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bioRxiv · 10.1101/2022.02.21.481370

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

Lim, C. S.·Bhandari, B. K.·Gardner, P. P.
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Abstract

MotivationAlmost 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. ResultsHere 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 implementationLazyPair and the source code and data for reproducing our analysis are freely available at https://github.com/Gardner-BinfLab/PPI_Analysis_2022 and https://doi.org/10.5281/zenodo.6071630. The web server version and the source code are freely available at https://tisigner.com/lazypair/ and https://github.com/Gardner-BinfLab/TISIGNER-ReactJS, respectively.

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Lim, C. S., Bhandari, B. K., Gardner, P. P.. 2022-02-22. LazyPair: scalable prediction of protein-protein interactions and interaction types. https://doi.org/10.1101/2022.02.21.481370

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