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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. The online version of this article (doi:10.1007/s11568-011-9152-7) contains supplementary material, which is available to authorized users.
Highlights
The astonishing success of genomics has delivered an ever-increasing flow of sequence data
A total of 110 genes of Streptococcus pneumoniae TIGR4 strain were acquired from Database of Essential Genes (DEG) database
The present study was formulated to evolve a novel strategy, which demonstrates the utilization of codon usage genomics coupled with subtractive genomics pertaining to potent drug target identification
Summary
The astonishing success of genomics has delivered an ever-increasing flow of sequence data. The sequence data serve as the raw material for in silico target discovery (Read et al 2001). The strategies for drug design and development are progressively shifting from the genetic approach to the genomic approach (Galperin and Koonin 1999). Genomics and bioinformatics provide new opportunities for finding optimal targets. With the phenomenal growth of microbial sequence databases, it has become possible to use in silico comparisons among genomes to identify potential targets at the beginning of the drug discovery process. More than 500 complete microbial genomes and human genome sequence data have been published and are publicly available (for an updated list of published genomes, vide http://www.genomesonline.org/)
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