Skip to main content
Log in

Computational Prediction of Protein–Protein Interactions

  • Review
  • Published:
Molecular Biotechnology Aims and scope Submit manuscript

Abstract

Recently a number of computational approaches have been developed for the prediction of protein–protein interactions. Complete genome sequencing projects have provided the vast amount of information needed for these analyses. These methods utilize the structural, genomic, and biological context of proteins and genes in complete genomes to predict protein interaction networks and functional linkages between proteins. Given that experimental techniques remain expensive, time-consuming, and labor-intensive, these methods represent an important advance in proteomics. Some of these approaches utilize sequence data alone to predict interactions, while others combine multiple computational and experimental datasets to accurately build protein interaction maps for complete genomes. These methods represent a complementary approach to current high-throughput projects whose aim is to delineate protein interaction maps in complete genomes. We will describe a number of computational protocols for protein interaction prediction based on the structural, genomic, and biological context of proteins in complete genomes, and detail methods for protein interaction network visualization and analysis.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Mendelsohn, A. R., & Brent, R. (1999). Protein interaction methods—toward an endgame. Science, 284(5422), 1948–1950.

    PubMed  CAS  Google Scholar 

  2. Eisenberg, D., Marcotte, E. M., Xenarios, I., & Yeates, T. O. (2000). Protein function in the post-genomic era. Nature, 405(6788), 823–826.

    PubMed  CAS  Google Scholar 

  3. Huynen, M., Snel, B., Lathe, W., & Bork, P. (2000). Exploitation of gene context. Current Opinion in Structural Biology, 10(3), 366–370.

    PubMed  CAS  Google Scholar 

  4. Grigoriev, A. (2001). A relationship between gene expression and protein interactions on the proteome scale: Analysis of the bacteriophage T7 and the yeast Saccharomyces cerevisiae. Nucleic Acids Research, 29(17), 3513–3519.

    PubMed  CAS  Google Scholar 

  5. Ge, H., Liu, Z., Church, G. M., & Vidal, M. (2001). Correlation between transcriptome and interactome mapping data from Saccharomyces cerevisiae. Nature Genetics, 29(4), 482–486.

    PubMed  CAS  Google Scholar 

  6. Jansen, R., Greenbaum, D., & Gerstein, M. (2002). Relating whole-genome expression data with protein–protein interactions. Genome Research, 12(1), 37–46.

    PubMed  CAS  Google Scholar 

  7. Marcotte, E. M., Pellegrini, M., Thompson, M. J., Yeates, T. O., & Eisenberg, D. (1999). A combined algorithm for genome-wide prediction of protein function. Nature, 402(6757), 83–86.

    PubMed  CAS  Google Scholar 

  8. Jansen, R., Yu, H., Greenbaum, D., Kluger, Y., Krogan, N. J., Chung, S., Emili, A., Snyder, M., Greenblatt, J. F., & Gerstein, M. (2003). A Bayesian networks approach for predicting protein–protein interactions from genomic data. Science, 302(5644), 449–453.

    PubMed  CAS  Google Scholar 

  9. Sussman, J. L., Lin, D., Jiang, J., Manning, N. O., Prilusky, J., Ritter, O., & Abola, E. E. (1998). Protein Data Bank (PDB): Database of three-dimensional structural information of biological macromolecules. Acta Crystallographica. Section D, Biological Crystallography, 54(Pt 6 Pt 1), 1078–1084.

    PubMed  CAS  Google Scholar 

  10. Chothia, C., & Janin, J. (1975). Principles of protein–protein recognition. Nature, 256(5520), 705–708.

    PubMed  CAS  Google Scholar 

  11. Gallet, X., Charloteaux, B., Thomas, A., & Brasseur, R. (2000). A fast method to predict protein interaction sites from sequences. Journal of Molecular Biology, 302(4), 917–926.

    PubMed  CAS  Google Scholar 

  12. Korn, A. P., & Burnett, R. M. (1991). Distribution and complementarity of hydropathy in multisubunit proteins. Proteins-Structure Function and Genetics, 9(1), 37–55.

