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Title |
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Alpha helical trans-membrane proteins: Enhanced prediction using a Bayesian approach
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Authors |
Paul D. Taylor1, Christopher P. Toseand2,Teresa K. Attwood3 and DarrenR.Flower1*
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Affiliation |
1 The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK; 2 National Institute for Medical Research, The Ridgeway, Mill Hill, London, NW7 1AA, UK; 3Faculty of Life Sciences & School of Computer Science,The University of Manchester, Oxford Road, Manchester M13 9PT, UK
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darren.flower@jenner.ac.uk; *Corresponding author
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Phone |
+44 1635 577954
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Fax |
+44 1635 577908
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Article Type |
Prediction Model
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Date |
received October 06, 2006; accepted November 01, 2006; published online November 14, 2006
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Abstract |
Membrane proteins, which constitute approximately 20% of most genomes, are poorly tractable targets for experimental structure determination, thus analysis by bioinformatic prediction and modelling makes an important contribution to their on-going study. Membrane proteins form two main classes: alpha helical and beta barrel transmembrane proteins. By using a method based on Bayesian Networks, which provides a flexible and powerful framework for statistical inference, we addressed α-helical topology prediction. This method has accuracies of 77.4% for prokaryotic proteins and 61.4% for eukaryotic proteins. The method described here represents an important advance in the computational determination of membrane protein topology and offers a useful, and complementary, tool for the analysis of membrane proteins for a range of applications.
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Keywords
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transmembrane Protein; alpha helix; static full Bayesian model; prediction; amino acid descriptors |
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Citation |
Taylor et al., Bioinformation 1(6): 234-236 (2006)
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Edited by |
P. Kangueane
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ISSN |
0973-2063
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Publisher |
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Copyright |
Publisher
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Copyright Transfer Statement |
The authors of published articles in Bioinformation automatically transfer the copyright to the publisher upon formal acceptance. However, the authors reserve right to use the information contained in the article for non commercial purposes.
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License |
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.
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