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.
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References
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/29.1.323
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-y
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-S1
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.1994.1666
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.20527
Dayhoff, M., Schwartz, R., Orcutt, B.C.: A model of evolutionary change in proteins. Atlas Protein Seq. Struct. 5, 345–352 (1978)
Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge (1998)
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/gkv1344
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/gkw300
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.106.082313
Frishman, D.: Structural Bioinformatics of Membrane Proteins (2010). https://doi.org/10.1007/978-3-7091-0045-5
Frishman, D.: Structural Bioinformatics of Membrane Proteins. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-7091-0045-5
Henikoff, S., Henikoff, J.: Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci. 89(22), 10915–10919 (1992)
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)00012-1
Heringa, J.: Local weighting schemes for protein multiple sequence alignment. Comput. Chem. 26(5), 459–477 (2002). http://www.sciencedirect.com/science/article/pii/S0097848502000086
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/gkv1178
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
Jones, D., Taylor, W., Thornton, J.: A mutation data matrix for transmembrane proteins. FEBS Lett. 339(3), 269–275 (1994)
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.2004.03.016
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/gks1169
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)
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.S182
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)
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/16.9.760
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/btm636
Shafrir, Y., Guy, H.R.: STAM: simple transmembrane alignment method. Bioinformatics 20(5), 758–769 (2004). https://doi.org/10.1093/bioinformatics/btg482
Tusnády, G.E., Simon, I.: The hmmtop transmembrane topology prediction server. Bioinformatics 17(9), 849–850 (2001). https://doi.org/10.1093/bioinformatics/17.9.849
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)
Wang, L., Jiang, T.: On the complexity of multiple sequence alignment. J. Comput. Biol. 1, 337–348 (1994)
Wu, S., Manber, U.: Fast text searching: allowing errors. Commun. ACM 35(10), 83–91 (1992)
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.
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Zambrano-Vega, C., Oviedo, B., Villamar-Torres, R., Botto-Tobar, M., Barros-Rodríguez, M. (2019). An Overview of Multiple Sequence Alignment Methods Applied to Transmembrane Proteins. In: Botto-Tobar, M., Pizarro, G., Zúñiga-Prieto, M., D’Armas, M., Zúñiga Sánchez, M. (eds) Technology Trends. CITT 2018. Communications in Computer and Information Science, vol 895. Springer, Cham. https://doi.org/10.1007/978-3-030-05532-5_30
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