Predict allosteric pockets on proteins
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Updated
Mar 28, 2022 - Python
Predict allosteric pockets on proteins
A Python package to interactively compute, analyze and visualize protein allosteric communication (residue interaction) networks and delta-networks.
Reveal protein energy centers.
RINFAIRE: Dynamic residue interaction networks from protein crystallographic multiconformer models
Network models of protein conformational entropy from dynamics
Tools for studying long ranged —allosteric— effects in elastic networks (e.g. of proteins) on a mechanical basis.
ASDParser
Data and code for Computational Analysis of Dynamic Allostery and Control in the SARS-CoV-2 Main Protease
Pure-PyTorch LAMMPS-AWSEM frustration analysis. 14-53x faster than frustrapy on a single GPU. Byte-comparable to frustratometeR.
Paper VII of Statistical Pharmacology via Kakutani Dichotomy: kakutani_pharma, a Python pipeline for Kakutani indices of MD conformational ensembles. Ledoit-Wolf regularized CKI with an exact three-way decomposition, split-trajectory null subtraction, within-half block bootstrap, and pocket-centred shell-scaling exponents. Validated on a synthetic
Can ligand structure alone tell an allosteric modulator from an orthosteric one? A ChEMBL 37 benchmark showing how much apparent accuracy is split-scheme artefact and target look-up
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