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Nucleic Acids Research Advance Access originally published online on May 5, 2007
Nucleic Acids Research 2007 35(Web Server issue):W429-W432; doi:10.1093/nar/gkm256
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Nucleic Acids Research, 2007, Vol. 35, No. suppl_2 W429-W432
© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Articles

Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server

Lukas Käll1,*, Anders Krogh2 and Erik L.L. Sonnhammer1

1Center for Genomics and Bioinformatics, Karolinska Institutet, S-17177 Stockholm, Sweden and 2Department of Molecular Biology, University of Copenhagen, Ole Maaloes Vej 5, 2200 Copenhagen, Denmark

*To whom correspondence should be addressed. Tel: +1 206 616 5021; Fax: +1 206 685 7301; Email: lukall{at}u.washington.edu

Received January 26, 2007. Revised March 22, 2007. Accepted April 8, 2007.

When using conventional transmembrane topology and signal peptide predictors, such as TMHMM and SignalP, there is a substantial overlap between these two types of predictions. Applying these methods to five complete proteomes, we found that 30–65% of all predicted signal peptides and 25–35% of all predicted transmembrane topologies overlap. This impairs predictions of 5–10% of the proteome, hence this is an important issue in protein annotation.

To address this problem, we previously designed a hidden Markov model, Phobius, that combines transmembrane topology and signal peptide predictions. The method makes an optimal choice between transmembrane segments and signal peptides, and also allows constrained and homology-enriched predictions.

We here present a web interface (http://phobius.cgb.ki.se and http://phobius.binf.ku.dk) to access Phobius.

Present addresses: Lukas Käll, Department of Genome Sciences, University of Washington, Seattle WA, USA

Erik L.L. Sonnhammer, Stockholm Bioinformatics Center, Stockholm University, Stockholm, Sweden


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