Skip to main content
SpringerLink
Log in

The Challenges of Interpreting Phosphoproteomics Data: A Critical View Through the Bioinformatics Lens

  • Conference paper
  • First Online:
Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9874))

Abstract

During the last decade, there has been great progress in high-throughput (HTP) phosphoproteomics and hundreds or even thousands of phosphorylation sites (p-sites) can now be detected in a single experiment. This success is attributable to a combination of very sensitive Mass Spectrometry instruments, better phosphopeptide enrichment techniques and bioinformatics software that are capable of detecting peptides and localizing p-sites. These new technologies have opened up a whole new level of gene regulation to be studied, with great potential for therapeutics and synthetic biology. Nevertheless, many challenges remain to be resolved; these concern the biases and noise of these proteomic technologies, the biological noise that is present, as well as the incompleteness of the current datasets. Despite these problems, the datasets published so far appear to represent a good sample of a complete phosphoproteome of some organisms and are capable of revealing their major properties.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Krüger, R., Kübler, D., Pallissé, R., Burkovski, A., Lehmann, W.D.: Protein and proteome phosphorylation stoichiometry analysis by element mass spectrometry. Anal. Chem. 78, 1987–1994 (2006)

    Article  Google Scholar 

  2. Nishi, H., Shaytan, A., Panchenko, A.R.: Physicochemical mechanisms of protein regulation by phosphorylation. Front. Genet. 5, 270 (2014)

    Article  Google Scholar 

  3. Strumillo, M., Beltrao, P.: Towards the computational design of protein post-translational regulation. Bioorg. Med. Chem. 23, 2877–2882 (2015)

    Article  Google Scholar 

  4. Cohen, P.: The regulation of protein function by multisite phosphorylation–a 25 year update. Trends Biochem. Sci. 25, 596–601 (2000)

    Article  Google Scholar 

  5. Amoutzias, G.D., He, Y., Lilley, K.S., Van de Peer, Y., Oliver, S.G.: Evaluation and properties of the budding yeast phosphoproteome. Mol. Cell. Proteomics MCP 11, M111.009555 (2012)

    Article  Google Scholar 

  6. Cohen, P.: The origins of protein phosphorylation. Nat. Cell Biol. 4, E127–E130 (2002)

    Article  Google Scholar 

  7. Sadowski, I., Breitkreutz, B.-J., Stark, C., Su, T.-C., Dahabieh, M., Raithatha, S., Bernhard, W., Oughtred, R., Dolinski, K., Barreto, K., Tyers, M.: The PhosphoGRID Saccharomyces cerevisiae protein phosphorylation site database: version 2.0 update. Database J. Biol. Databases Curation. 2013, bat026 (2013)

    Google Scholar 

  8. Amoutzias, G.D., Bornberg-Bauer, E., Oliver, S.G., Robertson, D.L.: Reduction/oxidation-phosphorylation control of DNA binding in the bZIP dimerization network. BMC Genom. 7, 107 (2006)

    Article  Google Scholar 

  9. Papadopoulou, N., Chen, J., Randeva, H.S., Levine, M.A., Hillhouse, E.W., Grammatopoulos, D.K.: Protein kinase A-induced negative regulation of the corticotropin-releasing hormone R1alpha receptor-extracellularly regulated kinase signal transduction pathway: the critical role of Ser301 for signaling switch and selectivity. Mol. Endocrinol. Baltim. Md. 18, 624–639 (2004)

    Article  Google Scholar 

  10. Zhang, K., Lin, W., Latham, J.A., Riefler, G.M., Schumacher, J.M., Chan, C., Tatchell, K., Hawke, D.H., Kobayashi, R., Dent, S.Y.R.: The Set1 methyltransferase opposes Ipl1 aurora kinase functions in chromosome segregation. Cell 122, 723–734 (2005)

    Article  Google Scholar 

  11. Oliveira, A.P., Sauer, U.: The importance of post-translational modifications in regulating Saccharomyces cerevisiae metabolism. FEMS Yeast Res. 12, 104–117 (2012)

    Article  Google Scholar 

  12. Oliveira, A.P., Ludwig, C., Picotti, P., Kogadeeva, M., Aebersold, R., Sauer, U.: Regulation of yeast central metabolism by enzyme phosphorylation. Mol. Syst. Biol. 8, 623 (2012)

    Article  Google Scholar 

  13. Deschênes-Simard, X., Kottakis, F., Meloche, S., Ferbeyre, G.: ERKs in cancer: friends or foes? Cancer Res. 74, 412–419 (2014)

