Incorporating hidden Markov models for identifying protein kinase-specific phosphorylation sites.
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.
Huang
Hsien-Da
HD
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
Lee
Tzong-Yi
TY
Tzeng
Shih-Wei
SW
Wu
Li-Cheng
LC
Horng
Jorng-Tzong
JT
Tsou
Ann-Ping
AP
Huang
Kuan-Tsae
KT
D019295:Computational Biology; D030562:Databases, Protein; D008390:Markov Chains; D008958:Models, Molecular; D010766:Phosphorylation; D011494:Protein Kinases / Q000737:chemistry*; D011506:Proteins / Q000737:chemistry
D011506:Proteins; D011494:Protein Kinases