A novel in silico approach to identify potential therapeutic targets in human bacterial pathogens

Abstract

In recent years, genome-sequencing projects of pathogens and humans have revolutionized microbial drug target identification. Of the several known genomic strategies, subtractive genomics has been successfully utilized for identifying microbial drug targets. The present work demonstrates a novel genomics approach in which codon adaptation index (CAI), a measure used to predict the translational efficiency of a gene based on synonymous codon usage, is coupled with subtractive genomics approach for mining potential drug targets. The strategy adopted is demonstrated using respiratory pathogens, namely, Streptococcus pneumoniae and Haemophilus influenzae as examples. Our approach identified 8 potent target genes (Streptococcus pneumoniae-2, H. influenzae-6), which are functionally significant and also play key role in host-pathogen interactions. This approach facilitates swift identification of potential drug targets, thereby enabling the search for new inhibitors. These results underscore the utility of CAI for enhanced in silico drug target identification.

Electronic supplementary material: The online version of this article (doi:10.1007/s11568-011-9152-7) contains supplementary material, which is available to authorized users.

Keywords: Bacterial pathogens; CAI value; Codon usage; Drug targets; Subtractive genomics.

Fig. 1

Codon usage analysis and drug target identification workflow

Fig. 2

Frequency of significant genes with CAI value above 0.75 in Haemophilus influenzae (a) and Streptococcus pneumoniae (b), CAI frequency of complete datasets: H. influenzae (c) and Streptococcus pneumoiae, Number in boxes over bar indicates the frequency. A total of 97 out of 638 and 35 out of 110 were identified as significant genes in H. influenzae and S. pneumoniae, respectively