Biocomputational strategies for microbial drug target identification.
The complete genome sequences of about 300 bacteria (mostly pathogenic) have been determined, and many more such projects are currently in progress. The detection of bacterial genes that are non-homologous to human genes and are essential for the survival of the pathogen represent a promising means of identifying novel drug targets. We present a subtractive genomics approach for the identification of putative drug targets in microbial genomes and demonstrate its execution using Pseudomonas aeruginosa as an example. The resultant analyses are in good agreement with the results of systematic gene deletion experiments. This strategy enables rapid potential drug target identification, thereby greatly facilitating the search for new antibiotics. It should be recognized that there are limitations to this computational approach for drug target identification. Distant gene relationships may be missed since the alignment scores are likely to have low statistical significance. In conclusion, the results of such a strategy underscore the utility of large genomic databases for in silico systematic drug target identification in the post-genomic era.
Sakharkar
Kishore R
KR
Human Genome Laboratory, Department of Microbiology, Yong loo Lin School of Medicine, National University of Singapore, Singapore.
Sakharkar
Meena K
MK
Chow
Vincent T K
VT
D000900:Anti-Bacterial Agents / Q000494:pharmacology*; D001426:Bacterial Proteins / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D015195:Drug Design; D005798:Genes, Bacterial / Q000187:drug effects*; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D011550:Pseudomonas aeruginosa / Q000187:drug effects / Q000235:genetics; D016415:Sequence Alignment; D012984:Software
D000900:Anti-Bacterial Agents; D001426:Bacterial Proteins