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All Projects → lucidrains → tr-rosetta-pytorch

lucidrains / tr-rosetta-pytorch

Licence: MIT license
Implementation of trRosetta and trDesign for Pytorch, made into a convenient package, for protein structure prediction and design

Programming Languages

python
139335 projects - #7 most used programming language

Labels

deep-learningartificial-intelligenceprotein-designprotein-folding

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trRosetta - Pytorch

Implementation of trRosetta and trDesign for Pytorch, made into a convenient package, for protein structure prediction and design. The concept of trDesign will also be abstracted into a wrapper in this repository, so that it can be applied to Alphafold2 once it is replicated. Please join the efforts there if you would like to see this happen!

The original repository can be found here

Update - Xander has released trDesign for Pytorch!

Install

$ pip install tr-rosetta-pytorch

Usage

As a command-line tool, to run a structure prediction

$ tr_rosetta <input-file.a3m>

Code

import torch
from tr_rosetta_pytorch import trRosettaNetwork

model = trRosettaNetwork(
    filters = 64,
    kernel = 3,
    num_layers = 61
).cuda()

x = torch.randn(1, 526, 140, 140).cuda()

theta, phi, distance, omega = model(x)

Citations

@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},
    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},
    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}
}
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