Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2015, Article ID 165186, 7 pages
http://dx.doi.org/10.1155/2015/165186
Research Article

Protein Complex Discovery by Interaction Filtering from Protein Interaction Networks Using Mutual Rank Coexpression and Sequence Similarity

1Institute of Biochemistry and Biophysics, University of Tehran, Enghelab Avenue, P.O. Box 13145-1384, Tehran, Iran
2Science College, University of Tehran, Tehran, Iran

Received 19 August 2014; Revised 19 November 2014; Accepted 1 December 2014

Academic Editor: Zhirong Sun

Copyright © 2015 Ali Kazemi-Pour et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. X. Wang, J. Yue, X. Ren et al., “Modularity analysis based on predicted protein-protein interactions provides new insights into pathogenicity and cellular process of Escherichia coli O157:H7,” Theoretical Biology and Medical Modelling, vol. 8, no. 1, article 47, 2011. View at Publisher · View at Google Scholar · View at Scopus
  2. L. Shi, Y.-R. Cho, and A. Zhang, “Prediction of protein function from connectivity of protein interaction networks,” International Journal of Computational Bioscience, vol. 1, 2010. View at Publisher · View at Google Scholar
  3. G. D. Kritikos, C. Moschopoulos, M. Vazirgiannis, and S. Kossida, “Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme,” BMC Bioinformatics, vol. 12, article 239, 2011. View at Publisher · View at Google Scholar · View at Scopus
  4. C. Brun, F. Chevenet, D. Martin, J. Wojcik, A. Guénoche, and B. Jacq, “Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network,” Genome Biology, vol. 5, article R6, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. Z. Lubovac, J. Gamalielsson, and B. Olsson, “Combining functional and topological properties to identify core modules in protein interaction networks,” Proteins: Structure, Function and Genetics, vol. 64, no. 4, pp. 948–959, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. P. W. Lord, R. D. Stevens, A. Brass, and C. A. Goble, “Investigating semantic similarity measures across the gene ontology: the relationship between sequence and annotation,” Bioinformatics, vol. 19, no. 10, pp. 1275–1283, 2003. View at Publisher · View at Google Scholar · View at Scopus
  7. J. Wang, X. Zhou, J. Zhu, C. Zhou, and Z. Guo, “Revealing and avoiding bias in semantic similarity scores for protein pairs,” BMC Bioinformatics, vol. 11, article 290, 2010. View at Publisher · View at Google Scholar · View at Scopus
  8. T. Joshi and D. Xu, “Quantitative assessment of relationship between sequence similarity and function similarity,” BMC Genomics, vol. 8, article 222, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. Z.-H. You, Y.-K. Lei, L. Zhu, J. Xia, and B. Wang, “Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis,” BMC Bioinformatics, vol. 14, no. 8, article S10, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. Y.-K. Lei, Z.-H. You, Z. Ji, L. Zhu, and D.-S. Huang, “Assessing and predicting protein interactions by combining manifold embedding with multiple information integration,” BMC Bioinformatics, vol. 13, supplement 7, article S3, 2012. View at Publisher · View at Google Scholar · View at Scopus
  11. Z.-H. You, Y.-K. Lei, J. Gui, D.-S. Huang, and X. Zhou, “Using manifold embedding for assessing and predicting protein interactions from high-throughput experimental data,” Bioinformatics, vol. 26, no. 21, pp. 2744–2751, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. J.-F. Xia, K. Han, and D.-S. Huang, “Sequence-based prediction of protein-protein interactions by means of rotation forest and autocorrelation descriptor,” Protein and Peptide Letters, vol. 17, no. 1, pp. 137–145, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. J.-F. Xia, X.-M. Zhao, and D.-S. Huang, “Predicting protein-protein interactions from protein sequences using meta predictor,” Amino Acids, vol. 39, no. 5, pp. 1595–1599, 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Shen, J. Zhang, X. Luo et al., “Predicting protein-protein interactions based only on sequences information,” Proceedings of the National Academy of Sciences of the United States of America, vol. 104, no. 11, pp. 4337–4341, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Peri, J. D. Navarro, R. Amanchy et al., “Development of human protein reference database as an initial platform for approaching systems biology in humans,” Genome Research, vol. 13, no. 10, pp. 2363–2371, 2003. View at Publisher · View at Google Scholar · View at Scopus
