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.

Global landscape of protein complexes in the yeast Saccharomyces cerevisiae


Identification of protein–protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization–time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein–protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein–protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.

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: The yeast interactome encompasses a large proportion of the predicted proteome.
Figure 2: Machine learning generates a core data set of protein–protein interactions.
Figure 3: Organization of the yeast protein–protein interaction network into protein complexes.
Figure 4: Characterization of three previously unreported protein complexes and Iwr1, a novel RNAPII-interacting factor.


  1. Goffeau, A. et al. Life with 6000 genes. Science 274, 546, 563–567 (1996)

    Article  ADS  CAS  PubMed  Google Scholar 

  2. Hughes, T. R. et al. Functional discovery via a compendium of expression profiles. Cell 102, 109–126 (2000)

    Article  CAS  PubMed  Google Scholar 

  3. Martzen, M. R. et al. A biochemical genomics approach for identifying genes by the activity of their products. Science 286, 1153–1155 (1999)

    Article  CAS  PubMed  Google Scholar 

  4. Zhu, H. & Snyder, M. Protein chip technology. Curr. Opin. Chem. Biol. 7, 55–63 (2003)

    Article  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

  6. Kumar, A. et al. Subcellular localization of the yeast proteome. Genes Dev. 16, 707–719 (2002)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ross-Macdonald, P. et al. Large-scale analysis of the yeast genome by transposon tagging and gene disruption. Nature 402, 413–418 (1999)

    Article  ADS  CAS  PubMed  Google Scholar 

  8. Winzeler, E. A. et al. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901–906 (1999)

    Article  CAS  PubMed  Google Scholar 

  9. Tong, A. H. et al. Systematic genetic analysis with ordered arrays of yeast deletion mutants. Science 294, 2364–2368 (2001)

    Article  ADS  CAS  PubMed  Google Scholar 

  10. Schuldiner, M. et al. Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell 123, 507–519 (2005)

    Article  CAS  PubMed  Google Scholar 

  11. Uetz, P. et al. A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature 403, 623–627 (2000)

    Article  ADS  CAS  PubMed  Google Scholar 

  12. Ito, T. et al. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl Acad. Sci. USA 98, 4569–4574 (2001)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  13. Gavin, A. C. et al. Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415, 141–147 (2002)

    Article  ADS  CAS  PubMed  Google Scholar 

  14. Ho, Y. et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415, 180–183 (2002)

    Article  ADS  CAS  PubMed  Google Scholar 

  15. Xia, Y. et al. Analyzing cellular biochemistry in terms of molecular networks. Annu. Rev. Biochem. 73, 1051–1087 (2004)

    Article  PubMed  Google Scholar 

  16. von Mering, C. et al. Comparative assessment of large-scale data sets of protein–protein interactions. Nature 417, 399–403 (2002)

    Article  ADS  CAS  PubMed  Google Scholar 

  17. Butland, G. et al. Interaction network containing conserved and essential protein complexes in Escherichia coli. Nature 433, 531–537 (2005)

    Article  ADS  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

  19. Rigaut, G. et al. A generic protein purification method for protein complex characterization and proteome exploration. Nature Biotechnol. 17, 1030–1032 (1999)

    Article  CAS  Google Scholar 

  20. Link, A. J. et al. Direct analysis of protein complexes using mass spectrometry. Nature Biotechnol. 17, 676–682 (1999)

    Article  CAS  Google Scholar 

  21. McCormack, A. L. et al. Direct analysis and identification of proteins in mixtures by LC/MS/MS and database searching at the low-femtomole level. Anal. Chem. 69, 767–776 (1997)

    Article  CAS  PubMed  Google Scholar 

  22. Krogan, N. J. et al. High-definition macromolecular composition of yeast RNA-processing complexes. Mol. Cell 13, 225–239 (2004)

    Article  CAS  PubMed  Google Scholar 

  23. Mewes, H. W. et al. MIPS: analysis and annotation of proteins from whole genomes. Nucleic Acids Res. 32, D41–D44 (2004)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mitchell, T. Machine Learning (McGraw Hill, 1997)

