Summary
As extensive mass spectrometry-based mapping of the phosphoproteome progresses, computational anal ysis of phosphorylation-dependent signaling becomes increasingly important. The linear sequence motifs that surround phosphorylated residues have successfully been used to characterize kinase–substrate spe cificity. Here, we briefly describe the available resources for predicting kinase-specific phosphorylation from sequence properties. We address the strengths and weaknesses of these resources, which are based on methods ranging from simple consensus patterns to more advanced machine-learning algorithms. Furthermore, a protocol for the use of the artificial neural network based predictors, NetPhos and NetPhosK, is provided. Finally, we point to possible developments with the intention of providing the community with improved and additional phosphorylation predictors for large-scale modeling of cellular signaling networks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Manning G, Whyte D, Martinez R, Hunter T, Sudarsanam S. (2002) The protein kinase complement of the human genome. Science 298, 1912–34.
Pawson T. (2002) Regulation and targets of receptor tyrosine kinases. Eur J Cancer 38, S3–10.
Seet B, Dikic I, Zhou M, Pawson T. (2006) Reading protein modifications with inter action domains. Nat Rev Mol Cell Biol 7, 473–83.
Bork P, Koonin E. (1996) Protein sequence motifs. Curr Opin Struct Biol 6, 366–76.
Songyang Z, Blechner S, Hoagland N, Hoek-stra M, Piwnica-Worms H, Cantley L. (1994) Use of an oriented peptide library to deter mine the optimal substrates of protein kinases. Curr Biol 4, 973–82.
Kreegipuu A, Blom N, Brunak S, Jar v J. (1998) Statistical analysis of protein kinase specificity determinants. FEBS Lett 430, 45–50.
Beausoleil S, Jedrychowski M, Schwartz D, et al. (2004) Large-scale characterization of Hela cell nuclear phosphoproteins. Proc Natl Acad Sci USA 101, 12130–5.
Olsen J, Blagoev B, Gnad F, et al. (2006) Global, in vivo, and site-specific phosphoryla-tion dynamics in signaling networks. Cell 127, 635–48.
Linding R, Jensen L, Ostheimer G, et al. (2007) Systematic discovery of in vivo phos-phorylation networks. Cell 129, 1415–26.
Hjerrild M, Stensballe A, Rasmussen T, et al. (2004) Identification of phosphorylation sites in protein kinase a substrates using artificial neural networks and mass spectrometry. J Pro-teome Res 3, 426–33.
Manning B, Tee A, Logsdon M, Blenis J, Can-tley L. (2002) Identification of the tuberous sclerosis complex-2 tumor suppressor gene product tuber in as a target of the phosphoi-nositide 3-kinase/Akt pathway. Mol Cell 10, 151–62.
Miller M, Hanke S, Hinsby A, et al. (2008) Motif decomposition of the phosphotyrosine proteome reveals a new N-terminal binding motif for ship2. Mol Cell Proteomics 7, 181–92.
Puntervoll P, Linding R, Gemund C, et al. (2003) Elm server: a new resource for inves tigating short functional sites in modular eukaryotic proteins. Nucleic Acids Res 31, 3625–30.
Amanchy R, Periaswamy B, Mathivanan S, Reddy R, Tattikota S, Pandey A. (2007) A curated compendium of phosphorylation motifs. Nat Biotechnol 25, 285–6.
Mulder N, Apweiler R, Attwood T, et al. (2003) The interpro database, 2003 brings increased coverage and new features. Nucleic Acids Res 31, 315–8.
Peri S, Navarro J, Amanchy R, et al. (2003) Development of human protein reference database as an initial platform for approaching systems biology in humans. Genome Res 13, 2363–71.
Yaffe M, Leparc G, Lai J, Obata T, Volinia S, Cantley L. (2001) A motif-based profile scan ning approach for genome-wide prediction of signaling pathways. Nat Biotechnol 19, 348–53.
Obenauer J, Cantley L, Yaffe M. (2003) Scan-site 2.0: proteome-wide prediction of cell signalling interactions using short sequence motifs. Nucleic Acids Res 31, 3635–41.
Zhou F, Xue Y, Chen G, Yao X. (2004) GPS: a novel group-based phosphorylation predict ing and scoring method. Biochem Biophys Res Commun 325, 1443–8.
Xue Y, Zhou F, Zhu M, Ahmed K, Chen G, Yao X. (2005) GPS: a comprehensive www server for phosphorylation sites prediction. Nucleic Acids Res 33, W184–7.
Huang H, Lee T, Tzeng S, Horng J. (2005) KinasePhos: a web tool for identifying protein kinase-specific phosphorylation sites. Nucleic Acids Res 33, W226–9.
Blom N, Sicheritz-Ponten T, Gupta R, Gam-meltoft S, Brunak S. (2004) Prediction of posttranslational glycosylation and phos-phorylation of proteins from the amino acid sequence. Proteomics 4, 1633–49.
Xue Y, Li A, Wang L, Feng H, Yao X. (2006) PPSP: prediction of PK-specific phosphoryla-tion site with Bayesian decision theory. BMC Bioinformatics 7, 163.
Kim J, Lee J, Oh B, Kimm K, Koh I. (2004) Prediction of phosphorylation sites using SVMs. Bioinformatics 20, 3179–84.
Blom N, Gammeltoft S, Brunak S. (1999) Sequence and structure-based prediction of eukaryotic protein phosphorylation sites. J Mol Biol 294, 1351–62.
Diella F, Cameron S, Gemund C, et al. (2004) Phospho.ELM: a database of experimentally verified phosphorylation sites in eukaryotic proteins. BMC Bioinformatics 5, 79.
Wu C. (1997) Artificial neural networks for molecular sequence analysis. Comput Chem 21, 237–56.
Brinkworth R, Breinl R, Kobe B. (2003) Structural basis and prediction of substrate specificity in protein serine/threonine kinases. Proc Natl Acad Sci USA 100, 74–9.
Manke I, Nguyen A, Lim D, Stewart M, Elia A, Yaffe M. (2005) MAPKAP kinase-2 is a cell cycle checkpoint kinase that regulates the G2/M transition and S phase progression in response to UV irradiation. Mol Cell 17, 37–48.
Ingrell C, Miller M, Jensen O, Blom N. (2007) NetPhosYeast: prediction of protein phosphorylation sites in yeast. Bioinformatics 23, 895–7.
Araki R, Fukumura R, Fujimori A, et al. (1999) Enhanced phosphorylation of p53 serine 18 following DNA damage in DNA-dependent protein kinase catalytic subunit-deficient cells. Cancer Res 59, 3543–6.
Saito S, Goodarzi A, Higashimoto Y, et al. (2002) ATM mediates phosphoryla-tion at multiple p53 sites, including ser(46), in response to ionizing radiation. J Biol Chem 277, 12491–4.
Dumaz N, Milne D, Meek D. (1999) Pro tein kinase CK1 is a p53-threonine 18 kinase which requires prior phosphorylation of serine 15. FEBS Lett 463, 312–6.
Kreegipuu A, Blom N, Brunak S. (1999) Phos-phoBase, a database of phosphorylation sites: release 2.0. Nucleic Acids Res 27, 237–9.
Acknowledgments
The authors would like to thank Rune Linding, Lars Juhl Jensen, Majbritt Hjerrild, Steen Gammeltoft, Thomas Sicheritz-Ponten and Søren Brunak.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Miller, M.L., Blom, N. (2009). Kinase-Specific Prediction of Protein Phosphorylation Sites. In: Graauw, M.d. (eds) Phospho-Proteomics. Methods in Molecular Biology™, vol 527. Humana Press. https://doi.org/10.1007/978-1-60327-834-8_22
Download citation
DOI: https://doi.org/10.1007/978-1-60327-834-8_22
Publisher Name: Humana Press
Print ISBN: 978-1-60327-833-1
Online ISBN: 978-1-60327-834-8
eBook Packages: Springer Protocols