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
  • 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: [email protected].
  • 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: [email protected].
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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