tpjo8uPhb/AODvRuYEEIIITySZOc8fDTpSZp8uoUYKxTq4USPO+k2fJG3wxJCCCFENSTZOQcns4/xw/MP0nKHGYCDMSriZi7goZvu9nJkQgghhDgbSXbOIv2r5ZSkzqFlDjiAHYmhPLT4G/T+Qd4OTQghhBDnQJKdanw4rgtNv/iDkFLIN0B2r/voNmiet8MSQgghxHmQZMeDnBMHWDPgEVrttACwv76aa+a8QoeETl6NSwghhBDnT5KdCn7//DVK58zn+lxwqGCHMZxHFn2HTm/wdmhCCCGEuACS7Jxmt9lYNa4L1361B50NTvlDXu9/0e352d4OTQghhBD/gCQ7QNaxv/m9/79ptccKwN8N1Fw35zUS441ejkwIIYQQ/9RVn+z88tEiVPOW0Pwk2FWw49ZIHlv4HT6+Om+HJoQQQogacNUmO3abjY9HP0yzb//G1w4nA6Hgucd44tlp3g5NCCGEEDXoqkx2Thzaw7pBXWn9ZykA+xppaDn3LZKuv9HLkQkhhBCipl11yc5P/52L9uU3aHYKbGrY2SmaRxd8LdtWQgghxBVK7e0AzsWSJUto1KgRfn5+tGvXjt9+++28r2G32Xh/yL2ET3+DuqcgNwhOjHqSbkt+lERHCCGEuILV+mTngw8+YMiQIYwbN44tW7Zw8803c99993H48OHzus43T3Qk/ttD+NhhbxMNce++z929JlykqIUQQghRW6gURVG8HUR1brrpJtq2bcvSpUtd9zVv3pyHH36Y1NTUs76+oKCA4OBgNlzTFD8fDTvvqE+X+V+j0V51O3hCCCHEZaPs+zs/P5+goH82j7JWf+NbrVY2bdrEmDFj3O6/++67SU9P9/gai8WCxWJx3c7PzwfgcIAdbb8nub/bMIpNposXtBBCCCH+sYKCAgBqYk2mVic7OTk52O12IiMj3e6PjIwkIyPD42tSU1OZMmVKpfsf27of+kxx/hFCCCHEZSE3N5fg4OB/dI1aneyUUalUbrcVRal0X5mxY8cybNgw1+1Tp07RoEEDDh8+/I8/rCtNQUEB9evX58iRI/94ifBKI59N1eSz8Uw+l6rJZ1M1+Wyqlp+fT1xcHKGhof/4WrU62QkPD0ej0VRaxcnKyqq02lNGp9Oh01U+XRUcHCx/kaoQFBQkn00V5LOpmnw2nsnnUjX5bKomn03V1Op/fpaqVp/G8vX1pV27dqxevdrt/tWrV5OUlOSlqIQQQghxOanVKzsAw4YNIzk5mfbt25OYmMhrr73G4cOH6du3r7dDE0IIIcRloNYnO48//ji5ublMnTqVEydO0LJlS77++msaNGhwTq/X6XRMmjTJ49bW1U4+m6rJZ1M1+Ww8k8+lavLZVE0+m6rV5GdT6/vsCCGEEEL8E7W6ZkcIIYQQ4p+SZEcIIYQQVzRJdoQQQghxRZNkRwghhBBXtCs62VmyZAmNGjXCz8+Pdu3a8dtvv3k7JK9LTU3lhhtuIDAwkIiICB5++GH+/PNPb4dVK6WmpqJSqRgyZIi3Q6kVjh07Ro8ePQgLC8NgMNCmTRs2bdrk7bC8zmazMX78eBo1aoRer6dx48ZMnToVh8Ph7dAuuV9//ZUHH3yQ6OhoVCoVn332mdvjiqIwefJkoqOj0ev1dOrUiZ07d3on2Eusus+mtLSU0aNH06pVK/z9/YmOjiYlJYXjx497L+BL6Gx/b8rr06cPKpWKBQsWnNd7XLHJzgcffMCQIUMYN24cW7Zs4eabb+a+++7j8OHD3g7Nq9asWUP//v1Zt24dq1evxmazcffdd1NcXOzt0GqVjRs38tprr9G6dWtvh1IrnDx5EqPRiI+PD9988w27du1i7ty51KlTx9uhed3s2bNZtmwZixcvZvfu3cyZM4cXX3yRRYsWeTu0S664uJj4+HgWL17s8fE5c+Ywb948Fi9ezMaNG4mKiuKuu+6isLDwEkd66VX32ZhMJjZv3syECRPYvHkzn3zyCXv37uVf//qXFyK99M7296bMZ599xvr164mOjj7/N1GuUDfeeKPSt29ft/uaNWumjBkzxksR1U5ZWVkKoKxZs8bbodQahYWFStOmTZXVq1crt956qzJ48GBvh+R1o0ePVjp27OjtMGqlzp07K08//bTbff/+97+VHj16eCmi2gFQPv30U9dth8OhREVFKbNmzXLdV1JSogQHByvLli3zQoTeU/Gz8WTDhg0KoBw6dOjSBFVLVPXZHD16VImJiVH++OMPpUGDBsr8+fPP67pX5MqO1Wpl06ZN3H333W7333333aSnp3spqtopPz8foEYGrV0p+vfvT+fOnbnzzju9HUqt8cUXX9C+fXu6dOlCREQECQkJvP76694Oq1bo2LEjP/74I3v37gVg27Zt/P7779x///1ejqx2OXDgABkZGW6/l3U6Hbfeeqv8XvYgPz8flUolq6eAw+EgOTmZkSNH0qJFiwu6Rq3voHwhcnJysNvtlYaFRkZGVhoqejVTFIVhw4bRsWNHWrZs6e1waoX333+fzZs3s3HjRm+HUqvs37+fpUuXMmzYMF544QU2bNjAoEGD0Ol0pKSkeDs8rxo9ejT5+fk0a9YMjUaD3W5nxowZPPHEE94OrVYp+93r6ffyoUOHvBFSrVVSUsKYMWN48sknZTgozq1irVbLoEGDLvgaV2SyU0alUrndVhSl0n1XswEDBrB9+3Z+//13b4dSKxw5coTBgwfz/fff4+fn5+1wahWHw0H79u2ZOXMmAAkJCezcuZOlS5de9cnOBx98wDvvvMN7771HixYt2Lp1K0OGDCE6OpqePXt6O7xaR34vV6+0tJRu3brhcDhYsmSJt8Pxuk2bNvHyyy+zefPmf/T35IrcxgoPD0ej0VRaxcnKyqr0r4qr1cCBA/niiy/4+eefiY2N9XY4tcKmTZvIysqiXbt2aLVatFota9asYeHChWi1Wux2u7dD9Jp69epx/fXXu93XvHnzq77gH2DkyJGMGTOGbt260apVK5KTkxk6dCipqaneDq1WiYqKApDfy9UoLS2la9euHDhwgNWrV8uqDvDbb7+RlZVFXFyc6/fyoUOHGD58OA0bNjzn61yRyY6vry/t2rVj9erVbvevXr2apKQkL0VVOyiKwoABA/jkk0/46aefaNSokbdDqjXuuOMOduzYwdatW11/2rdvT/fu3dm6dSsajcbbIXqN0Wis1KJg79695zyQ90pmMplQq91/lWo0mqvy6Hl1GjVqRFRUlNvvZavVypo1a67638twJtHZt28fP/zwA2FhYd4OqVZITk5m+/btbr+Xo6OjGTlyJN999905X+eK3cYaNmwYycnJtG/fnsTERF577TUOHz5M3759vR2aV/Xv35/33nuPzz//nMDAQNe/soKDg9Hr9V6OzrsCAwMr1S75+/sTFhZ21dc0DR06lKSkJGbOnEnXrl3ZsGEDr732Gq+99pq3Q/O6Bx98kBkzZhAXF0eLFi3YsmUL8+bN4+mnn/Z2aJdcUVERf/31l+v2gQMH2Lp1K6GhocTFxTFkyBBmzpxJ06ZNadq0KTNnzsRgMPDkk096MepLo7rPJjo6mscee4zNmzfz5ZdfYrfbXb+bQ0ND8fX19VbYl8TZ/t5UTPx8fHyIioriuuuuO/c3+ecHxWqvV155RWnQoIHi6+urtG3bVo5XK85jfZ7+LF++3Nuh1Upy9PyM//3vf0rLli0VnU6nNGvWTHnttde8HVKtUFBQoAwePFiJi4tT/Pz8lMaNGyvjxo1TLBaLt0O75H7++WePv1969uypKIrz+PmkSZOUqKgoRafTKbfccouyY8cO7wZ9iVT32Rw4cKDK380///yzt0O/6M7296aiCzl6rlIURTmvFEwIIYQQ4jJyRdbsCCGEEEKUkWRHCCGEEFc0SXaEEEIIcUWTZEcIIYQQVzRJdoQQQghxRZNkRwghhBBXNEl2hBBCCHFFk2RHCHHZmDx5Mm3atHHd7tWrFw8//PAlj+PgwYOoVCq2bt16yd9bCHH+JNkRQvxjvXr1QqVSoVKp8PHxoXHjxowYMYLi4uKL+r4vv/wyK1asOKfnSoIixNXrip2NJYS4tO69916WL19OaWkpv/32G8888wzFxcUsXbrU7XmlpaX4+PjUyHsGBwfXyHWEEFc2WdkRQtQInU5HVFQU9evX58knn6R79+589tlnrq2nt956i8aNG6PT6VAUhfz8fJ577jkiIiIICgri9ttvZ9u2bW7XnDVrFpGRkQQGBtK7d29KSkrcHq+4jeVwOJg9ezbXXHMNOp2OuLg4ZsyYATinbgMkJCSgUqno1KmT63XLly+nefPm+Pn50axZM5YsWeL2Phs2bCAhIQE/Pz/at2/Pli1bavCTE0JcbLKyI4S4KPR6PaWlpQD89ddffPjhh6xatQqNRgNA586dCQ0N5euvvyY4OJhXX32VO+64g7179xIaGsqHH37IpEmTeOWVV7j55pt5++23WbhwIY0bN67yPceOHcvrr7/O/Pnz6dixIydOnGDPnj2AM2G58cYb+eGHH2jRooVrkvTrr7/OpEmTWLx4MQkJCWzZsoVnn30Wf39/evbsSXFxMQ888AC3334777zzDgcOHGDw4MEX+dMTQtSofzisVAghlJ49eyoPPfSQ6/b69euVsLAwpWvXrsqkSZMUHx8fJSsry/X4jz/+qAQFBSklJSVu12nSpIny6quvKoqiKImJiUrfvn3dHr/pppuU+Ph4j+9bUFCg6HQ65fXXX/cYY9lk6S1btrjdX79+feW9995zu2/atGlKYmKioiiK8uqrryqhoaFKcXGx6/GlS5d6vJYQonaSbSwhRI348ssvCQgIwM/Pj8TERG655RYWLVoEQIMGDahbt67ruZs2baKoqIiwsDACAgJcfw4cOMDff/8NwO7du0lMTHR7j4q3y9u9ezcWi4U77rjjnGPOzs7myJEj9O7d2y2O6dOnu8URHx+PwWA4pziEELWPbGMJIWrEbbfdxtKlS/Hx8SE6OtqtCNnf39/tuQ6Hg3r16vHLL79Uuk6dOnUu6P31ev15v8bhcADOraybbrrJ7bGy7TZFUS4oHiFE7SHJjhCiRvj7+3PNNdec03Pbtm1LRkYGWq2Whg0benxO8+bNWbduHSkpKa771q1bV+U1mzZtil6v58cff+SZZ56p9HhZjY7dbnfdFxkZSUxMDPv376d79+4er3v99dfz9ttvYzabXQlVdXEIIWof2cYSQlxyd955J4mJiTz88MN89913HDx4kPT0dMaPH8///d//ATB48GDeeust3nrrLfbu3cukSZPYuXNnldf08/Nj9OjRjBo1ipUrV/L333+zbt063nzzTQAiIiLQ6/V8++23ZGZmkp+fDzgbFaampvLyyy+zd+9eduzYwfLly5k3bx4ATz75JGq1mt69e7Nr1y6+/vprXnrppYv8CQkhapIkO0KIS06lUvH1119zyy238PTTT3PttdfSrVs3Dh48SGRkJACPP/44EydOZPTo0bRr145Dhw7Rr1+/aq87YcIEhg8fzsSJE2nevDmPP/44WVlZAGi1WhYuXMirr75KdHQ0Dz30EADPPPMMb7zxBitWrKBVq1bceuutrFixwnVUPSAggP/973/s2rWLhIQExo0bx+zZsy/ipyOEqGkqRTakhRBCCHEFk5UdIYQQQlzRJNkRQgghxBVNkh0hhBBCXNEk2RFCCCHEFU2SHSGEEEJc0STZEUIIIcQVTZIdIYQQQlzRJNkRQgghxBVNkh0hhBBCXNEk2RFCCCHEFU2SHSGEEEJc0STZEUIIIcQV7f8BhP4C8BULGwcAAAAASUVORK5CYII=\n" 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\n", + "text/plain": [ + "
" + ] }, "metadata": {}, "output_type": "display_data" @@ -1998,9 +3048,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "Immediately we see that the LOTO split method is way more difficult to model than the random split. The PCM metrics show that even though the true and predicted values are somewhat correlated (Pearson's $r$), the PCM model features are not able to explain the variance in the target variable ($R^{2}$). We can also see this in the shape of the predicted vs. observed graph, where the datapoints are not aggregated around the unit line that would define a perfect fit. Rather, the predictions are aggregated around the mean bioactivity values in the training set." ] @@ -2012,11 +3060,13 @@ "## Discussion\n", "\n", "In this talktorial we created a PCM model for the four adenosine receptors based on a random split of the data. This model performed pretty well on our test set. Compared to independent QSAR models trained for each of the adenosine receptors, the performance of the PCM model is slightly better, which indicates that the PCM model is able to extrapolate between targets. However, there are several elements that could have an effect of the observed results:\n", + "\n", "* The four adenosine receptors had enough data on their own to be able to train individual models. The true advantage of a PCM model could be more relevant in a target set where some targets have very little data.\n", "* The QSAR models are only trained on 22 features compared to 1,300 for the PCM model. QSAR models trained on molecular fingerprints would probably have better chances of achieving better performance.\n", "* For better comparison, it would be good to train several models of the same type and calculate aggregated metrics, so that statistically significant results could be derived.\n", "\n", "Moreover, we trained four PCM models on three adenosine receptors and validated them on the remaining receptor, following a leave one target out (LOTO) split method. We did this to evaluate whether these PCM models could be used to predict bioactivity for a target for which the model has never seen any data in training. We immediately derive some observations:\n", + "\n", "* The LOTO split is a more challenging form of validation than the random split, since the random split allows data leakage between targets.\n", "* While the descriptors used in the PCM model trained on random split allowed for a good performance, in order to get a good performance in the LOTO split, we would need to search more carefully to find the optimal descriptors. Similarly, we could opt for a selection of the binding pocket prior to protein descriptor generation. Additionally, we could optimize the model parameters.\n" ] @@ -2039,7 +3089,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2053,7 +3103,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.4" + "version": "3.9.13" }, "widgets": { "application/vnd.jupyter.widget-state+json": { @@ -2065,4 +3115,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} From e016da81933b537803903766b35cf17234f7dcee Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Tue, 8 Nov 2022 13:37:08 +0100 Subject: [PATCH 48/62] Update HTML formatting to Markdown --- .../talktorial.ipynb | 128 +++++++++--------- 1 file changed, 64 insertions(+), 64 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index 77f5468d..82eb2a1f 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -69,12 +69,12 @@ "### References\n", "\n", "* Papyrus scripts [GitHub](https://github.com/OlivierBeq/Papyrus-scripts)\n", - "* Papyrus dataset preprint: [ChemRvix (2021)](https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0)\n", - "* Molecular descriptors (Modred): [J. Cheminf., 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y)\n", + "* Papyrus dataset preprint: [*ChemRvix* (2021)](https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0)\n", + "* Molecular descriptors (Modred): [*J. Cheminf.*, 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y)\n", "* Protein descriptors (ProDEC) [GitHub](https://github.com/OlivierBeq/ProDEC)\n", "* Regression metrics [(Scikit learn)](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics)\n", "* XGBoost [Documentation](https://xgboost.readthedocs.io/en/stable/index.html)\n", - "* Proteochemometrics review: [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub)\n", + "* Proteochemometrics review: [*Drug Discov.* (2019), **32**, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub)\n", "\n" ] }, @@ -100,7 +100,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Proteochemometrics (PCM) models a biological endpoint (e.g. compound activity) via supervised ML algorithms based on a series of features derived from chemical compounds and target proteins. PCM is an extension of a more widespread bioactivity modeling technique, Quantitative Structure Activity Relationship (QSAR) modeling, which relies solely on chemical features and that was introduced on Talktorial T007. Explore that talktorial to know more about the basic principle of activity prediction using ML.\n", + "Proteochemometrics (PCM) models a biological endpoint (e.g. compound activity) via supervised ML algorithms based on a series of features derived from chemical compounds and target proteins. PCM is an extension of a more widespread bioactivity modeling technique, Quantitative Structure Activity Relationship (QSAR) modeling, which relies solely on chemical features and that was introduced on **Talktorial T007**. Explore that talktorial to know more about the basic principle of activity prediction using ML.\n", "\n", "To successfully apply PCM modeling, we need a large dataset of molecule-protein pairs with known bioactivity values, a way of describing molecules and proteins, and a ML algorithm to train a model. Then, we can make predictions for new molecule-protein pairs.\n" ] @@ -134,9 +134,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The Papyrus dataset is a highly curated compilation of bioactivity data intended for modeling in drug discovery. Apart from the bioactivity data contained in the [ChEMBL database](https://www.ebi.ac.uk/chembl/) (see also Talktorial T001), the Papyrus dataset contains binary data for classification tasks from the [ExCAPE-DB](https://solr.ideaconsult.net/search/excape/), and bioactivity data from a number of kinase-specific papers (Figure 1). The Papyrus dataset consists of almost 60M compound-protein pairs, representing data of around 1.2M unique compounds and 7K proteins across 499 different organisms.\n", + "The Papyrus dataset is a highly curated compilation of bioactivity data intended for modeling in drug discovery. Apart from the bioactivity data contained in the [ChEMBL database](https://www.ebi.ac.uk/chembl/) (see also **Talktorial T001**), the Papyrus dataset contains binary data for classification tasks from the [ExCAPE-DB](https://solr.ideaconsult.net/search/excape/), and bioactivity data from a number of kinase-specific papers (Figure 1). The Papyrus dataset consists of almost 60M compound-protein pairs, representing data of around 1.2M unique compounds and 7K proteins across 499 different organisms.\n", "\n", - "The aggregated bioactivity data is standardized, repaired, and normalized to form the Papyrus dataset, which is updated with every new version of ChEMBL released. The Papyrus dataset contains \"high quality\" data associated with pChEMBL values for regression or classification tasks. pChEMBL value is a canonical activity metric defined as $-log_{10}(molar IC_{50}, XC_{50}, EC_{50}, AC_{50}, Ki, Kd, or potency)$. Moreover, \"low quality\" data that is only associated with an active/inactive label can be used for classification tasks (read more about ML applications in Talktorial T007)." + "The aggregated bioactivity data is standardized, repaired, and normalized to form the Papyrus dataset, which is updated with every new version of ChEMBL released. The Papyrus dataset contains \"high quality\" data associated with pChEMBL values for regression or classification tasks. pChEMBL value is a canonical activity metric defined as $-log_{10}(molar IC_{50}, XC_{50}, EC_{50}, AC_{50}, Ki, Kd, or potency)$. Moreover, \"low quality\" data that is only associated with an active/inactive label can be used for classification tasks (read more about ML applications in **Talktorial T007**)." ] }, { @@ -169,16 +169,16 @@ } }, "source": [ - "For the ML models used in PCM, molecules need to be converted into a list of features. In Talktorial T007, molecular fingerprints were introduced. In this talktorial, we will use a different type of representation that is often used on its own or in combination with fingerprints: molecular descriptors.\n", + "For the ML models used in PCM, molecules need to be converted into a list of features. In **Talktorial T007**, molecular fingerprints were introduced. In this talktorial, we will use a different type of representation that is often used on its own or in combination with fingerprints: molecular descriptors.\n", "\n", - "Molecular descriptors are the \"final result of a logical and mathematical procedure, which transforms chemical information encoded within a symbolic representation of a molecule into a useful number or the result of some standardized experiment\" ([J. Cheminf., 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y)). These descriptors can be, for example, molecular weight, ring count, Eccentric Connectivity Index (calculated from the 2D structure), or Geometrical Index (calculated from the 3D structure).\n", + "**Molecular descriptors** are the \"final result of a logical and mathematical procedure, which transforms chemical information encoded within a symbolic representation of a molecule into a useful number or the result of some standardized experiment\" ([*J. Cheminf.*, 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y)). These descriptors can be, for example, molecular weight, ring count, Eccentric Connectivity Index (calculated from the 2D structure), or Geometrical Index (calculated from the 3D structure).\n", "\n", "In this talktorial, we use [Modred](https://github.com/mordred-descriptor/mordred) as a software engine to calculate molecular descriptors. Modred calculates more than 1,800 molecular descriptors, including the ones implemented in RDKit. The algorithm starts with an automatic preprocessing step that is common for all possible descriptors calculated. For simplicity, here we calculate only four types of descriptors from the vast list of possibilities from Modred, excluding their 3D representation. These four descriptors are:\n", "\n", - "* ABC Index: 2 descriptors that represent the atom-bond connectivity index or the Graovac-Ghorbani atom-bond connectivity index (see Modred ABCIndex [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.ABCIndex.html))\n", - "* Acid-Base: 2 descriptors that count acidic and basic groups, respectively (see Modred AcidBase [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.AcidBase.html?highlight=acidbase))\n", - "* Atom count: 16 descriptors that represent a count of different types of atoms (see Modred AtomCount [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.AtomCount.html?highlight=atomcount))\n", - "* Balaban J index: 1 descriptor that represents a topological index (see Modred BalabanJ [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.BalabanJ.html?highlight=balaban#module-mordred.BalabanJ))" + "* **ABC Index**: 2 descriptors that represent the atom-bond connectivity index or the Graovac-Ghorbani atom-bond connectivity index (see Modred `ABCIndex` [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.ABCIndex.html))\n", + "* **Acid-Base**: 2 descriptors that count acidic and basic groups, respectively (see Modred `AcidBase` [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.AcidBase.html?highlight=acidbase))\n", + "* **Atom count**: 16 descriptors that represent a count of different types of atoms (see Modred `AtomCount` [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.AtomCount.html?highlight=atomcount))\n", + "* **Balaban J index**: 1 descriptor that represents a topological index (see Modred `BalabanJ` [docs](https://mordred-descriptor.github.io/documentation/master/api/mordred.BalabanJ.html?highlight=balaban#module-mordred.BalabanJ))" ] }, { @@ -196,17 +196,17 @@ } }, "source": [ - "As done for molecules, the proteins of interest need to be converted to a list of features or protein descriptors. Protein descriptors used in PCM applications are commonly based on the protein sequence and represent physicochemical characteristics of the amino acids that make up the sequence (e.g. Z-scales). Other protein descriptors represent topological (e.g. ST-scales) or electrostatic properties (e.g. MS-WHIM) of the protein sequence. Moreover, if structural information is available, protein descriptors can be derived from the 3D structure of the protein (e.g. sPairs) or the ligand-protein interaction in 3D (e.g. interaction fingerprints). Finally, with the widespread use of deep learning, protein embeddings can be obtained after parsing the protein sequence through the network (e.g. UniRep, AlphaFold embeddings). To read more about protein descriptors, check out these selection of articles ([Brief. Bioinform.,18, (2017)](https://pubmed.ncbi.nlm.nih.gov/26873661/), [Int. J. Mol. Sci., 22, (2021)](https://pubmed.ncbi.nlm.nih.gov/34884688/), [Comput. Struct. Biotechnol. J., 20, (2022)](https://pubmed.ncbi.nlm.nih.gov/35222841/)).\n", + "As done for molecules, the proteins of interest need to be converted to a list of features or protein descriptors. Protein descriptors used in PCM applications are commonly based on the protein sequence and represent physicochemical characteristics of the amino acids that make up the sequence (e.g. Z-scales). Other protein descriptors represent topological (e.g. ST-scales) or electrostatic properties (e.g. MS-WHIM) of the protein sequence. Moreover, if structural information is available, protein descriptors can be derived from the 3D structure of the protein (e.g. sPairs) or the ligand-protein interaction in 3D (e.g. interaction fingerprints). Finally, with the widespread use of deep learning, protein embeddings can be obtained after parsing the protein sequence through the network (e.g. UniRep, AlphaFold embeddings). To read more about protein descriptors, check out these selection of articles ([*Brief. Bioinform.*,18, (2017)](https://pubmed.ncbi.nlm.nih.gov/26873661/), [*Int. J. Mol. Sci.*, 22, (2021)](https://pubmed.ncbi.nlm.nih.gov/34884688/), [*Comput. Struct. Biotechnol. J.*, 20, (2022)](https://pubmed.ncbi.nlm.nih.gov/35222841/)).