Browsing by Author "Tzeng, SW"

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Incorporating hidden Markov models for identifying protein kinase-specific phosphorylation sites
(2005-07-30)
Huang, HD
;
Lee, TY
;
Tzeng, SW
;
Wu, LC
;
Horng, JT
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.
KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites
(2005-07-01)
Huang, HD
;
Lee, TY
;
Tzeng, SW
;
Horng, JT
;
生物資訊及系統生物研究所
KinasePhos is a novel web server for computationally identifying catalytic kinase-specific phosphorylation sites. The known phosphorylation sites from public domain data sources are categorized by their annotated protein kinases. Based on the profile hidden Markov model, computational models are learned from the kinase-specific groups of the phosphorylation sites. After evaluating the learned models, the model with highest accuracy was selected from each kinase-specific group, for use in a web-based prediction tool for identifying protein phosphorylation sites. Therefore, this work developed a kinase-specific phosphorylation site prediction tool with both high sensitivity and specificity. The prediction tool is freely available at http://KinasePhos.mbc. nctu.edu.tw/.