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From Protein Interaction Networks to Protein Function

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Part of the book series: Computational Biology ((COBO,volume 9))

Abstract

The recent availability of large-scale protein-protein interaction data provides new opportunities for characterizing a protein’s function within the context of its cellular interactions, pathways and networks. In this paper, we review computational approaches that have been developed for analyzing protein interaction networks in order to predict protein function.

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References

  1. M. Ashburner, C. Ball, J. Blake, D. Botstein, H. Butler, J. Cherry, et al. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat. Genet., 25(1):25–29, 2000.

    Google Scholar 

  2. S. Asthana, O. King, F. Gibbons, and F. Roth. Predicting protein complex membership using probabilistic network reliability. Genome Res., 14:1170–1175, 2004.

    Article  Google Scholar 

  3. V. Arnau, S. Mars, and I. Marin. Iterative cluster analysis of protein interaction data. Bioinformatics, 21:364–378, 2005.

    Article  Google Scholar 

  4. B. Adamcsek, G. Palla, I. Farkas, I. Derenyi, and T. Vicsek. Cfinder: locating cliques and overlapping modules in biological networks. Bioinformatics, 22: 1021–1023, 2006.

    Article  Google Scholar 

  5. T. Aittokallio and B. Schwikowski. Graph-based methods for analysing networks in cell biology. Briefings in Bioinformatics, 7:243–255, 2006.

    Article  Google Scholar 

  6. M. Altaf-Ul-Amin, Y. Shinbo, K. Mihara, K. Kurokawa, and S. Kanaya. Development and implementation of an algorithm for detection of protein complexes in large interaction networks. BMC Bioinformatics, 7:207, 2006.

    Article  Google Scholar 

  7. J. Bader. Greedily building protein networks with confidence. Bioinformatics, 19:1869–1874, 2003.

    Article  Google Scholar 

  8. C. Brun, F. Chevenet, D. Martin, J. Wojcik, A. Guenoche, and B. Jacq. Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome Biol., 5:R6, 2003.

    Article  Google Scholar 

  9. G. Bader and C. Hogue. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 4:2, 2003.

    Article  Google Scholar 

  10. A. Bauer and B. Kuster. Affinity purification-mass spectrometry. Eur. J. Biochem., 270:570–578, 2003.

    Article  Google Scholar 

  11. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121–167, 1998.

    Article  Google Scholar 

  12. S. Brohee and J. van Helden. Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics, 7:488, 2006.

    Article  Google Scholar 

  13. M. Blatt, S. Wiseman, and E. Domany. Superparamagnetic clustering of data. Phys. Rev. Lett., 76:3251–3254, 1996.

    Article  Google Scholar 

  14. D. Bu, Y. Zhao, L. Cai, H. Xue, X. Zhu, H. Lu, et al. Topological structure analysis of the protein-protein interaction network in budding yeast. Nucl. Acids. Res., 31:2443–2450, 2003.

    Article  Google Scholar 

  15. Y.-R. Cho, W. Hwang, M. Ramanathan, and Aidong Zhang. Semantic integration to identify overlapping functional modules in protein interaction networks. BMC Bioinformatics, 8:265, 2007.

    Google Scholar 

  16. Thomas H. Cormen, Charles E. Leiserson, and Ronald L. Rivest. Introduction to Algorithms. MIT Press/McGraw-Hill, 1990.

    Google Scholar 

  17. S. Carroll and V. Pavlovic. Protein classification using probabilistic chain graphs and the Gene Ontology structure. Bioinformatics, 22:1871–1878, 2006.

    Article  Google Scholar 

  18. H. Chua, W.-K. Sung, and L. Wong. Exploiting indirect neighbors and topological weight to predict protein function from protein-protein interactions. Bioinformatics, 22:1623–1630, 2006.

    Article  Google Scholar 

  19. J. Chen and B. Yuan. Detecting functional modules in the yeast protein-protein interaction network. Bioinformatics, 22:2283–2290, 2006.

    Article  Google Scholar 

  20. M. Deng, T. Chen, and F. Sun. An integrated probabilistic model for functional prediction of proteins. In Proc. 7th Annual RECOMB, pages 95–103. ACM, 2003.

    Google Scholar 

  21. R. Dunn, F. Dudbridge, and C. Sanderson. The use of edge-betweenness clustering to investigate biological function in protein interaction networks. BMC Bioinformatics, 6:39, 2005.

    Article  Google Scholar 

  22. E. Dalhaus, D. S. Johnson, C. Papadimitriou, P. Seymour, and M. Yannakakis. The complexity of the multiway cuts. In Proc. 24th Annual STOC, pages 241–251. ACM, 1992.

    Google Scholar 

  23. M. Deng, F. Sun, and T. Chen. Assessment of the reliability of protein-protein interactions and protein function prediction. In Pac. Symp. Biocomput., pages 140–151, 2003.

