T032: Compound activity: Proteochemometrics

.github/workflows/ci.yml

@@ -7,7 +7,6 @@ on:
   pull_request:
     branches:
       - "master"
-      - "base-ci-env-fix"
   schedule:
     # Run a cron job once weekly on Monday
     - cron: "0 3 * * 1"
@@ -75,6 +74,8 @@ 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"
@@ -83,9 +84,15 @@ 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
 
+      - name: Environment Information (after T032 installation)
+        shell: bash -l {0}
+        run: |
+          conda info --all
+          conda list
+
   format:
     name: Black
     runs-on: ubuntu-latest

.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
+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

devtools/test_env.yml

@@ -15,6 +15,7 @@ dependencies:
   # 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
   - seaborn
@@ -44,6 +45,7 @@ dependencies:
   - tqdm
   - lxml
   - kissim
+  - mordred
   ## CI tests
   - pytest
   - pytest-xdist
@@ -67,3 +69,12 @@ dependencies:
       - sphinxext-opengraph
       # 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

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

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
 ------------------

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"]}

teachopencadd/talktorials/T032_compound_activity_proteochemometrics/README.md

@@ -0,0 +1,57 @@
+# 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:
+
+- 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 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
+    * 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*
+
+* Download Papyrus dataset
+* Data preparation
+    * Filter activity data for targets of interest
+    * Align target sequences
+    * Calculate protein descriptors
+    * Calculate compound descriptors
+* Proteochemometrics modeling
+    * Helper functions
+    * Preprocessing
+    * Model training and validation
+        * Random split PCM model
+        * Random split QSAR models
+        * Leave one target out split PCM model
+
+
+### 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)
+* 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)
+
+

teachopencadd/talktorials/T032_compound_activity_proteochemometrics/T032_env.yml

@@ -0,0 +1,27 @@
+name: teachopencadd_t032
+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
+  - pytest
+  - pip:
+      - https://github.com/OlivierBeq/Papyrus-scripts/tarball/master
+      - prodec
+      - rich-msa
+      # Dependency for ClustalO webservice (also conda installable via -c bioconda)
+      - xmltramp2
+

teachopencadd/talktorials/T032_compound_activity_proteochemometrics/data/README.md

@@ -0,0 +1,9 @@
+# T032 · Compound activity: Proteochemometrics
+## Data
+
+This folder stores input and output data for the Jupyter 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. 

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----------------------
+------------------------------------------------------------
+------

teachopencadd/talktorials/T032_compound_activity_proteochemometrics/images/README.md

@@ -0,0 +1,8 @@
+# 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`

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.