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Genome-Wide Prediction and Analysis of Protein-Protein Functional Linkages in Bacteria pp 19–32Cite as

Co-Evolutionary Signals Within Genome Sequences Reflect Functional Dependence of Proteins

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Part of the book series: SpringerBriefs in Systems Biology ((BRIEFSBIOSYS,volume 2))

Abstract

In the course of evolution, proteins involved in a particular biological process or pathway are often subjected to the same selection pressure and adaptive constraints through various molecular mechanisms. Thus, proteins that are working together in the cell often co-evolve and show similar evolutionary trajectories. One of the evolutionary constraints that act on functionally related proteins, is the concerted appearance of genes encoding them in the organisms for which their function is indispensable, and disappearance otherwise. Likewise, physically interacting proteins are expected to have correlated mutations in their sequences and/or nucleotide sequence of genes encoding them in order to maintain binding interfaces. These two forms of co-evolutionary behavior of genes and their products in order to maintain their function leave pattern over the long evolutionary periods. In the post-genomic era, these co-evolutionary patterns have been utilized to reconstruct genome-scale protein–protein interactions and biological pathways using various methods. In this review, we have described the basic principles of these methods and the novel strategies to improve their prediction qualities.

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References

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

    Article  PubMed  CAS  Google Scholar 

  2. Aravind, L., et al.: Evidence for massive gene exchange between archaeal and bacterial hyperthermophiles. Trends Genet. 14(11), 442–444 (1998)

    Article  PubMed  CAS  Google Scholar 

  3. Watanabe, H., et al.: Genome plasticity as a paradigm of eubacteria evolution. J. Mol. Evol. 44(Suppl 1), S57–S64 (1997)

    Article  PubMed  CAS  Google Scholar 

  4. Koonin, E.V.: Evolution of genome architecture. Int. J. Biochem. Cell Biol. 41(2), 298–306 (2009)

    Article  PubMed  CAS  Google Scholar 

  5. Lawrence, J.G.: Selfish operons and speciation by gene transfer. Trends Microbiol. 5(9), 355–359 (1997)

    Article  PubMed  CAS  Google Scholar 

  6. Gaasterland, T., Ragan, M.A.: Microbial genescapes: phyletic and functional patterns of ORF distribution among prokaryotes. Microb. Comp. Genomics 3(4), 199–217 (1998)

    PubMed  CAS  Google Scholar 

  7. Kensche, P.R., et al.: Practical and theoretical advances in predicting the function of a protein by its phylogenetic distribution. J. R. Soc. Interface 5(19), 151–170 (2008)

    Article  PubMed  CAS  Google Scholar 

  8. Koonin, E.V., Mushegian, A.R.: Complete genome sequences of cellular life forms: glimpses of theoretical evolutionary genomics. Curr. Opin. Genet. Dev. 6(6), 757–762 (1996)

    Article  PubMed  CAS  Google Scholar 

  9. Koonin, E.V., Mushegian, A.R., Bork, P.: Non-orthologous gene displacement. Trends Genet. 12(9), 334–336 (1996)

    Article  PubMed  CAS  Google Scholar 

  10. Slonim, N., Elemento, O., Tavazoie, S.: Ab initio genotype-phenotype association reveals intrinsic modularity in genetic networks. Mol. Syst. Biol. 2, 2006 0005 (2006)

    Google Scholar 

  11. Singh, A.H., et al.: Modularity of stress response evolution. Proc. Natl. Acad. Sci. USA 105(21), 7500–7505 (2008)

    Article  PubMed  CAS  Google Scholar 

  12. Koonin, E.V.: Orthologs, paralogs, and evolutionary genomics. Annu. Rev. Genet. 39, 309–338 (2005)

    Article  PubMed  CAS  Google Scholar 

  13. Overbeek, R., et al.: Use of contiguity on the chromosome to predict functional coupling. Silico Biol. 1(2), 93–108 (1999)

    CAS  Google Scholar 

  14. Tatusov, R.L., et al.: The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res. 28(1), 33–36 (2000)

    Article  PubMed  CAS  Google Scholar 

  15. Pellegrini, M., et al.: Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc. Natl. Acad. Sci. USA 96(8), 4285–4288 (1999)

    Article  PubMed  CAS  Google Scholar 

  16. Date, S.V., Marcotte, E.M.: Discovery of uncharacterized cellular systems by genome-wide analysis of functional linkages. Nat. Biotechnol. 21(9), 1055–1062 (2003)

