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J Comput Chem. 2005 Jul 30;26(10):1032-41.

Incorporating hidden Markov models for identifying protein kinase-specific phosphorylation sites.

Author information

1
Department of Biological Science and Technology, Institute of Bioinformatics, National Chiao Tung University, Hsin-Chu 300, Taiwan, Republic of China. bryan@mail.nctu.edu.tw

Abstract

Protein phosphorylation, which is an important mechanism in posttranslational modification, affects essential cellular processes such as metabolism, cell signaling, differentiation, and membrane transportation. Proteins are phosphorylated by a variety of protein kinases. In this investigation, we develop a novel tool to computationally predict catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the concepts of profile Hidden Markov Models (HMM), computational models are trained from the kinase-specific groups of phosphorylation sites. After evaluating the trained models, we select the model with highest accuracy in each kinase-specific group and provide a Web-based prediction tool for identifying protein phosphorylation sites. The main contribution here is that we have developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity.

PMID:
15889432
DOI:
10.1002/jcc.20235
[Indexed for MEDLINE]

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