KinasePhos is to computationally predict phosphorylation sites within given protein sequences. The known phosphorylation sites are categorized by substrate sequences and their corresponding protein kinase classes. Profile Hidden Markov Model (HMM) is applied for learning to each group of sequences surrounding to the phosphorylation residues. By comparing to other approaches previously developed, our method has higher accuracy and provides not only the location of the phosphorylation sites, but also the corresponding catalytic protein kinases
Molecular Bioinformatics Center, National Chiao Tung University
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KinasePhos 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns.
Wong YH, Lee TY, Liang HK, Huang CM, Wang TY, Yang YH, Chu CH, Huang HD, Ko MT, Hwang JK.
Nucleic Acids Res. 2007 Jul;35(Web Server issue):W588-94.