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

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.

GPS 5.0: An Update on the Prediction of Kinase-specific Phosphorylation Sites in Proteins

In eukaryotes,protein phosphorylation is specifically catalyzed by numerous protein kinases(PKs),faithfully orchestrates various biological processes,and reversibly determines cellular dynamics and plasticity.Here we report an updated algorithm of Group-based Prediction System(GPS)5.0 to improve the performance for predicting kinase-specific phosphorylation sites(p-sites).Two novel methods,position weight determination(PWD)and scoring matrix optimization(SMO),were developed.Compared with other existing tools,GPS 5.0 exhibits a highly competitive accuracy.Besides serine/threonine or tyrosine kinases,GPS 5.0 also supports the prediction of dual-specificity kinase-specific p-sites.In the classical module of GPS 5.0,617 individual predictors were constructed for predicting p-sites of 479 human PKs.To extend the application of GPS5.0,a species-specific module was implemented to predict kinase-specific p-sites for 44,795 PKs in161 eukaryotes.The online service and local packages of GPS 5.0 are freely available for academic research at http://gps.biocuckoo.cn.