Skip to main content

Prediction of Protein–Protein Interactions: A Study of the Co-evolution Model

  • Protocol
  • First Online:
Computational Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 541))

Abstract

The concept of molecular co-evolution drew attention in recent years as the basis for several algorithms for the prediction of protein–protein interactions. While being successful on specific data, the concept has never been tested on a large set of proteins. In this chapter we analyze the feasibility of the co-evolution principle for protein–protein interaction prediction through one of its derivatives, the correlated divergence model. Given two proteins, the model compares the patterns of divergence of their families and assigns a score based on the correlation between the two. The working hypothesis of the model postulates that the stronger the correlation the more likely is that the two proteins interact. Several novel variants of this model are considered, including algorithms that attempt to identify the subset of the database proteins (the homologs of the query proteins) that are more likely to interact. We test the models over a large set of protein interactions extracted from several sources, including BIND, DIP, and HPRD.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Schwikowski, B., Uetz, P. and Fields, S. A network of protein-protein interactions in yeast. Nat Biotechnol. 2000, 18:1257–61.

    Article  PubMed  CAS  Google Scholar 

  2. Ho, Y., Gruhler, A., Heilbut, A., et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 2003, 415:180–83.

    Article  Google Scholar 

  3. Ihmels, J., Levy, R. and Barkai, N. Principles of ranscriptional control in the metabolic network of Saccharomyces cerevisiae. Nat Biotechnol. 2004, 22:86–92.

    Article  PubMed  CAS  Google Scholar 

  4. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D. and Alon, U. Network motifs: simple building blocks of complex networks. Science 2002, 298:824–27.

    Article  PubMed  CAS  Google Scholar 

  5. Fields, S. and Song, O. A novel genetic system to detect protein-protein interactions. Nature 1989, 340:245–46.

    Article  PubMed  CAS  Google Scholar 

  6. Uetz, P., Giot, L., Cagney, G., et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 2000, 403:623–27.

    Article  PubMed  CAS  Google Scholar 

  7. Sobolev, V., Sorokine, A., Prilusky, J., Abola, E. E. and Edelman, M. Automated analysis of interatomic contacts in proteins. Bioinformatics 1999, 4:327–32.

    Article  Google Scholar 

  8. Gallet, X., Charloteaux, B., Thomas, A. and Brasseur, R. A fast method to predict protein interaction sites from sequences. J. Mol. Biol. 2000, 302:917–26.

    Article  PubMed  CAS  Google Scholar 

  9. Espadaler, J., Romero-Isart, O., Jackson, R. M. and Oliva, B. Prediction of protein–protein interactions using distant conservation of sequence patterns and structure relationships. Bioinformatics 2005, 21(16):3360–68.

    Article  PubMed  CAS  Google Scholar 

  10. Teodoro, M., Phillips, G. and Kavraki, L. Molecular docking: A problem with thousands of degrees of freedom. IEEE International Conference on Robotics and Automation (ICRA 2001), 2001 May, Seoul, Korea, pp. 960–966.

    Google Scholar 

  11. Lu, L., Lu, H. and Skolnick, J. MULTIPROSPECTOR: An Algorithm for the prediction of protein-protein interactions by multimeric threading. Proteins 2002, 49:350–64.

    Article  PubMed  CAS  Google Scholar 

  12. Kini, R. M. and Evans, H. J. A hypothetical structural role for proline residues in the flanking segments of protein-protein interaction sites. Biochem. Biophys. Res. Commun. 1995, 212:1115–24.

    CAS  Google Scholar 

  13. Aytuna, A. S., Gursoy, A. and Keskin, O. Prediction of protein–protein interactions by combining structure and sequence conservation in protein interfaces. Bioinformatics 2005, 21(12):2850–55.

    Article  PubMed  CAS  Google Scholar 

  14. Clackson, T. and Wells, J. A. A hot spot of binding energy in a hormonereceptor interface. Science 1995, 267:383–86.

    Article  PubMed  CAS  Google Scholar 

  15. Thorn, K. S. and Bogan, A. A. ASEdb: A database of Alanine mutations and their effect on the free energy of binding in protein interactions. Bioinformatics 2001, 1:284–85.

