Beta barrel trans-membrane proteins:enhanced prediction using a Bayesian approach

Taylor, Paul D., Toseland, Christopher P., Attwood, Teresa K. and Flower, Darren R. (2006). Beta barrel trans-membrane proteins:enhanced prediction using a Bayesian approach. Bioinformation, 1 (6), pp. 231-233.

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: Life & Health Sciences > Pharmacy
Life & Health 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: 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)
Published Date: 2006
Authors: Taylor, Paul D.
Toseland, Christopher P.
Attwood, Teresa K.
Flower, Darren R. ( 0000-0002-8542-7067)

Download

[img]

Version: Published Version

License: Creative Commons Attribution


Export / Share Citation


Statistics

Additional statistics for this record