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.

Divisions: College of Health & Life Sciences > Aston Pharmacy School
College of Health & Life Sciences
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: trans-membrane protein,alpha helix,static full Bayesian model,prediction,amino acid descriptors
Publication ISSN: 0973-2063
Full Text Link:
Related URLs: http://www.bioi ... net/001/001.htm (Publisher URL)
PURE Output Type: Article
Published Date: 2006
Published Online Date: 2006-11-14
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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