Author: Stacho, Ladislav
Author: Manuch, Jan
Author: Safaei, Javad
Background: Complex intracellular signaling networks monitor diverse environmental inputs to evoke appropriateand coordinated effector responses. Defective signal transduction underlies many pathologies, including cancer,diabetes, autoimmunity and about 400 other human diseases. Therefore, there is high impetus to define thecomposition and architecture of cellular communications networks in humans. The major components ofintracellular signaling networks are protein kinases and protein phosphatases, which catalyze the reversiblephosphorylation of proteins. Here, we have focused on identification of kinase-substrate interactions throughprediction of the phosphorylation site specificity from knowledge of the primary amino acid sequence of thecatalytic domain of each kinase.Results: The presented method predicts 488 different kinase catalytic domain substrate specificity matrices in 478typical and 4 atypical human kinases that rely on both positive and negative determinants for scoring individualphosphosites for their suitability as kinase substrates. This represents a marked advancement over existing methodssuch as those used in NetPhorest (179 kinases in 76 groups) and NetworKIN (123 kinases), which consider onlypositive determinants for kinase substrate prediction. Comparison of our predicted matrices with experimentallyderivedmatrices from about 9,000 known kinase-phosphosite substrate pairs revealed a high degree ofconcordance with the established preferences of about 150 well studied protein kinases. Furthermore for many ofthe better known kinases, the predicted optimal phosphosite sequences were more accurate than the consensusphosphosite sequences inferred by simple alignment of the phosphosites of known kinase substrates.Conclusions: Application of this improved kinase substrate prediction algorithm to the primary structures of over23, 000 proteins encoded by the human genome has permitted the identification of about 650, 000 putativephosphosites, which are posted on the open source PhosphoNET website (http://www.phosphonet.ca).
Safaei et al. Proteome Science 2011, 9(Suppl 1):S6
Prediction of 492 Human Protein Kinase Substrate Specificities
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