hryk/psap

CLI interface for the PSAP classifier. PSAP implements a RandomForest approach to predict the probability of proteins to mediate protein phase separation (PPS)

psap

CLI interface for the PSAP classifier. PSAP implements a RandomForest approach to predict the probability of proteins to mediate protein phase separation (PPS). Initially, a set of protein sequences is annotated with biochemical features wich are subsequently used to train a RandomForest (scikit-learn) classifier. The trained classifier is exported to json format and can be used to predict the llps class probability (PSAP_score) for new samples.

The default model was trained on the human reference proteome with a list of literature curated PPS proteins for positive class labeling. Both can be found in /data.

Publication | Mierlo, G., Jansen, J. R. G., Wang, J., Poser, I., van Heeringen, S. J., & Vermeulen, M. (2021). Predicting protein condensate formation using machine learning. Cell Reports, 34(5), 108705. https://doi.org/10.1016/j.celrep.2021.108705.

• Free software: MIT license

Getting Started

1. Install psap

pip install psap

2. Train classifier

psap train -f /path/to/peptide-trainingset.fasta -l /path/top/known/pps-proteins.txt (optional)  -o /output/directory (optional)

The trained RandomForest classifier is exported to json format and stored in the output directory.

3. Predict llps score for peptide instances

psap predict -f /path/to/peptid-testset.fasta -m /path/to/model.json (optional) -o /output/directory (optional)

When no model (-m) is provided psap loads the default classifier stored in /data/model.

4. Annotate petides (optional)

psap annotate -f /path/to/peptide.fasta  -l /path/top/known/pps-proteins.txt (optional) -o /output/directory (optional)

Annotates a peptide fasta with biochemical features. This step is included in train and predict.

Credits

This package was adapted from the cookiecutter and the audreyr/cookiecutter-pypackage project template.