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  Unraveling Kinase Activation Dynamics Using Kinase-Substrate Relationships from Temporal Large-Scale Phosphoproteomics Studies

Domanova, W., Krycer, J., Chaudhuri, R., Yang, P., Vafaee, F., Fazakerley, D., et al. (2016). Unraveling Kinase Activation Dynamics Using Kinase-Substrate Relationships from Temporal Large-Scale Phosphoproteomics Studies. PLoS One, 11(6): e0157763. doi:10.1371/journal.pone.0157763.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-103A-F Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-103B-D
Genre: Journal Article

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 Creators:
Domanova, Westa1, Author
Krycer, James1, Author
Chaudhuri, Rima1, Author
Yang, Pengyi1, Author
Vafaee, Fatemeh1, Author
Fazakerley, Daniel1, Author
Humphrey, Sean2, Author              
James, David1, Author
Kuncic, Zdenka1, Author
Affiliations:
1external, ou_persistent22              
2Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society, ou_1565159              

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Free keywords: INSULIN-RESISTANCE; ELONGATION-FACTOR-2 KINASE; PHOSPHORYLATION NETWORKS; PROTEIN; REVEALS; PATTERNS; SURVIVAL; DATABASE; COMPLEX; SITES
 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.

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Language(s): eng - English
 Dates: 2016
 Publication Status: Published online
 Pages: 14
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 11 (6) Sequence Number: e0157763 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: /journals/resource/1000000000277850