From Phosphosites to Kinases

  • Stephanie Munk
  • Jan C. Refsgaard
  • Jesper V. Olsen
  • Lars J. JensenEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1355)


Kinases play a pivotal role in propagating the phosphorylation-mediated signaling networks in living cells. With the overwhelming quantities of phosphoproteomics data being generated, the number of identified phosphorylation sites (phosphosites) is ever increasing. Often, proteomics investigations aim to understand the global signaling modulation that takes place in different biological conditions investigated. For phosphoproteomics data, identifying the kinases central to mediating this response is key. This has prompted several efforts to catalogue the immense amounts of phosphorylation data and known or predicted kinases responsible for the modifications. However, barely 20 % of the known phosphosites are assigned to a kinase, initiating various bioinformatics efforts that attempt to predict the responsible kinases. These algorithms employ different approaches to predict kinase consensus sequence motifs, mostly based on large scale in vivo and in vitro experiments. The context of the kinase and the phosphorylated proteins in a biological system is equally important for predicting association between the enzymes and substrates, an aspect that is also being tackled with available bioinformatics tools. This chapter summarizes the use of the larger phosphorylation databases, and approaches that can be applied to predict kinases that phosphorylate individual sites or that are globally modulated in phosphoproteomics datasets.

Key words

Phosphoproteomics Kinases NetPhorest NetworKIN Phospho.ELM PHOSIDA PhoshoSitePlus 



This work was in part funded by the Novo Nordisk Foundation Center for Protein Research [NNF14CC0001].


  1. 1.
    Olsen JV et al (2010) Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci Signal 3(104):ra3PubMedGoogle Scholar
  2. 2.
    Sharma K et al (2014) Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Rep 8(5):1583–1594CrossRefPubMedGoogle Scholar
  3. 3.
    Manning G et al (2002) The protein kinase complement of the human genome. Science 298(5600):1912–1934CrossRefPubMedGoogle Scholar
  4. 4.
    Dinkel H et al (2011) Phospho.ELM a database of phosphorylation sites--update 2011. Nucleic Acids Res 39(Database issue):D261–D267PubMedCentralCrossRefPubMedGoogle Scholar
  5. 5.
    Rask-Andersen M et al (2014) Advances in kinase targeting: current clinical use and clinical trials. Trends Pharmacol Sci 35(11):604–620CrossRefPubMedGoogle Scholar
  6. 6.
    Hornbeck PV et al (2012) PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Res 40(Database issue):D261–D270PubMedCentralCrossRefPubMedGoogle Scholar
  7. 7.
    Gnad F, Gunawardena J, Mann M (2011) PHOSIDA 2011: the posttranslational modification database. Nucleic Acids Res 39(Database issue):D253–D260PubMedCentralCrossRefPubMedGoogle Scholar
  8. 8.
    Gnad F et al (2007) PHOSIDA (phosphorylation site database): management, structural and evolutionary investigation, and prediction of phosphosites. Genome Biol 8(11):R250PubMedCentralCrossRefPubMedGoogle Scholar
  9. 9.
    Olsen JV et al (2006) Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 127(3):635–648CrossRefPubMedGoogle Scholar
  10. 10.
    Miller ML et al (2008) Linear motif atlas for phosphorylation-dependent signaling. Sci Signal 1(35):2Google Scholar
  11. 11.
    Yaffe MB et al (2001) A motif-based profile scanning approach for genome-wide prediction of signaling pathways. Nat Biotechnol 19(4):348–353CrossRefPubMedGoogle Scholar
  12. 12.
    Obenauer JC, Cantley LC, Yaffe MB (2003) Scansite 2.0: proteome-wide prediction of cell signaling interactions using short sequence motifs. Nucleic Acids Res 31(13):3635–3641PubMedCentralCrossRefPubMedGoogle Scholar
  13. 13.
    Songyang Z et al (1994) Use of an oriented peptide library to determine the optimal substrates of protein kinases. Curr Biol 4(11):973–982CrossRefPubMedGoogle Scholar
  14. 14.
    Horn H et al (2014) KinomeXplorer: an integrated platform for kinome biology studies. Nat Methods 11(6):603–604CrossRefPubMedGoogle Scholar
  15. 15.
    Linding R et al (2007) Systematic discovery of in vivo phosphorylation networks. Cell 129(7):1415–1426PubMedCentralCrossRefPubMedGoogle Scholar
  16. 16.
    Franceschini A et al (2013) STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 41(Database issue):D808–D815PubMedCentralCrossRefPubMedGoogle Scholar
  17. 17.
    Cox J, Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26(12):1367–1372CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Stephanie Munk
    • 1
  • Jan C. Refsgaard
    • 1
    • 2
  • Jesper V. Olsen
    • 1
  • Lars J. Jensen
    • 2
    Email author
  1. 1.Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
  2. 2.Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark

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