The purpose of this work is to enhance KinasePhos,a machine learning-based kinasespecific phosphorylation site prediction tool.Experimentally verified kinase-specific phosphorylation data were collected from PhosphoSi...The purpose of this work is to enhance KinasePhos,a machine learning-based kinasespecific 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 demonstrated 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 accuracies 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.展开更多
基金The authors express their gratitude toward all database developers mentioned and quoted in this article for their important work and the data they shared.The author also would like to thank users for their comments and suggestions on the previous version of KinasePhos.This work was supported by the National Natural Science Foundation of China(Grant No.32070659)the Science,Technology and Innovation Commission of Shenzhen Municipality(Grant No.JCYJ20200109150003938)+1 种基金the Guangdong Province Basic and Applied Basic Research Fund(Grant No.2021A1515012447)the Ganghong Young Scholar Development Fund(Grant No.2021E007),China.This work is supported by the Warshel Institute for Computational Biology funding from Shenzhen City and Longgang District,China.
文摘The purpose of this work is to enhance KinasePhos,a machine learning-based kinasespecific 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 demonstrated 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 accuracies 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.
