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一种整合的药效团/对接/3D-QSAR 方法,用于筛选大型产品库以寻找未来的肉毒神经毒素 A 抑制剂。

An Integrated Pharmacophore/Docking/3D-QSAR Approach to Screening a Large Library of Products in Search of Future Botulinum Neurotoxin A Inhibitors.

机构信息

Department of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy.

Department of Biomedical and Biotechnological Sciences, Section of Pharmacology, University of Catania, Via S. Sofia 67, 95123 Catania, Italy.

出版信息

Int J Mol Sci. 2020 Dec 12;21(24):9470. doi: 10.3390/ijms21249470.

Abstract

Botulinum toxins are neurotoxins produced by . This toxin can be lethal for humans as a cause of botulism; however, in small doses, the same toxin is used to treat different conditions. Even if the therapeutic doses are effective and safe, the adverse reactions could be local and could unmask a subclinical impairment of neuromuscular transmissions. There are not many cases of adverse events in the literature; however, it is possible that sometimes they do not occur as they are transient and, if they do occur, there is no possibility of a cure other than to wait for the pharmacological effect to end. Inhibition of botulinum neurotoxin type A (BoNT/A) effects is a strategy for treating botulism as it can provide an effective post-exposure remedy. In this paper, 13,592,287 compounds were screened through a pharmacophore filter, a 3D-QSAR model, and a virtual screening; then, the compounds with the best affinity were selected. Molecular dynamics simulation studies on the first four compounds predicted to be the most active were conducted to verify that the poses foreseen by the docking were stable. This approach allowed us to identify compounds with a calculated inhibitory activity in the range of 316-500 nM.

摘要

肉毒杆菌毒素是由 产生的神经毒素。这种毒素会导致人类因肉毒中毒而致命;然而,在小剂量下,同样的毒素被用于治疗不同的疾病。即使治疗剂量有效且安全,不良反应也可能是局部的,并且可能会暴露出神经肌肉传递的亚临床损伤。文献中报道的不良事件案例并不多;但是,有时它们可能不会发生,因为它们是短暂的,如果确实发生了,除了等待药物作用结束之外,没有任何治疗方法。抑制肉毒梭菌神经毒素 A(BoNT/A)的作用是治疗肉毒中毒的一种策略,因为它可以提供有效的暴露后治疗方法。在本文中,通过药效团过滤、3D-QSAR 模型和虚拟筛选筛选了 13592287 种化合物,然后选择了具有最佳亲和力的化合物。对预测最活跃的前四种化合物进行了分子动力学模拟研究,以验证对接预测的构象是稳定的。这种方法使我们能够识别出计算出的抑制活性在 316-500 nM 范围内的化合物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce0e/7764241/f036d2c6c474/ijms-21-09470-g001.jpg

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