Installation:

These instructions will walk you through installing the program in the terminal.

• Requirements:
  • If you are using a Windows OS, you need to install and use WSL.
  • You need to have conda installed.

Clone the GitHub

git clone https://github.com/Collinformatics/CleaveNet

Create conda environment:

• If you are using MacOS run:

  conda env create -f environment_mac.yml
  

• If not, run:

   conda env create -f environment.yml
  

Activate the virtual environment:

  conda activate cleavenet

Test GPU activation:

  python testGPU.py

If you are using an NVIDIA GPU, you can monitor GPU usage with:

  watch -n 1 nvidia-smi

Note:

If the line "import cleavenet" gives you an error you'll need to add the working directory to PYTHONPATH:

export PYTHONPATH="$PYTHONPATH:$PWD"

All training data should be saved in the "data" directory.

Generator:

Training:

• Train a model that can generate protein substrates.

  Multiple parameters can be adjusted, to print the options run:

  python src/train_generator.py --help
  

• You can train the generator with:

  python src/train_generator.py --data-path <filepath>
  

Predictor:

• Train a model that can predict substrate activity.

• The external validation set (--data-pathEV) is optional, if a file is not provided 5% of the dataset will be used for this set.

Multiple parameters can be adjusted, to print the options run:

  python src/train_generator.py --help

Training:

python src/train_predictor.py --data-path <filename> --data-pathEV <filename>