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Molecular Docking, Drug-Likeness and ADMET Analysis, Application of Density Functional Theory (DFT) and Molecular Dynamics (MD) Simulation to the Phytochemicals from Withania Somnifera as Potential Antagonists of Estrogen Receptor Alpha (ER- α).
Current Computer-Aided Drug Design ( IF 1.7 ) Pub Date : 2021-01-01 , DOI: 10.2174/1573409916999200730181611
Alamgir Hossain 1
Affiliation  

INTRODUCTION Breast cancer is one of the leading causes of death of women every year. Estrogen receptor alpha (ER- α) is an important pathway that is responsible for the development of breast cancer. Tamoxifen is the most commonly used drug to treat breast cancer. But the main drawback of using this drug is that it increases the risk of uterine cancer, stroke, and pulmonary embolism. METHODS In this research, the in-silico approach was followed to get the anticancer agent from Withania somnifera as the root extract of the plant is active against breast cancer. For this, 15 bioactive molecules were subjected to molecular docking and 9 molecules were obtained comparing the consensus binding affinity of H3B-9224. RESULTS After rescoring, drug-likeness analysis and ADMET analysis of the molecules were carried out and 3 molecules remained. These 3 molecules showed good ADMET properties, which are crucial requirements in the drug discovery process. Their activity was checked by applying density functional theory (DFT) and all of them showed good reactivity. Their binding interaction was also evaluated. CONCLUSION Finally, the stability of those molecules was evaluated by applying molecular dynamics (MD) simulation. After this simulation, 2 molecules remained that had good stability with the protein during the simulation period.

中文翻译:

分子对接、药物相似性和 ADMET 分析、密度泛函理论 (DFT) 和分子动力学 (MD) 模拟在睡茄中作为雌激素受体 α (ER-α) 潜在拮抗剂的植物化学物质中的应用。

引言 乳腺癌是每年导致女性死亡的主要原因之一。雌激素受体α(ER-α)是导致乳腺癌发生的重要途径。他莫昔芬是治疗乳腺癌最常用的药物。但使用这种药物的主要缺点是它会增加患子宫癌、中风和肺栓塞的风险。方法 在这项研究中,采用计算机内方法从催眠睡茄中提取抗癌剂,因为该植物的根提取物具有抗乳腺癌活性。为此,对 15 个生物活性分子进行了分子对接,并获得了 9 个分子,比较了 H3B-9224 的共有结合亲和力。结果重新评分后,对分子进行了药物相似性分析和ADMET分析,剩余3个分子。这 3 种分子显示出良好的 ADMET 特性,这是药物发现过程中的关键要求。通过应用密度泛函理论(DFT)检查它们的活性,并且它们都显示出良好的反应性。还评估了它们的结合相互作用。结论 最后,通过应用分子动力学 (MD) 模拟来评估这些分子的稳定性。在该模拟之后,在模拟期间保留了 2 个与蛋白质具有良好稳定性的分子。通过应用分子动力学(MD)模拟来评估这些分子的稳定性。在该模拟之后,在模拟期间保留了 2 个与蛋白质具有良好稳定性的分子。通过应用分子动力学(MD)模拟来评估这些分子的稳定性。在该模拟之后,在模拟期间保留了 2 个与蛋白质具有良好稳定性的分子。
更新日期:2020-07-30
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