    CAS  Google Scholar 

  13. Young, L., Jernigan, R. L., & Covell, D. G. (1994). A role for surface hydrophobicity in protein–protein recognition. Protein Science, 3(5), 717–729.

    Article  PubMed  CAS  Google Scholar 

  14. Mueller, T. D., & Feigon, J. (2002). Solution structures of UBA domains reveal a conserved hydrophobic surface for protein–protein interactions. Journal of Molecular Biology, 319(5), 1243–1255.

    PubMed  CAS  Google Scholar 

  15. Lijnzaad, P., & Argos, P. (1997). Hydrophobic patches on protein subunit interfaces: Characteristics and prediction. Proteins-Structure Function and Genetics, 28(3), 333–343.

    CAS  Google Scholar 

  16. Janin, J., Miller, S., & Chothia, C. (1988). Surface, subunit interfaces and interior of oligomeric proteins. Journal of Molecular Biology, 204(1), 155–164.

    PubMed  CAS  Google Scholar 

  17. Argos, P. (1988). An investigation of protein subunit and domain interfaces. Protein Engineering, 2(2), 101–113.

    PubMed  CAS  Google Scholar 

  18. Jones, S., & Thornton, J. M. (1996). Principles of protein–protein interactions. Proceedings of the National Academy of Sciences of the United States of America, 93(1), 13–20.

    PubMed  CAS  Google Scholar 

  19. Ofran, Y., & Rost, B. (2003). Analysing six types of protein–protein interfaces. Journal of Molecular Biology, 325(2), 377–387.

    PubMed  CAS  Google Scholar 

  20. Jones, S., & Thornton, J. M. (1997). Analysis of protein–protein interaction sites using surface patches. Journal of Molecular Biology, 272(1), 121–132.

    PubMed  CAS  Google Scholar 

  21. Jones, S., & Thornton, J. M. (1997). Prediction of protein–protein interaction sites using patch analysis. Journal of Molecular Biology, 272(1), 133–143.

    PubMed  CAS  Google Scholar 

  22. Kim, W. K., Henschel, A., Winter, C., & Schroeder, M. (2006). The many faces of protein–protein interactions: A compendium of interface geometry. PLoS Computational Biology, 2(9), e124.

    PubMed  Google Scholar 

  23. Kim, W. K., & Ison, J. C. (2005). Survey of the geometric association of domain-domain interfaces. Proteins, 61(4), 1075–1088.

    PubMed  CAS  Google Scholar 

  24. Davis, F. P., & Sali, A. (2005). PIBASE: A comprehensive database of structurally defined protein interfaces. Bioinformatics, 21(9), 1901–1917.

    PubMed  CAS  Google Scholar 

  25. Aloy, P., & Russell, R. B. (2003). InterPreTS: Protein Interaction Prediction through Tertiary Structure. Bioinformatics, 19(1), 161–162.

    PubMed  CAS  Google Scholar 

  26. Enright, A. J., & Ouzounis, C. A. (2001). Functional associations of proteins in entire genomes by means of exhaustive detection of gene fusions. Genome Biology, 2(9), RESEARCH0034.

    PubMed  CAS  Google Scholar 

  27. Snel, B., Lehmann, G., Bork, P., & Huynen, M. A. (2000). STRING: A web-server to retrieve and display the repeatedly occurring neighbourhood of a gene. Nucleic Acids Research, 28(18), 3442–3444.

    PubMed  CAS  Google Scholar 

  28. Overbeek, R., Larsen, N., Pusch, G. D., D’Souza M, Selkov, E Jr., Kyrpides, N., Fonstein, M., Maltsev, N., & Selkov, E. (2000). WIT: Integrated system for high-throughput genome sequence analysis and metabolic reconstruction. Nucleic Acids Research, 28(1), 123–125.

    PubMed  CAS  Google Scholar 

  29. Mellor, J. C., Yanai, I., Clodfelter, K. H., Mintseris, J., & DeLisi, C. (2002). Predictome: A database of putative functional links between proteins. Nucleic Acids Research, 30(1), 306–309.

