PKIS: computational identification of protein kinases for experimentally discovered protein phosphorylation sites.
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.
Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. mhwang@ustc.edu.cn.
Liang
L
Zou
Mang
M
Wang
Yi
Y
Shen
Jie
J
Liao
Ao
A
Li
Minghui
M
Wang
D000818:Animals; D016203:CDC2 Protein Kinase / Q000737:chemistry*; D047390:Casein Kinase II / Q000737:chemistry*; D019295:Computational Biology / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D020407:Internet; D047908:Intracellular Signaling Peptides and Proteins / Q000737:chemistry*; D010766:Phosphorylation; D011237:Predictive Value of Tests; D011505:Protein-Tyrosine Kinases / Q000737:chemistry*; D019859:Proto-Oncogene Proteins c-met / Q000737:chemistry*; D015398:Signal Transduction; D013379:Substrate Specificity; D060388:Support Vector Machine; D000072377:Syk Kinase
D047908:Intracellular Signaling Peptides and Proteins; C491743:MET protein, human; D011505:Protein-Tyrosine Kinases; D019859:Proto-Oncogene Proteins c-met; C000606896:SYK protein, human; D000072377:Syk Kinase; D047390:Casein Kinase II; D016203:CDC2 Protein Kinase