首页|KinasePhos 3.0:Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites

KinasePhos 3.0:Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites

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The purpose of this work is to enhance KinasePhos,a machine learning-based kinase-specific phosphorylation site prediction tool.Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSitePlus,UniProtKB,the GPS 5.0,and Phospho.ELM.In total,41,421 experimentally verified kinase-specific phosphorylation sites were identified.A total of 1380 unique kinases were identified,including 753 with existing classification information from KinBase and the remaining 627 annotated by building a phylogenetic tree.Based on this kinase classification,a total of 771 predictive models were built at the individual,family,and group levels,using at least 15 experimentally verified substrate sites in positive training datasets.The improved models demon-strated their effectiveness compared with other prediction tools.For example,the prediction of sites phosphorylated by the protein kinase B,casein kinase 2,and protein kinase A families had accura-cies of 94.5％,92.5％,and 90.0％,respectively.The average prediction accuracy for all 771 models was 87.2％.For enhancing interpretability,the SHapley Additive exPlanations(SHAP)method was employed to assess feature importance.The web interface of KinasePhos 3.0 has been redesigned to provide comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins.Additionally,considering the large scale of phosphoproteomic data,a downloadable prediction tool is available at https://awi.cuhk.edu.cn/KinasePhos/download.html or https://github.com/tom-209/KinasePhos-3.0-executable-file.

Kinase-specific phosphorylationPhosphorylation site predictionPhosphorylationSHAP feature importanceKinase

Renfei Ma、Shangfu Li、Wenshuo Li、Lantian Yao、Hsien-Da Huang、Tzong-Yi Lee

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Warshel Institute for Computational Biology,School of Medicine,The Chinese University of Hong Kong,Shenzhen 518172,China

School of Life Sciences,University of Science and Technology of China,Hefei 230027,China

School of Science and Engineering,The Chinese University of Hong Kong,Shenzhen 518172,China

School of Life and Health Sciences,School of Medicine,The Chinese University of Hong Kong,Shenzhen 518172,China

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National Natural Science Foundation of ChinaScience,Technology and Innovation Commission of Shenzhen MunicipalityGuangdong Province Basic and Applied Basic Research FundGanghong Young Scholar Development Fund,ChinaWarshel Institute for Computational Biology funding from Shenzhen City and Longgang District,China

32070659JCYJ202001091500039382021A15150124472021E007

2023

10.1016/j.gpb.2022.06.004

基因组蛋白质组与生物信息学报(英文版)
中国科学院北京基因组研究所

基因组蛋白质组与生物信息学报(英文版)

CSTPCDCSCD
影响因子：0.495
ISSN：1672-0229
年,卷(期)：2023.21(1)
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