Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis



Experience from different fields of life sciences suggests that accessible, complete reference maps of the components of the system under study are highly beneficial research tools. Examples of such maps include libraries of the spectroscopic properties of molecules, or databases of drug structures in analytical or forensic chemistry. Such maps, and methods to navigate them, constitute reliable assays to probe any sample for the presence and amount of molecules contained in the map. So far, attempts to generate such maps for any proteome have failed to reach complete proteome coverage1,2,3. Here we use a strategy based on high-throughput peptide synthesis and mass spectrometry to generate an almost complete reference map (97% of the genome-predicted proteins) of the Saccharomyces cerevisiae proteome. We generated two versions of this mass-spectrometric map, one supporting discovery-driven (shotgun)3,4 and the other supporting hypothesis-driven (targeted)5,6 proteomic measurements. Together, the two versions of the map constitute a complete set of proteomic assays to support most studies performed with contemporary proteomic technologies. To show the utility of the maps, we applied them to a protein quantitative trait locus (QTL) analysis7, which requires precise measurement of the same set of peptides over a large number of samples. Protein measurements over 78 S. cerevisiae strains revealed a complex relationship between independent genetic loci, influencing the levels of related proteins. Our results suggest that selective pressure favours the acquisition of sets of polymorphisms that adapt protein levels but also maintain the stoichiometry of functionally related pathway members.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it


Prices may be subject to local taxes which are calculated during checkout

Figure 1: Generation of a reference mass-spectrometric map for the yeast proteome.
Figure 2: Composition and use of the spectral libraries.
Figure 3: Quantitative trait analysis from the targeted proteomic data set.


  1. Deutsch, E. W. The PeptideAtlas Project. Methods Mol. Biol. 604, 285–296 (2010)

    Article  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

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

    Article  ADS  CAS  Google Scholar 

  5. Picotti, P. et al. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell 138, 795–806 (2009)

    Article  CAS  Google Scholar 

  6. Anderson, L. & Hunter, C. L. Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Mol. Cell. Proteomics 5, 573–588 (2006)

    Article  CAS  Google Scholar 

  7. Foss, E. J. et al. Genetic basis of proteome variation in yeast. Nature Genet. 39, 1369–1375 (2007)

    Article  CAS  Google Scholar 

  8. Taussig, M. J. et al. ProteomeBinders: planning a European resource of affinity reagents for analysis of the human proteome. Nature Methods 4, 13–17 (2007); erratum. 4, 187 (2007)

    Article  CAS  Google Scholar 

  9. Deutsch, E. W., Lam, H. & Aebersold, R. PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows. EMBO Rep. 9, 429–434 (2008)

    Article  CAS  Google Scholar 

  10. Lam, H. & Aebersold, R. Spectral library searching for peptide identification via tandem MS. Methods Mol. Biol. 604, 95–103 (2010)

    Article  CAS  Google Scholar 

  11. Lam, H. et al. Building consensus spectral libraries for peptide identification in proteomics. Nature Methods 5, 873–875 (2008)

    Article  CAS  Google Scholar 

  12. Craig, R. et al. Using annotated peptide mass spectrum libraries for protein identification. J. Proteome Res. 5, 1843–1849 (2006)

    Article  CAS  Google Scholar 

  13. Lange, V. et al. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol. Syst. Biol. 4, 222 (2008)

    Article  Google Scholar 

  14. Lange, V. et al. Targeted quantitative analysis of Streptococcus pyogenes virulence factors by multiple reaction monitoring. Mol. Cell. Proteomics 7, 1489–1500 (2008)

    Article  CAS  Google Scholar 

  15. Ahrens, C. H. et al. Generating and navigating proteome maps using mass spectrometry. Nature Rev. Mol. Cell Biol. 11, 789–801 (2010)

    Article  CAS  Google Scholar 

  16. Mallick, P. et al. Computational prediction of proteotypic peptides for quantitative proteomics. Nature Biotechnol. 25, 125–131 (2007)

    Article  CAS  Google Scholar 

  17. Picotti, P. et al. A database of mass spectrometric assays for the yeast proteome. Nature Methods 5, 913–914 (2008)

    Article  CAS  Google Scholar 

  18. Krokhin, O. V. Sequence-specific retention calculator. Algorithm for peptide retention prediction in ion-pair RP-HPLC: application to 300- and 100-A pore size C18 sorbents. Anal. Chem. 78, 7785–7795 (2006)

    Article  CAS  Google Scholar 

  19. Craig, R. & Beavis, R. C. TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20, 1466–1467 (2004)

    Article  CAS  Google Scholar 

  20. Röst, H., Malmstrom, L. & Aebersold, R. A computational tool to detect and avoid redundancy in selected reaction monitoring. Mol. Cell. Proteomics 11, 540–549 (2012)