\n", "\n", "For protein descriptors based on the protein sequence, an aspect to take into account is that for ML the length of the protein descriptor needs to be the same. However, most proteins do not have the same sequence length. To solve this issue, there are two main approaches:\n", "\n", - "* Multiple sequence alignment (MSA): If the entire protein is to be included in the model, a MSA can be performed. The final descriptor has as many entries as the number of features per amino acid multiplied by the number of aligned positions. To account for gaps in the alignment, zeros are introduced in the descriptor. A MSA is a tool to identify common patterns between three or more biological sequences, usually DNA, RNA, or protein. One of the most common tools to perform MSA is Clustal Omega (or ClustalO), available as a [webtool](https://www.ebi.ac.uk/Tools/msa/clustalo/).\n", - "* Binding pocket selection: To avoid unnecessary features, a binding pocket of the same length can be selected for each protein. Normally, the binding pocket selection is preceded by a multiple sequence alignment and driven by known structural or mutagenesis data.\n", + "* **Multiple sequence alignment (MSA)**: If the entire protein is to be included in the model, a MSA can be performed. The final descriptor has as many entries as the number of features per amino acid multiplied by the number of aligned positions. To account for gaps in the alignment, zeros are introduced in the descriptor. A MSA is a tool to identify common patterns between three or more biological sequences, usually DNA, RNA, or protein. One of the most common tools to perform MSA is Clustal Omega (or ClustalO), available as a [webtool](https://www.ebi.ac.uk/Tools/msa/clustalo/).\n", + "* **Binding pocket selection**: To avoid unnecessary features, a binding pocket of the same length can be selected for each protein. Normally, the binding pocket selection is preceded by a multiple sequence alignment and driven by known structural or mutagenesis data.\n", "\n", - "Other options are available when proteins are not of the same family or do not share a binding pocket (see [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub))\n", + "Other options are available when proteins are not of the same family or do not share a binding pocket (see [*Drug Discov.* (2019), **32**, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub))\n", "\n", - "In this talktorial, we will focus on physicochemical protein descriptors, mainly Z-scales ([J. Med. Chem, 30 (1987)](https://pubs.acs.org/doi/10.1021/jm00390a003)). The Z-scales descriptor assigns three pre-determined values (Z1, Z2, Z3) to each amino acid in the sequence. The Z1, Z2, and Z3 values are the first principal components of a principal component analysis (PCA) including 29 different physicochemical variables to characterize the amino acids.\n", - "Since we are calculating activity for four proteins with very high sequence similarity (Adenosine receptors A1, A2A, A2B, and A3), we will use multiple sequence alignment prior to calculation of the Z-scales. To calculate Z-scales we will use [ProDEC](https://github.com/OlivierBeq/ProDEC), an open source resource that compiles a large number of protein descriptors." + "In this talktorial, we will focus on physicochemical protein descriptors, mainly **Z-scales** ([*J. Med. Chem*, 30 (1987)](https://pubs.acs.org/doi/10.1021/jm00390a003)). The Z-scales descriptor assigns three pre-determined values (Z~1~, Z~2~, Z~3~) to each amino acid in the sequence. The Z~1~, Z~2~, and Z~3~ values are the first principal components of a principal component analysis (PCA) including 29 different physicochemical variables to characterize the amino acids.\n", + "Since we are calculating activity for four proteins with very high sequence similarity (Adenosine receptors A1, A2A, A2B, and A3), we will use **multiple sequence alignment** prior to calculation of the Z-scales. To calculate Z-scales we will use [ProDEC](https://github.com/OlivierBeq/ProDEC), an open source resource that compiles a large number of protein descriptors." ] }, { @@ -224,7 +224,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The ML principles for PCM modeling are equivalent to those explained for QSAR modeling. However, in this talktorial we will explore a supervised ML application other than classification, this is regression. For regression tasks, a continuous target variable is needed, for example pChEMBL values.\n", + "The ML principles for PCM modeling are equivalent to those explained for QSAR modeling. However, in this talktorial we will explore a supervised ML application other than classification, this is **regression**. For regression tasks, a continuous target variable is needed, for example pChEMBL values.\n", "\n", "**Note**: Target variable is the variable we want to predict in ML. Not to be confused with (protein) target." ] @@ -246,11 +246,11 @@ "source": [ "Similarly to classification tasks, in supervised ML regression applications the model is first fitted to a training set and subsequently the predictive performance is evaluated on a test set. Therefore, the original dataset needs to be split between training and test sets. The split needs to ensure that the fitting process has enough data, and that the test set is representative. Normally, the distribution between train and test set is 80/20 or 70/30. Depending on the applicability domain, the split can be done in multiple ways. In PCM modeling, some of the most common splitting methods are:\n", "\n", - "* Random split: This method is not particularly relevant in drug discovery applications as it does not reflect the reality of a drug discovery campaign and it will most likely lead to data leaks between the training and test set. This is, very similar data will be found in both sets, which will lead to an overestimation of the predictive performance of the model. This type of split is commonly used, however, as a baseline and point of reference for other splitting methods, or as a starting point for quick model comparisons.\n", - "* Leave one target out (LOTO) split: To evaluate the ability of the model to extrapolate to targets not previously seen, one of the targets can be completely moved to the test set. In a big enough set, instead of one \"some\" targets can be moved to the test set (i.e. Leave some targets out, or LSTO).\n", - "* Leave one compound cluster out (LOCCO) split: This method evaluates the ability of the model to extrapolate to compounds with properties not previously seen by the model. Clustering can be done based on different molecular characteristics, such as physicochemical properties or scaffold, for example (see Talktorial T005 to learn more about clustering). One (or several, LSCCO) cluster(s) can then be left out for testing. This method prevents data leaking in terms of chemistry between training and test sets.\n", - "* Temporal split: This method was developed in order to account for the usual timeline of drug discovery campaigns, where chemical series are populated sequentially over time. In this approach, the molecules included in the training set are those released until a certain date and the rest (most novel) are included in the test set.\n", - "* Stratified split per target: This method can be applied to any of the splitting methods described above (except LOTO), and aims to include data of all targets in both the training and test set, so that additional target-compound interactions can be extracted by the model. (NOTE: stratification can be also done in regards to other reference points apart from targets, for example classes in classification tasks, to make sure that the distribution is similar across training and test set)." + "* **Random split**: This method is not particularly relevant in drug discovery applications as it does not reflect the reality of a drug discovery campaign and it will most likely lead to data leaks between the training and test set. This is, very similar data will be found in both sets, which will lead to an overestimation of the predictive performance of the model. This type of split is commonly used, however, as a baseline and point of reference for other splitting methods, or as a starting point for quick model comparisons.\n", + "* **Leave one target out (LOTO) split**: To evaluate the ability of the model to extrapolate to targets not previously seen, one of the targets can be completely moved to the test set. In a big enough set, instead of one \"some\" targets can be moved to the test set (i.e. Leave some targets out, or LSTO).\n", + "* **Leave one compound cluster out (LOCCO) split**: This method evaluates the ability of the model to extrapolate to compounds with properties not previously seen by the model. Clustering can be done based on different molecular characteristics, such as physicochemical properties or scaffold, for example (see **Talktorial T005** to learn more about clustering). One (or several, LSCCO) cluster(s) can then be left out for testing. This method prevents data leaking in terms of chemistry between training and test sets.\n", + "* **Temporal split**: This method was developed in order to account for the usual timeline of drug discovery campaigns, where chemical series are populated sequentially over time. In this approach, the molecules included in the training set are those released until a certain date and the rest (most novel) are included in the test set.\n", + "* **Stratified split per target**: This method can be applied to any of the splitting methods described above (except LOTO), and aims to include data of all targets in both the training and test set, so that additional target-compound interactions can be extracted by the model. (**NOTE:** stratification can be also done in regards to other reference points apart from targets, for example classes in classification tasks, to make sure that the distribution is similar across training and test set)." ] }, { @@ -284,15 +284,15 @@ "metadata": {}, "source": [ "\n", - "To evaluate the predictive performance of a regression model, there are several metrics that in simple terms measure the differences between the true target values and the predictions made by the model. These metrics can be used in cross-validation on the training set (see Talktorial T007) or in the test set. The most commonly used metrics include:\n", + "To evaluate the predictive performance of a regression model, there are several metrics that in simple terms measure the differences between the true target values and the predictions made by the model. These metrics can be used in cross-validation on the training set (see **Talktorial T007**) or in the test set. The most commonly used metrics include:\n", "\n", - "* Coefficient of determination ($R^{2}$ score): Represents the portion of variance of the target variable that has been explained by the independent variables (features) in the model. $R^{2}$ score varies between 1.0 (best score) and minus infinite, where 0.0 represents a model that always predicts the average target variable. As the variance is dataset-dependent, it might not be a meaningful metric to compare between datasets. When dealing with linear regression, and model fitting and evaluation are performed on a single dataset, $R^{2}$ is equivalent to the square of the Pearson correlation coefficient, described below, and can be noted as $r^{2}$.\n", - "* Pearson's correlation coefficient (Pearson's $r$): Is a measure of the linear correlation between the true and predicted values of the target variable. It is calculated as the covariance of the two variables divided by the product of their standard deviation. Pearson's $r$ can vary between 1.0 (a perfect positive correlation) and -1.0 (a perfect negative correlation), where 1.0 would represent a perfect prediction.\n", - "* Mean absolute error (MAE): Measures the average absolute difference between the predicted and the true values. MAE is interpreted based on the scale of the data, and it varies between infinite and 0.0 (best).\n", - "* Mean squared error (MSE): Measures the average of the squares of the difference between the predicted and the true values. It varies between infinite and 0.0 (best).\n", - "* Root mean square error (RMSE): It is the square root of the MSE and represents the standard deviation of the prediction errors with respect to the line of best fit. RMSE is a measure of accuracy and it cannot be applied to compare between datasets, as it is scale-dependent. It varies between infinite and 0.0 (best).\n", + "* **Coefficient of determination ($R^{2}$ score)**: Represents the portion of variance of the target variable that has been explained by the independent variables (features) in the model. $R^{2}$ score varies between 1.0 (best score) and minus infinite, where 0.0 represents a model that always predicts the average target variable. As the variance is dataset-dependent, it might not be a meaningful metric to compare between datasets. When dealing with linear regression, and model fitting and evaluation are performed on a single dataset, $R^{2}$ is equivalent to the square of the Pearson correlation coefficient, described below, and can be noted as $r^{2}$.\n", + "* **Pearson's correlation coefficient (Pearson's $r$)**: Is a measure of the linear correlation between the true and predicted values of the target variable. It is calculated as the covariance of the two variables divided by the product of their standard deviation. Pearson's $r$ can vary between 1.0 (a perfect positive correlation) and -1.0 (a perfect negative correlation), where 1.0 would represent a perfect prediction.\n", + "* **Mean absolute error (MAE)**: Measures the average absolute difference between the predicted and the true values. MAE is interpreted based on the scale of the data, and it varies between infinite and 0.0 (best).\n", + "* **Mean squared error (MSE)**: Measures the average of the squares of the difference between the predicted and the true values. It varies between infinite and 0.0 (best).\n", + "* **Root mean square error (RMSE)**: It is the square root of the MSE and represents the standard deviation of the prediction errors with respect to the line of best fit. RMSE is a measure of accuracy and it cannot be applied to compare between datasets, as it is scale-dependent. It varies between infinite and 0.0 (best).\n", "\n", - "To learn more about evaluation metrics, you can consult scikit learn's regression metrics [Docs](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics)." + "To learn more about evaluation metrics, you can consult scikit learn's `regression metrics` [Docs](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics)." ] }, { @@ -314,7 +314,7 @@ } }, "source": [ - "Different ML algorithms can be used to train PCM models. Some of them include support vector machines (SVM), random forest (RF), and neural networks (NN), which were described in Talktorial T007. RF models have been used extensively in PCM applications due to their efficiency in large datasets and resistance to overfitting with more features. However, deep learning applications are also gaining momentum. See [J. Cheminform., 45, (2017)](https://pubmed.ncbi.nlm.nih.gov/29086168/) for a comparative use of ML methods in PCM modeling.\n", + "Different ML algorithms can be used to train PCM models. Some of them include support vector machines (SVM), **random forest (RF)**, and neural networks (NN), which were described in **Talktorial T007**. RF models have been used extensively in PCM applications due to their efficiency in large datasets and resistance to overfitting with more features. However, deep learning applications are also gaining momentum. See [*J. Cheminform.*, 45, (2017)](https://pubmed.ncbi.nlm.nih.gov/29086168/) for a comparative use of ML methods in PCM modeling.\n", "In this talktorial, we will use RF. RF is a decision tree-based algorithm, more in detail a bagging ensemble method. This means that there are multiple decision trees trained independently with subsets of features and data and the final prediction is made from a consensus between the independent predictions.\n" ] }, @@ -339,11 +339,11 @@ "source": [ "The possibility to predict bioactivity for multiple targets in one model with PCM is very interesting in drug discovery and expands the applicability domain of QSAR modeling. Some applications of this technique are listed below and help answer the following questions in drug discovery:\n", "\n", - "* Poly-pharmacology: Is it possible to target several proteins of interest simultaneously with one single drug?\n", - "* Off-target prediction: What other proteins do these compounds target apart from the intended therapeutic target? Are maybe these off-targets responsible for side effects?\n", - "* Selectivity prediction: Do certain novel compounds target one protein isoform while avoiding others (off-targets) known to cause adverse effects?\n", + "* **Poly-pharmacology**: Is it possible to target several proteins of interest simultaneously with one single drug?\n", + "* **Off-target prediction**: What other proteins do these compounds target apart from the intended therapeutic target? Are maybe these off-targets responsible for side effects?\n", + "* **Selectivity prediction**: Do certain novel compounds target one protein isoform while avoiding others (off-targets) known to cause adverse effects?\n", "\n", - "To know more about applications of PCM in drug discovery, have a look at this review [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub)." + "To know more about applications of PCM in drug discovery, have a look at this review [*Drug Discov.* (2019), **32**, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub)." ] }, { @@ -431,7 +431,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To work with the Papyrus dataset, we use the papyrus_scripts [library](https://github.com/OlivierBeq/Papyrus-scripts). This library allows us to download, read, and explore the dataset. Many other features, including bioactivity modeling, are possible using the papyrus_scripts. If you want to dive into them, feel free to follow the [notebook with simple examples](https://github.com/OlivierBeq/Papyrus-scripts/blob/master/notebook_examples/simple_examples.ipynb). By default, the download_papyrus function retrieves bioactivity, target and other information for the latest version of the Papyrus dataset. The data retrieved consists of the highest quality continuous bioactivity data (Papyrus++) without stereochemistry annotated (i.e. nostereo=True and stereo=False). Check out the [documentation](https://github.com/OlivierBeq/Papyrus-scripts/blob/master/src/papyrus_scripts/download.py) to learn more about the options available." + "To work with the Papyrus dataset, we use the `papyrus_scripts` [library](https://github.com/OlivierBeq/Papyrus-scripts). This library allows us to download, read, and explore the dataset. Many other features, including bioactivity modeling, are possible using the `papyrus_scripts`. If you want to dive into them, feel free to follow the [notebook with simple examples](https://github.com/OlivierBeq/Papyrus-scripts/blob/master/notebook_examples/simple_examples.ipynb). By default, the `download_papyrus` function retrieves bioactivity, target and other information for the latest version of the Papyrus dataset. The data retrieved consists of the highest quality continuous bioactivity data (Papyrus++) without stereochemistry annotated (i.e. `nostereo=True` and `stereo=False`). Check out the [documentation](https://github.com/OlivierBeq/Papyrus-scripts/blob/master/src/papyrus_scripts/download.py) to learn more about the options available." ] }, { @@ -530,7 +530,7 @@ } }, "source": [ - "From the Papyrus dataset, we want to extract continuous activity data for all compound-protein pairs for the four human Adenosine receptors; unique compound-target activity values are reported by aggregating data from different assays available. To account for the different types of bioactivity units available, a canonical activity metric defined by ChEMBL is used, pChEMBL value. From the activity aggregation strategies available with the Papyrus set, we will use the Mean (pchembl_value_Mean variable).\n", + "From the Papyrus dataset, we want to extract continuous activity data for all compound-protein pairs for the four human Adenosine receptors; unique compound-target activity values are reported by aggregating data from different assays available. To account for the different types of bioactivity units available, a canonical activity metric defined by ChEMBL is used, pChEMBL value. From the activity aggregation strategies available with the Papyrus set, we will use the Mean (`pchembl_value_Mean` variable).