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通过基于结构的药物筛选鉴定具有抗分枝杆菌潜在抗菌活性的化合物。

Identification of compounds with potential antibacterial activity against Mycobacterium through structure-based drug screening.

机构信息

Department of Bioscience and Bioinformatics, Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka-shi, Fukuoka, Japan.

出版信息

J Chem Inf Model. 2013 May 24;53(5):1200-12. doi: 10.1021/ci300571n. Epub 2013 Apr 19.

Abstract

To identify novel antibiotics against Mycobacterium tuberculosis, we performed a hierarchical structure-based drug screening (SBDS) targeting the enoyl-acyl carrier protein reductase (InhA) with a compound library of 154,118 chemicals. We then evaluated whether the candidate hit compounds exhibited inhibitory effects on the growth of two model mycobacterial strains: Mycobacterium smegmatis and Mycobacterium vanbaalenii. Two compounds (KE3 and KE4) showed potent inhibitory effects against both model mycobacterial strains. In addition, we rescreened KE4 analogs, which were identified from a compound library of 461,383 chemicals through fingerprint analysis and genetic algorithm-based docking simulations. All of the KE4 analogs (KES1-KES5) exhibited inhibitory effects on the growth of M. smegmatis and/or M. vanbaalenii. Based on the predicted binding modes, we probed the structure-activity relationships of KE4 and its analogs and found a correlative relationship between the IC50 values and the interaction residues/LogP values. The most potent inhibitor, compound KES4, strongly and stably inhibited the long-term growth of the model bacteria and showed higher inhibitory effects (IC50 = 4.8 μM) than isoniazid (IC50 = 5.4 μM), which is a first-line drug for tuberculosis therapy. Moreover, compound KES4 did not exhibit any toxic effects that impede cell growth in several mammalian cell lines and enterobacteria. The structural and experimental information of these novel chemical compounds will likely be useful for the development of new anti-TB drugs. Furthermore, the methodology that was used for the identification of the effective chemical compound is also likely to be effective in the SBDS of other candidate medicinal drugs.

摘要

为了寻找针对结核分枝杆菌的新型抗生素,我们针对烯酰基辅酶 A 还原酶 (InhA) 进行了基于层次结构的药物筛选 (SBDS),使用了包含 154118 种化合物的化合物库。然后,我们评估了候选命中化合物是否对两种模型分枝杆菌菌株(耻垢分枝杆菌和分枝杆菌 vanbaalenii)的生长具有抑制作用。两种化合物(KE3 和 KE4)对两种模型分枝杆菌菌株均表现出强大的抑制作用。此外,我们重新筛选了 KE4 的类似物,这些类似物是通过指纹分析和基于遗传算法的对接模拟从包含 461383 种化合物的化合物库中鉴定出来的。KE4 的所有类似物(KES1-KES5)均对耻垢分枝杆菌和/或分枝杆菌 vanbaalenii 的生长具有抑制作用。基于预测的结合模式,我们研究了 KE4 及其类似物的构效关系,并发现 KE4 的 IC50 值与相互作用残基/LogP 值之间存在相关性。最有效的抑制剂化合物 KES4 强烈且稳定地抑制了模型细菌的长期生长,并且对模型细菌的抑制作用(IC50 = 4.8 μM)高于一线抗结核药物异烟肼(IC50 = 5.4 μM)。此外,化合物 KES4 对几种哺乳动物细胞系和肠杆菌没有表现出任何毒副作用,这些毒副作用会阻碍细胞生长。这些新型化学化合物的结构和实验信息可能有助于开发新的抗结核药物。此外,用于鉴定有效化学化合物的方法也可能有效地用于其他候选药物的 SBDS。

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