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bioRxiv · 10.1101/2023.10.03.559246

Nphos: Database and Predictor of Protein N-phosphorylation

Ming-Xiao, Z.·Ruo-Fan, D.·Qiang, C.·Junhua, M.·Fulai, L.·Songsen, F.·Biling, H.·Yan, L.·Ji, Z.-L.·Yufen, Z.
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Abstract

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/.

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Ming-Xiao, Z., Ruo-Fan, D., Qiang, C., Junhua, M., Fulai, L., Songsen, F., Biling, H., Yan, L., Ji, Z.-L., Yufen, Z.. 2023-10-05. Nphos: Database and Predictor of Protein N-phosphorylation. https://doi.org/10.1101/2023.10.03.559246

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