Computational and Statistical Methods for High-Throughput Mass Spectrometry-Based PTM Analysis

Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1558)

Abstract

Cell signaling and functions heavily rely on post-translational modifications (PTMs) of proteins. Their high-throughput characterization is thus of utmost interest for multiple biological and medical investigations. In combination with efficient enrichment methods, peptide mass spectrometry analysis allows the quantitative comparison of thousands of modified peptides over different conditions. However, the large and complex datasets produced pose multiple data interpretation challenges, ranging from spectral interpretation to statistical and multivariate analyses. Here, we present a typical workflow to interpret such data.

Key words

Proteomics Bioinformatics Post-translational modifications (PTMs) 

Notes

Acknowledgments

VS was funded by the Danish Council for Independent Research and the EU ELIXIR consortium (Danish ELIXIR node). This work was conducted as part of the EuPA Bioinformatics Community (EuBIC) initiative supported by the European Proteomics Association (EuPA).

References

  1. 1.
    Minguez P, Letunic I, Parca L et al (2013) PTMcode: a database of known and predicted functional associations between post-translational modifications in proteins. Nucleic Acids Res 41:D306–D311CrossRefPubMedGoogle Scholar
  2. 2.
    Hunter T (2000) Signaling—2000 and beyond. Cell 100:113–127CrossRefPubMedGoogle Scholar
  3. 3.
    Munoz J, Heck AJ (2014) From the human genome to the human proteome. Angewandte Chem Int Ed Engl 53:10864–10866CrossRefGoogle Scholar
  4. 4.
    Altelaar AF, Munoz J, Heck AJ (2013) Next-generation proteomics: towards an integrative view of proteome dynamics. Nat Rev Genet 14:35–48CrossRefPubMedGoogle Scholar
  5. 5.
    Solari FA, Dell’Aica M, Sickmann A et al (2015) Why phosphoproteomics is still a challenge. Mol Biosyst 11(6):1487–1493CrossRefPubMedGoogle Scholar
  6. 6.
    Olsen JV, Mann M (2013) Status of large-scale analysis of post-translational modifications by mass spectrometry. Mol Cell Proteomics 12:3444–3452CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Tran JC, Zamdborg L, Ahlf DR et al (2011) Mapping intact protein isoforms in discovery mode using top-down proteomics. Nature 480:254–258CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Chait BT (2006) Chemistry. Mass spectrometry: bottom-up or top-down? Science 314:65–66CrossRefPubMedGoogle Scholar
  9. 9.
    Perkel JM (2015) Top-down proteomics: turning protein mass spec upside-down. Science 349:1243–1245CrossRefGoogle Scholar
  10. 10.
    Gevaert K, Van Damme P, Ghesquiere B et al (2007) A la carte proteomics with an emphasis on gel-free techniques. Proteomics 7:2698–2718CrossRefPubMedGoogle Scholar
  11. 11.
    Vaudel M, Barsnes H, Bjerkvig R et al (2016) Practical considerations for omics experiments in biomedical sciences. Curr Pharm Biotechnol 17:105–114CrossRefPubMedGoogle Scholar
  12. 12.
    Schwämmle V, Verano-Braga T, Roepstorff P (2015) Computational and statistical methods for high-throughput analysis of post-translational modifications of proteins. J Proteomics 129:3–15CrossRefPubMedGoogle Scholar
  13. 13.
    Bantscheff M, Schirle M, Sweetman G et al (2007) Quantitative mass spectrometry in proteomics: a critical review. Anal Bioanal Chem 389:1017–1031CrossRefPubMedGoogle Scholar
  14. 14.
    Bantscheff M, Lemeer S, Savitski MM et al (2012) Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present. Anal Bioanal Chem 404:939–965CrossRefPubMedGoogle Scholar
  15. 15.
    Geiger T, Cox J, Ostasiewicz P et al (2010) Super-SILAC mix for quantitative proteomics of human tumor tissue. Nat Methods 7:383–385CrossRefPubMedGoogle Scholar
  16. 16.
    McAlister GC, Huttlin EL, Haas W et al (2012) Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses. Anal Chem 84:7469–7478CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Ross PL, Huang YN, Marchese JN et al (2004) Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 3:1154–1169CrossRefPubMedGoogle Scholar
  18. 18.
    Thompson A, Schafer J, Kuhn K et al (2003) Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75:1895–1904CrossRefPubMedGoogle Scholar
  19. 19.
    Vaudel M, Sickmann A, Martens L (2010) Peptide and protein quantification: a map of the minefield. Proteomics 10:650–670CrossRefPubMedGoogle Scholar
  20. 20.
    Edwards AV, Edwards GJ, Schwämmle V et al (2014) Spatial and temporal effects in protein post-translational modification distributions in the developing mouse brain. J Proteome Res 13:260–267CrossRefPubMedGoogle Scholar
  21. 21.
    Edwards AV, Schwämmle V, Larsen MR (2014) Neuronal process structure and growth proteins are targets of heavy PTM regulation during brain development. J Proteomics 101:77–87CrossRefPubMedGoogle Scholar
  22. 22.
    Martens L, Hermjakob H, Jones P et al (2005) PRIDE: the proteomics identifications database. Proteomics 5:3537–3545CrossRefPubMedGoogle Scholar
  23. 23.
