Melak Tilahun, Gakkhar Sunita
Department of Computer Science, Dilla University, P.O. Box 419, Dilla, SNNPR, Ethiopia.
Department of Mathematics, IIT Roorkee, Roorkee, Uttarakhand 247667, India.
Biomed Res Int. 2015;2015:212061. doi: 10.1155/2015/212061. Epub 2015 Nov 5.
Potential drug targets of Mycobacterium tuberculosis H37Rv were identified through systematically integrated comparative genome and network centrality analysis. The comparative analysis of the complete genome of Mycobacterium tuberculosis H37Rv against Database of Essential Genes (DEG) yields a list of proteins which are essential for the growth and survival of the pathogen. Those proteins which are nonhomologous with human were selected. The resulting proteins were then prioritized by using the four network centrality measures: degree, closeness, betweenness, and eigenvector. Proteins whose centrality value is close to the centre of gravity of the interactome network were proposed as a final list of potential drug targets for the pathogen. The use of an integrated approach is believed to increase the success of the drug target identification process. For the purpose of validation, selective comparisons have been made among the proposed targets and previously identified drug targets by various other methods. About half of these proteins have been already reported as potential drug targets. We believe that the identified proteins will be an important input to experimental study which in the way could save considerable amount of time and cost of drug target discovery.
通过系统整合的比较基因组学和网络中心性分析，确定了结核分枝杆菌H37Rv的潜在药物靶点。将结核分枝杆菌H37Rv的全基因组与必需基因数据库（DEG）进行比较分析，得出了一份对该病原体生长和存活至关重要的蛋白质列表。选择了那些与人类无同源性的蛋白质。然后使用度、接近度、介数和特征向量这四种网络中心性度量对所得蛋白质进行优先级排序。中心性值接近相互作用组网络重心的蛋白质被提议作为该病原体潜在药物靶点的最终列表。据信，采用综合方法可提高药物靶点识别过程的成功率。为了进行验证，已在所提议的靶点与先前通过其他各种方法确定的药物靶点之间进行了选择性比较。这些蛋白质中约有一半已被报道为潜在的药物靶点。我们相信，所确定的蛋白质将为实验研究提供重要的输入信息，从而有可能节省药物靶点发现过程中大量的时间和成本。
