title: Beta barrel trans-membrane proteins: Enhanced prediction using a Bayesian approach.
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 beta-barrel topology prediction. The beta-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.
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