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Open Access Highly Accessed Methodology article

Membrane protein orientation and refinement using a knowledge-based statistical potential

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 2013, 14:276  doi:10.1186/1471-2105-14-276

Published: 18 September 2013

Abstract

Background

Recent increases in the number of deposited membrane protein crystal structures necessitate the use of automated computational tools to position them within the lipid bilayer. Identifying the correct orientation allows us to study the complex relationship between sequence, structure and the lipid environment, which is otherwise challenging to investigate using experimental techniques due to the difficulty in crystallising membrane proteins embedded within intact membranes.

Results

We have developed a knowledge-based membrane potential, calculated by the statistical analysis of transmembrane protein structures, coupled with a combination of genetic and direct search algorithms, and demonstrate its use in positioning proteins in membranes, refinement of membrane protein models and in decoy discrimination.

Conclusions

Our method is able to quickly and accurately orientate both alpha-helical and beta-barrel membrane proteins within the lipid bilayer, showing closer agreement with experimentally determined values than existing approaches. We also demonstrate both consistent and significant refinement of membrane protein models and the effective discrimination between native and decoy structures. Source code is available under an open source license from http://bioinf.cs.ucl.ac.uk/downloads/memembed/ webcite.

Keywords:
Membrane protein; Statistical potential; Orientation; Refinement; Genetic algorithm