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

Paul D. Taylor; Christopher P. Toseland; Teresa K. Attwood; Darren R. Flower

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

Paul D. Taylor, Christopher P. Toseland, Teresa K. Attwood, Darren R. Flower

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	231-233
Number of pages	3
Journal	Bioinformation
Volume	1
Issue number	6
Early online date	7 Oct 2006
Publication status	Published - 2006

Bibliographical note

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.

Keywords

beta barrel transmembrane protein
prokaryotic membrane proteins
Bayesian networks
prediction method
sub-cellular location

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Beta barrel trans-membrane proteins
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.
Final published version, 96.1 KBLicence: CC BY 3.0

Cite this

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title = "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.",

keywords = "beta barrel transmembrane protein, prokaryotic membrane proteins, Bayesian networks , prediction method, sub-cellular location",

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AU - Toseland, Christopher P.

AU - Attwood, Teresa K.

AU - Flower, Darren R.

N1 - 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.

PY - 2006

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N2 - 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.

AB - 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.

KW - beta barrel transmembrane protein

KW - prokaryotic membrane proteins

KW - Bayesian networks

KW - prediction method

KW - sub-cellular location

UR - http://www.bioinformation.net/001/001.htm

M3 - Article

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SP - 231

EP - 233

JO - Bioinformation

JF - Bioinformation

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ER -

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

Abstract

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Keywords

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