    Article  Google Scholar 

  14. Reimand, J., Wagih, O., Bader, G.D.: The mutational landscape of phosphorylation signaling in cancer. Sci. Rep. 3, 2651 (2013)

    Article  Google Scholar 

  15. Khadjavi, A., Barbero, G., Destefanis, P., Mandili, G., Giribaldi, G., Mannu, F., Pantaleo, A., Ceruti, C., Bosio, A., Rolle, L., Turrini, F., Fontana, D.: Evidence of abnormal tyrosine phosphorylated proteins in the urine of patients with bladder cancer: the road toward a new diagnostic tool? J. Urol. 185, 1922–1929 (2011)

    Article  Google Scholar 

  16. Jers, C., Soufi, B., Grangeasse, C., Deutscher, J., Mijakovic, I.: Phosphoproteomics in bacteria: towards a systemic understanding of bacterial phosphorylation networks. Expert Rev. Proteomics 5, 619–627 (2008)

    Article  Google Scholar 

  17. Schwartz, D., Church, G.M.: Collection and motif-based prediction of phosphorylation sites in human viruses. Sci. Signal 3, rs2 (2010)

    Article  Google Scholar 

  18. Doll, S., Burlingame, A.L.: Mass spectrometry-based detection and assignment of protein posttranslational modifications. ACS Chem. Biol. 10, 63–71 (2015)

    Article  Google Scholar 

  19. Engholm-Keller, K., Larsen, M.R.: Technologies and challenges in large-scale phosphoproteomics. Proteomics 13, 910–931 (2013)

    Article  Google Scholar 

  20. Olsen, J.V., Mann, M.: Status of large-scale analysis of post-translational modifications by mass spectrometry. Mol. Cell. Proteomics MCP. 12, 3444–3452 (2013)

    Article  Google Scholar 

  21. Bodenmiller, B., Mueller, L.N., Mueller, M., Domon, B., Aebersold, R.: Reproducible isolation of distinct, overlapping segments of the phosphoproteome. Nat. Methods 4, 231–237 (2007)

    Article  Google Scholar 

  22. Lienhard, G.E.: Non-functional phosphorylations? Trends Biochem. Sci. 33, 351–352 (2008)

    Article  Google Scholar 

  23. Olsen, J.V., Vermeulen, M., Santamaria, A., Kumar, C., Miller, M.L., Jensen, L.J., Gnad, F., Cox, J., Jensen, T.S., Nigg, E.A., Brunak, S., Mann, M.: Quantitative phosphoproteomics reveals widespread full phosphorylation site occupancy during mitosis. Sci. Signal 3, ra3 (2010)

    Article  Google Scholar 

  24. Soufi, B., Kelstrup, C.D., Stoehr, G., Fröhlich, F., Walther, T.C., Olsen, J.V.: Global analysis of the yeast osmotic stress response by quantitative proteomics. Mol. Biosyst. 5, 1337–1346 (2009)

    Article  Google Scholar 

  25. Landry, C.R., Levy, E.D., Michnick, S.W.: Weak functional constraints on phosphoproteomes. Trends Genet. TIG 25, 193–197 (2009)

    Article  Google Scholar 

  26. Lee, D.C.H., Jones, A.R., Hubbard, S.J.: Computational phosphoproteomics: from identification to localization. Proteomics 15, 950–963 (2015)

    Article  Google Scholar 

  27. Gruhler, A., Olsen, J.V., Mohammed, S., Mortensen, P., Faergeman, N.J., Mann, M., Jensen, O.N.: Quantitative phosphoproteomics applied to the yeast pheromone signaling pathway. Mol. Cell. Proteomics MCP 4, 310–327 (2005)

    Article  Google Scholar 

  28. Holt, L.J., Tuch, B.B., Villén, J., Johnson, A.D., Gygi, S.P., Morgan, D.O.: Global analysis of Cdk1 substrate phosphorylation sites provides insights into evolution. Science 325, 1682–1686 (2009)

    Article  Google Scholar 

  29. Li, X., Gerber, S.A., Rudner, A.D., Beausoleil, S.A., Haas, W., Villén, J., Elias, J.E., Gygi, S.P.: Large-scale phosphorylation analysis of alpha-factor-arrested Saccharomyces cerevisiae. J. Proteome Res. 6, 1190–1197 (2007)

    Article  Google Scholar 

  30. Gauci, S., Helbig, A.O., Slijper, M., Krijgsveld, J., Heck, A.J.R., Mohammed, S.: Lys-N and trypsin cover complementary parts of the phosphoproteome in a refined SCX-based approach. Anal. Chem. 81, 4493–4501 (2009)

