title: A predictor of membrane class: Discriminating alpha-helical and beta-barrel membrane proteins from non-membranous proteins.
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 alpha-helical membrane proteins at 94.4% accuracy, beta-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.
authors.affiliation: The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK.
authors.forename: Paul D
authors.initials: PD
authors.lastname: Taylor
authors.forename: Christopher P
authors.initials: CP
authors.lastname: Toseland
authors.forename: Teresa K
authors.initials: TK
authors.lastname: Attwood
authors.forename: Darren R
authors.initials: DR
authors.lastname: Flower