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An Overview of Multiple Sequence Alignment Methods Applied to Transmembrane Proteins

  • Cristian Zambrano-Vega
  • Byron Oviedo
  • Ronald Villamar-Torres
  • Miguel Botto-Tobar
  • Marcos Barros-Rodríguez
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 895)

Abstract

Transmembrane proteins (TMPs) have received a great deal of attention playing a fundamental role in cell biology and are considered to constitute around 30% of proteins at genomic scale. Multiple Sequence Alignment (MSA) problem has been studied for some years and researchers have proposed many heuristic and stochastic techniques tailored for sequences of soluble proteins, considering that there are a few particular differences that ought to be taken into consideration aligning TMPs sequences, these techniques are therefore not optimal to align this special class of proteins. There is a small number of MSA methods applied specifically to TMPs. In this review, we have summarized the features, implementations and performance results of three MSA methods applied to TMPs: PRALINE\(^\mathrm{TM}\), TM-Coffee and TM-Aligner. These methods have illustrated impressive advances in the accuracy and computational efforts aligning TMPs sequences.

Keywords

Multiple Sequence Alignment Transmembrane proteins Computational bilogy 

Notes

Acknowledgement

This work has been supported by the 5th convocation of Fondo Competitivo de Investigación Científica y Tecnológica FOCICYT of the Universidad Técnica Estatal de Quevedo from Ecuador.