    Article  Google Scholar 

  31. Sharma, K., D’Souza, R.C.J., Tyanova, S., Schaab, C., Wiśniewski, J.R., Cox, J., Mann, M.: Ultradeep human phosphoproteome reveals a distinct regulatory nature of Tyr and Ser/Thr-based signaling. Cell Rep. 8, 1583–1594 (2014)

    Article  Google Scholar 

  32. Choudhary, G., Wu, S.-L., Shieh, P., Hancock, W.S.: Multiple enzymatic digestion for enhanced sequence coverage of proteins in complex proteomic mixtures using capillary LC with ion trap MS/MS. J. Proteome Res. 2, 59–67 (2003)

    Article  Google Scholar 

  33. Wiśniewski, J.R., Mann, M.: Consecutive proteolytic digestion in an enzyme reactor increases depth of proteomic and phosphoproteomic analysis. Anal. Chem. 84, 2631–2637 (2012)

    Article  Google Scholar 

  34. Goffeau, A., Barrell, B.G., Bussey, H., Davis, R.W., Dujon, B., Feldmann, H., Galibert, F., Hoheisel, J.D., Jacq, C., Johnston, M., Louis, E.J., Mewes, H.W., Murakami, Y., Philippsen, P., Tettelin, H., Oliver, S.G.: Life with 6000 genes. Science 274(546), 563–567 (1996)

    Google Scholar 

  35. Oliver, S.G., van der Aart, Q.J., Agostoni-Carbone, M.L., Aigle, M., Alberghina, L., Alexandraki, D., Antoine, G., Anwar, R., Ballesta, J.P., Benit, P.: The complete DNA sequence of yeast chromosome III. Nature 357, 38–46 (1992)

    Article  Google Scholar 

  36. Beltrao, P., Trinidad, J.C., Fiedler, D., Roguev, A., Lim, W.A., Shokat, K.M., Burlingame, A.L., Krogan, N.J.: Evolution of phosphoregulation: comparison of phosphorylation patterns across yeast species. PLoS Biol. 7, e1000134 (2009)

    Article  Google Scholar 

  37. De Godoy, L.M.F., Olsen, J.V., Cox, J., Nielsen, M.L., Hubner, N.C., Fröhlich, F., Walther, T.C., Mann, M.: Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251–1254 (2008)

    Article  Google Scholar 

  38. Wu, R., Dephoure, N., Haas, W., Huttlin, E.L., Zhai, B., Sowa, M.E., Gygi, S.P.: Correct interpretation of comprehensive phosphorylation dynamics requires normalization by protein expression changes. Mol. Cell. Proteomics MCP. 10, M111.009654 (2011)

    Article  Google Scholar 

  39. Landry, C.R., Freschi, L., Zarin, T., Moses, A.M.: Turnover of protein phosphorylation evolving under stabilizing selection. Front. Genet. 5, 245 (2014)

    Article  Google Scholar 

  40. Moses, A.M., Hériché, J.-K., Durbin, R.: Clustering of phosphorylation site recognition motifs can be exploited to predict the targets of cyclin-dependent kinase. Genome Biol. 8, R23 (2007)

    Article  Google Scholar 

  41. Iakoucheva, L.M., Radivojac, P., Brown, C.J., O’Connor, T.R., Sikes, J.G., Obradovic, Z., Dunker, A.K.: The importance of intrinsic disorder for protein phosphorylation. Nucleic Acids Res. 32, 1037–1049 (2004)

    Article  Google Scholar 

  42. Ingrell, C.R., Miller, M.L., Jensen, O.N., Blom, N.: NetPhosYeast: prediction of protein phosphorylation sites in yeast. Bioinformatics 23, 895–897 (2007)

    Article  Google Scholar 

  43. Mok, J., Kim, P.M., Lam, H.Y.K., Piccirillo, S., Zhou, X., Jeschke, G.R., Sheridan, D.L., Parker, S.A., Desai, V., Jwa, M., Cameroni, E., Niu, H., Good, M., Remenyi, A., Ma, J.-L.N., Sheu, Y.-J., Sassi, H.E., Sopko, R., Chan, C.S.M., De Virgilio, C., Hollingsworth, N.M., Lim, W.A., Stern, D.F., Stillman, B., Andrews, B.J., Gerstein, M.B., Snyder, M., Turk, B.E.: Deciphering protein kinase specificity through large-scale analysis of yeast phosphorylation site motifs. Sci. Signal 3, ra12 (2010)

