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Open Access Research article

Detecting pore-lining regions in transmembrane protein sequences

Timothy Nugent and David T Jones*

Author Affiliations

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

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BMC Bioinformatics 2012, 13:169  doi:10.1186/1471-2105-13-169

Published: 17 July 2012

Abstract

Background

Alpha-helical transmembrane channel and transporter proteins play vital roles in a diverse range of essential biological processes and are crucial in facilitating the passage of ions and molecules across the lipid bilayer. However, the experimental difficulties associated with obtaining high quality crystals has led to their significant under-representation in structural databases. Computational methods that can identify structural features from sequence alone are therefore of high importance.

Results

We present a method capable of automatically identifying pore-lining regions in transmembrane proteins from sequence information alone, which can then be used to determine the pore stoichiometry. By labelling pore-lining residues in crystal structures using geometric criteria, we have trained a support vector machine classifier to predict the likelihood of a transmembrane helix being involved in pore formation. Results from testing this approach under stringent cross-validation indicate that prediction accuracy of 72% is possible, while a support vector regression model is able to predict the number of subunits participating in the pore with 62% accuracy.

Conclusion

To our knowledge, this is the first tool capable of identifying pore-lining regions in proteins and we present the results of applying it to a data set of sequences with available crystal structures. Our method provides a way to characterise pores in transmembrane proteins and may even provide a starting point for discovering novel routes of therapeutic intervention in a number of important diseases. This software is freely available as source code from: http://bioinf.cs.ucl.ac.uk/downloads/memsat-svm/ webcite.

Keywords:
Transmembrane; Membrane; Protein; Pore; Channel; Prediction; Support vector machine; SVM