Vetrivel Umashankar, Subramanian Gurunathan, Dorairaj Sudarsanam
Center of Bioinformatics, Vision Research Foundation, Sankara Nethralaya, Chennai, Tamilnadu 600 006 India.
Hugo J. 2011 Dec;5(1-4):25-34. doi: 10.1007/s11568-011-9152-7. Epub 2011 Apr 8.
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.
近年来,病原体和人类的基因组测序项目彻底改变了微生物药物靶点的识别。在几种已知的基因组策略中,消减基因组学已成功用于识别微生物药物靶点。目前的工作展示了一种新的基因组学方法,其中密码子适应指数(CAI),一种基于同义密码子使用情况预测基因翻译效率的指标,与消减基因组学方法相结合以挖掘潜在的药物靶点。以呼吸道病原体肺炎链球菌和流感嗜血杆菌为例展示了所采用的策略。我们的方法鉴定出8个有效的靶基因(肺炎链球菌 - 2个,流感嗜血杆菌 - 6个),这些基因在功能上具有重要意义,并且在宿主 - 病原体相互作用中也起着关键作用。这种方法有助于快速鉴定潜在的药物靶点,从而能够寻找新的抑制剂。这些结果强调了CAI在增强计算机辅助药物靶点识别方面的效用。
本文的在线版本(doi:10.1007/s11568 - 011 - 9152 - 7)包含补充材料,授权用户可以获取。