H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing

This repo contains code for H-Packer, a method for side-chain packing based upon rotationally equivariant convolutional neural networks.

Currently supported features

• Packing side-chain conformations of a full structure, providing a backbone structure and desired sequence information
• Refining side-chain conformations of a full structure
• Add and pack side-chains in parts of a structure (keeping some of the structure constant)
• Apply mutations and selectively pack the surrounding side-chains

Coming soon

• Training new HPacker models

Installation

Create the hpacker conda environment by running the following

conda env create -f env.yml

to install the necessary dependencies.

Then run

pip install .

to install the code in this repo as a package.

If you're going to make edits to the code, run

pip install -e .

so you can test your changes.

Usage

As simple as a few lines of code:

from hpacker import HPacker
# Initialize HPacker object by passing it a tutple of paths to the pre-trained models, and the backbone-only structure that you want to add side-chains to
hpacker = HPacker(['pretrained_models/initial_guess','pretrained_models/refinement','pretrained_models/initial_guess_conditioned'], 'T0950_bb_only.pdb')
hpacker.reconstruct_sidechains(num_refinement_iterations=5)
hpacker.write_pdb('reconstructed_from_bb_only_T0950.pdb')

See the provided hpacker.ipynb notebook for more examples, as well as explanations of the inner workings of H-Packer.

Training HPacker

Coming soon

Limitations

• Cannot process hetero residues, since they do not play nice with BioPython's internal_coords module.

Citation

If you used H-Packer or learned something from it, please cite us:

@misc{visani2023hpacker,
      title={H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing}, 
      author={Gian Marco Visani and William Galvin and Michael Neal Pun and Armita Nourmohammad},
      year={2023},
      eprint={2311.09312},
      archivePrefix={arXiv},
      primaryClass={q-bio.BM}
}