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. 2013 Aug 13;14:247.
doi: 10.1186/1471-2105-14-247.

PKIS: Computational Identification of Protein Kinases for Experimentally Discovered Protein Phosphorylation Sites

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Free PMC article

PKIS: Computational Identification of Protein Kinases for Experimentally Discovered Protein Phosphorylation Sites

Liang Zou et al. BMC Bioinformatics. .
Free PMC article

Abstract

Background: Dynamic protein phosphorylation is an essential regulatory mechanism in various organisms. In this capacity, it is involved in a multitude of signal transduction pathways. Kinase-specific phosphorylation data lay the foundation for reconstruction of signal transduction networks. For this reason, precise annotation of phosphorylated proteins is the first step toward simulating cell signaling pathways. However, the vast majority of kinase-specific phosphorylation data remain undiscovered and existing experimental methods and computational phosphorylation site (P-site) prediction tools have various limitations with respect to addressing this problem.

Results: To address this issue, a novel protein kinase identification web server, PKIS, is here presented for the identification of the protein kinases responsible for experimentally verified P-sites at high specificity, which incorporates the composition of monomer spectrum (CMS) encoding strategy and support vector machines (SVMs). Compared to widely used P-site prediction tools including KinasePhos 2.0, Musite, and GPS2.1, PKIS largely outperformed these tools in identifying protein kinases associated with known P-sites. In addition, PKIS was used on all the P-sites in Phospho.ELM that currently lack kinase information. It successfully identified 14 potential SYK substrates with 36 known P-sites. Further literature search showed that 5 of them were indeed phosphorylated by SYK. Finally, an enrichment analysis was performed and 6 significant SYK-related signal pathways were identified.

Conclusions: In general, PKIS can identify protein kinases for experimental phosphorylation sites efficiently. It is a valuable bioinformatics tool suitable for the study of protein phosphorylation. The PKIS web server is freely available at http://bioinformatics.ustc.edu.cn/pkis.

Figures

Figure 1
Figure 1
Prediction performance of models with different single-side window sizes m. (A) The escalating trend for AUC with the improvement of m. The slope of the left side is larger than that of the right. (B) The optimal m for kinases is diverse. Sensitivity was evaluated when the corresponding specificity was greater than or equal to 99%.
Figure 2
Figure 2
Difference of amino acid distributions in positive and negative data. Panels (A) and (B) represent distinct amino acid distribution patterns in CK2 alpha’s positive and negative datasets, respectively. Panels (C) and (D) represent different amino acid distribution patterns in CDC2’s positive and negative datasets, respectively. The X-axis represents the single side window size m.
Figure 3
Figure 3
Performance of two sequence encoding strategies: CMS and binary encoding. (A) Performance of CK2 alpha models using the CMS and binary encoding strategies. (B) Performance of CDC2 models using CMS and binary encoding strategies. The red lines represent the CMS method and the black lines represent the binary method.
Figure 4
Figure 4
Comparing with kinase-specific P-site prediction tools: KinasePhos2.0, Musite, and GPS2.1 at high specificities. Panel (A) depicts the performance of the tool in CK2 alpha kinase and (B) illustrates the performance in CDC2 kinase. The ROC curves of PKIS are plotted in red solid lines.

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