Assigning Confidence Scores to Protein–Protein Interactions

  • Jingkai Yu
  • Thilakam Murali
  • Russell L. FinleyJr.
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 812)

Abstract

Screens for protein–protein interactions using assays like the yeast two-hybrid system have generated volumes of useful data. The protein interactions from these screens have been used to develop a better understanding of the functions of individual proteins, regulatory pathways, molecular machines, and entire biological systems. The value of this data, however, is limited by the inherent frequency of false positives that arise in most protein interaction screens. Appreciable numbers of false positives can crop up in both low-throughput and high-throughput screens, and even in screens that employ stringent criteria for defining a positive. A number of classification systems have been used to help distinguish false positives from biologically relevant true positives. This chapter describes a system for assigning a confidence score to each interaction based on the probability that it is a true positive. Such confidence scores can be used to prioritize interactions for validation. The scores are also useful for network analysis methods that take advantage of probabilistic edge weights. The scoring method does not rely on gold standard datasets of reliable true positives and true negatives, and thus circumvents the challenges associated with obtaining such datasets. Moreover, the scoring method uses data features that are largely assay-independent, making it useful for interactions obtained from a variety of different technologies and screening methods.

Key words

Interactome mapping Protein–protein interaction Protein networks Confidence scores 

References

  1. 1.
    Edwards, A. M., Kus, B., Jansen, R., Greenbaum, D., Greenblatt, J., and Gerstein, M. (2002) Bridging structural biology and genomics: assessing protein interaction data with known complexes, Trends Genet 18, 529–536.PubMedCrossRefGoogle Scholar
  2. 2.
    Huang, H., Jedynak, B. M., and Bader, J. S. (2007) Where have all the interactions gone? Estimating the coverage of two-hybrid protein interaction maps, PLoS Comput Biol 3, e214.Google Scholar
  3. 3.
    von Mering, C., Krause, R., Snel, B., Cornell, M., Oliver, S. G., Fields, S., and Bork, P. (2002) Comparative assessment of large-scale data sets of protein-protein interactions, Nature 417, 399–403.CrossRefGoogle Scholar
  4. 4.
    Uetz, P., and Finley, R. L., Jr. (2005) From protein networks to biological systems, FEBS Lett 579, 1821–1827.PubMedCrossRefGoogle Scholar
  5. 5.
    Levy, E. D., Landry, C. R., and Michnick, S. W. (2009) How perfect can protein interactomes be?, Sci Signal 2, pe11.Google Scholar
  6. 6.
    Yu, H., Braun, P., Yildirim, M. A., Lemmens, I., Venkatesan, K., Sahalie, J., Hirozane-Kishikawa, T., Gebreab, F., Li, N., Simonis, N., Hao, T., Rual, J. F., Dricot, A., Vazquez, A., Murray, R. R., Simon, C., Tardivo, L., Tam, S., Svrzikapa, N., Fan, C., de Smet, A. S., Motyl, A., Hudson, M. E., Park, J., Xin, X., Cusick, M. E., Moore, T., Boone, C., Snyder, M., Roth, F. P., Barabasi, A. L., Tavernier, J., Hill, D. E., and Vidal, M. (2008) High-quality binary protein interaction map of the yeast interactome network, Science 322, 104–110.PubMedCrossRefGoogle Scholar
  7. 7.
    Schwartz, A. S., Yu, J., Gardenour, K. R., Finley, R. L., Jr., and Ideker, T. (2009) Cost-effective strategies for completing the interactome, Nature methods 6, 55–61.PubMedCrossRefGoogle Scholar
  8. 8.
    Suthram, S., Shlomi, T., Ruppin, E., Sharan, R., and Ideker, T. (2006) A direct comparison of protein interaction confidence assignment schemes, BMC Bioinformatics 7, 360.PubMedCrossRefGoogle Scholar
  9. 9.
    Braun, P., Tasan, M., Dreze, M., Barrios-Rodiles, M., Lemmens, I., Yu, H., Sahalie, J. M., Murray, R. R., Roncari, L., de Smet, A. S., Venkatesan, K., Rual, J. F., Vandenhaute, J., Cusick, M. E., Pawson, T., Hill, D. E., Tavernier, J., Wrana, J. L., Roth, F. P., and Vidal, M. (2009) An experimentally derived confidence score for binary protein-protein interactions, Nature methods 6, 91–97.PubMedCrossRefGoogle Scholar
  10. 10.
    Lin, X., Liu, M., and Chen, X. W. (2009) Assessing reliability of protein-protein interactions by integrative analysis of data in model organisms, BMC Bioinformatics 10 Suppl 4, S5.Google Scholar
  11. 11.
    Yu, J., and Finley, R. L., Jr. (2009) Combining multiple positive training sets to generate confidence scores for protein-protein interactions, Bioinformatics 25, 105–111.PubMedCrossRefGoogle Scholar
  12. 12.
    Bader, G. D., and Hogue, C. W. (2003) An automated method for finding molecular complexes in large protein interaction networks, BMC Bioinformatics 4, 2.PubMedCrossRefGoogle Scholar