References

  1. 1.
    Bahr, A., Thompson, J.D., Thierry, J.C., Poch, O.: BAliBASE (benchmark alignment database): enhancements for repeats, transmembrane sequences and circular permutations. Nucleic Acids Res. 29(1), 323–326 (2001).  https://doi.org/10.1093/nar/29.1.323CrossRefGoogle Scholar
  2. 2.
    Bhat, B., Ganai, N.A., Andrabi, S.M., Shah, R.A., Singh, A.: TM-Aligner: multiple sequence alignment tool for transmembrane proteins with reduced time and improved accuracy. Sci. Rep. 7(1), 1–8 (2017).  https://doi.org/10.1038/s41598-017-13083-yCrossRefGoogle Scholar
  3. 3.
    Chang, J.M., Di Tommaso, P., Taly, J.F., Notredame, C.: Accurate multiple sequence alignment of transmembrane proteins with PSI-Coffee. BMC Bioinform. 13(4), S1 (2012).  https://doi.org/10.1186/1471-2105-13-S4-S1CrossRefGoogle Scholar
  4. 4.
    Cserzö, M., Bernassau, J.M., Simon, I., Maigret, B.: New alignment strategy for transmembrane proteins. J. Mol. Biol. 243(3), 388–396 (1994).  https://doi.org/10.1006/jmbi.1994.1666CrossRefGoogle Scholar
  5. 5.
    Thompson, J.D., Koehl, P., Ripp, R., Poch, O.: BAliBASE 3.0: latest developments of the multiple sequence alignment benchmark. Proteins: Struct. Funct. Bioinform. 61(1), 127–136 (2005).  https://doi.org/10.1002/prot.20527CrossRefGoogle Scholar
  6. 6.
    Dayhoff, M., Schwartz, R., Orcutt, B.C.: A model of evolutionary change in proteins. Atlas Protein Seq. Struct. 5, 345–352 (1978)Google Scholar
  7. 7.
    Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge (1998)CrossRefGoogle Scholar
  8. 8.
    Finn, R.D., et al.: The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res. 44(D1), D279–D285 (2016).  https://doi.org/10.1093/nar/gkv1344CrossRefGoogle Scholar
  9. 9.
    Floden, E.W., Tommaso, P.D., Chatzou, M., Magis, C., Notredame, C., Chang, J.M.: PSI/TM-Coffee: a web server for fast and accurate multiple sequence alignments of regular and transmembrane proteins using homology extension on reduced databases. Nucleic Acids Res. 44(W1), W339–W343 (2016).  https://doi.org/10.1093/nar/gkw300CrossRefGoogle Scholar
  10. 10.
    Forrest, L.R., Tang, C.L., Honig, B.: On the accuracy of homology modeling and sequence alignment methods applied to membrane proteins. Biophys. J. 91(2), 508–517 (2006).  https://doi.org/10.1529/biophysj.106.082313CrossRefGoogle Scholar
  11. 11.
    Frishman, D.: Structural Bioinformatics of Membrane Proteins (2010).  https://doi.org/10.1007/978-3-7091-0045-5CrossRefGoogle Scholar
  12. 12.
    Frishman, D.: Structural Bioinformatics of Membrane Proteins. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-7091-0045-5CrossRefzbMATHGoogle Scholar
  13. 13.
    Henikoff, S., Henikoff, J.: Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. 89(22), 10915–10919 (1992)CrossRefGoogle Scholar
  14. 14.
    Heringa, J.: Two strategies for sequence comparison: profile-preprocessed and secondary structure-induced multiple alignment. Comput. Chem. 23(3), 341–364 (1999).  https://doi.org/10.1016/S0097-8485(99)00012-1CrossRefGoogle Scholar
  15. 15.
    Heringa, J.: Local weighting schemes for protein multiple sequence alignment. Comput. Chem. 26(5), 459–477 (2002). http://www.sciencedirect.com/science/article/pii/S0097848502000086CrossRefGoogle Scholar
  16. 16.
    Isberg, V., et al.: GPCRdb: an information system for g protein-coupled receptors. Nucleic Acids Res. 44(D1), D356–D364 (2016).  https://doi.org/10.1093/nar/gkv1178CrossRefGoogle Scholar
  17. 17.
    Jimenez-Morales, D., Adamian, L., Liang, J.: Detecting remote homologues using scoring matrices calculated from the estimation of amino acid substitution rates of beta-barrel membrane proteins. In: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1347–1350, August 2008.  https://doi.org/10.1109/IEMBS.2008.4649414
  18. 18.
    Jones, D., Taylor, W., Thornton, J.: A mutation data matrix for transmembrane proteins. FEBS Lett. 339(3), 269–275 (1994)CrossRefGoogle Scholar
  19. 19.
    Käll, L., Krogh, A., Sonnhammer, E.L.: A combined transmembrane topology and signal peptide prediction method. J. Mol. Biol. 338(5), 1027–1036 (2004).  https://doi.org/10.1016/j.jmb.2004.03.016CrossRefGoogle Scholar
  20. 20.
    Kozma, D., Simon, I., Tusnády, G.E.: PDBTM: protein data bank of transmembrane proteins after 8 years. Nucleic Acids Res. 41(D1), D524–D529 (2013).  https://doi.org/10.1093/nar/gks1169CrossRefGoogle Scholar
  21. 21.
    Krogh, A., Larsson, B., Von Heijne, G., Sonnhammer, E.L.: Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes1. J. Mol. Biol. 305(3), 567–580 (2001)CrossRefGoogle Scholar
  22. 22.
    Müller, T., Rahmann, S., Rehmsmeier, M.: Non-symmetric score matrices and the detection of homologous transmembrane proteins. Bioinformatics 17(suppl1), S182–S189 (2001).  https://doi.org/10.1093/bioinformatics/17.suppl_1.S182CrossRefGoogle Scholar
  23. 23.
    Newby, Z.E., et al.: A general protocol for the crystallization of membrane proteins for x-ray structural investigation. Nat. Protoc. 4(5), 619 (2009)CrossRefGoogle Scholar
  24. 24.
    Ng, P.C., Henikoff, J.G., Henikoff, S.: PHAT: a transmembrane-specific substitution matrix. Bioinformatics 16(9), 760–766 (2000).  https://doi.org/10.1093/bioinformatics/16.9.760CrossRefGoogle Scholar
  25. 25.
    Pirovano, W., Feenstra, K.A., Heringa, J.: Praline\(^{\rm TM}\): a strategy for improved multiple alignment of transmembrane proteins. Bioinformatics 24(4), 492–497 (2008).  https://doi.org/10.1093/bioinformatics/btm636CrossRefGoogle Scholar
  26. 26.
    Shafrir, Y., Guy, H.R.: STAM: simple transmembrane alignment method. Bioinformatics 20(5), 758–769 (2004).  https://doi.org/10.1093/bioinformatics/btg482CrossRefGoogle Scholar
  27. 27.
    Tusnády, G.E., Simon, I.: The hmmtop transmembrane topology prediction server. Bioinformatics 17(9), 849–850 (2001).  https://doi.org/10.1093/bioinformatics/17.9.849CrossRefGoogle Scholar
  28. 28.
    Wallin, E., Heijne, G.V.: Genome-wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms. Protein Sci. 7(4), 1029–1038 (1998)CrossRefGoogle Scholar
  29. 29.
    Wang, L., Jiang, T.: On the complexity of multiple sequence alignment. J. Comput. Biol. 1, 337–348 (1994)CrossRefGoogle Scholar
  30. 30.
    Wu, S., Manber, U.: Fast text searching: allowing errors. Commun. ACM 35(10), 83–91 (1992)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Facultad de Ciencias de la IngenieríaUniversidad Técnica Estatal de QuevedoQuevedoEcuador
  2. 2.Université de MontpellierMontpellierFrance
  3. 3.Eindhoven University of TechnologyEindhovenThe Netherlands
  4. 4.Universidad de GuayaquilGuayaquilEcuador
  5. 5.Universidad Técnica de AmbatoAmbatoEcuador

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