Zhao Ming-Xiao, Chen Qiang, Li Fulai, Fu Songsen, Huang Biling, Zhao Yufen
Department of Chemical Biology, Key Laboratory for Chemical Biology of Fujian Province, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian 361005, China.
Department of Biochemistry and Molecular Biology, and Zhejiang Key Laboratory of Pathophysiology, Medical School of Ningbo University, Ningbo 315211, China.
Brief Bioinform. 2023 Mar 19;24(2). doi: 10.1093/bib/bbad090.
Protein 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.
蛋白质磷酸化是主要的蛋白质翻译后修饰之一，是调节各种生命活动所必需的。调节人类蛋白质磷酸化的激酶和磷酸酶已成为治疗各种疾病（尤其是癌症）的靶点。发现蛋白质磷酸化位点的高通量实验方法既费力又耗时。新兴的数据库和预测工具为研究界提供了重要的基础设施。迄今为止，已经分别开发了60多个公开可用的磷酸化数据库和预测工具。在本综述中，我们全面总结了主要在线磷酸化数据库和预测工具的现状及适用性，从而帮助研究人员快速选择最适合其项目的工具。此外，还强调了这些数据库和预测工具的组织策略及局限性，这可能有助于在计算机上开发出更好的蛋白质磷酸化预测工具。
