title: Unraveling Kinase Activation Dynamics Using Kinase-Substrate Relationships from Temporal Large-Scale Phosphoproteomics Studies.
abstract: In response to stimuli, biological processes are tightly controlled by dynamic cellular signaling mechanisms. Reversible protein phosphorylation occurs on rapid time-scales (milliseconds to seconds), making it an ideal carrier of these signals. Advances in mass spectrometry-based proteomics have led to the identification of many tens of thousands of phosphorylation sites, yet for the majority of these the kinase is unknown and the underlying network topology of signaling networks therefore remains obscured. Identifying kinase substrate relationships (KSRs) is therefore an important goal in cell signaling research. Existing consensus sequence motif based prediction algorithms do not consider the biological context of KSRs, and are therefore insensitive to many other mechanisms guiding kinase-substrate recognition in cellular contexts. Here, we use temporal information to identify biologically relevant KSRs from Large-scale In Vivo Experiments (KSR-LIVE) in a data-dependent and automated fashion. First, we used available phosphorylation databases to construct a repository of existing experimentally-predicted KSRs. For each kinase in this database, we used time-resolved phosphoproteomics data to examine how its substrates changed in phosphorylation over time. Although substrates for a particular kinase clustered together, they often exhibited a different temporal pattern to the phosphorylation of the kinase. Therefore, although phosphorylation regulates kinase activity, our findings imply that substrate phosphorylation likely serve as a better proxy for kinase activity than kinase phosphorylation. KSR-LIVE can thereby infer which kinases are regulated within a biological context. Moreover, KSR-LIVE can also be used to automatically generate positive training sets for the subsequent prediction of novel KSRs using machine learning approaches. We demonstrate that this approach can distinguish between Akt and Rps6kb1, two kinases that share the same linear consensus motif, and provide evidence suggesting IRS-1 S265 as a novel Akt site. KSR-LIVE is an open-access algorithm that allows users to dissect phosphorylation signaling within a specific biological context, with the potential to be included in the standard analysis workflow for studying temporal high-throughput signal transduction data.
authors.affiliation: Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.|School of Physics, The University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: Westa
authors.identifier: http://orcid.org/0000-0003-3215-4883
authors.initials: W
authors.lastname: Domanova
authors.affiliation: Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.|School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: James
authors.initials: J
authors.lastname: Krycer
authors.affiliation: Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.|School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: Rima
authors.initials: R
authors.lastname: Chaudhuri
authors.affiliation: National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, Durham, NC 27709, United States of America.
authors.forename: Pengyi
authors.initials: P
authors.lastname: Yang
authors.affiliation: Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.|School of Mathematics and Statistics, The University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: Fatemeh
authors.initials: F
authors.lastname: Vafaee
authors.affiliation: Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.|School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: Daniel
authors.initials: D
authors.lastname: Fazakerley
authors.affiliation: Department of Proteomics and Signal Transduction, Max Planck Institute for Biochemistry, Martinsried, 82152, Germany.
authors.forename: Sean
authors.initials: S
authors.lastname: Humphrey
authors.affiliation: Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.|School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia.|Sydney Medical School, The University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: David
authors.initials: D
authors.lastname: James
authors.affiliation: Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia.|School of Physics, The University of Sydney, Sydney, NSW 2006, Australia.
authors.forename: Zdenka
authors.initials: Z
authors.lastname: Kuncic
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D006801:Humans; D010750:Phosphoproteins / Q000378:metabolism*; D010766:Phosphorylation; D011494:Protein Kinases / Q000378:metabolism*; D040901:Proteomics / Q000379:methods; D015203:Reproducibility of Results; D013379:Substrate Specificity; D064878:Web Browser
chemical_list: D010750:Phosphoproteins; D011494:Protein Kinases