SARS-CoV-1/2 NSP1与DNA聚合酶α-引物酶的结合通过减少DNA与DNA聚合酶α-引物酶的相互作用抑制DNA复制。

IF 5.3 2区 化学 Q1 CHEMISTRY, MEDICINAL
Hung Van Nguyen*, Nguyen Le Ngoc Lan and Mai Suan Li*, 
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引用次数: 0

摘要

全面了解SARS-CoV-1 (SARS-CoV-1 NSP1)和SARS-CoV-2 (SARS-CoV-2 NSP1)与Pol α-primase结合的非结构蛋白1的原子水平机制,对推进治疗COVID-19的小分子抑制剂的开发具有重要意义。在这项研究中,我们使用全原子操纵分子动力学(all-atom SMD)和粗粒度伞样采样(粗粒度US)模拟来评估SARS-CoV-1 NSP1和SARS-CoV-2 NSP1对Pol α-启动酶的结合亲和力。我们的全原子SMD和粗粒度US模拟一致表明,与SARS-CoV-1 NSP1相比,SARS-CoV-2 NSP1对Pol α-引物酶具有更强的亲和力,这意味着SARS-CoV-2在阻碍DNA复制以合成DNA方面比SARS-CoV-1具有更大的风险。通过对这些配合物内相互作用能的能量分解分析,我们确定静电相互作用是观察到的结合亲和力差异的主要贡献者。我们发现,在SARS1 NSP1-Pol α-引物酶中,Asp33与Arg616之间的氢键,以及Asp33与SARS2 NSP1-Pol α-引物酶中Arg616和Lys655之间的氢键,是SARS-CoV-1 NSP1和SARS-CoV-2 NSP1与Pol α-引物酶相互作用的关键。与SARS-CoV-1 NSP1相比,Asp33在SARS-CoV-2 NSP1中表现出更高的溶解度和稳定性,增强了其与Pol α-引物酶的关联。这一发现为旨在抑制这些实体之间相互作用的创新策略奠定了基础,为针对COVID-19的治疗干预提供了有希望的途径。我们还利用MM-PBSA法估计了DNA与Pol α-引物酶、SARS1 NSP1-Pol α-引物酶和SARS2 NSP1-Pol α-引物酶的结合自由能。结果显示:Pol α-引物酶-DNA < SARS1 NSP1-Pol α-引物酶-DNA < SARS2 NSP1-Pol α-引物酶-DNA,表明SARS-CoV-1 NSP1和SARS-CoV-2 NSP1都减少了DNA与Pol α-引物酶的结合,表明DNA合成受损。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Binding of SARS-CoV-1/2 NSP1 to DNA Polymerase α-Primase Inhibits DNA Replication through Reduction of Interaction between DNA and DNA Polymerase α-Primase

A comprehensive understanding of the atomic level mechanism governing the binding nonstructural protein 1 of SARS-CoV-1 (SARS-CoV-1 NSP1) and SARS-CoV-2 (SARS-CoV-2 NSP1) to Pol α-primase is important to advance the development of small molecule inhibitors for the treatment COVID-19. In this study, we use both all-atom steered molecular dynamics (all-atom SMD) and coarse-grained umbrella sampling (coarse-grained US) simulations to assess the binding affinity of SARS-CoV-1 NSP1 and SARS-CoV-2 NSP1 to Pol α-primase. Our all-atom SMD and coarse-grained US simulations consistently indicate that SARS-CoV-2 NSP1 exhibits stronger affinity for Pol α-primase compared to SARS-CoV-1 NSP1, implying that SARS-CoV-2 poses a greater risk than SARS-CoV-1 in impeding DNA replication for DNA synthesis. Through an energetic decomposition analysis of the interaction energies within these complexes, we identify electrostatic interactions as the primary contributors to the observed difference in binding affinity. We found that hydrogen bonds between Asp33 and Arg616 in SARS1 NSP1-Pol α-primase, and Asp33 with Arg616 and Lys655 in SARS2 NSP1-Pol α-primase, are critical for the interaction of both SARS-CoV-1 NSP1 and SARS-CoV-2 NSP1 with Pol α-primase. Asp33 in SARS-CoV-2 NSP1 shows increased solubility and stability compared to SARS-CoV-1 NSP1, enhancing its association with Pol α-primase. This finding lays the groundwork for innovative strategies aimed at inhibiting the interaction between these entities, offering promising avenues for therapeutic intervention against COVID-19. We also estimated the binding free energy of DNA to Pol α-primase, SARS1 NSP1-Pol α-primase, and SARS2 NSP1-Pol α-primase using the MM-PBSA method. The results show the order: Pol α-primase-DNA < SARS1 NSP1-Pol α-primase-DNA < SARS2 NSP1-Pol α-primase-DNA, indicating that both SARS-CoV-1 NSP1 and SARS-CoV-2 NSP1 reduce DNA binding to Pol α-primase, suggesting impaired DNA synthesis.

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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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