hsm - Biophysical prediction of protein-peptide interactions and signaling networks using machine learning.
This repository implements the hierarchical statistical mechanical (HSM) model described in the paper Biophysical prediction of protein-peptide interactions and signaling networks using machine learning.
An associated website is available at proteinpeptide.io. The website is built to facilitate interactions with results from the model including: (1) specific domain-peptide and protein-protein predictions, (2) the resulting networks, and (3) structures colored using the inferred energy functions from the model. Code for the website is available via the parallel repo: aqlaboratory/hsm-web. Note that the results on the website were obtained using an old model.
This file documents how this package might be used, the location of associated data, and other metadata.
The model was implemented in Python (>= 3.5) primarily using TensorFlow (>= 1.14) (Software Requirements). To work with this repository, either download pre-processed data (see below) or include new data. The folder contains three major directories: train/, predict/, and publication_analysis/. Each directory is accompanied by a README.md file detailing usage.
To train / re-train new models, use the train.py script in train/. To make predictions using a model, use one of two scripts, predict_domains.py and predict_proteins.py, for predicting either domain-peptide interactions or protein-protein interactions. Scripts are designed with a CLI and should be used from the command line:
python [SCRIPT] [OPTIONS]Options for any script may be listed using the -h/--help flag.
To reproduce analysis and figures presented in the paper Biophysical prediction of protein-peptide interactions and signaling networks using machine learning, use the scripts in publication_analysis/.
Pre-trained models are released with this repo. An alternative use case would be to train / re-train a new model in the train/ code and make new predictions using the predict/ code.
We identified an issue in the original datasets used to train the model published in Biophysical prediction of protein-peptide interactions and signaling networks using machine learning. We have released corrected datasets on figshare (doi:10.6084/m9.figshare.22105529) (published on February 16, 2023), and replaced the original models released with this repo with corrected ones (on January 9, 2023). Please verify that you use the corrected models for all predictions (see documentation in predict/).
All associated data may be downloaded from figshare (doi:10.6084/m9.figshare.22105529).
- Python (>= 3.5)
- TensorFlow (1.14)
- numpy (1.18)
- scipy (1.4)
- scikit-learn (0.20)
- tqdm (4.41) (Progressbar. Not strictly necessary for functionality; needed to ensure package runs.)
Please reference the associated publication:
Cunningham, J.M., Koytiger, G., Sorger, P.K., & AlQuraishi, M. "Biophysical prediction of protein-peptide interactions and signaling networks using machine learning." Nature Methods (2020). doi:10.1038/s41592-019-0687-1. (citation.bib)
See also, a website at proteinpeptide.io for exploring the associated analyses (code: aqlaboratory/hsm-web). Note that the results on the website were obtained using an old model.
This work was supported by the following sources:
| Funder | Grant number |
|---|---|
| NIH | U54-CA225088 |
| NIH | P50-GM107618 |
| DARPA / DOD | W911NF-14-1-0397 |
This repository is released under an MIT License