Generating novel protein sequences using Gibbs sampling of masked language models

This repository represents the code supporting the work done in Generating novel protein sequences using Gibbs sampling of masked language models . Since then, the code has been continuously updated. For the version of the code that was used in that preprint, see: here

More recently, this repo was used to generate sequences from ESM-MSA in work described in work published in Nature Biotechnology: Computational Scoring and Experimental Evaluation of Enzymes Generated by Neural Networks.

Install

PIP Installation

Clone the repo and specify your CUDA version. If you want to run on CPU instead of GPU set CUDA_VERSION="cpu".

# clone repo and change directories
git clone https://github.com/seanrjohnson/protein_gibbs_sampler.git
cd protein_gibbs_sampler

# install pytorch
# NOTE: MAKE SURE TO ADJUST YOUR CUDA VERSION OR SET TO CPU IF NECESSARY
CUDA_VERSION="cpu" # set to cu117

# install torch core
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/$CUDA_VERSION
# get torch version
TORCH_VERSION=`python -c "import torch; print(torch.__version__.split('+')[0])"`
# install torch extensions
pip install torch-scatter -f https://data.pyg.org/whl/torch-$TORCH_VERSION+$CUDA_VERSION.html
pip install torch-sparse -f https://data.pyg.org/whl/torch-$TORCH_VERSION+$CUDA_VERSION.html
pip install torch-cluster -f https://data.pyg.org/whl/torch-$TORCH_VERSION+$CUDA_VERSION.html
pip install torch-spline-conv -f https://data.pyg.org/whl/torch-$TORCH_VERSION+$CUDA_VERSION.html
pip install torch-geometric

# install ESM
echo "[*] Installing ESM and its dependencies"
pip install -q git+https://github.com/facebookresearch/esm.git
pip install -e .

# test install
pytest .

Conda Installation

Make a new conda environment called protein_gibbs_sampler

# clone repo and change directories
git clone https://github.com/seanrjohnson/protein_gibbs_sampler.git
cd protein_gibbs_sampler
conda env create --name protein_gibbs_sampler -f conda_env.yml

# activate environment
conda activate protein_gibbs_sampler

# test install
pytest .

If you have a CUDA compatible GPU, everything should pass, otherwise there will be one skipped test.

Running the tests for the first time might take a while because model weights need to be downloaded.

Generating new protein sequences from the command line

This package contains four command line programs to make it easy to generate new sequences. For good performance, it is recommended to use GPU, but they will still run on a CPU, just excruciatingly slow for everything but small proteins.

pgen_esm.py

Given a seed sequence, generates new sequences.

pgen_esm_input.tsv

test_seq	{'num_iters': 20, 'burnin': 10, 'mask': True, 'in_order':False, 'num_positions_percent': 10, 'top_k': 1, 'seed_seq': "MEPAATGQEAEECAHSGRGEAWEEV"}
test_seq2	{'num_iters': 20, 'burnin': 10, 'mask': True, 'in_order':False, 'num_positions_percent': 10, 'top_k': 1, 'seed_seq': "MLEGADIVIIPAGV"}

Note that for pgen_msa.py, if 'top_k' is not defined, then all rounds are burnin rounds.

pgen_esm.py -o pgen_out -i pgen_esm_input.tsv --num_output_sequences 10

For detailed help: pgen_esm.py -h

pgen_esm_from_fasta.py

Like pgen_esm.py except that the seed sequences come from fasta files, instead of being defined in the sampler arguments. The sequences in the fasta can either be aligned or unaligned. If they are aligned, then gaps will be removed before sampling, but setting --keep_gap_positions will add the gaps back in after sampling. If the fasta contains more than one sequence, then a random sequence will be selected for each round of sampling.

pgen_esm_from_fasta.py -o pgen_from_fasta_out -i pgen_msa_input.tsv --num_output_sequences 10 --keep_gap_positions

For detailed help: pgen_esm_from_fasta.py -h

pgen_msa.py

Given a seed msa, uses esm-msa to generate new sequences.

This program will randomly mask and resample across the entire MSA.

fasta_input1.fasta

>s1
MEPAATGQEAE--AHSGRGEAWEEV
>s2
MCP-ATGR-AEMCAHS--GEAWLLV
>s3
MEQ-AGGRLAEM-AHHC-GEAWLLV

fasta_input2.fasta

>s1
MLEGADIVIIP-GV
>s2
MLDG---VLLPGAV
>s3
M-EPADILVV--GV

pgen_msa_input.tsv

test_seq	{'num_iters': 20, 'burnin': 10, 'mask': True, 'in_order':False, 'num_positions_percent': 10, 'top_k': 1}	fasta_input1.fasta
test_seq2	{'num_iters': 20, 'burnin': 10, 'mask': True, 'in_order':False, 'num_positions_percent': 10, 'top_k': 1}	fasta_input2.fasta

Note that for pgen_msa.py, if 'top_k' is not defined, then all rounds are burnin rounds.

pgen_msa.py -o pgen_msa_out -i pgen_esm_msa_input.tsv --num_output_sequences 10 

For detailed help: pgen_msa.py -h

pgen_msa_revised.py

This program will randomly mask and resample a single sequence from the MSA. It can also generate new MSAs from a the top phmmer hits in a list of reference sequences.

example

pgen_msa_revised.py --templates list_of_sequences_to_resample.fasta --references large_list_of_reference_sequences.fasta -o generated_sequences.fasta --device gpu --alignment_size 32 --passes 3 --burn_in 2

For detailed help and additional options: pgen_msa_revised.py -h

Calculating sequence probabilities

ESM and ESM-MSA models can be used to assign probabilities to protein sequences and individual positions within protein sequences

likelihood_esm.py

Use the single-sequence ESM models to calculate sequence likelihoods.

likelihood_esm.py -i {input} -o {output} --model esm1v --csv --device gpu --score_name esm1v-mask6 --mask_distance 6 --positionwise positionwise_sequence_probabilities.csv

For detailed help and additional options: likelihood_esm.py -h

likelihood_esm_msa.py

Use the mutliple sequence alignment ESM model to calculate sequence likelihoods.

We have noticed that the probability scores from ESM-MSA are best from partially masked sequences. Masking the entire target sequence works very poorly. Not masking any positions gives higher than expected scores for highly divergent targets.

Note that in some cases --reference_msa can actually be an unaligned fasta file, because the sequences may be re-aligned at runtime, depending on the subset_strategy.

likelihood_esm_msa.py -i seqs_to_calculate_probabilities_for.fasta -o whole_sequence_probabilities.csv --reference_msa reference_sequences.fasta --device gpu --subset_strategy top_hits --alignment_size 31 --count_gaps --mask_distance 6 --csv --positionwise positionwise_sequence_probabilities.csv

For detailed help and additional options: likelihood_esm_msa.py -h

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}
}

ESM-MSA

Paper

@article{rao2021msa,
  author = {Rao, Roshan and Liu, Jason and Verkuil, Robert and Meier, Joshua and Canny, John F. and Abbeel, Pieter and Sercu, Tom and Rives, Alexander},
  title={MSA Transformer},
  year={2021},
  doi={10.1101/2021.02.12.430858},
  url={https://www.biorxiv.org/content/10.1101/2021.02.12.430858v1},
  journal={bioRxiv}
}