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Bioinformatics Identification of Coevolving Residues

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1123))

Abstract

Positions in a protein are thought to coevolve to maintain important structural and functional interactions over evolutionary time. The detection of putative coevolving positions can provide important new insights into a protein family in the same way that knowledge is gained by recognizing evolutionarily conserved characters and characteristics. Putatively coevolving positions can be detected with statistical methods that identify covarying positions. However, positions in protein alignments can covary for many other reasons than coevolution; thus, it is crucial to create high-quality multiple sequence alignments for coevolution inference. Furthermore, it is important to understand common signs and sources of error. When confounding factors are accounted for, coevolution is a rich resource for protein engineering information.

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References

  1. Kimura M (1968) Evolutionary rate at the molecular level. Nature 217:624–626

    Article  CAS  PubMed  Google Scholar 

  2. Kimura M, Ota T (1974) On some principles governing molecular evolution. Proc Natl Acad Sci U S A 71:2848–2852

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  3. Kleinstiver BP, Fernandes AD, Gloor GB, Edgell DR (2010) A unified genetic, computational and experimental framework identifies functionally relevant residues of the homing endonuclease I-BmoI. Nucleic Acids Res. doi:10.1093/nar/gkp1223

    PubMed Central  PubMed  Google Scholar 

  4. Dickson R, Wahl L, Fernandes A, Gloor G (2010) Identifying and seeing beyond multiple sequence alignment errors using intra-molecular protein covariation. PLoS ONE 5:e11082

    Article  PubMed Central  PubMed  Google Scholar 

  5. Dickson RJ, Gloor GB (2013) The MIp toolset: an efficient algorithm for calculating Mutual Information in protein alignments. arXiv, Ithaca, NY

    Google Scholar 

  6. Dickson RJ, Gloor GB (2012) Protein sequence alignment analysis by local covariation: coevolution statistics detect benchmark alignment errors. PLoS ONE 7:e37645

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Talavera G, Castresana J (2007) Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst Biol 56:564

    Article  CAS  PubMed  Google Scholar 

  8. Privman E, Penn O, Pupko T (2012) Improving the performance of positive selection inference by filtering unreliable alignment regions. Mol Biol Evol 29:1–5

    Article  CAS  PubMed  Google Scholar 

  9. Martin LC, Gloor GB, Dunn SD, Wahl LM (2005) Using information theory to search for co-evolving residues in proteins. Bioinformatics 21:4116–4124

    Article  CAS  PubMed  Google Scholar 

  10. Kawrykow A et al (2012) Phylo: a citizen science approach for improving multiple sequence alignment. PLoS ONE 7:e31362

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  11. Khatib F, DiMaio F, Cooper S (2011) Crystal structure of a monomeric retroviral protease solved by protein folding game players. Nat Struct Mol Biol 18:1175–1177. doi:10.1038/nsmb.2119

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Clamp M, Cuff J, Searle SM, Barton GJ (2004) The Jalview Java alignment editor. Bioinformatics 20:426–427

    Article  CAS  PubMed  Google Scholar 

  13. Waterhouse AM, Procter JB, Martin DMA, Clamp M, Barton GJ (2009) Jalview Version 2–a multiple sequence alignment editor and analysis workbench. Bioinformatics 25:1189–1191

    Article  CAS  PubMed  Google Scholar 

  14. Söding J (2005) Protein homology detection by HMM-HMM comparison. Bioinformatics 21:951–960

    Article  PubMed  Google Scholar 

  15. Eddy SR (2011) Accelerated profile HMM searches. PLoS Comput Biol 7:e1002195

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Marchler-Bauer A et al (2009) CDD: specific functional annotation with the Conserved Domain Database. Nucleic Acids Res 37:D205–D210

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Punta M et al (2012) The Pfam protein families database. Nucleic Acids Res 40:D290–D301

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  19. Katoh K, Toh H (2008) Recent developments in the MAFFT multiple sequence alignment program. Brief Bioinform 9:286–298

    Article  CAS  PubMed  Google Scholar 

  20. Loytynoja A, Goldman N (2008) Phylogeny-aware gap placement prevents errors in sequence alignment and evolutionary analysis. Science 320:1632–1635

    Article  PubMed  Google Scholar 

  21. Edgar RC (2004) MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinform 5:113

    Article  Google Scholar 

  22. Gilbert D (2002) Sequence file format conversion with command-line readseq.. doi:10.1002/0471250953.bia01es00

    Google Scholar 

  23. Hogue CW (1997) Cn3D: a new generation of three-dimensional molecular structure viewer. Trends Biochem Sci 22:314–316

    Article  CAS  PubMed  Google Scholar 

  24. Wang Y, Geer LY, Chappey C, Kans JA, Bryant SH (2000) Cn3D: sequence and structure views for Entrez. Trends Biochem Sci 25:300–302

    Article  CAS  PubMed  Google Scholar 

  25. Ash RB (1965) Information theory. Courier Dover, New York

    Google Scholar 

  26. Cover TM, Thomas JA (1991) Elements of information theory. Wiley, New York

    Book  Google Scholar 

  27. Dunn SD, Wahl LM, Gloor GB (2008) Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction. Bioinformatics 24:333–340

    Article  CAS  PubMed  Google Scholar 

  28. R Development Core Team (2008) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://www.R-project.org.29.

  29. Ellson J, Gansner E, Koutsofios L, North S, Woodhull G (2002) Graphviz—open source graph drawing tools. Springer, Heidelberg, pp 594–597

    Google Scholar 

  30. Bromham L (2009) Reading the story in DNA. Oxford University Press, USA

    Google Scholar 

  31. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410

    CAS  PubMed  Google Scholar 

  32. Henikoff S, Henikoff JG (1992) Amino acid substitution matrices from protein blocks. Proc Natl Acad Sci U S A 89:10915–10919

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  33. Altschul SF (1998) Generalized affine gap costs for protein sequence alignment. Proteins 32:88–96

    Article  CAS  PubMed  Google Scholar 

  34. Burger L, van Nimwegen E (2010) Disentangling direct from indirect co-evolution of residues in protein alignments. PLoS Comput Biol 6:e1000633

    Google Scholar 

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Dickson, R.J., Gloor, G.B. (2014). Bioinformatics Identification of Coevolving Residues. In: Edgell, D. (eds) Homing Endonucleases. Methods in Molecular Biology, vol 1123. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-968-0_15

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  • DOI: https://doi.org/10.1007/978-1-62703-968-0_15

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-967-3

  • Online ISBN: 978-1-62703-968-0

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