/protein_gibbs_sampler

Gibbs sampling for generating protein sequences

Primary LanguageJupyter Notebook

Generating novel protein sequences using Gibbs sampling of masked language models

This repository represents the code supporting the work done in our manuscript: Generating novel protein sequences using Gibbs sampling of masked language models

Running the Code

Setup container environment:

# From root of this repo
# Create container -- starts container running in detached mode and root of 
# 	repo mounted to /workspace in the container
make run-cpu # cpu-only
# make run # GPU pass through

# Attach to container
make attach

# ---Runnining inside container---
# Pip install the source for this package
pip install -e .

Additional Commands:

# Additional Commands
make start
make stop
make shell
make remove

Table of Contents

References

This repository represents work building on related resources as cited below.

bert-gen

Github Paper

@article{wang2019bert,
  title={BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model},
  author={Wang, Alex and Cho, Kyunghyun},
  journal={arXiv preprint arXiv:1902.04094},
  year={2019}
}

ESM

Github Paper

@article{rives2019biological,
  author={Rives, Alexander and Meier, Joshua and Sercu, Tom and Goyal, Siddharth and Lin, Zeming and Guo, Demi and Ott, Myle and Zitnick, C. Lawrence and Ma, Jerry and Fergus, Rob},
  title={Biological Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences},
  year={2019},
  doi={10.1101/622803},
  url={https://www.biorxiv.org/content/10.1101/622803v3},
  journal={bioRxiv}
}

ProTrans

Github Paper

@article {Elnaggar2020.07.12.199554,
	author = {Elnaggar, Ahmed and Heinzinger, Michael and Dallago, Christian and Rehawi, Ghalia and Wang, Yu and Jones, Llion and Gibbs, Tom and Feher, Tamas and Angerer, Christoph and Steinegger, Martin and BHOWMIK, DEBSINDHU and Rost, Burkhard},
	title = {ProtTrans: Towards Cracking the Language of Life{\textquoteright}s Code Through Self-Supervised Deep Learning and High Performance Computing},
	elocation-id = {2020.07.12.199554},
	year = {2020},
	doi = {10.1101/2020.07.12.199554},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Computational biology and bioinformatics provide vast data gold-mines from protein sequences, ideal for Language Models (LMs) taken from Natural Language Processing (NLP). These LMs reach for new prediction frontiers at low inference costs. Here, we trained two auto-regressive language models (Transformer-XL, XLNet) and two auto-encoder models (Bert, Albert) on data from UniRef and BFD containing up to 393 billion amino acids (words) from 2.1 billion protein sequences (22- and 112 times the entire English Wikipedia). The LMs were trained on the Summit supercomputer at Oak Ridge National Laboratory (ORNL), using 936 nodes (total 5616 GPUs) and one TPU Pod (V3-512 or V3-1024). We validated the advantage of up-scaling LMs to larger models supported by bigger data by predicting secondary structure (3-states: Q3=76-84, 8 states: Q8=65-73), sub-cellular localization for 10 cellular compartments (Q10=74) and whether a protein is membrane-bound or water-soluble (Q2=89). Dimensionality reduction revealed that the LM-embeddings from unlabeled data (only protein sequences) captured important biophysical properties governing protein shape. This implied learning some of the grammar of the language of life realized in protein sequences. The successful up-scaling of protein LMs through HPC to larger data sets slightly reduced the gap between models trained on evolutionary information and LMs. Availability ProtTrans: \<a href="https://github.com/agemagician/ProtTrans"\>https://github.com/agemagician/ProtTrans\</a\>Competing Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554},
	eprint = {https://www.biorxiv.org/content/early/2020/07/21/2020.07.12.199554.full.pdf},
	journal = {bioRxiv}
}