\n", "\n", "|Receptor|UniProt accession|\n", "|---|---|\n", @@ -678,9 +678,9 @@ "source": [ "For PCM modeling, we keep from our bioactivity dataset three variables:\n", "\n", - "* Bioactivity (pchembl_value_mean), which is our target variable to predict\n", - "* Target IDs (accession), which is the UniProt code to link the protein descriptors that we will calculate with ProDEC\n", - "* Compound IDs (SMILES), to link the compound descriptors that we will calculate with Mordred" + "* Bioactivity (`pchembl_value_mean`), which is our target variable to predict\n", + "* Target IDs (`accession`), which is the UniProt code to link the protein descriptors that we will calculate with ProDEC\n", + "* Compound IDs (`SMILES`), to link the compound descriptors that we will calculate with Mordred" ] }, { @@ -799,7 +799,7 @@ "metadata": {}, "source": [ "In order to ensure protein descriptors are of the same length, we first need to align the target sequences. We do this by creating a MSA with the software Clustal Omega (ClustalO). To begin with, we extract the protein sequences from the target files in Papyrus. The sequences could also be obtained from UniProt, but this way we ensure we are always retrieving the canonical isoform sequence.\n", - "Since Papyrus also contains bioactivity data for different mutants and species, the main protein identifier (target_id variable) consists of the UniProt accession code and the mutant ('WT' for wild type). Even though we are interested in the wild type, to map our targets of interest we calculate a new variable called accession to be consistent with the rest of the talktorial." + "Since Papyrus also contains bioactivity data for different mutants and species, the main protein identifier (`target_id` variable) consists of the UniProt accession code and the mutant ('WT' for wild type). Even though we are interested in the wild type, to map our targets of interest we calculate a new variable called `accession` to be consistent with the rest of the talktorial." ] }, { @@ -1141,30 +1141,30 @@ ], "text/plain": [ "╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n", - "│ 0 AA2BR_H… \u001b[1;36m 1\u001b[0m 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│\n", "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n" ] }, @@ -1896,7 +1896,7 @@ "\n", "Ultimately, we want a model that can predict compound activity data towards a target of interest for compound-target pairs that it has never seen before. By combining several targets in one model, we expect the model to be able to learn the similarities and differences between targets and use the additional data to make better predictions.\n", "\n", - "We start by defining a few functions that will help us split the data (split_train_test) and train and validate a PCM regression model (train_validate_pcm_model). The validation will be done on the test set and the performance will be assessed using regression metrics such as Person's $r$, $R^{2}$ and $MAE$. This function will also plot the correlation between true and predicted values, making a distinction between the different targets in the test set to assess whether the PCM model has a different performance per protein. Finally, we will define a function (train_validate_qsar_model) to train a QSAR model for a single target based on a random split. The output of this function will be comparable to that of the PCM model for comparison purposes." + "We start by defining a few functions that will help us split the data (`split_train_test`) and train and validate a PCM regression model (`train_validate_pcm_model`). The validation will be done on the test set and the performance will be assessed using regression metrics such as Person's $r$, $R^{2}$ and $MAE$. This function will also plot the correlation between true and predicted values, making a distinction between the different targets in the test set to assess whether the PCM model has a different performance per protein. Finally, we will define a function (`train_validate_qsar_model`) to train a QSAR model for a single target based on a random split. The output of this function will be comparable to that of the PCM model for comparison purposes." ] }, { @@ -3115,4 +3115,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file From fadc2d64fdecba48f2f3239d9a659a7ec3a5d806 Mon Sep 17 00:00:00 2001 From: Marina Gorostiola Gonzalez Date: Tue, 8 Nov 2022 16:10:32 +0100 Subject: [PATCH 49/62] Update conflicting formatting in regression evaluation metrics --- .../talktorial.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index 82eb2a1f..f932cd42 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -286,8 +286,8 @@ "\n", "To evaluate the predictive performance of a regression model, there are several metrics that in simple terms measure the differences between the true target values and the predictions made by the model. These metrics can be used in cross-validation on the training set (see **Talktorial T007**) or in the test set. The most commonly used metrics include:\n", "\n", - "* **Coefficient of determination ($R^{2}$ score)**: Represents the portion of variance of the target variable that has been explained by the independent variables (features) in the model. $R^{2}$ score varies between 1.0 (best score) and minus infinite, where 0.0 represents a model that always predicts the average target variable. As the variance is dataset-dependent, it might not be a meaningful metric to compare between datasets. When dealing with linear regression, and model fitting and evaluation are performed on a single dataset, $R^{2}$ is equivalent to the square of the Pearson correlation coefficient, described below, and can be noted as $r^{2}$.\n", - "* **Pearson's correlation coefficient (Pearson's $r$)**: Is a measure of the linear correlation between the true and predicted values of the target variable. It is calculated as the covariance of the two variables divided by the product of their standard deviation. Pearson's $r$ can vary between 1.0 (a perfect positive correlation) and -1.0 (a perfect negative correlation), where 1.0 would represent a perfect prediction.\n", + "* **Coefficient of determination (**$R^{2}$ **score)**: Represents the portion of variance of the target variable that has been explained by the independent variables (features) in the model. $R^{2}$ score varies between 1.0 (best score) and minus infinite, where 0.0 represents a model that always predicts the average target variable. As the variance is dataset-dependent, it might not be a meaningful metric to compare between datasets. When dealing with linear regression, and model fitting and evaluation are performed on a single dataset, $R^{2}$ is equivalent to the square of the Pearson correlation coefficient, described below, and can be noted as $r^{2}$.\n", + "* **Pearson's correlation coefficient (Pearson's** $r$**)**: Is a measure of the linear correlation between the true and predicted values of the target variable. It is calculated as the covariance of the two variables divided by the product of their standard deviation. Pearson's $r$ can vary between 1.0 (a perfect positive correlation) and -1.0 (a perfect negative correlation), where 1.0 would represent a perfect prediction.\n", "* **Mean absolute error (MAE)**: Measures the average absolute difference between the predicted and the true values. MAE is interpreted based on the scale of the data, and it varies between infinite and 0.0 (best).\n", "* **Mean squared error (MSE)**: Measures the average of the squares of the difference between the predicted and the true values. It varies between infinite and 0.0 (best).\n", "* **Root mean square error (RMSE)**: It is the square root of the MSE and represents the standard deviation of the prediction errors with respect to the line of best fit. RMSE is a measure of accuracy and it cannot be applied to compare between datasets, as it is scale-dependent. It varies between infinite and 0.0 (best).\n", From 778062db4d5f7bc5346dcbf872f31c390d342f15 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 21 Nov 2022 20:43:00 +0000 Subject: [PATCH 50/62] T032: Move pip installs from env file to notebook itself --- devtools/test_env.yml | 15 ++++++++----- .../talktorial.ipynb | 22 ++++++++++++++++++- 2 files changed, 30 insertions(+), 7 deletions(-) diff --git a/devtools/test_env.yml b/devtools/test_env.yml index d7eb70ba..668c28d1 100644 --- a/devtools/test_env.yml +++ b/devtools/test_env.yml @@ -70,11 +70,14 @@ dependencies: - black-nb - nbsphinx-link - sphinxext-opengraph - # T032 - we might move some of this to conda-forge - - https://github.com/OlivierBeq/Papyrus-scripts/tarball/master - - prodec - - rich-msa - # Dependency for ClustalO webservice (also conda installable via -c bioconda) - - xmltramp2 # TeachOpenCADD itself - ../ + + # T032 + # The following pip packages are currently installed in the notebook itself because they are only used there, thereby avoiding the addition of more dependencies to our already quite large environment file. + # Follow this discussion on how we try to simplify our environment setup in the future: https://github.com/volkamerlab/teachopencadd/discussions/277 + # - https://github.com/OlivierBeq/Papyrus-scripts/tarball/master + # - prodec + # - rich-msa + # Dependency for ClustalO webservice (also conda installable via -c bioconda) + # - xmltramp2 diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index f932cd42..27412879 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -360,11 +360,31 @@ "In the practical section of this talktorial we will create a PCM regression model for the four adenosine receptors (A1, A2A, A2B, A3) with data from the Papyrus dataset and molecular and protein descriptors as features." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we start, let's install a few packages that are not part of TeachOpenCADD's [global enviroment file](https://github.com/volkamerlab/teachopencadd/blob/master/devtools/test_env.yml) because they are only relevant to this notebook (this setup will change in the future, see discussion [here](https://github.com/volkamerlab/teachopencadd/discussions/277))." + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "%%bash\n", + "pip install prodec rich-msa xmltramp2\n", + "pip install git+https://github.com/OlivierBeq/Papyrus-scripts.git" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "import json\n", "from pathlib import Path\n", From 13f7b5cd5263179343eca1e9b93986e4afc845b9 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 21 Nov 2022 20:43:27 +0000 Subject: [PATCH 51/62] Satisfy black-nb --- .../T032_compound_activity_proteochemometrics/talktorial.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index 27412879..1edf37a8 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -1022,7 +1022,7 @@ " dict\n", " Protein annotations deposited in the GPCRdb.\n", " \"\"\"\n", - " \n", + "\n", " url = f\"https://www.ebi.ac.uk/Tools/common/tools/help/index.html?tool=clustalo#!/Submit32job/post_run\"\n", " url = f\"https://gpcrdb.org/services/protein/{entry_name}/\"\n", " response = requests.get(url)\n", From 5d524517fc260bda2b4fd02cec7680f722fafb61 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 21 Nov 2022 20:44:24 +0000 Subject: [PATCH 52/62] Regenerate READMEs --- .../T032_compound_activity_proteochemometrics/README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md index 50cbf5c3..2596f56a 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md @@ -37,7 +37,7 @@ While activity data is very abundant for some protein targets, there are still a * Calculate compound descriptors * Proteochemometrics modeling * Helper functions - * Preprocessing + * Preprocessing * Model training and validation * Random split PCM model * Random split QSAR models @@ -47,11 +47,11 @@ While activity data is very abundant for some protein targets, there are still a ### References * Papyrus scripts [GitHub](https://github.com/OlivierBeq/Papyrus-scripts) -* Papyrus dataset preprint: [ChemRvix (2021)](https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0) -* Molecular descriptors (Modred): [J. Cheminf., 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y) +* Papyrus dataset preprint: [*ChemRvix* (2021)](https://chemrxiv.org/engage/chemrxiv/article-details/617aa2467a002162403d71f0) +* Molecular descriptors (Modred): [*J. Cheminf.*, 10, (2018)](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-018-0258-y) * Protein descriptors (ProDEC) [GitHub](https://github.com/OlivierBeq/ProDEC) * Regression metrics [(Scikit learn)](https://scikit-learn.org/stable/modules/model_evaluation.html#regression-metrics) * XGBoost [Documentation](https://xgboost.readthedocs.io/en/stable/index.html) -* Proteochemometrics review: [Drug Discov. (2019), 32, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub) +* Proteochemometrics review: [*Drug Discov.* (2019), **32**, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub) From 37321576c31874af430bb5efc1f5e499e0194b22 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 21 Nov 2022 21:01:41 +0000 Subject: [PATCH 53/62] T032: Rerun notebook & add NBVAL_CHECK_OUTPUT checks --- .../talktorial.ipynb | 213 +++++++++--------- 1 file changed, 111 insertions(+), 102 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index 1edf37a8..ee226d14 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -373,14 +373,21 @@ "metadata": {}, "outputs": [], "source": [ - "%%bash\n", - "pip install prodec rich-msa xmltramp2\n", - "pip install git+https://github.com/OlivierBeq/Papyrus-scripts.git" + "!pip install prodec rich-msa xmltramp2" ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install git+https://github.com/OlivierBeq/Papyrus-scripts.git" + ] + }, + { + "cell_type": "code", + "execution_count": 4, "metadata": { "tags": [] }, @@ -412,7 +419,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -431,7 +438,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -456,7 +463,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": { "collapsed": false, "jupyter": { @@ -474,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "metadata": { "collapsed": false, "jupyter": { @@ -496,7 +503,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "31aca1d3d0c34d6d91014aeb8509b21a", + "model_id": "262b8dd13bf2485496146b0ac53fcc0d", "version_major": 2, "version_minor": 0 }, @@ -511,8 +518,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 409 ms, sys: 59.5 ms, total: 469 ms\n", - "Wall time: 735 ms\n" + "CPU times: user 1.86 s, sys: 829 ms, total: 2.69 s\n", + "Wall time: 57.3 s\n" ] } ], @@ -562,7 +569,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": { "collapsed": false, "jupyter": { @@ -633,7 +640,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": { "collapsed": false, "jupyter": { @@ -647,7 +654,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "68acd18cb79c470fa374cf851acd8433", + "model_id": "e6132a1eee284886aa6d717d91b1aeb2", "version_major": 2, "version_minor": 0 }, @@ -671,7 +678,7 @@ }, { "data": { - "image/png": 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\n", 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" ] @@ -705,7 +712,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": { "collapsed": false, "jupyter": { @@ -793,7 +800,7 @@ "464 5.6500 " ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -824,7 +831,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": { "collapsed": false, "jupyter": { @@ -940,7 +947,7 @@ "82 MPNNSTALSLANVTYITMEIFIGLCAIVGNVLVICVVKLNPSLQTT... P0DMS8 " ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -963,7 +970,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": { "collapsed": false, "jupyter": { @@ -1004,7 +1011,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -1043,7 +1050,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": { "collapsed": false, "jupyter": { @@ -1088,7 +1095,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": { "collapsed": false, "jupyter": { @@ -1117,7 +1124,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 17, "metadata": { "collapsed": false, "jupyter": { @@ -1161,30 +1168,30 @@ ], "text/plain": [ "╭────────────────────────────────────────── Multiple sequence alignment ──────────────────────────────────────────╮\n", - "│ 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│\n", "╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯\n" ] }, @@ -1235,7 +1242,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": { "collapsed": false, "jupyter": { @@ -1263,7 +1270,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": { "collapsed": false, "jupyter": { @@ -1292,7 +1299,7 @@ " 'Patent': None}" ] }, - "execution_count": 16, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -1305,7 +1312,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": { "collapsed": false, "jupyter": { @@ -1349,7 +1356,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 21, "metadata": { "collapsed": false, "jupyter": { @@ -1363,7 +1370,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "84a5bb53ffa64379aa411f3ded76beac", + "model_id": "315d66f818334fd9b3bc5ee912c42218", "version_major": 2, "version_minor": 0 }, @@ -1548,7 +1555,7 @@ "[4 rows x 1279 columns]" ] }, - "execution_count": 18, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -1582,7 +1589,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": { "collapsed": false, "jupyter": { @@ -1661,7 +1668,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": { "collapsed": false, "jupyter": { @@ -1676,7 +1683,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████████| 6898/6898 [00:11<00:00, 626.03it/s]\n" + "100%|██████████████████████████████████████| 6898/6898 [00:09<00:00, 741.92it/s]\n" ] }, { @@ -1874,7 +1881,7 @@ "[5 rows x 23 columns]" ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1943,7 +1950,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 24, "metadata": { "collapsed": false, "jupyter": { @@ -2024,7 +2031,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 25, "metadata": { "collapsed": false, "jupyter": { @@ -2075,7 +2082,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 26, "metadata": { "collapsed": false, "jupyter": { @@ -2130,7 +2137,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 27, "metadata": { "collapsed": false, "jupyter": { @@ -2224,7 +2231,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 28, "metadata": { "collapsed": false, "jupyter": { @@ -2312,7 +2319,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 29, "metadata": { "collapsed": false, "jupyter": { @@ -2518,7 +2525,7 @@ "[5 rows x 1303 columns]" ] }, - "execution_count": 26, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -2528,7 +2535,8 @@ "ar_pcm_dataset = ar_dataset.merge(protein_features, on=\"accession\")\n", "ar_pcm_dataset = ar_pcm_dataset.merge(molecular_features, on=\"SMILES\")\n", "\n", - "ar_pcm_dataset.head()" + "ar_pcm_dataset.head()\n", + "# NBVAL_CHECK_OUTPUT" ] }, { @@ -2544,7 +2552,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 30, "metadata": { "collapsed": false, "jupyter": { @@ -2750,7 +2758,7 @@ "[5 rows x 25 columns]" ] }, - "execution_count": 27, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -2759,7 +2767,8 @@ "# Add molecular features to bioactivity dataset to generate QSAR dataset\n", "ar_qsar_dataset = ar_dataset.merge(molecular_features, on=\"SMILES\")\n", "\n", - "ar_qsar_dataset.head()" + "ar_qsar_dataset.head()\n", + "# NBVAL_CHECK_OUTPUT" ] }, { @@ -2797,7 +2806,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 31, "metadata": { "collapsed": false, "jupyter": { @@ -2826,7 +2835,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -2835,15 +2844,15 @@ "text": [ "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.6838750730061094,\n", - " \"R2 score\": 0.4629920934701346,\n", - " \"MAE\": 0.6424857789562605\n", + " \"Pearson r\": 0.6921704181980155,\n", + " \"R2 score\": 0.4729408196152224,\n", + " \"MAE\": 