MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification


Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope–labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.

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Figure 1: Three-dimensional peak detection.
Figure 2: Automatic large-scale SILAC pair detection.
Figure 3: Accurate masses and individual peptide mass errors.
Figure 4: Peptide score (P-score) distributions.
Figure 5: High rate of identified MS/MS spectra.
Figure 6: Proteome-wide accurate quantification and significance.


  1. 1

    Allison, D.B., Cui, X., Page, G.P. & Sabripour, M. Microarray data analysis: from disarray to consolidation and consensus. Nat. Rev. Genet. 7, 55–65 (2006).

    CAS  Article  Google Scholar 

  2. 2

    Patterson, S.D. & Aebersold, R.H. Proteomics: the first decade and beyond. Nat. Genet. 33 Suppl, 311–323 (2003).

    CAS  Article  Google Scholar 

  3. 3

    Nesvizhskii, A.I., Vitek, O. & Aebersold, R. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods 4, 787–797 (2007).

    CAS  Article  Google Scholar 

  4. 4

    Aebersold, R. & Mann, M. Mass spectrometry-based proteomics. Nature 422, 198–207 (2003).

    CAS  Article  Google Scholar 

  5. 5

    Steen, H. & Mann, M. The ABC's (and XYZ's) of peptide sequencing. Nat. Rev. Mol. Cell Biol. 5, 699–711 (2004).

    CAS  Article  Google Scholar 

  6. 6

    Sadygov, R.G., Cociorva, D. & Yates, J.R. III. Large-scale database searching using tandem mass spectra: looking up the answer in the back of the book. Nat. Methods 1, 195–202 (2004).

    CAS  Article  Google Scholar 

  7. 7

    Ong, S.E. & Mann, M. Mass spectrometry-based proteomics turns quantitative. Nat. Chem. Biol. 1, 252–262 (2005).

    CAS  Article  Google Scholar 

  8. 8

    Bantscheff, M., Schirle, M., Sweetman, G., Rick, J. & Kuster, B. Quantitative mass spectrometry in proteomics: a critical review. Anal. Bioanal. Chem. 389, 1017–1031 (2007).

    CAS  Article  Google Scholar 

  9. 9

    Listgarten, J. & Emili, A. Statistical and computational methods for comparative proteomic profiling using liquid chromatography-tandem mass spectrometry. Mol. Cell. Proteomics 4, 419–434 (2005).

    CAS  Article  Google Scholar 

  10. 10

    Colinge, J. & Bennett, K.L. Introduction to computational proteomics. PLOS Comput. Biol. 3, e114 (2007).

    Article  Google Scholar 

  11. 11

    Matthiesen, R. Methods, algorithms and tools in computational proteomics: a practical point of view. Proteomics 7, 2815–2832 (2007).

    CAS  Article  Google Scholar 

  12. 12

    Mead, J.A., Shadforth, I.P. & Bessant, C. Public proteomic MS repositories and pipelines: available tools and biological applications. Proteomics 7, 2769–2786 (2007).

    CAS  Article  Google Scholar 

  13. 13

    Perkins, D.N., Pappin, D.J., Creasy, D.M. & Cottrell, J.S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999).

    CAS  Article  Google Scholar 

  14. 14

    Cox, J. & Mann, M. Is proteomics the new genomics? Cell 130, 395–398 (2007).

    CAS  Article  Google Scholar 

  15. 15

    Senko, M.W., Beu, S.C. & McLafferty, F.W. Determination of monoisotopic masses and ion populations for large biomolecules from resolved isotopic distributions. J. Am. Soc. Mass Spectrom. 6, 229–233 (1995).

    CAS  Article  Google Scholar 

  16. 16

    Ong, S.E. et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol. Cell. Proteomics 1, 376–386 (2002).

    CAS  Article  Google Scholar 

  17. 17

    Blagoev, B., Ong, S.E., Kratchmarova, I. & Mann, M. Temporal analysis of phosphotyrosine-dependent signaling networks by quantitative proteomics. Nat. Biotechnol. 22, 1139–1145 (2004).

    CAS  Article  Google Scholar 

  18. 18

    Sokal, A.D. Monte Carlo Methods in Statistical Physics: Foundations and New Algorithms (Lausanne, Switzerland, 1996).

    Google Scholar 

  19. 19

    Zubarev, R. & Mann, M. On the proper use of mass accuracy in proteomics. Mol. Cell. Proteomics 6, 377–381 (2007).

    CAS  Article  Google Scholar 

  20. 20

    Olsen, J.V. et al. Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap. Mol. Cell. Proteomics 4, 2010–2021 (2005).

    CAS  Article  Google Scholar 

  21. 21

    Olsen, J.V. & Mann, M. Improved peptide identification in proteomics by two consecutive stages of mass spectrometric fragmentation. Proc. Natl. Acad. Sci. USA 101, 13417–13422 (2004).

