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  3. Deciphering the functional landscape of phosphosites with deep neural network

Deciphering the functional landscape of phosphosites with deep neural network

  • Cell Rep. 2023 Sep 1;42(9):113048. doi: 10.1016/j.celrep.2023.113048.
Zhongjie Liang 1,  Tonghai Liu 2,  Qi Li 2,  Guangyu Zhang 3,  Bei Zhang 4,  Xikun Du 5,  Jingqiu Liu 4,  Zhifeng Chen 4,  Hong Ding 4,  Guang Hu 1,  Hao Lin 3,  Fei Zhu 6,  Cheng Luo 7
Affiliations

Affiliations

  • 1 Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China; Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou 215123, China.
  • 2 Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528437, China; State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.
  • 3 School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
  • 4 State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China.
  • 5 Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou 215123, China.
  • 6 School of Computer Science and Technology, Soochow University, Suzhou 215006, China. Electronic address: zhufei@suda.edu.cn.
  • 7 Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan 528437, China; State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, UCAS, Hangzhou 310024, China; School of Life Science and Technology, Shanghai Tech University, 100 Haike Road, Shanghai 201210, China; School of Pharmacy, Fujian Medical University, Fuzhou 350122, China. Electronic address: cluo@simm.ac.cn.
PMID: 37659078 DOI: 10.1016/j.celrep.2023.113048
Abstract

Current biochemical approaches have only identified the most well-characterized kinases for a tiny fraction of the phosphoproteome, and the functional assignments of phosphosites are almost negligible. Herein, we analyze the substrate preference catalyzed by a specific kinase and present a novel integrated deep neural network model named FuncPhos-SEQ for functional assignment of human proteome-level phosphosites. FuncPhos-SEQ incorporates phosphosite motif information from a protein sequence using multiple convolutional neural network (CNN) channels and network features from protein-protein interactions (PPIs) using network embedding and deep neural network (DNN) channels. These concatenated features are jointly fed into a heterogeneous feature network to prioritize functional phosphosites. Combined with a series of in vitro and cellular biochemical assays, we confirm that NADK-S48/50 phosphorylation could activate its enzymatic activity. In addition, ERK1/2 are discovered as the primary kinases responsible for NADK-S48/50 phosphorylation. Moreover, FuncPhos-SEQ is developed as an online server.

Keywords

CP: Genomics; CP: Molecular biology; NADK; deep neural network; enzymatic activity; evolutionary features; functional phosphosite; network embedding; protein-protein interaction (PPI).

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