Journal of Medical Systems

, Volume 30, Issue 1, pp 39–44 | Cite as

Computational Approaches for Predicting Protein–Protein Interactions: A Survey

Research Article

Abstract

Discovery of the protein interactions that take place within a cell can provide a starting point for understanding biological regulatory pathways. Global interaction patterns among proteins, for example, can suggest new drug targets and aid the design of new drugs by providing a clearer picture of the biological pathways in the neighborhoods of the drug targets. High-throughput experimental screens have been developed to detect protein–protein interactions, however, they show high rates of errors in terms of false positives and false negatives. Many computational approaches have been proposed to tackle the problem of protein–protein interaction prediction. They range from comparative genomics based methods to data integration based approaches. Challenging properties of protein–protein interaction data have to be addressed appropriately before a higher quality interaction map with better coverage can be achieved. This paper presents a survey of major works in computational prediction of protein–protein interactions, explaining their assumptions, main ideas, and limitations.

Keywords

Protein–protein interactions Yeast two-hybrid Computational prediction 

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References

  1. Walhout AJM, Vidal M (2001) Protein interactions maps for model organisms. Nat Rev Mol Cell Biol 2:55–62CrossRefGoogle Scholar
  2. Alberts B (1998) The cell as a collection of protein machines: Preparing the next generation of molecular biologists. Cell 92:291–294CrossRefGoogle Scholar
  3. Semple JI, Sanderson CM, Campbell RD (2002) The jury is out on “guilt by association” trials. Brief Funct Genomics Proteomics 1(1):40–52CrossRefGoogle Scholar
  4. von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, Bork P (2002) Comparative assessment of large-scale data sets of protein–protein interactions. Nature 417(6887):399–403CrossRefGoogle Scholar
  5. Deane CM, Salwinski L, Xenarios I, Eisenberg D. (2002) Protein interactions: Two methods for assessment of the reliability of high throughput observations. Mol and Cell Proteomics 1:349–356CrossRefGoogle Scholar
  6. Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y (2001) A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci USA 98(8):4569–4574Google Scholar
  7. Mrowka R, Patzak A, Herzel H (2001) Is there a bias in proteome research? Genome Res 11(12):1971–1973CrossRefGoogle Scholar
  8. Edwardsm AM, Kus B, Jansen R, Greenbaum D, Greenblatt J, Gerstein M (2002) Bridging structural biology and genomics: Assessing protein interaction data with known complexes. Trends Genet 18(10):529–536CrossRefGoogle Scholar
  9. Fields S, Song O-K (1989) A novel genetic system to detect protein–protein interactions. Nature 340(6230):245–246CrossRefGoogle Scholar
  10. Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, Lockshon D, Narayan V, Srinivasan M, Pochart P, et al. (2000) A comprehensive analysis of protein–protein interactions in Saccharomyces Cerevisiae. Nature 403(6770):623–627CrossRefGoogle Scholar
  11. Li S, Armstrong CM, Bertin N, Ge H, Milstein S, Boxem M, Vidalain PO, Han JD, Chesneau A, Hao T, et al. (2004) A map of the interactome network of the metazoan C Elegans Science 303(5657):540–543Google Scholar
  12. Giot L, et al. (2003) A protein interaction map of Drosophila melanogaster. Science 302:1727–1736Google Scholar
  13. Ho Y, Gruhler A, Heilbut A, Bader GD, Moore L, Adams S-L, Millar A, Taylor P, Bennett K, Boutilier K, et al. (2002) Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415(6868):180–183CrossRefGoogle Scholar
  14. Gavin A-C, Bosche M, Krause R, Grandi P, Marzioch M, Bauer A, Schultz J, Rick JM, Michon A-M, Cruciat C-M, et al. (2002) Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415(6868):141–147CrossRefGoogle Scholar
  15. Bader GD, Hogue CWV (2002) Analyzing yeast protein–protein interaction data obtained from different sources. Nat Biotechnol 20(10):991–997CrossRefGoogle Scholar
  16. Valencia A, Pazos F (2002) Computational methods for the prediction of protein interactions. Curr Opin Struct Biol 12:368–373CrossRefGoogle Scholar
  17. Dandekar T, Snel B, Huynen M, Bork P (1998) Conservation of gene order: A fingerprint of proteins that physically interact. Science 23:324–328Google Scholar
  18. Marcotte EM, Pellegrini M, Ng H-L, Rice DW, Yeates TO, Eisenberg D (1999) Detecting protein function and protein–protein interactions from genome sequences. Science 285:751–753CrossRefGoogle Scholar
  19. Enright AJ, Iliopoulos I, Kyrpides NC, Ouzounis CA (1999) Protein interactions maps for complete genomes based on gene fusion events. Nature 402(6747):86–90Google Scholar
  20. Pellegrini M, Marcotte EM, Thompson MJ, Eisenberg D, Yeates TO (1999) Assigning protein functions by comparative genome analysis: Protein phylogenetic profiles. Proc Natl Acad Sci USA 96(8):4285–4288Google Scholar