    CAS  Article  Google Scholar 

  22. 22

    Elias, J.E. & Gygi, S.P. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat. Methods 4, 207–214 (2007).

    CAS  Article  Google Scholar 

  23. 23

    Käll, L., Storey, J.D., MacCoss, M.J. & Nobel, W.S. Assigning significance to peptides identified by tandem mass spectrometry using decoy databases. J. Proteome Res. 7, 29–34 (2008).

    Article  Google Scholar 

  24. 24

    Nesvizhskii, A.I. & Aebersold, R. Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell. Proteomics 4, 1419–1440 (2005).

    CAS  Article  Google Scholar 

  25. 25

    Selbach, M. et al. Widespread changes in protein synthesis induced by microRNAs. Nature 455, 58–63 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Bonaldi, T. et al. Combined use of RNAi and quantitative proteomics to study gene function in Drosophila. Mol. Cell 31, 762–772 (2008).

    CAS  Article  Google Scholar 

  27. 27

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Statist. Soc. B 57, 289–300 (1995).

    Google Scholar 

  28. 28

    Laporte, J. et al. MTM1 mutations in X-linked myotubular myopathy. Hum. Mutat. 15, 393–409 (2000).

    CAS  Article  Google Scholar 

  29. 29

    Wishart, M.J. & Dixon, J.E. PTEN and myotubularin phosphatases: from 3-phosphoinositide dephosphorylation to disease. Trends Cell Biol. 12, 579–585 (2002).

    CAS  Article  Google Scholar 

  30. 30

    Matys, V. et al. TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res. 31, 374–378 (2003).

    CAS  Article  Google Scholar 

  31. 31

    Swinnen, J.V. et al. Stimulation of tumor-associated fatty acid synthase expression by growth factor activation of the sterol regulatory element-binding protein pathway. Oncogene 19, 5173–5181 (2000).

    CAS  Article  Google Scholar 

  32. 32

    Liu, T., Belov, M.E., Jaitly, N., Qian, W.J. & Smith, R.D. Accurate mass measurements in proteomics. Chem. Rev. 107, 3621–3653 (2007).

    CAS  Article  Google Scholar 

  33. 33

    Kuster, B., Schirle, M., Mallick, P. & Aebersold, R. Scoring proteomes with proteotypic peptide probes. Nat. Rev. Mol. Cell Biol. 6, 577–583 (2005).

    CAS  Article  Google Scholar 

  34. 34

    de Godoy, L.M. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251–1254 (2008).

    CAS  Article  Google Scholar 

  35. 35

    Huh, W.K. et al. Global analysis of protein localization in budding yeast. Nature 425, 686–691 (2003).

    CAS  Article  Google Scholar 

  36. 36

    Ghaemmaghami, S. et al. Global analysis of protein expression in yeast. Nature 425, 737–741 (2003).

    CAS  Article  Google Scholar 

  37. 37

    King, N.L. et al. Analysis of the Saccharomyces cerevisiae proteome with PeptideAtlas. Genome Biol. 7, R106 (2006).

    Article  Google Scholar 

  38. 38

    Graumann, J. et al. SILAC-labeling and proteome quantitation of mouse embryonic stem cells to a depth of 5111 proteins. Mol. Cell Proteomics 7, 672–683 (2008).

    CAS  Article  Google Scholar 

  39. 39

    Eriksson, J. & Fenyo, D. Improving the success rate of proteome analysis by modeling protein-abundance distributions and experimental designs. Nat. Biotechnol. 25, 651–655 (2007).

    CAS  Article  Google Scholar 

  40. 40

    Mann, M. & Kelleher, N.L. Special feature: precision proteomics: The case for high resolution and high mass accuracy. Proc. Natl. Acad. Sci. USA. published online, doi:10.1073/pnas.0800788105 (25 September 2008).

  41. 41

    Cox, J., Hubner, N.C. & Mann, M. How much peptide sequence information is contained in ion trap tandem mass spectra? J. Am. Soc. Mass. Spectrom. published online, doi:10.1016/j.jasms.2008.07.024 (7 August 2008).

  42. 42

    Ong, S.E. & Mann, M. A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC). Nat. Protocols 1, 2650–2660 (2006).

    CAS  Article  Google Scholar 

  43. 43

    Rappsilber, J., Mann, M. & Ishihama, Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protocols 2, 1896–1906 (2007).

    CAS  Article  Google Scholar 

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We thank all the other members of the Proteomics and Signal Transduction group for help with the development of MaxQuant. Shubin Ren helped in developing the 3D data viewer used in MaxQuant. Nina Hubner measured the data used in this analysis. This work was supported by the Max-Planck Society and by the 6th Framework Program of the European Union (Interaction Proteome LSHG-CT-2003-505520 and HEROIC LSHG-CT-2005-018883).

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Correspondence to Jürgen Cox or Matthias Mann.

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Cox, J., Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol 26, 1367–1372 (2008).

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