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Filtering and Interpreting Large-Scale Experimental Protein–Protein Interaction Data

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Network Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 781))

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Abstract

Rarely acting in isolation, it is invariably the physical associations among proteins that define their biological activity, necessitating the study of the cellular meshwork of protein–protein interactions (PPI) before a full appreciation of gene function can be achieved. The past few years have seen a marked expansion in the both the sheer volume and number of organisms for which high-quality interaction data is available, with high-throughput interaction screening and detection techniques showing consistent improvement both in scale and sensitivity. Although techniques for large-scale PPI mapping are increasingly being applied to new organisms, including human, there is a corresponding need to rigorously evaluate, benchmark, and impartially filter the results. This chapter explores methods for PPI dataset evaluation, including a survey of previous techniques applied by landmark studies in the field and a discussion of promising new experimental approaches. We further outline practical suggestions and useful tools for interpreting newly generated PPI data. As the majority of large-scale experimental data has been generated for the budding yeast S. cerevisiae, most of the techniques and datasets described are from the perspective of this model unicellular eukaryote; however, extensions to other organisms including mammals are mentioned where possible.

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References

  1. Musso GA, Z Zhang, et al. (2007). Experimental and computational procedures for the assessment of protein complexes on a genome-wide scale. Chem Rev 1078: 3585–3600.

    Article  Google Scholar 

  2. Sanderson CM (2009). The Cartographers toolbox: building bigger and better human protein interaction networks. Brief Funct Genomic Proteomic 81: 1–11.

    Google Scholar 

  3. Cagney G (2009). Interaction networks: lessons from large-scale studies in yeast. Proteomics 920: 4799–4811.

    Article  Google Scholar 

  4. Fields S and O Song (1989). A novel genetic system to detect protein–protein interactions. Nature 3406230: 245–246.

    Article  Google Scholar 

  5. Uetz P, L Giot, et al. (2000). A comprehensive analysis of protein–protein interactions in Saccharomyces cerevisiae. Nature 4036770: 623–627.

    Google Scholar 

  6. Ito T, T Chiba, et al. (2001). A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci USA 988: 4569–4574.

    Article  Google Scholar 

  7. von Mering C, R Krause, et al. (2002). Comparative assessment of large-scale data sets of protein–protein interactions. Nature 4176887: 399–403.

    Article  Google Scholar 

  8. Ito T, K Ota, et al. (2002). Roles for the two-hybrid system in exploration of the yeast protein interactome. Mol Cell Proteomics 18: 561–566.

    Google Scholar 

  9. Yu H, P Braun, et al. (2008). High-quality binary protein interaction map of the yeast interactome network. Science 3225898: 104–110.

    Article  Google Scholar 

  10. Vidalain PO, M Boxem, et al. (2004). Increasing specificity in high-throughput yeast two-hybrid experiments. Methods 324: 363–370.

    Article  Google Scholar 

  11. Li S, CM Armstrong, et al. (2004). A map of the interactome network of the metazoan C. elegans. Science 3035657: 540–543.

    Article  Google Scholar 

  12. Stanyon CA, G Liu, et al. (2004). A Drosophila protein-interaction map centered on cell-cycle regulators. Genome Biol 512: R96.

    Article  Google Scholar 

  13. Formstecher E, S Aresta, et al. (2005). Protein interaction mapping: a Drosophila case study. Genome Res 153: 376–384.

    Article  Google Scholar 

  14. Rual JF, K Venkatesan, et al. (2005). Towards a proteome-scale map of the human protein–protein interaction network. Nature 4377062: 1173–1178.

    Article  Google Scholar 

  15. Stelzl U, U Worm, et al. (2005). A human protein–protein interaction network: a resource for annotating the proteome. Cell 1226: 957–968.

    Article  Google Scholar 

  16. Lievens S, I Lemmens, et al. (2009). Mammalian two-hybrids come of age. Trends Biochem Sci 3411: 579–588.

    Article  Google Scholar 

  17. Suter B, S Kittanakom, et al. (2008). Two-hybrid technologies in proteomics research. Curr Opin Biotechnol 194: 316–323.

    Article  Google Scholar 

  18. Walhout AJ and M Vidal (1999). A genetic strategy to eliminate self-activator baits prior to high-throughput yeast two-hybrid screens. Genome Res 911: 1128–1134.

    Article  Google Scholar 

  19. Helbig AO, AJ Heck, et al. (2010). Exploring the membrane proteome – challenges and analytical strategies. J Proteomics 735: 868–878.

    Article  Google Scholar 

  20. Johnsson N and A Varshavsky (1994). Split ubiquitin as a sensor of protein interactions in vivo. Proc Natl Acad Sci USA 9122: 10340–10344.

    Article  Google Scholar 

  21. Miller JP, RS Lo, et al. (2005). Large-scale identification of yeast integral membrane protein interactions. Proc Natl Acad Sci USA 10234: 12123–12128.

    Article  Google Scholar 

  22. Harris MA, J Clark, et al. (2004). The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 32Database issue: D258–261.

    Google Scholar 

  23. Tarassov K, V Messier, et al. (2008). An in vivo map of the yeast protein interactome. Science 3205882: 1465–1470.

    Article  Google Scholar 

  24. Remy I and SW Michnick (1999). Clonal selection and in vivo quantitation of protein interactions with protein-fragment complementation assays. Proc Natl Acad Sci USA 9610: 5394–5399.

    Article  Google Scholar 

  25. Pelletier JN, KM Arndt, et al. (1999). An in vivo library-versus-library selection of optimized protein–protein interactions. Nat Biotechnol 177: 683–690.

