LightMHC: A Light Model for pMHC Structure Prediction with Graph Neural Networks

Welcome to LightMHC, a Python framework for predicting peptide-MHC (pMHC) structures using deep learning. This repository houses the codebase for LightMHC, which is designed for class-I pMHC prediction with peptide sequences ranging from 8 to 13 amino acids. The paper associated with this repository was accepted at the Machine Learning for Structural Biology workshop at the NeurIPS 2023 conference.

Requirements

This project has the following dependencies:

• Conda
• Python 3.8
• CUDA 11.1 to use a GPU
• PyRosetta license credentials

Installation

1. Clone this repository to your local machine

2. Install the conda environment by running:

conda create --yes --name lightmhc_env python=3.8
conda env update --name lightmhc_env --file environment.yml

If you have a MacOS system or do not have a CUDA-enabled GPU on your laptop, you can install the CPU-only version with:

conda create --yes --name lightmhc_env python=3.8
conda env update --name lightmhc_env --file environment_no_gpu.yml

3. Install PyRosetta, replacing the placeholders with your own credentials: Linux/Ubuntu operating system

wget https://USERNAME:[email protected]/linux-64/pyrosetta-2021.34+release.5eb89ef-py38_0.tar.bz2
conda activate lightmhc_env
conda install pyrosetta-2021.34+release.5eb89ef-py38_0.tar.bz2

MacOS operating system

wget https://USERNAME:[email protected]/osx-64/pyrosetta-2021.34+release.5eb89ef1fc1-py38_0.tar.bz2
conda activate lightmhc_env
conda install pyrosetta-2021.34+release.5eb89ef1fc1-py38_0.tar.bz2

4. Install LightMHC using pip:

pip install -e .

Usage

To perform pMHC structure prediction using LightMHC, follow these steps:

1. Prepare a CSV file with the following columns:

• pdb_id: The PDB ID of the structure.
• peptide: The peptide sequence (8-13 amino acids).
• mhc: The MHC sequence (excluding signal peptide residues, e.g., starting at 'GSH').

2. Run the following command, specifying the input CSV path, output directory, and the number of CPU cores to use:

python inference.py data.input_csv_path=YOUR_INPUT_DIR data.output_dir=YOUR_OUTPUT_DIR model.n_cpus=YOUR_CPU_NUMBER

Examples

We have provided an example input CSV file and the resulting structures in the examples/ directory.

Acknowledgements

This project includes code adapted from Abanades et al., 2023.

Abanades B, Wong WK, Boyles F, Georges G, Bujotzek A, Deane CM. ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins. Commun Biol. 2023 May 29.

Citing this work

If you use LightMHC in your work, please cite it using:

@article{delaunay2023lightmhc,
	author = {Antoine P Delaunay and Yunguan Fu and Nikolai Gorbushin and Robert McHardy and Bachir A Djermani and Liviu Copoiu and Michael Rooney and Maren Lang and Andrey Tovchigrechko and Ugur Sahin and Karim Beguir and Nicolas Lopez Carranza},
	title = {LightMHC: A Light Model for pMHC Structure Prediction with Graph Neural Networks},
	year = {2023},
	doi = {10.1101/2023.11.21.568015},
	publisher = {Cold Spring Harbor Laboratory},
	journal = {bioRxiv}
}

If you have any questions or feedback on the code and models, please feel free to reach out to us.

Thank you for your interest in our work!

License

LightMHC: A Light Model for pMHC Structure Prediction with Graph Neural Networks © 2023 by InstaDeep Ltd is licensed under CC BY-NC-SA 4.0

Disclaimer of Warranties

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