    Article  Google Scholar 

  44. Xue, Y., Gao, X., Cao, J., Liu, Z., Jin, C., Wen, L., Yao, X., Ren, J.: A summary of computational resources for protein phosphorylation. Curr. Protein Pept. Sci. 11, 485–496 (2010)

    Article  Google Scholar 

  45. Trost, B., Kusalik, A.: Computational Prediction of Eukaryotic Phosphorylation Sites. Bioinformatics 27, 2927–2935 (2011)

    Article  Google Scholar 

  46. Manning, G., Plowman, G.D., Hunter, T., Sudarsanam, S.: Evolution of protein kinase signaling from yeast to man. Trends Biochem. Sci. 27, 514–520 (2002)

    Article  Google Scholar 

  47. Chi, A., Huttenhower, C., Geer, L.Y., Coon, J.J., Syka, J.E.P., Bai, D.L., Shabanowitz, J., Burke, D.J., Troyanskaya, O.G., Hunt, D.F.: Analysis of phosphorylation sites on proteins from Saccharomyces cerevisiae by electron transfer dissociation (ETD) mass spectrometry. Proc. Natl. Acad. Sci. U. S. A. 104, 2193–2198 (2007)

    Article  Google Scholar 

  48. Yachie, N., Saito, R., Sugiyama, N., Tomita, M., Ishihama, Y.: Integrative features of the yeast phosphoproteome and protein-protein interaction map. PLoS Comput. Biol. 7, e1001064 (2011)

    Article  Google Scholar 

  49. Amoutzias, G.D., He, Y., Gordon, J., Mossialos, D., Oliver, S.G., Van de Peer, Y.: Posttranslational regulation impacts the fate of duplicated genes. Proc. Natl. Acad. Sci. U. S. A. 107, 2967–2971 (2010)

    Article  Google Scholar 

  50. Schweiger, R., Linial, M.: Cooperativity within proximal phosphorylation sites is revealed from large-scale proteomics data. Biol. Direct. 5, 6 (2010)

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported and implemented under the “ARISTEIA ΙΙ” Action of the “operational programme education and lifelong learning” and is co-funded by the European Social Fund (ESF) and National Resources (code 4288 to G.D.A). G.D.A acknowledges additional support by research grants from the Postgraduate Programme ‘Applications of Molecular Biology-Genetics, Diagnostic Biomarkers’, code 3817 of the Department of Biochemistry & Biotechnology, University of Thessaly, Greece. S.G.O. acknowledges support from the Wellcome Trust (grant no. 104967/Z/14/Z). Y.V.d.P acknowledges the Multidisciplinary Research Partnership “Bioinformatics: from nucleotides to networks” Project (no. 01MR0310 W) of Ghent University.

Author information

Authors and Affiliations

  1. Bioinformatics Laboratory, Department of Biochemistry and Biotechnology, University of Thessaly, Larisa, Greece

    Panayotis Vlastaridis & Grigoris D. Amoutzias

  2. Department of Biochemistry, Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK

    Stephen G. Oliver

  3. Department of Plant Systems Biology, VIB, Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052, Ghent, Belgium

    Yves Van de Peer

  4. Bioinformatics Institute Ghent, Technologiepark 927, 9052, Ghent, Belgium

    Yves Van de Peer

  5. Department of Genetics, Genomics Research Institute, University of Pretoria, Pretoria, 0028, South Africa

    Yves Van de Peer

Authors
  1. Panayotis Vlastaridis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stephen G. Oliver
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yves Van de Peer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Grigoris D. Amoutzias
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Grigoris D. Amoutzias .

Editor information

Editors and Affiliations

  1. CNR, Istituto per le Applicazioni del Calcolo, Naples, Italy

    Claudia Angelini

  2. Center for Statistics in the Biomedical Sciences, Vita-Salute San Raffaele University, Milano, Italy

    Paola MV Rancoita

  3. DIBRIS, University of Genoa, Genova, Italy

    Stefano Rovetta

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Vlastaridis, P., Oliver, S.G., Van de Peer, Y., Amoutzias, G.D. (2016). The Challenges of Interpreting Phosphoproteomics Data: A Critical View Through the Bioinformatics Lens. In: Angelini, C., Rancoita, P., Rovetta, S. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2015. Lecture Notes in Computer Science(), vol 9874. Springer, Cham. https://doi.org/10.1007/978-3-319-44332-4_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-44332-4_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44331-7

  • Online ISBN: 978-3-319-44332-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation