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- W1571196974 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 important potential advance in the computational analysis of membrane protein structure. It represents a useful tool for the characterisation of membrane proteins with a wide variety of potential applications." @default.
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- W1571196974 date "2006-10-07" @default.
- W1571196974 modified "2023-09-23" @default.
- W1571196974 title "A predictor of membrane class: Discriminating alpha-helical and beta-barrel membrane proteins from non-membranous proteins." @default.
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