https://github.com/lucidrains/tr-rosetta-pytorch
Implementation of trRosetta and trDesign for Pytorch, made into a convenient package, for protein structure prediction and design
https://github.com/lucidrains/tr-rosetta-pytorch
artificial-intelligence deep-learning protein-design protein-folding
Last synced: 16 days ago
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Implementation of trRosetta and trDesign for Pytorch, made into a convenient package, for protein structure prediction and design
- Host: GitHub
- URL: https://github.com/lucidrains/tr-rosetta-pytorch
- Owner: lucidrains
- License: mit
- Created: 2021-02-04T20:28:55.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-08-27T06:08:22.000Z (over 3 years ago)
- Last Synced: 2025-03-27T23:33:04.171Z (about 1 month ago)
- Topics: artificial-intelligence, deep-learning, protein-design, protein-folding
- Language: Python
- Homepage:
- Size: 80.6 MB
- Stars: 79
- Watchers: 4
- Forks: 10
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
## 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
```bash
$ pip install tr-rosetta-pytorch
```
## Usage
As a command-line tool, to run a structure prediction
```bash
$ tr_rosetta
```
## Code
```python
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
```bibtex
@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}
}
```
```bibtex
@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}
}
```