Potential inhibitory activity of phytoconstituents against black fungus: In silico ADMET, molecular docking and MD simulation studies

IF 3.1 Q2 TOXICOLOGY
Narmin Hamaamin Hussen , Aso Hameed Hasan , Joazaizulfazli Jamalis , Sonam Shakya , Subhash Chander , Harsha Kharkwal , Sankaranaryanan Murugesan , Virupaksha Ajit Bastikar , Pramodkumar Pyarelal Gupta
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引用次数: 12

Abstract

Mucormycosis or “black fungus” has been currently observed in India, as a secondary infection in COVID-19 infected patients in the post-COVID-stage. Fungus is an uncommon opportunistic infection that affects people who have a weak immune system. In this study, 158 antifungal phytochemicals were screened using molecular docking against glucoamylase enzyme of Rhizopus oryzae to identify potential inhibitors. The docking scores of the selected phytochemicals were compared with Isomaltotriose as a positive control. Most of the compounds showed lower binding energy values than Isomaltotriose (-6.4 kcal/mol). Computational studies also revealed the strongest binding affinity of the screened phytochemicals was Dioscin (-9.4 kcal/mol). Furthermore, the binding interactions of the top ten potential phytochemicals were elucidated and further analyzed. In-silico ADME and toxicity prediction were also evaluated using SwissADME and admetSAR online servers. Compounds Piscisoflavone C, 8-O-methylaverufin and Punicalagin exhibited positive results with the Lipinski filter and drug-likeness and showed mild to moderate of toxicity. Molecular dynamics (MD) simulation (at 300 K for 100 ns) was also employed to the docked ligand-target complex to explore the stability of ligand-target complex, improve docking results, and analyze the molecular mechanisms of protein-target interactions.

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植物成分对黑木耳的潜在抑制活性:硅ADMET、分子对接和MD模拟研究
印度目前已观察到毛霉病或“黑菌”,作为COVID-19感染患者在COVID-19后阶段的继发感染。真菌是一种罕见的机会性感染,影响免疫系统较弱的人。本研究通过对米根霉葡萄糖淀粉酶的分子对接,筛选了158种抗真菌植物化学物质,以确定潜在的抑制剂。将所选植物化学物质的对接分数与作为阳性对照的异麦芽糖三糖进行比较。大多数化合物的结合能值低于异麦芽糖糖(-6.4 kcal/mol)。计算研究还显示,筛选的植物化学物质结合亲和力最强的是薯蓣皂苷(-9.4 kcal/mol)。此外,对十大潜在植物化学物质的结合相互作用进行了阐明和进一步分析。还使用SwissADME和admetSAR在线服务器评估了计算机ADME和毒性预测。化合物Piscisoflavone C、8- o - methylverufin和Punicalagin经Lipinski滤镜检测呈阳性,呈药物相似性,毒性为轻至中度。对对接的配体-靶标配合物进行分子动力学(MD)模拟(300 K, 100 ns),探索配体-靶标配合物的稳定性,改进对接结果,分析蛋白-靶标相互作用的分子机制。
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
Computational Toxicology
Computational Toxicology Computer Science-Computer Science Applications
CiteScore
5.50
自引率
0.00%
发文量
53
审稿时长
56 days
期刊介绍: Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs
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