RGN2-Replica (WIP)

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).

Install

$ pip install rgn2-replica

To load sample dataset

from datasets import load_from_disk
ds = load_from_disk("data/ur90_small")
print(ds['train'][0])

To convert to pandas for exploration

df = ds['train'].to_pandas()
df.sample(5)

To train ProteinLM

Run the following command with default parameters

python -m scripts.lmtrainer

This will start the run using sample dataset in repo directory on CPU.

TO-DO LIST: ordered by priority

• Provide basic package and file structure

• RGN2:

  • Contribute adaptation of RGN1 for different ops
    • Simple LSTM with:
      • Inputs (B, L, emb_dim)
      • Outputs (B, L, 4) (4 features which should be outputs of linear projections)
  • Find a good (and reproducible) training scheme
  • Benchmark regression vs classification of torsional alphabet

• Language Model:

  • basic tokenizer by @gurvindersingh
  • basic architecture by @gurvindersingh
  • adapt for desired outputs
  • find a combination of pretraining losses

• To be merged when first versions of RGN are ready:

  • Geometry module
  • Adapt functionality from MP-NeRF:
    • Sidechain building
    • Full backbone from CA
    • Fast loss functions and metrics
    • Modifications to convert LSTM cell into RGN cell

• Contirbute trainer classes / functionality.

  • Sequence preprocessing for AminoBERT
    • inverted fragments
    • sequence masking
    • loss function wrapper v1 by @DrHB
    • Sample dataset by @gurvindersingh
    • Dataloder
    • ...

• Contribute Data Infra for training:

  • Sequences: UniParc sequences, etc @gurvindersingh
  • Structures: will use the amazing sidechainnet work by Jonathan King

• Contribute Rosetta Scripts ( contact me by email ([email protected]) / discord to get a key for Rosetta if interested in doing this part. )

• NOTES:

• Use functionality provided in MP-NeRF wherever possible (avoid repetition).

Contribute:

Hey there! New ideas are welcome: open/close issues, fork the repo and share your code with a Pull Request.

Currently the main discussions / conversation 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

Citations:

@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}