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.

HybridSucc:A Hybrid-learning Architecture for General and Species-specific Succinylation Site Prediction

As an important protein acylation modification,lysine succinylation(Ksucc)is involved in diverse biological processes,and participates in human tumorigenesis.Here,we collected 26,243 non-redundant known Ksucc sites from 13 species as the benchmark data set,combined 10 types of informative features,and implemented a hybrid-learning architecture by integrating deep-learning and conventional machine-learning algorithms into a single framework.We constructed a new tool named HybridSucc,which achieved area under curve(AUC)values of 0.885 and 0.952 for general and human-specific prediction of Ksucc sites,respectively.In comparison,the accuracy of HybridSucc was 17.84%-50.62%better than that of other existing tools.Using HybridSucc,we conducted a proteome-wide prediction and prioritized 370 cancer mutations that change Ksucc states of 218 important proteins,including PKM2,SHMT2,and IDH2.We not only developed a high-profile tool for predicting Ksucc sites,but also generated useful candidates for further experimental consideration.The online service of HybridSucc can be freely accessed for academic research at http://hybridsucc.biocuckoo.org/.