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Bioinformation. 2006 Nov 14;1(6):234-6.

Alpha helical trans-membrane proteins: Enhanced prediction using a Bayesian approach.

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The Jenner Institute, University of Oxford, Compton,Newbury, Berkshire, RG20 7NN, UK.


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.


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