Alpha helical trans-membrane proteins: Enhanced prediction using a Bayesian approach.
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.
Taylor
Paul D
PD
The Jenner Institute, University of Oxford, Compton,Newbury, Berkshire, RG20 7NN, UK.
Toseland
Christopher P
CP
Attwood
Teresa K
TK
Flower
Darren R
DR