Using ProtMAX to create high-mass-accuracy precursor alignments from label-free quantitative mass spectrometry data generated in shotgun proteomics experiments

Journal name:
Nature Protocols
Year published:
Published online


Recently, new software tools have been developed for improved protein quantification using mass spectrometry (MS) data. However, there are still limitations especially in high-sample-throughput quantification methods, and most of these relate to extensive computational calculations. The mass accuracy precursor alignment (MAPA) strategy has been shown to be a robust method for relative protein quantification. Its major advantages are high resolution, sensitivity and sample throughput. Its accuracy is data dependent and thus best suited for precursor mass-to-charge precision of ∼1 p.p.m. This protocol describes how to use a software tool (ProtMAX) that allows for the automated alignment of precursors from up to several hundred MS runs within minutes without computational restrictions. It comprises features for 'ion intensity count' and 'target search' of a distinct set of peptides. This procedure also includes the recommended MS settings for complex quantitative MAPA analysis using ProtMAX (

At a glance


  1. Workflow diagram from MS analysis to a quantitative protein data matrix.
    Figure 1: Workflow diagram from MS analysis to a quantitative protein data matrix.
  2. Target list example.
    Figure 2: Target list example.

    This file is a tab-delimited text file with two columns. Column A contains m/z values; column B contains Rt in minutes.

  3. ProtMAX output file.
    Figure 3: ProtMAX output file.

    Samples 1 and 2 correspond to the test sample in the Equipment section. (a) Charge state (no information for target approach). (b) Sum across all samples. (c) Sum of ion intensity counts specific for sample 1 (can also be SC or Ion count, depending on the selected method). (d) Scan number of the most intense ion signal of the corresponding m/z value specific for sample 1. (e) Rt of the most intense ion signal of the corresponding m/z value specific for sample 1.

  4. Graphical user interface of the Xcalibur MS instrument setup.
    Figure 4: Graphical user interface of the Xcalibur MS instrument setup.

    The settings for the protein analysis are indicated by green circles that are numbered according to the steps in Box 1 (Instrument settings for the protein shotgun LC-MS/MS analysis using an Orbitrap) in which they are described. (a) MS1 settings and internal Lock Mass calibration. (b) MS/MS Scan Events. (c) Data-dependent settings for MS/MS scan events.

  5. Graphical user interface of ProtMAX.
    Figure 5: Graphical user interface of ProtMAX.

    The settings for the unbiased peptide matrix generation are indicated by green circles that are numbered according to the PROCEDURE steps in which they are described.


