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.
|Number of pages||6|
|Publication status||Published - 2006|
Bibliographical noteThis 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.
- alpha-helical membrane proteins
- beta-barrel membrane proteins
- membrane protein discrimination
- Bayesian network
- alignment-free prediction
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.