A structural-based virtual screening and in vitro validation reveals novel effective inhibitors for SARS-CoV-2 helicase and endoribonuclease.

IF 2.7 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Ibrahim M Ibrahim, Abdo A Elfiky, Sara H Mahmoud, Mahmoud ElHefnawi
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引用次数: 0

Abstract

Researchers worldwide are looking for molecules that might disrupt the COVID-19 life cycle. Endoribonuclease, which is responsible for processing viral RNA to avoid detection by the host defense system, and helicase, which is responsible for unwinding the RNA helices for replication, are two key non-structural proteins. This study performs a hierarchical structure-based virtual screening approach for NSP15 and helicase to reach compounds with high binding probabilities. In this investigation, we incorporated a variety of filtering strategies for predicting compound interactions. First, we evaluated 756,275 chemicals from four databases using a deep learning method (NCI, Drug Bank, Maybridge, and COCONUT). Following that, two docking techniques (extra precision and induced fit) were utilized to evaluate the compounds' binding affinity, followed by molecular dynamic simulation supported by the MM-GBSA free binding energy calculation. Remarkably, two compounds (90616 and CNP0111740) exhibited high binding affinity values of -66.03 and -12.34 kcal/mol for helicase and NSP15, respectively. The VERO-E6 cell line was employed to test their in vitro therapeutic impact. The CC50 for CNP0111740 and 90616 were determined to be 102.767 μg/ml and 379.526 μg/ml, while the IC50 values were 140.176 μg/ml and 5.147 μg/ml, respectively. As a result, the selectivity index for CNP0111740 and 90616 is 0.73 and 73.73, respectively. Finally, these compounds were found to be novel, effective inhibitors for the virus; however, further in vivo validation is needed.Communicated by Ramaswamy H. Sarma.

基于结构的虚拟筛选和体外验证揭示了新型有效的 SARS-CoV-2 螺旋酶和内切核酸酶抑制剂。
全世界的研究人员都在寻找可能破坏 COVID-19 生命周期的分子。内切核酸酶和螺旋酶是两种关键的非结构蛋白,前者负责处理病毒 RNA 以避免被宿主防御系统检测到,后者负责解开 RNA 螺旋以进行复制。本研究针对 NSP15 和螺旋酶采用了一种基于层次结构的虚拟筛选方法,以筛选出具有高结合概率的化合物。在这项研究中,我们采用了多种筛选策略来预测化合物的相互作用。首先,我们使用深度学习方法评估了四个数据库(NCI、Drug Bank、Maybridge 和 COCONUT)中的 756275 种化学物质。随后,我们利用两种对接技术(额外精度和诱导拟合)评估了化合物的结合亲和力,并在 MM-GBSA 自由结合能计算的支持下进行了分子动力学模拟。值得注意的是,两种化合物(90616 和 CNP0111740)与螺旋酶和 NSP15 的结合亲和力分别为 -66.03 和 -12.34 kcal/mol。我们采用 VERO-E6 细胞系来测试它们的体外治疗效果。CNP0111740和90616的CC50分别为102.767 μg/ml和379.526 μg/ml,IC50分别为140.176 μg/ml和5.147 μg/ml。因此,CNP0111740 和 90616 的选择性指数分别为 0.73 和 73.73。最后,这些化合物被认为是新型、有效的病毒抑制剂,但还需要进一步的体内验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Biomolecular Structure & Dynamics
Journal of Biomolecular Structure & Dynamics 生物-生化与分子生物学
CiteScore
8.90
自引率
9.10%
发文量
597
审稿时长
2 months
期刊介绍: The Journal of Biomolecular Structure and Dynamics welcomes manuscripts on biological structure, dynamics, interactions and expression. The Journal is one of the leading publications in high end computational science, atomic structural biology, bioinformatics, virtual drug design, genomics and biological networks.
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