    PubMed  CAS  Google Scholar 

  30. Tatusov, R. L., Koonin, E. V., & Lipman, D. J. (1997). A genomic perspective on protein families. Science, 278(5338), 631–637.

    PubMed  CAS  Google Scholar 

  31. Karp, P. D., Riley, M., Saier, M., Paulsen, I. T., Paley, S. M., & Pellegrini-Toole, A. (2000). The EcoCyc and MetaCyc databases. Nucleic Acids Research, 28(1), 56–59.

    PubMed  CAS  Google Scholar 

  32. Vastrik, I., D’Eustachio P, Schmidt, E., Joshi-Tope, G., Gopinath, G., Croft, D., de Bono, B., Gillespie, M., Jassal, B., Lewis, S., Matthews, L., Wu, G., Birney, E., & Stein, L. (2007). Reactome: A knowledge base of biologic pathways and processes. Genome Biology, 8(3), R39.

    PubMed  Google Scholar 

  33. Kanehisa, M., & Goto, S. (2000). KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research, 28(1), 27–30.

    PubMed  CAS  Google Scholar 

  34. Campagne, F., Neves, S., Chang, C. W., Skrabanek, L., Ram, P. T., Iyengar, R., Weinstein, H. (2004). Quantitative information management for the biochemical computation of cellular networks. Science STKE, 2004(248), pl11.

    Google Scholar 

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

    PubMed  CAS  Google Scholar 

  36. Bader, G. D., Betel, D., & Hogue, C. W. (2003). BIND: The Biomolecular Interaction Network Database. Nucleic Acids Research, 31(1), 248–250.

    PubMed  CAS  Google Scholar 

  37. Xenarios, I., Rice, D. W., Salwinski, L., Baron, M. K., Marcotte, E. M., & Eisenberg, D. (2000). DIP: The database of interacting proteins. Nucleic Acids Research, 28(1), 289–291.

    PubMed  CAS  Google Scholar 

  38. Hermjakob, H., Montecchi-Palazzi, L., Lewington, C., Mudali, S., Kerrien, S., Orchard, S., Vingron, M., Roechert, B., Roepstorff, P., Valencia, A., Margalit, H., Armstrong, J., Bairoch, A., Cesareni, G., Sherman, D., Apweiler, R. (2004). IntAct—an open source molecular interaction database. Nucleic Acids Research, 32(Database issue), D452–D455.

    PubMed  CAS  Google Scholar 

  39. Zanzoni, A., Montecchi-Palazzi, L., Quondam, M., Ausiello, G., Helmer-Citterich, M., & Cesareni, G. (2002). MINT: A Molecular INTeraction database. FEBS Letters, 513(1), 135–140.

    PubMed  CAS  Google Scholar 

  40. Stein, A., Russell, R. B., & Aloy, P. (2005). 3did: Interacting protein domains of known three-dimensional structure. Nucleic Acids Research, 33(Database issue), D413–D417.

    PubMed  CAS  Google Scholar 

  41. Winter, C., Henschel, A., Kim, W. K., & Schroeder, M. (2006). SCOPPI: A structural classification of protein–protein interfaces. Nucleic Acids Research, 34(Database issue), D310–D314.

    PubMed  CAS  Google Scholar 

  42. Finn, R. D., Marshall, M., & Bateman, A. (2005). iPfam: Visualization of protein–protein interactions in PDB at domain and amino acid resolutions. Bioinformatics, 21(3), 410–412.

    PubMed  CAS  Google Scholar 

  43. Pagel, P., Oesterheld, M., Stumpflen, V., & Frishman, D. (2006). The DIMA web resource–exploring the protein domain network. Bioinformatics, 22(8), 997–998.

    PubMed  CAS  Google Scholar 

  44. Bowers, P. M., Pellegrini, M., Thompson, M. J., Fierro, J., Yeates, T. O., & Eisenberg, D. (2004). Prolinks: A database of protein functional linkages derived from coevolution. Genome Biology, 5(5), R35.