  13. 13.
    Goldberg, D. S., and Roth, F. P. (2003) Assessing experimentally derived interactions in a small world, Proc Natl Acad Sci USA 100, 4372–4376.PubMedCrossRefGoogle Scholar
  14. 14.
    Mosca, R., Pons, C., Fernandez-Recio, J., and Aloy, P. (2009) Pushing structural information into the yeast interactome by high-throughput protein docking experiments, PLoS Comput Biol 5, e1000490.PubMedCrossRefGoogle Scholar
  15. 15.
    Prieto, C., and De Las Rivas, J. (2010) Structural domain-domain interactions: assessment and comparison with protein-protein interaction data to improve the interactome, Proteins 78, 109–117.PubMedCrossRefGoogle Scholar
  16. 16.
    Ge, H., Liu, Z., Church, G. M., and Vidal, M. (2001) Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae, Nat Genet 29, 482–486.PubMedCrossRefGoogle Scholar
  17. 17.
    Rhodes, D. R., Tomlins, S. A., Varambally, S., Mahavisno, V., Barrette, T., Kalyana-Sundaram, S., Ghosh, D., Pandey, A., and Chinnaiyan, A. M. (2005) Probabilistic model of the human protein-protein interaction network, Nat Biotechnol 23, 951–959.PubMedCrossRefGoogle Scholar
  18. 18.
    Stein, A., Russell, R. B., and Aloy, P. (2005) 3did: interacting protein domains of known three-dimensional structure, Nucleic Acids Res 33, D413–417.PubMedCrossRefGoogle Scholar
  19. 19.
    Bader, J. S., Chaudhuri, A., Rothberg, J. M., and Chant, J. (2004) Gaining confidence in high-throughput protein interaction networks, Nat Biotechnol 22, 78–85.PubMedCrossRefGoogle Scholar
  20. 20.
    Beyer, A., Bandyopadhyay, S., and Ideker, T. (2007) Integrating physical and genetic maps: from genomes to interaction networks, Nat Rev Genet 8, 699–710.PubMedCrossRefGoogle Scholar
  21. 21.
    Tong, A. H., Lesage, G., Bader, G. D., Ding, H., Xu, H., Xin, X., Young, J., Berriz, G. F., Brost, R. L., Chang, M., Chen, Y., Cheng, X., Chua, G., Friesen, H., Goldberg, D. S., Haynes, J., Humphries, C., He, G., Hussein, S., Ke, L., Krogan, N., Li, Z., Levinson, J. N., Lu, H., Menard, P., Munyana, C., Parsons, A. B., Ryan, O., Tonikian, R., Roberts, T., Sdicu, A. M., Shapiro, J., Sheikh, B., Suter, B., Wong, S. L., Zhang, L. V., Zhu, H., Burd, C. G., Munro, S., Sander, C., Rine, J., Greenblatt, J., Peter, M., Bretscher, A., Bell, G., Roth, F. P., Brown, G. W., Andrews, B., Bussey, H., and Boone, C. (2004) Global mapping of the yeast genetic interaction network, Science 303, 808–813.PubMedCrossRefGoogle Scholar
  22. 22.
    Bowers, P. M., Pellegrini, M., Thompson, M. J., Fierro, J., Yeates, T. O., and Eisenberg, D. (2004) Prolinks: a database of protein functional linkages derived from coevolution, Genome Biol 5, R35.PubMedCrossRefGoogle Scholar
  23. 23.
    Giot, L., Bader, J. S., Brouwer, C., Chaudhuri, A., Kuang, B., Li, Y., Hao, Y. L., Ooi, C. E., Godwin, B., Vitols, E., Vijayadamodar, G., Pochart, P., Machineni, H., Welsh, M., Kong, Y., Zerhusen, B., Malcolm, R., Varrone, Z., Collis, A., Minto, M., Burgess, S., McDaniel, L., Stimpson, E., Spriggs, F., Williams, J., Neurath, K., Ioime, N., Agee, M., Voss, E., Furtak, K., Renzulli, R., Aanensen, N., Carrolla, S., Bickelhaupt, E., Lazovatsky, Y., DaSilva, A., Zhong, J., Stanyon, C. A., Finley, R. L., Jr., White, K. P., Braverman, M., Jarvie, T., Gold, S., Leach, M., Knight, J., Shimkets, R. A., McKenna, M. P., Chant, J., and Rothberg, J. M. (2003) A protein interaction map of Drosophila melanogaster, Science 302, 1727–1736.PubMedCrossRefGoogle Scholar
  24. 24.
    Stanyon, C. A., Liu, G., Mangiola, B. A., Patel, N., Giot, L., Kuang, B., Zhang, H., Zhong, J., and Finley, R. L., Jr. (2004) A Drosophila protein-interaction map centered on cell-cycle regulators, Genome Biol 5, R96.PubMedCrossRefGoogle Scholar
  25. 25.
    Formstecher, E., Aresta, S., Collura, V., Hamburger, A., Meil, A., Trehin, A., Reverdy, C., Betin, V., Maire, S., Brun, C., Jacq, B., Arpin, M., Bellaiche, Y., Bellusci, S., Benaroch, P., Bornens, M., Chanet, R., Chavrier, P., Delattre, O., Doye, V., Fehon, R., Faye, G., Galli, T., Girault, J. A., Goud, B., de Gunzburg, J., Johannes, L., Junier, M. P., Mirouse, V., Mukherjee, A., Papadopoulo, D., Perez, F., Plessis, A., Rosse, C., Saule, S., Stoppa-Lyonnet, D., Vincent, A., White, M., Legrain, P., Wojcik, J., Camonis, J., and Daviet, L. (2005) Protein interaction mapping: a Drosophila case study, Genome Res 15, 376–384.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Jingkai Yu
    • 1
  • Thilakam Murali
    • 2
  • Russell L. FinleyJr.
    • 2
  1. 1.National Key Laboratory of Biochemical EngineeringChinese Academy of SciencesBeijingChina
  2. 2.Center for Molecular Medicine and GeneticsWayne State University School of MedicineDetroitUSA

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