LazyPair: scalable prediction of protein-protein interactions and interaction types

Motivation Almost all cellular processes require protein-protein interactions. Common interaction types include binding, post-translational modifications, and catalysis. However, existing prediction tools do not take these interaction types into account and do not scale well on proteome-wide prediction. Results Here we show that a random forest classifier trained on per-residue physicochemical and biochemical properties is useful for predicting protein-protein interactions. Counterintuitively, we find that training random forests by individual interaction types improves accuracy. Furthermore, a combination of these specialised classifiers improves generalisability. We call our protein-protein interaction prediction tool LazyPair. More importantly, LazyPair outperforms the state-of-the-art in accuracy, generalisability and scalability. Availability and implementation LazyPair and the source code and data for reproducing our analysis are freely available at [https://github.com/Gardner-BinfLab/PPI\_Analysis\_2022][1] and . The web server version and the source code are freely available at and , respectively. ### Competing Interest Statement The authors have declared no competing interest. [1]: https://github.com/Gardner-BinfLab/PPI_Analysis_2022