Skip to main content

Thank you for visiting nature.com. 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.

  • Article
  • Published:

An experimentally derived confidence score for binary protein-protein interactions

Abstract

Information on protein-protein interactions is of central importance for many areas of biomedical research. At present no method exists to systematically and experimentally assess the quality of individual interactions reported in interaction mapping experiments. To provide a standardized confidence-scoring method that can be applied to tens of thousands of protein interactions, we have developed an interaction tool kit consisting of four complementary, high-throughput protein interaction assays. We benchmarked these assays against positive and random reference sets consisting of well documented pairs of interacting human proteins and randomly chosen protein pairs, respectively. A logistic regression model was trained using the data from these reference sets to combine the assay outputs and calculate the probability that any newly identified interaction pair is a true biophysical interaction once it has been tested in the tool kit. This general approach will allow a systematic and empirical assignment of confidence scores to all individual protein-protein interactions in interactome networks.

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

Access options

Buy this article

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

Figure 1: Strategy for deriving a confidence score for individual protein-protein interactions after high-throughput screening using data from several complementary follow-up interaction assays.
Figure 2: Schematic descriptions of complementary tool kit assays for binary protein interaction.
Figure 3: Evaluation of assay performance at different stringencies using hsPRS-v1 and hsRRS-v1.
Figure 4: Performance of assays against positive and random reference sets PRS and RRS.
Figure 5: Application of the integrated confidence score.

Similar content being viewed by others

References

  1. Barrios-Rodiles, M. et al. High-throughput mapping of a dynamic signaling network in mammalian cells. Science 307, 1621–1625 (2005).

    Article  CAS  Google Scholar 

  2. 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 

  3. Yu, H. et al. High-quality binary protein interaction map of the yeast interactome network. Science 322, 104–110 (2008).

    Article  CAS  Google Scholar 

  4. 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 

  5. Ramani, A.K., Bunescu, R.C., Mooney, R.J. & Marcotte, E.M. Consolidating the set of known human protein-protein interactions in preparation for large-scale mapping of the human interactome. Genome Biol. 6, R40 (2005).

    Article  Google Scholar 

  6. Ramani, A.K. et al. A map of human protein interactions derived from co-expression of human mRNAs and their orthologs. Mol. Syst. Biol. 4, 180 (2008).

    Article  Google Scholar 

  7. Chiang, T. et al. Coverage and error models of protein-protein interaction data by directed graph analysis. Genome Biol. 8, R186 (2007).

    Article  Google Scholar 

  8. 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 

  9. Sato, S. et al. A large-scale protein protein interaction analysis in Synechocystis sp. PCC6803. DNA Res. 14, 207–216 (2007).

    Article  CAS  Google Scholar 

  10. Gavin, A.C. et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  12. Han, J.D. et al. Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430, 88–93 (2004).

    Article  CAS  Google Scholar 

  13. Li, S. et al. A map of the interactome network of the metazoan C. elegans. Science 303, 540–543 (2004).

    Article  CAS  Google Scholar 

  14. Rual, J.F. et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature 437, 1173–1178 (2005).

    Article  CAS  Google Scholar 

  15. Vidalain, P.O. et al. Increasing specificity in high-throughput yeast two-hybrid experiments. Methods 32, 363–370 (2004).

    Article  CAS  Google Scholar 

  16. Walhout, A.J. & Vidal, M. High-throughput yeast two-hybrid assays for large-scale protein interaction mapping. Methods 24, 297–306 (2001).

    Article  CAS  Google Scholar 

  17. Eyckerman, S. et al. Design and application of a cytokine-receptor-based interaction trap. Nat. Cell Biol. 3, 1114–1119 (2001).

    Article  CAS  Google Scholar 

  18. Nyfeler, B., Michnick, S.W. & Hauri, H.P. Capturing protein interactions in the secretory pathway of living cells. Proc. Natl. Acad. Sci. USA 102, 6350–6355 (2005).

    Article  CAS  Google Scholar 

  19. Ramachandran, N. et al. Next-generation high-density self-assembling functional protein arrays. Nat. Methods 5, 535–538 (2008).

    Article  CAS  Google Scholar 

  20. Venkatesan, K. et al. An empirical framework for binary interactome mapping. Nat. Methods advance online publication, 10.1038/nmeth.1280 (7 December 2008).