    Google Scholar 

  24. M. Deng, Z. Tu, F. Sun, and T. Chen. Mapping gene ontology to proteins based on protein-protein interaction data. Bioinformatics, 20:895–902, 2004.

    Article  Google Scholar 

  25. M. Deng, K. Zhang, S. Mehta, T. Chen, and F. Sun. Prediction of protein function using protein-protein interaction data. J. Computational Biol., 10:947–960, 2003.

    Article  Google Scholar 

  26. A. Enright, S. Van Dongen, and C. Ouzounis. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res, 30:1575–1584, 2002.

    Article  Google Scholar 

  27. R. Fourer, D. M. Gay, and B. W. Kernighan. AMPL: A Modeling Language for Mathematical Programming. Brooks/Cole Publishing Company, Pacific Grove, CA, 2002.

    Google Scholar 

  28. S. Fields and O.-K. Song. A novel genetic system to detect protein-protein interactions. Nature, 340:245–246, 1989.

    Article  Google Scholar 

  29. M. Galperin and E. Koonin. Who’s your neighbor? New computational approaches for functional genomics. Nat. Biotechnol., 18:609–613, 2000.

    Article  Google Scholar 

  30. M. Girvan and M. Newman. Community structure in social and biological networks. Proc. Natl. Acad. Sci. USA, 99:7821–7826, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  31. D. Goldberg and F. Roth. Assessing experimentally derived interactions in a small world. Proc. Natl. Acad. Sci. USA, 100:4372–4376, 2003.

    Article  MATH  MathSciNet  Google Scholar 

  32. L. Hartwell, J. Hopfield, S. Leibler, and A. Murray. From molecular to modular cell biology. Nature, 402:C47–52, 1999.

    Article  Google Scholar 

  33. H. Hishigaki, K. Nakai, T. Ono, A. Tanigami, and T. Takagi. Assessment of prediction accuracy of protein function from protein–protein interaction data. Yeast, 18:523–531, 2001.

    Article  Google Scholar 

  34. ILOG CPLEX 7.1, 2000. http://www.ilog.com/products/cplex/.

  35. T. Joshi, Y. Chen, J. Becker, N. Alexandrov, and D. Xu. Genome-scale gene function prediction using multiple sources of high-throughput data in yeast. OMICS, 8:322–333, 2004.

    Article  Google Scholar 

  36. R. H. Jansen, H. Yu, D. Greenbaum, Y. Kluger, N. Krogan, S. Chung, et al. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science, 302:449–453, 2003.

    Article  Google Scholar 

  37. R. Kondor and J. Lafferty. Diffusion kernels on graphs and other discrete input spaces. In Proc. Intl. Conf. on Machine Learning, pages 315–322, 2002.

    Google Scholar 

  38. U. Karaoz, T. M. Murali, S. Levotsky, Y. Zheng, C. Ding, C. R. Cantor, and S. Kasif. Whole-genome annotation by using evidence integration in functional-linkage networks. Proc. Natl. Acad. Sci. USA, 101:2888–2893, 2004.

    Article  Google Scholar 

  39. M. Kirac, G. Ozsoyoglu, and J. Yang. Annotating proteins by mining protein interaction networks. Bioinformatics, 22:e260–e270, 2006.

    Article  Google Scholar 

  40. A. King, N. Przulj, and I. Jurisica. Protein complex prediction via cost-based clustering. Bioinformatics, 20:3013–3020, 2004.

    Article  Google Scholar 

  41. R. Krause, C. von Mering, and P. Bork. A comprehensive set of protein complexes in yeast: mining large-scale protein-protein interaction screens. Bioinformatics, 19:1901–1908, 2003.

    Article  Google Scholar 

  42. G. Lanckriet, T. Bie, N. Cristianini, M. Jordan, and W. Noble. A statistical framework for genomic data fusion. Bioinformatics, 20:2626–2635, 2004.

    Article  Google Scholar 

  43. I. Lee, S. Date, A. Adai, and E. Marcotte. A probabilistic functional network of yeast genes. Science, 306(2):1555–1558, 2004.

    Article  Google Scholar 

  44. S. Letovsky and S. Kasif. Predicting protein function from protein/protein interaction data: a probabilistic approach. Bioinformatics, 19 Suppl 1:i197–i204, 2003.

    Article  Google Scholar 

  45. F. Luo, Y. Yang, C. Chen, R. Chang, J. Zhou, and R. Scheuermann. Modular organization of protein interaction networks. Bioinformatics, 23:207–214, 2007.

    Article  Google Scholar 

  46. C. Myers, D. Robson, A. Wible, M. Hibbs, C. Chiriac, C. Theesfeld, et al. Discovery of biological networks from diverse functional genomics data. Genome Biol., 6:R114, 2005.