    Article  Google Scholar 

  16. Sprinzak, E. and Margalit, H. Correlated sequence-signatures as markers of protein-protein interaction. J. Mol. Biol. 2001, 311:681–92.

    Article  PubMed  CAS  Google Scholar 

  17. Aloy, P. and Russell, R. InterPreTS: Protein interaction prediction through tertiary structure. Bioinformatics 2003, 19:161–62.

    Article  PubMed  CAS  Google Scholar 

  18. Deng, M., Mehta, S., Sun, F. and Chen, T. Inferring domain-domain interactions from protein-protein interactions. Genome Res. 2002, 12:1540–48.

    Article  PubMed  CAS  Google Scholar 

  19. Liu, Y., Liu, N. and Zhao, H. Inferring protein–protein interactions through high-throughput interaction data from diverse organisms. Bioinformatics 2005, 21(15): 3279–85.

    Article  PubMed  CAS  Google Scholar 

  20. Chen, X. W. and Liu, M. Prediction of protein–protein interactions using random decision forest framework. Bioinformatics 2005, 21(24):4394–400.

    Article  PubMed  CAS  Google Scholar 

  21. Breiman, L. Random forests. Mach. Learn. 2001, 45:5–32.

    Article  Google Scholar 

  22. Han, D., Kim, H., Jang, W., Lee, S. and Suh, J. PreSPI: A domain combination based prediction system for protein–protein interaction. Nucl. Acids Res. 2004, 32(21):6312–20.

    Article  PubMed  CAS  Google Scholar 

  23. Marcotte, E. M., Pellegrini, M., Ng, H. L., Rice, D. W., Yeates, T. O. and Eisenberg, D. Detecting protein function and protein-protein interactions from genome sequences. Science 1999, 285:751–53.

    Article  PubMed  CAS  Google Scholar 

  24. Enright, A. J., Iliopoulos, I., Kyrpides, N. C. and Ouzounis, C. A. Protein interaction maps for complete genomes based on gene fusion events. Nature 1999, 402:86–90.

    Article  PubMed  CAS  Google Scholar 

  25. Park, D., Lee, S., Bolser, D., Schroeder, M., Lappe, M., Oh, D. and Bhak, J. Comparative interactomics analysis of protein family interaction networks using PSIMAP (protein structural interactome map). Bioinformatics 2005, 21(15):3234–40.

    Article  PubMed  CAS  Google Scholar 

  26. Huang, T., Tien, A., Huang, W., Lee, Y. G., Peng, C., Tseng, H., Kao, C. and Huang, C. F. POINT: A database for the prediction of protein–protein interactions based on the orthologous interactome. Bioinformatics 2004, 20(17):3273–76.

    Article  PubMed  CAS  Google Scholar 

  27. Sun, J., Xu, J., Liu, Z., Liu, Q., Zhao, A., Shi, T. and Li, Y. Refined phylogenetic profiles method for predicting protein–protein interactions. Bioinformatics 2005, 21(16):3409–15.

    Article  PubMed  CAS  Google Scholar 

  28. Dandekar, T., Snel, B., Huynen, M. and Bork, P. Conservation of gene order: A fingerprint of proteins that physically interact. Trends Biochem. Sci. 1998, 23:324–28.

    Article  PubMed  CAS  Google Scholar 

  29. Goh, C., Bogan, A., Joachimiak, M., Walther, D. and Cohen, F. Co-evolution of proteins with their interaction partners. J. Mol. Biol. 2000, 299:283–93.

    Article  PubMed  CAS  Google Scholar 

  30. Pazos, F. and Valencia, A. Similarity of phylogenetic trees as indicator of protein-protein interaction. Protein Eng. 2001, 14:609–14.

    Article  PubMed  CAS  Google Scholar 

  31. Tan, S., Zhang, Z. and Ng, S. ADVICE: Automated detection and validation of interaction by co-evolution. Nucl. Acids Res. 2004, 32:W69–W72.

