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Leveraging Protein Dynamics to Identify Functional Sites Models

文献类型：期刊论文

作者Zhu, Fei4,5; Yang, Sijie4; Meng, Fanwang3; Zheng, Yuxiang5; Ku, Xin2; Luo, Cheng1; Hu, Guang5; Liang, Zhongjie1,2,5
刊名JOURNAL OF CHEMICAL INFORMATION AND MODELING
出版日期2022-07-25
卷号62期号:14页码:3331-3345
ISSN号1549-9596
DOI10.1021/acs.jcim.2c004843331J
通讯作者Hu, Guang(huguang@suda.edu.cn) ; Liang, Zhongjie(zjliang@suda.edu.cn)
英文摘要Accurate prediction of post-translational modifications (PTMs) is of great significance in understanding cellular processes, by modulating protein structure and dynamics. Nowadays, with the rapid growth of protein data at different ''omics'' levels, machine learning models largely enriched the prediction of PTMs. However, most machine learning models only rely on protein sequence and little structural information. The lack of the systematic dynamics analysis underlying PTMs largely limits the PTM functional predictions. In this research, we present two dynamicscentric deep learning models, namely, cDL-PAU and cDL-FuncPhos, by incorporating sequence, structure, and dynamics-based features to elucidate the molecular basis and underlying functional landscape of PTMs. cDLPAU achieved satisfactory area under the curve (AUC) scores of 0.804-0.888 for predicting phosphorylation, acetylation, and ubiquitination (PAU) sites, while cDL-FuncPhos achieved an AUC value of 0.771 for predicting functional phosphorylation (FuncPhos) sites, displaying reliable improvements. Through a feature selection, the dynamics-based coupling and commute ability show large contributions in discovering PAU sites and FuncPhos sites, suggesting the allosteric propensity for important PTMs. The application of cDL-FuncPhos in three oncoproteins not only corroborates its strong performance in FuncPhos prioritization but also gains insight into the physical basis for the functions. The source code and data set of cDL-PAU and cDL-FuncPhos are available at https://github.com/ComputeSuda/PTM_ML.
WOS关键词POSTTRANSLATIONAL MODIFICATION SITES ; REGULATORY ELEMENTS ; STRUCTURAL-ANALYSIS ; SEQUENCE EVOLUTION ; UBIQUITINATION ; CROSSTALK ; MUTATIONS ; DBPTM ; TOOL
资助项目Key Laboratory of Systems Biomedicine (Ministry of Education) ; Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University[KLSB2019KF-02] ; State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences[SIMM2205KF-11] ; National Natural Science Foundation of China[31872723] ; National Natural Science Foundation of China[61303108] ; Natural Science Foundation of Jiangsu Province[BK20211102] ; Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions
WOS研究方向Pharmacology & Pharmacy ; Chemistry ; Computer Science
语种英语
WOS记录号WOS:000855024600001
出版者AMER CHEMICAL SOC
源URL[http://119.78.100.183/handle/2S10ELR8/302483]  
专题新药研究国家重点实验室
通讯作者Hu, Guang; Liang, Zhongjie
作者单位1.Chinese Acad Sci, Shanghai Inst Mat Med, State Key Lab Drug Res, Shanghai 201203, Peoples R China
2.Shanghai Jiao Tong Univ, Shanghai Ctr Syst Biomed, Key Lab Syst Biomed, Minist Educ, Shanghai 200240, Peoples R China
3.McMaster Univ, Dept Chem & Chem Biol, Hamilton, ON L8S 4L8, Canada
4.Soochow Univ, Sch Comp Sci & Technol, Suzhou 215006, Peoples R China
5.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		 Zhu, Fei,Yang, Sijie,Meng, Fanwang,et al. Leveraging Protein Dynamics to Identify Functional Sites Models[J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING,2022,62(14):3331-3345.
APA Zhu, Fei.,Yang, Sijie.,Meng, Fanwang.,Zheng, Yuxiang.,Ku, Xin.,...&Liang, Zhongjie.(2022).Leveraging Protein Dynamics to Identify Functional Sites Models.JOURNAL OF CHEMICAL INFORMATION AND MODELING,62(14),3331-3345.
MLA Zhu, Fei,et al."Leveraging Protein Dynamics to Identify Functional Sites Models".JOURNAL OF CHEMICAL INFORMATION AND MODELING 62.14(2022):3331-3345.

入库方式： OAI收割

来源：上海药物研究所

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