重新审视南印度传统植物对几种SARS-CoV-2目标的影响——一种计算机方法

IF 1.5 4区 医学 Q4 CHEMISTRY, MEDICINAL
Srikanth Jupudi, Srikala Rajala, Narasimha Rao Gaddam, Gomathi Swaminathan, Jaya Preethi Peesa, Kalirajan Rajagopal, Mohammed Afzal Azam
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引用次数: 1

摘要

背景:南印度泰卢固邦将庆祝一个名为“Ugadi”的新年,这是南印度的传统节日。ugadi pachadi中使用的成分也经常用于食物以及传统的阿育吠陀和悉达陀药物制剂。冠状病毒(cov)是包膜正义单链RNA病毒的一个多样化家族,可感染人类并有可能引起大规模疫情。目的:考虑到乌加地的益处,我们研究了从其成分中报道的各种植物化学成分对SARS-CoV-2五种靶标的结合模式。方法:通过AutoDock 1.5.6版本实现柔性配体对接仿真。使用GROMACS 2018.1软件进行50ns的分子动力学模拟,并使用g_mmpbsa工具计算蛋白质-配体复合物的结合自由能(ΔGbind)。利用薛定谔的Qikprop预测ADME。结果:从分子对接和MM/PBSA结果看,化合物Eriodictin与核衣壳N蛋白(6M3M) (-6.8 kcal/mol, - 82.46 kJ/mol)、SARS-CoV-2-hACE2配合物(6M0J) (-7.4 kcal/mol, -71.10 kJ/mol)和Mpro (6XR3) (-8.6 kcal/mol, -140.21 kJ/mol)的结合能最高。范德华和静电能项非常有利于总自由能结合。结论:化合物桔梗素、牡荆素、环蒿素- 3,24,27 -三醇、Agigenin、芒果苷、芒果铁酸、沙夫脱苷、27-羟基芒果铁酸、槲皮素、印楝树酚、立方豆素、异金毛素、异槲皮素、马柳卡宾、东方苷和原花青素二聚体与标准分子相比具有满意的结合能值。在体外和体内技术验证后,对高排名化合物的进一步迭代优化有助于发现治疗性抗sars - cov -2分子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Revisiting the South Indian Traditional Plants against Several Targets of SARS-CoV-2 - An in silico Approach.

Background: The south Indian Telugu states will celebrate a new year called 'Ugadi' which is a south Indian traditional festival. The ingredients used in ugadi pachadi have often also been used in food as well as traditional Ayurveda and Siddha medicinal preparations. Coronaviruses (CoVs) are a diverse family of enveloped positive-sense single-stranded RNA viruses which can infect humans and have the potential to cause large-scale outbreaks.

Objective: Considering the benefits of ugadi pachadi, we investigated the binding modes of various phytochemical constituents reported from its ingredients against five targets of SARS-CoV-2.

Methods: Flexible-ligand docking simulations were achieved through AutoDock version 1.5.6. Following 50ns of molecular dynamics simulation using GROMACS 2018.1 software and binding free energy (ΔGbind) of the protein-ligand complexes were calculated using the g_mmpbsa tool. ADME prediction was done using Qikprop of Schrodinger.

Results: From the molecular docking and MM/PBSA results compound Eriodictin exhibited the highest binding energy when complexed with nucleocapsid N protein (6M3M) (-6.8 kcal/mol, - 82.46 kJ/mol), bound SARS-CoV-2-hACE2 complex (6M0J) (-7.4 kcal/mol, -71.10 kJ/mol) and Mpro (6XR3) (-8.6 kcal/mol, -140.21 kJ/mol). Van der Waal and electrostatic energy terms highly favored total free energy binding.

Conclusion: The compounds Eriodictin, Vitexin, Cycloart-3, 24, 27-triol, Agigenin, Mangiferin, Mangiferolic acid, Schaftoside, 27-Hydroxymangiferonic acid, Quercetin, Azadirachtol, Cubebin, Isomangiferin, Isoquercitrin, Malicarpin, Orientin and procyanidin dimer exhibited satisfactory binding energy values when compared with standard molecules. The further iterative optimization of high-ranked compounds following validation by in vitro and in vivo techniques assists in discovering therapeutic anti-SARS-CoV-2 molecules.

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来源期刊
Current computer-aided drug design
Current computer-aided drug design 医学-计算机:跨学科应用
CiteScore
3.70
自引率
5.90%
发文量
46
审稿时长
>12 weeks
期刊介绍: Aims & Scope Current Computer-Aided Drug Design aims to publish all the latest developments in drug design based on computational techniques. The field of computer-aided drug design has had extensive impact in the area of drug design. Current Computer-Aided Drug Design is an essential journal for all medicinal chemists who wish to be kept informed and up-to-date with all the latest and important developments in computer-aided methodologies and their applications in drug discovery. Each issue contains a series of timely, in-depth reviews, original research articles and letter articles written by leaders in the field, covering a range of computational techniques for drug design, screening, ADME studies, theoretical chemistry; computational chemistry; computer and molecular graphics; molecular modeling; protein engineering; drug design; expert systems; general structure-property relationships; molecular dynamics; chemical database development and usage etc., providing excellent rationales for drug development.
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