af2rave package

AlphaFold2-RAVE package generates boltzman-ranked non-native conformations for proteins.

• Free software: MIT license
• Documentation: https://af2rave.readthedocs.io

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

This should take ~5 min depending on internet and computer. 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

Demo

A demonstration is available in the notebook folder. To run it in Google Colab, simply open the notebook, and replace github.com with githubtocolab.com. You can generate your own structures with Colab, or upload the structures of DDR1 we generated in datasets/DDR1 to the notebook. The notebook should give cluster centers in ~2 mins without structure generation or ~40 mins with AlphaFold2 part.

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:

1. 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
2. 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
3. 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

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.