  21. Eisenberg D, Marcotte EM, Xenarios I, Yeates TO (2000) Protein function in the post-genomic era. Nature 405(6788):823–826CrossRefGoogle Scholar
  22. Schachter V (2002) Bioinformatics of large-scale protein interaction networks. BioTech Comput Proteomics Suppl 32:S16–S27Google Scholar
  23. von Mering C, Huynen M, Jaeggi D, Schmidt S, Bork P, Snel B (2003) STRING: a database of predicted functional associations between proteins. Nucl Acids Res 13(1):258–261CrossRefGoogle Scholar
  24. Bowers PM, Pellegrini M, Thompson MJ, Fierro J, Yeates TO, Eisenberg D (2004) Prolinks: a database of protein functional linkages derived from coevolution. Genome Biol 5(5):R35CrossRefGoogle Scholar
  25. Mellor JC, Yanai I, Clodfelter KH, Mintseris J, DeLisi C (2002) Predictome: A database of putative functional links between proteins. Nucl Acids Res 30(1):306–309CrossRefGoogle Scholar
  26. Pazos F, Valencia A (2001) Similarity of phylogenetic trees as indicator of protein–protein interaction. Protein Eng 14(9):609–614CrossRefGoogle Scholar
  27. Gertz J, Elfond G, Shustrova A, Weisinger M, Pellegrini M, Cokus S, Rothschild B (2003) Inferring protein interactions from phylogenetic distance matrices. Bioinformatics 19(16):2039–2045CrossRefGoogle Scholar
  28. Matthews LR, Vaglio P, Reboul J, Ge H, Davis BP, Garrels J, Vincent S, Vidal M (2001) Identification of potential interaction networks using sequence-based searches for conserved protein–protein interactions or “interologs”. Genome Res 11(12):2120–2126CrossRefGoogle Scholar
  29. Wojcik J, Schachter V (2001) Protein–protein interaction map inference using interaction domain profile pairs. Bioinformatics 17(Suppl. 1):S296–S305Google Scholar
  30. Bock JR, Gough DA (2003) Whole-proteome interaction mining. Bioinformatics 19(1):125–134CrossRefGoogle Scholar
  31. Gomez SM, Rzhetsky A (2002) Towards the prediction of complete protein–protein interaction networks. Pac Symp Biocomput 413–424Google Scholar
  32. Deng M, Metha S, Sun F, Chen T (2002) Inferring domain–domain interactions from protein–protein interactions. In Proceedings of the 6th ACM International Conference on Research in Computational Molecular Biology (RECOMB), Washington, DC, USAGoogle Scholar
  33. Sprinzak E, Margalit H (2001) Correlated sequence-signatures as markers of protein–protein interaction. J Mol Biol 311(4):681–692CrossRefGoogle Scholar
  34. Kanaan SP, Huang C, Wuchty S, Chen D, Izaguirre JA (2005) Inferring protein–protein interactions from protein domain combinations. In Proceedings of the Ninth Annual International Conference on Research in Computational Molecular BiologyGoogle Scholar
  35. Lu L, Lu H, Skolnick J (2002) MULTIPROSPECTOR: an algorithm for the prediction of protein–protein interactions by multimeric threading. Proteins Struct Funct Genet 49(3):350–364CrossRefGoogle Scholar
  36. Bock JR, Gough DA (2001) Predicting protein–protein interactions from primary structure. Bioinformatics 17(5):455–460CrossRefGoogle Scholar
  37. Jansen R, Lan N, Qian J, Gerstein M (2002) Integration of genomic datasets to predict protein complexes in yeast. J Struct Funct Genomics 2(2):71–81CrossRefGoogle Scholar
  38. Jansen R, Yu H, Greenbaum D, Kluger Y, Krogan NJ, Chung S, Emili A, Snyder M, Greenblatt JF, Gerstein M (2003) A Bayesian networks approach for predicting protein–protein interactions from genomic data. Science 302:449–453CrossRefGoogle Scholar
  39. Zhang LV, Wong SL, King OD, Roth FP (2004) Predicting co-complexed protein pairs using genomic and proteomic data integration. BMC Bioinformatics 5(38)Google Scholar
  40. Weiss GM (1995) Learning with rare cases and small disjuncts. In Proceedings of the 12th International Conference on Machine Learning, pp. 558–565Google Scholar
  41. Stanyon CA, Liu G, Mangiola BA, Patel N, Giot L, Kuang B, Zhang H, Zhong J, Finley RL, Jr. (2004) A Drosophila protein-interaction map centered on cell-cycle regulators. Genome Biol 5(12):R96Google Scholar
  42. Grigoriev A (2003) On the number of protein–protein interactions in the yeast proteome. Nucl Acids Res 31(14):4157–4161CrossRefGoogle Scholar
  43. Legrain P, Wojcik J, Gauthier J-M (2001) Protein–protein interaction maps: A lead towards cellular functions. Trends Genet 17(6):346–352CrossRefGoogle Scholar
  44. Tucker CL, Gera JF, Uetz P (2001) Towards an understanding of complex protein networks. Trends Cell Biol 11(3):102–106CrossRefGoogle Scholar
  45. Sprinzak E, Sattath S, Margalit H (2003) How reliable are experimental protein–protein interaction data? J Mol Biol 327(5):919–923CrossRefGoogle Scholar
  46. Walhout AJM, Boulton SJ, Vidal M (2000) Yeast two-hybrid systems and protein interaction mapping projects for yeast and worm. Yeast 17:88–94CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Department of Computer ScienceWayne State University Detroit

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