Mazandu Gaston K, Mulder Nicola J
Computational Biology Group, Department of Clinical Laboratory Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Rondebosch 7701, South Africa.
Adv Bioinformatics. 2011;2011:801478. doi: 10.1155/2011/801478. Epub 2011 Nov 29.
Technological developments in large-scale biological experiments, coupled with bioinformatics tools, have opened the doors to computational approaches for the global analysis of whole genomes. This has provided the opportunity to look at genes within their context in the cell. The integration of vast amounts of data generated by these technologies provides a strategy for identifying potential drug targets within microbial pathogens, the causative agents of infectious diseases. As proteins are druggable targets, functional interaction networks between proteins are used to identify proteins essential to the survival, growth, and virulence of these microbial pathogens. Here we have integrated functional genomics data to generate functional interaction networks between Mycobacterium tuberculosis proteins and carried out computational analyses to dissect the functional interaction network produced for identifying drug targets using network topological properties. This study has provided the opportunity to expand the range of potential drug targets and to move towards optimal target-based strategies.
大规模生物学实验的技术发展，再加上生物信息学工具，为全基因组的全局分析打开了计算方法的大门。这提供了在细胞环境中研究基因的机会。这些技术产生的大量数据的整合为识别微生物病原体（传染病的病原体）中的潜在药物靶点提供了一种策略。由于蛋白质是可成药靶点，蛋白质之间的功能相互作用网络被用于识别这些微生物病原体生存、生长和毒力所必需的蛋白质。在这里，我们整合了功能基因组学数据以生成结核分枝杆菌蛋白质之间的功能相互作用网络，并进行了计算分析，以利用网络拓扑特性剖析为识别药物靶点而产生的功能相互作用网络。这项研究提供了扩大潜在药物靶点范围并朝着基于最佳靶点的策略发展的机会。
