Essential oil components interacting with insect odorant-binding proteins: a molecular modelling approach.

IF 2.3 3区 环境科学与生态学 Q3 CHEMISTRY, MULTIDISCIPLINARY SAR and QSAR in Environmental Research Pub Date : 2024-07-01 Epub Date: 2024-08-05 DOI:10.1080/1062936X.2024.2382973
K Fuentes-Lopez, M Ahumedo-Monterrosa, J Olivero-Verbel, K Caballero-Gallardo
{"title":"Essential oil components interacting with insect odorant-binding proteins: a molecular modelling approach.","authors":"K Fuentes-Lopez, M Ahumedo-Monterrosa, J Olivero-Verbel, K Caballero-Gallardo","doi":"10.1080/1062936X.2024.2382973","DOIUrl":null,"url":null,"abstract":"<p><p>Essential oils (EOs) are natural products currently used to control arthropods, and their interaction with insect odorant-binding proteins (OBPs) is fundamental for the discovery of new repellents. This in silico study aimed to predict the potential of EO components to interact with odorant proteins. A total of 684 EO components from PubChem were docked against 23 odorant binding proteins from Protein Data Bank using AutoDock Vina. The ligands and proteins were optimized using Gaussian 09 and Sybyl-X 2.0, respectively. The nature of the protein-ligand interactions was characterized using LigandScout 4.0, and visualization of the binding mode in selected complexes was carried out by Pymol. Additionally, complexes with the best binding energy in molecular docking were subjected to 500 ns molecular dynamics simulations using Gromacs. The best binding affinity values were obtained for the 1DQE-ferutidine (-11 kcal/mol) and 2WCH-kaurene (-11.2 kcal/mol) complexes. Both are natural ligands that dock onto those proteins at the same binding site as DEET, a well-known insect repellent. This study identifies kaurene and ferutidine as possible candidates for natural insect repellents, offering a potential alternative to synthetic chemicals like DEET.</p>","PeriodicalId":21446,"journal":{"name":"SAR and QSAR in Environmental Research","volume":" ","pages":"591-610"},"PeriodicalIF":2.3000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SAR and QSAR in Environmental Research","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1080/1062936X.2024.2382973","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/8/5 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Essential oils (EOs) are natural products currently used to control arthropods, and their interaction with insect odorant-binding proteins (OBPs) is fundamental for the discovery of new repellents. This in silico study aimed to predict the potential of EO components to interact with odorant proteins. A total of 684 EO components from PubChem were docked against 23 odorant binding proteins from Protein Data Bank using AutoDock Vina. The ligands and proteins were optimized using Gaussian 09 and Sybyl-X 2.0, respectively. The nature of the protein-ligand interactions was characterized using LigandScout 4.0, and visualization of the binding mode in selected complexes was carried out by Pymol. Additionally, complexes with the best binding energy in molecular docking were subjected to 500 ns molecular dynamics simulations using Gromacs. The best binding affinity values were obtained for the 1DQE-ferutidine (-11 kcal/mol) and 2WCH-kaurene (-11.2 kcal/mol) complexes. Both are natural ligands that dock onto those proteins at the same binding site as DEET, a well-known insect repellent. This study identifies kaurene and ferutidine as possible candidates for natural insect repellents, offering a potential alternative to synthetic chemicals like DEET.

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
精油成分与昆虫气味结合蛋白的相互作用:分子建模方法。
精油(EO)是目前用于控制节肢动物的天然产品,它们与昆虫气味结合蛋白(OBPs)的相互作用是发现新驱虫剂的基础。这项硅学研究旨在预测环氧乙烷成分与气味蛋白相互作用的潜力。研究人员使用 AutoDock Vina 将 PubChem 中的 684 种环氧乙烷成分与蛋白质数据库中的 23 种气味结合蛋白进行了对接。配体和蛋白质分别使用 Gaussian 09 和 Sybyl-X 2.0 进行了优化。使用 LigandScout 4.0 对蛋白质-配体相互作用的性质进行了表征,并使用 Pymol 对选定复合物中的结合模式进行了可视化。此外,还使用 Gromacs 对分子对接中结合能最佳的复合物进行了 500 ns 的分子动力学模拟。1DQE-ferutidine 复合物(-11 kcal/mol)和 2WCH-kaurene 复合物(-11.2 kcal/mol)获得了最佳结合亲和值。这两种配体都是天然配体,与著名的驱虫剂 DEET 在相同的结合位点上与这些蛋白质对接。这项研究发现,高烯烃和阿魏苷可能是天然驱虫剂的候选物质,为替代 DEET 等合成化学品提供了可能。
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
CiteScore
5.20
自引率
20.00%
发文量
78
审稿时长
>24 weeks
期刊介绍: SAR and QSAR in Environmental Research is an international journal welcoming papers on the fundamental and practical aspects of the structure-activity and structure-property relationships in the fields of environmental science, agrochemistry, toxicology, pharmacology and applied chemistry. A unique aspect of the journal is the focus on emerging techniques for the building of SAR and QSAR models in these widely varying fields. The scope of the journal includes, but is not limited to, the topics of topological and physicochemical descriptors, mathematical, statistical and graphical methods for data analysis, computer methods and programs, original applications and comparative studies. In addition to primary scientific papers, the journal contains reviews of books and software and news of conferences. Special issues on topics of current and widespread interest to the SAR and QSAR community will be published from time to time.
期刊最新文献
Enhanced prediction of beta-secretase inhibitory compounds with mol2vec technique and machine learning algorithms. Structure-based drug design of pre-clinical candidate nanopiperine: a direct target for CYP1A1 protein to mitigate hyperglycaemia and associated microbes. Structure-based pharmacophore modelling for ErbB4-kinase inhibition: a systematic computational approach for small molecule drug discovery for breast cancer. A deep learning model based on the BERT pre-trained model to predict the antiproliferative activity of anti-cancer chemical compounds. Discovery of novel pyrrolo[2,3-d]pyrimidine derivatives as anticancer agents: virtual screening and molecular dynamic studies.
×
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