Methods for the bioinformatic identification of bacterial lipoproteins encoded in the genomes of Gram-positive bacteria |
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Authors: | Obaidur Rahman Stephen P Cummings Dean J Harrington Iain C Sutcliffe |
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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 |
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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. |
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Keywords: | Lipoproteins Signal peptides Bioinformatics Genomics Firmicutes Actinobacteria |
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