    MATH  Google Scholar 

  25. Wolpert, D. H. Stacked generalization. Neural Netw. 5, 241–259 (1992)

    Article  Google Scholar 

  26. Jansen, R. & Gerstein, M. Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction. Curr. Opin. Microbiol. 7, 535–545 (2004)

    Article  CAS  PubMed  Google Scholar 

  27. Jansen, R. et al. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302, 449–453 (2003)

    Article  ADS  CAS  PubMed  Google Scholar 

  28. Barabasi, A. L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999)

    Article  ADS  MathSciNet  CAS  PubMed  Google Scholar 

  29. Enright, A. J., Van Dongen, S. & Ouzounis, C. A. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 30, 1575–1584 (2002)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Keogh, M. C. et al. Cotranscriptional Set2 methylation of Histone H3 lysine 36 recruits a repressive Rpd3 complex. Cell 123, 593–605 (2005)

    Article  CAS  PubMed  Google Scholar 

  31. Carrozza, M. J. et al. Histone H3 methylation by Set2 directs deacetylation of coding regions by Rpd3S to suppress spurious intragenic transcription. Cell 123, 581–592 (2005)

    Article  CAS  PubMed  Google Scholar 

  32. Lord, P. W., Stevens, R. D., Brass, A. & Goble, C. A. Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation. Bioinformatics 19, 1275–1283 (2003)

    Article  CAS  PubMed  Google Scholar 

  33. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Fraser, H. B., Wall, D. P. & Hirsh, A. E. A simple dependence between protein evolution rate and the number of protein-protein interactions. BMC Evol. Biol. 3, 11 (2003)

    Article  PubMed  PubMed Central  Google Scholar 

  35. Joy, M. P., Brock, A., Ingber, D. E. & Huang, S. High-betweenness proteins in the yeast protein interaction network. J. Biomed. Biotechnol. 2005, 96–103 (2005)

    Article  PubMed  PubMed Central  Google Scholar 

  36. Fourel, G., Revardel, E., Koering, C. E. & Gilson, E. Cohabitation of insulators and silencing elements in yeast subtelomeric regions. EMBO J. 18, 2522–2537 (1999)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Brigati, C., Kurtz, S., Balderes, D., Vidali, G. & Shore, D. An essential yeast gene encoding a TTAGGG repeat-binding protein. Mol. Cell. Biol. 13, 1306–1314 (1993)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Aravind, L. The BED finger, a novel DNA-binding domain in chromatin-boundary-element-binding proteins and transposases. Trends Biochem. Sci. 25, 421–423 (2000)

    Article  CAS  PubMed  Google Scholar 

  39. Regelmann, J. et al. Catabolite degradation of fructose-1,6-bisphosphatase in the yeast Saccharomyces cerevisiae: a genome-wide screen identifies eight novel GID genes and indicates the existence of two degradation pathways. Mol. Biol. Cell 14, 1652–1663 (2003)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Scholes, D. T., Banerjee, M., Bowen, B. & Curcio, M. J. Multiple regulators of Ty1 transposition in Saccharomyces cerevisiae have conserved roles in genome maintenance. Genetics 159, 1449–1465 (2001)

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Krogan, N. J. & Greenblatt, J. F. Characterization of a six-subunit holo-elongator complex required for the regulated expression of a group of genes in Saccharomyces cerevisiae. Mol. Cell. Biol. 21, 8203–8212 (2001)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Krogan, N. J. et al. Proteasome involvement in the repair of DNA double-strand breaks. Mol. Cell 16, 1027–1034 (2004)

    Article  CAS  PubMed  Google Scholar 

  43. Krogan, N. J. et al. RNA polymerase II elongation factors of Saccharomyces cerevisiae: a targeted proteomics approach. Mol. Cell. Biol. 22, 6979–6992 (2002)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Korber, P. & Horz, W. SWRred not shaken; mixing the histones. Cell 117, 5–7 (2004)

    Article  CAS  PubMed  Google Scholar 

  45. Hampsey, M. & Reinberg, D. Tails of intrigue: phosphorylation of RNA polymerase II mediates histone methylation. Cell 113, 429–432 (2003)