    PubMed  Google Scholar 

  45. Gollub, J., Ball, C. A., Binkley, G., Demeter, J., Finkelstein, D. B., Hebert, J. M., Hernandez-Boussard, T., Jin, H., Kaloper, M., Matese, J. C., Schroeder, M., Brown, P. O., Botstein, D., & Sherlock, G. (2003). The Stanford microarray database: Data access and quality assessment tools. Nucleic Acids Research, 31(1), 94–96.

    PubMed  CAS  Google Scholar 

  46. Brazma, A., Parkinson, H., Sarkans, U., Shojatalab, M., Vilo, J., Abeygunawardena, N., Holloway, E., Kapushesky, M., Kemmeren, P., Lara, G. G., Oezcimen, A., Rocca-Serra, P., & Sansone, S. A. (2003). ArrayExpress—a public repository for microarray gene expression data at the EBI. Nucleic Acids Research, 31(1), 68–71.

    PubMed  CAS  Google Scholar 

  47. Edgar, R., Domrachev, M., & Lash, A. E. (2002). Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research, 30(1), 207–210.

    PubMed  CAS  Google Scholar 

  48. Enright, A. J., & Ouzounis, C. A. (2001). BioLayout—an automatic graph layout algorithm for similarity visualization. Bioinformatics, 17(9), 853–854.

    PubMed  CAS  Google Scholar 

  49. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., Ideker, T. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498–2504.

    PubMed  CAS  Google Scholar 

  50. Hu, Z., Mellor, J., Wu, J., Yamada, T., Holloway, D., & Delisi, C. (2005). VisANT: Data-integrating visual framework for biological networks and modules. Nucleic Acids Research, 33(Web Server issue), W352–W357.

    PubMed  CAS  Google Scholar 

  51. Lawrence, M. C., & Colman, P. M. (1993). Shape complementarity at protein–protein interfaces. Journal of Molecular Biology, 234(4), 946–950.

    PubMed  CAS  Google Scholar 

  52. Gabb, H. A., Jackson, R. M., & Sternberg MJE: (1997). Modelling protein docking using shape complementarity, electrostatics and biochemical information. Journal of Molecular Biology, 272(1), 106–120.

    PubMed  CAS  Google Scholar 

  53. Shoichet, B. K., & Kuntz, I. D. (1991). Protein docking and complementarity. Journal of Molecular Biology, 221(1), 327–346.

    PubMed  CAS  Google Scholar 

  54. Mintseris, J., Wiehe, K., Pierce, B., Anderson, R., Chen, R., Janin, J., & Weng, Z. (2005). Protein–protein docking benchmark 2.0: An update. Proteins, 60(2), 214–216.

    PubMed  CAS  Google Scholar 

  55. Janin, J., Henrick, K., Moult, J., Eyck, L. T., Sternberg, M. J., Vajda, S., Vakser, I., & Wodak, S. J. (2003). CAPRI: A critical assessment of predicted interactions. Proteins, 52(1), 2–9.

    PubMed  CAS  Google Scholar 

  56. Aloy, P., & Russell, R. B. (2002). Potential artefacts in protein-interaction networks. FEBS Letters, 530(1–3), 253–254.

    PubMed  CAS  Google Scholar 

  57. Casari, G., Sander, C., & Valencia, A. (1995). A method to predict functional residues in proteins. Nature Structural Biology, 2(2), 171–178.

    PubMed  CAS  Google Scholar 

  58. Lichtarge, O., Bourne, H. R., & Cohen, F. E. (1996). An evolutionary trace method defines binding surfaces common to protein families. Journal of Molecular Biology, 257(2), 342–358.

    PubMed  CAS  Google Scholar 

  59. Pazos, F., Helmer-Citterich, M., Ausiello, G., & Valencia, A. (1997). Correlated mutations contain information about protein–protein interaction. Journal of Molecular Biology, 271(4), 511–523.

    PubMed  CAS  Google Scholar 

  60. Zhou, H. X., & Shan, Y. B. (2001). Prediction of protein interaction sites from sequence profile and residue neighbor list. Proteins-Structure Function and Genetics, 44(3), 336–343.