0.6373598241125418\n", "}\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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/srKyrtMnEQRBuDZfrxhL8ciXqJ8moXeHfwe14/Et++7IQAdEsHNLe//99xk7diy7du0iNTXV7tzevXsJCgpi48aNJCcnM2PGDKZNm8bq1asdnrNr1y5KSkp4/PHHWb9+/RW929fXl/Pnz5Oens53331HcXExDz74IEaj0XbN9u3befHFF/nzzz/55ZdfMJlM9O7dm+Li4ip9blcKCgro1asX4eHh/P3337z11lu89tprrFy58orunzx5MuHh4Q7HBw0axPnz5+2+HnjgAe6//36Cg4Or+2MIgiBck+JCLZ8Oi+Ku937FRw9pQWB6bQaPz/u4podWs6TbnFarlQBJq9U6nNPr9dKhQ4ckvV5ftZfo8iQp+6gknf1bkrKPWb+/zoqKiiQfHx/pyJEj0qBBg6R58+ZVes/o0aOlbt26ORwfPny4NHXqVOmHH36QGjZsKFksFpfP+eCDDySNRmN37JtvvpEA6cCBAxXel5WVJQHS9u3bKx3rtXrnnXckjUYjlZSU2I4tWbJECg8Pr/Rzff/991KzZs2k5ORkCZD2799f4bVZWVmSm5ubtGHDhgqvqbY/X4IgCFcgKf5b6YduzaVDTZtJh5o2kzYPulvKz0mv6WFdM1e/v6+WmNmpKm0abHkWVt8D63rA6g7w+XPW49fR5s2badq0KU2bNmXIkCF88MEHlS7VaLVa/P397Y4VFhayZcsWhgwZQq9evSguLmbbtm1XNZYLFy7w8cfWfzW4ubm5fD/gMIbydu7cibe3t8uvxYsXV3j/7t27uf/++/Hw8LAde+CBB0hPT+f06dMV3peZmcmIESP46KOPUKvVFV5XZsOGDajVagYOHFjptYIgCNfbl8tHYhg9iXrpEjp3OPTUPTzx6V5qBYTV9NBuCiJBuSr0+fD1GDj1u/3xk7/BN2NhYByo/K7Lq+Pi4hgyZAgAffr0oaioiN9++42ePXs6vX737t189tlnfPfdd3bHP/30U5o0aULLli0BePLJJ4mLi6Nbt24u36/VavH29kaSJHQ6HQAPPfQQzZo5744rSRITJkygc+fOtGrVqsLndujQwS5XxhlXwVJGRgb169e3OxYSEmI716CBYz0JSZIYPnw4I0eOpEOHDi6DojLvv/8+Tz31FCqVqtJrBUEQrpcibR7fje5Dm72FAJwLkeE/ex4DejxewyO7uYhgpyqKsx0DnTInf7Oevw7BztGjR/nrr7/44osvAFAqlQwaNIj333/fabCTnJzMww8/zOzZs+nVq5fdufJBE8CQIUPo0qULFy5coFatWhWOwcfHh3379mEymdi+fTuvvvoqa9eurfD6MWPGcODAAXbt2uXys6lUKho3buzymsrIZDK778tmvC4/Xuatt96ioKCAadOmXdHzd+/ezaFDh9iwYUOVxikIglAV+7d/SdacGbTJsP4dd7C9N31Wf4evn8gjvJwIdqqipKBq569RXFwcJpOJiIhLWfWSJOHm5kZ+fj5+fpcCrEOHDtG9e3dGjBjBzJkz7Z5z6NAh9uzZw99//82UKVNsx81mM5988gmjRo2qcAxyudwWlDRr1oyMjAwGDRrEjh07HK4dO3Ys33zzDTt27KB27douP9vOnTvp27evy2umT5/O9OnTnZ4LDQ0lIyPD7ljZbqmyGZ7L/f777/z55592S19gnWV6+umn+fDDD+2Or1u3jrZt29K+fXuX4xQEQbheti56lnqf7aauAYo9IPXJGJ6Ytq6mh3XTEsFOVXj6Vu38NTCZTGzYsIEVK1bQu3dvu3MDBgxg06ZNjBkzBrDO6HTv3p1hw4axaNEih2fFxcXRpUsX3n77bbvjH330EXFxcS6Dncu9/PLLrFy5ki+//JJHH30UsAZgY8eO5csvv2Tbtm1Ol5AuV9VlrKioKKZPn47RaMTd3R2An3/+mfDwcIflrTJvvvkmCxcutH2fnp7OAw88wObNm+nYsaPdtUVFRXz22WcsWbKk0s8iCIJQ3Qrys/hxdD9a77fuak0NkxEyfwmP3fdwDY/sJlflFOeb3HXdjaXLk6QNj0rSHF/Hrw2PXpddWV9++aXk7u4uXbhwweHc9OnTpbZt20qSJEn//vuvFBQUJD399NPS+fPnbV9ZWVmSJEmS0WiUgoKCpDVr1jg859ixYxIgJSYmOh2Ds91YkiRJEyZMkFq3bm3b9TRq1ChJo9FI27ZtsxuDTqe71o9fqQsXLkghISHS4MGDpYMHD0pffPGF5OvrK7322mu2a/bs2SM1bdpUOnfunNNnpKSkVLgba926dZKnp6eUl1f5f1uxG0sQhOr096+fSD93ubTb6tOn75WKChx/F9wuxG6sm4XKDx56Cxr1sD/eqIf1+HXI14mLi6Nnz55oNBqHcwMGDCAxMZF9+/axZcsWsrOz2bRpE2FhYbave+65B4BvvvmG3Nxc2yxMeU2aNKF169bExcVd1dheeuklDh8+zJYt1n4ra9asQavV0rVrV7sxbN68+Ro++ZXRaDT88ssvnDt3jg4dOjB69GgmTJjAhAkTbNfodDqOHj1KaWnpVT8/Li6Oxx57zG6pUBAE4Xr7fH4sspfnUTtTolAFR5+7n0Eb9+Dl4/i7QHAkk6QrLC17iyooKECj0aDVavH1tV9WKikpISUlhQYNGuDp6XntL9HnW5ORSwqsS1deQddtF5Zw66i2P1/CTU2rM5JTZKSgpBRflRuBXu5o1O41PSzhNnEh9zy/jH6QVkl6AM5EyKi9aAWtOrnObbwduPr9fbVEzk51UPmJ4EYQ7kDpF/RM2XqAncdzbMe6NAlk6YA2hNcSZQmEqtnz4waKFi2hVbb1+6SOtXj4nZ9QeVV/PujtTixjCYIgXAOtzugQ6ADsOJ7D1K0H0OqMFdwpCJX7bNaTuE1eQng2FKjh+Au9ePLD3SLQuUZiZkcQBOEa5BQZHQKdMjuO55BTZBTLWcJVy8s8y+8vPkTrf0sASKktp8GSVXS8x3nBWOHK1OjMzo4dO+jfvz/h4eHIZDK++uor27nS0lKmTJlC69at8fLyIjw8nNjYWNLT02tuwIIgCBcVlLhOcC+s5LwgXG73t3EkDuhNy39LsABJ0f70+N/fNBeBTpXVaLBTXFxMZGSk007cOp2Offv2MWvWLPbt28cXX3zBsWPHeOihh2pgpIIgCPZ8PSvuAwfgU8l5QSjvs2kDUE17jbAc0KohZcyDPPl+PB6qynv1CZWr0WWsvn37Vlgtt2wLcXlvvfUW9957L6mpqdStW/dGDFEQBMGpQG93ujQJZIeTpawuTQIJ9BZLWELlcs6nsP3FR2l9yADAqbpyGi9fQ6e2XWp4ZLeXWypBWavVIpPJXPZsMhgMFBQU2H0JgiBUN43anaUD2tClSaDd8S5NAlk2oI3I1xEqtfOLt/l3QD9aHDJgkUHSfUH0/PpvmopAp9rdMgnKJSUlTJ06laeeesrlfvslS5Ywb968GzgyQRDuVOG1VLw1uB05RUYKS0rx8XQj0FvU2RFcM5tMfD5tAM1+OIa7CS54Q95zj/DkKNGG5nq5JYKd0tJSnnzySSwWC++8847La6dNm2ZXLbegoIA6depc7yEKgnCH0qhFcCNcuYzU4ySMG0ibI9bSBCfqy2nx6ntEtY6u4ZHd3m76YKe0tJQnnniClJQUfv/990qrKHp4eDh0rxYEQRCEmrZt8xvI3vg/mueDWQYH7w9l4Js/4uYufmddbzd1zk5ZoHP8+HF+/fVXAgICanpIN5WEhAQUCgV9+vRxOJeUlMTgwYOpU6cOKpWK5s2bs2rVKrtrtm3bhkwms32pVCpatmzJu+++6/K9l98XEBBA9+7diY+Pt7vuvffe47777sPPzw8/Pz969uzJX3/9VfUPXonU1FT69++Pl5cXgYGBjBs3DqPRdYG3rl272n0mmUzGk08+aXfNokWLiI6ORq1Wu8wbEwRBKM9sMvHphAfxm/9/BOdDng+kTRrE4LV/iEDnBqnRmZ2ioiJOnDhh+z4lJYXExET8/f0JDw9n4MCB7Nu3j2+//Raz2UxGRgYA/v7+uLuLaeP333+fsWPHsm7dOocdanv37iUoKIiNGzdSp04dEhISeP7551EoFIwZM8buOUePHsXX1xe9Xs///vc/Ro0aRaNGjejRo8flr3R6X3Z2NgsXLuTBBx/k2LFjBAcHA9agaPDgwURHR+Pp6cny5cvp3bs3ycnJREREVP8PBDCbzTz44IMEBQWxa9cucnNzGTZsGJIk8dZbb7m8d8SIEcyfP9/2vUplX+7faDTy+OOPExUVddVNUgVBuDOlpRzir3FPEnncWnfpeEMFbVasp37zDjU8sjtMlfumV8Eff/whAQ5fw4YNk1JSUpyeA6Q//vjjit/hqkW8Xq+XDh06JOn1+ip9jgslF6RTF05JSVlJ0qkLp6QLJReq9LwrUVRUJPn4+EhHjhyRBg0aJM2bN6/Se0aPHi1169bN9n3Zzz8/P9/uuoYNG0rLly+v8DnO7jtw4IAESN98802F95lMJsnHx0f68MMPKx3rtfr+++8luVwupaWl2Y598sknkoeHh9M/A2Xuv/9+6aWXXrqid3zwwQeSRqOp9Lrq+vMlCMKt6deNy6Qd9zaTDjVtJh1o3kz6+MUekqm0tKaHdctw9fv7atXozE7Xrl2RXDRdd3XuZpFRnMGchDkkpCfYjsWExzA3ei6hXqHX7b2bN2+madOmNG3alCFDhjB27FhmzZqFTCar8B6tVou/v3+F5yVJ4qeffuLs2bN07Njxisei0+n44IMPAHBzq7iQmk6no7S01OUYUlNTadGihcv3DRkyhLVr1zo9t3v3blq1akV4eLjt2AMPPIDBYGDv3r1069atwudu2rSJjRs3EhISQt++fZkzZw4+Pj4uxyIIgnA5s8nElgkP0vLXVJQWyPUF3einGTx8Zk0P7Y510yco38y0Bq1DoAMQnx7P3IS5LOuyDI2H5rq8Oy4ujiFDhgDQp08fioqK+O233+jZ03lZ8d27d/PZZ5/x3XffOZyrXbs2YK1RZLFYmD9/Pl26VF7noew+nU6HJEm0b9/e5dLX1KlTiYiIqHCMAOHh4SQmJrp8r6sk9YyMDEJCQuyO+fn54e7ublsGdebpp5+mQYMGhIaG8u+//zJt2jSSkpIcClsKgiC4kno8iX0vDyHyhAmAo42V3P36Ruo2iazhkd3ZRLBTBXkleQ6BTpn49HjySvKuS7Bz9OhR/vrrL7744gsAlEolgwYN4v3333caSCQnJ/Pwww8ze/ZsevXq5XB+586d+Pj4YDAY+OuvvxgzZgz+/v6MGjXK5Th27tyJl5cX+/fvZ8qUKaxfv77CmZ3ly5fzySefsG3bNjw9PSt8plKppHHjxi7fWxlns1uSJLmc9RoxYoTtf7dq1YomTZrQoUMH9u3bx913312l8QiCcGf4ef1C1O9somkBlCoguWc9nljxLQql+FVb08R/gSooNBZW6fy1iouLw2Qy2SX5SpKEm5sb+fn5+Pn52Y4fOnSI7t27M2LECGbOdD6F2qBBA9vuopYtW7Jnzx4WLVpUabBTdt9dd91FSUkJjz76KP/++6/D1v/XXnuNxYsX8+uvv9KmTRuXz6zqMlZoaCh79uyxO5afn09paanDjI8rd999N25ubhw/flwEO4IguGQ2mdjy0gO0/CMdpQVyNFD60nMMfmpSTQ9NuEgEO1Xg4+46n6Oy89fCZDKxYcMGVqxYQe/eve3ODRgwgE2bNtl2WyUnJ9O9e3eGDRvGokWLrvgdCoUCvV5/VeMaOnQo8+fP55133uHll1+2HX/11VdZuHAhP/30Ex06VL77oKrLWFFRUSxatIjz588TFhYGwM8//4yHhwft27e/sg+D9WdXWlpqe4YgCIIzpw//w4EJw4lMMQNw5C437l31KRENXP+jTbixRLBTBf6e/sSExxCfHu9wLiY8Bn/PihNxr9W3335Lfn4+zz33HBqN/RLZwIEDiYuLY8yYMSQnJ9OtWzd69+7NhAkTbPkqCoWCoKAgu/uysrIoKSmxLWN99NFHDBw48KrGJZfLGT9+PAsXLuSFF15ArVazfPlyZs2axccff0z9+vVtY/D29sbb29vpc6q6jNW7d29atGjB0KFDefXVV8nLy2PSpEmMGDHCFiSlpaXRo0cPNmzYwL333svJkyfZtGkT/fr1IzAwkEOHDjFx4kTatWtHTEyM7dmpqank5eWRmpqK2Wy2BWWNGzeu8PMIgnD7+mndHHz+7zOaFIJRAYcfaMjjy78Wy1Y3oyrv57rJXe+t5+eLzksv/PyC1Gp9K9vXCz+/IJ0vOl+VYVfoP//5j9SvXz+n5/bu3SsB0t69e6U5c+Y43bZfr1492/WXb/1XKpVSgwYNpEmTJklFRUUVjqGiLetFRUWSn5+ftGzZMkmSJKlevXpOxzBnzpyq/hhcOnPmjPTggw9KKpVK8vf3l8aMGSOVlJTYzpeVNSgrYZCamip16dJF8vf3l9zd3aVGjRpJ48aNk3Jzc+2eO2zYsKsqhSC2ngvC7cloKJE+fuF+6WAz67bybZ2aSX989mZND+u2U51bz2WSdAvs766CgoICNBoNWq3WYfmjpKSElJQUGjRo4DJptjJag5a8kjwKjYX4uPvg7+l/3XZhCbeO6vrzJQjCzePkwQQOvTKCxqctABxu5k70m58TWrdJDY/s9uPq9/fVEnNt1UDjoRHBjSAIwm3uuzXT8I/7isZFYFDC0X5NGbj4c7FsdQsQ/4UEQRAEwQWDXseXYx+gdXwOcgky/EH5yjgGPep6x6pw8xDBjiAIgiBU4Oj+bZyY8iKRqdZlq0MtPbh/9ZcEhjWo4ZEJV0MEO4IgCILgxLdvTSTwg+9pqAODGxzr35InFn9e08MSroEIdgRBEAShHINex5cv9qJ1Qh5y4HwgqKZO4on/PFfTQxOukQh2BEEQBOGiw3//Ssq0cUSes25UTm7tSY93vsUvKKKSO4WbmQh2BEEQBAH45vVxhG74hQZ60LvDiYcjeWLBpzU9LKEaiGBHEARBuKPpiwv4evQDRO65AEB6EHjPmMYTfWJrdmBCtRHBjiAIgnDHOpDwHekzXyEy3bps9W+kml7vfEutANEX73Yigh1BEAThjvTVqyOJ2LSdeiWgc4eUgffw+OwNNT0s4TqQ1/QAhGuXkJCAQqGgT58+Dudyc3Pp06cP4eHheHh4UKdOHcaMGUNBQYHLZ9avXx+ZTIZMJkOlUtGsWTNeffVVyncVSUpKYvDgwdSpUweVSkXz5s1ZtWpVtX++y0mSxNy5cwkPD0elUtG1a1eSk5Ov+P5PP/0UmUzGI488Ynd8zZo1tGnTBl9fX3x9fYmKiuKHH36o5tELgnCzKC7UsnnIvTSN2453CZwLkSG9PoeBItC5bYlg5xb2/vvvM3bsWHbt2kVqaqrdOblczsMPP8w333zDsWPHWL9+Pb/++isjR46s9Lnz58/n/PnzHD58mEmTJjF9+nTeffdd2/m9e/cSFBTExo0bSU5OZsaMGUybNo3Vq1dX+2csb/ny5axcuZLVq1fz999/ExoaSq9evSgsLKz03jNnzjBp0iTuu+8+h3O1a9dm6dKl/PPPP/zzzz90796dhx9++KoCKUEQbg37t3/Jrv5RtPnH+vfGwbu96fjVNjr0eLKGRyZcV1VuJXqTu95dzyVJkkovXJBKTp6UdImJUsnJU1LphQtVet6VKCoqknx8fKQjR45IgwYNkubNm1fpPatWrZJq167t8pp69epJr7/+ut2xu+++W3rsscdc3jd69GipW7dulY7hWlksFik0NFRaunSp7VhJSYmk0WiktWvXurzXZDJJMTEx0rp166Rhw4ZJDz/8cKXv8/Pzk9atW1elMYuu54Jwc9m66Fnp7zbWTuV/t2kmbV30bE0PSXChOruei5mdKio9n0HahImc6vcgpwc9yal+/UibOInS8xnX9b2bN2+madOmNG3alCFDhvDBBx/YLTVdLj09nS+++IL777//it8hSRLbtm3j8OHDuLm5ubxWq9Xi7+/v8pq+ffvi7e3t8qsiKSkpZGRk0Lt3b9sxDw8P7r//fhISEly+d/78+QQFBfHcc5UXBDObzXz66acUFxcTFRVV6fWCINz8CvKz+Oype2i+IQEvA6SGylC8uZjHpsfV9NCEG0QkKFeBSaslfeZMdPHxdsd1u3aRPmsWESteQ6m5Pt3Q4+LiGDJkCAB9+vShqKiI3377jZ49e9pdN3jwYL7++mv0ej39+/dn3bp1lT57ypQpzJw5E6PRSGlpKZ6enowbN67C63fv3s1nn33Gd9995/K569atQ6/XX8Gnc5SRYQ0eQ0JC7I6HhIRw5syZCu+Lj48nLi6OxMREl88/ePAgUVFRlJSU4O3tzZdffkmLFi2uaayCUC30+VCcDSUF4KkBr0BQ+dX0qG45//z2Kfnz59M60/qPwQMdfHjw7R/x1rj+x5lwexHBThWYc3MdAp0yul27MOfmXpdg5+jRo/z111988cUXACiVSgYNGsT777/vEOy8/vrrzJkzh6NHjzJ9+nQmTJjAO++84/L5r7zyCsOHDyc7O5sZM2bQvXt3oqOjnV6bnJzMww8/zOzZs+nVq5fL50ZEVL0CqUwms/tekiSHY2UKCwsZMmQI7733HoGBgS6f27RpUxITE7lw4QJbt25l2LBhbN++XQQ8Qs3QpsHXY+DU75eONeoBD70FGlHJ90p9Pj+WBp//TW0jFHnCuafuZ9DktTU9LKEGiGCnCiyVJMZaCouuy3vj4uIwmUx2wYMkSbi5uZGfn4+f36V//YWGhhIaGkqzZs0ICAjgvvvuY9asWYSFVVxDIjAwkMaNG9O4cWO2bt1K48aN6dSpk0MgdejQIbp3786IESOYOXNmpePu27cvO3fudHlNUZHzn1loaChgneEpP/asrCyH2Z4yJ0+e5PTp0/Tv3992zGKxdi5WKpUcPXqURo0aAeDu7k7jxo0B6NChA3///TerVq3i//7v/yr9XIJQrfT5joEOwMnf4JuxMDBOzPBU4kLueX4Z/SCtkqwzyWfCZYQvfJVHox+s4ZEJNUUEO1Ug9/Gp5HzFOSjXymQysWHDBlasWGGXvwIwYMAANm3axJgxY5zeW5bTYzAYrvh9fn5+jB07lkmTJrF//37bLEpycjLdu3dn2LBhLFq06IqeVZVlrAYNGhAaGsovv/xCu3btADAajWzfvp1ly5Y5vadZs2YcPHjQ7tjMmTMpLCxk1apV1KlTp8L3SZJ0VT8nQag2xdmOgU6Zk79Zz4tgp0J//byRggWLaJVt/T6pYy36r/4RL5/rk1Ig3BpEsFMFioAA1J07o9u1y+GcunNnFAEB1f7Ob7/9lvz8fJ577jk0ly2RDRw4kLi4OMaMGcP3339PZmYm99xzD97e3hw6dIjJkycTExND/fr1r+qdL774IsuWLWPr1q0MHDiQ5ORkunXrRu/evZkwYYItn0ahUBAUFFThc6qyjCWTyRg/fjyLFy+mSZMmNGnShMWLF6NWq3nqqads18XGxhIREcGSJUvw9PSkVatWds+pVasWgN3x6dOn07dvX+rUqUNhYSGffvop27Zt48cff7zm8QrCNStxXQur0vN3sC2zB9Poq0QijFCghsyhvXjy5TdreljCTUAEO1Wg1GgIX7CA9Fmz7AIedefOhC9ccF3ydeLi4ujZs6dDoAPWmZ3Fixezb98+VCoV7733Hi+//DIGg4E6derw2GOPMXXq1Kt+Z1BQEEOHDmXu3Lk89thjbNmyhezsbDZt2sSmTZts19WrV4/Tp09X5eO5NHnyZPR6PaNHjyY/P5+OHTvy888/41Nuhi01NRW5/Oo2GWZmZjJ06FDOnz+PRqOhTZs2/Pjjj5XmIAnCdeHpW7Xzd6D87DR+G/UfWv1bAkBKbRkNlrxJx3t6VnKncKeQSa72K98GCgoK0Gg0aLVafH3t/5IoKSkhJSWFBg0a4Onpec3vMGm1mHNzsRQWIffxRhEQcN12YQm3jur68yXcYfT58Plz1iWryzXqIXJ2LrP72zj0S18jLAcswMFofx5+6wdUXiIovNW5+v19tcTMTjVQajQiuBEEoXqo/Ky7rr4Zax/wlO3GEoGOzWfTB3LX/5KpVQpaNeQ8048nx66o6WEJNyER7AiCINxsNBHWGRxbnR1f8AoSgc5FOedT2D7mUVonWzcRnKorp/Gyt+nUrmuNjku4eYlgRxAE4Wak8hPBjRM7v1yD6dU3aZEHFhkcjAnk0bd+wkOlrumhCTcxEewIgiDcrm6jKsxmk4nPpw+k6fdH8TDBBW/Ie+4Rnhy1pKaHJtwCRLADLntKCcK1En+uhBp1G1Vhzkg9TsK4gbQ5YgTgZD05zV97j6jWziu7C8Ll7uhgp6y5pU6nQ6VS1fBohNuN0Wj9i1mhUNTwSIQ7waVdoYXIvb1QZCWgPLfH/qJbsArzti1vIVv5Ds3zwSyDg/eHMPDNn3Bz96jpoQm3kDs62FEoFNSqVYusrCwA1Gp1hX2WBOFqWCwWsrOzUavVKJV39P+bCTdA6fkMh6bE6uhOhI9dj9sPw8FYfOniW6QKs9lkYsvkh2n+0ynczZDvA4UvPMHg/86r6aEJt6A7/m/hsp5LZQGPIFQXuVxO3bp1RQAtXFcmrdYh0AHQJfxJOhDx+Iso9yy3v+kmr8KclnKIv156kshjpQAcb6Cgzcr11G/eoYZHJtyq7vhgRyaTERYWRnBwMKWlpTU9HOE24u7uftXVnAXhaplzcx0CnTK6hD8xj37a8S/6m7gK8+8fv4byzTiaXQCTHP7tHsETb/yIQsyQClUg/vRcpFAoRG6FIAi3HEthoevzeqP9gUY9rDV7bjJmk4nPJv6Hlr+ewc0Mub6gG/00g4fPrOmhCbcBEewIgiDcwuTlesM5Pa9yv/TNTVqFOfV4EvteHkLbEyYAjjVS0u6NjdRtElnDIxNuFyLYEQRBuIUpAgJQd+5s14y4jLpzDIrajeG/v920VZh/3bAYz9Uf0bTAumyV3LMuj6/8TixbCdVKJBQIgiDcwpQaDeELFqDu3NnuuLpzZ8IXLkQZWh9qd4DAu26qQMdsMvHJmJ6ELP2IgALI0UDWjGd48s2fRKAjVDvxJ0oQBKEit0gFYrewUCJWvHaxzk4Rch9vFAEBN22D4tSj+0h8OZa2p8wAHGnixr1vfkpEgxY1PDLhdlWjMzs7duygf//+hIeHI5PJ+Oqrr+zOS5LE3LlzCQ8PR6VS0bVrV5KTk2tmsIIg3Bz0+ZBzDM79AznHrd9fD9o02PIsrL4H1vWA1R3g8+esx29CSo0Gj4YNUUW2waNhw5s20Pkpbi5nhzxNk1NmjApI6teQh77cJwId4bqq0WCnuLiYyMhIVq9e7fT88uXLWblyJatXr+bvv/8mNDSUXr16UVjJ7gNBEG5TNyoA0ec7tlqASxWIr1eAdRsrNRr4ZGQ3Il7bjH8hZPlB/uwXeFLk5wg3gEy6SRr4yGQyvvzySx555BHAOqsTHh7O+PHjmTJlCgAGg4GQkBCWLVvGCy+8cEXPLSgoQKPRoNVq8fW9eWtLCIJQCX2+NdC5PAAB6y6j6myBkHPMGlBVZMzf1hyYisZ5Cyx93UgnDyZw6JURND5tAeBwM3ei3/yc0LpNanhkws2sOn9/37ThdEpKChkZGfTu3dt2zMPDg/vvv5+EhIQKgx2DwYDBYLB9X1Bwc1cKFQThChVnOw90oPpbIFRWYbii87dR883q8v3a6fit+5LGRWBUwpF+TRm4+HMxmyPcUDftbqyMjAwAQkJC7I6HhITYzjmzZMkSNBqN7atOnTrXdZyCINwg1xqAXIvKKgw7Oy+WvuwY9Do+HdGFequ+pFYRZPpDwYJxDFr+lQh0hBvupg12ylzeV0iSJJe9hqZNm4ZWq7V9nT179noPURCEG+FaApBr5RVknZFxpqIKxFcy81SO1qAlRZvCgewDpGhT0Bq0VRz0zeNo4g5+ffgeIndmI5fgUAsPWm39nvseHVXTQxPuUDdteF3WoDMjI4OwsDDb8aysLIfZnvI8PDzw8PC47uMTBOEGKwtATv7meK66WyCo/KxLT9+MtX+fqwrEVzHzlFGcwZyEOSSkJ9iOxYTHMDd6LqFeoVUdfY36dvUkAt//joY6MLjBsf+04IklW2t6WMId7qYNdho0aEBoaCi//PIL7dq1A8BoNLJ9+3aWLVtWw6MTBOGGu5YApCo0EdakZ1uycSUViK9w5klr0DoEOgDx6fHMTZjLsi7L0Hhc/23jWp2RnCIjBSWl+KrcCPRyR6N2r/zGChj0Or58sRetE/KQA+cDwXPyBJ54aET1DVoQrlGNBjtFRUWcOHHC9n1KSgqJiYn4+/tTt25dxo8fz+LFi2nSpAlNmjRh8eLFqNVqnnrqqRoctSAINeZqA5CqUvld+bM9fCH2f1CSD0pPOPcX/LkGjMV2M095JXkOgU6Z+PR48kryrnuwk35Bz5StB9h5PMd2rEuTQJYOaEN4LdVVP+/w37+SMu0lIs9Zd1slt/Kk+9vf4B8iciaFm0ONBjv//PMP3bp1s30/YcIEAIYNG8b69euZPHkyer2e0aNHk5+fT8eOHfn555/xqaTxnSAIt7GrCUBuFGe7sBp2hQFxsO8j6PeqbcyFRtd1wio7X1VandEh0AHYcTyHqVsP8Nbgdlc1w/PNGy8RsuFnGuhA7w4nHo7kiQWfVvewBaFKajTY6dq1K67K/MhkMubOncvcuXNv3KAEQbhlVPdSzDWpaBfWqW0gk8Mja8DnUh6Oj7vrf6xVdr6qcoqMDoFOmR3Hc8gpMl7Rz1BfXMDXox8gcs8FANKDwHvGNJ7oE1udwxWEanHT5uwIgiC4Ut1LMdfM5S6s38FQYBfs+Hv6ExMeQ3x6vMPlMeEx+Hv6X6+RAlBQUuryfGEl5wH+/fMHzs2YSGSa9R+r/0aq6PXOd9QKCKvkTkGoGTf91nNBEITLaXVG9v57hpltvPitXyA/D6jH1M7h/HMmn6lbD6DVGW/cYCraheXuhanjZAx5ZvRJSRhOpWDSatF4aJgbPZeY8Bi7y2PCo5l7z2Q0hdnXtSaPr6eby/M+lZz/6tWR6EZNoF6ahN4dkp9sz+Ob94lAR7ipiZkdQRBuOYrcbFq9/yolCQmUhTW9o6OJGjOZwV+lXPFSzFWpqA2Es11Y7l6U9l1P+lufoEt4zHZY3bkz4QsWEBoWyrIuy8gryaPQcAEfgx7/k3+geafzpYTm61R1OdDbnS5NAtnhZCmrS5NAAr2d/9yKC7V8O6o3bf6xBnfnQmT4zZ7NwB5PVvsYBaG63TS9sa4X0RtLEG4vJq2WtAkT0cU7LgN5REfz82Nj6NSmPm3rVmMSs6s2EO5qazPSctvhTR0nk7blBLqEPx0epe7cmYgVr1m7kt/Ifl/lpF/QM3XrAbuAp0uTQJYNaEOYkyXAxJ1fkzl7GnXPW39dHGznRZ93vsfXL7jaxyYIZe6I3liCIAjOmHNznQY6AIaEBLqNehllJUsxV6WyNhAD4xzq/5iDOqJL2Oj0cbpduzDn5lqDnRvZ76uc8Foq3hrcjpwiI4Ulpfh4uhHo7Ty5+4sl/6Xup/HUNUCxB5x5IoonZrxf7WMShOtJBDuCIFSvSrp+V3UHlaXQ9dZsr1I9PpcvxVSlE/mVBCSBd9nV/7FkV9zSxvoZitDqjKiLL+AyLLs8H6gaO6pr1K5/7kXaPL4f9QCt9xUBcDZURtC8RQy4/9Frep8g1CQR7AiCUH0q6fpdHTuo5JXU2fIJqIXOaEajvrIxVcai17reyVEWkJSr/yM3nnL9TC8vxnyynwUxHtR3daG7N+QcA0MReNaC7ybCuT3QaRTUvgfyTkKtetbdXtU4A7T3ty3kzZ9D60zrstWBDj48+PaPeGuu704xQbheRLAjCEL1qGS5p+Thd5my9aTrYnayYutzjMXWL1Ut8A6x+0WuCAhA3bkzul27HIagionh85M6tu8q97zKlqBcBAnarFzcdD7IenyK3D8YhZcSpf4sKNwuVUh2kqDsaozqzp35PaOUncdz+KVOGM806I4y5eL43L0wtXsRc1BHLKVy5LkGFKe/RentidY7iLwuYyn0mIaP2Yz/id/RJLxt/Tk1fRBTj1cxFxmwFBYi9/ayjtWcBx4+VzUDtHV+LPU//5vaRijyhHODuzBoyv9d0b2CcLMSwY4gCNWjkuUeuS6nwmJ2/5zJx1OfCQVnYOer1oJ8ZS6bhVFqNIQvWED6zJl2uTse0dFcePEVVnyVgs5otu7Ikl97TkxJWjr5s2ahT7jU2kEd3YnwsYNx+2G4dWZl8GdOG5Daxjhrll3Ao+7cmYA5c5ny3kEAXt+VRfTgRTRlBsq0PeV2cF3K91FHdyJo/hxmHXmVPw68ajseE9qRuYM+JPSLUZQ2f5b0abPRxZcba0y0dazf/wfqdKp0JutC7nl+Gf0fWiXpADgTLiN84as8Gv1ghfcIwq1C7MYSBKF6nPsH1vWo8LTxmV+4a02203Mzuofx34CDyJK/sA90LpIa9UDbby0pxW62PB9dXj4F57NR6osxqbz4I7OUN//KRGc0A/DNi9G04gTyuJ72Dys/eyLXIK8ViCLA35owfJGrHV/qmGgiBjVFmbCo0h1TJq0Wc24ulsIi5D7eKAICSC6w8PDb5YISdwUvdw5mQBMfChcutQtYLr0zBu9JY8k3XmBHcRJrUzaiN+mJCY9i5V1jyJ6/qsL7giY8ifrrWGvAU8FY//p5IwULFxGRZf0+qWMt+q/+ES+f69+QVBAqUp2/v0VRQUEQqkdlXb89Kj7fq64cmU+I00AHQHbyNy5kp/PoOwn0WLGdsZ/sx+DpzaP/O0eP73N4YOsZlu5KtwU6ancFGpU7RuVl+T0X69+kbTnBqSFjOf1ULKf69SNt4iRKz2fYLnO140sXn4C5bj9w97o0O1QBpUaDR8OGGJs045x3MAe1FjzcFIzp3hi1u8L6PKOZRb+fJzvH4DRgsb4zHvfMfEoHjaRrXCJxbZejUqrYl5WIzODp8j6DIpiMQR/C2T+djnXL7MEoJ1kDnUIVHBvRkyc/3C0CHeG2IoIdQRCqh1eQdabDmUY9sKgD6dIk0OlpX5kOTAaXj3c3F9n+947jOcz++l9m/aeF02tn/acFCSdz+e2sBVOD7rbjpnYvXlwmsq9/o9u1i/RZszBptUDlO74s+Zkw/Dt4YoN13C4qHp/L0zHm4330WLmdR99JoM8bO0lMzefNwe1sAQ+AUl/s8p2SwfrzMcTvxnflR2zuEsdP932MKS3d5X2KohLmntiMNvpFu91d+dlpfP54O1p9lojKCKcjZHitXcXDE99y+TxBuBWJYEcQhOqh8rPmhVwe8FzMufH0te666tU8mKmdw/lpQD1+6xfIjsEN8fENAze18+deZFR4232/43gOd9et5RBAdWkSyN11axHs68Er36Zy9N5FtoDHWv/GsdAfXKp/gz4fycv1zjC5mwRFmfDVKEz7v8Fw7BD6/ftsLSHKpOXrmPLFAXaeyLW7f9eJXNbHp/Bs5wao3RVM7RxOaJDrmRSZh8elscYn4F1o4sLcxeB6lztytZr4jD3k1etkm31L+O4D9j/ak5YHS7AASVH+dP3mT1p27O36YYJwixIJyoIgVB9NhF29GTx9rTM+F/NEwmupeKtnBJmzZ6OLj8cIGIH0mBjCZ7yM21194NiPDo+VGnbnl1SLw3G90WxXHM9X5UaozIgpL4ug4gK+6l+bnSml/FV7Dt06ziGwyHXPLEvBBbTaeHbLPbkrJgpD/G6Ha9Qx0Siy94BXpPOE4ostIXQaf87k6oi/LNAps+tELrP+04Lnm3mRP28uxa1bo46KQrfbyTujotAnJtkd89CVkpOwG32bSJf3lVwspVMok4FXEJ9NH8hd/0vGrxS0asge3pcnx610+XMRhFudCHYEQahe5erNXM6k1doCnfJ08fGkL4LQWUtQNHgQ5R9TrVuqARp2x/TAMl5/27F2jY+nm11xvNLzGQ67tLpFR1M4ZjIDP0/l+4druxy6XKYnTxPO7IRpxE1YjhfYBTweMVEEzp6KctP9mFr9TvqryypcEvNesJgLepPDO9TuCsbdG0K3EDfqFGSSvcyalKzfv5+IFa9Zn1EucFFHReEfO5S0iZPsx6q2zoTlbdjg/L6YaAJeeIEvcnaiUqpQF3mwdUJXWh+yBnyn6shpvPxtOrXr6vJnIgi3AxHsCIJww7hO/I3HeDqVvI92ED4nHjcPPVhMoMsBs5GXOwfz+q4sWxLy5U0rdblZZM+c4ZCsa0hIwM/zTbZPnYxxz18Vz4LExKBI30ah193oTXqeS5zMyOeGcN+4WBQ6Axa1ikJ3C4FGE/puG5HpcLkkFlh4gWg3+L1fID4BGhRqBUZDDj6eIeQsWGRNdF67xjZeSacjbeIk/GNj8R8Wi8zNDZlCQfGfe0ibOAlJp7M93yPm0ozN5fdJBgMyDw/c6tSh1GTk/gt3Ucc0gLMv/JcWuWCRQWIrd+7xvZsGoc2u/D+eINzCRLAjCMINU6otcHleMhisszzzFhI09b9kKnUUSkZ8DJk8HpRO7Nho8nIykHv44uUfivfFGZ2M4gxKz5+ucFeS5113kTNvPvr9iRXOgoTPegXlx93wafYRAHqTntePv8frgEqpIq7tcnxf3UBagvW+iFVvuPwslrQ0zo8aBYABa72csCkvkbFs+aUAx2CflC3pdOSuXQuATK2mwedb0B886BDoFE+MZVfOTjpfXGqTdDryNmzAn1hUbSNBsj6r6OdfiD/2P1rEZ+BhggtecKa+F3cfLMbMn6TPmnWpKakg3MZEsCMIwg2h1RnRylz3wCpLwtXFJ5BrGMFDCeNt52JCOzI3sDFhWTvhjyW2xGetpzcHU3YT49aCWmvXIBkMyD080SUmkrdhA5JOh6ptpC2IcDoLEhoKMut+Df8zfxITHkV8+m5UShUjGwyhf6378My8gCx2GLo2keRt2GCXMOz8w9h/q0v4k/PLFfgPeRrdnr+RdDrbM2RqNf6x1kCl/PjRZ2OaPIMGMgPmCznkqZX8UvAXa/dPBqDdxaU24/4kIla8Rt6Gj2yfU6tWkBYB7Y5bZ8JO1pXhpXMjMvnSri+7pqSCcBsTu7EEQbghcoqMfH/OgEd0tNPzlyfhlhZq7c7HZ+xh7qE4tE37Wg+c/A2+fwVljpZoXQTZy5dzbuQo0l4az9mRI9EnWQMAmVptN4NSNntSdu25kaMwpqaSPn8ppm5L0SS8zdy7J9KtTlfi2i6n27pEch57inOjRts9tyT5EOqoqCv6LGV08fHIVSrbuPSJSXh17Wp93pEj6BOTkHl4YDGU4NWpEzIvf+r6GTiCB+oTb+OmKuSvwgPoTXrbUtuxl/pR/8ut5G3caJutOtJIhcHDTIvjZswy2N/Rl3qpEqE5jgnalsIih2OCcLsRMzuCINwQBSXWCsdRYybjw3IM5dswOEnCNasdZ07iM/aQd/d4yuYhTLVaYkzYQ8H3Pzjk4ZR97x8bi6KSmQu3iAj0+xMx+72MsvY9hOanMa/lJAqmz0eX4Py5qvbt8Y8dancMrLk//kOedkgotn0urZb8TzfjHxtL3oYN1NvwIdmr38Zv0BN2MzMA6uhowubOpIFGRnHPpQT/OpVl4a3I6zKYQrMRHw8N/pp6yC4WJLQgsa+tijYH9bibId8HztZW0/+ZZZzbM8rpeOQ+3k6PC8LtRAQ7giDcEL6ebuiMZgZ/lcK4x8YwePIU5OfTQQb6xCS7JFyPmCi2FR9w+pxCs8FazE/pibk0EGV2odOEY7AGIYGjRqIICKDu+g8wa7UOS1zqqChKkpPxj43FUqyDB5aQ5qnGcva8LbfG2TKTIjCA1BdG4vf444RMmYxFm4NCaQDvIFIGP2NdpqrgPn1SEv7DhyGtXYspPx+/QU8gV6nwe3IQAcOG2canS0jg/LxFRPy3C8UNYkhsvwh/qQAPXRFh/hF41grBZJRjzM8gz0dBVrBEh8QSAI7Xk+FbqKTNYR1U0BVI3bkzioCAqv6nFYSbngh2BEG4IQK93enSJJAdx3NYuiudZo3vwU8j4bP6slmemBgKJg6x5aWU5c3c59UGZbGBoEI1pjP/otz/NpZuHyEZHLd3l5Gp1Sj9/clYuMhhO3fEitfI3/wZfoOeIG3iJCJWrsDs4026uydL965ggud/bM+4PB/GOs5owhcuIH/zZwDoDyQSPnYwsjM/omrbBn3igQrvi1jxGnIvL+QBAbiFhJC5bJl9E8+L40ubOAldfDzmUU/i+/MkArqv5v7VaQD8PrEtdS4UkT5zJkea+6JSmGl2EkxySGrtQWSSASWlACiDgx12oak7dyZ84QKRryPcEUSwIwjCDaFRu7N0QBumbj3AjuM56ErNjL44y9Nt1Mso9cUEBPtz3CTjo4w30Zv0tl1Q3is2YEj8EE1sLFJbD/Se0bg99ghyuYTMVHGrBv/YWDKWLHG+xCWX4T9kyKUZJUnis5xf2XMmmcHNBmPOk9uekbfhI8dnxCeABCEzpnP68SeQdDrSZXIinmpF+LinKTrer+L7LBIhUyYTsXE9mQuXOOwiK78El7t2LRa9Edmp3wnsYd3N1qt5MBFSEWdnTCW+OInID0pwM0OuL6SHqWmfdGn3ljoqisI/tuHbrx8h06ch6fS2pqQi0BHuFCLYEQThhgmvpbJVPDaYzOiMZpbuSufNi52/n/IxUbsgj0ltX8ZgNtDRq+XFQMdxtxFYZ4FCZ84gdPEiMhcustuiDeDVqaPd9eXp4hPwHzr00j1hwazda+0mbpEsvNDgaQJjou12cjk8IyEBqaTE9gxdfALGyRNwP/87qlY9yZg12/l9u3dj0elQyNQVN/HcvRv/YbEAyFXWXWwKYyG9mgfzTv9Qzv39Gwcy/qLDKesS1bFGCsJCWtM6IdHh5wOg8PcXwY1wxxLBjiAIN1RZxWOtzkiXJoH8cyafLYPr0uyv6SgS/sDL3QsGrmd5YAwKdXvOJqwhYORIh1kSmVqNqnVrTJmZuIeHU3/TRgp++52899+35eIgd73htGyXliommi8v7EBv0gPw5/k/iW0RS+DMqUin01w+w1JcTMDIkba8HCwyzI0ewZKV5frdJlOlO6EkgwF1dCdrewpAodbw1qMBbH91EJ4/pdO0wLpsldjag/bHILBXJ1SxLyAZDLjXq4cyLEwEOIKACHYEQbjOTFot5txcLIWFyH18UQRYZxjKlrUSj6VYA52UP6w3dBoFf76N5tQ29D0+AXCYXak4jyaGBls+QzKZ0B84gEWvdzk2mYcHHjFRFE4cyuqLOUJl9CY9uVmphIRFuHyGQqNBn5TkkJcTMnmyw7XlE5YBlGrXzU8VGg3h455G+X0sUqMeyNX+bJ3yOC23ZaC0QI4GMkPUdLi4bFV+DA2//04EOoJwkQh2BEG4bpz1qlLHxBA2dxbuinzC3b0IbeSJ/Ne/Lt1U+x7YYa1yXLZ8c3ml4YrzaOLJWLyE0FnWpRtlQADqmGinS0XqmBgMtQPZ9lxb1u6fbJvVKaPx0KDydaf0/HnU0dHoEpw9Ixp90gGneTn6AwdQx8TYPruzAC1g5EiXz3bXSLgpLEgNunK26XASB3Uj8pS1SOCRxgpaj5xF09M5yB6Q4dmyhW3HV2l2tthlJQjliKKCgiBcFyat1iHQAWtAcn7OfIzFcnj3fuTfTYQBceDudfHGS4GNInsP6phoh2rFqraRFW83j49HKjGi0GiQzGZCZ85EHWNfyFAdE0PIrJkole70r3UfnzVcyJdtV/NykxGolCqiw6NRoAA/DbnffE3ItKmoLyuGqI6OJnTWLDKXLnU6jswlSwmZOsVWeNBZgJa3YQP+Q4c4PjsmmtCZM7AofNDnu/NDVhhnX3iJJqfMGBXwT1tP7jphonTSXPT796Pu0IG0CRNtBRULfvwRSed6VksQ7iRiZkcQhOvCZdPPhARKc0cgb/ciyj3LrQeHfw9H/gdKle065f63CZu+neK9++22Tl8+03M545nTpL00HgCvrl0JnTULU0Ym5gItysBAZB6eYDKhXWK/5btrTBRdp6xGCg7glzO/IAu9B/f/PgLvvE3A0CEET5yApagYhY83Mh9fjKdTHJKiy0g6HcYzZ1C1b0/wK5PAYnFIdC7fxDNkymRK08/b6g6lDHgcZZtW/K04SeuEXBQSZPlBboCaDok6yvpR6OLjybFYbDu3yo6JvleCcIkIdgRBuC4shYUuz5u1WszBnaDjZMxBHbGcNyAP6IHCKwjlXX0gbS+mPm9jMZtxr1uXkGlT0SclkblkaaV9qcqfL962jQyjEdXdd6Nq2YLst1ajioxEn5TkMDtkiN+NepmM3GnDeDvxbVZ3X83sf+fw5vPzCJQFYS7UoaylQebpSdbKFdR6+GGX41AGBKBq0xpLQQFmrdbpNWXtK1SRkZwbdanKcYa/G0Xn/6btGetuq8N3KQlNl2h+wjG4Kr9zy3ZM9L0SBBsR7AiCcM0qSj4GkPv4uLxX5uGBWVKTseUEuoSNtuPqmCjC5y4FyUL6vMX2xfZiYmjwxVYki8UuH6Y8Z32pdAkJBI0dQ/abb9kCA1db0mvphvNykxG0KK7FDx3Wkb14KWftxhGN/5AhlBw9WnFOUFQUhhMncK9bj+K//8arQ3uXP4/yjUOTmntR72wxoWfAqIRDXevQp+9LZFTQggKcz3aJvleCYCWCHUEQronT5OPOnQlfsAC3sFAUAQGVBiRenTqiS/jT7pwufjdFf++n4LvvHIII/f79mLKyyF2/Hv8hT4PF4lAZ+fIeWzZmc6XLYPKAAMIXLsDNPYhByk4o8MCcm0/JkaOXjTEBmZs7QWPGoG7bjhyLdFl/rGj8Y2NJe3kCqshI1B06IPf2dp3onJiECTjQxp22B4uRS5DpDxf81DzYf+JVzWbZPo/oeyUIgAh2BEG4BhUmH+/aZZcrEjZ/Hudnzbb7BV8WkORv2VLh8z3q13c6