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Modeling human phosphorylation networks through kinome-wide profiling


Biography

Overview
Protein phosphorylation is the most common reversible post-translational modification in eukaryotes, yet signaling networks comprising protein kinases, their regulators, and their substrates are only partially elucidated. The overall goals of the proposed project are to build a comprehensive collection of consensus phosphorylation motifs for the entire collection of protein kinases encoded in the human genome using arrayed positional scanning peptide libraries, and integrate this data into web-accessible tools that are currently available to the entire biomedical community. The resulting dataset of protein kinase specificity motifs and informatics tools will: (1) allow functional annotation of a large number of proteins whose phosphorylation sites already have been, or currently are, being mapped in high-throughput phosphoproteomic mass- spectrometry experiments and datasets that have been previously funded by the NIH by now identifying the relevant kinase and signaling pathways responsible for these modifications; (2) allow the identification of new protein kinase substrates relevant to human health and disease and place them within the context of specific signal transduction pathways; and (3) provide a general set of kinase tools useful for structural and drug inhibitor studies and for the therapeutic targeting of specific signaling pathways implicated in human disease. To illustrate the utility of our approach, we will investigate predicted substrates of protein kinases in the Hippo signaling pathway, a conserved tumor suppressor pathway important in regulating cell proliferation, differentiation and survival. This project will serve to increase our fundamental understanding of how specificity is achieved by protein kinases, will identify critical connections in signaling networks, and will provide a general resource for researchers studying signal transduction and protein phosphorylation.
R01GM104047
TURK, BENJAMIN E

Time
2013-09-10
2018-10-31
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.