Multi-class subcellular location prediction for bacterial proteins


Two algorithms, based onBayesian Networks (BNs), for bacterial subcellular location prediction, are explored in this paper: one predicts all locations for Gram+ bacteria and the other all locations for Gram- bacteria. Methods were evaluated using different numbers of residues (from the N-terminal 10 residues to the whole sequence) and residue representation (amino acid-composition, percentage amino acid-composition or normalised amino acid-composition). The accuracy of the best resulting BN was compared to PSORTB. The accuracy of this multi-location BN was roughly comparable to PSORTB; the difference in predictions is low, often less than 2%. The BN method thus represents both an important new avenue of methodological development for subcellular location prediction and a potentially value new tool of true utilitarian value for candidate subunit vaccine selection.

Divisions: College of Health & Life Sciences > Aston Pharmacy School
College of Health & Life Sciences
Additional Information: This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
Uncontrolled Keywords: Bayesian network,prediction method,subcellular location,membrane protein,periplasmic protein,secreted protein
Publication ISSN: 0973-2063
Last Modified: 03 Jun 2024 07:11
Date Deposited: 14 Jul 2014 08:30
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Related URLs: http://www.bioi ... net/001/001.htm (Publisher URL)
PURE Output Type: Article
Published Date: 2006
Authors: Taylor, Paul D.
Attwood, Teresa K.
Flower, Darren R. (ORCID Profile 0000-0002-8542-7067)



Version: Published Version

License: Creative Commons Attribution Non-commercial

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