RegPhos: A system to explore the protein kinase-substrate phosphorylation network in humans

Tzong Yi Lee, Justin Bo Kai Hsu, Wen Chi Chang, Hsien Da Huang

Research output: Contribution to journalArticle

45 Citations (Scopus)

Abstract

Protein phosphorylation catalyzed by kinases plays crucial regulatory roles in intracellular signal transduction. With the increasing number of experimental phosphorylation sites that has been identified by mass spectrometry-based proteomics, the desire to explore the networks of protein kinases and substrates is motivated. Manning et al. have identified 518 human kinase genes, which provide a starting point for comprehensive analysis of protein phosphorylationnetworks. In this study, a knowledgebase is developed to integrate experimentally verified protein phosphorylation data and protein- protein interaction data for constructing the protein kinase-substrate phosphorylation networks in human. A total of 21 110 experimental verified phosphorylation sites within 5092 human proteins are collected. However, only 4138 phosphorylation sites (̃20%) have the annotation of catalytic kinases from public domain. In order to fully investigate how protein kinases regulate the intracellular processes, a published kinase-specific phosphorylation site prediction tool, named KinasePhos is incorporated for assigning the potential kinase. The web-based system, RegPhos, can let users input a group of human proteins; consequently, the phosphorylation network associated with the protein subcellular localization can be explored. Additionally, time-coursed microarray expression data is subsequently used to represent the degree of similarity in the expression profiles of network members. A case study demonstrates that the proposed scheme not only identify the correct network of insulin signaling but also detect a novel signaling pathway that may cross-talk with insulin signaling network. This effective system is now freely available at http://RegPhos.mbc. nctu.edu.tw.

Original languageEnglish
Pages (from-to)D777-D787
JournalNucleic acids research
Volume39
Issue numberSUPPL. 1
DOIs
Publication statusPublished - 2011 Jan 1

All Science Journal Classification (ASJC) codes

  • Genetics

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