Data and code for Computational Analysis of Dynamic Allostery and Control in the SARS-CoV-2 Main Protease
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Updated
Jul 22, 2020 - Python
Data and code for Computational Analysis of Dynamic Allostery and Control in the SARS-CoV-2 Main Protease
ASDParser
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
Alignment-independent distance correlation (dCor) analysis for pH-dependent activation of hENT3 (Tran & Lin, Biophys. J. 2026)
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
Tools for studying long ranged —allosteric— effects in elastic networks (e.g. of proteins) on a mechanical basis.
RINFAIRE: Dynamic residue interaction networks from protein crystallographic multiconformer models
Pure-PyTorch LAMMPS-AWSEM frustration analysis. 14-53x faster than frustrapy on a single GPU. Byte-comparable to frustratometeR.
Reveal protein energy centers.
Network models of protein conformational entropy from dynamics
Predict allosteric pockets on proteins
A Python package to interactively compute, analyze and visualize protein allosteric communication (residue interaction) networks and delta-networks.
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