W+IfG0vOmrXodu9G9+ce/GNj8R8Wa6srU/DjT3Y9tsqTe1/6xX95YCBTqwl44Xl8unalND0dU0amrT+Vqm1b6q3/gDPDn8GSm2u7R9W6NSWHD6EMDibgv88RPGkSkqkUc0EBpowMlEFBhC9ZjNzDE2XdOhRu20bwy+PJkuHQGiJk2jR2vzCY4joy7j5g7UyefJec2udkND2pQxkURFHCboeWD+Wfcflsluh7JQiXiGBHEISrZs7Jrjj5uFyuiHtEBGELF1CamopZq0Xm4YE+MYn8zzYTMnECKU8Mts2mKIODsRQVIff2QaZUIFOrHYKW8vV2ynJdytTb/Cn6pCSngY46Kgpkl9aJ9IlJtsDBtiV840ZyXn/D7p6y/lSZS5YSvnAB50aNBqzBkU/vXmQuXuJ0ZilzyVIiVq6wJUmrY6IJfP4FTFotqnZ3Wys3Gwy41a1L4U8/8/3aaYRfKCK0GAxucLClig6JetszDSdOoGrTGq+OHcmRyy/byh9N4AsvcHbkqHLHYkTfK0EoRwQ7giBcHW0alqwzTk+VFc2TjEYK9+8DHzWlvl54NmmEh7YQS2ERmv4P4j/oIcyFemQqFfXWf+AYNMTE2AKN8sGLq11Y5oIC/GOHAjgNQKTSUluAk7dhAxErrLV8VJGRzmv2XNafKnjiBNs5/9hYMpcudXlP+bHq4hPIsUj49u0DJhPnRo7CIyYK97Ej2B4fR9uDpciBjAAo8C3bbWW/LKdq25awRQuJWLkC0/nzGM+csQaP/yaj+/sfIlauQDIYkHl44Fa7Dm6hoRX+rAThTiOCHUEQrpw+H74eg7zFiw6nKqpq7BETBVNG4hUSSkhYHVD5ASDJz1H7jZUOgQ5Yt04jSXbbqcF5XoptaHv3UXLsKKrISNvSlm0macsWgidNInTWTDIWLUYXH2/b8u3bu1fFycrldjlZioptx131y9InJVkDI5mMiFVvIPfwtC2J+Q+LtXYgj4khZ1BPMic9x93nrLut/m2qIHrSG/ialXZjLwv4dAkJSHo9yrAwzLm5tlkjZxp+/12F5wThTiSCHUEQrlxxNpz6HUVQB9TRneySiyuqamyI343HMtg/ri9RlhI0DbuDJoJz8lqE+RRXXBwwIYHA50fYBRWmrKwKdz+VHDlC0OjRZK16w751Q3Q0/kOHcHrg49ZxPvssIVMmY87LQ6HRYCkudnhWeWUzNHJvr0sH5Qq7flhlAU3+li2EL1xA1uuvO+TlRKx4DclkQuHry76GMsKnzqOBDkrc4FCHAO7enYuPzsy5l8ZWOJay3VWKgADUnTuj27XL4RqRqyMIjkSwIwjClSspAKzF/sLHricdbAGPq9kOQ/xuGo2LJa+WGc03Y2FgHGqLAXNhgcvXSWYztdeusc10lCQfwj/WOtOiu2wrePCkiRjPniV45gyK9FqKtNkE+UWg+/FX0mfOsvWkkrm5AWBMTSVz6TIiVq5wOQaZhwfq6Ghknp5ErHoDmVqNe0QEeR984NAPq976D8he/bbd2GRqNarISOQqFSUmI9+vHkvrvwqQA+lBYHjuKXpd8EU1LBJlYKDLsZTtrlJqNIQvWED6rFl2AY+6c2eRqyMITohgRxCEK+fpa/2/xmLcfhhOxOMvYh79NBa9EcnTdVNLZYkJVa1ADC1exHz4FBq1Cnx8Xd4jlZZyrlziLViDh3ofb8I0NAPJaMQtIoKS5GROPzHIlt+jjokmdPZMpBIjnq1bUf/BB8lYssQhOGmweTPG9DSXu5xMWdmETJtK6gsjqfWf/+DTswcZixY57YeVuWQpwRNepnjnTiSdzm5pb/9X67HIS4hMt15/oLUHax5w54O77iX32fHAxV5ZFY3lshkbt7BQIla8drHOURFyH28UAQGXAh19vnUmrqQAPDXgFWhbQhSEO40IdgRBuHJeQdCoB5z8DYzFKPcst/0lYnj0+wpvk6nVhAY3JGv+Ik6Vm/UInT+v4qJ80dEO26kBVO3aYvFSkffRRlStW5P/yadOA4+seQtRtWkDQP7GTU6vyVi8GN/+/yF0zmwyFixwKGAYOmsmFp2Osy9PIGzGdPI2fGTty+VkvDK1GlWbNsjkcmq/9SaUmpAkC/mbPyO+KInG2hJ89KB3h0PNVUR7t6W4RVtMXpfykMonTtslWVcwY6PUaJzP4mjT4OsxcOr3S8ca9YCH3gKN6y7ugnA7kkmSJNX0IK6ngoICNBoNWq0WX1/X/4oUBOEKaNPgm7HWgKdMox6YHniLtOlz0O1y3JIeumA+BT/+6BAkyNRq6qxZQ867/2cfaERHEzp3DulLl1Dy+zbbcY+YKPQTh5FUdIxHgnsiLzGQ8sijtl1gl+fQqNrfDWazw+xQebXXriH/0834DXrCuj1dAmVwEIV/bCP/k08Ifmkcqg4dyFy0CF18AhGr3nBIDi4/g1M+SJFF30tiSTKR+6x5QeeCwejpScPUEgDcNv8fJ8ik+ZuXfjblPwuAW+3aKIOCrnxpSp8PW561D3TKNOoBA+PEDI9wS6jO39839cyOyWRi7ty5bNq0iYyMDMLCwhg+fDgzZ85ELhcN2wWhRmgirL8wi3MwlVgw68FSrENeXErYvHmcnz3Hrg6MKiYatzat0M2a7fAoSafj7KhR1NvwIaahQ1F4eyP3UqEwpJGm0vLHkOZ0GfUUfkpfPGRKSgq1BLn501ATCsU6zDod9b/YiszDg6xXX7NfpoqKwrdfX4ynT7v8OJLBQPG2bfg9OcgWFKmjolB36ED4wgXkbfgIZXDwpWDEyY4wZ8nZZ8I9kZ36i8gM6/cHWihpfMKM2lhiuybE4kWQ7hzqsYNJlyzoEv601Q9Sx8QQvmjh1W8hv5hE7tTJ36znRbAj3GGuONh58803r/ih48aNu6bBXG7ZsmWsXbuWDz/8kJYtW/LPP//wzDPPoNFoeOmll6rlHYIgXAOVH6UXDKTPsa+i7NWjB2EL5mPW6zAVaJG8PDH4qshNOVnhoySdjtK0NNJeGk/991bg+e1gAPwefYfEkhPco2mD4tVVlCTsRh4QYK3Ls/SyujwXd1zp/vrLlrej272bzGXLCXml4n5ScCl4kYxGux1W7vXqoT9wwFqosFzNHH1iksPS2+XJ2fvaqGl6TIdXCeg84OQDLWjzzSGHd8tVKhRuLXH7YSQRjz+D+aX/YrGokPv6ogi8itmc8kpcJ31Xel4QbkNXHOy8/vrrdt9nZ2ej0+moVasWABcuXECtVhMcHFxtwc7u3bt5+OGHefDBBwGoX78+n3zyCf/880+1PF8QhGtTUbuI4t9+47zBQMSK11A1agz6fA4UpBCmCcSxrvElZQFHaXBdMoZ9gf+FNDThHVgYEkPejPnoE6yBTfjCBc7r8iQkOK3Lo4uPR3p5fMVJvzHRKAMDiVj1Bu5162G+cIG0CRNtycUhU6dQ76MNSHo99bduRTKVYikqwvfBfmQuWWprg1EWDJUoZRy9S8HdB6yf9mwImN086dPjedK+GW//7qgoin/8Bf3BA4SPeQe3wx+gjB5e9Zwaz0qm+ys7Lwi3oSsOdlJSUmz/++OPP+add94hLi6Opk2bAnD06FFGjBjBCy+8UG2D69y5M2vXruXYsWPcddddJCUlsWvXLt54441qe4cgCFfPnJt7Re0itJIXnm4aZO76Svs6ecRE8Xnu77x+/D1iwjoxL6IdvoUS+vhL9yiDgyuuy1OuAGB5pRkZzisrX2yzcCZ22KVdXBfr4aTPnGVbwsqYPcdurP7PDKfkyBFCZ8/CUlyMpbgYhY8Pp2t7oigtIfKQCYDElkqaHTXjaSpBcdkMTVnX9LKCgenIiFi8DGV1JA+XTyK/XKMe1vOCcIe5ppydWbNm8fnnn9sCHYCmTZvy+uuvM3DgQJ5++ulqGdyUKVPQarU0a9YMhUKB2Wxm0aJFDB48uMJ7DAYDhnJTzgUFYspWEKqbpbCwkvNFpF/QM2XrAdrW82CwptBlK4f8LVsonhjL2v2TAdiXnUSqpYTwvFz75xYV2f63s6RkuV8th55aMqWStAkTbU1D5V5eyBQKDCdOcHbkKLtry8ZWFuhcHljpk5KQe3iS9+WHnH9lsu34gX5NaJxdgtoAxZ5w9C61bXZHHRWFRa+3qxekDAy0C7J08fGYCw0oQyr5wV8JlZ9115WTJHIeekvk6wh3pGsKds6fP09paanDcbPZTGZmZpUHVWbz5s1s3LiRjz/+mJYtW5KYmMj48eMJDw9n2LBhTu9ZsmQJ8+bNq7YxCILgSO7j4/oCby+mbD3AzuM57D2jYGRsQ86+MNgacAwfhkypRFGrFpLJhLmgAPWEFxm45xn0Jmvzy2dbPUtO1hmaa5pT/p8rZZ3LK2pNoY6JtuupVTZrVL5paJ24dSj8/e1mbMrT7d5N8KSJTmeQ/GNjyVm71nau2F3OyUZy2nx/HIAzYYDM0y7QKettVX72SBUZ6dCw1FJJgcWrYksiL6uz42ud0RGBjnCHuqat5/379yc1NZW4uDjat2+PTCbjn3/+YcSIEdSpU4dvvvmmWgZXp04dpk6dyosvXurDs3DhQjZu3MiRI0ec3uNsZqdOnTpi67kgVCOTVkvaxEkVtivwXriYLf+co1uIG0p9MWFhfpQkJpG1ZKntl3xZt3O3kBDMxcVIPl4UeljQlxQS7hmC/p+9eDRqZO2WLpNbt5K3a0vehxtQtWmDPimpwjwc/yFDyN/8GX6DnnBoJtrgm68x5eRy9tlnK/x8dT/8kFQn/6CqvXaNbcfWqbqeeJSUEJFlPZfYyo0+Cz7GXVuEuaDAVuwwc+kyu2KH5Zevymv4zRd43NW8kp+8INw5anzr+fvvv8+wYcO49957cbtYet1kMvHAAw+wbt26Kg2oPJ1O57DFXKFQYLFYKrzHw8MDDxfNAgVBqDqX7QoWLURvMPDAF6spSUjACJzBWqSvzto1nB05quJu59HRRMyZjSkjk4Lvf3BY8lJ1aE/I9GmYc3Iqbt4Zn0DgiBEEjR5N6ij7ZSp1VBT6g/+iatXK5eeTe3tdWiZr1/biTJQfFr31WXsjVbQ4rEdlhEIVnGjkRbt/i3G7UIilpIT8TzejT0zEPzbW2o5CknALC0OfnOw00FFHd0Lhq7rSH78gCFfpmoKdoKAgvv/+e44dO8aRI0eQJInmzZtz1113Vevg+vfvz6JFi6hbty4tW7Zk//79rFy5kmdd/ItMEIQbo6J2BQD502dQkmBfQFAXH08OEvU/3wKlpWQuXeqYE5OYCBYLSBb8nhxEwLBhto7hut27QS4jdNYsZErXf3WZtVpy3n0Pv8cftwVFZUtKJYcOozebXLeIyM21Fjtcu9YuqPJ981UONlPQPsm63HY6HJQWD9r9ay0aKBmN5H+2hdC5czBlZGDOz7d+rqQDtqRnVdu2tl1cYA10wic9L/pZCcJ1VKUKykajkZSUFBo1aoSykr98rkVhYSGzZs3iyy+/JCsri/DwcAYPHszs2bNxd3e/omeICsqCcGMZTp3iVL8HKzxfe+0aAKc9ryJWvEbexo0OHcPL573U/3wLppycSqsinxs5igZffkFpZiYKjQbzhQugVCL38EAyGFBoalm7k1+2Qyt05kzMxcVkX9a5/Hh9Fd5FesJywAIktXajZXIp7paye2MImTEdSafDVFiIW0AAmcuW29ch6tqVoDEvInNTImmzkSuMKIzpKFv0AE34lfx4BeGOUePLWDqdjrFjx/Lhhx8CcOzYMRo2bMi4ceMIDw9n6tSpVRpUGR8fH9544w2x1VwQbnJanZGcIiNavZHGFyrpZF4upw4u7ary6dnDGnxc1lKiLBgpq6Fj0emshf0q2coOUJqRiVud2pRmZDjsrvJ+4AFCZs5AKinBUlSE3NsHU1YmhtOnkcnlduPYG6mi5SE9nqVQoIbTd0fQblfapXfGRBMwbBiSTm/7DGWfK3DEf0Eux6LXo9+fyJnYYdRbtxZV3Vrg7gWqu0XisCBcZ9cU7EybNo2kpCS2bdtGnz59bMd79uzJnDlzqi3YEQTh5le2xXzn8RwAfhpQz+X15dstlN9VVVGDTbCvoSP38iJ/yxbCFy4AuazCWSDrxTIylyzB94EH7HtWqdXUeuRhMhcucsgLCpkx3ToLBBSoFKTWwbZslVJbhjve9B+7AvOQfORqNUgScm9vZEolpenpBAyNRdW6DXkbNpB7cRmsbAdW2ZKY3KKFtT3FdnBBuEGuKdj56quv2Lx5M506dUImk9mOt2jRgpMnKy4LLwjCra1sBqegpBRflRveHkoWfXeIl+/2551oDRQWogz0IismxmnRwfKzLmUBQNmMi9+Tg1y+WzIYUMdEU3L0KPU+eB9TXh6hc+diSkuz7tjy8ECfmHRp23l0NMqAAPT7E/EfOtTuWc56WcHFFhNLlhAyeTKpAzrjvm0XrY6BRQZJHXy4r/1garVtZ62Xgwy5ry8ytZrMefMcgq7yW+DLB2vq6E4osvdYLzz5m7UejmjOKQjX1TUFO9nZ2QQHBzscLy4utgt+BEG4fVw+gwPw2fMdWRIVQP6C+Zy7GDjI1GrqrF1DDtgFPJfPukSseA25SmWb7XDWYLM8hUaDf2wsaS9PQH3vvQSNeRHT+fMog4LIeW+d47uGDiF79dv4x8Y6LJ1d3suqvOJ9e/n63Yk0/ekkHia44AVnGvrw8JjV5Lz7f5xb++6l98TEEPjC8+j3J9o94/KlN7gYrEV3InzsYJQ/DL90sWjOKQjX3TUFO/fccw/fffcdY8eOBbAFOO+99x5RUVHVNzpBEG4KWp3RLtBRuyt4uXMwkR46MmbOt5shkXQ6zo4cRcjUqQRPmYyxuAg3Ly/0SUl2267TJk6i9luXGgy7zMOJicai15M+cxYBzz2HT+9e1t1c8Qm2ej0hEydgLi5G7uWFKTOT9JmzsOTmOp0xujz4sX1OtYK0OmbafGedoT5ZV4aXzo3uMU/bFRMso4uPJ8dicejJBY7tK9zr1ibi8cbWQMdYbP9i0ZxTEK6rawp2lixZQp8+fTh06BAmk4lVq1aRnJzM7t272b59e3WPURCEGpZTZLQLdLYMrkuzv6ZTWmeR0+BE0unImD2bBl9+QZG7CQ+NJ4VtG+LeLhLDxV5Xkk4H5XZx5m3YQMSK1wAnLSWGDLFt3TZlZ9vq88jUaqetHdRRUYQvXGCdRZLAlJNtNz5ns0hHGqnxy9PR4iiYZZDY2p3IA0aUGF3OBFXUkwsuBVXqmBiUp79CuWe50+tEc05BuL7klV/iKDo6mvj4eHQ6HY0aNeLnn38mJCSE3bt30759++oeoyAINayg5FJ7mJc7B9Psr+koUv7AUljk4i4wpqZSOHQUih1/ESb5EjJ6NPW//IL6P3xH8PdfoIgIRx0dDViDn7SJk1C1b0+9jzdR96MN1P9iK8GTJoJCQZ23VwPg2bIVqshI226ninJv8jZ8hH9sLMrgIFTt2qEuN+usT0yyvdeCxD9tPWl4WkdIPuR7Q3IzNe0PGG3/GqxoJohKzss8PFBHRxM2bw7KC8nObxbNOQXhurvm4jitW7e2bT0XBOH25uvpZvvfverKUST8AYDcx9vlfeV3W2XMmm07ro6KInDUSORBntZZEUmyBSyqli3Ifmu1Q2Vl/6FD7HpeRax4DeRy8jZsIGDkSLuGoGWFCANfeJ7CP7Yhk8nw7dsH/2GxSEYjyuBgNA8/RPKiWZzO2E+HxBIATtST0+SlOfhNsO+bVVk+kbPz6pho3EJDCZ07B8lkwdTjVetfuEe/u3SR2I0lCDfENQU73bp1Y8iQIQwcOBCNqPopCLcFrc6I1lCAzqJFZypC4+5DoDoAjYeGQG93ujQJZMfxHNzNlzqeKwxpqKOj7SoCl1FHRYHJVOHMSw7g268vpqxsVJGR+A+LReHnR/abbzpen5AAkmTLjSk7H/D8COcNQcuCoYvFRz1btUSuUlm3iQcGkr36bf7J3Efg+QKaXwCTHA7eF0zPXmPwqdeCrMs+U2X5RKasrMuOxRA6fRoFP/5EblycNUDr3Jnwectx6zUPSrSiOacg3EDXVEF53LhxbNmyhQsXLtCvXz+GDh1Kv379rriq8Y0kKigLQuXOX9CTWpDOe4eXsSfj0i/06PBo5kXPI9QrlPMX9Ow4eYZ2dcFYkEqYWxhqnQWFSsP5eQvsWyBc3HmFXM65F0ZW+N7aa9egDAqi9Nw55B6eyH19OPPU0xVeX//zLZhzcm0zNw22fk7G/AUVFhcMnTObjHn2CdQeUR350zOVVjvO42aGPB9ID1dzb2A7QubOJmvJUvwef9wuSLPtMPu/d+13fcXEEDz+JYp27MSzZQvrlnQPD9xCQzn91NOOPbA6dyZixWuiNYQgXIHq/P19ze0iLBYLv/76Kx9//DFffvklCoWCgQMH8vTTT3P//fdXaVDVSQQ7guCaVmfkh8On+DVnpV2gUyY6PJrlXZajN+mZkzCH/Vn7iWu7HO8VGyhJ2G3Xvdyi12MuLESfmETehg2EL1lM2kvjK3x3xKo3AGzX1F7zDudGjXZ5fdpL463B1DPDca9bF+Pp0w7LV2VBRoOvviTlkUdt9+f6KsgJtND0lPWvvWP1ZfjlKwjSmgCov/VzTg8YeKkJ6MWlMZmHByXJh/Du2hXJUIJkMiFXq5Gp1UglJZwZMtQusCkbpzMNv/8Oj4YNHY6btNqLfcYKkfv4ogjwF0GRcEer8XYRAHK5nN69e9O7d2/Wrl3L//73PxYtWkRcXBxms7lKgxIE4cbJKTIS6mdmz7+OgQ5AQnoCOfoclv+9nIT0BF5uMsIW6ABYcnNtAUrogvkU/PiTbfbjqnNdKqnTVXa9PikJuYcnGQsWuCzmZy64tKU7+S4vQjKLaXqqbNkqhMjtGci59O89S5E14VrS6ZzuvlLf04GcNfZb0NUx0XbvrOxzG7UFHErNx1flRqCXOxq1O6XnM0ifOdN+1qhzZ8IXLMAtLNTlz0QQhMpd026s8jIyMli7di3Lli3jwIEDdOjQoTrGJQjCDVJQUoqbm+vdRlpDAQnp1qDiPq82tkDncplLlhI2YwrqmBjgUq6LM+qoKEzZ2ej/vbRLqbLry6ov+8fGWuveOOmjVbYLC6ytJSxI/BPpQdMTxQRqIUcDRxt78Z/HpiHHPriSe7tOuMZicVJrJ8HuneqYGNs4nUk3KXn0nQR6rNjO2E/2o83KdQh0AHS7dpE+axYmrdb1mARBqNQ1zewUFBSwdetWPv74Y7Zt20bDhg156qmn+PTTT2ncuHF1j1EQhOvI19MNd5nrKWK1UmX738piAyYX15rNFvynTCSwcBRylQrNgAGYzqdjzs+3LTWVHDlCwLPPgFyOqbCQ2mveQRkcjKW4GN++fdEfSCJzyVLbTElZDlD6zFkEjByJT6+eLuveBE+cgFdUJ87lnOFEAwUdkqzB3NHGCpo9NZ524U1QBAZYl6HKvcOUleUyEbn4zz0VvtN/WCzqzp0JmzeXjMVLnF7nER3Nz5mXtvHvOJ6DNt3HaWsNsAY85txcsZwlCFV0TcFOSEgIfn5+PPHEEyxevJh77rmnusclCEI1qygnJNDbnVStFx1Do5zm7HQMjcJDqb70HC/nSzRl28yzli63X1q6uG08fdp0666kmBhCZ83EePYsyOT4du1KxqJF9vfERNNg6+eY8vOxFBSgT0yyFRXM2/ARns2bufyspWlp/PTOdMLTddxVCKUKONhBQ9s9F5Dmr+As9stPqshIWzAVvnABcFlhw5gYQmdMJ2Xg4xW+U+Hja0s+Dps5k3SDAd2uXbbzHtHRFIyZzJtfpdiPtaDw8kfZqayWkSAIlbvqYEeSJFatWsWQIUNQq9WV3yAIQo1zlROiCQvFo9CLpxpOAl6zC3g6hkbxVMNJKC1qYsJjiE+PZ2fxAbrGRNkqIZepsMDfZdvG9fv3Y8rMIu+D9ajatCEvKcnp0lDGgoWEzptLxlur0Scl2Xpp+T05CLfatSv8rCbg2y0LaX1ch9IC2bVA959u9PJuSu6etXbvAKi34UMKf/3NlnOTPnMWtV9fiWLyK5gLCpF7e2HW6zGWGhx2V5Wn8KuF0t0COcdwsxQRsXAG5mITliIdpSo1Hx8r5M2vUtAZ7XMaTSqvCp8JldcyEgShcledsyNJEmPGjCEtLe16jEcQhGpm0morzgmZOZPc89loVO58kqCluWI0b3b+hNdi4niz8yc0V4zmkwQtvh6+zI2eS3R4NGtTNlI8IRaPGPvcGq9OHZ0u/4B1lkTVNhIol2+TkICqXduK70lIQCouxv+Z4dRZs4a8jRtJHf4MaS+Np/CXX20VkMvL9HfjdD057XbloLTA4cZyFGYFMZ2fsL3f7h3xCViKishduxZJp0OmVlN75Upy/u//SHn0MVKHDeP0gIHkrlmDu9oLVYzjO8EaOCq8PWDLs7D6HnivG8q1kXgkTEZVP4Acv1CW7kp3CHQA/sgsteU4OX1uQIDTc4IgXLmrntmRy+U0adKE3NxcmjRpcj3GJAhCNTLn5lacExIfT8m5TF47dJpZ/2nBzK/+5a1fL/1DpkuTQJYNaING7Y6GUBbcO5eighR0xiJqzZ2AR4mSC/kZ6D3kmEyu/+1U1lKhrM+UTK22FvpzNfbCIvQHDqLft8+ujo9dH62Lxw82V1P7rI7GZ8CohAMtPbk7SY8cmet2D0ol9bd+jkWnQ65SkbXydYciibr4BDIXLSF00iQyWeE4QzZ/LspfJ8Gp3+2fffI3+GYs4Q+/ayvKeLm9uSaGz59P5pw5dste6s6dCV+4QOTrCEI1uKacneXLl/PKK6+wZs0aWrVqVd1jEgShGlkKXeeEKPXF/HLY+kv41ccjKSoxUVhSio+nG4He1q3RYK3HY8nJpuFHfa3fd53MBEMK+3MOsLzLcjy0FpfvKduOXRZ4+MfGgsX1PQofH7w6tCd39Wq742V9tPxjY/EfP5b/vfEibXbnoZAg0w/y/dV0SNLBxd1WrraCy+RyzgyNJWLlCiw6ndNq0ADF27ZR+vRT+PZ5gJApryAVFyH31aAIDEJZmgXHvr90sbsXpnYvYg7qiEVvRJ6Vy1v9GzPpBzm/HL5UbblX82Be69sQWZGWoBdfRD75FZArkCnkKPxFnR1BqC7XFOwMGTIEnU5HZGQk7u7uqC7711leXl61DE4QhKqT+/i4PG/NGcnhl8NZTO1rolGw8xyRnCIjqpKL26DdvTA1fIhJ+blo/N0wFCjJU+jxcZLLA/bbxssCD1XbSIr/3ONi91MMyGXI3NwczoE14Pn38/fRff8u7VKtQdOhJnIi0mQ0O6kr95xoTFnZlKanOx2X4cQJIla8RsnRY6jvblfxD+riOzNmzabhxrfw/HbApd5WJeW2h7t7Udp3PelvfYIuYeOld3XuzFvz5pHerzkF+lJ8VW6EGwvInDbFaS6VCHQEofpcU7DzxhtvVPMwBEG4XhQBAag7d7ZbIilz+VbownLdzctodUZyiozkFhsJVvjYfpkXzXsDU3wCuRev8+3elYCZ08hZuMQu4CnfxBPAlJ2NOiYGyWCwX44qv/spKorQ6dMoTU+vsPZNUksv6p8uJiwHDEo43L0eHQtCMBz/y+7doTNmIFksFP5uv8SkjonBf8jTpM+cRfBL4/Du7DxvpryyQM2iN1oPXFym4oHFtmtM7V68GOj8aXevbtcuMufMoe6K11AG+WHSakmbOrvC+jpX01ZCVF8WBNeuuV3ErUK0ixCEi7uxZs1yuhX6v9+n8uS9dWlXpxa+Kjf8vdxtlX3TL+iZsvUAO4/nEDesA6fOpjHc+1/S1/3h8MscwKt7N/ynTEJfrKVIm41Z7Ult/3qUnk5F0ulQBgVhOH0aZUAAcg8PUoc/47Q1gz4xCVX7uzk34nnqbviQnLX/Z1teMqk9OdhCSeTeIuQSZASA/JkhNPjnHJ7NmqFqfzcKX1/kbm7WPBxvbwq3bweT2da/yr1OXWRqFQU//Yxn82bkfbAe3e7dBIwcid7J7jCwBmCqSGu+UcONb+Hx7YBLJ8fth+8mwcnfMPxnK6eGjK3wv0VZuwjDqVOc6vdgpddd0X9bUX1ZuA3dFO0iTp48yQcffMDJkydZtWoVwcHB/Pjjj9SpU4eWLVtWaVCCIFQvt7BQIla8RmlODrmZeRS7qfg5s5R136eydEAbPohPYfXvJ2zX92oezKt9GmDOyGZuEzC3qUdeqZ59WRaerHsvugTnRfOKf/8DvyeeoPCjDRRPiOW5xEl822Mz+vUfoouPp/baNWRMn2GryaOOiUYXn+BQILB8FWWLwYD/0CEgSRw/+y9GWRHt/ikB4N+mCuqmSoT9c46gMS9iys/HLSSEzGXLHNpI+McOJW3CRCSdjtpr13Bu5CjUMdGo27ZFn2RdYrPNNMllzu+fOAl1TDSK7MuKC+ovWJezvhl7adanAmV1cyrLpbqS+joud9pd5eyQINzOrinY2b59O3379iUmJoYdO3awaNEigoODOXDgAOvWrePzzz+v7nEKglBFSo0GpUaDPCCcRVsPsON4DmO6N+aD+BTiT+TarlO7KxjT2pcL06agL5esGxwdzaJZcynNzXL2eBvJYMAQvxsvYN5Lk8lR6vGc8jweyyy25OSyBOOIFa+BRXJYwioLLMquTZ82nX/vr0udpEJ8dVDiBv+2UNEhSQ9Yk4clgwH/2KFkLlnqWLfn4vf+sbHok5Js+UO6+ARyLJdqANkSn599luAJEzBlZYPM2sYibeIkVO3a4jd3OspPH7A+2N0LbfSL5Km8KDTm4tP/VUJzXPcGLKubU1ku1ZXU13G5005UXxYEm2vqjTV16lQWLlzIL7/8gru7u+14t27d2F1BzQxBEG4O4bVUvDW4Hb9NuJ9+rULtAh2AcfeG4Lt6uV2gA2BISEC7fAlqX9e/pMvyWgzxu+msasU7Se/wzP5XKJwUi1u9ekSseoM6a9fiHxtL+sxZqCIjqb12DXU/2kDttWtQRUbaNdUswcy+hqU0/+EIvjpID4Rz4ZcCnTK63btRBge7rPXj1akj/rFDyduwwe54+Ro8kk5H7urVnBkai8LfDwBVZBsCNq7DNPcl8mTZ0H4YuHuRMehDJhtP89D/BvL090/z0NePsF138Irq5pTlUlV2nSvVMTskCHeCawp2Dh48yKOPPupwPCgoiNzcXCd3CIJwpbQGLSnaFA5kHyBFm4LWUP2NIDVqdxoFe2MwOW797hbiRkkF26+9mjZFn3RlzToBpKJiDuYcZHP71dR67SNOP/IoaS+N5+zIkZQcOULd/1uLql1bJIMBhY8P+sQk8jZssAU6qaGe7F0+iXb/liIH/m3lgW+RjMZn9E7fX9a1vCKS2WwLpGRqNQEjR1J77RrkKhV11q4lYORIZBcrw0s6Heb8fHI/+ojUEDkP7n+BHKUevxO/Qe170Ea/yJyTn5Fw3j53afbBZZinPI/6sgKE6pgYW90ck1aLOS+P0BnTHQKjq6mvUx2zQ4JwJ7imZaxatWpx/vx5GjRoYHd8//79REREVMvABOFOlFGcwZyEObYO4wAx4THMjZ5LqJdjsqnWoCWvJI9CYyE+7j74e/qj8bjyZQtfT8dt3Up9MRVlnajaRpI+Yyb11n9gXS4qFxR5de1qzZvJzSVi1RvIPTyR+fnx2b1rKJn/mt2Mi0ytxm/QE2StWHlZD6poGmzeTMHPP/PLjvU0PlaIjx707nCkQxD9Y+dzbuSoCj+PopIAQSottQU6ESteI2/DR3b5QuqoqEv9stq1RRkYiE+fByiWybk39F7qyFVoEt7GNHgLmS0HkPC9tVeWSqliZIMh3OfVBmWxAUtJCQEL5hCaeQpLkQ65yh1F6XmUGg+7hOKy5OzAEf9F5u6BopYGRUDAFS89udppJ6ovC8Il1xTsPPXUU0yZMoUtW7Ygk8mwWCzEx8czadIkYmNjq3uMgnBH0Bq0DoEOQHx6PHMT5rKsyzK7QOZqAyNnAr3d6dIkkH/O5PNy52B61ZUThBupFVwvGQz4Pf44WStWomrThqAxL2LWakGpRKmp5SR4iSF0+jQKOnTAf1gsksGA3MMTSbI476MVn0DKonkkGg/TLqkYgHPBoHrmGR6O7ImiVi1bUvPl1NHRlJ4/X3Hdnuho26xThX28Ln4fMnUKyqAgzsQOu9i8NJpFC6eh2dAHjMWcM3iRYi4ArIFOXNvleK/YQEnCGltH+NyYaEKmjkX1w1NgtH4WU4P7SJ+50JZnI+l05K5dS+7atag7d77qhGKlRkP4ggUOO+1E9WVBsHdNW89LS0sZPnw4n376KZIkoVQqMZvNPPXUU6xfvx6FQnE9xnpNxNZz4VaRok3hoa8eqvD8N498QwONdTZVa9Ayecdkh8AIrAHP5YGRK+cv6DHmn6X2jskoUv7A1HEyaVtOON1aXnf9B1hKSmyzK2UzJKbsbAp++LHC4oC+D/QmY/Ycu+ekDn/G4doz4Z7IpBLqnrd+f7Ctmg4xQ/GRu+Pd9X7MBQW4BQeTsXixw26pkCmTQS4HmYzMpUsduqiHTJtG1oqVFP/xh203VkXqfbyJ1P+OsGv82XDTajx+GorlgSUUBd1NpqeCx34YxMtNRtBtXSIlCc4+ezQRAxui3LMcAMNTf3LqoccqfO+Vbje/3KU6O0XIfbyvanZIEG5WNb713M3NjU2bNrFgwQL27duHxWKhXbt2oleWIFRBodF1smn583kleU4DHbDOBOWV5F1xsBPmUYK0cwqylD8AUO5/m/Cx60kHu4BHFRODvHYEluMnbcfKdi/VXfdexYnB8fHWrePlmLWOeUj7WqtpelyHVwnoPOBwMzWPjn8bubc3WStWkvPWW4A1wAqZOpXgCRMoTUtD5u6OKSsLmZcXGbPnoE9KsraRGDrUVrfHlJ2NVFqK3xOP4/fkIORqtTVn57nn8L6/CwAWnQ6ZmxtF8QmY8vIcOpxbdEakwZ8h27kC31PjkLpOJjo8ivu82lCSsKaCz56AedRg21+0luKKu6bDtScUl+20EwTBuWuuswPQsGFDGjZsiNls5uDBg+Tn5+Pn51ddYxOEO4qPu+tk0/LnryYwqlRxNrLLGljqihLxnjsBP70MdAbw9mGX7gDD4ofwbeQ7dtdKOh2m7Gy7Y5cXCnQLCaHe5k+xFBSg27sPmaen7doSpYyjTRXcfdAaCJwNAZ8hw2i/4kNbUGSX76NSoQwKRCaTofDzQ+Hri1tYGOb8fNt1l9ftAayzOaNGA1D/yy+IeON15B6eTvOGfPs8gEyttgt4SkPrk+F2Af+6HdCc24Mm4W1mjNyO4ngaBS5+vLa6Ow27I/cWCcWCUBOuKdgZP348rVu35rnnnsNsNnP//feTkJCAWq3m22+/pWvXrtU8TEG4/fl7+hMTHkN8umPdlJjwGPw9/W3fX01gVF5Z64eCEmtvpkAvdzQlF39Vu3th6jAeY70HKbqQjT4/jx3FSRi93Nmbspc/z/+JSqkihVwarf8As1aL3MMTXWKiXfDiKvnXP3YoJUeO4NOzB+qYaA6d2YeitITIZGumS1JLJZGadtSP6k/hSBUytdpuFkgeEGBNjl68xD5AiY4mdOYMAseOtVVJLhtb2e6usho/6phosFgwZWQ4XXrTxSeQuWSJrfYOgEdMFJ9n/8Lrx98jJrQjcwd9SOjmYWiSv8QU1MdlsCNXuUPDrtBpJArjmYpzjkRCsSBcN9cU7Hz++ecMGWKdlv7f//7HqVOnOHLkCBs2bGDGjBnEV1DkShCEimk8NMyNnsvchLl2AU9Z0nH5ZamrCYzKlG/9UKZLk0De/08tlGXNK1dvRhf/ge1815go/OZM5/1/37+UiLt8PakJ9kUAy4IXXXxCpcm/qshIsle/zd9hOhr8U4LaAMWecLSJms7ekfgPHcqZ2FhUbdvi0727LeEXIHzhAodAB0CXkEDGosX4PtDbLhen/O4qmYcH6qgoAl94Aam01HVNnvgE/IcOBayBTvHEWNbunwxAfMYe5gLLol9Es/MNCp7ug2dMNCXOApiYaBSBQVA3CpChTFpH+JjnSJcsdkuEIqFYEK6va0pQ9vT05MSJE9SuXZvnn38etVrNG2+8QUpKCpGRkRQUuPp3zo0lEpSFW42z7eReJTg0esxVljAnYY7TwOjy3VhanZExn+y3C3TKvPNYA3qb95L23u9Ok5LVMdH89py16F5Fibhe3bsTMmUyGXPn4T8s1i7guHxJq0TjzR8rXqTNQetMS2qYDM3Tw2nZ+F6HOjtlTURty09bP+f0gIEV/uycJR6ro6Lw7dcXz+bNKfz1N/I2bKDO2jWY8/NJe2l8hc+qu/Ej0t2K2Fl8gLUpG9Gb7Gv7fNPlDRp8+Bh4BVHyyOdkLl1t358qJobwMU/g9sNw6DQKzv0Dp7ZZZ9DavYg5qCMWvRG5rwZF7cYoA0IqHIsg3IlqPEE5JCSEQ4cOERYWxo8//sg771jX8HU63U21E0sQbkUaD43dLE7p+QzSKmj0uKzLsiuqs5NTZHQa6ABM+jaVfUM7oktY7PS8Lj6B+8ZZZzkqSsQt/v13pJdeQtW+PfKLRfnAcUnrVF1P3EtKaHOx40RiGw+6/3cpeeNe5hwfODxXl5BA4PPP254llZQ4fX+ZsqUqu2fs3k3IlMmUZmfj2bwZtVeuROHri0XnOlnYrPHisQTHHWNlCs0Xc3GKs/H8ZzkRy1/HrC2+tCPKvRTlhm7Wbee174Ed1u7uGItR7llu/5fvmL8BEewIwvVyTcHOM888wxNPPEFYWBgymYxevXoBsGfPHpo1a1atAxSEO9mVNHrUaBpUcPclBSWlFZ7TGc3oC4pd3q82gNFksFtSulxpejqqli2w6C/NgJRf0tobqaLFYT0qIxSq4NQ9EQyYEYfhVAp5rl4ul9k6jkulFX8OuNSq4nLmwiL0SQdQtWqJxWANmEzZ2RXX5ImJodRH5fJdPoqLrXIa9YB+r6LUhKAsn3KTc8xWXweTYxBmp+TmmQ0XhNvRNbWLmDt3LuvWreP5558nPj4ej4t/wSgUCqZOnVqtAxSEO9mVNHq8Es4qJZcn83K9C0jjH4bSt5J8EhnkfbQRLBZbOwlV20iy9v/JwWYK2idZA53T4ZCv8SRyRxqG1FQUtVw/16LXo4qMxKdXT4r/3FNxq4qYaLtWFeUpvLzQ79vHuZGjSHtpPKefehr3Bg0IHDXS4XnqmGjCJ/4XhQdEh0c7fV5MeDT+PnWsMzID40DjpHK8V5A1EAJQOg/CbDzFErsgXE/XvPV84EDHdfNhw4ZVaTCCINirrkaPZZWSdzhZyoppHMARoxvB0dEYnPTEUsVEk6A/hL+nH0EV7SS62BNLl5BA4MiRBL/8MllyGXsP/IrZW6L1ETMWIKmVGy0PleJusc6uyJRKFH5+qKOj7VpP2D13fyK5a9eiimxD3oYN1k7p4NApPWTqNE4PGuT4jJho9P8etHu+pNNx9vkXCJk5g5CZM5CMBqRiHXJPGcr031B+9Rhez/zCoPoTMVsk9mRcepctYbyyKtUqP3joLfhmLJz727oj69Q2x+sa9bAGRoIgXDfXHOz89ttvvP766xw+fBiZTEazZs0YP348PXv2rM7xCcIdrdoaPSr0zHo0kLQCBVhU7D1VStyODNrX82NYdH3GbD3AB2On4CNbjv6yJNuAeTN5YvsTAMRNWI4vOFQv9o8dStrESQDI5DKy164hwf00zd5LwLMUCtRwqqGau0+B//PP2ZKVlf7+6PbuxX/4MJAkh+3kwePHc2b4cNQxMbiFhNqKGPrHxtraT8g8PNAnJiGZSlG1a2ef2xQdTejMmaQ4SWqWdDoyps+g3tdfkhfqSe01PS8tOwGGYi1jPirguS6jebrzOGSKEupo/AlUB1x5/zFNBAyMw1yUjaz1k8h+nIzsZLmaRo16WAMilahPJgjX0zUFO6tXr+bll19m4MCBvPTSSwD8+eef9OvXj5UrVzJmzJhqHaQg3Kmqo9Gjsx5a0eHRfD9hNkpzLR5Zk4DOaEapMhPxXBcso5607hJSuaMoTeeIe75tJ9JziZP5ftr7+J8bahdolHUSB8jNPc+etB20PWoGIKW2DHeDO+1OUXH9neHDUJfrn1X2XJQKIl5fiXv9+kglJbYZoMuLBqqjonCrUwf/IU/jP3SI3TOMZ885VEMuTyooxK9YjqHPRhSZu1HufxuMxeSa1UABXiUS4Xmg1JsJCZfwlAOVrEqVl27wZMo3+ew9k8/LnWfR6545+Mh0aGoFoPQNFoGOINwA17T1PCIigmnTpjkENW+//TaLFi0iPT292gZYVWLruXCrKz2fUWGjR7dQ10sprnpoRYdHM7/VeGrJ5Bjkarx/HIf8skrKAKf/+xO/p/7FfV5tcNMZCfCvjelAMplLljoEESld7kKVfIzQXLAAiZGe9J+7kfzXXkfVujX6pCTnCcEXE5AvD2IiVr2BWaul4MefKDl6lLrv/h8lhw6hDAqyFQ4szcrEvWFDLDodaeNfdhhTZX2wyp9XR3cifOxglIc/IPnueUh68H5rGSXllsDKdsK5hVXebNXVlv8uTQJ5a3A7NGr3Sp8jCHeiGt96XlBQQJ8+fRyO9+7dmylTplRpQIIg2HMLCyVixWvX1OjRVQ+thPQEdI2fIOTDx3CP/cahZQQA7l6E6j3pHpeIPt7a0fsc1jyYOmvWcHbUKCSdDgsS+zv50yrhGB4m0HpB5sPRDHhyCllvvIGqdWt8evV02sYBrPk3/sNiHY4rNBrcatcmY/YcZGo1ppwca9Xj8sFHTAyhM2dy+qUhTmdw9IlJqGNinCZ6l+Ua2caR8CfpMhkRs6bQNOMf0t/7zSGXqPxOOHCsf1T+v4tJq2VmGy+UTcCs8uL3zFLe/CsTndHMjuM55BQZRbAjCDfANQU7Dz30EF9++SWvvPKK3fGvv/6a/v37V8vABEG45FobPWoNlfTQulgrRlZywel5U7sXyVyyCv1lxQZ18QnkAPU//YSM5H3s/XAx7f/MB+BkXTl+dVsS5duGzCVL0e3eTfHvf+DZ3HVZistr5KhjYlAEBdl2nPnHxpK3/kMn7R3iyVi4AL/HH3caTOVt2ECDrZ+TsXCRfT7PZblGl563G3Pu06AMdpqMDaDftw/LhQucn7/Aaf0jt7BQSs9nUDRzJpb4eC5W5KF3dDRRYyYz+KsUdEYzhS5KAgiCUH2uONh58803bf+7efPmLFq0iG3bthF1cdvmn3/+SXx8PBMnTqzWAaalpTFlyhR++OEH9Ho9d911F3FxcbRv375a3yMItyO10svhmEqpYmiLobQJbINJqSJl2Bf4qwLRuHvZJegCmIM6okvY6PTZuvgEdn4fh9tn39AiDywySOrkx71SE0p3/YVqyGi74KOiGjjOzqtjovGPHcrpx58gYuUK67jbOi5zlR9L8Esv2VVftn3edm1BLiN07myMp1KQSkpwi4ig8Nff7HKNyrPoXNfF8Y+N5fz8+Q7BUNmsT/jiRU7rIxkSEvBhOeMeG8PSXen4VFISQBCE6nHFwc7rr79u972fnx+HDh3i0KFDtmO1atXi/fffZ+bMmdUyuPz8fGJiYujWrRs//PADwcHBnDx5klq1alXL8wXhdicze9MxNMq2dVqlVLG8y3I2Hd7EuwfetV0XE9aJuUO3EvrRgEsBj7sXFovzAMWCxL62Ktqs+wZ3E+R7Q+6gnjzadySnL5aluHymRp+Y5KKIXzTKwEBqr1mDMjiIkiNHQCazu89ZdeTyTLl51Fm7hrMjR11qNxETTeDIkSiOfgbmUvK2nECfeIB6Gz5E1TaS8OaLHRqGAuAfglnpWeG7XAZeu3ZZO7BXUB/JkJBAt1Evk9AkkEBvsYQlCDfCFQc7Kf/f3n2HN1mvfxx/ZzRp0gXdtGUKyi4ICG0REAQHbtkCojiAslEQkalSQJaIFEGFgx4XelB/igNRQChLtoCCskcppaUradIkz++P0NDQxSiklPt1XVznynpy+8hpP37H/T18uNBzqampqFQqgq7TSb3Tp0+natWqLFlysY18jRo1rst3CVERafChV62XUKlmsel0En3q9+G/+//LptPu01IbTm9ikkrF9AemE/DNYPAJwdb1S1S5OiLfnusWCM5pLJwJU2i+09kr558aaiLvfoRWxqrYzqa4rnnpSE6xPXLiYgkbM4a8M2fQhgRztE9fFJPpwjRTX9fn1IaSOxqjgtT3FlFj+RfYz51DpdViOXIEr0p6tGsWQZ6JiGEfYbX4kTJ7jvu6nwIHhhqaNmGt7RCnMk7TLi4Gy4YiDgstZVtHaf2RfPLMTH+ysazXEeIGueIOyufPnyc+Pp7g4GDCwsIIDQ0lODiYwYMHc/78+TIt7ttvv6V58+Z07dqV0NBQmjZtyuLFi0v8jMViITMz0+2PELeqIB8dnyZl0NBrEJ89uIJ7q3UsFHTybTi1kdRqLVGe/o687j9zcso8Dj/2JKfGvopp5058WrXk2NMdsWvs1PvHgV0FO1pVosYRB0H/puDdsAHa0FDX9fJHZPLl98gxREdTbekSqn7wPlELEzE0asyRHj058fwL5J086RpdMW3c6OzHc+Fz2rAwjHFFdzR2NTXcsAEsFuzp6dgzM8k7dpzk2QvIu9d5fp8qdTfWo0cJ7NuHyLfnUnXhQoIGDMC8axfpy5dT86svCRkzmnqmSrT1a4px7Aj0cZd0WG7dGq+oIjomF1Baf6SgsECqVColvAkhyswVLVBOS0sjJiaGkydP8tRTT1GvXj0URWH//v0sXbqU1atXk5SUROXKZdM34tChQyQmJjJy5EheffVVtmzZwtChQ9Hr9fTtW3jnBkBCQgKTJ08uk+8X4mYXYNQx+dGGvPLVbt777TTvvxBa4vszrVnY/1rHqS8PYUra6DrI8+x/lvLzxqU0/jMXLzuk+UHGU51ptnwTitGIoXFj1AYDKoOBakuXYM/IQG004te+PSlcHMlRTCbMu3ZhiG5c5HqZ/NGg/JPStcHBRL49F01AJRRFIXjAAFIpuamh9fjxQqeZn7JYiOwxHEeNh8j8OKFQ9+XIObNBpSL5jTfcrq1u3w7juJE4stJQm8yEBNdA5eMPtrzid3i1bu3sCl1CfySv4OAS/z0IIcrWFfXZGT58OKtXr+aXX34hLMz9hN7k5GQ6depEhw4dCq3vuVo6nY7mzZuTVGC4eejQoWzdupWNRcz7g3Nkx1Jgbj8zM5OqVatKnx1xSzuTmct5sxW19gxPfPdEke8xaA2sbP8ZvufNWE+exiu8CiqdF3tnTOTY6V3U/df5o+JADRWV0zVUr98CQ7NmGBrUJ/3zL6jcvRvpn3+Bd926rg7JmkqV0AQHYz+XBiiovLywHDzImWnTCwWd/F47+dNW+QeIul6PiyV8wgRQFKxHjrg1Diy43ia/b05+YMqvRX97nQs7soo4liIuFv/77iN5wsRCr+njYljTvwkLD3/Mj60/JWPyVCw7dxZdY4H+R9fSH0kIUbZ9dq4o7NSoUYP33nuP++67r8jXf/zxRwYMGMCRI0euqah81atXp2PHjrz//vuu5xITE3njjTc4efLkZV1DmgqKW93p82b2HTpKuwg72Wo1Y3bNZcMlvXcMWgOftZiPZvp7mHdc/EW+r54vvl+tIjgDbGrY2UjPnbtyUeNcPFz9k/9y9p35GKKjyf3rLyp371Y4AMTGEj5xAkf7P4cjLa3okFBgdCawb98Smw8GDxxA6nvvFXtGV3GB6UqaC15K+3kiv+fspsMHu1zfWzBMAXhFRaENCXG1CLBlZGDPyHD2IcoxoQ7wd3tdCFEyjzUVPH36NA0aNCj29YYNG5KcnHxNBRUUFxfH33//7fbcgQMHqF69epl9hxAVWYbJSl76Cdr/ORbVt78SoPNhfP8fmKQ43NbuTG40Gs10Z4AIGjCAs/9ZygbzLpostaB1wDl/SA73ofmuHLgQdAAUm82tIeClIQbAlJRE8pTXqTLuVU4MHMTJUS8RNvYVQkcMJy85Ga/ISHL37nVNa5W402njRhz9nib4hRcLT2fFxRE6bBjHBg68sLDZvZbSdnOV9LrGZKGj/11kb0i8+H6Tya3OWiu/dwWZvNPJhbae5/fgQcKOEDfcFYWd4OBgjhw5QlRUVJGvHz58uEx3Zo0YMYLY2FimTp1Kt27d2LJlC4sWLWLRokWlf1gIgcOUTtTvL6M69JvzCWsOeeZ0Goc0pne93ljsFvQaPfWy/Tl3ITicq+LPgZ+20vywc9D371oqglPVNDiQU+j6aqMRcAaFkvvgbCB05AjA2fdGV706R/s9g2IyuUZIImfPAkVB7VPy4aZKbi7HR4ykxpfLsaekYM/IcE1nnU1MJOKN10GtLlTLpeuB8qe38neaqS78sxTFyy+AyhY1JZ0x78g8DzhHdIrqsVOw87KM7ghxY11R2Ln//vsZN24cq1atQqdz3zJpsVgYP358kcdIXK0WLVqwYsUKxo4dy5QpU6hZsyZz587lqaeeKrPvEKKiOnXeTOW8NNT5QeeCTJvZrccOwLd1nFvC99xhJHLmDG7PgjwN7GkVTIy9FpZDWwpdv+DOKJVeX+rIicNkJmrxIrwiIsg7cYKIhEt63Cx0rrmp+fWKEq+j0utRTCbyjh51TXsZmkTjXa8uxiZNyEtJwatq4f8gM+/chU+7dq6pNrfDSGNj8b+vEyqjsfBaorhYgshEg6bEutQqM5jTsZ8rvseOaf167OfOSdgR4ga7orAzefJkmjdvTp06dYiPj6duXWf793379rFgwQIsFgsfffRRmRb40EMP8dBDD5XpNYWoqDIsGaTlppFhycJi8cLgrcZwSWdkP03h3i65OjXbovVE7zGhdcDZSnA2xEizHSZCZvUjTaUptMYmfNw4FLsdY1ysM0i0allibRp/P9Tees5MnVpoN1V+jxvFZMJhsbhON7+U21lWanWxp6iHT5xQKLikLVtG9WX/IWXW7CKn2s7MnEnYK2PcFikbY1sRMbg7Xt/2w9Y0HmNsK0xJhbfuG2NboTm1BiJDcGSZS7wPjqySxoeEENfDFYWdqKgoNm7cyKBBgxg7diz5a5tVKhUdO3Zk/vz5VK1a9boUKoQoWXJOMhOTJrod/BkbEcvk7v8h/POnAedZV+GmSvxY9x3M3irW5ezi+11fcXD8cJoddgCwv46GGiHRhCRtRwHX6Eng031BUdAEBJC97nfsmZmcW7KU0GHDObtgAf4PP1RiSLFnZpK6cGHhIxYuBI/Avn05t3Ah9sxMQocPJ0VV/BZzY1wsOBxFrxHauJHk198gbOwrJI+f4HpeMZmwnTtX5MJncH5X2MsvU/PLj3FYHWgcGWjObkb7Qz+w5qDd8S4RQ5ZyCtwCj+uk9B/6QaP7UPsFlvjvSe1X8jSdEKLsXdFurILS09M5ePAgALVr1yYwsOT/g3uK7MYSFU2GyUpqtpXM3Dz8DV4E++iwKDm8lvQKG4s44TyuSiumG+pg9G3CqXc+dftFvffuakTsOEblbLBq4M/mlWi620zV2XNI+/jjYsOGIToa/wfuRxsSAjod5q1/YGh5F/qIiEJnRhnjYgkbOxb7+fMo2TmFj2a4IGphImn/WYYhOhpDszsx79qN3z3tsKWkgErl2mJuaNqE8PHjsR4/zonnX3C7RsH1OGo/P7DZyNm4yfV9UYkLODFwULH3tsbiWXgdWkzOPVMI+LA16HywNY3HHtISh9mK2scbtdqGwxiF48wR1AadMxDteNc5ejZ4KzavME6OeqnYHjuyZkeIy+Oxrec3Iwk7oiLJSDlH5ukUvHNzMFbyxeblQG/U8I8Kev7YpdjPrenwBdkTZriCjg3Y1VhHkz1WNAqcqQzpof7E3dPLuXDXZsOrSgQqjZq808mg4mLYiI52Cz0hw4dxtHsPAKp/8gnZ69Y5r2G1unZaFeyrUzA0FQw8UQsTQVE4OeolIhKmcnLY8EKLiVV6PSgKtvR0tJUruwWX/AaIhfvzxBE+dgyKAorZzJFu3Yu9TzU/f59/dZXZfiaPnoa92Pwbk5d8FpVK7QpphqbRRLwyFK/lD7ofnHpbB+jyARgqS48dIcqAx7aeCyE8J/fkKdLHj8eclOTaFZQ/hUJgySeKe5kUV9BJDtaRY8yj2W4rAPvu0NJi3AJCjJVxmHPR+PnisFqxp53DvHsPfg/cT96xY3jXq+vsNOxwgFbrWmCs9vNzrY9R+1zc0aQNCyNl9uxSp61c7w8JcZ2LpdLr3YOO1Yo2NBSVlxeg4uSol6j+0TK36xa13RycO8GSp04lfOJE8k6cwBgXW0xjwTj2q0J4/qujfPF4LU7OXo1pw+sXXy+wtujUtHlEdolHu3mG88XbOsAj74DB2T3eq0o4kbNmYj93DkdWNmo/XzRBQTKiI4SHSNgR4iZgy8jgzIQJmC9ZD2NK2sQpoMqkkSV+XpXtHIHY1cCH6kdyCE8Fixb2t69O5+4TSE98nyOXjIaEjRmNsYU35OVxcuQoACLnzsF29qz7GVjbthE5dw6nxr6KSqfDvGsX5xYuJGphYpGhAnDrzQPOIIGiuEZ6cvfuo+rCRFITFxZafBw8cACKyUTWb2vcgkuJW9+TNqGYzJwc9ZLzMFKHUmjBddDYsTyw7CAzOtVAMzuh9LVFr4xB2+g+8PYHnxBX0MmnDQiQcCNEOSFhR4ibgP3cueK3MydtIjTLSlx4SzYkb8agNTCgZm/u9mmMNseCV0AlHBoj2xvrabInB7UCyUFgbdea+6M7kv7++0WOhpxJmIYhOhrznj1EzppJ7sGDqPXeZP7wY6GgEDxwANWX/cd5tlT+OViX2cTPGBND4DP9UBSF6p/8F8Vmc44WqVQYmzfHvGuX2+GgvOhcp5P24YduwaXUre9mM5GzZ6HYbISOGoVitWBLTXUGtJ27yLMrvN+3OU3I4Ghx97pASHOYLFCneYnfKYQoHyTsCFGOFLX4OMCow5aZVfIH01KYVLs7U70MPB/2JL6zlpGblIgNOBSiJ1dv5c4TzuV5f96uodpxaN7R2a+q2N1JF36xn1u4EBSFsHGvcuaNN4vc/ZQKhI0Z7TYakt/ErzheVasStTCR3L37UAFn584ttLA5eMAAvBs15ORwZ0PCwL59Uel0RCUmolKpMP35J8bmzQl82nloaEkUq9XtOAjX2qGRo9A1acLPtdswbf3f7Ole8onm+aHq0l1VtoyMC9NWWaj9/NEEBcrIjhDlhIQdIcqJU+fNjPlqN78fTHU916ZOMNOebIx/KV2F1QYvwj/vy5R+SWSMfx1TkjOQ7GzoQ61DOUSchVwv+KtddZqsOgqUPvJS8D2mpCTIyysxGDmy3fvHmHfuwhgTU/QZV3GxZP36G+fmzydo8GDO/ec/haeNNiSRCvg/+CBB/fvjXb9ekT118gNLyGvjCH99CtqQELfOyGnLlmFo0oScTZsLd082GIlauJBDugDm/e+Q85+5lHut0usxto5DU6BbfEnHQ3hVkQXJQniahB0hyoEMk7VQ0AFYdzCVV77azbRONdDHxmIpqodNbCs0Zzdji2xJXnoupg1JWNWwt74X0X/moAZOB0O2r4F7ByZw/rkMNCYL6spRKMlnS6yr4OiMvZRmeGofH7fH+YdxApfsjooldPhwjj7dDwC/Nndzbv78Iq9p2pBESHw83nfcUXQzwI0bQa0m/MtPsCh55E6dU2i7fNWFiThyczk19tWimxDGxaEaPNr1OM8vAGNcXJHThsaYGGwpKURMes3twM/rejyEOR1yzkJuJngHgE9wofVBQoiSqT1dgBACUrOthYJOvnUHU8nyMpA1eDT62Fi31/J3Y6nS/+Rgy6noTDmcCNdzqgo0/TMPNbCnrga/bBV1jphRTCZOBar4p4qKPF8DtpSzzsXBRTDGxWL+c6/rscrPp8j35VPsdrdrKSaTa3t6taVLqPrB+0QtTMTQqLFrN1fUewtR8vJKvq7NBpQw3bZhAzZHHqY3i975lfreIsy791C5a9did2v5vDOdoXeF0aZOMNqAACImvooxtlWh+1Fl/Fj8Kh3BS5Xmer7E9VQXjoe4ahknYfmzML8FvN8B5jeHL/s7nxdCXDYZ2RGiHMjMLfkXvlqlYv6eTJo9MZgOg0ZQyZGL0d8Xm85BukrB+4F5HN6fxv6f3iYqw4KfGcw62FfPQLNdF48vsHhrGPzrAAC+vjOR0IgqhI0ZzZm3ZrpPwcTEEPziizhyc1EZjeiaRvMPZwkobtt2TAzZa9cR2LcPcDGYKCYT5l27MEQ3duur412vLieHDSdowAD8Ot5b8j+70YjjkgaEl/LJ03A2qfgwFNinN0Cxu7UsSUk8OHQUj7auS4BRB0Y7kV1rYx/0lLOZYH7zwM/aOnvr3PmY67OOrJLXU1318RDmdPhmMBz61f35f1fDt0NcPX2EEKWTsCNEOeDv7VXi6xq1ismPNmTiN3+S463izpreWJVMKnn5UTUgjJPncslc0IfoP5y/eE+GgtnbPegY4+JYlek80NOgNRCc542KXOw5Ofjffx+BfXq7GveZd+7i+ICBzqaBY8dwqkEoQ3aNZ/mr78JUVaFglN8kEHAdLaH29UXt7Y1itWJLTSVq9mzXGpr86TFDk2hQlBLX9qh9fC701yleaYEDRUGxWkt8SxB5+FQyOB/4BKNN3Xaxj05Bt3VwbjW/QO3nV+J1r/p4iJyzhYNOvn9XO1+XsCPEZZGwI0Q5EOyro02dYNYVMZXVpk4wQRd2ZU16IorJmyaxdP3F0ZX7rfXp8Okeok85d1vtaexNA5+GqDf+4XqPMSaGoFdHs3CTc4RjQM3e6Hz9cdgckJfndoZUQaaNG/EfM5z+257DbDOjRo3/fZ0I7NMbtcGIw2zCvHOX26jNuYULURmN1Pj8M85MTSi0Tb1qYiKmP5y1KRYL2WvXETxgAKlQ6L2BvXtz5i3nAZ0+99xDzm/uJ7jnv0+lLflHmTY0tNRApPUvEFoMlZ1NAr8d4gwW+S5pHgigCQrC2Lp1scdDFFzIfEVyM6/tdSGEi4QdIcoBk9XOoHtqY1cUNvxzcY3H3XWCmf5kYwKMOjIsGUzZNMnt/KtHd+l59Nfd+OaCSQ/76xppfhAC+zbH8HR/t5GaZNMZzDbnSM/dPo1RabWkJi6kco/ij08A0GVm8nncNLL0vvicV3HkwqngUQsT3bZyFxTYty9npk0repu6Wo2haVPAuQD63Acf4N2oIcEDB8CLL2DPyHDVnB+iki25VJ7wKorVUuR5XdlJG0sYHYrDpLGjrxJS/MLjokJJQKRzqsi1OLj45oERkydyasKkwrux3nj96hcne5fSHr+014UQLhJ2hPCwDJOV0V/tZtvRdJ5tXZNn42pisTnQa9WkZFkw6jQApOWmuU4098qDYb/AXTudnZGPh4F+5DDu/nYLJtNG1+hK/jZrQ5NoDLogfov7lOzUZALzjJBrKdTJuChadS41D22EE39gqjfO9XxJW8t9WrUsvpvxhg2Ejn4JY8f2eOm8MTRtysnhI6j2wfsc7dmrmM8kEWrOI+PlpzEOfZowqzfkXBxVAorc+aWPjcVvwljuX98TgA9GzsAHB5YN7t2iiw0lhsqucGPLyMB++hyOrGPufXQyTuK19mUiuzTEPrCHc42PfwCaKjXQhl7DtnOfEOdIUsGRpXyXTKUJIUomYUcIDyu4E2v+r/8Uev2uGoEEGHVkWZ3TMHWTdTz3g4lqyc7Xt0ZrmdsRljWpR0S1WpxSFMw7dxe9zTomhqC+fUBrwXFhM2aJ/XAubGvnthawbibqOy9OdxW7tTwmBtQlb/RUckykfbQM09rfiZw1k3Qvr1J3ZeUdPYr/F1+QNbIPZ8glr4/7qNLJUS+51gtp/PxI0xr57ngu9e0W14hW/52jGdC/N3cP7YvGZMFu1ONbpRZepYSSYvvoTJ6I19qX4cBKtKx0/4Fa4GDQq3IFU2lCiJJJ2BHCw0rbiZV14XU/nR9Pbtfx8BoTRgvk6OHb9j6saOJs/OfjsOH1Qz8iu8ZjHzOC5BlvF92XBgjs2we1r3PhbPH9cOKIGNwN7Q/9oHoiANpTq11TQflby/MDBoqCJiCA7HW/4zCbKYlitRI6KJ6jW/4ocF6Vo8TPqPR6zBuS8EEh66W+VLpkZ5hiMnFu4UKMcXFEzp6FWtGR9MducrxVtAyPYXPyRsw2M3MOLmbOhc/ERcQxve70Er+3xD46EyYR2aUhWlYW/mBZLCK+zKk0IUTJpM+OEB7m7+2FUadhXPsqrOkXSVIff9Y8E8m49lUw6jT4eXuRmZ7Clhe70P0nZ9A5VgUSehtdQad91XsIy7Fj6bSMPN+mKA4NhkaNURmNhb7PtHGj8wRxoxFjXJxbP5yohYlEvj2XakuXUOW1l/D6oZ9zq7XWuXtKu2sRoRPHYYyLAy4GjLRlHwFw7LnnObdwIeYdO4vv3xMTQ86mzZxJmEbEG687FzarVORs2lxyz5+duwCwbNiIr8OL8IkTMBbqOxRL2MQJnOQ8PgYr7/RsyuPRtRl310RiI9zfGxcRx6TYSQToS15TU2IfnQ0bsIe0LP7DZbGI2FAZgm+HqObO/5WgI8QVk5EdITws2FfHD8/UImrdaDRJF3cbPVOzPZ2emc6xrV9zaPqbNE5xPv/Hnd6kDX6KoVVbYLFbCPQOpL49lLPjJ2JK2uT6vDEmhshZM912SuXLO3GCjJU/UGXCeE5PmYJpQ5JrussYF0uViRPQ+aqgxt1w4Ec4sRVuv5+8ev05O+0tDI0aOUeHDM6t2pqgIOznzhGRMBW13hvz3r0EDxpIqlpd7DZ1xWQidJTztHbFYilxWixszBiO9Oh58Z5pAzgzbTqGxo0J7NvHbSH26WkJ/Na7Hlty9jEpdhK1QsIBX2a0mUFabhpZ1iz8dH4EegeWGnTgMvromEvY0i6LiIUoFyTsCHEDFXVYZIDOgf/vY1Addt9WrT38K1u29eO2NekEWyHLAKd6taX9wPFM3jiZ93/9DwAj6jxP8Ac7MRcIOlBwyqpvocXCKr2e7J9+AiB00gTsOdkoWTmo/XxR+RnRfd0N/COg/WvQ4jmwWbDV682pcc5zt3J+/Y2gAQPI/esvKnfvRmriwkJTYD4PP0jY6JfJO+3ev6dg+HJk57jquXRarOBn8s6ccQtsOrzI+fU3cn4tvBUd4O4XuzPn4GImJU1iepvpBOgDXH+uVKl9dPyLuaYsIhai3JCwI8QNUuwi10nj8Dqx2e296TYtv+yvQsO96QAcjVQR9eYs7mkay+h1o9l46mKwuNunMeYNiUV+p2njRkKGDCZt2TJXWDDGxLimhLJ/+omAgc9z77ZnXIt446q0YvoTiwhY8gCsnuKcPlk3E/vD/3MdMAoXGgJCsUcwnJ38BmFjRhe7PR1A7es8gqLgIulLg9mlU1vGuDhyzpc82qIxOaf3NpzaQFpu2lWFHNe1SuujU6VG4V1TsohYiHJF1uwIcQOUuMh14hvYmsa7ntuUE8r2NaE03GvHAey6y4+2Sz+hYXQr0nPTaenTgP81eYdv68xkRZP5BOoqF7k2J589I4PIWTNRGY34tGtH6KiRGJo2IfLtuVRduBAvm8KSO2dh0DqnpDac3kSaLQdaDYRDayCqBQAOk/sp6YrFgqFJdIlnVqHRFFpXk88YG4stxTk3l7ZsGUEDB2CMK7wGJ7BvH9KWLQOcW8nzRo7F7lvyaIvdePEA0/xdbFdLGxBAxOuvY2zd2r22/D46oRcWEQ/eCs+tdv5vlw+ci4uFEOWCjOwIcQOUush1YA+0wPLT1amdlEdAHmQa4UxrHT0eexjMyXDiLIHet3HP+zvJTUrEduHz2XGxxa7NAUClIu2jj6j+0Udgtxc6Pdyn/T3UGjaMlU3fI+t8Cg5fA/7aUI406kJm9Rb4VKqB16D1VE53/28jlV6PYrFc+m1urEeOED5xPMmTX8dU4MR2Y2ws4ZMmYraa8F7yNjajjv+Zt1Fl+MPc+fIwMtJP4zAaOK9zoFGMRC1OROXjy0f/WJj3+UG+fya62FPg9XExrMnZ7Xrspys5GF0OryrhRM6aeWEKMhu1ny+aoKCLvXkK9OMRQpQ/EnaEuAFKW+SamprGxl1RNNzv3GZ+OEpFrZY2WvadBpsXwrqZ2FqOJv3Lz8hNunTKKAmUotfm5E9ZmTYkocTHc/ad+W5BR2U0UrlrV1KmTXd7PjMuluyRfXhu5wTMNjOtqrRiXP1h6ONiXA35zDt34dOqhJ1IgEqj4XTKYTJe7sNtqpEoWTmo/Hz4VzmLyZDD078/7Zo+i42IpU/VRixPTaJeVD1AIcIQgt++7/FdP5cjT6xk2vpTABy1aKg8eDSVVG+5hUh9XAw5o/qycMdoAOIiYgn0DiyxxsulDQi4+m7IQgiPkrAjxA1Q0iLXAzWN+L8+kQbnwAHsaeXP41X/Qn/PS86gc2gNAPaQlpg2fFzkNUxJSQS/8HyhBoIFD+hU7PZCU06BffsWueYm90I/mwH9ezPn4GI2nd7ETI2eV8eNgDed27/Tli3D794OGGNj3UZtCn6/eecu8u6JpveWIW6vxUbEMD78FZbdtwRzngl/lQY/UxqmPBv+oXfil5tF4NFNBCS9C9YcbDXbs+rYxT48VSp5M3NzJjPHjSQ8vSd5+JGud7AqcwsLd4zGbDMTF96SSS3GXNN6HSFExSBhR4gboLhFrn80MdBorwl9HmT4QOpT7ejxZB9Y8oBzrcy6ma73lrjFGVDpdEQtTCx251N+E8GCDE2iiz3WwbJhI3cP7etqwLf2xFq639Gdg/2b0Gb4M0TqQ1E57ITEx6O8+AI5Gze5FkLnB6305V/yb3v3tSutqrTiuUbP45V+hLqf9Xb28blAqdUeR/3H0Hw31PWcrWZ7/r7rTeZ8egxwHowa7u/NzK7RGDIPoVv2BHqdD/bYeO6p3ormoZPx0+icYclmL/GeCSFuDRJ2hLgB8he5nho/HtP69WQYNZyIUmi+0zmFc6iamtozEmnVpA2c2ev8kM19PYzaoCvxO9Q6hbPL/uPWayefPi4Gm77w/91LW3OjuWRRstlmZuHhj2nRpDEp02e4H8oZF0eNzz4l78wZzNu2k778S4LHjcWcs5X57edjsVvQa/TsTt1N/Op47gyJZnpsPAFrZgBgr9WevM5vk2XXEzBwM2pLFql5ev7vXxtzPj2GyWqnTYGDUQFQhbl2QgWsmYHbGM5tHaDloBL/+YQQtwYJO0LcIPmLXH//6l1U739EgwPgUMHuu0PoMn8VXroLO4hUGqjVztW1OJ/m7GaMsa2KDDPG2FZoT60mYkhPToF7c8G4WILHjsGaZyX89SmcSZjmGu1R6fWFrlVQwV1NAHqNngE1e+M7a5nbNnSV0YihUSPsaWlojAb877uXSt2eYLeSw4SNEy69LODc9ZXSeRT66h05Y9WxN0NHc69QQoO8gTAADCYrHfytPBodQSXlPNq8LNS2E2AOvrgoWM6PEkKUQsKOEDeI3Wbjq9f7UPfHg+hscN4X0p97nJ4Dprq/Ua2BlgMgK9kZei6s2dHueJeIIUuLDDMRo/qj/boLgPNsrOEvYLfpUaxWcjZt5ni3ns7ppbg4qi5M5PiAgSgm56nhxa25uXRXU9uotvjkKnQL7MDxpIt9fVRGY9GHjsbFUXviqxi0Btci5Eudz8tla0oYWrWKQD892RYboQVeDzDqCMg7C98MhkO/XnwhP8wERMr5UUKIUqkURVE8XcT1lJmZSUBAABkZGfj7S+t24RnJxw6SNORJ6v3t3G31Tw019d9azG2NiuhBY06Hr+MhojHUexR+fPXiL3qdD7Z7pmOu3AJVThZeSgaatO1o9Q6o0sQ59aXVY7OoODn3C7dppnzGuFjCRw/DkXICtY8BlU9lTk9/2y1AecfFkj2qD/0vLPZtG9WW1+sM4/ykBIK6d+fksOGu9wYNGIB5166iT02Pi2N1/8bMObi4yPsyr/WnvP9bNlMeaUj3RRt5v29zmlQrEFLM6bD8Wfegk+9aTxUXQpRrZfn7W0Z2hLjOfvtsNpq5i6l3Huwq+LNdFZ58+4eL01aXMlSGB99yTs1smOds7tfqRRQFrH5RnHZU4sFFf7KyVzg1PnuyyEvYH/qqyKADF7aqZ/XEsPrCWVM6HyJ7DMf2yhjsWSY0RgMYDeRoc/kw5k0MxkAClUpkvjaV3KSNqPr2dS+3hEXOpg0b6DjiOeZQOOy0DI9h9zE77arp8Mk6xK89/DDoU8DMxQCTc7booANlc6q4EOKWIGFHiOvEbrOx/OVHqPfzYXR2SPOD7AHd6dF/UqH3ZpispGZbyczNw9/gRbBPCAFdPsCacYbUc6lYHb6sOuZgzvoTvNMzBJPVzqpjDp6p2R7t4cJhwJGnKrG2S3d2KSFNODP9LfcFx7GtuGNIT7w+exhLp2UkX3it4NEOUPoi58p5WuLCW7Ih+eKRGLERsfSsOYowm53bN491OwDVbYqqtFPDy+JUcSFEhSdhR4jr4OThfWwd2p3og84+xwdraWgyZxnV7riz0HtPnTcz5qvd/H4w1fVcmzrBTHuyMWfzwnj0o3+BDNdrO46fJ652EHPWpxDb803qq8ahKjj6Uasd6sphJdZXcGeXrWk8p975zG3BMTjXBZ3CuQaoYDi69HTy0hY5e6mtTG88hLQ7epFls+Cn0aHXV+Gn/SbuPjEJzSUHoPLvaueoVpcPSj81XE4VF0JcBjkbS4gytvq/MzjU40nuOGjDpoZd90bR+dudRQadDJO1UNABWHcwlVe+2k2lIrabf7j+MM/E1aRptUp0/fQYe1pMQ+n7LXRbBr0+h6jmaI6txBjbqsj6jHExaM5eHGWxR7QrFHTymZI2YQ9p6RaO8k8nN0RHE7UwEU3lyhjj4or+rthWaE79RsCqCdQ8tIHGH/eg5n+ewJpp5Z5ICgedfPlTVD4hzpGeosip4kKIyyQjO0KUEbvNxvIRD9Jg9XG0DjjnD7mD+9Cj76vFfiY121oo6ORbdzAVq91BmzrBrCvwHpPVztBPdzD+ofpMergBZqsNq9GAbsNwVBe2X2t1PkS89BWnZmncT1mPbUXEK0PRchaqLwOtHkd2yf17HGYrXtk73La9KyaTa52Ozz1tqTJ6MKen2913icW2ImJIT7Q/9HM2DmzlPP08vxty58icQt/lJjcTgm+XreVCiGsmYUeIMnDs4C52DO9N9L/Oaau/a2tp9vanVL2tYfEfMqcTaT9DUh9/rFo/Vh11MGd9Cibrxa6/ORYb055szCtf7XYLPM2rV6bd7SFUqWS48EzlQtuvzysBKK+9TnVrBqqUQ6gNOjRnN6Nd/qBb12J1z6IPKHW9btCh/f3CtneVCtOGi6NAxthWVHnhCby+7ULk8xOwjxqMI+3Mxe/KDzoANgvKbR042Xoac5YcomOv8JJvav4UlWwtF0JcI9l6LsQ1+nnpGxgX/JegTMjTwL6O1ek68zs02hL+WyLjZKHeMfnHInS90C0YYPXIttwW6utawJyVm4eftxfBvrqLXYSL8W9KNg/PX88PLzSi6q9DUB8qMDKi88HWNB57RDvseVoUm0LOps2u4x7yGeNiiRw/FK3lNBgDseU6sBOIPfUEGi+HM9DseBd7VEvUnWeieu9utyDlZmAS+EeQofiQmm3FT8km5OdBrtEoN5exrdyWkXHhFPIs1H7+aIIC5aBOISoQ2XouRDmQZ7Xw1YgHafDbKbQOOFsJbEP706PXSyV/0JxeuEkeoD38K3cwjhGtx/Pmr6dpUyeYYF9noAkwlh5uLhXsq6N59coczvYiu8Ub3KEozp1bOh/yHlh6YVHyxYNFjbGxRM6a6TpPyxjbiojB3dEmTYVm/WD9XFT3z2DkyjM0CalEx2pqdIa2WGt2ZtUxB9UOW+lUtVXx4cU/AgyVCbjwzwO+Vz1FlXc6mVOvveY+Rde6NRGvv45XlVJGjIQQtxwZ2RHiKhzZ/we7R/ajzmHnCMxft3vR6p0vqFK9bukfTj0A81sUf+0ea5mwwcL0JxsXmKa6OqfOm9l/OpMhn+5gROtQOlZTU1mn4/ybs4ptOBg2+iVUahUagwqtzgGonF2dfYI5lO1F+1lri/wuo07DjmH10a8cXnR4CYgs8nOY069oisqWkcHJkaPcgo6rhtatiZw1U0Z4hKgAZGRHCA/6cfF4/Bd9SZ0ssGrgr/tvo8v0r0uetiqolN4wkcY83unZ9IpHcooSUclAjsWGyWrnzV9P8ybw05PVcZTQcFCl9UJ/W+0iX884m17sd5msdv42+dH4StfX5J9xdZns584VGXQATOvXYz93TsKOEMKNhB0hLlOe1cJXQ+6j4bozaBRIqQzKyEF07zrkyi5USm8YL2NAmQSdfKF+ercdXVpzDtYS3u/INhX7mr+3V4nf5aP3AoPvdV087MjKKuX17Ov23UKIm5P02RHiMhzctYGfHr6T6LXOoLO/no56X35HuysNOnDDe8cEGHVMe7IxbeoEA2Az+JT4frWfb7GvBfvqXNe5VME1RteT2s+vlNeLr18IcWu6qcJOQkICKpWK4cOHe7oUcQv5PnEsqc8+x21HHVi0sOfRujy6fBuhkbdd3QUNlZ1rWC4NPNexd0xEJQPv9GzK6pFt8Q8PKb4JYOvWaIKCir3OpcEpX5s6wUx/snGZjkgVRxMUhLF16yJfK61+IcSt6aZZoLx161a6deuGv78/99xzD3Pnzr2sz8kCZXG1LGYTK4bcR6MNqagVSA4ErzEjaP3oC2XzBVe4MLcs5Z1O5tT48ZjWr3c9Z2zdmog3XscrvPTdTFezFb4sXWv9Qojy75ZboJydnc1TTz3F4sWLeeONNzxdjrgF/L1jDf+MiSf6mAOAvQ30tJu/guAqNcvuS65wYW5Z8qoSTuSsmRf61GSj9vNFExR02Qt7r2YrfFm61vqFELeWmyLsxMfH07lzZ+69995Sw47FYsFS4BTmzEw5FVlcme/eGUXwkpXUMoHFCw480pBuby73dFllThsQcFOHg5u9fiHEjVPuw85nn33G9u3b2bp162W9PyEhgcmTJ1/nqkRFZM7J5JvBD9BoYxpq4HQweI8dTbfOz3i6NCGEENegXC9QPn78OMOGDePjjz/G29v7sj4zduxYMjIyXH+OHz9+nasUFcHezT+z5pFWRF8IOnsbedN0xS/EStARQoibXrleoPz111/z+OOPo9FoXM/Z7XZUKhVqtRqLxeL2WlFkgbIozTezhlDl41/wM4NZB/8+1oSuUz71dFlCCHFLu2UWKHfo0IE9e/a4PffMM89Qt25dxowZU2rQEaIkOVkZ/N/g+4nefB6AkyHgP34cXTv19mxhQgghylS5Djt+fn40bNjQ7TkfHx+CgoIKPS/Eldid9D2nXnuZ6FPOgc0/o410XPAdlYKqeLgyIYQQZa1chx0hrocVMwYQ9claqueCSQdHurSg64Rlni5LCCHEdXLThZ01a9Z4ugRxk8rOSOP7QffTeJvzbKUTYSoCJ0zmyQ5dPVyZEEKI6+mmCztCXI0da1eQMnEcjZOd01Z7mvly//zv8a8c6uHKhBBCXG8SdkSF97+p/an2eRLVLJCjh2M94ug29n1PlyWEEOIGkbAjKqzM9BR+HPQgjXbkAHCsioqwKQk8cfejHq5MCCHEjSRhR1RIf6z+jPQpU2h0xjlttbu5Pw8l/oyPnxwvIIQQtxoJO6LC+XJKX2p+uZUoK2QZ4FSvtnR/eaGnyxJCCOEhEnZEhXH+3GlWDepMw11mAI5GqIiaOovHWj3g4cqEEEJ4koQdUSFs/nEZ2W8m0PCs8/GulpV4dMFPGHzkiBAhhLjVSdgRN70vxveg9je7iLBCphHO9OlIjxHzPF2WEEKIckLCjrhppZ05zq/xj9Doz1wADkepqZnwNi1b3OvhyoQQQpQnEnbETWnjdx9gnjaTBqngAPbEBvL4u6vQG4yeLk0IIUQ5I2FH3HS+GPskt3+3j0p5kGGE1Gc702PwTE+XJYQQopySsCNuGqmnD7M2/nEa7bMAcKiamtozEmnVpI2HKxNCCFGeSdgRN4XfVyRif2se9dPAoYI9rUN4fN6PMm0lhBCiVBJ2RLlmt9n4cuyT1P3hADobnPeFtP6P0WNggqdLE0IIcZOQsCPKreRjB0ka2oXGf1kB+KeGmvpvLSamUayHKxNCCHEzkbAjyqU1n89FNfc96qWDXQV72obTZd6PeOn0ni5NCCHETUbCjihX7DYby0c/Sr2fDqGzQ7ofZL3YjZ7PTfZ0aUIIIW5SEnZEuXHy8D62DO1B9ME8AA7W1NB49lJq1Gvu4cqEEELczCTsiHJh9X9noJu3hLoZYFPDn+0j6Tb3RzRa+SsqhBDi2shvEuFRdpuN5SM70+CXY2gdcM4fTIOeome/1zxdmhBCiApCwo7wmGMHd7F9RG+i/7EB8HdtLXfO+ZhqdaI9XJkQQoiKRMKO8Iifl76BccF/uSMT8jSw997qdJv1nUxbCSGEKHPym0XcUHabjeXD7qPBb6fQOiA1APKG9adnr5c8XZoQQogKSsKOuGGO7P+D3SP7EX3YDsBft3tx19ufEVmzvocrE0IIUZFJ2BE3xE/vT8T3vS+okwVWDey/rxZdZ3wj01ZCCCGuO/lNI66rPKuFL4feR6O1Z9AokFIZlOEv0qP7cE+XJoQQ4hYhYUdcN//uSWLfy8/T5IgDgP11dcTO+5LwanU8XJkQQohbiYQdcV18nziWwA++pnY2WLTw94N30GXqlzJtJYQQ4oaT3zyiTFnMJlYMuY9GG1JRK5AcCNqXh9L98YGeLk0IIcQtSsKOKDN/71jDP2PiiT7mnLba10BP2/krCK5S08OVCSGEuJVJ2BFl4rt3RhG8ZCW1TGDxggMPN6Db1C89XZYQQgghYUdcG4vZxIr4jjRKSkMNnA4Gwysv0e2h/p4uTQghhAAk7IhrsH/rLxweO5ToEwoAext502HBd1QOifRwZUIIIcRFEnbEVfl2zlDCPlpFTROYdfDPo9F0e/0zT5clhBBCFCJhR1wRc04m3wy6j+jN5wE4FQK+48bS7f6+ni1MCCGEKIaEHXHZdid9z6nXXib6lHPa6s9oIx0XfEeloCoerkwIIYQonoQdcVm+fmsAkf9dS/VcMOngcJcWdJ2wzNNlCSGEEKWSsCNKlJOVwXcDO9L4jywAToSpqDxhAl069PBwZUIIIcTlkbAjirVj7QpSJo6jcbJz2mrPnb7c/+73+FcO9XBlQgghxOWTsCOK9L+p/an2eRLVLJCjh2PdY+n26geeLksIIYS4YhJ2hJvM9BR+jO9Mo+3ZABwLVxE6+U2eaPu4hysTQgghro6EHeGybfVy0qZMpNEZ57TV7uZ+dH73R3wDAj1cmRBCCHH11J4uoCQJCQm0aNECPz8/QkNDeeyxx/j77789XVaF9NWUvjBiAlFnFLK94a9n29L94y0SdIQQQtz0ynXYWbt2LfHx8WzatIlVq1Zhs9no1KkTOTk5ni6twjh/7jTLuzej/idbMVrhaIQK/YKZPD56oadLE0IIIcqESlEUxdNFXK6zZ88SGhrK2rVradOmzWV9JjMzk4CAADIyMvD397/OFd5ctvz8MZmvv0nkWefjXS0r8fD8H/HxC/BsYUIIIW55Zfn7+6Zas5ORkQFAYGDxUysWiwWLxeJ6nJmZed3ruhktn9CT277eSaQVsgyQ3LcjPUbM83RZQgghRJkr19NYBSmKwsiRI2ndujUNGzYs9n0JCQkEBAS4/lStWvUGVln+pZ89yZddmtLwi50YrHA4SoXfond4RIKOEEKICuqmmcaKj4/n+++/Z/369URFRRX7vqJGdqpWrSrTWMDG7z7APG0mVVLBAeyJCeTR+T9g8Lm174sQQojy55abxhoyZAjffvst69atKzHoAOj1evR6/Q2q7ObxxatduP3/9lIpDzKMcLbfA/QYOtvTZQkhhBDXXbkOO4qiMGTIEFasWMGaNWuoWbOmp0u66aSePszawY/TaK9ztOtQNTW1p79Lq6btPFqXEEIIcaOU67ATHx/PJ598wjfffIOfnx/JyckABAQEYDAYPFxd+ff7ikRsb82jfho4VLAnLpjH3/kJvcHo6dKEEEKIG6Zcr9lRqVRFPr9kyRL69et3Wde4Fbee2202vny1C3es/Bu9Dc77Qlr/x+g8MMHTpQkhhBCX5ZZZs1OOc1i5lXzsIElDu9D4LysA/1ZXU2/mYmIaxXq4MiGEEMIzynXYEVdmzfJ3UM1eQL10sKtgT9swusz7CS+dLNgWQghx65KwUwHYbTaWj36Uej8dQmeHdD/IerEbPZ+b7OnShBBCCI+TsHOTO330LzYN6Ub0gTwADtbU0Hj2UmrUa+7hyoQQQojyQcLOTezXT2ainfcBdc+DTQ1774mg69s/odHKv1YhhBAin/xWvAnZbTa+GPUQDX45ipcdzvmDaVAvevQb7+nShBBCiHJHws5N5tjBXWwf0Zsm/9gAOHCblqZzP6ZanWgPVyaEEEKUTxJ2biK/LJuK9/yPuCPzwrTVvdXoOvt7mbYSQgghSiC/JW8CdpuNL4bfT8NfT6J1QGoAWIc+Q4+nRnu6NCGEEKLck7BTzh37ezs7R/SlySE7AH/V8eKueZ8RWbO+hysTQgghbg4Sdsqxnz6YhO/Cz6mTBVYN7L+vFl1nfCPTVkIIIcQVkN+a5VCe1cKXQ++n0dpkNAqkVAZl+Iv06D7c06UJIYQQNx0JO+XMv3uS2Pfy8zQ54gBgf10dsfO+JLxaHQ9XJoQQQtycJOyUIysXvkrl91dQOxusWvjrgdvpkvCVTFsJIYQQ10B+i5YDFrOJFUPvp9H6s6gVOBMImpcG0/2JeE+XJoQQQtz0JOx42N871/HP6IFEH3NOW+2rr6ftuysIrlLTw5UJIYQQFYOEHQ/6bv5LBH/4PbVMYPGCAw/Vp1vCV54uSwghhKhQJOx4gMVsYkV8RxolpaEGTgeD9+iRdHvkeU+XJoQQQlQ4EnZusP1bf+Hw2GFEn3BOW+1t6E37d78lMKyqhysTQgghKiYJOzfQt3OHEbbsZ2qawKyDfx6Nptvrn3m6LCGEEKJCk7BzA5hzMvlm0H1Ebz4PwKkQ8B03lm739/VsYUIIIcQtQMLOdfbnph84MW4U0ScV5+NoAx0XfE+loCoerkwIIYS4NUjYuY6+njmIiP/+RnWzc9rq0BPN6DrpY0+XJYQQQtxSJOxcBzlZGXw3qBONt2YCcCJMReUJE+jSoYeHKxNCCCFuPRJ2ytjO37/hzISxND7tnLba09SH+xesxL9yqIcrE0IIIW5NEnbK0P8SnqPaZxuoZoEcPRztFkO3cR96uiwhhBDiliZhpwxkZ6SxcuB9NNqeDcDxcBUhk9/kybaPe7gyIYQQQkjYuUbbVi8nbcpEGp1xTlvtbu5H53d/xDcg0MOVCSGEEAIk7FyTr6b0pcaXW4myQrY3nOjZhu5j3vN0WUIIIYQoQMLOVTh/7jSrBj1Ew10mAI5GqIh44y0ej+3s4cqEEEIIcSkJO1doy88fk/nGmzRMcT7e1bISD8//ER+/AM8WJoQQQogiSdi5Assn9uK2FTuItEKWAU73vpceo97xdFlCCCGEKIGEncuQfvYkvwx6mIZ7zAAciVRRbepcHm3ZycOVCSGEEKI0EnZKkfT9EnITZtAwFRzAnphAHp3/AwYff0+XJoQQQojLIGGnBF+M60qdb/+kch5kGOFsvwfoMXS2p8sSQgghxBWQsFOE1NOHWTv4cRrttQBwqKqa2jPepVXTdh6tSwghhBBXTsLOJdZ/s4i8GXOofw4cKtgTF8zj7/yE3mD0dGlCCCGEuAoSdi6w22x8Na4rt3//F3obnPeBtP6P0GPQdE+XJoQQQohrIGEHSDn5L+vjn6DRX1YA/q2u5o4Zi4iJjvNwZUIIIYS4Vrd82Fmz/B1UsxdQLx3sKtjTNowu837CS6f3dGlCCCGEKAO3bNix22x8OeYx6v74Lzo7pPtB5gtd6Pn8654uTQghhBBl6JYMO6eP/sWmod1o/HceAAdramg460Ni69/l4cqEEEIIUdZuubDz66ez0L79PnXPg00Ne9tF8OTclTJtJYQQQlRQak8XcDkWLFhAzZo18fb2plmzZvz+++9XfA27zcZnw+8n+I33CTkP5/zh9Ohe9FiwWoKOEEIIUYGV+7Dz+eefM3z4cMaNG8eOHTu4++67eeCBBzh27NgVXeeHnq2J/vEoXnY4cJuGav/9jE79xl+nqoUQQghRXqgURVE8XURJWrZsyZ133kliYqLruXr16vHYY4+RkJBQ6uczMzMJCAhgS+06eHtp2NuhKl3nrESjveVm8IQQQoibRv7v74yMDPz9r+08ynL9G99qtbJt2zZeeeUVt+c7depEUlJSkZ+xWCxYLBbX44yMDACO+drRDuzFgz1GkmMyXb+ihRBCCHHNMjMzASiLMZlyHXZSU1Ox2+2EhYW5PR8WFkZycnKRn0lISGDy5MmFnu+y8xC8ONn5RwghhBA3hXPnzhEQEHBN1yjXYSefSqVye6woSqHn8o0dO5aRI0e6Hp8/f57q1atz7Nixa75ZFU1mZiZVq1bl+PHj1zxEWNHIvSme3JuiyX0pntyb4sm9KV5GRgbVqlUjMDDwmq9VrsNOcHAwGo2m0ChOSkpKodGefHq9Hr2+8O6qgIAA+YtUDH9/f7k3xZB7Uzy5N0WT+1I8uTfFk3tTPLX62vdSlevdWDqdjmbNmrFq1Sq351etWkVsbKyHqhJCCCHEzaRcj+wAjBw5kj59+tC8eXNiYmJYtGgRx44dY8CAAZ4uTQghhBA3gXIfdrp37865c+eYMmUKp0+fpmHDhqxcuZLq1atf1uf1ej0TJ04scmrrVif3pnhyb4on96Zocl+KJ/emeHJvileW96bc99kRQgghhLgW5XrNjhBCCCHEtZKwI4QQQogKTcKOEEIIISo0CTtCCCGEqNAqdNhZsGABNWvWxNvbm2bNmvH77797uiSPS0hIoEWLFvj5+REaGspjjz3G33//7emyyqWEhARUKhXDhw/3dCnlwsmTJ+nduzdBQUEYjUaaNGnCtm3bPF2Wx9lsNl577TVq1qyJwWCgVq1aTJkyBYfD4enSbrh169bx8MMPExERgUql4uuvv3Z7XVEUJk2aREREBAaDgXbt2rF3717PFHuDlXRv8vLyGDNmDI0aNcLHx4eIiAj69u3LqVOnPFfwDVTa35uCXnzxRVQqFXPnzr2i76iwYefzzz9n+PDhjBs3jh07dnD33XfzwAMPcOzYMU+X5lFr164lPj6eTZs2sWrVKmw2G506dSInJ8fTpZUrW7duZdGiRTRu3NjTpZQL6enpxMXF4eXlxQ8//MC+ffuYNWsWlSpV8nRpHjd9+nQWLlzI/Pnz2b9/PzNmzOCtt97inXfe8XRpN1xOTg7R0dHMnz+/yNdnzJjB7NmzmT9/Plu3biU8PJyOHTuSlZV1gyu98Uq6NyaTie3btzN+/Hi2b9/O//73Pw4cOMAjjzzigUpvvNL+3uT7+uuv2bx5MxEREVf+JUoFdddddykDBgxwe65u3brKK6+84qGKyqeUlBQFUNauXevpUsqNrKwspU6dOsqqVauUtm3bKsOGDfN0SR43ZswYpXXr1p4uo1zq3Lmz8uyzz7o998QTTyi9e/f2UEXlA6CsWLHC9djhcCjh4eHKtGnTXM/l5uYqAQEBysKFCz1Qoedcem+KsmXLFgVQjh49emOKKieKuzcnTpxQIiMjlT///FOpXr26MmfOnCu6boUc2bFarWzbto1OnTq5Pd+pUyeSkpI8VFX5lJGRAVAmB61VFPHx8XTu3Jl7773X06WUG99++y3Nmzena9euhIaG0rRpUxYvXuzpssqF1q1bs3r1ag4cOADArl27WL9+PQ8++KCHKytfDh8+THJystvPZb1eT9u2beXnchEyMjJQqVQyego4HA769OnDyy+/TIMGDa7qGuW+g/LVSE1NxW63FzosNCwsrNChorcyRVEYOXIkrVu3pmHDhp4up1z47LPP2L59O1u3bvV0KeXKoUOHSExMZOTIkbz66qts2bKFoUOHotfr6du3r6fL86gxY8aQkZFB3bp10Wg02O123nzzTXr27Onp0sqV/J+9Rf1cPnr0qCdKKrdyc3N55ZVX6NWrlxwOinOqWKvVMnTo0Ku+RoUMO/lUKpXbY0VRCj13Kxs8eDC7d+9m/fr1ni6lXDh+/DjDhg3j559/xtvb29PllCsOh4PmzZszdepUAJo2bcrevXtJTEy85cPO559/zscff8wnn3xCgwYN2LlzJ8OHDyciIoKnn37a0+WVO/JzuWR5eXn06NEDh8PBggULPF2Ox23bto23336b7du3X9Pfkwo5jRUcHIxGoyk0ipOSklLovypuVUOGDOHbb7/lt99+IyoqytPllAvbtm0jJSWFZs2aodVq0Wq1rF27lnnz5qHVarHb7Z4u0WOqVKlC/fr13Z6rV6/eLb/gH+Dll1/mlVdeoUePHjRq1Ig+ffowYsQIEhISPF1auRIeHg4gP5dLkJeXR7du3Th8+DCrVq2SUR3g999/JyUlhWrVqrl+Lh89epRRo0ZRo0aNy75OhQw7Op2OZs2asWrVKrfnV61aRWxsrIeqKh8URWHw4MH873//49dff6VmzZqeLqnc6NChA3v27GHnzp2uP82bN+epp55i586daDQaT5foMXFxcYVaFBw4cOCyD+StyEwmE2q1+49SjUZzS249L0nNmjUJDw93+7lstVpZu3btLf9zGS4GnYMHD/LLL78QFBTk6ZLKhT59+rB79263n8sRERG8/PLL/PTTT5d9nQo7jTVy5Ej69OlD8+bNiYmJYdGiRRw7dowBAwZ4ujSPio+P55NPPuGbb77Bz8/P9V9ZAQEBGAwGD1fnWX5+foXWLvn4+BAUFHTLr2kaMWIEsbGxTJ06lW7durFlyxYWLVrEokWLPF2axz388MO8+eabVKtWjQYNGrBjxw5mz57Ns88+6+nSbrjs7Gz++ecf1+PDhw+zc+dOAgMDqVatGsOHD2fq1KnUqVOHOnXqMHXqVIxGI7169fJg1TdGSfcmIiKCLl26sH37dr777jvsdrvrZ3NgYCA6nc5TZd8Qpf29uTT4eXl5ER4ezh133HH5X3LtG8XKr3fffVepXr26otPplDvvvFO2VyvObX1F/VmyZImnSyuXZOv5Rf/3f/+nNGzYUNHr9UrdunWVRYsWebqkciEzM1MZNmyYUq1aNcXb21upVauWMm7cOMVisXi6tBvut99+K/Lny9NPP60oinP7+cSJE5Xw8HBFr9crbdq0Ufbs2ePZom+Qku7N4cOHi/3Z/Ntvv3m69OuutL83l7qarecqRVGUK4pgQgghhBA3kQq5ZkcIIYQQIp+EHSGEEEJUaBJ2hBBCCFGhSdgRQgghRIUmYUcIIYQQFZqEHSGEEEJUaBJ2hBBCCFGhSdgRQtw0Jk2aRJMmTVyP+/Xrx2OPPXbD6zhy5AgqlYqdO3fe8O8WQlw5CTtCiGvWr18/VCoVKpUKLy8vatWqxUsvvUROTs51/d63336bpUuXXtZ7JaAIceuqsGdjCSFurPvvv58lS5aQl5fH77//znPPPUdOTg6JiYlu78vLy8PLy6tMvjMgIKBMriOEqNhkZEcIUSb0ej3h4eFUrVqVXr168dRTT/H111+7pp4+/PBDatWqhV6vR1EUMjIyeOGFFwgNDcXf35/27duza9cut2tOmzaNsLAw/Pz86N+/P7m5uW6vXzqN5XA4mD59OrVr10av11OtWjXefPNNwHnqNkDTpk1RqVS0a9fO9bklS5ZQr149vL29qVu3LgsWLHD7ni1bttC0aVO8vb1p3rw5O3bsKMM7J4S43mRkRwhxXRgMBvLy8gD4559/+OKLL/jqq6/QaDQAdO7cmcDAQFauXElAQADvvfceHTp04MCBAwQGBvLFF18wceJE3n33Xe6++24++ugj5s2bR61atYr9zrFjx7J48WLmzJlD69atOX36NH/99RfgDCx33XUXv/zyCw0aNHCdJL148WImTpzI/Pnzadq0KTt27OD555/Hx8eHp59+mpycHB566CHat2/Pxx9/zOHDhxk2bNh1vntCiDJ1jYeVCiGE8vTTTyuPPvqo6/HmzZuVoKAgpVu3bsrEiRMVLy8vJSUlxfX66tWrFX9/fyU3N9ftOrfddpvy3nvvKYqiKDExMcqAAQPcXm/ZsqUSHR1d5PdmZmYqer1eWbx4cZE15p8svWPHDrfnq1atqnzyySduz73++utKTEyMoiiK8t577ymBgYFKTk6O6/XExMQiryWEKJ9kGksIUSa+++47fH198fb2JiYmhjZt2vDOO+8AUL16dUJCQlzv3bZtG9nZ2QQFBeHr6+v6c/jwYf79918A9u/fT0xMjNt3XPq4oP3792OxWOjQocNl13z27FmOHz9O//793ep444033OqIjo7GaDReVh1CiPJHprGEEGXinnvuITExES8vLyIiItwWIfv4+Li91+FwUKVKFdasWVPoOpUqVbqq7zcYDFf8GYfDATinslq2bOn2Wv50m6IoV1WPEKL8kLAjhCgTPj4+1K5d+7Lee+edd5KcnIxWq6VGjRpFvqdevXps2rSJvn37up7btGlTsdesU6cOBoOB1atX89xzzxV6PX+Njt1udz0XFhZGZGQkhw4d4qmnniryuvXr1+ejjz7CbDa7AlVJdQghyh+ZxhJC3HD33nsvMTExPPbYY/z0008cOXKEpKQkXnvtNf744w8Ahg0bxocffsiHH37IgQMHmDhxInv37i32mt7e3owZM4bRo0ezbNky/v33XzZt2sQHH3wAQGhoKAaDgR9//JEzZ86QkZEBOBsVJiQk8Pbbb3PgwAH27NnDkiVLmD17NgC9evVCrVbTv39/9u3bx8qVK5k5c+Z1vkNCiLIkYUcIccOpVCpWrlxJmzZtePbZZ7n99tvp0aMHR44cISwsDIDu3bszYcIExowZQ7NmzTh69CgDBw4s8brjx49n1KhRTJgwgXr16tG9e3dSUlIA0Gq1zJs3j/fee4+IiAgeffRRAJ577jnef/99li5dSqNGjWjbti1Lly51bVX39fXl//7v/9i3bx9NmzZl3LhxTJ8+/TreHSFEWVMpMiEthBBCiApMRnaEEEIIUaFJ2BFCCCFEhSZhRwghhBAVmoQdIYQQQlRoEnaEEEIIUaFJ2BFCCCFEhSZhRwghhBAVmoQdIYQQQlRoEnaEEEIIUaFJ2BFCCCFEhSZhRwghhBAVmoQdIYQQQlRo/w+JoYKlM49FyQAAAABJRU5ErkJggg==\n", 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" ] @@ -2889,7 +2898,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 33, "metadata": { "collapsed": false, "jupyter": { @@ -2906,33 +2915,33 @@ "text": [ "=== QSAR model performance A1 ===\n", "{\n", - " \"Pearson r\": 0.6061543418780471,\n", - " \"R2 score\": 0.3663100021241771,\n", - " \"MAE\": 0.5906718239123281\n", + " \"Pearson r\": 0.5986860794442124,\n", + " \"R2 score\": 0.35708264748315865,\n", + " \"MAE\": 0.5975955476925413\n", "}\n", "=== QSAR model performance A2A ===\n", "{\n", - " \"Pearson r\": 0.6362163694962808,\n", - " \"R2 score\": 0.40386774292895244,\n", - " \"MAE\": 0.6906853759160689\n", + " \"Pearson r\": 0.6331428179843668,\n", + " \"R2 score\": 0.3984324237653284,\n", + " \"MAE\": 0.6998201581163358\n", "}\n", "=== QSAR model performance A2B ===\n", "{\n", - " \"Pearson r\": 0.7015289644139334,\n", - " \"R2 score\": 0.48148295104576777,\n", - " \"MAE\": 0.5557563859787753\n", + " \"Pearson r\": 0.7006823641193349,\n", + " \"R2 score\": 0.4803217299541849,\n", + " \"MAE\": 0.5573997234421912\n", "}\n", "=== QSAR model performance A3 ===\n", "{\n", - " \"Pearson r\": 0.6627839368015453,\n", - " \"R2 score\": 0.43602890319497334,\n", - " \"MAE\": 0.6918106133161609\n", + " \"Pearson r\": 0.6643990005833391,\n", + " \"R2 score\": 0.43854963831859717,\n", + " \"MAE\": 0.6908388511158647\n", "}\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -2976,7 +2985,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 34, "metadata": { "collapsed": false, "jupyter": { @@ -2997,9 +3006,9 @@ "Test set has 3519 datapoints (27.667 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.24073383293582623,\n", - " \"R2 score\": -0.11296092040813255,\n", - " \"MAE\": 0.8604964690588454\n", + " \"Pearson r\": 0.2577150000359349,\n", + " \"R2 score\": -0.09531568827653203,\n", + " \"MAE\": 0.8537743338477594\n", "}\n", "Not plotting A2A. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2B. Performance can only be plotted for the left out target in LOTO split\n", @@ -3010,9 +3019,9 @@ "Test set has 3991 datapoints (31.378 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.18760181006210028,\n", - " \"R2 score\": -0.05928985099256723,\n", - " \"MAE\": 0.9722787268838569\n", + " \"Pearson r\": 0.21008560538267065,\n", + " \"R2 score\": -0.042261684276110545,\n", + " \"MAE\": 0.9620344947955969\n", "}\n", "Not plotting A1. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2B. Performance can only be plotted for the left out target in LOTO split\n", @@ -3023,9 +3032,9 @@ "Test set has 1988 datapoints (15.63 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.014694410599454713,\n", - " \"R2 score\": -0.2494809399878013,\n", - " \"MAE\": 0.9773176706991492\n", + " \"Pearson r\": 0.0032209100786820756,\n", + " \"R2 score\": -0.2650510712411036,\n", + " \"MAE\": 0.9803939410025996\n", "}\n", "Not plotting A1. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2A. Performance can only be plotted for the left out target in LOTO split\n", @@ -3036,9 +3045,9 @@ "Test set has 3221 datapoints (25.324 %)\n", "=== PCM model performance ===\n", "{\n", - " \"Pearson r\": 0.10940003095698604,\n", - " \"R2 score\": -0.260020559620582,\n", - " \"MAE\": 1.0512621250471534\n", + " \"Pearson r\": 0.10347100605749118,\n", + " \"R2 score\": -0.2670385587615447,\n", + " \"MAE\": 1.05459014925041\n", "}\n", "Not plotting A1. Performance can only be plotted for the left out target in LOTO split\n", "Not plotting A2A. Performance can only be plotted for the left out target in LOTO split\n", @@ -3047,7 +3056,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -3123,7 +3132,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.13" + "version": "3.9.14" }, "widgets": { "application/vnd.jupyter.widget-state+json": { @@ -3135,4 +3144,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} From 4e5da17b295f82b5e2b7e9c4be27f27b0b0079a4 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 21 Nov 2022 21:18:39 +0000 Subject: [PATCH 54/62] T032: Fix typos --- .../talktorial.ipynb | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index ee226d14..2a17d5b1 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -102,7 +102,7 @@ "source": [ "Proteochemometrics (PCM) models a biological endpoint (e.g. compound activity) via supervised ML algorithms based on a series of features derived from chemical compounds and target proteins. PCM is an extension of a more widespread bioactivity modeling technique, Quantitative Structure Activity Relationship (QSAR) modeling, which relies solely on chemical features and that was introduced on **Talktorial T007**. Explore that talktorial to know more about the basic principle of activity prediction using ML.\n", "\n", - "To successfully apply PCM modeling, we need a large dataset of molecule-protein pairs with known bioactivity values, a way of describing molecules and proteins, and a ML algorithm to train a model. Then, we can make predictions for new molecule-protein pairs.\n" + "To successfully apply PCM modeling, we need a large dataset of molecule-protein pairs with known bioactivity values, a way of describing molecules and proteins, and an ML algorithm to train a model. Then, we can make predictions for new molecule-protein pairs.\n" ] }, { @@ -196,11 +196,11 @@ } }, "source": [ - "As done for molecules, the proteins of interest need to be converted to a list of features or protein descriptors. Protein descriptors used in PCM applications are commonly based on the protein sequence and represent physicochemical characteristics of the amino acids that make up the sequence (e.g. Z-scales). Other protein descriptors represent topological (e.g. ST-scales) or electrostatic properties (e.g. MS-WHIM) of the protein sequence. Moreover, if structural information is available, protein descriptors can be derived from the 3D structure of the protein (e.g. sPairs) or the ligand-protein interaction in 3D (e.g. interaction fingerprints). Finally, with the widespread use of deep learning, protein embeddings can be obtained after parsing the protein sequence through the network (e.g. UniRep, AlphaFold embeddings). To read more about protein descriptors, check out these selection of articles ([*Brief. Bioinform.*,18, (2017)](https://pubmed.ncbi.nlm.nih.gov/26873661/), [*Int. J. Mol. Sci.*, 22, (2021)](https://pubmed.ncbi.nlm.nih.gov/34884688/), [*Comput. Struct. Biotechnol. J.*, 20, (2022)](https://pubmed.ncbi.nlm.nih.gov/35222841/)).\n", + "As done for molecules, the proteins of interest need to be converted to a list of features or protein descriptors. Protein descriptors used in PCM applications are commonly based on the protein sequence and represent physicochemical characteristics of the amino acids that make up the sequence (e.g. Z-scales). Other protein descriptors represent topological (e.g. ST-scales) or electrostatic properties (e.g. MS-WHIM) of the protein sequence. Moreover, if structural information is available, protein descriptors can be derived from the 3D structure of the protein (e.g. sPairs) or the ligand-protein interaction in 3D (e.g. interaction fingerprints). Finally, with the widespread use of deep learning, protein embeddings can be obtained after parsing the protein sequence through the network (e.g. UniRep, AlphaFold embeddings). To read more about protein descriptors, check out this selection of articles ([*Brief. Bioinform.*,18, (2017)](https://pubmed.ncbi.nlm.nih.gov/26873661/), [*Int. J. Mol. Sci.*, 22, (2021)](https://pubmed.ncbi.nlm.nih.gov/34884688/), [*Comput. Struct. Biotechnol. J.*, 20, (2022)](https://pubmed.ncbi.nlm.nih.gov/35222841/)).\n", "\n", "For protein descriptors based on the protein sequence, an aspect to take into account is that for ML the length of the protein descriptor needs to be the same. However, most proteins do not have the same sequence length. To solve this issue, there are two main approaches:\n", "\n", - "* **Multiple sequence alignment (MSA)**: If the entire protein is to be included in the model, a MSA can be performed. The final descriptor has as many entries as the number of features per amino acid multiplied by the number of aligned positions. To account for gaps in the alignment, zeros are introduced in the descriptor. A MSA is a tool to identify common patterns between three or more biological sequences, usually DNA, RNA, or protein. One of the most common tools to perform MSA is Clustal Omega (or ClustalO), available as a [webtool](https://www.ebi.ac.uk/Tools/msa/clustalo/).\n", + "* **Multiple sequence alignment (MSA)**: If the entire protein is to be included in the model, an MSA can be performed. The final descriptor has as many entries as the number of features per amino acid multiplied by the number of aligned positions. To account for gaps in the alignment, zeros are introduced in the descriptor. An MSA is a tool to identify common patterns between three or more biological sequences, usually DNA, RNA, or protein. One of the most common tools to perform MSA is Clustal Omega (or ClustalO), available as a [webtool](https://www.ebi.ac.uk/Tools/msa/clustalo/).\n", "* **Binding pocket selection**: To avoid unnecessary features, a binding pocket of the same length can be selected for each protein. Normally, the binding pocket selection is preceded by a multiple sequence alignment and driven by known structural or mutagenesis data.\n", "\n", "Other options are available when proteins are not of the same family or do not share a binding pocket (see [*Drug Discov.* (2019), **32**, 89-98](https://www.sciencedirect.com/science/article/pii/S1740674920300111?via%3Dihub))\n", @@ -433,7 +433,7 @@ "metadata": {}, "source": [ "**Note**: We will lateron use the ClustalO web service to align multiple sequences. In order to use the service, we need to provide an email address (see the [docs](https://www.ebi.ac.uk/seqdb/confluence/display/JDSAT/Clustal+Omega+Help+and+Documentation)).\n", - "Please set your email address here; for the purpose of this template talktorial, we set the email to `None` and use pre-calculated data (see \"Practical\" section of this talktoria and [this discussion](https://github.com/volkamerlab/teachopencadd/discussions/283))." + "Please set your email address here; for the purpose of this template talktorial, we set the email to `None` and use pre-calculated data (see \"Practical\" section of this talktorial and [this discussion](https://github.com/volkamerlab/teachopencadd/discussions/283))." ] }, { @@ -648,7 +648,10 @@ }, "pycharm": { "name": "#%%\n" - } + }, + "tags": [ + "nbshpinx-thumbnail" + ] }, "outputs": [ { @@ -825,7 +828,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In order to ensure protein descriptors are of the same length, we first need to align the target sequences. We do this by creating a MSA with the software Clustal Omega (ClustalO). To begin with, we extract the protein sequences from the target files in Papyrus. The sequences could also be obtained from UniProt, but this way we ensure we are always retrieving the canonical isoform sequence.\n", + "In order to ensure protein descriptors are of the same length, we first need to align the target sequences. We do this by creating an MSA with the software Clustal Omega (ClustalO). To begin with, we extract the protein sequences from the target files in Papyrus. The sequences could also be obtained from UniProt, but this way we ensure we are always retrieving the canonical isoform sequence.\n", "Since Papyrus also contains bioactivity data for different mutants and species, the main protein identifier (`target_id` variable) consists of the UniProt accession code and the mutant ('WT' for wild type). Even though we are interested in the wild type, to map our targets of interest we calculate a new variable called `accession` to be consistent with the rest of the talktorial." ] }, From a6cd9dfe7b7a48e25b6453963eb9f4a68cdd185f Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 21 Nov 2022 21:22:18 +0000 Subject: [PATCH 55/62] T032: Remove thumbnail (talktorial has no pure png outputs we can use) --- .../talktorial.ipynb | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index 2a17d5b1..525fdd1b 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -649,9 +649,7 @@ "pycharm": { "name": "#%%\n" }, - "tags": [ - "nbshpinx-thumbnail" - ] + "tags": [] }, "outputs": [ { From 5fd7aae62b45a5c1531fc9155e43421e5d14f180 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 21 Nov 2022 21:31:46 +0000 Subject: [PATCH 56/62] T032: Fix typo [skip ci] --- .../T032_compound_activity_proteochemometrics/talktorial.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb index 525fdd1b..cb3ecb48 100644 --- a/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb +++ b/teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb @@ -364,7 +364,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Before we start, let's install a few packages that are not part of TeachOpenCADD's [global enviroment file](https://github.com/volkamerlab/teachopencadd/blob/master/devtools/test_env.yml) because they are only relevant to this notebook (this setup will change in the future, see discussion [here](https://github.com/volkamerlab/teachopencadd/discussions/277))." + "Before we start, let's install a few packages that are not part of TeachOpenCADD's [global environment file](https://github.com/volkamerlab/teachopencadd/blob/master/devtools/test_env.yml) because they are only relevant to this notebook (this setup will change in the future, see discussion [here](https://github.com/volkamerlab/teachopencadd/discussions/277))." ] }, { From 3c0eb9d427511a90187965e9fa0c8c5d39025fcd Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 2 Jan 2023 18:51:06 +0000 Subject: [PATCH 57/62] Env: Sync env with latest master --- devtools/test_env.yml | 65 +++++++++++++++++++++---------------------- 1 file changed, 31 insertions(+), 34 deletions(-) diff --git a/devtools/test_env.yml b/devtools/test_env.yml index 668c28d1..351f9702 100644 --- a/devtools/test_env.yml +++ b/devtools/test_env.yml @@ -1,54 +1,53 @@ -name: teachopencadd +name: teachopencadd channels: - conda-forge - defaults dependencies: - python>=3.8 - pip - - jupyter - jupyterlab>=3 + # Workaround for jupyterlab, see https://github.com/volkamerlab/teachopencadd/issues/310 + - jsonschema>=4.3.0 - nglview>=3 - # https://github.com/volkamerlab/teachopencadd/issues/262 + # Workaround for nglview, see https://github.com/volkamerlab/teachopencadd/issues/262 - ipywidgets<8 - # Explicitly add numpy because of https://github.com/volkamerlab/teachopencadd/issues/150 - - numpy + # New numpy version 1.2.4 too young, e.g. caused + # https://github.com/volkamerlab/teachopencadd/issues/299 + - numpy<1.24 - scikit-learn - scipy # API changed after v2.6, see https://github.com/volkamerlab/teachopencadd/issues/265 - #- tensorflow<=2.6 + - tensorflow<=2.6 - seaborn - #- matplotlib-venn - # Remove jsonschema once this issue is fixed: https://github.com/Yelp/bravado/issues/478 - - jsonschema<4.0.0 - #- bravado - #- requests - #- requests-cache + - matplotlib-venn + - bravado + - requests + - requests-cache - redo - #- suds-community - #- beautifulsoup4 - #- chembl_webresource_client - #- pypdb + - suds-community + - beautifulsoup4 + - chembl_webresource_client + - pypdb - biopython<=1.77 - #- biopandas + - biopandas - rdkit==2021.09.5 - #- openbabel - #- opencadd - #- biotite>=0.34.0 - #- smina - #- mdanalysis>=1.0.0 - #- mdtraj - #- plip - #- openmm - # depends on openff-toolkit->ambertools -> not available on Windows yet! + - openbabel + - opencadd + - biotite>=0.34.0 + - smina + - mdanalysis>=1.0.0 + - mdtraj + - plip + - openmm + # Dependency not included, see https://github.com/volkamerlab/teachopencadd/issues/313 # - openmmforcefields - #- pdbfixer - #- tqdm - #- lxml - #- kissim + - pdbfixer + - tqdm + - lxml + - kissim - mordred ## CI tests - # Workaround for https://github.com/computationalmodelling/nbval/issues/153 - - pytest 5.