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Bioinformatics Advance Access published online on January 19, 2007
Bioinformatics, doi:10.1093/bioinformatics/btl677
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Improving the accuracy of transmembrane protein topology prediction using evolutionary information

David T. Jones 1,*

1Department of Computer Science, University College London, Gower Street, London WC1E 6BT, United Kingdom

*To whom correspondence should be addressed. David T. Jones, dtj{at}cs.ucl.ac.uk


  Abstract

Motivation: Many important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion are mediated by membrane proteins. Unfortunately, as these proteins are not water soluble, it is extremely hard to experimentally determine the structure of membrane proteins and so improved methods for predicting the structure of these proteins are vital in biological research. In order to improve transmembrane topology prediction, we evaluate the combined use of both integrated signal peptide prediction and evolutionary information in a single algorithm.

Results: A new method (MEMSAT3) for predicting transmembrane protein topology from sequence profiles is described and benchmarked with full cross-validation on a standard data set of 184 transmembrane proteins. The method is found to predict both the correct topology and the locations of transmembrane segments for 80% of the test set. This compares with accuracies of 62-72% for other popular methods on the same benchmark. By using a second neural network specifically to discriminate transmembrane from globular proteins, a very low overall false positive rate (0.5%) can also be achieved in detecting transmembrane proteins.

Availability: An implementation of the described method is available both as a web server (http://www.psipred.net) and as downloadable source code from http://bioinf.cs.ucl.ac.uk. Both the server and source code files are free to non-commercial users. Benchmark and training data is also available from http://bioinf.cs.ucl.ac.uk/memsat.

Associate Editor: Thomas Lengauer

Received on October 4, 2006; revised on January 3, 2007; accepted on January 4, 2007

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