  1. Michalski, A., Cox, J. & Mann, M. More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS. J. Proteome Res. 10, 17851793 (2011).
  2. Michalski, A. et al. Mass spectrometry-based proteomics using Q Exactive, a high-performance benchtop quadrupole Orbitrap mass spectrometer. Mol. Cell. Proteomics 10, M111.011015 (2011).
  3. Thakur, S.S. et al. Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation. Mol. Cell Proteomics 10, M110.003699 (2011).
  4. Griffin, N.M. et al. Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis. Nat. Biotechnol. 28, 8389 (2010).
  5. Ishihama, Y. et al. Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol. Cell Proteomics 4, 12651272 (2005).
  6. Liu, H., Sadygov, R.G. & Yates, J.R. A model for random sampling and estimation of relative protein abundance in shotgun proteomics. Anal. Chem. 76, 41934201 (2004).
  7. Lu, P., Vogel, C., Wang, R., Yao, X. & Marcotte, E.M. Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat. Biotechnol. 25, 117124 (2007).
  8. Paoletti, A.C. et al. Quantitative proteomic analysis of distinct mammalian Mediator complexes using normalized spectral abundance factors. Proc. Natl. Acad. Sci. USA 103, 1892818933 (2006).
  9. Schulze, W.X. & Usadel, B. Quantitation in mass-spectrometry-based proteomics. Annu. Rev. Plant Biol. 61, 491516 (2010).
  10. Schwanhausser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337342 (2011).
  11. Silva, J.C., Gorenstein, M.V., Li, G.Z., Vissers, J.P.C. & Geromanos, S.J. Absolute quantification of proteins by LCMSE—a virtue of parallel MS acquisition. Mol. Cell Proteomics 5, 144156 (2006).
  12. Baerenfaller, K. et al. Genome-scale proteomics reveals Arabidopsis thaliana gene models and proteome dynamics. Science 320, 938941 (2008).
  13. Brunner, E. et al. A high-quality catalog of the Drosophila melanogaster proteome. Nat. Biotechnol. 25, 576583 (2007).
  14. de Godoy, L.M.F. et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature 455, 12511260 (2008).
  15. Graumann, J. et al. Stable isotope labeling by amino acids in cell culture (SILAC) and proteome quantitation of mouse embryonic stem cells to a depth of 5,111 proteins. Mol. Cell Proteomics 7, 672683 (2008).
  16. Malmstrom, J. et al. Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature 460, 762U112 (2009).
  17. Nagaraj, N. et al. Deep proteome and transcriptome mapping of a human cancer cell line. Mol. Syst. Biol. 7, 548 (2011).
  18. Pavelka, N. et al. Statistical similarities between transcriptomics and quantitative shotgun proteomics data. Mol. Cell Proteomics 7, 631644 (2008).
  19. 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, 13671372 (2008).
  20. De Vos, R.C.H. et al. Untargeted large-scale plant metabolomics using liquid chromatography coupled to mass spectrometry. Nat. Protoc. 2, 778791 (2007).
  21. Hoehenwarter, W. et al. A rapid approach for phenotype-screening and database independent detection of cSNP/protein polymorphism using mass accuracy precursor alignment. Proteomics 8, 42144225 (2008).
  22. Hoehenwarter, W. et al. MAPA distinguishes genotype-specific variability of highly similar regulatory protein isoforms in potato tuber. J. Proteome Res. 10, 29792991 (2011).
  23. Chen, Y., Hoehenwarther, W. & Weckwerth, W. Comparative analysis of phytohormone-responsive phosphoproteins in Arabidopsis thaliana using TiO2-phosphopeptide enrichment and mass accuracy precursor alignment. Plant J. 63, 117 (2010).
  24. Doerfler, H. et al. Granger causality in integrated GC-MS and LC-MS metabolomics data reveals the interface of primary and secondary metabolism. Metabolomics. (25 October 2012).
  25. Mari, A. et al. Phytochemical composition of Potentilla anserina L. analyzed by an integrative GC-MS and LC-MS metabolomics platform. Metabolomics. (17 November 2012).
  26. Lee, K.A., Farnsworth, C., Yu, W. & Bonilla, L.E. 24-hour lock mass protection. J. Proteome Res. 10, 880885 (2011).
  27. Isaacson, T. et al. Sample extraction techniques for enhanced proteomic analysis of plant tissues. Nat. Protoc. 2, 769774 (2006).
  28. Sheoran, I.S. et al. Compatibility of plant protein extraction methods with mass spectrometry for proteome analysis. Plant Sci. 176, 99104 (2009).
  29. Sun, X. & Weckwerth, W. COVAIN: a toolbox for uni- and multivariate statistics, time-series and correlation network analysis and inverse estimation of the differential Jacobian from metabolomics covariance data. Metabolomics 8, 8193 (2012).

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Author information


  1. Department of Molecular Systems Biology, University of Vienna, Vienna, Austria.

    • Volker Egelhofer,
    • Wolfgang Hoehenwarter,
    • David Lyon,
    • Wolfram Weckwerth &
    • Stefanie Wienkoop
  2. Present address: Proteome Analysis Research Group, Leibniz Institute for Plant Biochemistry, Halle, Germany.

    • Wolfgang Hoehenwarter


V.E. developed the algorithm of the ProtMAX tool. W.H. contributed to the concept and optimization of the ProtMAX tool of protein analysis and writing of the manuscript. D.L. contributed to the concept of the ProtMAX tool and writing of the manuscript. W.W. conceived the concept of the MAPA strategy for proteomics. S.W. conceived Preferences and feature upgrading with the target approach of the ProtMAX tool and was responsible for project coordination and writing of the manuscript.

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The authors declare no competing financial interests.

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