    CAS  Google Scholar 

  61. Fariselli, P., Pazos, F., Valencia, A., & Casadio, R. (2002). Prediction of protein–protein interaction sites in heterocomplexes with neural networks. European Journal of Biochemistry, 269(5), 1356–1361.

    PubMed  CAS  Google Scholar 

  62. Ofran, Y., & Rost, B. (2003). Predicted protein–protein interaction sites from local sequence information. FEBS Letters, 544(1–3), 236–239.

    PubMed  CAS  Google Scholar 

  63. Aloy, P., & Russell, R. B. (2002). Interrogating protein interaction networks through structural biology. Proceedings of the National Academy of Sciences of the United States of America, 99(9), 5896–5901.

    PubMed  CAS  Google Scholar 

  64. Tamames, J., Casari, G., Ouzounis, C., & Valencia, A. (1997). Conserved clusters of functionally related genes in two bacterial genomes. Journal of Molecular Evolution, 44(1), 66–73.

    PubMed  CAS  Google Scholar 

  65. Dandekar, T., Snel, B., Huynen, M., & Bork, P. (1998). Conservation of gene order: A fingerprint of proteins that physically interact. Trends in Biochemical Sciences, 23(9), 324–328.

    PubMed  CAS  Google Scholar 

  66. Overbeek, R., Fonstein, M., D’Souza M, Pusch, G. D., & Maltsev, N. (1999). The use of gene clusters to infer functional coupling. Proceedings of the National Academy of Sciences of the United States of America, 96(6), 2896–2901.

    PubMed  CAS  Google Scholar 

  67. Zorio, D. A., Cheng, N. N., Blumenthal, T., & Spieth, J. (1994). Operons as a common form of chromosomal organization in C. elegans. Nature, 372(6503), 270–272.

    PubMed  CAS  Google Scholar 

  68. Blumenthal, T. (1998). Gene clusters and polycistronic transcription in eukaryotes. Bioessays, 20(6), 480–487.

    PubMed  CAS  Google Scholar 

  69. Snel, B., Bork, P., & Huynen, M. A. (2002). Genomes in flux: The evolution of archaeal and proteobacterial gene content. Genome Research, 12(1), 17–25.

    PubMed  CAS  Google Scholar 

  70. Kunin, V., Cases, I., Enright, A. J., de Lorenzo, V., & Ouzounis, C. A. (2003). Myriads of protein families, and still counting. Genome Biology, 4(2), 401.

    PubMed  Google Scholar 

  71. Ouzounis, C. (1999). Orthology: Another terminology muddle. Trends in Genetics, 15(11), 445.

    PubMed  CAS  Google Scholar 

  72. Pellegrini, M., Marcotte, E. M., Thompson, M. J., Eisenberg, D., & Yeates, T. O. (1999). Assigning protein functions by comparative genome analysis: Protein phylogenetic profiles. Proceedings of the National Academy of Sciences of the United States of America, 96(8), 4285–4288.

    PubMed  CAS  Google Scholar 

  73. Ouzounis, C., & Kyrpides, N. (1996). The emergence of major cellular processes in evolution. FEBS Letters, 390(2), 119–123.

    PubMed  CAS  Google Scholar 

  74. Rivera, M. C., Jain, R., Moore, J. E., & Lake, J. A. (1998). Genomic evidence for two functionally distinct gene classes. Proceedings of the National Academy of Sciences of the United States of America, 95(11), 6239–6244.

    PubMed  CAS  Google Scholar 

  75. Marcotte, E. M., Xenarios, I., van der Bliek, A. M., & Eisenberg, D. (2000). Localizing proteins in the cell from their phylogenetic profiles. Proceedings of the National Academy of Sciences of the United States of America, 97(22), 12115–12120.

    PubMed  CAS  Google Scholar 

  76. Bowers, P. M., O’Connor BD, Cokus, S. J., Sprinzak, E., Yeates, T. O., & Eisenberg, D. (2005). Utilizing logical relationships in genomic data to decipher cellular processes. FEBS Journal, 272(20), 5110–5118.

