Title

A Predictor of Membrane Class: Discriminating α-helical and β-barrel membrane proteins from non-membranous proteins

Authors

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

Affiliation

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

E-mail*

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

Phone

+44 1635 577954

Fax

+44 1635 577908

Article Type

Prediction Model

Date

received September 20, 2006; accepted October 02, 2006; published online October 07, 2006
 

Abstract

Accurate protein structure prediction remains an active objective of research in bioinformatics. Membrane proteins comprise approximately 20% of most genomes. They are, however, poorly tractable targets of experimental structure determination. Their analysis using bioinformatics thus makes an important contribution to their on-going study. Using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we have addressed the alignment-free discrimination of membrane from non-membrane proteins. The method successfully identifies prokaryotic and eukaryotic α-helical membrane proteins at 94.4% accuracy, β-barrel proteins at 72.4% accuracy, and distinguishes assorted non-membranous proteins with 85.9% accuracy. The method here is an important potential advance in the computational analysis of membrane protein structure. It represents a useful tool for the characterisation of membrane proteins with a wide variety of potential applications.
 

Keywords

α-helical membrane proteins; β-barrel membrane proteins; membrane protein discrimination; Bayesian Network; alignment-free prediction

Citation

Taylor et al., Bioinformation 1(6): 208-213 (2006)

Edited by

P. Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.