    Article  CAS  PubMed  Google Scholar 

  46. Sampath, V. & Sadhale, P. Rpb4 and Rpb7: a sub-complex integral to multi-subunit RNA polymerases performs a multitude of functions. IUBMB Life 57, 93–102 (2005)

    Article  CAS  PubMed  Google Scholar 

  47. Eissenberg, J. C. et al. dELL is an essential RNA polymerase II elongation factor with a general role in development. Proc. Natl Acad. Sci. USA 99, 9894–9899 (2002)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  48. Allison, L. A., Moyle, M., Shales, M. & Ingles, C. J. Extensive homology among the largest subunits of eukaryotic and prokaryotic RNA polymerases. Cell 42, 599–610 (1985)

    Article  CAS  PubMed  Google Scholar 

Download references


We thank M. Chow, N. Mohammad, C. Chung and V. Fong for their assistance with the creation of the web resources. We are grateful to J. van Helden and S. Brohée for sharing information on their comparison of clustering methods before publication. This research was supported by grants from Genome Canada and the Ontario Genomics Institute (to J.F.G. and A.E.), the Canadian Institutes of Health Research (to A.E., N.J.K., J.F.G., S.J.W., S.P. and C.J.I.), the National Cancer Institute of Canada with funds from the Canadian Cancer Society (to J.F.G.), the Howard Hughes Medical Institute (to J.S.W. and E.O.), the McLaughlin Centre for Molecular Medicine (to S.J.W. and S.P.), the Hospital for Sick Children (to J.M.P.-A.), the National Sciences and Engineering Research Council (to N.J.K., T.R.H. and A.E.) and the National Institutes of Health (to A.S., M.G., A.P. and H.Y.).

Author information

Authors and Affiliations


Corresponding authors

Correspondence to Andrew Emili or Jack F. Greenblatt.

Ethics declarations

Competing interests

Protein interaction information from this paper has been provided to the BioGRID database (, as well as the International Molecular Interaction Exchange consortium (IMEx, consisting of BIND, DIP, IntAct, MINT and Mpact (MIPS). Reprints and permissions information is available at The authors declare no competing financial interests.

Supplementary information

Supplementary Notes

This file contains Supplementary Discussion and Supplementary Methpds on generating the interaction network, visualization, and quality assessment. (PDF 124 kb)

Supplementary Figures 1–5

Supplementary Figure 1 details the co-localization of MIPS, Gavin, Ho, Core, Extended Core datasets. Supplementary Figure 2 details the semantic similarity (GO biological processes) for all. Supplementary Figure 3 details the cytoscape view indicating comparison with MIPS. Supplementary Figure 4 details the essentiality versus conservation, degree of connectivity and betweenness. Supplementary Figure 5 details the IWR1 complex data. (PDF 2567 kb)

Supplementary Figure 6

Guide to the yeast interactome database (PDF 3394 kb)

Supplementary Table Legends

This file contains a detailed text guide to the contents of the Supplementary Tables. (PDF 63 kb)

Supplementary Tables 1–3

Supplementary Table 1 is a list of all the 4562 proteins whose purification was attempted. Supplementary Table 2 is a list of all the 2357 proteins whose purification was successful. Supplementary Table 3 is a list of 4087 proteins that were identified via MS. (XLS 379 kb)

Supplementary Tables 4–6

Supplementary Table 4 is a list of 71 proteins that were identified in more than 3% of all the successful protein purifications. Supplementary Table 5 is a list of 2357 protein-protein interactions in the intersection dataset. Supplementary Table 6 is a list of 5496 protein-protein interactions in the merged dataset. (XLS 804 kb)

Supplementary Tables 7, 8 and 10

Supplementary Table 7 is a list of 7123 protein-protein interactions in the core dataset. Supplementary Table 8 is a list of 14317 protein-protein interactions in the extended dataset. Supplementary Table 10 is a list of protein complexes and their component subunits as identified by the Markov Cluster Algorithm. (TXT 6809 kb)

Supplementary Table 9

Complete list of all the putative S. cerevisiae protein-protein interactions identified in this study. (XLS 2884 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Krogan, N., Cagney, G., Yu, H. et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637–643 (2006).

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

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing