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Bioinformatics Advance Access published online on May 12, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn221
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© The Author (2008). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

OCTOPUS:Improvingtopologypredictionbytwo-track ANN-basedpreferencescoresandanextended topologicalgrammar

Håkan Viklund 1 and Arne Elofsson 1,*

DepartmentofBiochemistyandBiophysics/CenterforBiomembraneResearch/Stockholm BioinformaticsCenter,TheArrheniusLaboratoriesforNaturalSciences,StockholmUniversity SE-10691Stockholm,Sweden.

*To whom correspondence should be addressed. Dr. Håkan Viklund, E-mail: hakanv{at}sbc.su.se


  Abstract

Motivation: As {alpha}-helical transmembrane proteins constitute roughly 25% of a typical genome and are vital parts of many essential biological processes, structural knowledge of these proteins is necessary for increasing our understanding of such processes. Because structural knowledge of transmembrane proteins is difficult to attain experimentally, improved methods for prediction of structural features of these proteins are important.

Results: OCTOPUS, a new method for predicting transmembrane protein topology is presented and benchmarked using a data set of 124 sequences with known structures. Using a novel combination of hidden Markov models and artificial neural networks, OCTOPUS predicts the correct topology for 94% of the sequences. In particular, OCTOPUS is the first topology predictor to fully integrate modeling of reentrant/membrane-dipping regions and transmembrane hairpins in the topological grammar.

Availability: OCTOPUS is available as a web-server at http://octopus.cbr.su.se.

Contact: hakanv{at}sbc.su.se

Associate Editor: Prof. Burkhard Rost

Received on October 22, 2007; revised on May 1, 2008; accepted on May 3, 2008

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