a School of Biotechnology , KIIT University , Bhubaneswar , Odisha , India.
b Department of Bioinformatics , Odisha University of Agriculture and Technology , Bhubaneswar , Odisha , India.
J Biomol Struct Dyn. 2019 Aug;37(13):3388-3398. doi: 10.1080/07391102.2018.1515116. Epub 2018 Nov 4.
The emergence of multidrug-resistant () has become one of the major hurdles in the treatment of tuberculosis (TB). Drug-resistant has evolved with various strategies to avoid killing by the anti-tubercular drugs. Thus, there is a rising need to develop effective anti-TB drugs to improve the treatment of these strains. Traditional drug design approach has earned little success due to time and the cost involved in the process of development of anti-infective drugs. Numerous reports have demonstrated that several mutations in the drug target sites cause emergence of drug-resistant strains. In this study, we performed computational mutational analysis of , , and genes, which are the primary targets for first-line isoniazid (INH) drug. virtual drug screening was performed to identify the potent drugs from a ChEMBL compound library to improve the treatment of INH-resistant . Further, these compounds were analyzed for their binding efficiency against active drug binding cavity of wild-type and mutant InhA, FabD and AhpC proteins. The drug efficacy of predicted lead compounds was verified by molecular docking using wild-type and mutant InhA, FabD and AhpC protein template models. Different and pharmacophore analysis predicted three potent lead compounds with better drug-like properties against both wild-type and mutant InhA, FabD, and AhpC proteins as compared to INH drug, and thus may be considered as effective drugs for the treatment of INH-resistant strains. We hypothesize that this work may accelerate drug discovery process for the treatment of drug-resistant TB. Communicated by Ramaswamy H. Sarma.
耐多药 () 的出现已成为治疗结核病 (TB) 的主要障碍之一。耐药性通过各种策略进化，以避免被抗结核药物杀死。因此，迫切需要开发有效的抗结核药物来改善这些菌株的治疗效果。由于抗感染药物开发过程中的时间和成本，传统的药物设计方法几乎没有取得成功。大量报道表明，药物靶位的几个突变导致了耐药菌株的出现。在这项研究中，我们对 、 和 基因进行了计算突变分析，这些基因是一线异烟肼 (INH) 药物的主要靶点。对 ChEMBL 化合物库进行了虚拟药物筛选，以鉴定出能够改善 INH 耐药性治疗的有效药物。此外，还分析了这些化合物与 野生型和突变型 InhA、FabD 和 AhpC 蛋白的活性药物结合腔的结合效率。使用 野生型和突变型 InhA、FabD 和 AhpC 蛋白模板模型进行分子对接验证了预测先导化合物的药物功效。与 INH 药物相比，不同的 和药效团分析预测了三种具有更好药物特性的潜在先导化合物，它们对 InhA、FabD 和 AhpC 蛋白的野生型和突变型均具有更好的疗效，因此可能被认为是治疗 INH 耐药性的有效药物。我们假设这项工作可能会加速治疗耐药性结核病的药物发现过程。由 Ramaswamy H. Sarma 传达。
