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Analyzing yeast protein–protein interaction data obtained from different sources

Nature Biotechnology volume 20, pages 991–997 (2002)Cite this article

Abstract

High-throughput methods for detecting protein interactions, such as mass spectrometry and yeast two-hybrid assays, continue to produce vast amounts of data that may be exploited to infer protein function and regulation. As this article went to press, the pool of all published interaction information on Saccharomyces cerevisiae was 15,143 interactions among 4,825 proteins, and power-law scaling supports an estimate of 20,000 specific protein interactions. To investigate the biases, overlaps, and complementarities among these data, we have carried out an analysis of two high-throughput mass spectrometry (HMS)–based protein interaction data sets from budding yeast, comparing them to each other and to other interaction data sets. Our analysis reveals 198 interactions among 222 proteins common to both data sets, many of which reflect large multiprotein complexes. It also indicates that a “spoke” model that directly pairs bait proteins with associated proteins is roughly threefold more accurate than a “matrix” model that connects all proteins. In addition, we identify a large, previously unsuspected nucleolar complex of 148 proteins, including 39 proteins of unknown function. Our results indicate that existing large-scale protein interaction data sets are nonsaturating and that integrating many different experimental data sets yields a clearer biological view than any single method alone.

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Figure 1: Visual representation of molecular complexes in protein interaction networks found using the k-core method.
Figure 2: Overlap of the spoke models of TAP and HMS-PCI.
Figure 3: Functional annotation matrices26 showing the distribution of interactions of six data sets.

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References

  1. Fields, S. Proteomics. Proteomics in genomeland. Science 291, 1221–1224 (2001).

    Article  CAS  Google Scholar 

  2. Pawson, T., Gish, G.D. & Nash, P. SH2 domains, interaction modules and cellular wiring. Trends Cell Biol. 11, 504–511 (2001).

    Article  CAS  Google Scholar 

  3. Marcotte, E.M. et al. Detecting protein function and protein–protein interactions from genome sequences. Science 285, 751–753 (1999).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  6. Pandey, A. & Mann, M. Proteomics to study genes and genomes. Nature 405, 837–846 (2000).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  8. Bader, G.D. et al. BIND—The biomolecular interaction network database. Nucleic Acids Res. 29, 242–245 (2001).

    Article  CAS  Google Scholar 

  9. 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  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  11. Tong, A.H. et al. A combined experimental and computational strategy to define protein interaction networks for peptide recognition modules. Science 295, 321–324 (2002).

    Article  CAS  Google Scholar 

  12. Drees, B.L. et al. A protein interaction map for cell polarity development. J. Cell Biol. 154, 549–571 (2001).

    Article  CAS  Google Scholar 

  13. Fromont-Racine, M. et al. Genome-wide protein interaction screens reveal functional networks involving Sm-like proteins. Yeast 17, 95–110 (2000).

    Article  CAS  Google Scholar 

  14. Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).

    Article  CAS  Google Scholar 

  15. Mewes, H.W. et al. MIPS: a database for genomes and protein sequences. Nucleic Acids Res. 28, 37–40 (2000).

    Article  CAS  Google Scholar 

  16. Costanzo, M.C. et al. YPD, PombePD and WormPD: model organism volumes of the BioKnowledge library, an integrated resource for protein information. Nucleic Acids Res. 29, 75–79 (2001).

    Article  CAS  Google Scholar 

  17. Andersen, J.S. et al. Directed proteomic analysis of the human nucleolus. Curr. Biol. 12, 1–11 (2002).

    Article  Google Scholar 

  18. Harnpicharnchai, P. et al. Composition and functional characterization of yeast 66S ribosome assembly intermediates. Mol. Cell 8, 505–515 (2001).

    Article  CAS  Google Scholar 

  19. Schwikowski, B., Uetz, P. & Fields, S. A network of protein–protein interactions in yeast. Nat.Biotechnol. 18, 1257–1261 (2000).

    Article  CAS  Google Scholar 

  20. Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N. & Barabasi, A.L. The large-scale organization of metabolic networks. Nature 407, 651–654 (2000).

    Article  CAS  Google Scholar 

  21. Jeong, H., Mason, S.P., Barabasi, A.L. & Oltvai, Z.N. Lethality and centrality in protein networks. Nature 411, 41–42 (2001).

    Article  CAS  Google Scholar 

  22. Pruitt, K.D. & Maglott, D.R. RefSeq and LocusLink: NCBI gene-centered resources. Nucleic Acids Res. 29, 137–140 (2001).

    Article  CAS  Google Scholar 

  23. Chervitz, S.A. et al. Using the Saccharomyces Genome Database (SGD) for analysis of protein similarities and structure. Nucleic Acids Res. 27, 74–78 (1999).

    Article  CAS  Google Scholar 

  24. Norris, V. et al. Hypothesis: hyperstructures regulate bacterial structure and the cell cycle. Biochimie 81, 915–920 (1999).

    Article  CAS  Google Scholar 

  25. Xenarios, I. et al. DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res. 30, 303–305 (2002).

    Article  CAS  Google Scholar 

  26. Ge, H., Liu, Z., Church, G.M. & Vidal, M. Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nat. Genet. 29, 482–486 (2001).

    Article  CAS  Google Scholar 

  27. Olson, M.O., Dundr, M. & Szebeni, A. The nucleolus: an old factory with unexpected capabilities. Trends Cell Biol. 10, 189–196 (2000).

    Article  CAS  Google Scholar 

  28. Visintin, R. & Amon, A. The nucleolus: the magician's hat for cell cycle tricks. Curr. Opin. Cell. Biol. 12, 752 (2000).

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Acknowledgements

We thank Mike Tyers, Charlie Boone, and Tony Pawson for helpful discussions. This work was supported in part from grants from the Canadian Institutes of Health Research (CIHR), the Ontario Research and Development Challenge Fund and MDS-Sciex to C.H. G.D.B. is supported by an Ontario Graduate Scholarship (OGS).

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  1. Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, ON, Canada, M5G 1X5

    Gary D. Bader & Christopher W.V. Hogue

  2. Department of Biochemistry, University of Toronto, Toronto, ON, Canada, M5S 1A8

    Gary D. Bader & Christopher W.V. Hogue

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Correspondence to Christopher W.V. Hogue.

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Bader, G., Hogue, C. Analyzing yeast protein–protein interaction data obtained from different sources. Nat Biotechnol 20, 991–997 (2002). https://doi.org/10.1038/nbt1002-991

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