Title

Beta barrel trans-membrane proteins: Enhanced prediction using a Bayesian approach

Authors

Paul D. Taylor¹, Christopher P. Toseland², Teresa K. Attwood³ and Darren R. Flower^1,*

Affiliation

¹The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK; ²National Institute for Medical Research, The Ridgeway, Mill Hill, London, NW7 1AA, UK; ³Faculty of Life Sciences & School of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PT, UK

Email

darren.flower@jenner.ac.uk; *Corresponding author

Phone

+44 1635 577954

Fax

+44 1635 577908

Article Type

Prediction Model

Date

received October 04, 2006; accepted October 06, 2006; published online October 07, 2006

Abstract

Membrane proteins, which constitute approximately 20% of most genomes, form two main classes: alpha helical and beta barrel transmembrane proteins. Using methods based on Bayesian Networks, a powerful approach for statistical inference, we have sought to address β-barrel topology prediction. The β-barrel topology predictor reports individual strand accuracies of 88.6%. The method outlined here represents a potentially important advance in the computational determination of membrane protein topology.

Keywords

Beta Barrel Transmembrane Protein; Prokaryotic membrane proteins; Bayesian Networks; Prediction Method; Sub-cellular Location

Citation

Taylor et al., Bioinformation 1(6): 231-233 (2006) 

Edited by

P. Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

Copyright

Publisher

Copyright Transfer Statement

The authors of published articles in Bioinformation automatically transfer the copyright to the publisher upon formal acceptance. However, the authors reserve right to use the information contained in the article for non commercial purposes.

License

This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.