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.

Divisions: College of Health & Life Sciences > Aston Pharmacy School
College of Health & Life Sciences
College of Health & Life Sciences > Chronic and Communicable Conditions
Additional Information: 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.
Uncontrolled Keywords: beta barrel transmembrane protein,prokaryotic membrane proteins,Bayesian networks ,prediction method,sub-cellular location
Full Text Link:
Related URLs: http://www.bioi ... net/001/001.htm (Publisher URL)
PURE Output Type: Article
Published Date: 2006
Published Online Date: 2006-10-07
Authors: Taylor, Paul D.
Toseland, Christopher P.
Attwood, Teresa K.
Flower, Darren R. (ORCID Profile 0000-0002-8542-7067)

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License: Creative Commons Attribution


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