align-plm

Get Started

Environment

conda env create -f env.yml
conda activate align

Data

Download from Tranception

• MSA_weights (download & unzip MSA_weights.zip)
• MSA_files (download & unzip MSA_ProteinGym.zip)
• Tranception checkpoints (Small and Large) then place under ckpt/

Download DMS data from ProteinGym then place under proteingym/

Split the dataset.

python split_dataset.py

Resource constraints

The allocated memory depends on the length of the protein, which is very diverse. Therefore, batch size is handled by the functions in core/utils/get_batch.py. It is hard-coded for GPU with 24GB memory, so you may change the value considering your resource constraints.

Fitness Prediction in ProteinGym

Pipeline for Single DMS assay

python pipeline.py --DMS_id IF1_ECOLI_Kelsic_2016 

Run for all ProteinGym benchmarks

python 1_sft_all.py
python 2_ref_dist.py
python 3_generate_pairs.py
python 4_align_score.py

The scoring files will be saved under dms-results.

python performance.py --input_scoring_files_folder dms-results/$exp_name$ --performance_by_depth

This will evaluate the experiment in terms of correlation metrics.

Fitness Optimization in AAV and ACE2

See tasks/

Pipeline for Custom DMS assay

See tasks/pipeline.py. You may

1. Set target_seq value as wild-type sequence
2. Set preference argument as root directory containing the dataset
3. Set DMS_id argument as directory containing your DMS assay
4. All datasets should consist of columns mutant, mutated_sequence, and DMS_score. mutant should be in conventional format, e.g. R42Q, where multiple mutations joined by : symbol. Due to the behavior of pandas join functions in tranception scoring utils, I recommend to delete other columns.

$preference$/
   $DMS_id$/
      dms_train.csv
      dms_test.csv
      dms_val.csv
      sft.csv

Reproduce the Figures

See draw_figure.ipynb. Note that total.csv contains Spearman's rho correlation of Ours, Alpha-Missense,Tranception Large, Tranception Large (no retrieval), EVE, and ESM1v in ProteinGym benchmark.

TODO

• Support LoRA