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FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics

文献类型：期刊论文

作者Zhang, Guangyu4; Zhang, Cai4; Cai, Mingyue3; Luo, Cheng2; Zhu, Fei4; Liang, Zhongjie1,3
刊名INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES
出版日期2024-05-01
卷号266页码:11
关键词Functional phosphosite Deep learning AlphaFold protein structure Dynamics Protein protein interaction
ISSN号0141-8130
DOI10.1016/j.ijbiomac.2024.131180
通讯作者Zhu, Fei(zhufei@suda.edu.cn) ; Liang, Zhongjie(zjliang@suda.edu.cn)
英文摘要Phosphorylation modifications play important regulatory roles in most biological processes. However, the functional assignment for the vast majority of the identified phosphosites remains a major challenge. Here, we provide a deep learning framework named FuncPhos-STR as an online resource, for functional prediction and structural visualization of human proteome-level phosphosites. Based on our reported FuncPhos-SEQ framework, which was built by integrating phosphosite sequence evolution and protein-protein interaction (PPI) information, FuncPhos-STR was developed by further integrating the structural and dynamics information on AlphaFold protein structures. The characterized structural topology and dynamics features underlying functional phosphosites emphasized their molecular mechanism for regulating protein functions. By integrating the structural and dynamics, sequence evolutionary, and PPI network features from protein different dimensions, FuncPhosSTR has advantage over other reported models, with the best AUC value of 0.855. Using FuncPhos-STR, the phosphosites inside the pocket regions are accessible to higher functional scores, theoretically supporting their potential regulatory mechanism. Overall, FuncPhos-STR would accelerate the functional identification of huge unexplored phosphosites, and facilitate the elucidation of their allosteric regulation mechanisms. The web server of FuncPhos-STR is freely available at http://funcptm.jysw.suda.edu.cn/str.
WOS关键词REGULATORY ELEMENTS ; PHOSPHORYLATION ; RESOURCE ; PLATFORM ; REVEALS ; BIOLOGY
资助项目Natural Science Foundation of China[22377089] ; Natural Science Foundation of China[81821005] ; Natural Science Foundation of China[92253303] ; Priority Academic Program Development of Jiangsu Higher Education Institutions, China
WOS研究方向Biochemistry & Molecular Biology ; Chemistry ; Polymer Science
语种英语
WOS记录号WOS:001223574600001
出版者ELSEVIER
源URL[http://119.78.100.183/handle/2S10ELR8/311270]  
专题新药研究国家重点实验室
通讯作者Zhu, Fei; Liang, Zhongjie
作者单位1.Soochow Univ, Jiangsu Prov Engn Res Ctr Precis Diagnost & Therap, Suzhou 215123, Peoples R China
2.Chinese Acad Sci, State Key Lab Drug Res, Shanghai Inst Mat Med, 555 Zuchongzhi Rd, Shanghai 201203, Peoples R China
3.Soochow Univ, Ctr Syst Biol, Sch Biol & Basic Med Sci, Dept Bioinformat, Suzhou 215123, Peoples R China
4.Soochow Univ, Sch Comp Sci & Technol, Suzhou 215006, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Guangyu,Zhang, Cai,Cai, Mingyue,et al. FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics[J]. INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES,2024,266:11.
APA Zhang, Guangyu,Zhang, Cai,Cai, Mingyue,Luo, Cheng,Zhu, Fei,&Liang, Zhongjie.(2024).FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics.INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES,266,11.
MLA Zhang, Guangyu,et al."FuncPhos-STR: An integrated deep neural network for functional phosphosite prediction based on AlphaFold protein structure and dynamics".INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES 266(2024):11.

入库方式： OAI收割

来源：上海药物研究所

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