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

Xiaokun Hong; Jiyang Lv; Zhengxin Li; Yi Xiong; Jian Zhang; Hai-Feng Chen

doi:10.1016/j.ijbiomac.2023.125233

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

Int J Biol Macromol. 2023 Jul 15:243:125233. doi: 10.1016/j.ijbiomac.2023.125233. Epub 2023 Jun 6.

Authors

Xiaokun Hong¹, Jiyang Lv¹, Zhengxin Li¹, Yi Xiong¹, Jian Zhang², Hai-Feng Chen³

Affiliations

¹ State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.
² Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200025, China. Electronic address: jian.zhang@sjtu.edu.cn.
³ State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, Department of Bioinformatics and Biostatistics, National Experimental Teaching Center for Life Sciences and Biotechnology, School of Life Sciences and Biotechnology, Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China. Electronic address: haifengchen@sjtu.edu.cn.

PMID: 37290543
DOI: 10.1016/j.ijbiomac.2023.125233

Abstract

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.

Keywords: Machine learning; Phosphorylation; Protein-protein interaction; Sequence-based model.

MeSH terms

Artificial Intelligence*
Computational Biology / methods
Machine Learning
Phosphorylation
Proteins*
Signal Transduction

Substances

Proteins