Alpha helical 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, 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.
Original languageEnglish
Pages (from-to)234-236
Number of pages3
Issue number6
Early online date14 Nov 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.


  • trans-membrane protein
  • alpha helix
  • static full Bayesian model
  • prediction
  • amino acid descriptors


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