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Deciphering the functional landscape of phosphosites with deep neural network

文献类型：期刊论文

作者Liang, Zhongjie8; Liu, Tonghai6,7; Li, Qi6,7; Zhang, Guangyu5; Zhang, Bei6; Du, Xikun8; Liu, Jingqiu6; Chen, Zhifeng6; Ding, Hong6; Hu, Guang1,8
刊名CELL REPORTS
出版日期2023-09-26
卷号42期号:9页码:23
ISSN号2211-1247
DOI10.1016/j.celrep.2023.113048
通讯作者Zhu, Fei(zhufei@suda.edu.cn) ; Luo, Cheng(cluo@simm.ac.cn)
英文摘要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.
WOS关键词POSTTRANSLATIONAL MODIFICATIONS ; PROTEIN-PHOSPHORYLATION ; STRUCTURAL-ANALYSIS ; SEQUENCE EVOLUTION ; RESOURCE ; ASSOCIATIONS ; INFORMATION ; CYTOSCAPE ; RESIDUES ; DATABASE
资助项目National Centre for Protein Science Shanghai (Protein Expression and Purification system) - National Key Ramp;D Program of China[2022YFC3400500] ; National Centre for Protein Science Shanghai (Protein Expression and Purification system) - National Key Ramp;D Program of China[2021ZD0203900] ; National Centre for Protein Science Shanghai (Protein Expression and Purification system) ; National Key Ramp;D Program of China[2022YFC3400500] ; National Key Ramp;D Program of China[2021ZD0203900] ; National Natural Science Foundation of China[22377089] ; National Natural Science Foundation of China[32000915] ; National Natural Science Foundation of China[61303108] ; National Natural Science Foundation of China[81821005] ; National Natural Science Foundation of China[92253303] ; National Multidisciplinary Innovation Team of Traditional Chinese Medicine - National Administration of Traditional Chinese Medicine[ZYYCXTD-202004] ; High-level new Ramp;D Institute[2019B090904008] ; High-level Innovative Research Institute[2021B0909050003] ; Department of Science and Technology of Guangdong Province ; State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences[SIMM2205KF-11] ; Priority Academic Program Development of Jiangsu Higher Education Institutions, China
WOS研究方向Cell Biology
语种英语
出版者CELL PRESS
WOS记录号WOS:001071972700001
源URL[http://119.78.100.183/handle/2S10ELR8/307143]  
专题新药研究国家重点实验室
通讯作者Zhu, Fei; Luo, Cheng
作者单位1.Soochow Univ, Jiangsu Prov Engn Res Ctr Precis Diagnost & Therap, Suzhou 215123, Peoples R China
2.Fujian Med Univ, Sch Pharm, Fuzhou 350122, Peoples R China
3.Shanghai Tech Univ, Sch Life Sci & Technol, 100 Haike Rd, Shanghai 201210, Peoples R China
4.UCAS, Hangzhou Inst Adv Study, Sch Pharmaceut Sci & Technol, Hangzhou 310024, Peoples R China
5.Soochow Univ, Sch Comp Sci & Technol, Suzhou 215006, Peoples R China
6.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China
7.Chinese Acad Sci, Zhongshan Inst Drug Discovery, Shanghai Inst Mat Med, Zhongshan 528437, Peoples R China
8.Soochow Univ, Ctr Syst Biol, Sch Biol & Basic Med Sci, Dept Bioinformat, Suzhou 215123, Peoples R China
推荐引用方式
GB/T 7714		 Liang, Zhongjie,Liu, Tonghai,Li, Qi,et al. Deciphering the functional landscape of phosphosites with deep neural network[J]. CELL REPORTS,2023,42(9):23.
APA Liang, Zhongjie.,Liu, Tonghai.,Li, Qi.,Zhang, Guangyu.,Zhang, Bei.,...&Luo, Cheng.(2023).Deciphering the functional landscape of phosphosites with deep neural network.CELL REPORTS,42(9),23.
MLA Liang, Zhongjie,et al."Deciphering the functional landscape of phosphosites with deep neural network".CELL REPORTS 42.9(2023):23.

入库方式： OAI收割

来源：上海药物研究所

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