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

M. D. Obaidur Rahman, Stephen Cummings, Dean Harrington, Iain Sutcliffe

Research output: Contribution to journalArticlepeer-review

77 Citations (Scopus)

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
Original languageEnglish
Pages (from-to)2377-2382
JournalWorld Journal of Microbiology and Biotechnology
Volume24
Issue number11
DOIs
Publication statusPublished - Nov 2008

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