Sequence-based machine learning method for predicting the effects of phosphorylation on protein-protein interactions

Protein phosphorylation, catalyzed by kinases, is an important biochemical process, which plays an essential role in multiple cell signaling pathways. Meanwhile, protein-protein interactions (PPI) constitute the signaling pathways. Abnormal phosphorylation status on protein can regulate protein functions through PPI to evoke severe diseases, such as Cancer and Alzheimer's disease. Due to the limited experimental evidence and high costs to experimentally identify novel evidence of phosphorylation regulation on PPI, it is necessary to develop a high-accuracy and user-friendly artificial intelligence method to predict phosphorylation effect on PPI. Here, we proposed a novel sequence-based machine learning method named PhosPPI, which achieved better identification performance (Accuracy and AUC) than other competing predictive methods of Betts, HawkDock and FoldX. PhosPPI is now freely available in web server (https://phosppi.sjtu.edu.cn/). This tool can help the user to identify functional phosphorylation sites affecting PPI and explore phosphorylation-associated disease mechanism and drug development.