    Article  PubMed  CAS  Google Scholar 

  32. Izarzugaza, J. M. G., Juan, D., Pons, C., Ranea, J. A. G., Valencia, A. and Pazos, F. TSEMA: Interactive prediction of protein pairings between interacting families. Nucl. Acids Res. 2006, 34:W315–W319.

    Article  PubMed  CAS  Google Scholar 

  33. Pazos, F., Helmer-Citterich, M., Ausiello, G. and Valencia, A. Correlated mutations contain information about protein-protein interaction. J. Mol. Biol. 1997, 271:511–23.

    Article  PubMed  CAS  Google Scholar 

  34. Valencia, A. and Pazos, F. Computational methods for the prediction of protein interactions. Curr. Opin. Struct. Biol. 2002, 12:368–73.

    Article  PubMed  CAS  Google Scholar 

  35. Pazos, F. and Valencia, A. In silico two-hybrid system for the selection of physically interacting protein pairs. Proteins 2002, 47:219–27.

    Article  PubMed  CAS  Google Scholar 

  36. Jansen, R., Yu, H., Greenbaum, D., Kluger, Y., Krogan, N. J., Chung, S., Emili, A., Snyder, M., Greenblatt, J. F. and Gerstein, M. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 2003, 302(17):449–53.

    Article  PubMed  CAS  Google Scholar 

  37. Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., Harris, M. A., Hill, D. P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J. C., Richardson, J. E., Ringwald, M., Rubin, G. M. and Sherlock G. Gene ontology: Tool for the unification of biology. The gene ontology consortium. Nat. Genet. 2000, 25(1):25–29.

    Article  PubMed  CAS  Google Scholar 

  38. Mewes, H. W., Frishman, D., Guldener, U., Mannhaupt, G., Mayer, K., Mokrejs, M., Morgenstern, B., Munsterkotter, M., Rudd, S. and Weil B. MIPS: A database for genomes and protein sequences. Nucl. Acids Res. 2002, 30(1):31–34.

    Article  PubMed  CAS  Google Scholar 

  39. Ben-Hur, A. and Noble, W. S. Kernel methods for predicting protein–protein interactions. Bioinformatics 2005, 21(Suppl. 1):i38–i46.

    Article  PubMed  CAS  Google Scholar 

  40. Gobel, U., Sander, C., Schneider, R. and Valencia, A. Correlated mutations and residue contacts in proteins. Proteins 1994, 18:309–17.

    Article  PubMed  CAS  Google Scholar 

  41. Birkland, A. and Yona, G. The BIOZON database: A hub of heterogeneous biological data. Nucl. Acids Res. 2006, 34:D235–D242.

    Article  PubMed  CAS  Google Scholar 

  42. Bader, G. D., Donaldson, I., Wolting, C., Ouellette, B. F., Pawson, T. and Hogue, C. W. BIND – The biomolecular interaction network database. Nucl. Acids Res. 2001, 29:242–45.

    Article  PubMed  CAS  Google Scholar 

  43. Xenarios, I., Fernandez, E., Salwinski, L., Duan, X. J., Thompson, M. J., Marcotte, E. M. and Eisenberg, D. DIP: The database of interacting proteins: 2001 update. Nucl. Acids Res. 2001, 29:239–241.

    Article  PubMed  CAS  Google Scholar 

  44. Katoh, K., Misawa, K., Kuma, K. and Miyata, T. MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucl. Acids Res. 2002, 30(14):3059–66.

    Article  PubMed  CAS  Google Scholar 

  45. Katoh, K., Kuma, K., Toh, H. and Miyata, T. MAFFT version 5: Improvement in accuracy of multiple sequence alignment. Nucl. Acids Res. 2005, 33(2):511–18.

    Article  PubMed  CAS  Google Scholar 

  46. Higgins, D. G., Thompson, J. D. and Gibson, T. J. Using CLUSTAL for multiple sequence alignments. Methods Enzymol. 1996, 266:383–402.

    Article  PubMed  CAS  Google Scholar 

  47. Altschul, S. F., Madden, T. L., Schaffer, A. A., Zhang, J., Zhang, Z., Miller, W. and Lipman, D. J. Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucl. Acids Res. 1997, 25:3389–402.