    PubMed  CAS  Google Scholar 

  77. Pagel, P., Wong, P., & Frishman, D. (2004). A domain interaction map based on phylogenetic profiling. Journal of Molecular Biology, 344(5), 1331–1346.

    PubMed  CAS  Google Scholar 

  78. Galperin, M. Y., & Koonin, E. V. (2000). Who’s your neighbor? New computational approaches for functional genomics. Nature Biotechnology, 18(6), 609–613.

    PubMed  CAS  Google Scholar 

  79. Barker, D., & Pagel, M. (2005). Predicting functional gene links from phylogenetic-statistical analyses of whole genomes. PLoS Computational Biology, 1(1), e3.

    PubMed  Google Scholar 

  80. Enright, A. J., Iliopoulos, I., Kyrpides, N. C., & Ouzounis, C. A. (1999). Protein interaction maps for complete genomes based on gene fusion events. Nature, 402(6757), 86–90.

    PubMed  CAS  Google Scholar 

  81. Marcotte, E. M., Pellegrini, M., Ng, H. L., Rice, D. W., Yeates, T. O., & Eisenberg, D. (1999). Detecting protein function and protein–protein interactions from genome sequences. Science, 285(5428), 751–753.

    PubMed  CAS  Google Scholar 

  82. Suhre, K., & Claverie, J. M. (2004). FusionDB: A database for in-depth analysis of prokaryotic gene fusion events. Nucleic Acids Research, 32(Database issue), D273–D276.

    PubMed  CAS  Google Scholar 

  83. Enright, A. J., Van Dongen, S., & Ouzounis, C. A. (2002). An efficient algorithm for large-scale detection of protein families. Nucleic Acids Research, 30(7), 1575–1584.

    PubMed  CAS  Google Scholar 

  84. Pazos, F., & Valencia, A. (2002). In silico two-hybrid system for the selection of physically interacting protein pairs. Proteins, 47(2), 219–227.

    PubMed  CAS  Google Scholar 

  85. Gobel, U., Sander, C., Schneider, R., & Valencia, A. (1994). Correlated mutations and residue contacts in proteins. Proteins, 18(4), 309–317.

    PubMed  CAS  Google Scholar 

  86. Jothi, R., Cherukuri, P. F., Tasneem, A., & Przytycka, T. M. (2006). Co-evolutionary analysis of domains in interacting proteins reveals insights into domain-domain interactions mediating protein–protein interactions. Journal of Molecular Biology, 362(4), 861–875.

    PubMed  CAS  Google Scholar 

  87. Shoemaker, B. A., & Panchenko, A. R. (2007). Deciphering protein–protein interactions. Part I. Experimental techniques and databases. PLoS Computational Biology, 3(3), e42.

    PubMed  Google Scholar 

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

    PubMed  CAS  Google Scholar 

  89. Snel, B., Bork, P., & Huynen, M. A. (2002). The identification of functional modules from the genomic association of genes. Proceedings of the National Academy of Sciences of the United States of America, 99(9), 5890–5895.

    PubMed  CAS  Google Scholar 

  90. von Mering, C., Huynen, M., Jaeggi, D., Schmidt, S., Bork, P., & Snel, B. (2003). STRING: A database of predicted functional associations between proteins. Nucleic Acids Research, 31(1), 258–261.

    Google Scholar 

  91. Tatusov, R. L., Fedorova, N. D., Jackson, J. D., Jacobs, A. R., Kiryutin, B., Koonin, E. V., Krylov, D. M., Mazumder, R., Mekhedov, S. L., Nikolskaya, A. N., Rao, B. S., Smirnov, S., Sverdlov, A. V., Vasudevan, S., Wolf, Y. I., Yin, J. J., & Natale, D. A. (2003). The COG database: An updated version includes eukaryotes. BMC Bioinformatics, 4(1), 41.

