To eventually become an unofficial working Pytorch implementation of RGN2, an state of the art model for MSA-less Protein Folding for particular use when no evolutionary homologs are available (ie. for protein design).
$ pip install rgn2-replica-
Provide basic package and file structure
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Contribute adaptation of RGN1 for different ops.
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Contirbute trainer classes / functionality
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Adapt functionality from MP-NeRF:
- Sidechain building
- Full backbone from CA
- Fast loss functions and metrics
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Contribute Rosetta Scripts (contact me by email/discord to get a key for Rosetta if interested in doing this part. )
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NOTES:
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Use functionality provided in MP-NeRF wherever possible (avoid repetition).
Hey there! New ideas are welcome: open/close issues, fork the repo and share your code with a Pull Request.
Currently, the main discussions / conversatino about the model development is happening in this discord server under the /self-supervised-learning channel.
Clone this project to your computer:
git clone https://github.com/EricAlcaide/pysimplechain
Please, follow this guideline on open source contribtuion
@article {Chowdhury2021.08.02.454840,
author = {Chowdhury, Ratul and Bouatta, Nazim and Biswas, Surojit and Rochereau, Charlotte and Church, George M. and Sorger, Peter K. and AlQuraishi, Mohammed},
title = {Single-sequence protein structure prediction using language models from deep learning},
elocation-id = {2021.08.02.454840},
year = {2021},
doi = {10.1101/2021.08.02.454840},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2021/08/04/2021.08.02.454840},
eprint = {https://www.biorxiv.org/content/early/2021/08/04/2021.08.02.454840.full.pdf},
journal = {bioRxiv}
}
@article{alquraishi_2019,
author={AlQuraishi, Mohammed},
title={End-to-End Differentiable Learning of Protein Structure},
volume={8},
DOI={10.1016/j.cels.2019.03.006},
URL={https://www.cell.com/cell-systems/fulltext/S2405-4712(19)30076-6}
number={4},
journal={Cell Systems},
year={2019},
pages={292-301.e3}