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通过基于片段的药物设计(FBDD)和分子动力学模拟策略,发现新型有效抑制神经氨酸酶关键功能的流感药物。

Discovery of novel potent drugs for influenza by inhibiting the vital function of neuraminidase via fragment-based drug design (FBDD) and molecular dynamics simulation strategies.

机构信息

Group of Computational and Medicinal Chemistry, Laboratory of Molecular Chemistry and Environment, University of Biskra, Biskra, Algeria.

Faculty of Pharmacy, Middle East University Amman, Amman, Jordan.

出版信息

J Biomol Struct Dyn. 2024 Nov;42(18):9294-9308. doi: 10.1080/07391102.2023.2251065. Epub 2023 Aug 28.

DOI:10.1080/07391102.2023.2251065
PMID:37640004
Abstract

The current work describes a fragment linking methodology to generate new neuraminidase inhibitors. A total number of 28,977 fragments from Zinc 20 have been obtained and screened for neuraminidase receptor affinity. Using Schrödinger software, the highest-scoring 270 fragment hits (with scores greater than -7.6) were subjected to fragment combining to create 100 new molecules. These 100 novel compounds were studied using XP docking to evaluate the molecular interaction modes and their binding affinity to neuraminidase receptor. The top ten molecules were selected, for ADMET, drug-likeness features. Based on these characteristics, the best four developed molecules and Zanamivir were submitted to a molecular dynamics simulation investigation to estimate their dynamics within the neuraminidase receptor using Gromacs software. All MD simulation findings show that the generated complexes are very stable when compared to the clinical inhibitor (Zanamivir). In addition, the four designed neuraminidase inhibitors formed very stable complexes with neuraminidase receptor (with total binding energies ranging from -83.50 to -107.85 Kj/mol) according to the total binding energy calculated by MM-PBSA. For the objective of developing new influenza medications, these novel molecules have the potential to be further evaluated and for influenza drug discovery.Communicated by Ramaswamy H. Sarma.

摘要

当前的工作描述了一种连接片段的方法,用于生成新的神经氨酸酶抑制剂。从 Zinc 20 中获得了总共 28977 个片段,并对其进行了神经氨酸酶受体亲和力筛选。使用 Schrödinger 软件,对得分最高的 270 个片段(得分大于-7.6)进行片段组合,以创建 100 个新分子。使用 XP 对接研究这 100 种新化合物,以评估它们与神经氨酸酶受体的分子相互作用模式和结合亲和力。选择排名前十的分子进行 ADMET、药物相似性特征研究。基于这些特性,最好的四个开发分子和扎那米韦被提交给分子动力学模拟研究,以使用 Gromacs 软件估计它们在神经氨酸酶受体中的动力学。所有 MD 模拟结果表明,与临床抑制剂(扎那米韦)相比,生成的复合物非常稳定。此外,根据 MM-PBSA 计算的总结合能,这四个设计的神经氨酸酶抑制剂与神经氨酸酶受体形成非常稳定的复合物(总结合能范围从-83.50 到-107.85 Kj/mol)。为了开发新的流感药物,这些新分子有可能进一步评估和用于流感药物发现。由 Ramaswamy H. Sarma 传达。

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