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 journalArticlepeer-review


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 languageEnglish
Pages (from-to)231-233
Number of pages3
Issue number6
Early online date7 Oct 2006
Publication statusPublished - 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.


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


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