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Methods for the bioinformatic identification of bacterial lipoproteins encoded in the genomes of Gram-positive bacteria
Authors:Obaidur Rahman  Stephen P Cummings  Dean J Harrington  Iain C Sutcliffe
Institution:(1) Northumbria University, Newcastle upon Tyne, NE1 8ST, UK;(2) University of Bradford, West Yorkshire, BD7 1DP, UK;(3) Biomolecular and Biomedical Research Centre, School of Applied Science, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK
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 () performs best individually but that a consensus approach, incorporating outputs from predictors of general signal peptide properties, is most informative. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.
Keywords:Lipoproteins  Signal peptides  Bioinformatics  Genomics            Firmicutes            Actinobacteria
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