Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions

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The official implementation of our ICLR 2025 Spotlight paper "Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions", which establishes a bidirectional connection between log-likelihood in protein inverse folding models and $\Delta\Delta G$ values.

Environment

1. Clone the repository

git clone https://github.com/aim-uofa/BA-DDG
cd BA-DDG

2. Prepare the environment

conda env create -f env.yml
conda activate BA-DDG

Dataset

Dataset	Download Script
SKEMPI v2	data/get_skempi_v2.sh

Usage

Download the trained weights from Google Driver and put them into the ./ckpt folder.

Inference, BA-Cycle, unsupervised verison

Load the pre-trained ProteinMPNN model and make inference in an unsupervised setting.

cd training
python train_skempi.py --config_path ../config/inference_ba-cycle_skempi.json

You can choose different pre-trained ProteinMPNN weights by modifying the ckpt_path parameter in the config file. Available full protein backbone models include: vanilla_model_weights/v_48_002.pt, v_48_010.pt, v_48_020.pt, v_48_030.pt, and soluble_model_weights/v_48_002.pt, v_48_010.pt, v_48_020.pt, v_48_030.pt.

Inference, BA-DDG, supervised version

Load the pre-trained BA-DDG model and make inference.

cd training
python train_skempi.py --config_path ../config/inference_ba-ddg_skempi.json

Train, BA-DDG

You can set the wandb flag to use Weights & Biases.

cd training
python train_skempi.py --config_path ../config/train_ba-ddg_skempi.json --use_wandb

Citation

@article{jiao2024boltzmannaligned,
  title   = {Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions},
  author  = {Xiaoran Jiao and Weian Mao and Wengong Jin and Peiyuan Yang and Hao Chen and Chunhua Shen},
  year    = {2024},
  journal = {arXiv preprint arXiv: 2410.09543},
  url     = {https://arxiv.org/abs/2410.09543v1},
  pdf     = {https://arxiv.org/pdf/2410.09543.pdf}
}

Feedback

If you have any issue about this work, please feel free to contact Xiaoran Jiao.

License

For non-commercial academic use, this project is licensed under the 2-clause BSD License. For commercial use, please contact Chunhua Shen.