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Bioinformatics 2006 22(14):e290-e297; doi:10.1093/bioinformatics/btl209
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

A combinatorial pattern discovery approach for the prediction of membrane dipping (re-entrant) loops

Gorka Lasso 1, John F. Antoniw 2 and Jonathan G.L. Mullins 1,*

1 Membrane Proteins Structural Bioinformatics Group, School of Medicine, Swansea University Singleton Park, Swansea SA2 8PP, Wales, UK
2 Wheat Pathogenesis Programme, Plant Pathogen Interactions Division, Rothamsted Research Harpenden, Hertfordshire AL5 2JQ, England, UK

*To whom correspondence should be addressed.

Motivation: Membrane dipping loops are sections of membrane proteins that reside in the membrane but do not traverse from one side to the other, rather they enter and leave the same side of the membrane. We applied a combinatorial pattern discovery approach to sets of sequences containing at least one characterised structure described as possessing a membrane dipping loop. Discovered patterns were found to be composed of residues whose biochemical role is known to be essential for function of the protein, thus validating our approach.

TMLOOP (http://membraneproteins.swan.ac.uk/TMLOOP) was implemented to predict membrane dipping loops in polytopic membrane proteins. TMLOOP applies discovered patterns as weighted predictive rules in a collective motif method (a variation of the single motif method), to avoid inherent limitations of single motif methods in detecting distantly related proteins. The collective motif method applies several, partially overlapping patterns, which pertain to the same sequence region, allowing proteins containing small variations to be detected. The approach achieved 92.4% accuracy in sensitivity and 100% reliability in specificity. TMLOOP was applied to the Swiss-Prot database, identifying 1392 confirmed membrane dipping loops, 75 plausible membrane dipping loops hitherto uncharacterised by topology prediction methods or experimental approaches and 128 false positives (8.0%).

Contact: j.g.l.mullins{at}swansea.ac.uk


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