    Article  Google Scholar 

  21. Brem, R. B. et al. Genetic dissection of transcriptional regulation in budding yeast. Science 296, 752–755 (2002)

    Article  ADS  CAS  Google Scholar 

  22. Zuk, O. et al. The mystery of missing heritability: genetic interactions create phantom heritability. Proc. Natl Acad. Sci. USA 109, 1193–1198 (2012)

    Article  ADS  CAS  Google Scholar 

  23. Michaelson, J. J. et al. Data-driven assessment of eQTL mapping methods. BMC Genomics 11, 502 (2010)

    Article  Google Scholar 

  24. Ackermann, M., Clément-Ziza, M., Michaelson, J. J. & Beyer, A. Teamwork: improved eQTL mapping using combinations of machine learning methods. PLoS ONE 7, e40916 (2012)

    Article  ADS  CAS  Google Scholar 

  25. Colón, M. et al. Saccharomyces cerevisiae Bat1 and Bat2 aminotransferases have functionally diverged from the ancestral-like Kluyveromyces lactis orthologous enzyme. PLoS ONE 6, e16099 (2011)

    Article  ADS  Google Scholar 

  26. Young, E. T. & Pilgrim, D. Isolation and DNA sequence of ADH3, a nuclear gene encoding the mitochondrial isozyme of alcohol dehydrogenase in Saccharomyces cerevisiae. Mol. Cell. Biol. 5, 3024–3034 (1985)

    Article  CAS  Google Scholar 

  27. de Smidt, O., du Preez, J. C. & Albertyn, J. Molecular and physiological aspects of alcohol dehydrogenases in the ethanol metabolism of Saccharomyces cerevisiae. FEMS Yeast Res. 12, 33–47 (2012)

    Article  CAS  Google Scholar 

  28. Reiter, L. et al. mProphet: automated data processing and statistical validation for large-scale SRM experiments. Nature Methods 8, 430–435 (2011)

    Article  CAS  Google Scholar 

  29. Gillet, L. C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell Proteomics 11, O111–016717 (2012)

    Article  Google Scholar 

  30. Fraser, H. B., Moses, A. M. & Schadt, E. E. Evidence for widespread adaptive evolution of gene expression in budding yeast. Proc. Natl Acad. Sci. USA 107, 2977–2982 (2010)

    Article  ADS  CAS  Google Scholar 

Download references


This project has been funded in part by ETH Zurich, the Swiss National Science Foundation (3100A0-107679), the National Heart, Lung and Blood Institute, National Institutes of Health (N01-HV-28179), the National Science Foundation MRI (grant 0923536), the Luxembourg Centre for Systems Biomedicine and the University of Luxembourg, and by, the Swiss initiative for systems biology. P.P. is supported by a Foerderungsprofessur grant from the Swiss National Science Foundation (PP00P3_133670), by a European Union Seventh Framework Program Reintegration grant (FP7-PEOPLE-2010-RG-277147) and by a Promedica Stiftung (2-70669-11), H. L. is supported by the University Grant Council of the Hong Kong Special Administrative Region Government, China (HKUST DAG08/09.EG02). A.B. is supported by the Klaus Tschira Foundation and by a European Union FP7 HEALTH grant (HEALTH-F4-2008-223539). R.A. is supported by the European Research Council (ERC-2008-AdG 233226) and by, the Swiss Initiative for Systems Biology.

Author information

Authors and Affiliations



P.P. and M.C.-Z. carried out the experiments; M.C.-Z., H.L., E.W.D., O.R., L.R., P.P., J.J.M., A.B. and R.A. conceived the data analysis pipeline; P.P., M.C.-Z., H.L., D.S.C., A.S., E.W.D., H.R., Z.S., O.R., L.R., J.J.M. and Q.S. analysed the data; A.S., A.F., Q.S. and U.K. performed mass-spectrometry measurements; P.P., A.B., M.C.-Z., S.A. and R.A. designed the experiments; P.P., M.C.-Z., A.B., H.L., H.R., S.A. and R.A. wrote the manuscript; B.W. and R.L.M. supervised part of the project; and R.A., A.B. and P.P. supervised the project.

Corresponding authors

Correspondence to Paola Picotti, Andreas Beyer or Ruedi Aebersold.

Ethics declarations

Competing interests

O.R. and L.R. are employees of Biognosys AG, Switzerland.

Supplementary information

Supplementary Information

This file contains a Supplementary Discussion, Supplementary Figures 1-20, Supplementary Text and Data, Supplementary Tables 1-4, Supplementary Methods and additional references. (PDF 3718 kb)

Supplementary Data

This file contains the Supplementary Data used in this study. (XLSX 4758 kb)

PowerPoint slides

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Picotti, P., Clément-Ziza, M., Lam, H. et al. A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature 494, 266–270 (2013).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research