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Hidden Markov Models pp 43–61Cite as

Predicting Beta Barrel Transmembrane Proteins Using HMMs

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1552))

Abstract

Transmembrane beta-barrels (TMBBs) constitute an important structural class of membrane proteins located in the outer membrane of gram-negative bacteria, and in the outer membrane of chloroplasts and mitochondria. They are involved in a wide variety of cellular functions and the prediction of their transmembrane topology, as well as their discrimination in newly sequenced genomes is of great importance as they are promising targets for antimicrobial drugs and vaccines. Several methods have been applied for the prediction of the transmembrane segments and the topology of beta barrel transmembrane proteins utilizing different algorithmic techniques. Hidden Markov Models (HMMs) have been efficiently used in the development of several computational methods used for this task. In this chapter we give a brief review of different available prediction methods for beta barrel transmembrane proteins pointing out sequence and structural features that should be incorporated in a prediction method. We then describe the procedure of the design and development of a Hidden Markov Model capable of predicting the transmembrane beta strands of TMBBs and discriminating them from globular proteins.

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Authors and Affiliations

  1. Department of Cell Biology and Biophysics, Faculty of Biology, National and Kapodistrian University of Athens, Panepistimiopolis, Athens, 15701, Greece

    Georgios N. Tsaousis & Stavros J. Hamodrakas

  2. Department of Computer Science and Biomedical Informatics, University of Thessaly, Papasiopoulou 2-4, Lamia, 35100, Greece

    Pantelis G. Bagos

Authors
  1. Georgios N. Tsaousis
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  2. Stavros J. Hamodrakas
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  3. Pantelis G. Bagos
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Correspondence to Pantelis G. Bagos .

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Editors and Affiliations

  1. University of Leeds School of Molecular and Cellular Biology, Leeds, United Kingdom

    David R. Westhead

  2. University of Leeds School of Cellular and Molecular Biology, Leeds, United Kingdom

    M. S. Vijayabaskar

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Tsaousis, G.N., Hamodrakas, S.J., Bagos, P.G. (2017). Predicting Beta Barrel Transmembrane Proteins Using HMMs. In: Westhead, D., Vijayabaskar, M. (eds) Hidden Markov Models. Methods in Molecular Biology, vol 1552. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6753-7_4

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