In Silico Study on Natural Chemical Compounds from Citric Essential Oils as Potential Inhibitors of an Omicron (BA.1) SARS-CoV-2 Mutants' Spike Glycoprotein.

Olha Ovchynnykova, Jordhan D Booth, Trey M Cocroft, Kostyantyn M Sukhyy, Karina Kapusta
{"title":"In Silico Study on Natural Chemical Compounds from Citric Essential Oils as Potential Inhibitors of an Omicron (BA.1) SARS-CoV-2 Mutants' Spike Glycoprotein.","authors":"Olha Ovchynnykova, Jordhan D Booth, Trey M Cocroft, Kostyantyn M Sukhyy, Karina Kapusta","doi":"10.2174/0115734099275132231213055138","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>SARS-CoV-2's remarkable capacity for genetic mutation enables it to swiftly adapt to environmental changes, influencing critical attributes, such as antigenicity and transmissibility. Thus, multi-target inhibitors capable of effectively combating various viral mutants concurrently are of great interest.</p><p><strong>Objective: </strong>This study aimed to investigate natural compounds that could unitedly inhibit spike glycoproteins of various Omicron mutants. Implementation of various in silico approaches allows us to scan a library of compounds against a variety of mutants in order to find the ones that would inhibit the viral entry disregard of occurred mutations.</p><p><strong>Methods: </strong>An extensive analysis of relevant literature was conducted to compile a library of chemical compounds sourced from citrus essential oils. Ten homology models representing mutants of the Omicron variant were generated, including the latest 23F clade (EG.5.1), and the compound library was screened against them. Subsequently, employing comprehensive molecular docking and molecular dynamics simulations, we successfully identified promising compounds that exhibited sufficient binding efficacy towards the receptor binding domains (RBDs) of the mutant viral strains. The scoring of ligands was based on their average potency against all models generated herein, in addition to a reference Omicron RBD structure. Furthermore, the toxicity profile of the highest-scoring compounds was predicted.</p><p><strong>Results: </strong>Out of ten built homology models, seven were successfully validated and showed to be reliable for In Silico studies. Three models of clades 22C, 22D, and 22E had major deviations in their secondary structure and needed further refinement. Notably, through a 100 nanosecond molecular dynamics simulation, terpinen-4-ol emerged as a potent inhibitor of the Omicron SARS-CoV-2 RBD from the 21K clade (BA.1); however, it did not show high stability in complexes with other mutants. This suggests the need for the utilization of a larger library of chemical compounds as potential inhibitors.</p><p><strong>Conclusion: </strong>The outcomes of this investigation hold significant potential for the utilization of a homology modeling approach for the prediction of RBD's secondary structure based on its sequence when the 3D structure of a mutated protein is not available. This opens the opportunities for further advancing the drug discovery process, offering novel avenues for the development of multifunctional, non-toxic natural medications.</p>","PeriodicalId":93961,"journal":{"name":"Current computer-aided drug design","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current computer-aided drug design","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/0115734099275132231213055138","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Background: SARS-CoV-2's remarkable capacity for genetic mutation enables it to swiftly adapt to environmental changes, influencing critical attributes, such as antigenicity and transmissibility. Thus, multi-target inhibitors capable of effectively combating various viral mutants concurrently are of great interest.

Objective: This study aimed to investigate natural compounds that could unitedly inhibit spike glycoproteins of various Omicron mutants. Implementation of various in silico approaches allows us to scan a library of compounds against a variety of mutants in order to find the ones that would inhibit the viral entry disregard of occurred mutations.

Methods: An extensive analysis of relevant literature was conducted to compile a library of chemical compounds sourced from citrus essential oils. Ten homology models representing mutants of the Omicron variant were generated, including the latest 23F clade (EG.5.1), and the compound library was screened against them. Subsequently, employing comprehensive molecular docking and molecular dynamics simulations, we successfully identified promising compounds that exhibited sufficient binding efficacy towards the receptor binding domains (RBDs) of the mutant viral strains. The scoring of ligands was based on their average potency against all models generated herein, in addition to a reference Omicron RBD structure. Furthermore, the toxicity profile of the highest-scoring compounds was predicted.

