SPURS

Environment

# get esm
conda create -n spurs python=3.7 pip
conda activate spurs


pip install -e .
pip install git+https://github.com/facebookresearch/esm.git

pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113

Reproduce result

Download data.tar.gz from link.

Data

tar -xzvf data.tar.gz

Stability Prediction

Evaluation on the test sets:

# general usage, only model and test dataset should be specified.
python ./test.py  experiment_path={checkpoint_path} datamodule._target_={dataset_name} data_split=test ckpt_path=best.ckpt mode=predict 

while model checkpoints can be selected from data/checkpoints, and datamodule can be selected from megascale and domainome.

## SPURS on Megascale and ten test sets
python ./test.py  experiment_path=data/checkpoints/spurs datamodule._target_=megascale data_split=test ckpt_path=best.ckpt mode=predict 


### ThermoMPNN on Domainome
python ./test.py  experiment_path=data/checkpoints/ThermoMPNN datamodule._target_=domainome data_split=test ckpt_path=best.ckpt mode=predict 

Results on Megascale and ten test sets can be processed using convert.ipynb

Functional Site Identification

An example can be found at functional_site_identification.ipynb. This is the reuslt for Fig3h in the paper (UniProt ID: P00327, PDB ID: 1QLH).

Fitness Prediction

To run fitness prediction

cd combining-evolutionary-and-assay-labelled-data
export PROJECT_ROOT=$PWD/../
python run_proteingym.py

This command will use all accessible CPU cores by default. If you want to use a specific range of CPUs, such as CPU0-80, you can use:

taskset -c 0-80 python run_proteingym.py

SPURS-augmented models were built upon the Augmented models framework (Hsu et al., Nat Biotechnol, 2022). We adapted the code from the original GitHub repo (commit fdaa5bb) and retained only the necessary files. A DDGPredictor is added to introduce predicted ddG into the regression model.

Inference

from spurs.inference import get_SPURS, parse_pdb
# ~ 10s
model, cfg = get_SPURS('./data/checkpoints/spurs')
pdb_name = 'DOCK1_MOUSE'
pdb_path = './data/inference_example/' + pdb_name + '.pdb'
chain = 'A'
pdb = parse_pdb(pdb_path, pdb_name, chain, cfg)
# ~ 1s
result = model(pdb,return_logist=True)