Protein phosphorylation catalyzed by kinase plays crucial regulatory role in intracellular signal transduction that transmits information from the cell surface to the nucleus, where they ultimately effect transcriptional changes. With the full annotation of human kinome identified by Manning et al., there is a starting point for comprehensive analysis of intracellular protein phosphorylation networks. Mass spectrometry-based proteomics have enabled the large-scale mapping of in vivo phosphorylation sites. In order to fully and accurately investigate the phosphorylation networks, the experimentally validated phosphorylation site databases have been integrated. However, only 20% experimental phosphorylation sites have the annotation of catalytic kinases, covering 350 kinases (67%). Experimental identification of kinase-specific phosphorylation sites is an inconvenient work and usually limited by the availability of detailed data on the kinase-specific substrates. In silico prediction could be a promising strategy to conduct preliminary analyses and could greatly reduce the number of potential targets that need further in vivo or in vitro confirmation.
The presented method, namely RegPhos, was designed to link experimentally validated phosphorylation sites to protein kinases. Due to the fact that signaling proteins are modular in the sense that they contain domains (catalytic or interaction) and linear motifs (phosphorylation or binding sites), which mediate interactions between proteins, the protein-protein interaction, protein functional association, and cellular localization are incorporated. Investigating into the predictive power of the context of protein associations, physical protein interactions play the dominant role among the primary experimental data, whereas gene coexpression contributes un-robust correlation between kinase and substrate genes. Physical protein interactions were imported and merged from numerous repositories, and the reliability of each individual interaction was assessed based on the promiscuity of the interaction partners. After the evaluation, the improved predictive power gained from using context of protein association underlines the importance of kinase-substrate interactions in the specificity of protein phosphorylation within cells. The predictive specificity of kinase groups with similar consensus motifs can be improved by the consideration of protein association.
We would also suggest that this underlines the utility of protein association data in modeling cellular processes.
To complete the intracellular processes about protein kinases and phosphorylation, the
identified kinase-substrate interactions were adopted to fully construct the intracellular phosphorylation networks starting from membrane receptor to transcription factors. The discovered phosphorylation networks were validated by calculating the Pearson correlation coefficient of gene expression patterns between kinase and substrate genes across 9 time-coursed experiment series of Affymetrix GeneChip Human Genome U133 Array Set HG-U133A platform (GPL96) collected from Gene Expression Omnibus repository. As illustrated in case study, the discovered phosphorylation networks with highly correlated expression pattern demonstrated that they may be involved in insulin signaling pathway or EGF signaling pathway.
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