Overview
Installation
It is strongly recommended a separate environment for this package, either with conda or venv.
After activating the environment, simply run:
pip install git+https://github.com/tiwarylab/af2rave.git
If you want the folding module installed, too. You need to install ColabFold separately. One way to do it is with conda and download its parameters.
conda install colabfold
python -m colabfold.download
Bibliography
The main article describing the method is:
Da Teng, Vanessa J. Meraz, Akashnathan Aranganathan, Xinyu Gu, and Pratyush Tiwary, AlphaFold2-RAVE: Protein Ensemble Generation with Physics-Based Sampling, ChemRxiv (2025) https://doi.org/10.26434/chemrxiv-2025-q3mwr
AlphaFold2-RAVE:
Bodhi P. Vani, Akashnathan Aranganathan, Dedi Wang, and Pratyush Tiwary, AlphaFold2-RAVE: From Sequence to Boltzmann Ranking, J. Chem. Theory Comput. 2023, 19, 14, 4351–4354, https://doi.org/10.1021/acs.jctc.3c00290
Bodhi P. Vani, Akashnathan Aranganathan and Pratyush Tiwary, Exploring Kinase Asp-Phe-Gly (DFG) Loop Conformational Stability with AlphaFold2-RAVE, J. Chem. Inf. Model. 2024, 64, 7, 2789–2797, https://doi.org/10.1021/acs.jcim.3c01436
Xinyu Gu, Akashnathan Aranganathan and Pratyush Tiwary, Empowering AlphaFold2 for protein conformation selective drug discovery with AlphaFold2-RAVE, eLife, 2024, https://doi.org/10.7554/eLife.99702.3
SPIB:
Dedi Wang and Pratyush Tiwary, State predictive information bottleneck, J. Chem. Phys. 154, 134111 (2021), https://doi.org/10.1063/5.0038198
AMINO:
Pavan Ravindra, Zachary Smith and Pratyush Tiwary, Automatic mutual information noise omission (AMINO): generating order parameters for molecular systems, Mol. Syst. Des. Eng., 2020,5, 339-348, https://doi.org/10.1039/C9ME00115H