title: In silico analysis of Burkholderia pseudomallei genome sequence for potential drug targets.
abstract: Recent advances in DNA sequencing technology have enabled elucidation of whole genome information from a plethora of organisms. In parallel with this technology, various bioinformatics tools have driven the comparative analysis of the genome sequences between species and within isolates. While drawing meaningful conclusions from a large amount of raw material, computer-aided identification of suitable targets for further experimental analysis and characterization, has also led to the prediction of non-human homologous essential genes in bacteria as promising candidates for novel drug discovery. Here, we present a comparative genomic analysis to identify essential genes in Burkholderia pseudomallei. Our in silico prediction has identified 312 essential genes which could also be potential drug candidates. These genes encode essential proteins to support the survival of B. pseudomallei including outer-inner membrane and surface structures, regulators, proteins involved in pathogenenicity, adaptation, chaperones as well as degradation of small and macromolecules, energy metabolism, information transfer, central/intermediate/miscellaneous metabolism pathways and some conserved hypothetical proteins of unknown function. Therefore, our in silico approach has enabled rapid screening and identification of potential drug targets for further characterization in the laboratory.
authors.affiliation: School of Biosciences and Biotechnology, Faculty of Science and Technology, University Kebangsaan Malaysia, 43600 Bangi, Malaysia.
authors.forename: Chan-Eng
authors.initials: CE
authors.lastname: Chong
authors.forename: Boon-San
authors.initials: BS
authors.lastname: Lim
authors.forename: Sheila
authors.initials: S
authors.lastname: Nathan
authors.forename: Rahmah
authors.initials: R
authors.lastname: Mohamed
mesh_terms: D016957:Burkholderia pseudomallei / Q000187:drug effects* / Q000235:genetics* / Q000472:pathogenicity; D019295:Computational Biology; D030541:Databases, Genetic; D024881:Drug Resistance, Bacterial / Q000235:genetics; D005798:Genes, Bacterial / Q000187:drug effects; D016680:Genome, Bacterial; D023281:Genomics / Q000706:statistics & numerical data; D006801:Humans; D012984:Software