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How one can use binary classifiers, variational auto-encoder, TICA, PCA, distance and dihedral order parameters as CVs in context of pepsin-like aspartic proteases e.g. BACE1 and plasmepsin-II.

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sbhakat/PAP-review-inputs

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PAP-review-inputs

This repository corresponds to the review article entititled Beyond benzamide-trypsin & alanine dipeptide: pepsin-like aspartic proteases (PAPs) as model systems for combining biomolecular simulation with biophysical experiments It contains orer parameters as a part of Plumed files and other inputs necessary to perform further computational study.

Informations on the folder:

  1. WT-TIP3P: contains plumed.dat file and PCA eigenvector (ev1.dat) necessary to peform 1D PCA well-tempered metadynamics with Plumed 1.3.
  2. 2D-PCA: contains inputs to perform 2D PCA metadynamics with Plumed 2.5 or higher.
  3. Metadynamics-torsion: metadynamics with torsion CVs (chi1 and chi2 angles of Tyr)
  4. Metadynamics unbining: metadynamics simulation which using centre of mass CV to facilitate ligand unbining from plasmepsin-II.
  5. Metadynamics-COM: 2D metadynamics simulation using COM CVs described here https://www.biorxiv.org/content/10.1101/2020.04.27.062539v1
  6. Plumed-PasAg: plumed input which uses passive-agressive classifier
  7. Plumed-TICs: plumed input for TICA using dihedral angles.
  8. Plumed-reweight: reweighting script
  9. TICA-generation: scripts to perform TICA analyses on plasmepsin-II.
  10. plasmepsin-tica-tip4p-md.ipynb contains TICA generation using resid 69-91 (numbering according to .tpr file)
  11. variational-autoencoder: shows how to use variational autoencoder (MSM VDE) on the TICs. similar in spirit of using RAVE on SGOOP.

Softwares required:

  1. Plumed 1.3, 2.5
  2. Python 3.6 or higher
  3. MDtraj
  4. Gromacs patched with plumed
  5. SciPy for machine learning
  6. Gnuplot (plotting)
  7. Jupyter Notebook
  8. MSMBuilder
  9. Variational Dynamic Encoder (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7398762/), Github: https://github.com/msmbuilder/vde

Please contact [email protected] if you need more information.

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How one can use binary classifiers, variational auto-encoder, TICA, PCA, distance and dihedral order parameters as CVs in context of pepsin-like aspartic proteases e.g. BACE1 and plasmepsin-II.

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