KinasePhos 3.0 a machine-learning-based kinase-specific phosphorylation site prediction tool

Introduction

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, UniProt, 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. 

Publications

  1. KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-specific Phosphorylation Sites
    Renfei Ma, Shangfu Li, Wenshuo Li, Lantian Yao, Hsien-Da Huang, Tzong-Yi Lee, 2021/11/4 -

Credits

  1. R M marenfei@cuhk.edu.cn
    Investigator

    Warshel institute for computational biology, Chinese University of Hong Kong-Shenzhen, China

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Summary
AccessionBT007291
Tool TypeApplication
Category
PlatformsWindows
Technologies
User InterfaceWebpage
Latest Releaseversion3.0 (December 29, 2021)
Download Count816
Country/RegionChina
Submitted ByR M
Fundings

The improved models were observed to be more effective than other prediction tools. For example, the prediction of sites phosphorylated by the Akt, CK2, and PKA 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 with the goal of providing comprehensive annotations of kinase-specific phosphorylation sites on multiple proteins.