  21. Bader, G.D., Betel, D. & Hogue, C.W. BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res. 31, 248–250 (2003).

    Article  CAS  Google Scholar 

  22. Chatr-aryamontri, A. et al. MINT: the Molecular INTeraction database. Nucleic Acids Res. 35, D572–D574 (2007).

    Article  CAS  Google Scholar 

  23. Mishra, G.R. et al. Human protein reference database–2006 update. Nucleic Acids Res. 34, D411–D414 (2006).

    Article  CAS  Google Scholar 

  24. Pagel, P. et al. The MIPS mammalian protein-protein interaction database. Bioinformatics 21, 832–834 (2005).

    Article  CAS  Google Scholar 

  25. Salwinski, L. et al. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res. 32, D449–D451 (2004).

    Article  CAS  Google Scholar 

  26. Cusick, M.E. et al. Literature-curated protein interaction datasets. Nat. Methods (in the press).

  27. Rual, J.F. et al. Human ORFeome version 1.1: a platform for reverse proteomics. Genome Res. 14, 2128–2135 (2004).

    Article  CAS  Google Scholar 

  28. Ben-Hur, A. & Noble, W.S. Choosing negative examples for the prediction of protein-protein interactions. BMC Bioinformatics 7 (suppl. 1), S2 (2006).

    Article  Google Scholar 

  29. Qi, Y., Bar-Joseph, Z. & Klein-Seetharaman, J. Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins 63, 490–500 (2006).

    Article  CAS  Google Scholar 

  30. Vidal, M. et al. Reverse two-hybrid and one-hybrid systems to detect dissociation of protein-protein and DNA-protein interactions. Proc. Natl. Acad. Sci. USA 93, 10315–10320 (1996).

    Article  CAS  Google Scholar 

  31. Lemmens, I. et al. Heteromeric MAPPIT: a novel strategy to study modification-dependent protein-protein interactions in mammalian cells. Nucleic Acids Res. 31, e75 (2003).

    Article  Google Scholar 

  32. Lemmens, I., Lievens, S., Eyckerman, S. & Tavernier, J. Reverse MAPPIT detects disruptors of protein-protein interactions in human cells. Nat. Protoc. 1, 92–97 (2006).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank S. Michnick and N. Ramachandran for reagents and technical help for the PCA and wNAPPA assays, respectively. We thank A. Datti, T. Sun and F. Vizeacoumar from the SMART Robotics Facility at the Samuel Lunenfeld Research Institute for help with the automated version of LUMIER assay. We thank all members of the Vidal, Tavernier, Roth, and Wrana laboratories for helpful discussions, Agencourt Biosciences for sequencing assistance, and A. Bird and D. Maher for administrative assistance. This work was supported by contributions from the W.M. Keck Foundation awarded to M.V., F.P.R. and D.E.H.; by the Ellison Foundation awarded to M.V.; by Institute Sponsored Research funds from the Dana-Farber Cancer Institute Strategic Initiative awarded to M.V. and CCSB; by US National Institutes of Health grants 5P50HG004233 and 2R01HG001715 awarded to M.V., F.P.R. and D.E.H., R01 ES015728 awarded to M.V., 5U54CA112952 awarded to J. Nevins (M.V. subcontract), 5U01CA105423 awarded to S.H. Orkin (M.V. project), R01 HG003224 awarded to F.P.R. and F32 HG004098 awarded to M.T.; by a University of Ghent grant GOA12051401 and the Fonds Wetenschappelijk Onderzoek– Vlanderen (FWO-V) G.0031.06 awarded to J.T., by a postdoctoral fellowship from the FWO-V awarded to I.L.; and by a grant from Genome Canada and funds from the Ontario Genomics Institute awarded to J.L.W. M.V. is a Chercheur Qualifié Honoraire from the Fonds de la Recherche Scientifique (FRS-FNRS, French Community of Belgium).

Author information

Authors and Affiliations

Authors

Contributions

P.B., M.T. and M.D. coordinated experiments and data analysis. P.B., M.D., J.M.S., J.-F.R., R.R.M. and H.Y. performed high-throughput Gateway cloning. P.B., H.Y. and J.M.S. implemented, developed and analyzed wNAPPA and PCA experiments. J.-F.R., K.V. and M.E.C. established PRSv1.0 and RRS reference sets. I.L., A.-S. de S., J.T. and K.V. coordinated, performed and analyzed MAPPIT experiments. M.B.-R., L.R. and J.L.W. coordinated, performed and analyzed LUMIER experiments. M.T. and F.P.R. developed the regression model. M.V. conceived the project. M.V., T.P., J.L.W. and D.E.H. developed the concepts underlying the overall strategy. D.E.H., F.P.R. and M.V. co-directed the project.

Corresponding authors

Correspondence to Pascal Braun, Jan Tavernier, Jeffrey L Wrana, Frederick P Roth or Marc Vidal.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6, Supplementary Table 1, Supplementary Data, Supplementary Methods, Supplementary Protocols 1–5 (PDF 2582 kb)

Supplementary Table 2

PM_IDs and full names for all hsPRS_v1 protein pairs (XLS 77 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Braun, P., Tasan, M., Dreze, M. et al. An experimentally derived confidence score for binary protein-protein interactions. Nat Methods 6, 91–97 (2009). https://doi.org/10.1038/nmeth.1281

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.1281

This article is cited by

Search

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