Taylor, Paul D, Toseland, Christopher P, Attwood, Teresa K, Flower, Darren R (2006) Beta barrel trans-membrane proteins: Enhanced prediction using a Bayesian approach. Bioinformation, 1 (6). pp. 231-3. ISSN 0973-2063. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:47859)
| The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. |
Abstract
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.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science |
| Divisions: | Divisions > Division of Natural Sciences > Biosciences |
| Depositing User: | Chris Toseland |
| Date Deposited: | 07 Apr 2015 10:59 UTC |
| Last Modified: | 16 Nov 2021 10:19 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/47859 (The current URI for this page, for reference purposes) |
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