Abstract
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 language | English |
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Pages (from-to) | 234-236 |
Number of pages | 3 |
Journal | Bioinformation |
Volume | 1 |
Issue number | 6 |
Early online date | 14 Nov 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
- trans-membrane protein
- alpha helix
- static full Bayesian model
- prediction
- amino acid descriptors