A predictor of membrane class: Discriminating alpha-helical and beta-barrel membrane proteins from non-membranous proteins.

ABSTRACT: Accurate protein structure prediction remains an active objective of research in bioinformatics. Membrane proteins comprise approximately 20% of most genomes. They are, however, poorly tractable targets of experimental structure determination. Their analysis using bioinformatics thus makes an important contribution to their on-going study. Using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we have addressed the alignment-free discrimination of membrane from non-membrane proteins. The method successfully identifies prokaryotic and eukaryotic alpha-helical membrane proteins at 94.4% accuracy, beta-barrel proteins at 72.4% accuracy, and distinguishes assorted non-membranous proteins with 85.9% accuracy. The method here is an

SUBMITTER: Taylor PD 

PROVIDER: S-EPMC1891694 | biostudies-literature | 2006 Oct

REPOSITORIES: biostudies-literature