Title

Toward bacterial protein sub-cellular location prediction: single-class discrimminant models for all gram- and gram+ compartments

Authors

Paul D. Taylor ¹, Teresa K. Attwood ² and Darren R. Flower ^1,*

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

Email

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

Phone

+44 1635 577954

Fax

+44 1635 577908

Article Type

Prediction Model

Date

received November 24, 2006; accepted December 02, 2006; published online December 02, 2006

Abstract

Based on Bayesian Networks, methods were created that address protein sequence-based bacterial subcellular location prediction. Distinct predictive algorithms for the eight bacterial subcellular locations were created. Several variant methods were explored. These variations included differences in the number of residues considered within the query sequence - which ranged from the N-terminal 10 residues to the whole sequence - and residue representation - which took the form of amino acid composition, percentage amino acid composition, or normalised amino acid composition. The accuracies of the best performing networks were then compared to PSORTB. All individual location methods outperform PSORTB except for the Gram+ cytoplasmic protein predictor, for which accuracies were essentially equal, and for outer membrane protein prediction, where PSORTB outperforms the binary predictor. The method described here is an important new approach to method development for subcellular location prediction.  It is also a new, potentially valuable tool for candidate subunit vaccine selection.

Keywords

Bayesian Networks; prediction method; subcellular location; membrane protein; periplasmic protein; secreted protein

Citation

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.