Protein phosphorylation database and prediction tools

Author:

Zhao Ming-Xiao12ORCID,Chen Qiang34,Li Fulai5,Fu Songsen5,Huang Biling5,Zhao Yufen12567

Affiliation:

1. Department of Chemical Biology , Key Laboratory for Chemical Biology of Fujian Province, College of Chemistry and Chemical Engineering, , Xiamen, Fujian 361005 , China

2. Xiamen University , Key Laboratory for Chemical Biology of Fujian Province, College of Chemistry and Chemical Engineering, , Xiamen, Fujian 361005 , China

3. Department of Biochemistry and Molecular Biology , and Zhejiang Key Laboratory of Pathophysiology, , Ningbo 315211 , China

4. Medical School of Ningbo University , and Zhejiang Key Laboratory of Pathophysiology, , Ningbo 315211 , China

5. Institute of Drug Discovery Technology, Ningbo University , Ningbo 315211 , China

6. Key Laboratory of Bioorganic Phosphorus Chemistry and Chemical Biology , Department of Chemistry, , Beijing 100084 , China

7. Tsinghua University , Department of Chemistry, , Beijing 100084 , China

Abstract

AbstractProtein phosphorylation, one of the main protein post-translational modifications, is required for regulating various life activities. Kinases and phosphatases that regulate protein phosphorylation in humans have been targeted to treat various diseases, particularly cancer. High-throughput experimental methods to discover protein phosphosites are laborious and time-consuming. The burgeoning databases and predictors provide essential infrastructure to the research community. To date, >60 publicly available phosphorylation databases and predictors each have been developed. In this review, we have comprehensively summarized the status and applicability of major online phosphorylation databases and predictors, thereby helping researchers rapidly select tools that are most suitable for their projects. Moreover, the organizational strategies and limitations of these databases and predictors have been highlighted, which may facilitate the development of better protein phosphorylation predictors in silico.

Funder

National Key Research and Development Program of China

Project supported by Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences

Scientific Research Grant of Ningbo University

Ningbo Top Talent Project

National Natural Science Foundation of China

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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