    Vizcaino JA, Deutsch EW, Wang R et al (2014) ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat Biotechnol 32:223–226CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Vaudel M, Venne AS, Berven FS et al (2014) Shedding light on black boxes in protein identification. Proteomics 14:1001–1005CrossRefPubMedGoogle Scholar
  25. 25.
    Kessner D, Chambers M, Burke R et al (2008) ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24:2534–2536CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    French WR, Zimmerman LJ, Schilling B et al (2015) Wavelet-based peak detection and a new charge inference procedure for MS/MS implemented in ProteoWizard’s msConvert. J Proteome Res 14:1299–1307CrossRefPubMedGoogle Scholar
  27. 27.
    Vaudel M, Barsnes H, Berven FS et al (2011) SearchGUI: an open-source graphical user interface for simultaneous OMSSA and X!Tandem searches. Proteomics 11:996–999CrossRefPubMedGoogle Scholar
  28. 28.
    Craig R, Beavis RC (2004) TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20:1466–1467CrossRefPubMedGoogle Scholar
  29. 29.
    Tabb DL, Fernando CG, Chambers MC (2007) MyriMatch: highly accurate tandem mass spectral peptide identification by multivariate hypergeometric analysis. J Proteome Res 6:654–661CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Dorfer V, Pichler P, Stranzl T et al (2014) MS Amanda, a universal identification algorithm optimized for high accuracy tandem mass spectra. J Proteome Res 13:3679–3684CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Kim S, Pevzner PA (2014) MS-GF+ makes progress towards a universal database search tool for proteomics. Nat Commun 5:5277CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Craig R, Cortens JP, Beavis RC (2004) Open source system for analyzing, validating, and storing protein identification data. J Proteome Res 3:1234–1242CrossRefPubMedGoogle Scholar
  33. 33.
    Eng JK, Jahan TA, Hoopmann MR (2013) Comet: an open-source MS/MS sequence database search tool. Proteomics 13:22–24CrossRefPubMedGoogle Scholar
  34. 34.
    Diament BJ, Noble WS (2011) Faster SEQUEST searching for peptide identification from tandem mass spectra. J Proteome Res 10:3871–3879CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Cox J, Neuhauser N, Michalski A et al (2011) Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res 10:1794–1805CrossRefPubMedGoogle Scholar
  36. 36.
    Vaudel M, Burkhart JM, Zahedi RP et al (2015) PeptideShaker enables reanalysis of MS-derived proteomics data sets. Nat Biotechnol 33:22–24CrossRefPubMedGoogle Scholar
  37. 37.
    Schwämmle V, Leon IR, Jensen ON (2013) Assessment and improvement of statistical tools for comparative proteomics analysis of sparse data sets with few experimental replicates. J Proteome Res 12:3874–3883CrossRefPubMedGoogle Scholar
  38. 38.
    Barsnes H, Vaudel M, Martens L (2015) JSparklines: making tabular proteomics data come alive. Proteomics 15:1428–1431CrossRefPubMedGoogle Scholar
  39. 39.
    Polpitiya AD, Qian W-J, Jaitly N et al (2008) DAnTE: a statistical tool for quantitative analysis of -omics data. Bioinformatics 24:1556–1558CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 3:Article3PubMedGoogle Scholar
  41. 41.
    Breitling R, Armengaud P, Amtmann A et al (2004) Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett 573:83–92CrossRefPubMedGoogle Scholar
  42. 42.
    Storey JD (2002) A direct approach to false discovery rates. J R Stat Soc Series B Stat Methodol 64:479–498CrossRefGoogle Scholar
  43. 43.
    Colaert N, Degroeve S, Helsens K et al (2011) Analysis of the resolution limitations of peptide identification algorithms. J Proteome Res 10:5555–5561CrossRefPubMedGoogle Scholar
  44. 44.
    Knudsen GM, Chalkley RJ (2011) The effect of using an inappropriate protein database for proteomic data analysis. PLoS One 6:e20873CrossRefPubMedPubMedCentralGoogle Scholar
  45. 45.
    Muth T, Kolmeder CA, Salojarvi J et al (2015) Navigating through metaproteomics data: a logbook of database searching. Proteomics 15:3439–3453CrossRefPubMedGoogle Scholar
  46. 46.
    Vaudel M, Sickmann A, Martens L (2014) Introduction to opportunities and pitfalls in functional mass spectrometry based proteomics. Biochim Biophys Acta 1844:12–20CrossRefPubMedGoogle Scholar
  47. 47.
    Chalkley RJ, Clauser KR (2012) Modification site localization scoring: strategies and performance. Mol Cell Proteomics 11:3–14CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    Beausoleil SA, Villen J, Gerber SA et al (2006) A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nat Biotechnol 24:1285–1292CrossRefPubMedGoogle Scholar
  49. 49.
    Taus T, Kocher T, Pichler P et al (2011) Universal and confident phosphorylation site localization using phosphoRS. J Proteome Res 10:5354–5362CrossRefPubMedGoogle Scholar
  50. 50.
    Vaudel M, Breiter D, Beck F et al (2013) D-score: a search engine independent MD-score. Proteomics 13:1036–1041CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Protein Research Group, Department of Biochemistry and Molecular BiologyUniversity of Southern DenmarkOdenseDenmark
  2. 2.Proteomics Unit, Department of BiomedicineUniversity of BergenBergenNorway

Personalised recommendations