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Computational Biology and Chemistry
Volume 28, Issue 3, July 2004, Pages 189-194
 
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doi:10.1016/j.compbiolchem.2004.02.004    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2004 Elsevier Ltd. All rights reserved.

Scoring hidden Markov models to discriminate β-barrel membrane proteins

Yong Denga, 1, Qi Liub, 1 and Yi-Xue LiCorresponding Author Contact Information, b, Corresponding Author Contact Information, E-mail The Corresponding Author, 1

a School of Electronics & Information Technology, Shanghai Jiao Tong University, Shanghai 200030, PR China b Bioinformation Center, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, PR China

Received 4 February 2004; 
Revised 26 February 2004; 
accepted 26 February 2004. 
Available online 26 June 2004.

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Abstract

A new method is presented for identification of β-barrel membrane proteins. It is based on a hidden Markov model (HMM) with an architecture obeying these proteins’ construction principles. Once the HMM is trained, log-odds score relative to a null model is used to discriminate β-barrel membrane proteins from other proteins. The method achieves only 10% false positive and false negative rates in a six-fold cross-validation procedure. The results compare favorably with existing methods. This method is proposed to be a valuable tool to quickly scan proteomes of entirely sequenced organisms for β-barrel membrane proteins.

Author Keywords: β-Barrel membrane proteins; Hidden Markov model; Log-odds score

Article Outline

1. Introduction
2. Materials and methods
2.1. Datasets
2.2. Cross-validation
2.3. HMM training
2.4. Scoring sequences
3. Results and discussion
4. Comparison with other methods
4.1. Screening of genomic data
5. Conclusions
Acknowledgements
References




 
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