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Methods for the bioinformatic identification of bacterial lipoproteins encoded in the genomes of Gram-positive bacteria

Methods for the bioinformatic identification of bacterial lipoproteins encoded in the genomes of... Bacterial lipoproteins are a diverse and functionally important group of proteins that are amenable to bioinformatic analyses because of their unique signal peptide features. Here we have used a dataset of sequences of experimentally verified lipoproteins of Gram-positive bacteria to refine our previously described lipoprotein recognition pattern (G+LPP). Sequenced bacterial genomes can be screened for putative lipoproteins using the G+LPP pattern. The sequences identified can then be validated using online tools for lipoprotein sequence identification. We have used our protein sequence datasets to evaluate six online tools for efficacy of lipoprotein sequence identification. Our analyses demonstrate that LipoP ( http://www.cbs.dtu.dk/services/LipoP/ ) performs best individually but that a consensus approach, incorporating outputs from predictors of general signal peptide properties, is most informative. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png World Journal of Microbiology and Biotechnology Springer Journals

Methods for the bioinformatic identification of bacterial lipoproteins encoded in the genomes of Gram-positive bacteria

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References (27)

Publisher
Springer Journals
Copyright
Copyright © 2008 by Springer Science+Business Media B.V.
Subject
Chemistry; Microbiology ; Environmental Engineering/Biotechnology; Biochemistry, general; Biotechnology; Applied Microbiology
ISSN
0959-3993
eISSN
1573-0972
DOI
10.1007/s11274-008-9795-2
Publisher site
See Article on Publisher Site

Abstract

Bacterial lipoproteins are a diverse and functionally important group of proteins that are amenable to bioinformatic analyses because of their unique signal peptide features. Here we have used a dataset of sequences of experimentally verified lipoproteins of Gram-positive bacteria to refine our previously described lipoprotein recognition pattern (G+LPP). Sequenced bacterial genomes can be screened for putative lipoproteins using the G+LPP pattern. The sequences identified can then be validated using online tools for lipoprotein sequence identification. We have used our protein sequence datasets to evaluate six online tools for efficacy of lipoprotein sequence identification. Our analyses demonstrate that LipoP ( http://www.cbs.dtu.dk/services/LipoP/ ) performs best individually but that a consensus approach, incorporating outputs from predictors of general signal peptide properties, is most informative.

Journal

World Journal of Microbiology and BiotechnologySpringer Journals

Published: Jun 27, 2008

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