pytorch implementation of trDesign

Related tags

Deep Learningtrdesign-pytorch
Overview

trdesign-pytorch

This repository is a PyTorch implementation of the trDesign paper based on the official TensorFlow implementation. The initial port of the trRosetta network was done by @lucidrains.

Figure 1: trDesign Architecture

Figure 1 of De novo protein design by deep network hallucination (p. 12, Anishchenko et al., CC-BY-ND)

Requirements

Requires python 3.6+

pip install matplotlib numpy torch

Usage (protein design):

  1. Edit src/config.py to set the experiment configuration.
  2. Run python design.py
  3. All results will be saved under results/

Design Configuration Options

  • Sequence length (int)
  • AA_weight (float): how strongly we want the amino acid type composition to be 'natural'
  • RM_AA (str): disable specific amino acid types
  • n_models (int): how many trRosetta model ensembles we want to use during the MCMC loop
  • sequence constraint (str): fix a subset of the sequence residues to specific amino acids
  • target_motif (path): optimize a sequence with a target motif provided as an .npz file
  • MCMC options

Usage (protein structure prediction):

python predict.py example.a3m
# or
python predict.py example.fasta

To get a .pdb from the resulting .npz you need to request the trRosetta package from the original authors.

Then you can run:

python trRosetta.py example.npz example.fasta output.pdb -w /tmp

References

@article {Yang1496,
  author = {Yang, Jianyi and Anishchenko, Ivan and Park, Hahnbeom and Peng, Zhenling and Ovchinnikov, Sergey and Baker, David},
  title = {Improved protein structure prediction using predicted interresidue orientations},
  year = {2020},
  doi = {10.1073/pnas.1914677117},
  URL = {https://www.pnas.org/content/117/3/1496},
  eprint = {https://www.pnas.org/content/117/3/1496.full.pdf},
  journal = {Proceedings of the National Academy of Sciences}
}
@article {Anishchenko2020.07.22.211482,
  author = {Anishchenko, Ivan and Chidyausiku, Tamuka M. and Ovchinnikov, Sergey and Pellock, Samuel J. and Baker, David},
  title = {De novo protein design by deep network hallucination},
  year = {2020},
  doi = {10.1101/2020.07.22.211482},
  URL = {https://www.biorxiv.org/content/early/2020/07/23/2020.07.22.211482},
  eprint = {https://www.biorxiv.org/content/early/2020/07/23/2020.07.22.211482.full.pdf},
  journal = {bioRxiv}
}
@article {Tischer2020.11.29.402743,
  author = {Tischer, Doug and Lisanza, Sidney and Wang, Jue and Dong, Runze and Anishchenko, Ivan and Milles, Lukas F. and Ovchinnikov, Sergey and Baker, David},
  title = {Design of proteins presenting discontinuous functional sites using deep learning},
  year = {2020},
  doi = {10.1101/2020.11.29.402743},
  URL = {https://www.biorxiv.org/content/early/2020/11/29/2020.11.29.402743},
  eprint = {https://www.biorxiv.org/content/early/2020/11/29/2020.11.29.402743.full.pdf},
  journal = {bioRxiv}
}
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Learn Ventures Inc.
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