In silico analysis of potential diagnostic targets from Burkholderia pseudomallei

Administration of appropriate therapeutic regimes for infections arising from pathogenic species of Burkholderia is critically dependent upon rapid and accurate diagnoses. The purpose of this work is to establish a bioinformatic pipeline to assess protein sequences for their potential as diagnostic targets for the detection of Burkholderia species. Data are presented showing both a bioinformatic methodology for prediction of surface-associated and secreted proteins and its application to a test dataset of proteins from the pathogen B. pseudomallei. A subset of proteins, known to be produced by the organism, is identified which represents potential targets for development of new diagnostic reagents. In addition, a 'reverse diagnostics' bioinformatics approach has been established which can now be extended to whole genome analyses.