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. 2007 Jul;35(Web Server issue):W588-94.
doi: 10.1093/nar/gkm322. Epub 2007 May 21.

KinasePhos 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns

Yung-Hao Wong  1 , Tzong-Yi Lee, Han-Kuen Liang, Chia-Mao Huang, Ting-Yuan Wang, Yi-Huan Yang, Chia-Huei Chu, Hsien-Da Huang, Ming-Tat Ko, Jenn-Kang Hwang
Affiliations

KinasePhos 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns

Yung-Hao Wong et al. Nucleic Acids Res. 2007 Jul.

Abstract

Due to the importance of protein phosphorylation in cellular control, many researches are undertaken to predict the kinase-specific phosphorylation sites. Referred to our previous work, KinasePhos 1.0, incorporated profile hidden Markov model (HMM) with flanking residues of the kinase-specific phosphorylation sites. Herein, a new web server, KinasePhos 2.0, incorporates support vector machines (SVM) with the protein sequence profile and protein coupling pattern, which is a novel feature used for identifying phosphorylation sites. The coupling pattern [XdZ] denotes the amino acid coupling-pattern of amino acid types X and Z that are separated by d amino acids. The differences or quotients of coupling strength C(XdZ) between the positive set of phosphorylation sites and the background set of whole protein sequences from Swiss-Prot are computed to determine the number of coupling patterns for training SVM models. After the evaluation based on k-fold cross-validation and Jackknife cross-validation, the average predictive accuracy of phosphorylated serine, threonine, tyrosine and histidine are 90, 93, 88 and 93%, respectively. KinasePhos 2.0 performs better than other tools previously developed. The proposed web server is freely available at http://KinasePhos2.mbc.nctu.edu.tw/.

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Figures

Figure 1.
Figure 1.
The system flow of KinasePhos 2.0.
Figure 2.
Figure 2.
The comparison for the average precision (Prec), sensitivity (Sn), specificity (Sp) and accuracy (Acc) among the models trained with various features in phosphoserine, phosphothreonine, phosphotyrosine and phosphohistidine.
Figure 3.
Figure 3.
The web interface of KinasePhos 2.0.
See this image and copyright information in PMC

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