    Google Scholar 

  26. Mewes HW, C Amid, et al. (2004). MIPS: analysis and annotation of proteins from whole genomes. Nucleic Acids Res 32Database issue: D41–44.

    Google Scholar 

  27. Fritze CE and TR Anderson (2000). Epitope tagging: general method for tracking recombinant proteins. Methods Enzymol 327: 3–16.

    Article  PubMed  CAS  Google Scholar 

  28. Puig O, F Caspary, et al. (2001). The tandem affinity purification (TAP) method: a general procedure of protein complex purification. Methods 243: 218–229.

    Article  Google Scholar 

  29. Ho Y, A Gruhler, et al. (2002). Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 4156868: 180–183.

    Article  Google Scholar 

  30. Krogan NJ, G Cagney, et al. (2006). Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 4407084: 637–643.

    Article  Google Scholar 

  31. Gavin AC, P Aloy, et al. (2006). Proteome survey reveals modularity of the yeast cell machinery. Nature 4407084: 631–636.

    Article  Google Scholar 

  32. Goll J and P Uetz (2006). The elusive yeast interactome. Genome Biol 76: 223.

    Google Scholar 

  33. Collins SR, P Kemmeren, et al. (2007). Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae. Mol Cell Proteomics 63: 439–450.

    Google Scholar 

  34. Bader GD and CW Hogue (2002). Analyzing yeast protein–protein interaction data obtained from different sources. Nat Biotechnol 2010: 991–997.

    Article  Google Scholar 

  35. Gavin AC, M Bosche, et al. (2002). Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 4156868: 141–147.

    Article  Google Scholar 

  36. Jansen R and M Gerstein (2004). Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction. Curr Opin Microbiol 75: 535–545.

    Article  Google Scholar 

  37. Franzosa E, B Linghu, et al. (2009). Computational reconstruction of protein–­protein interaction networks: algorithms and issues. Methods Mol Biol 541: 89–100.

    Article  PubMed  CAS  Google Scholar 

  38. Hu P, SC Janga, et al. (2009). Global functional atlas of Escherichia coli encompassing previously uncharacterized proteins. PLoS Biol 74: e96.

    Article  Google Scholar 

  39. Skrabanek L, HK Saini, et al. (2008). Computational prediction of protein–protein interactions. Mol Biotechnol 381: 1–17.

    Article  Google Scholar 

  40. Frank E, M Hall, et al. (2004). Data mining in bioinformatics using Weka. Bioinformatics 2015: 2479–2481.

    Article  Google Scholar 

  41. Sing T, O Sander, et al. (2005). ROCR: visualizing classifier performance in R. Bioinformatics 2120: 3940–3941.

    Article  Google Scholar 

  42. Shannon P, A Markiel, et al. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 1311: 2498–2504.

    Article  Google Scholar 

  43. Maere S, K Heymans, et al. (2005). BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 2116: 3448–3449.

    Article  Google Scholar 

  44. Cline MS, M Smoot, et al. (2007). Integration of biological networks and gene expression data using Cytoscape. Nat Protoc 210: 2366–2382.

    Article  Google Scholar 

  45. Vlasblom J, S Wu, et al. (2006). GenePro: a Cytoscape plug-in for advanced visualization and analysis of interaction networks. Bioinformatics 2217: 2178–2179.

    Article  Google Scholar 

  46. Rivera CG, R Vakil, et al. (2010). NeMo: Network Module identification in Cytoscape. BMC Bioinformatics 11 Suppl 1: S61.

    Google Scholar 

  47. Yeung N, MS Cline, et al. (2008). Exploring biological networks with Cytoscape software. Curr Protoc Bioinformatics Chapter 8: Unit 8 13.

    Google Scholar 

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Acknowledgments

AE and ZZ acknowledge a Team Grant from the Canadian Institute of Health Research (CIHR MOP#82940).

Author information

Authors and Affiliations

  1. Cardiovascular Division, Brigham & Women’s Hospital, Boston, MA, USA

    Gabriel Musso

  2. Harvard Medical School, Boston, MA, USA

    Gabriel Musso

  3. Banting and Best Department of Medical Research, Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada

    Andrew Emili & Zhaolei Zhang

  4. Department of Molecular Genetics and Microbiology, University of Toronto, Toronto, ON, Canada

    Andrew Emili & Zhaolei Zhang

Authors
  1. Gabriel Musso
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  2. Andrew Emili
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  3. Zhaolei Zhang
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Corresponding author

Correspondence to Zhaolei Zhang .

Editor information

Editors and Affiliations

  1. Conway Institute, Sch. Biomolecular & Biomedical Science, University College Dublin, Belfield, Dublin, 4, Ireland

    Gerard Cagney

  2. Donnelly Ctr Cell. & Biomolecular Res., Banting & Best Dept. Medical Research, University of Toronto, College St. 160, Toronto, M5S 3E1, Ontario, Canada

    Andrew Emili

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Musso, G., Emili, A., Zhang, Z. (2011). Filtering and Interpreting Large-Scale Experimental Protein–Protein Interaction Data. In: Cagney, G., Emili, A. (eds) Network Biology. Methods in Molecular Biology, vol 781. Humana Press. https://doi.org/10.1007/978-1-61779-276-2_14

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  • DOI: https://doi.org/10.1007/978-1-61779-276-2_14

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-275-5

  • Online ISBN: 978-1-61779-276-2

  • eBook Packages: Springer Protocols

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