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Advances in Computational Methods for Transmembrane Protein Structure Prediction

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From Protein Structure to Function with Bioinformatics

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Abstract

Transmembrane (TM) proteins fulfill many crucial cellular functions such as substrate transport, biogenesis and signalling, and make up a significant fraction of any given proteome. Estimates suggest that up to 30% of all human genes may encode α-helical TM proteins, while β-barrel TM proteins, which are found in the outer-membrane of gram-negative bacteria, mitochondria and chloroplast, are encoded by 2–3% of genes. However, relatively few high resolution TM protein structures are known, making it all the more important to extract as much structural information as possible from amino acid sequences. In this chapter, we review the existing methods for the identification, topology prediction and three-dimensional modelling of TM proteins, including a discussion of the recent advances in identifying residue-residue contacts from large multiple sequence alignments that have enabled impressive gains to be made in the field of TM protein structure prediction.

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Authors and Affiliations

  1. Thomson Reuters, Corporate Research and Development, 30 South Colonnade, Canary Wharf, EC2A 4EG, London, UK

    Tim Nugent

  2. Bioinformatics Group, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

    David Jones

  3. Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York City, USA

    Sikander Hayat

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  3. Sikander Hayat
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Correspondence to Tim Nugent .

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  1. Institute of Integrative Biology, University of Liverpool, Liverpool, United Kingdom

    Daniel J. Rigden

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Nugent, T., Jones, D., Hayat, S. (2017). Advances in Computational Methods for Transmembrane Protein Structure Prediction. In: J. Rigden, D. (eds) From Protein Structure to Function with Bioinformatics. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-1069-3_5

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