    PubMed  Google Scholar 

  92. Altschul, S. F., Madden, T. L., Schaffer, A. A., Zhang, J., Zhang, Z., Miller, W., & Lipman, D. J. (1997). Gapped BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Research, 25(17), 3389–3402.

    PubMed  CAS  Google Scholar 

  93. Iliopoulos, I., Enright, A. J., Poullet, P., & Ouzounis, C. (2003). Mapping functional associations in the entire genome of Drosophila melanogaster. Comparative and Functional Genomics, 4, 337–341.

    CAS  PubMed  Google Scholar 

  94. Smith, T. F., & Waterman, M. S. (1981). Identification of common molecular subsequences. Journal of Molecular Biology, 147(1), 195–197.

    PubMed  CAS  Google Scholar 

  95. Enright, A. J. (2002). Computational analysis of protein function in complete genomes (p 241). Cambridge: University of Cambridge.

    Google Scholar 

  96. Enright, A. J., Kunin, V., & Ouzounis, C. A. (2003). Protein families and TRIBES in genome sequence space. Nucleic Acids Research, 31(15), 4632–4638.

    PubMed  CAS  Google Scholar 

  97. Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., Aach, J., Ansorge, W., Ball, C. A., Causton, H. C., Gaasterland, T., Glenisson, P., Holstege, F. C., Kim, I. F., Markowitz, V., Matese, J. C., Parkinson, H., Robinson, A., Sarkans, U., Schulze-Kremer, S., Stewart, J., Taylor, R., Vilo, J., & Vingron, M. (2001). Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nature Genetics, 29(4), 365–371.

    PubMed  CAS  Google Scholar 

  98. Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences of the United States of America, 95(25), 14863–14868.

    PubMed  CAS  Google Scholar 

  99. Walsh, S., Anderson, M., & Cartinhour, S. W. (1998). ACEDB: A database for genome information. Methods of Biochemical Analysis, 39, 299–318.

    Article  PubMed  CAS  Google Scholar 

  100. Bader, G. D., Cary, M. P., & Sander, C. (2006). Pathguide: A pathway resource list. Nucleic Acids Research, 34(Database issue), D504–D506.

    PubMed  CAS  Google Scholar 

  101. Lambrix, P., Habbouche, M., & Perez, M. (2003). Evaluation of ontology development tools for bioinformatics. Bioinformatics, 19(12), 1564–1571.

    PubMed  CAS  Google Scholar 

  102. Orchard, S., Hermjakob, H., & Apweiler, R. (2003). The proteomics standards initiative. Proteomics, 3(7), 1374–1376.

    PubMed  CAS  Google Scholar 

  103. Hucka, M., Finney, A., Sauro, H. M., Bolouri, H., Doyle, J. C., Kitano, H., Arkin, A. P., Bornstein, B. J., Bray, D., Cornish-Bowden, A., Cuellar, A. A., Dronov, S., Gilles, E. D., Ginkel, M., Gor, V., Goryanin, I. I., Hedley, W. J., Hodgman, T. C., Hofmeyr, J. H., Hunter, P. J., Juty, N. S., Kasberger, J. L., Kremling, A., Kummer, U., Le Novere, N., Loew, L. M., Lucio, D., Mendes, P., Minch, E., Mjolsness, E. D., Nakayama, Y., Nelson, M. R., Nielsen, P. F., Sakurada, T., Schaff, J. C., Shapiro, B. E., Shimizu, T. S., Spence, H. D., Stelling, J., Takahashi, K., Tomita, M., Wagner, J., & Wang, J. (2003). The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models. Bioinformatics, 19(4), 524–531.

Download references

Acknowledgment

The authors would like to thank Ronald Jansen for providing information about Bayesian network based prediction of protein–protein interactions and the graph used for Fig. 4.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Anton J. Enright.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Skrabanek, L., Saini, H.K., Bader, G.D. et al. Computational Prediction of Protein–Protein Interactions. Mol Biotechnol 38, 1–17 (2008). https://doi.org/10.1007/s12033-007-0069-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12033-007-0069-2

Keywords

Navigation