Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model

🚀 Introduction (ProtREM)

📑 Results

News

• [2024.xx.xx]

Downloads

• ProteinGym a2m homology sequences (EVCouplings): https://huggingface.co/datasets/tyang816/ProtREM/blob/main/aa_seq_aln_a2m.tar.gz. The original a2m files are downloaded at ProteinGym.
• ProteinGym a3m homology sequences (ColabFold): https://huggingface.co/datasets/tyang816/ProtREM/blob/main/aa_seq_aln_a3m.tar.gz
• Uniref 100 database: https://ftp.uniprot.org/pub/databases/uniprot/uniref/uniref100/uniref100.fasta.gz

Paper Results

🛫 Requirement

Conda Enviroment

Please make sure you have installed Anaconda3 or Miniconda3.

conda env create -f environment.yml
conda activate protrem

# We need HMMER and EVCouplings for MSA
# pip install hmmer
# pip install https://github.com/debbiemarkslab/EVcouplings/archive/develop.zip

Other Requirement

Install plmc

git clone https://github.com/debbiemarkslab/plmc.git
cd plmc
make all-openmp

Hardware

• For direct use of inference, we recommend at least 10G of graphics memory, such as RTX 3080
• For searching homology sequences, 8 cores cpu.

🧬 Zero-shot Prediction for Mutants

Evaluation on ProteinGym

Prepare for the processed data

cd data/proteingym_v1
wget https://huggingface.co/datasets/tyang816/ProtREM/blob/main/aa_seq_aln_a2m.tar.gz
# unzip homology files
tar -xzf aa_seq_aln_a2m.tar.gz
# unzip fasta sequence files
tar -xzf aa_seq.tar.gz
# unzip pdb structure files
tar -xzf pdbs.tar.gz
# unzip structure sequence files
tar -xzf struc_seq.tar.gz
# unzip DMS substitution csv files
tar -xzf substitutions.tar.gz

Start inference

protein_dir=proteingym_v1
python compute_fitness.py \
    --base_dir data/$protein_dir \
    --out_scores_dir result/$protein_dir

Your own dataset

What you need at least

data/<your_protein_dir_name>
|——aa_seq # amino acid sequences
|——|——protein1.fasta
|——|——protein2.fasta
|——aa_seq_aln_a2m # homology sequences of EVCouplings
|——|——protein1.a2m
|——|——protein2.a2m
|——pdbs # structures
|——|——protein1.pdb
|——|——protein2.pdb
|——struc_seq # structure sequences
|——|——protein1.fasta
|——|——protein2.fasta
|——substitutions # mutant files
|——|——protein1.csv
|——|——protein2.csv

Search homology sequences by JackHmmer

# step 1: search homology sequences
# your protein name, eg. fluorescent_protein
protein_dir=<your_protein_dir_name>
# your protein path, eg. data/fluorescent_protein/aa_seq/GFP.fasta
query_protein_name=<your_protein_name>
protein_path=data/$protein_dir/aa_seq/$query_protein_name.fasta
# your unipror dataset path
database=<your_path>/uniref100.fasta
evcouplings \
    -P output/$protein_dir/$query_protein_name \
    -p $query_protein_name \
    -s $protein_path \
    -d $database \
    -b "0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9" \
    -n 5 src/single_config_monomer.txt
# ⚠ 👆 Repeat the searching process until all your proteins are done

# step 2: select a2m file
protein_dir=<your_protein_dir_name>
python src/data/select_msa.py \
    --input_dir output/$protein_dir \
    --output_dir data/$protein_dir

Get pdb files for your protein

You can use AlphaFold3 server, AlphaFold database, ESMFold and other tools to obtain structures.

⚠ For wet-lab experiments, please try to get high quality structures as possible as you can.

Get structure sequences for PLM

protein_dir=<your_protein_dir_name>
python src/data/get_struc_seq.py \
    --pdb_dir data/$protein_dir/pdbs \
    --out_dir data/$protein_dir/struc_seq

Start inference

protein_dir=<your_protein_dir_name>
python compute_fitness.py \
    --base_dir data/$protein_dir \
    --out_scores_dir result/$protein_dir

🙌 Citation

Please cite our work if you have used our code or data.

@article{tan2024protrem,
  title={Retrieval-Enhanced Mutation Mastery: Augmenting Zero-Shot Prediction of Protein Language Model},
  author={Tan, Yang and Wang, Ruilin and Wu, Banghao and Hong, Liang and Zhou, Bingxin},
  journal={arXiv:2410.21127},
  year={2024}
}