Movements and confirmational changes within proteins are important for understanding protein function. Common methods for studying protein dynamics include molecular dynamics (MD) simulations and normal model analyses (NMA). Deep learning methods have also been applied to probe protein dynamics.
Table of contents
Structure Prediction-based
Subsampled AF2
High-throughput prediction of protein conformational distributions with subsampled AlphaFold2
AFCluster
Predicting multiple conformations via sequence clustering and AlphaFold2
AF2Complex
AF2Complex predicts direct physical interactions in multimeric proteins with deep learning
StELa
Searching for Structure: Characterizing the Protein Conformational Landscape with Clustering-Based Algorithms
Af2 Conformations
Sampling alternative conformational states of transporters and receptors with AlphaFold2
Elastic Network Models-based
ProDy
ProDy: Protein Dynamics Inferred from Theory and Experiments
A collection of tools for studying different aspects of protein dynamics.
ClustENMD
ClustENMD: efficient sampling of biomolecular conformational space at atomic resolution
Iterative sampling along Anisotropic Network Model (ANM) modes to for generating protein conformations with intermediate brief MD simulations. Part of the ProDy ecosystem.
Molecular Dynamics-based
Making it rain
Making it Rain: Cloud-Based Molecular Simulations for Everyone
User-friendly front-end for running molecular dynamics (MD) simulations with a variety of different Colab notebooks.
Trajectory Analysis
mRMSD
Analysis of Protein Folding Simulation with Moving Root Mean Square Deviation