Nphos: Database and Predictor of ProteinN-phosphorylation

Protein N -phosphorylation widely present in nature and participates in various biological functions. However, current knowledge on N -phosphorylation is extremely limited compared to that on O -phosphorylation. In this study, we collected 11,710 experimentally verified N -phosphosites of 7344 proteins from 39 species and subsequently constructed the database Nphos to share up-to-date information on protein N -phosphorylation. Upon these substantial data, we characterized the sequential and structural features of protein N -phosphorylation. Moreover, after comparing of hundreds of learning models, we chose and optimized gradient boosting decision tree (GBDT) models to predict three types of human N -phosphorylation, achieving mean areas under the receiver operating characteristic curve (AUC) of 90.56%, 91.24%, and 92.01% for pHis, pLys, and pArg, respectively. Meanwhile, we discovered 488,825 distinct N -phosphosites in the human proteome. The models were also deployed in Nphos for interactive N -phosphosite prediction. In summary, this work provides new insights and points for both flexible and focused investigations of N -phosphorylation. It will also facilitate a deeper and more systematic understanding of protein N -phosphorylation modification by providing a data and technical foundation. Nphos is freely available at http://www.bio-add.org and http://ppodd.org.cn/Nphos/ .