標題: Incorporating hidden Markov models for identifying protein kinase-specific phosphorylation sites
作者: Huang, HD
Lee, TY
Tzeng, SW
Wu, LC
Horng, JT
Tsou, AP
Huang, KT
生物資訊及系統生物研究所
Institude of Bioinformatics and Systems Biology
關鍵字: phosphorylation;protein kinase;profile hidden Markov model
公開日期: 30-Jul-2005
摘要: 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. (c) 2005 Wiley Periodicals, Inc.
URI: http://dx.doi.org/10.1002/jcc.20235
http://hdl.handle.net/11536/13467
ISSN: 0192-8651
DOI: 10.1002/jcc.20235
期刊: JOURNAL OF COMPUTATIONAL CHEMISTRY
Volume: 26
Issue: 10
起始頁: 1032
結束頁: 1041
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