In Silico Study of Alkaloid Compounds with Computational Approach for Selection of Drug Leads for COVID-19

A. A. Parikesit, Stephanie Audrey Victoria, I. Pramanda
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Abstract

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virulent source of COVID-19 disease. As a result of the rapid transmission of the viral agent and deficiency of specific drugs against the virus, a worldwide pandemic ensued with a terrifying death toll. Thus there is tremendous urgency to discover substances for the development of specific COVID-19 drugs. With increasing public interest in natural products, this study aims to discover alkaloid compounds capable of inhibiting SARS-CoV-2 with the assistance of bioinformatics. In this work, 298 alkaloids with reported antiviral properties were identified, and their biological activities were validated with QSAR analysis using the Pass Online server until only 7 alkaloids remained. Molecular docking studies for these 7 alkaloids onto SARS-CoV-2 3CLpro, a protein involved in viral replication, were carried out with AutoDock Vina, followed by in silico visualization of the protein-alkaloid interaction with Ligplot+ program and prediction of ADME-Tox properties of the alkaloids using Toxtree program and SwissADME online server. Fangchinoline, phenanthroindolizidine, and polyalthenol are predicted to have strong binding affinity with SARS-CoV-2 3CLpro. Visualization of the molecular interactions between the ligand and protein target, however, showed that homonojirimycin formed the most hydrogen bonds with the protein binding site. Most of the alkaloids have little to no violation of Lipinski’s Rule of 5, easy to moderate synthetic accessibility, and good pharmacokinetic properties. Fangchinoline, phenanthroindolizidine, and polyalthenol exhibited high binding affinity values to SARS-CoV-2 3CLpro, with polyalthenol predicted to possess the strongest binding interactions to the active site of the protein. Polyalthenol and phenanthroindolizidine confer the most versatility in terms of bioavailability, however, supplementary observation of phenanthroindolizidine for the prospect of mutagenicity is required before it can be recommended for further drug development
生物碱化合物的计算机研究及其在COVID-19药物先导物选择中的应用
新型严重急性呼吸综合征冠状病毒2 (SARS-CoV-2)是COVID-19疾病的毒力源。由于病毒剂的迅速传播和缺乏针对该病毒的特殊药物,随后发生了世界范围的大流行病,造成了可怕的死亡人数。因此,迫切需要发现用于开发新型冠状病毒特异性药物的物质。随着公众对天然产物的兴趣日益浓厚,本研究旨在借助生物信息学发现能够抑制SARS-CoV-2的生物碱化合物。在这项工作中,鉴定出298种具有抗病毒特性的生物碱,并使用Pass Online服务器进行QSAR分析验证其生物活性,直到只剩下7种生物碱。利用AutoDock Vina软件对这7种生物碱与参与病毒复制的蛋白sars - cov - 23clpro进行分子对接研究,然后利用Ligplot+程序对蛋白-生物碱相互作用进行计算机可视化,利用Toxtree程序和SwissADME在线服务器预测生物碱的ADME-Tox特性。预测芳喹啉、phenanthroindolizidine和聚乙醇醇与sars - cov - 23clpro具有较强的结合亲和力。然而,配体与蛋白质靶点之间的分子相互作用的可视化显示,homojirimycin与蛋白质结合位点形成了最多的氢键。大多数生物碱几乎没有违反利平斯基规则5,易于合成,并且具有良好的药代动力学性质。Fangchinoline、phenanthroindolizidine和聚醛醇对sars - cov - 23clpro具有很高的结合亲和力,其中聚醛醇与该蛋白活性位点的结合作用最强。在生物利用度方面,聚乙醇和吩anthroindolizidine具有最广泛的用途,然而,在将其推荐用于进一步的药物开发之前,需要对吩anthroindolizidine的致突变性前景进行补充观察
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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