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.
This is a preview of subscription content, access via your institution
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
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).
Patterson, S.D. & Aebersold, R.H. Proteomics: the first decade and beyond. Nat. Genet. 33 Suppl, 311–323 (2003).
Nesvizhskii, A.I., Vitek, O. & Aebersold, R. Analysis and validation of proteomic data generated by tandem mass spectrometry. Nat. Methods 4, 787–797 (2007).
Aebersold, R. & Mann, M. Mass spectrometry-based proteomics. Nature 422, 198–207 (2003).
Steen, H. & Mann, M. The ABC's (and XYZ's) of peptide sequencing. Nat. Rev. Mol. Cell Biol. 5, 699–711 (2004).
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).
Ong, S.E. & Mann, M. Mass spectrometry-based proteomics turns quantitative. Nat. Chem. Biol. 1, 252–262 (2005).
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).
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).
Colinge, J. & Bennett, K.L. Introduction to computational proteomics. PLOS Comput. Biol. 3, e114 (2007).
Matthiesen, R. Methods, algorithms and tools in computational proteomics: a practical point of view. Proteomics 7, 2815–2832 (2007).
Mead, J.A., Shadforth, I.P. & Bessant, C. Public proteomic MS repositories and pipelines: available tools and biological applications. Proteomics 7, 2769–2786 (2007).
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).
Cox, J. & Mann, M. Is proteomics the new genomics? Cell 130, 395–398 (2007).
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).
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).
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).
Sokal, A.D. Monte Carlo Methods in Statistical Physics: Foundations and New Algorithms (Lausanne, Switzerland, 1996).
Zubarev, R. & Mann, M. On the proper use of mass accuracy in proteomics. Mol. Cell. Proteomics 6, 377–381 (2007).
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).
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).
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).
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).
Nesvizhskii, A.I. & Aebersold, R. Interpretation of shotgun proteomic data: the protein inference problem. Mol. Cell. Proteomics 4, 1419–1440 (2005).
Selbach, M. et al. Widespread changes in protein synthesis induced by microRNAs. Nature 455, 58–63 (2008).
Bonaldi, T. et al. Combined use of RNAi and quantitative proteomics to study gene function in Drosophila. Mol. Cell 31, 762–772 (2008).
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).
Laporte, J. et al. MTM1 mutations in X-linked myotubular myopathy. Hum. Mutat. 15, 393–409 (2000).
Wishart, M.J. & Dixon, J.E. PTEN and myotubularin phosphatases: from 3-phosphoinositide dephosphorylation to disease. Trends Cell Biol. 12, 579–585 (2002).
Matys, V. et al. TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res. 31, 374–378 (2003).
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).
Liu, T., Belov, M.E., Jaitly, N., Qian, W.J. & Smith, R.D. Accurate mass measurements in proteomics. Chem. Rev. 107, 3621–3653 (2007).
Kuster, B., Schirle, M., Mallick, P. & Aebersold, R. Scoring proteomes with proteotypic peptide probes. Nat. Rev. Mol. Cell Biol. 6, 577–583 (2005).
de Godoy, L.M. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 1251–1254 (2008).
Huh, W.K. et al. Global analysis of protein localization in budding yeast. Nature 425, 686–691 (2003).
Ghaemmaghami, S. et al. Global analysis of protein expression in yeast. Nature 425, 737–741 (2003).
King, N.L. et al. Analysis of the Saccharomyces cerevisiae proteome with PeptideAtlas. Genome Biol. 7, R106 (2006).
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).
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).
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).
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).
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).
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).
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).
About this article
Cite this article
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). https://doi.org/10.1038/nbt.1511
This article is cited by
Data-independent acquisition-based mass spectrometry(DIA-MS) for quantitative analysis of patients with chronic hepatitis B
Proteome Science (2023)
Acta Neuropathologica Communications (2023)
Journal of Cheminformatics (2023)
BMC Biology (2023)
Heterogeneous effects of individual high-fat diet compositions on phenotype, metabolic outcome, and hepatic proteome signature in BL/6 male mice
Nutrition & Metabolism (2023)