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

BMC Bioinformatics

David T Jones¹  Timothy Nugent¹ 

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

关键词: Genetic algorithm;    Refinement;    Orientation;    Statistical potential;    Membrane protein;   

Others  :  1087760
DOI  :  10.1186/1471-2105-14-276

 received in 2013-05-03, accepted in 2013-09-05,  发布年份 2013

【 摘 要 】

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.

【 授权许可】

   
2013 Nugent and Jones; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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