  16. S. Van Dongen, MCL—graph clustering by flow simulation [Ph.D. thesis], University of Utrecht, 2000.
  17. A.-C. Gavin, P. Aloy, P. Grandi et al., “Proteome survey reveals modularity of the yeast cell machinery,” Nature, vol. 440, no. 7084, pp. 631–636, 2006. View at Publisher · View at Google Scholar · View at Scopus
  18. S. Srihari, K. Ning, and H. W. Leong, “Refining Markov Clustering for protein complex prediction by incorporating core-attachment structure.,” Genome informatics. International Conference on Genome Informatics, vol. 23, no. 1, pp. 159–168, 2009. View at Google Scholar · View at Scopus
  19. T. Obayashi and K. Kinoshita, “Rank of correlation coefficient as a comparable measure for biological significance of gene coexpression,” DNA Research, vol. 16, no. 5, pp. 249–260, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. T. Obayashi and K. Kinoshita, “COXPRESdb: a database to compare gene coexpression in seven model animals,” Nucleic Acids Research, vol. 39, no. 1, pp. D1016–D1022, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Reimand, T. Arak, and J. Vilo, “G:Profiler—a web server for functional interpretation of gene lists (2011 update),” Nucleic Acids Research, vol. 39, no. 2, pp. W307–W315, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. J. Reimand, L. Tooming, H. Peterson, P. Adler, and J. Vilo, “GraphWeb: mining heterogeneous biological networks for gene modules with functional significance,” Nucleic Acids Research, vol. 36, supplement 2, pp. W452–W459, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Ashburner, C. A. Ball, J. A. Blake et al., “Gene ontology: tool for the unification of biology,” Nature Genetics, vol. 25, no. 1, pp. 25–29, 2000. View at Publisher · View at Google Scholar · View at Scopus
  24. M. Kanehisa, S. Goto, M. Hattori et al., “From genomics to chemical genomics: new developments in KEGG,” Nucleic Acids Research, vol. 34, pp. D354–D357, 2006. View at Publisher · View at Google Scholar · View at Scopus
  25. G. Joshi-Tope, M. Gillespie, I. Vastrik et al., “Reactome: a knowledgebase of biological pathways,” Nucleic Acids Research, vol. 33, pp. D428–D432, 2005. View at Publisher · View at Google Scholar · View at Scopus
  26. E. Aygün, B. J. Oommen, and Z. Cataltepe, “Peptide classification using optimal and information theoretic syntactic modeling,” Pattern Recognition, vol. 43, no. 11, pp. 3891–3899, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. S. B. Needleman and C. D. Wunsch, “A general method applicable to the search for similarities in the amino acid sequence of two proteins,” Journal of Molecular Biology, vol. 48, no. 3, pp. 443–453, 1970. View at Publisher · View at Google Scholar · View at Scopus
  28. T. F. Smith and M. S. Waterman, “Identification of common molecular subsequences,” Journal of Molecular Biology, vol. 147, no. 1, pp. 195–197, 1981. View at Publisher · View at Google Scholar · View at Scopus
  29. B. Louie, R. Higdon, and E. Kolker, “A statistical model of protein sequence similarity and function similarity reveals overly-specific function predictions,” PLoS ONE, vol. 4, no. 10, Article ID e7546, 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. A. Ruepp, B. Brauner, I. Dunger-Kaltenbach et al., “CORUM: the comprehensive resource of mammalian protein complexes,” Nucleic Acids Research, vol. 36, no. 1, pp. D646–D650, 2008. View at Publisher · View at Google Scholar · View at Scopus
  31. P. Shannon, A. Markiel, O. Ozier et al., “Cytoscape: a software environment for integrated models of biomolecular interaction networks,” Genome Research, vol. 13, no. 11, pp. 2498–2504, 2003. View at Publisher · View at Google Scholar · View at Scopus