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  2. 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.
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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