A predictor of membrane class: discriminating α-helical and β-barrel membrane proteins from non-membranous proteins

Paul D. Taylor, Christopher P. Toseland, Teresa K. Attwood, Darren R. Flower

Research output: Contribution to journalArticle

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 α-helical membrane proteins at 94.4% accuracy, β-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.
Original languageEnglish
Pages (from-to)208-213
Number of pages6
JournalBioinformation
Volume1
Issue number6
Publication statusPublished - 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

  • alpha-helical membrane proteins
  • beta-barrel membrane proteins
  • membrane protein discrimination
  • Bayesian network
  • alignment-free prediction

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    Taylor, P. D., Toseland, C. P., Attwood, T. K., & Flower, D. R. (2006). A predictor of membrane class: discriminating α-helical and β-barrel membrane proteins from non-membranous proteins. Bioinformation, 1(6), 208-213.