Human Genome Laboratory, Department of Microbiology, Yong Loo Lin School of Medicine, National University of Singapore, Kent Ridge, 117 597 Singapore.
Indian J Microbiol. 2008 Jun;48(2):156-62. doi: 10.1007/s12088-008-0025-2. Epub 2008 Jun 13.
The availability of complete genome sequences of many bacterial species is facilitating numerous computational approaches for understanding bacterial genomes. One of the major incentives behind the genome sequencing of many pathogenic bacteria is the desire to better understand their diversity and to develop new approaches for controlling human diseases caused by these microorganisms. This task has become even more urgent with the rapid evolution of antibiotic resistance among many bacterial pathogens. Novel drug targets are required in order to design new antimicrobials against antibiotic-resistant pathogens. The complete genome sequences of an ever increasing number of pathogenic microbes constitute an invaluable resource and provide lead information on potential drug targets. This review focuses on in silico analyses of microbial genomes, their host-specific adaptations, with specific reference to genome architecture, design, evolution, and trends in computational identification of microbial drug targets. These trends underscore the utility of genomic data for systematic in silico drug target identification in the post-genomic era.
许多细菌物种的全基因组序列的可用性正在促进许多用于理解细菌基因组的计算方法。许多致病菌进行基因组测序的主要动机之一是希望更好地了解它们的多样性,并开发控制这些微生物引起的人类疾病的新方法。随着许多细菌病原体对抗生素耐药性的迅速演变,这一任务变得更加紧迫。需要新的药物靶点来设计针对抗药性病原体的新抗生素。越来越多的致病菌的完整基因组序列构成了宝贵的资源,并提供了潜在药物靶点的先导信息。本综述重点介绍了微生物基因组的计算机分析,及其宿主特异性适应性,特别是基因组结构、设计、进化以及计算识别微生物药物靶点的趋势。这些趋势强调了基因组数据在后基因组时代用于系统计算机药物靶点识别的实用性。