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Title

 

 

 

 

TATPred: a Bayesian method for the identification of twin arginine translocation pathway signal sequences

 

Authors

Paul D. Taylor1, Christopher P. Toseland1, Teresa K. Attwood2 and Darren R. Flower1,*

 

Affiliation

1The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK;  2Faculty of Life Sciences & School of Computer Science, The University of Manchester, Oxford Road, Manchester M13 9PT, UK,  author: The Jenner Institute, University of Oxford, Compton, Newbury, Berkshire, RG20 7NN, UK.

 

E-mail*

darren.flower@jenner.ac.uk; * Corresponding author

 

Phone

+44 1635 577954

 

Fax

+44 1635 577908

 

Article Type

Prediction Model

 

Date

received July 12, 2006; revised July 24, 2006; accepted July 24, 2006; published online July 25, 2006

 

Abstract

The twin arginine translocation (TAT) system ferries folded proteins across the bacterial membrane. Proteins are directed into this system by the TAT signal peptide present at the amino terminus of the precursor protein, which contains the twin arginine residues that give the system its name. There are currently only two computational methods for the prediction of TAT translocated proteins from sequence. Both methods have limitations that make the creation of a new algorithm for TAT-translocated protein prediction desirable. We have developed TATPred, a new sequence-model method, based on a Na´ve-Bayesian network, for the prediction of TAT signal peptides. In this approach, a comprehensive range of models was tested to identify the most reliable and robust predictor. The best model comprised 12 residues: three residues prior to the twin arginines and the seven residues that follow them. We found a prediction sensitivity of 0.979 and a specificity of 0.942.

 

Keywords

 

Twin arginine motif; Bayesian Network; TAT translocation; Signal sequence; Vaccine

 

Citation

Taylor et al., Bioinformation 1(5):184-187 (2006)

 

Edited by

P. Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.