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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

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Free PMC article

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. .
Free PMC article

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/.

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.

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References

    1. Berry EA, Dalby AR, Yang ZR. Reduced bio basis function neural network for identification of protein phosphorylation sites: comparison with pattern recognition algorithms. Comput. Biol. Chem. 2004;28:75–85. - PubMed
    1. Stock AM, Robinson VL, Goudreau PN. Two-component signal transduction. Annu. Rev. Biochem. 2000;69:183–215. - PubMed
    1. Manning G, Whyte DB, Martinez R, Hunter T, Sudarsanam S. The protein kinase complement of the human genome. Science. 2002;298:1912–1934. - PubMed
    1. Xue Y, Li A, Wang L, Feng H, Yao X. PPSP: prediction of PK-specific phosphorylation site with Bayesian decision theory. BMC Bioinformatics. 2006;7:163. - PMC - PubMed
    1. Huang HD, Lee TY, Tzeng SW, Horng JT. KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites. Nucleic Acids Res. 2005;33:W226–W229. - PMC - PubMed

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