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Phosphoproteomics-based Profiling of Kinase Activities in Cancer Cells

View ORCID ProfileJakob Wirbel, View ORCID ProfilePedro Cutillas, View ORCID ProfileJulio Saez-Rodriguez
doi: https://doi.org/10.1101/066019
Jakob Wirbel
1Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Faculty of Medicine, D-52074 Aachen, Germany
2Institute for Pharmacy and Molecular Biotechnology (IPMB), University of Heidelberg, D-69120 Heidelberg, Germany
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Pedro Cutillas
3Barts Cancer Institute, Queen Mary University of London, London, United Kingdom
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  • For correspondence: p.cutillas@qmul.ac.uk saezrodriguez@combine.rwth-aachen.de
Julio Saez-Rodriguez
1Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Faculty of Medicine, D-52074 Aachen, Germany
4European Molecular Biology Laboratory - European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, United Kingdom
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  • For correspondence: p.cutillas@qmul.ac.uk saezrodriguez@combine.rwth-aachen.de
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Abstract

Cellular signaling, predominantly mediated by phosphorylation through protein kinases, is found to be deregulated in most cancers. Accordingly, protein kinases have been subject to intense investigations in cancer research, to understand their role in oncogenesis and to discover new therapeutic targets. Despite great advances, an understanding of kinase dysfunctioning in cancer is far from complete.

A powerful tool to investigate phosphorylation is mass-spectrometry (MS)-based phosphoproteomics, which enables the identification of thousands of phosphorylated peptides in a single experiment. Since every phosphorylation event results from the activity of a protein kinase, high-coverage phosphoproteomics data should indirectly contain comprehensive information about the activity of protein kinases.

In this chapter, we discuss the use of computational methods to predict kinase activity scores from MS-based phosphoproteomics data. We start with a short explanation of the fundamental features of the phosphoproteomics data acquisition process from the perspective of the computational analysis. Next, we briefly review the existing databases with experimentally verified kinase-substrate relationships and present a set of bioinformatic tools to discover novel kinase targets. We then introduce different methods to infer kinase activities from phosphoproteomics data and these kinase-substrate relationships. We illustrate their application with a detailed protocol of one of the methods, KSEA (Kinase Substrate Enrichment Analysis). This method is implemented in Python within the framework of the open-source Kinase Activity Toolbox (kinact), which is freely available at http://github.com/saezlab/kinact/.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted July 26, 2016.
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Phosphoproteomics-based Profiling of Kinase Activities in Cancer Cells
Jakob Wirbel, Pedro Cutillas, Julio Saez-Rodriguez
bioRxiv 066019; doi: https://doi.org/10.1101/066019
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Phosphoproteomics-based Profiling of Kinase Activities in Cancer Cells
Jakob Wirbel, Pedro Cutillas, Julio Saez-Rodriguez
bioRxiv 066019; doi: https://doi.org/10.1101/066019

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