* + - pytest - pytest-xdist - pytest-cov - nbval @@ -64,8 +63,6 @@ dependencies: - sphinx-copybutton - sphinx-gallery - autodocsumm - ## temporary fix for rdkit on MacOS - - fontconfig==2.13.1 - pip: - black-nb - nbsphinx-link From a4ba93f9037f1629de5a5261baf8190205956b15 Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 2 Jan 2023 18:53:06 +0000 Subject: [PATCH 58/62] README: Sync with master README --- README.md | 235 ++++++------------------------------------------------ 1 file changed, 23 insertions(+), 212 deletions(-) diff --git a/README.md b/README.md index 55a3acc4..d81fe72e 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,5 @@ # TeachOpenCADD + A teaching platform for computer-aided drug design (CADD) using open source packages and data. ![TOC](https://img.shields.io/badge/Project-TeachOpenCADD-pink) @@ -10,21 +11,19 @@ A teaching platform for computer-aided drug design (CADD) using open source pack ![GitHub tag (latest by date)](https://img.shields.io/github/v/tag/volkamerlab/teachopencadd) [![GH Actions CI ](https://github.com/volkamerlab/teachopencadd/workflows/CI/badge.svg)](https://github.com/volkamerlab/teachopencadd/actions?query=branch%3Amaster+workflow%3ACI) [![GH Actions Docs](https://github.com/volkamerlab/teachopencadd/workflows/Docs/badge.svg)](https://projects.volkamerlab.org/teachopencadd/) -[![Conda Version](https://img.shields.io/conda/vn/conda-forge/teachopencadd.svg)](https://anaconda.org/conda-forge/teachopencadd) - -Open source programming packages for cheminformatics and structural bioinformatics are powerful tools to build modular, reproducible, and reusable pipelines for computer-aided drug design (CADD). While documentation for such tools is available, only few freely accessible examples teach underlying concepts focused on CADD applications, addressing especially users new to the field. +[![Anaconda-Server Badge](https://anaconda.org/conda-forge/teachopencadd/badges/downloads.svg)](https://anaconda.org/conda-forge/teachopencadd) -TeachOpenCADD is a teaching platform developed by students for students, which provides teaching material for central CADD topics. Since we cover both the theoretical as well as practical aspect of these topics, the platform addresses students and researchers with a biological/chemical as well as a computational background. - -Each topic is covered in an interactive Jupyter Notebook, using open source packages such as the Python packages `rdkit`, `pypdb`, `biopandas`, `nglview`, and `mdanalysis` (find the full list [here](https://github.com/volkamerlab/teachopencadd#external-resources)). Topics are continuously expanded and open for contributions from the community. Beyond their teaching purpose, the TeachOpenCADD material can serve as starting point for users’ project-directed modifications and extensions. +![GitHub closed pr](https://img.shields.io/github/issues-pr-closed-raw/volkamerlab/teachopencadd) ![GitHub open pr](https://img.shields.io/github/issues-pr-raw/volkamerlab/teachopencadd) ![GitHub closed issues](https://img.shields.io/github/issues-closed-raw/volkamerlab/teachopencadd) ![GitHub open issues](https://img.shields.io/github/issues/volkamerlab/teachopencadd) > If you use TeachOpenCADD in a publication, -> please [cite](https://github.com/volkamerlab/TeachOpenCADD/blob/master/README.md#citation) us! +> please [cite](https://projects.volkamerlab.org/teachopencadd/citation.html) us! > If you use TeachOpenCADD in class, please include a link back to our repository. > In any case, please [star](https://docs.github.com/en/get-started/exploring-projects-on-github/saving-repositories-with-stars) > (and tell your students to star) those repositories you consider useful for your learning/teaching activities. +## Description +

TeachOpenCADD topics
@@ -35,6 +34,12 @@ Each topic is covered in an interactive Jupyter Notebook, using open source pack

+Open source programming packages for cheminformatics and structural bioinformatics are powerful tools to build modular, reproducible, and reusable pipelines for computer-aided drug design (CADD). While documentation for such tools is available, only few freely accessible examples teach underlying concepts focused on CADD applications, addressing especially users new to the field. + +TeachOpenCADD is a teaching platform developed by students for students, which provides teaching material for central CADD topics. Since we cover both the theoretical as well as practical aspect of these topics, the platform addresses students and researchers with a biological/chemical as well as a computational background. + +Each topic is covered in an interactive Jupyter Notebook, using open source packages such as the Python packages `rdkit`, `pypdb`, `biopandas`, `nglview`, and `mdanalysis` (find the full list [here](https://projects.volkamerlab.org/teachopencadd/external_dependencies.html)). Topics are continuously expanded and open for contributions from the community. Beyond their teaching purpose, the TeachOpenCADD material can serve as starting point for users’ project-directed modifications and extensions. + ## Get started @@ -57,213 +62,19 @@ If you'd like to execute the provided notebooks, we offer two possibilities: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3626897.svg)](https://doi.org/10.5281/zenodo.3626897) [![KNIME Hub](https://img.shields.io/badge/KNIME%20Hub-TeachOpenCADD--KNIME-yellow.svg)](https://hub.knime.com/volkamerlab/spaces/Public/latest/TeachOpenCADD/TeachOpenCADD) -If you prefer to work in the context of a graphical interface, talktorials T001-T008 are also available as [KNIME workflows](https://hub.knime.com/volkamerlab/space/TeachOpenCADD/TeachOpenCADD). Questions regarding this version should be addressed using the "Discussion section" available at [this post](https://forum.knime.com/t/teachopencadd-knime/17174). You might need to create a KNIME account. - -## External resources - -### Python programming introduction - -The TeachOpenCADD platform is not a Python programming course from scratch but teaches how to solve tasks in cheminformatics and structural bioinformatics programmatically. -If you wish to get started first with a Python programming introduction before diving into the TeachOpenCADD material, here are a few great resources to do so: - -- [AI in Medicine course](https://github.com/volkamerlab/ai_in_medicine) by the [Volkamer Lab](https://volkamerlab.org/) and [Ritter Lab](https://psychiatrie-psychotherapie.charite.de/metas/person/person/address_detail/ritter-7/) at the Charité: Introduction to Python basics, Jupyter Notebook, and important data science packages such as Pandas, Matplotlib and Scikit-learn -- [Python for Chemists course](https://github.com/GDChCICTeam/python-for-chemists) by the [GDCh/CIC](https://en.gdch.de/network-structures/divisions/computers-in-chemistry-cic.html) team: Crash-course introduction to Python for natural scientists -- [MolSSI Education Resources](http://education.molssi.org/resources.html) by [The Molecular Sciences Software Institute](https://molssi.org/): Collection of tutorials on Python programming basics and data analysis but also more advanced material on software development and computational molecular science -- [Core lessons](https://software-carpentry.org/lessons/) by the [Software Carpentry](https://software-carpentry.org/): Introduction to Python, Git, command line interfaces and more - -### Cheminformatics resources - -The following resources are collections of interesting cheminformatics-related training material, blogs, and books. - -- [Curated list of resources from the RDKit UGM 2020](https://github.com/rdkit/UGM_2020/blob/master/info/curated_list_of_resources.md) -- [A Highly Opinionated List of Open Source Cheminformatics Resources](https://github.com/PatWalters/resources/blob/main/cheminformatics_resources.md) by Pat Walters -- [Awesome Cheminformatics](https://github.com/hsiaoyi0504/awesome-cheminformatics#resources) by Yi Hsiao - -### Structural bioinformatics resources - -- [Education & Tutorials of the Bonvin Lab](https://www.bonvinlab.org/education/molmod_online/) - -## Contact - -![GitHub closed pr](https://img.shields.io/github/issues-pr-closed-raw/volkamerlab/teachopencadd) ![GitHub open pr](https://img.shields.io/github/issues-pr-raw/volkamerlab/teachopencadd) ![GitHub closed issues](https://img.shields.io/github/issues-closed-raw/volkamerlab/teachopencadd) ![GitHub open issues](https://img.shields.io/github/issues/volkamerlab/teachopencadd) - -Please contact us if you have questions or suggestions! - -- If you have questions regarding our Jupyter Notebooks, please [open an issue](https://github.com/volkamerlab/teachopencadd/issues) on our GitHub repository. -- If you have ideas for new topics, please fill out our questionnaire: [contribute.volkamerlab.org](http://contribute.volkamerlab.org) -- For all other requests, please send us an email: teachopencadd@charite.de - -We are looking forward to hearing from you! +If you prefer to work in the context of a graphical interface, talktorials T001-T008 are also available as [KNIME workflows](https://hub.knime.com/volkamerlab/space/TeachOpenCADD/TeachOpenCADD). Questions regarding this version should be addressed using the "Discussion section" available at [this post](https://forum.knime.com/t/teachopencadd-knime/17174). You need to create a KNIME account to use the forum. -## License +## About TeachOpenCADD -This work is licensed under the Attribution 4.0 International (CC BY 4.0). -To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. +- [Contact](https://projects.volkamerlab.org/teachopencadd/contact.html) +- [Acknowledgments](https://projects.volkamerlab.org/teachopencadd/acknowledgments.html) +- [Citation](https://projects.volkamerlab.org/teachopencadd/citation.html) +- [License](https://projects.volkamerlab.org/teachopencadd/license.html) +- [Funding](https://projects.volkamerlab.org/teachopencadd/funding.html) -## Citation -If you make use of the TeachOpenCADD material in scientific publications, please cite our respective articles. It will help measure the impact of the TeachOpenCADD platform and future funding, thank you! - -### TeachOpenCADD Jupyter notebooks - -TeachOpenCADD Jupyter notebooks' main citation: Talktorials T001-T022 ([paper](https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkac267/6582172)) - -``` -@article{TeachOpenCADD2022, - author = {Sydow, Dominique and Rodríguez-Guerra, Jaime and Kimber, Talia B and Schaller, David and Taylor, Corey J and Chen, Yonghui and Leja, Mareike and Misra, Sakshi and Wichmann, Michele and Ariamajd, Armin and Volkamer, Andrea}, - title = {TeachOpenCADD 2022: open source and FAIR Python pipelines to assist in structural bioinformatics and cheminformatics research}, - journal = {Nucleic Acids Research}, - year = {2022}, - doi = {10.1093/nar/gkac267}, -} -``` - -TeachOpenCADD Jupyter notebooks' original citation: Talktorials T001-T010 ([paper](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-019-0351-x)) - -``` -@article{TeachOpenCADD2019, - author = {Sydow, Dominique and Morger, Andrea and Driller, Maximilian and Volkamer, Andrea}, - title = {{TeachOpenCADD: a teaching platform for computer-aided drug design using open source packages and data}}, - journal = {Journal of Cheminformatics}, - year = {2019}, - volume = {11}, - number = {1}, - pages = {29}, - doi = {10.1186/s13321-019-0351-x}, -} -``` - - -TeachOpenCADD Jupyter notebooks on kinase similarities: Talktorials T023-T028 ([paper](https://doi.org/10.33011/livecoms.3.1.1599)) - -``` -@article{TeachOpenCADDKinaseEdition, - author = {Kimber, Talia B and Sydow, Dominique and Volkamer, Andrea}, - title = {{Kinase similarity assessment pipeline for off-target prediction [v1.0]}}, - journal = {Living Journal of Computational Molecular Science}, - year = {2022}, - doi = {10.1186/s13321-019-0351-x}, -} -``` - -### TeachOpenCADD-KNIME - - -TeachOpenCADD KNIME workflows ([paper](https://pubs.acs.org/doi/10.1021/acs.jcim.9b00662)) - -``` -@article{TeachOpenCADDKNIME2019, - author = {Sydow, Dominique and Wichmann, Michele and Rodríguez-Guerra, Jaime and Goldmann, Daria and Landrum, Gregory and Volkamer, Andrea}, - title = {{TeachOpenCADD-KNIME: A Teaching Platform for Computer-Aided Drug Design Using KNIME Workflows}}, - journal = {Journal of Chemical Information and Modeling}, - year = {2019}, - volume = {59}, - number = {10}, - pages = {4083-4086}, - doi = {10.1021/acs.jcim.9b00662}, -} -``` - -### Teaching - -How to use the TeachOpenCADD material for teaching ([chapter](https://pubs.acs.org/doi/abs/10.1021/bk-2021-1387.ch010)) - -``` -@inbook{doi:10.1021/bk-2021-1387.ch010, - author = {Sydow, Dominique and Rodríguez-Guerra, Jaime and Volkamer, Andrea}, - title = {Teaching Computer-Aided Drug Design Using TeachOpenCADD}, - booktitle = {Teaching Programming across the Chemistry Curriculum}, - chapter = {10}, - pages = {135-158}, - doi = {10.1021/bk-2021-1387.ch010}, -} -``` - -## Acknowledgments - -### External resources - -#### Python packages - -- Cheminformatics and structural bioinformatics: - [`rdkit`](http://rdkit.org/), - [`openbabel`](https://openbabel.org/), - [`mdanalysis`](https://www.mdanalysis.org/), - [`biopython`](https://biopython.org/), - [`biopandas`](http://rasbt.github.io/biopandas/), - [`opencadd`](https://opencadd.readthedocs.io/en/latest/), - [`plip`](https://github.com/pharmai/plip), - [`openff`](https://github.com/openforcefield/openff-toolkit), - [`openff-toolkit`](https://github.com/openforcefield/openff-toolkit), - [`openmmforcefields`](https://github.com/openmm/openmmforcefields), - [`pdbfixer`](https://github.com/openmm/pdbfixer), - [`mdanalysis`](https://www.mdanalysis.org/), - [`biotite`](https://www.biotite-python.org/), - [`smina`](https://sourceforge.net/p/smina/discussion/) -- Data science (PyData stack): - [`numpy`](https://numpy.org/), - [`pandas`](https://pandas.pydata.org/), - [`scikit-learn`](https://scikit-learn.org/), - [`keras`](https://keras.io/), - [`jupyter`](https://jupyter.org/), - [`ipywidgets`](https://ipywidgets.readthedocs.io) -- Data visualization: - [`matplotlib`](https://matplotlib.org/),  - [`mpl_toolkits`](https://matplotlib.org/stable/api/toolkits/mplot3d.html), - [`matplotlib_venn`](https://github.com/konstantint/matplotlib-venn), - [`seaborn`](https://seaborn.pydata.org/), - [`nglview`](http://nglviewer.org/nglview/latest/) -- Web services clients: - [`pypdb`](https://github.com/williamgilpin/pypdb), - [`chembl_webresource_client`](https://github.com/chembl/chembl_webresource_client), - [`requests`](https://requests.readthedocs.io/en/latest/), - [`bravado`](https://bravado.readthedocs.io/en/stable/), - [`beautifulsoup4`](https://www.crummy.com/software/BeautifulSoup/bs4/doc/) -- Utilities: - [`tqdm`](https://tqdm.github.io/), - [`requests_cache`](https://requests-cache.readthedocs.io), - [`redo`](https://github.com/mozilla-releng/redo), - [`google-colab`](https://pypi.org/project/google-colab/), - [`condacolab`](https://pypi.org/project/condacolab/) -- Continuous integration: - [`pytest`](https://docs.pytest.org), - [`nbval`](https://nbval.readthedocs.io) -- Documentation: - [`sphinx`](https://www.sphinx-doc.org), - [`nbsphinx`](https://nbsphinx.readthedocs.io) -- Code style: - [`black-nb`](https://github.com/tomcatling/black-nb) - -#### Databases and webservers - -- [ChEMBL](https://www.ebi.ac.uk/chembl/) -- [RCSB PDB](https://www.rcsb.org/) -- [KLIFS](https://klifs.net/) -- [PubMed](https://pubchem.ncbi.nlm.nih.gov/) -- [ProteinsPlus](https://proteins.plus/) - -If we are using your resource and forgot to add it here, please contact us so that we can rectify this, thank you! - -### Funding - -Volkamer Lab's projects are supported by several public funding sources -(for more info see our [webpage](https://volkamerlab.org/)). - -### Contributors - -TeachOpenCADD has been initiated by the members of [Volkamer Lab](https://volkamerlab.org/), -Charité - Universitätsmedizin Berlin, with special thanks to -[dominiquesydow](https://github.com/dominiquesydow/), -[jaimergp](https://github.com/jaimergp/) and -[AndreaVolkamer](https://github.com/andreavolkamer). -The platform has been filled with life by our students from the CADD courses taught in the -bioinformatics program at Freie Universität Berlin. - -Many thanks to everyone who has contributed to TeachOpenCADD by working on talktorials -(check out the talktorial READMEs for author information - -[example](https://github.com/volkamerlab/teachopencadd/tree/master/teachopencadd/talktorials/T001_query_chembl)) -and/or by helping in any other way (see [GitHub contributors](https://github.com/volkamerlab/teachopencadd/graphs/contributors)). +## External resources -You are welcome to contribute to the project either by requesting new topics, -proposing ideas or getting involved in the development! -Please, use [this form](http://contribute.volkamerlab.org/) to let us know! +Please refer to our TeachOpenCADD website to find a list of external resources: +- [External packages and webservices](https://projects.volkamerlab.org/teachopencadd/external_dependencies.html) that are used in the TeachOpenCADD material +- [Further reading material](https://projects.volkamerlab.org/teachopencadd/external_tutorials_collections.html) on Python programming, cheminformatics, structural bioinformatics, and more. \ No newline at end of file From 4ee706daf23d507a2140f8c390f55e4fa05eeaaa Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 2 Jan 2023 18:53:30 +0000 Subject: [PATCH 59/62] CI: Sync with master CI --- .github/workflows/ci.yml | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index e3b935a7..5a9c095f 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -4,11 +4,9 @@ on: push: branches: - "master" - - "maintenance/.+" pull_request: branches: - "master" - - "maintenance/.+" schedule: # Run a cron job once weekly on Monday - cron: "0 3 * * 1" @@ -76,12 +74,19 @@ jobs: shell: bash -l {0} run: | PYTEST_ARGS="--nbval-lax --current-env --dist loadscope --numprocesses 2" - pytest $PYTEST_ARGS teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb - # if [ "$RUNNER_OS" != "Windows" ]; then - # pytest $PYTEST_ARGS teachopencadd/talktorials/T*/talktorial.ipynb - # else - # pytest $PYTEST_ARGS teachopencadd/talktorials/ --ignore=teachopencadd/talktorials/T008_md_simulation/talktorial.ipynb --ignore=teachopencadd/talktorials/T019_md_simulation/talktorial.ipynb - # fi + + # Ignore T019 under Windows, see https://github.com/volkamerlab/teachopencadd/issues/313 + PYTEST_IGNORE_T019="--ignore=teachopencadd/talktorials/T019_md_simulation/talktorial.ipynb" + + # Temporarily ignored notebooks, see https://github.com/volkamerlab/teachopencadd/issues/303 + PYTEST_IGNORE_T008="--ignore=teachopencadd/talktorials/T008_query_pdb/talktorial.ipynb" + + if [ "$RUNNER_OS" != "Windows" ]; then + # Temporarily ignore T019 + pytest $PYTEST_ARGS teachopencadd/talktorials/ $PYTEST_IGNORE_T008 $PYTEST_IGNORE_T019 + else + pytest $PYTEST_ARGS teachopencadd/talktorials/ $PYTEST_IGNORE_T008 $PYTEST_IGNORE_T019 + fi format: name: Black From 265ae707cf5902d13c7a50ad2a87a50655dcabdc Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 2 Jan 2023 20:04:10 +0000 Subject: [PATCH 60/62] CI: Drop T032 under Windows --- .github/workflows/ci.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 5a9c095f..e583cbde 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -77,6 +77,8 @@ jobs: # Ignore T019 under Windows, see https://github.com/volkamerlab/teachopencadd/issues/313 PYTEST_IGNORE_T019="--ignore=teachopencadd/talktorials/T019_md_simulation/talktorial.ipynb" + # Ignore T032 under Windows, see + PYTEST_IGNORE_T032="--ignore=teachopencadd/talktorials/T032_compound_activity_proteochemometrics/talktorial.ipynb" # Temporarily ignored notebooks, see https://github.com/volkamerlab/teachopencadd/issues/303 PYTEST_IGNORE_T008="--ignore=teachopencadd/talktorials/T008_query_pdb/talktorial.ipynb" @@ -85,7 +87,7 @@ jobs: # Temporarily ignore T019 pytest $PYTEST_ARGS teachopencadd/talktorials/ $PYTEST_IGNORE_T008 $PYTEST_IGNORE_T019 else - pytest $PYTEST_ARGS teachopencadd/talktorials/ $PYTEST_IGNORE_T008 $PYTEST_IGNORE_T019 + pytest $PYTEST_ARGS teachopencadd/talktorials/ $PYTEST_IGNORE_T008 $PYTEST_IGNORE_T019 $PYTEST_IGNORE_T032 fi format: From 4bf8bbcc62f14efebb5ace97188aac80bafc2f8a Mon Sep 17 00:00:00 2001 From: dominiquesydow Date: Mon, 2 Jan 2023 21:38:57 +0000 Subject: [PATCH 61/62] CI: Add env list after T032-specific package installations (tmp) --- .github/workflows/ci.yml | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index e583cbde..03eeaf2f 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -90,6 +90,12 @@ jobs: pytest $PYTEST_ARGS teachopencadd/talktorials/ $PYTEST_IGNORE_T008 $PYTEST_IGNORE_T019 $PYTEST_IGNORE_T032 fi + - name: Environment Information (after T032 installation) + shell: bash -l {0} + run: | + conda info --all + conda list + format: name: Black runs-on: ubuntu-latest From 63ea5c6d579778ec11a6d24c37634938c5ea8080 Mon Sep 17 00:00:00 2001 From: hamzaibrahim21 Date: Tue, 16 May 2023 11:42:09 +0200 Subject: [PATCH 62/62] trigger CI