ComPPlete (Completing the Protein-Protein Interaction Network) is a novel ML pipeline utilizing PPI network topology for strategic sampling of protein-protein non-interactions (PPNIs) by leveraging higher-order network characteristics that capture the inherent complementarity-driven mechanisms of PPIs. Integrating unsupervised pre-training in protein representation learning with topological PPNI samples, ComPPlete improves PPI prediction generalizability and interpretability, particularly in identifying potential binding sites locations on amino acid sequences. ComPPlete strengthens the prioritization of screening assays, facilitates the transferability of ML predictions across protein families and homodimers. ComPPlete establishes the foundation for a fundamental negative sampling methodology in graph machine learning by integrating insights from network topology.
ComPPlete is developed in Python 3. Required packages to run ComPPlete is available in: compplete_env.yml.
ComPPlete is developed with NVIDIA A100 GPUs with CUDA 12.0. Use the compplete_env.yml to set-up the Python 3 environment. Then use ComPPlete_Frontend.ipynb to run predictions. You require the amino acid sequences of both proteins.
Two ComPPlete interactomes are available here:
(1) ComPPlete PPI (Protein-Prtoein Interactions) interactome that has 4,582,765 PPI predictions
(2) ComPPlete PPNI (Protein-Prtoein None-Interactions) interactome that has 1,287,060 PPNI predictions.
The topological negatives obtained from contrastive-L3 approach. A total of 3,063,604 negatives between 5,037 proteins.
Trained models.
Use this notebook to run ComPPlete models.
This notebook offers the code to obtain binding profiles
If you find ComPPlete useful in your research, please add the following citation:
@article{ComPPlete,
title={Topology-Driven Negative Sampling Enhances Generalizability in Protein-Protein Interaction Prediction},
author={Chatterjee, Ayan and Ravandi, Babak and Philip, Naomi H and Abdelmessih, Mario and Mowrey, William R and Ricchiuto, Piero and Liang, Yupu and Ding, Wei and Mobarec, Juan C and Eliassi-Rad, Tina},
url = {https://www.biorxiv.org/content/10.1101/2024.04.27.591478},
journal={bioRxiv},
pages={2024--04},
year={2024},
}