    Article  Google Scholar 

  47. T. Murali, C.-J. Wu, and S. Kasif. The art of gene function prediction. Nat. Biotechnol., 24:1474–1475, 2006.

    Article  Google Scholar 

  48. E. Nabieva, K. Jim, A. Agarwal, B. Chazelle, and M. Singh. Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics, 21 Suppl. 1:i302–i310, 2005.

    Article  Google Scholar 

  49. J. Poyatos and L. Hurst. How biologically relevant are interaction-based modules in protein networks? Genome Biol., 5:R93, 2004.

    Article  Google Scholar 

  50. J. Pereira-Leal, A. Enright, and C. Ouzounis. Detection of functional modules from protein interaction networks. Proteins, 54:49–57, 2004.

    Article  Google Scholar 

  51. F. Radicchi, C. Castellano, F. Cecconi, V. Loreto, and D. Parisi. Defining and identifying communities in networks. Proc. Natl. Acad. Sci. USA, 101(2):2658–2663, 2004.

    Article  Google Scholar 

  52. A. Rives and T. Galitski. Modular organization of cellular networks. Proc. Natl. Acad. Sci. USA, 100(2):1128–1133, 2003.

    Article  Google Scholar 

  53. A. Ruepp, A. Zollner, D. Maier, K. Albermann, J. Hani, M. Mokrejs, et al. The FunCat, a functional annotation scheme for systematic classification of proteins from whole genomes. Nucleic Acids Res., 32:5539–5545, 2004.

    Article  Google Scholar 

  54. M. Samanta and S. Liang. Predicting protein functions from redundancies in large-scale protein interaction networks. Proc. Natl. Acad. Sci. USA., 100:12579–12583, 2003.

    Article  Google Scholar 

  55. V. Spirin and L. A. Mirny. Protein complexes and functional modules in molecular networks. Proc. Natl. Acad. Sci. USA., 100:12123–12128, 2003.

    Article  Google Scholar 

  56. N. Saitou and M. Nei. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol., 4:406–425, 1987.

    Google Scholar 

  57. E. Sprinzak, S. Sattath, and H. Margalit. How reliable are experimental protein-protein interaction data? J. Mol. Biol., 327(2):919–923, 2003.

    Article  Google Scholar 

  58. B. Schwikowski, P. Uetz, and S. Fields. A network of protein-protein interactions in yeast. Nat. Biotechnol., 18:1257–1261, 2000.

    Article  Google Scholar 

  59. R. Sharan, I. Ulitsky, and R. Shamir. Network-based prediction of protein function. Molecular Systems Biology, 3:88, 2007.

    Article  Google Scholar 

  60. O. Troyanskaya, K. Dolinski, A. Owen, R. Altman, and D. Botstein. A Bayesian framework for combining heterogeneous data sources for gene function prediction (in S. cerevisiae). Proc. Natl. Acad. Sci. USA, 100:8348–8353, 2003.

    Article  Google Scholar 

  61. K. Tsuda and W. Noble. Learning kernels from biological networks by maximizing entropy. Bioinformatics, 20 Suppl. 1:i326–i333, 2004.

    Article  Google Scholar 

  62. V Vapnik. Statistical Learning Theory. Wiley, 1998.

    Google Scholar 

  63. S. van Dongen. Graph clustering by flow simulation. PhD thesis, University of Utrecht, 2000.

    Google Scholar 

  64. A. Vazquez, A. Flammini, A. Maritan, and A. Vespignani. Global protein function prediction from protein-protein interaction networks. Nat Biotechnol., 21:697–700, 2003.

    Article  Google Scholar 

  65. C. von Mering, M. Huynen, D. Jaeggi, S. Schmidt, P. Bork, and B. Snel. STRING: a database of predicted functional associations between proteins. Nucleic Acids Res., 31:258–261, 2003.

    Article  Google Scholar 

  66. C. von Mering, R. Krause, B. Snel, M. Cornell, S. Oliver, S. Fields, and P. Bork. Comparative assessment of large-scale data sets of protein-protein interactions. Nature, 417:399–403, 2002.

    Article  Google Scholar 

  67. J. Yedidia, W. Freeman, and Y. Weiss. Understanding belief propagation and its generalizations. In Exploring artificial intelligence in the new millennium, pp. 239–269. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2003.

    Google Scholar 

  68. X. Zhu, M. Gerstein, and M. Snyder. Getting connected: analysis and principles of biological networks. Genes Dev, 21:1010–1024, 2007.

    Article  Google Scholar 

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Correspondence to Mona Singh .

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Singh, M. (2008). From Protein Interaction Networks to Protein Function. In: Panchenko, A., Przytycka, T. (eds) Protein-protein Interactions and Networks. Computational Biology, vol 9. Springer, London. https://doi.org/10.1007/978-1-84800-125-1_8

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  • DOI: https://doi.org/10.1007/978-1-84800-125-1_8

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84800-124-4

  • Online ISBN: 978-1-84800-125-1

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