Nucleic Acids Research, 2003, Vol. 31, No. 13 3613-3617
© 2003 Oxford University Press
PSORT-B: improving protein subcellular localization prediction for Gram-negative bacteria
Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada V5A 1S6 1 Department of Computing Science, Simon Fraser University, Burnaby, BC, Canada V5A 1S6 2 Institute of Enzymology, Hungarian Academy of Sciences H-1113 Budapest Karolina ut 29, Hungary 3 Bioinformatics and Computational Biology Track, Biological and Biomedical Sciences Program, Yale University, New Haven, CT 06520, USA 4 URBM, FUNDP Rue de Bruxelles, 61, B-5000, Namur, Belgium 5 Human Genome Center, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokane-dai, Minato-ku, Tokyo 108-8639, Japan
*To whom correspondence should be addressed. Tel: +1 6042915646; Fax: +1 6042915583; Email: brinkman{at}sfu.ca
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
Automated prediction of bacterial protein subcellular localization is an important tool for genome annotation and drug discovery. PSORT has been one of the most widely used computational methods for such bacterial protein analysis; however, it has not been updated since it was introduced in 1991. In addition, neither PSORT nor any of the other computational methods available make predictions for all five of the localization sites characteristic of Gram-negative bacteria. Here we present PSORT-B, an updated version of PSORT for Gram-negative bacteria, which is available as a web-based application at http://www.psort.org. PSORT-B examines a given protein sequence for amino acid composition, similarity to proteins of known localization, presence of a signal peptide, transmembrane alpha-helices and motifs corresponding to specific localizations. A probabilistic method integrates these analyses, returning a list of five possible localization sites with associated probability scores. PSORT-B, designed to favor high precision (specificity) over high recall (sensitivity), attained an overall precision of 97% and recall of 75% in 5-fold cross-validation tests, using a dataset we developed of 1443 proteins of experimentally known localization. This dataset, the largest of its kind, is freely available, along with the PSORT-B source code (under GNU General Public License).
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