title: Toward bacterial protein sub-cellular location prediction: single-class discrimminant models for all gram- and gram+ compartments.
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.
authors.affiliation: The Jenner Institute, University of Oxford, Compton,Newbury, Berkshire, RG20 7NN, UK.
authors.forename: Paul D
authors.initials: PD
authors.lastname: Taylor
authors.forename: Teresa K
authors.initials: TK
authors.lastname: Attwood
authors.forename: Darren R
authors.initials: DR
authors.lastname: Flower