title: Alpha helical trans-membrane proteins: Enhanced prediction using a Bayesian approach.
abstract: Membrane proteins, which constitute approximately 20% of most genomes, are poorly tractable targets for experimental structure determination, thus analysis by prediction and modelling makes an important contribution to their on-going study. Membrane proteins form two main classes: alpha helical and beta barrel trans-membrane proteins. By using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we addressed alpha-helical topology prediction. This method has accuracies of 77.4% for prokaryotic proteins and 61.4% for eukaryotic proteins. The method described here represents an important advance in the computational determination of membrane protein topology and offers a useful, and complementary, tool for the analysis of membrane proteins for a range of 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