A predictor of membrane class:discriminating α-helical and β-barrel membrane proteins from non-membranous proteins


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 α-helical membrane proteins at 94.4% accuracy, β-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.

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: alpha-helical membrane proteins,beta-barrel membrane proteins,membrane protein discrimination,Bayesian network,alignment-free prediction
Publication ISSN: 0973-2063
Last Modified: 08 Dec 2023 09:30
Date Deposited: 03 Jul 2014 07:56
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Related URLs: http://www.bioi ... net/001/001.htm (Publisher URL)
PURE Output Type: Article
Published Date: 2006
Authors: Taylor, Paul D.
Toseland, Christopher P.
Attwood, Teresa K.
Flower, Darren R. (ORCID Profile 0000-0002-8542-7067)



Version: Published Version

License: Creative Commons Attribution

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