    Article  PubMed  CAS  Google Scholar 

  17. Enault, F.: Annotation of bacterial genomes using improved phylogenomic profiles. Bioinformatics 19(Suppl 1), i105–i107 (2003)

    Article  PubMed  Google Scholar 

  18. Altschul, S.F., et al.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25(17), 3389–3402 (1997)

    Article  PubMed  CAS  Google Scholar 

  19. Kim, P.J., Price, N.D.: Genetic co-occurrence network across sequenced microbes. PLoS Comput. Biol. 7(12): p. e1002340

    Google Scholar 

  20. Galperin, M.Y., Koonin, E.V.: Functional genomics and enzyme evolution. Homologous and analogous enzymes encoded in microbial genomes. Genetica 106(1–2), 159–170 (1999)

    Article  PubMed  CAS  Google Scholar 

  21. Barker, D., Pagel, M.: Predicting functional gene links from phylogenetic-statistical analyses of whole genomes. PLoS Comput. Biol. 1(1), e3 (2005)

    Article  PubMed  Google Scholar 

  22. Zheng, Y., Roberts, R.J., Kasif, S.:Genomic functional annotation using co-evolution profiles of gene clusters. Genome Biol. 3(11), RESEARCH0060 (2002)

    Google Scholar 

  23. Sun, J., et al.: Refined phylogenetic profiles method for predicting protein–protein interactions. Bioinformatics 21(16), 3409–3415 (2005)

    Article  PubMed  CAS  Google Scholar 

  24. Jothi, R., Przytycka, T.M., Aravind, L.: Discovering functional linkages and uncharacterized cellular pathways using phylogenetic profile comparisons: a comprehensive assessment. BMC Bioinform. 8, 173 (2007)

    Article  Google Scholar 

  25. Karimpour-Fard, A., Hunter, L., Gill, R.T.: Investigation of factors affecting prediction of protein–protein interaction networks by phylogenetic profiling. BMC Genomics 8, 393 (2007)

    Article  PubMed  Google Scholar 

  26. Snitkin, E.S., et al.: Comparative assessment of performance and genome dependence among phylogenetic profiling methods. BMC Bioinform. 7, 420 (2006)

    Article  Google Scholar 

  27. Kim, Y., et al.: Inferring functional information from domain co-evolution. Bioinformatics 22(1), 40–49 (2006)

    Article  PubMed  CAS  Google Scholar 

  28. Pazos, F., et al.: Correlated mutations contain information about protein–protein interaction. J. Mol. Biol. 271(4), 511–523 (1997)

    Article  PubMed  CAS  Google Scholar 

  29. Gobel, U., et al.: Correlated mutations and residue contacts in proteins. Proteins 18(4), 309–317 (1994)

    Article  PubMed  CAS  Google Scholar 

  30. Fodor, A.A., Aldrich, R.W.: Influence of conservation on calculations of amino acid covariance in multiple sequence alignments. Proteins 56(2), 211–221 (2004)

    Article  PubMed  CAS  Google Scholar 

  31. Lockless, S.W., Ranganathan, R.: Evolutionarily conserved pathways of energetic connectivity in protein families. Science 286(5438), 295–299 (1999)

    Article  PubMed  CAS  Google Scholar 

  32. Gloor, G.B., et al.: Mutual information in protein multiple sequence alignments reveals two classes of coevolving positions. Biochemistry 44(19), 7156–7165 (2005)

    Article  PubMed  CAS  Google Scholar 

  33. Kann, M.G., et al.: Correlated evolution of interacting proteins: looking behind the mirrortree. J. Mol. Biol. 385(1), 91–98 (2009)

    Article  PubMed  CAS  Google Scholar 

  34. Wang, Z.O., Pollock, D.D.: Coevolutionary patterns in cytochrome c oxidase subunit I depend on structural and functional context. J. Mol. Evol. 65(5), 485–495 (2007)

    Article  PubMed  Google Scholar 

  35. Yeang, C.H., Haussler, D.: Detecting coevolution in and among protein domains. PLoS Comput. Biol. 3(11), e211 (2007)

    Article  PubMed  Google Scholar 

  36. Burger, L., van Nimwegen, E.: Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method. Mol. Syst. Biol. 4, 165 (2008)

    Article  PubMed  Google Scholar 

  37. Mintseris, J., Weng, Z.: Structure, function, and evolution of transient and obligate protein–protein interactions. Proc. Natl. Acad. Sci. USA 102(31), 10930–10935 (2005)

