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Protein Science (2004), 13:1908-1917. Published by Cold Spring Harbor Laboratory Press. Copyright © 2004 The Protein Society
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Best {alpha}-helical transmembrane protein topology predictions are achieved using hidden Markov models and evolutionary information

Håkan Viklund and Arne Elofsson

Stockholm Bioinformatics Center, Stockholm University, SE-10691 Stockholm, Sweden

(RECEIVED January 13, 2004; FINAL REVISION March 18, 2004; ACCEPTED March 29, 2004)



Abstract

Methods that predict the topology of helical membrane proteins are standard tools when analyzing any proteome. Therefore, it is important to improve the performance of such methods. Here we introduce a novel method, PRODIV-TMHMM, which is a profile-based hidden Markov model (HMM) that also incorporates the best features of earlier HMM methods. In our tests, PRODIV-TMHMM outperforms earlier methods both when evaluated on "low-resolution" topology data and on high-resolution 3D structures. The results presented here indicate that the topology could be correctly predicted for approximately two-thirds of all membrane proteins using PRODIV-TMHMM. The importance of evolutionary information for topology prediction is emphasized by the fact that compared with using single sequences, the performance of PRODIV-TMHMM (as well as two other methods) is increased by approximately 10 percentage units by the use of homologous sequences. On a more general level, we also show that HMM-based (or similar) methods perform superiorly to methods that focus mainly on identification of the membrane regions.

Keywords: transmembrane protein; {alpha}-helix; topology prediction; hidden Markov model; sequence profile

Abbreviations: HMM, hidden Markov model • TM, transmembrane

Reprint requests to: Arne Elofsson, Stockholm Bioinformatics Center, Stockholm University, SE-10691 Stockholm, Sweden; e-mail: arne{at}sbc.su.se; fax: 46-8-5537-8214.

Article and publication are at http://www.proteinscience.org/cgi/doi/10.1110/ps.04625404.


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