    Article  PubMed  CAS  Google Scholar 

  48. Do, C. B., Mahabhashyam, M. S. P., Brudno, M., and Batzoglou, S. PROBCONS: Probabilistic consistency-based multiple sequence alignment. Genome Res. 2005, 15:330–40.

    Article  PubMed  CAS  Google Scholar 

  49. Ramani, A. K. and Marcotte, E. M. Exploiting the co-evolution of interacting proteins to discover interaction specificity. J. Mol. Biol. 2003, 327:273–84.

    Article  PubMed  CAS  Google Scholar 

  50. Gertz, J., Elfond, G., Shustrova, A., Weisinger, M., Pellegrini, M., Cokus, S. and Rothschild, B. Inferring protein interactions from phylogenetic distance matrices. Bioinformatics 2003, 19(16):2039–45.

    Article  PubMed  CAS  Google Scholar 

  51. Henikoff, S. and Henikoff, J. G. Position-based sequence weights. J. Mol. Biol. 1994, 243:574–78.

    Article  PubMed  CAS  Google Scholar 

  52. Popescu, L. and Yona, G. Automation of gene assignments to metabolic pathways using high-throughput expression data. BMC Bioinformatics 2005, 6:217.

    Article  PubMed  Google Scholar 

  53. Miklos, G. and Rubin, G. The role of the genome project in determining gene function: Insights from model organisms. Cell 1996, 86:521–29.

    Article  PubMed  CAS  Google Scholar 

  54. Yona, G., Dirks, W., Rahman, R. and Lin, M. Effective similarity measures for expression profiles. Bioinformatics 2006, 22:1616–22.

    Article  PubMed  CAS  Google Scholar 

  55. Jothi, R., Kann, M. G. and Przytycka, T. M. Predicting protein–protein interaction by searching evolutionary tree automorphism space. Bioinformatics 2005, 21(Suppl. 1):i241–i250.

    Article  PubMed  CAS  Google Scholar 

  56. Carillo, H. and Lipman, D. The multiple sequence alignment problem in biology. SIAM J. Appl. Math. 1988, 48(5):1073–82.

    Article  Google Scholar 

  57. Lin, J. Divergence measures based on the Shannon entropy. IEEE Trans. Info. Theory 1991, 37(1):145–51.

    Article  Google Scholar 

  58. Hirsh, A. E. and Fraser, H. B. Protein dispensability and rate of evolution. Nature 2001, 411(6841):1046–49.

    Article  PubMed  CAS  Google Scholar 

  59. Jordan, I. K., Rogozin, I. B., Wolf, Y. I. and Koonin, E. V. Essential genes are more evolutionarily conserved than are nonessential genes in bacteria. Genome Res. 2002, 12(6):962–68.

    PubMed  CAS  Google Scholar 

  60. Remm, M., Storm, C. E. V. and Sonnhammer, E. L. L. Automatic clustering of orthologs and in-paralogs from pairwise species. J. Mol. Biol. 2001, 314:1041–52.

    Article  PubMed  CAS  Google Scholar 

  61. O'Brien, K. P., Remm, M. and Sonnhammer, E. L. L. Inparanoid: A comprehensive database of eukaryotic orthologs. Nucl. Acids Res. 2005, 33:D476–D480.

    Google Scholar 

  62. Sato, T., Yamanishi, Y., Kanehisa, M. and Toh, H. The inference of protein– protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships. Bioinformatics 2005, 21(17):3482–89.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Science Foundation under Grant No. 0133311 to Golan Yona, and by the National Science Foundation under Grant No. 0218521, as part of the NSF/NIH Collaborative Research in Computational Neuroscience Program.

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Humana Press, a part of Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Sharon, I., Davis, J.V., Yona, G. (2009). Prediction of Protein–Protein Interactions: A Study of the Co-evolution Model. In: Ireton, R., Montgomery, K., Bumgarner, R., Samudrala, R., McDermott, J. (eds) Computational Systems Biology. Methods in Molecular Biology, vol 541. Humana Press. https://doi.org/10.1007/978-1-59745-243-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-59745-243-4_4

  • Published:

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-905-5

  • Online ISBN: 978-1-59745-243-4

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics