Predicting the solvent accessibility of transmembrane residues from protein sequence

Yuan, Z., Zhang, F. S., Davis, M. J., Boden, M. and Teasdale, R. D. (2006) Predicting the solvent accessibility of transmembrane residues from protein sequence. Journal of Proteome Research, 5 5: 1063-1070. doi:10.1021/pr050397b


Author Yuan, Z.
Zhang, F. S.
Davis, M. J.
Boden, M.
Teasdale, R. D.
Title Predicting the solvent accessibility of transmembrane residues from protein sequence
Journal name Journal of Proteome Research   Check publisher's open access policy
ISSN 1535-3893
Publication date 2006-01-01
Sub-type Article (original research)
DOI 10.1021/pr050397b
Volume 5
Issue 5
Start page 1063
End page 1070
Total pages 8
Editor William S. Hancock
Place of publication Washington
Publisher American Chemical Society
Language eng
Subject C1
270104 Membrane Biology
780105 Biological sciences
Abstract In this study, we propose a novel method to predict the solvent accessible surface areas of transmembrane residues. For both transmembrane alpha-helix and beta-barrel residues, the correlation coefficients between the predicted and observed accessible surface areas are around 0.65. On the basis of predicted accessible surface areas, residues exposed to the lipid environment or buried inside a protein can be identified by using certain cutoff thresholds. We have extensively examined our approach based on different definitions of accessible surface areas and a variety of sets of control parameters. Given that experimentally determining the structures of membrane proteins is very difficult and membrane proteins are actually abundant in nature, our approach is useful for theoretically modeling membrane protein tertiary structures, particularly for modeling the assembly of transmembrane domains. This approach can be used to annotate the membrane proteins in proteomes to provide extra structural and functional information.
Keyword Lipid Exposed Residues
Transmembrane Helix Protein
Transmembrane Beta-barrel Protein
Protein Sequence Analysis
Support Vector Regression
Biochemical Research Methods
Secondary Structure Prediction
Multiple Linear-regression
Support Vector Machines
Real Value Prediction
Hidden Markov Model
Membrane-proteins
Data-bank
Surface
Improvement
Alignment
Q-Index Code C1

 
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Created: Wed, 15 Aug 2007, 19:06:09 EST