Vincent A. Obakachi, Vaderament-A. Nchiozem-Ngnitedem, Krishna K. Govender, Penny P. Govender
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Molecular docking and dynamics simulations (MDDS) were performed to assess their binding energy and stability within the ACE2 active site, comparing them to the reference inhibitor MLN-4067. The top six compounds were selected based on their docking performance, followed by Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) calculations to quantify binding affinities. Additionally, molecular electrostatic potential (MEP) analysis was conducted to visualize electron density regions relevant to binding interactions. Our results demonstrate that XAN71 and XAN72 exhibit superior binding affinities of -70.97 and − 69.85 kcal/mol, respectively, outperforming MLN-4067 (-61.33 kcal/mol). MD simulations revealed stable interactions with key ACE2 residues, primarily through hydrogen bonds and hydrophobic contacts. The Molecular Electrostatic Potential(MEP) analysis further elucidated critical electron density regions that enhance binding stability. 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引用次数: 0
摘要
由SARS-CoV-2引起的COVID-19大流行强调了迫切需要有效的抗病毒治疗,特别是针对疫苗耐药变体。本研究调查了天然山酮衍生物作为ACE2受体的潜在抑制剂,ACE2受体是病毒的关键切入点。我们对从獐牙菜中提取的91个口山酮化合物进行了计算评价,确定了两个有希望的候选化合物:8-O-[β-D-Xylopyranosyl-(1→6)-β- d -glucopyranosyl]-1,7-二羟基-3-甲氧基口山酮(XAN71)和8-O-[β-D-Xylopyranosyl-(1→6)-β- d -glucopyranosyl]-1-羟基-3,7-二甲氧基口山酮(XAN72)。通过分子对接和动力学模拟(MDDS)来评估它们在ACE2活性位点的结合能和稳定性,并将它们与参考抑制剂MLN-4067进行比较。通过分子力学/泊松-玻尔兹曼表面积(MM/PBSA)计算来量化结合亲和度。此外,进行了分子静电势(MEP)分析,以可视化与结合相互作用相关的电子密度区域。结果表明,XAN71和XAN72的结合亲和力分别为-70.97和- 69.85 kcal/mol,优于MLN-4067 (-61.33 kcal/mol)。MD模拟揭示了与关键ACE2残基的稳定相互作用,主要通过氢键和疏水接触。分子静电势(MEP)分析进一步阐明了提高结合稳定性的临界电子密度区。本研究确定了XAN71和XAN72是抑制ACE2的可行候选药物,为它们作为天然的基于xanthone的抗SARS-CoV-2药物的开发提供了结构基础。这些发现强调了用天然化合物靶向ACE2对抗COVID-19的潜力,特别是考虑到新出现的病毒变体。
In silico exploration of natural xanthone derivatives as potential inhibitors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication and cellular entry
The COVID-19 pandemic, caused by SARS-CoV-2, has underscored the urgent need for effective antiviral therapies, particularly against vaccine-resistant variants. This study investigates natural xanthone derivatives as potential inhibitors of the ACE2 receptor, a critical entry point for the virus. We computationally evaluated 91 xanthone compounds derived from Swertia chirayita, identifying two promising candidates: 8-O-[β-D-Xylopyranosyl-(1→6)-β-D-glucopyranosyl]-1,7-dihydroxy-3-methoxy xanthone (XAN71) and 8-O-[β-D-Xylopyranosyl-(1→6)-β-D-glucopyranosyl]-1-hydroxy-3,7-dimethoxy-xanthone (XAN72). Molecular docking and dynamics simulations (MDDS) were performed to assess their binding energy and stability within the ACE2 active site, comparing them to the reference inhibitor MLN-4067. The top six compounds were selected based on their docking performance, followed by Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) calculations to quantify binding affinities. Additionally, molecular electrostatic potential (MEP) analysis was conducted to visualize electron density regions relevant to binding interactions. Our results demonstrate that XAN71 and XAN72 exhibit superior binding affinities of -70.97 and − 69.85 kcal/mol, respectively, outperforming MLN-4067 (-61.33 kcal/mol). MD simulations revealed stable interactions with key ACE2 residues, primarily through hydrogen bonds and hydrophobic contacts. The Molecular Electrostatic Potential(MEP) analysis further elucidated critical electron density regions that enhance binding stability. This study establishes XAN71 and XAN72 as viable candidates for ACE2 inhibition, providing a structural basis for their development as natural xanthone-based therapeutics against SARS-CoV-2. These findings highlight the potential of targeting ACE2 with natural compounds to combat COVID-19, particularly in light of emerging viral variants.
期刊介绍:
The Journal of Computer-Aided Molecular Design provides a form for disseminating information on both the theory and the application of computer-based methods in the analysis and design of molecules. The scope of the journal encompasses papers which report new and original research and applications in the following areas:
- 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.