Molecular Mechanism-Driven Discovery of Novel Small Molecule Inhibitors against Drug-Resistant SARS-CoV-2 Mpro Variants.

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL Journal of Chemical Information and Modeling Pub Date : 2024-10-28 Epub Date: 2024-10-10 DOI:10.1021/acs.jcim.4c01206
Jingyi Yang, Beibei Fu, Rongpei Gou, Xiaoyuan Lin, Haibo Wu, Weiwei Xue
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Abstract

Under the selective pressure of nirmatrelvir, a peptidomimetic covalent drug targeting SARS-CoV-2 Mpro, various drug-resistant mutations on Mpro have been acquired in vitro. Among the mutations, L50F and E166V, along with the combination of L50F and E166V, are particularly representative and pose considerable obstacles to the effective treatment of COVID-19. Our previous study identified NMI-001 and NMI-002 as novel nonpeptide inhibitors that target SARS-CoV-2 Mpro, possessing unique scaffolds and binding modes different from those of nirmatrelvir. In view of these findings, we proposed a drug design strategy aimed at rapidly identifying inhibitors that can combat mutation-induced drug resistance. Initially, molecular dynamics (MD) simulation was employed to investigate the binding mechanisms of NMI-001 and NMI-002 against the three drug-resistant mutants (Mpro_L50F, Mpro_E166V, and Mpro_L50F+E166V). Then, we conducted two phases of high-throughput virtual screening. In the first phase, NMI-001 served as a template to perform scaffold hopping-based similarity search in a library of 15,742,661 compounds. In the second phase, 968 compounds exhibiting similarity to NMI-001 were evaluated via molecular docking and MD simulations. Six compounds that may be effective against at least one mutant were identified, and five compounds were procured for conducting in vitro assays. Finally, the compound Z1557501297 (NMI-003) exhibiting inhibitory effects against the E166V (IC50 = 27.81 ± 2.65 μM) and L50F+E166V (IC50 = 8.78 ± 0.74 μM) mutants was discovered. The binding modes referring to NMI-003-Mpro_E166V and NMI-003-Mpro_L50F+E166V were further elucidated at the atomic level. In summary, NMI-003 reported herein is the first compound with activity against E166V and L50F+E166V, which provides a good starting point to design novel antiviral drugs for the treatment of drug-resistant SARS-CoV-2.

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分子机制驱动的新型小分子抑制剂对抗药性 SARS-CoV-2 Mpro 变体的发现。
在针对 SARS-CoV-2 Mpro 的拟肽共价药物 nirmatrelvir 的选择性压力下,体外获得了 Mpro 上的各种耐药突变。在这些突变中,L50F 和 E166V 以及 L50F 和 E166V 的组合尤其具有代表性,它们对 COVID-19 的有效治疗构成了相当大的障碍。我们之前的研究发现,NMI-001 和 NMI-002 是针对 SARS-CoV-2 Mpro 的新型非肽抑制剂,具有与 nirmatrelvir 不同的独特支架和结合模式。有鉴于此,我们提出了一种药物设计策略,旨在快速找到能对抗突变引起的耐药性的抑制剂。首先,我们采用分子动力学(MD)模拟研究了 NMI-001 和 NMI-002 与三种耐药突变体(Mpro_L50F、Mpro_E166V 和 Mpro_L50F+E166V)的结合机制。然后,我们进行了两个阶段的高通量虚拟筛选。在第一阶段,以 NMI-001 为模板,在 15,742,661 个化合物库中进行基于支架跳跃的相似性搜索。在第二阶段,通过分子对接和 MD 模拟评估了与 NMI-001 相似的 968 种化合物。确定了 6 种可能对至少一种突变体有效的化合物,并采购了 5 种化合物进行体外试验。最后,发现了对 E166V(IC50 = 27.81 ± 2.65 μM)和 L50F+E166V (IC50 = 8.78 ± 0.74 μM)突变体具有抑制作用的化合物 Z1557501297(NMI-003)。在原子水平上进一步阐明了 NMI-003-Mpro_E166V 和 NMI-003-Mpro_L50F+E166V 的结合模式。总之,本文报道的 NMI-003 是第一个对 E166V 和 L50F+E166V 有活性的化合物,这为设计治疗耐药 SARS-CoV-2 的新型抗病毒药物提供了一个良好的起点。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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