    Article  PubMed  CAS  Google Scholar 

  38. Halperin, I., Wolfson, H., Nussinov, R.: Correlated mutations: advances and limitations. A study on fusion proteins and on the Cohesin–Dockerin families. Proteins 63(4), 832–845 (2006)

    Article  PubMed  CAS  Google Scholar 

  39. Pazos, F., Valencia, A.: Protein co-evolution, co-adaptation and interactions. EMBO J. 27(20), 2648–2655 (2008)

    Article  PubMed  CAS  Google Scholar 

  40. Ullsperger, C., Cozzarelli, N.R.: Contrasting enzymatic activities of topoisomerase IV and DNA gyrase from Escherichia coli. J. Biol. Chem. 271(49), 31549–31555 (1996)

    Article  PubMed  CAS  Google Scholar 

  41. Weiss, D.S.: Bacterial cell division and the septal ring. Mol. Microbiol. 54(3), 588–597 (2004)

    Article  PubMed  CAS  Google Scholar 

  42. Wang, X., Reyes-Lamothe, R., Sherratt, D.J.: Modulation of Escherichia coli sister chromosome cohesion by topoisomerase IV. Genes Dev. 22(17), 2426–2433 (2008)

    Article  PubMed  CAS  Google Scholar 

  43. Fryxell, K.J.: The coevolution of gene family trees. Trends Genet. 12(9), 364–369 (1996)

    Article  PubMed  CAS  Google Scholar 

  44. van Kesteren, R.E.: Co-evolution of ligand-receptor pairs in the vasopressin/oxytocin superfamily of bioactive peptides. J. Biol. Chem. 271(7), 3619–3626 (1996)

    Article  PubMed  Google Scholar 

  45. Sato, T., et al.: The inference of protein-protein interactions by co-evolutionary analysis is improved by excluding the information about the phylogenetic relationships. Bioinformatics 21(17), 3482–3489 (2005)

    Article  PubMed  CAS  Google Scholar 

  46. Goh, C.S., et al.: Co-evolution of proteins with their interaction partners. J. Mol. Biol. 299(2), 283–293 (2000)

    Article  PubMed  CAS  Google Scholar 

  47. Pazos, F., Valencia, A.: Similarity of phylogenetic trees as indicator of protein–protein interaction. Protein Eng. 14(9), 609–614 (2001)

    Article  PubMed  CAS  Google Scholar 

  48. Thompson, J.D., Gibson T.J., Higgins D.G.: Multiple sequence alignment using ClustalW and ClustalX. Curr. Protoc. Bioinform. Chapter 2, Unit 2 3 (2002)

    Google Scholar 

  49. Pazos, F., et al.: Assessing protein co-evolution in the context of the tree of life assists in the prediction of the interactome. J. Mol. Biol. 352(4), 1002–1015 (2005)

    Article  PubMed  CAS  Google Scholar 

  50. Choi, K., Gomez, S.M.: Comparison of phylogenetic trees through alignment of embedded evolutionary distances. BMC Bioinform. 10, 423 (2009)

    Article  Google Scholar 

  51. Hakes, L., et al.: Specificity in protein interactions and its relationship with sequence diversity and coevolution. Proc. Natl. Acad. Sci. USA 104(19), 7999–8004 (2007)

    Article  PubMed  CAS  Google Scholar 

  52. Muley, V.Y., Ranjan, A: Effect of Reference Genome Selection on the Performance of Computational Methods for Genome-wide Protein-Protein Interaction Prediction. PLoS ONE, In Press (2012)

    Google Scholar 

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Authors and Affiliations

  1. Center of Excellence in Epigenetics, Indian Institute of Science Education and Research (IISER), Pune, Maharashtra, India

    Vijaykumar Yogesh Muley

  2. Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology (IHBT), Palampur, Himachal Pradesh, India

    Vishal Acharya

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  1. Vijaykumar Yogesh Muley
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  2. Vishal Acharya
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Correspondence to Vijaykumar Yogesh Muley .

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© 2013 Vijaykumar Yogesh Muley

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Muley, V.Y., Acharya, V. (2013). Co-Evolutionary Signals Within Genome Sequences Reflect Functional Dependence of Proteins. In: Genome-Wide Prediction and Analysis of Protein-Protein Functional Linkages in Bacteria. SpringerBriefs in Systems Biology, vol 2. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4705-4_3

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