SCAI-BIO / STEP

Data and code for STEP (Siamese Twin deep sequence Embedding of Proteins) approach.

STEP

Data and code for STEP (Siamese Twin deep sequence Embedding of Proteins) approach.

Relevant files

1. ''ppi_finetuning.py'' tackles the finetuning and prediction for use case 1 (brain tissue-specific ppi prediction)
2. ''ppi_virhostnet_finetuning.py'' tackles the finetuning and prediction for use case 2 (sars-cov-2 spike ppi predisction)

Prepare Conda Environment

1. Create the appropriate Conda env

cd ~/git/STEP
conda env create -n STEP -f environment.yml

2. For GPU processing, install the correct pytorch package (see environment.yml for version and check out https://pytorch.org/get-started/previous-versions/ for specific commands)

pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113

3. Activate Conda env

conda activate STEP

Training a model

1. ''ppi_finetuning.py'': Use VSCode and run through the "Run and Debug" option.

cd ~/git/STEP
conda activate STEP

# set correct PYtHONPATH from the .env file, which is also used by VSCode
set -o allexport && source .env && set +o allexport  

# Print out all arguments
python src/ppi_finetuning.py --help

# Train a model
python src/ppi_finetuning.py --accelerator gpu --devices 2 --num_sanity_val_steps 0

Manuscript to cite

Madan, Sumit, Victoria Demina, Marcus Stapf, Oliver Ernst, and Holger Fröhlich. 2022. “Accurate Prediction of Virus-Host Protein-Protein Interactions via a Siamese Neural Network Using Deep Protein Sequence Embeddings.” Patterns 3 (9): 100551. https://doi.org/10.1016/j.patter.2022.100551.