Results: Out of ten built homology models, seven were successfully validated and showed to be reliable for In Silico studies. Three models of clades 22C, 22D, and 22E had major deviations in their secondary structure and needed further refinement. Notably, through a 100 nanosecond molecular dynamics simulation, terpinen-4-ol emerged as a potent inhibitor of the Omicron SARS-CoV-2 RBD from the 21K clade (BA.1); however, it did not show high stability in complexes with other mutants. This suggests the need for the utilization of a larger library of chemical compounds as potential inhibitors.

Conclusion: The outcomes of this investigation hold significant potential for the utilization of a homology modeling approach for the prediction of RBD's secondary structure based on its sequence when the 3D structure of a mutated protein is not available. This opens the opportunities for further advancing the drug discovery process, offering novel avenues for the development of multifunctional, non-toxic natural medications.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
从柠檬精油中提取的天然化学物质作为 SARS-CoV-2 突变体的 Omicron (BA.1) Spike 糖蛋白潜在抑制剂的硅学研究。
背景:SARS-CoV-2 基因突变能力极强,能迅速适应环境变化,影响抗原性和传播性等关键属性。因此,能够同时有效抑制各种病毒变异体的多靶点抑制剂备受关注:本研究旨在调查能联合抑制各种奥米克龙突变体尖峰糖蛋白的天然化合物。通过采用各种硅学方法,我们可以扫描针对各种突变体的化合物库,从而找到能够抑制病毒进入的化合物,而无需考虑发生的突变:方法:我们对相关文献进行了广泛的分析,汇编了一个来自柑橘精油的化合物库。生成了代表奥米克龙变体突变体的十个同源模型,包括最新的 23F 支系(EG.5.1),并针对这些模型对化合物库进行了筛选。随后,通过全面的分子对接和分子动力学模拟,我们成功鉴定出了对突变病毒株的受体结合域(RBD)具有足够结合效力的化合物。除了参考 Omicron RBD 结构外,配体的评分还基于它们对本文生成的所有模型的平均效力。此外,还预测了得分最高的化合物的毒性特征:结果:在建立的 10 个同源模型中,有 7 个已成功通过验证,并被证明可用于 In Silico 研究。22C、22D 和 22E 族的三个模型在二级结构上存在重大偏差,需要进一步完善。值得注意的是,通过 100 纳秒分子动力学模拟,21K 支系(BA.1)中的萜品烯-4-醇成为 Omicron SARS-CoV-2 RBD 的强效抑制剂;然而,它在与其他突变体的复合物中并没有表现出很高的稳定性。这表明需要利用更大的化合物库作为潜在的抑制剂:这项研究的结果为在没有突变蛋白质三维结构的情况下,根据序列利用同源建模方法预测 RBD 的二级结构提供了巨大的潜力。这为进一步推进药物发现过程提供了机会,为开发多功能、无毒的天然药物提供了新的途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Study on the Mechanism of Alpinia officinarum Hance in the Improvement of Insulin Resistance through Network Pharmacology, Molecular Docking and in vitro Experimental Verification. Synthesis, Biological Evaluation, Molecular Docking Studies and ADMET Prediction of Oxindole-Based Hybrids for the Treatment of Tuberculosis. Identifying Novel Inhibitors for Dengue NS2B-NS3 Protease by Combining Topological similarity, Molecular Dynamics, MMGBSA and SiteMap Analysis. Discovery of Two GSK3β Inhibitors from Sophora flavescens Ait. using Structure-based Virtual Screening and Bioactivity Evaluation. Berberine Ameliorates High-fat-induced Insulin Resistance in HepG2 Cells by Modulating PPARs Signaling Pathway.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1