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磺酰苯胺衍生物通过对接、虚拟筛选和 MD 模拟研究鉴定新型芳香酶抑制剂。

Sulfonanilide Derivatives in Identifying Novel Aromatase Inhibitors by Applying Docking, Virtual Screening, and MD Simulations Studies.

机构信息

Division of Applied Life Science (BK21 Plus), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Systems and Synthetic Agrobiotech Center (SSAC), Research Institute of Natural Science (RINS), Gyeongsang National University (GNU), 501 Jinju-daero, Jinju 52828, Republic of Korea.

Division of Quality of Life, Korea Research Institute of Standards and Science, Daejeon 34113, Republic of Korea.

出版信息

Biomed Res Int. 2017;2017:2105610. doi: 10.1155/2017/2105610. Epub 2017 Oct 17.

DOI:10.1155/2017/2105610
PMID:29312992
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5664374/
Abstract

Breast cancer is one of the leading causes of death noticed in women across the world. Of late the most successful treatments rendered are the use of aromatase inhibitors (AIs). In the current study, a two-way approach for the identification of novel leads has been adapted. 81 chemical compounds were assessed to understand their potentiality against aromatase along with the four known drugs. Docking was performed employing the CDOCKER protocol available on the Discovery Studio (DS v4.5). Exemestane has displayed a higher dock score among the known drug candidates and is labeled as reference. Out of 81 ligands 14 have exhibited higher dock scores than the reference. In the second approach, these 14 compounds were utilized for the generation of the pharmacophore. The validated four-featured pharmacophore was then allowed to screen Chembridge database and the potential Hits were obtained after subjecting them to Lipinski's rule of five and the ADMET properties. Subsequently, the acquired 3,050 Hits were escalated to molecular docking utilizing GOLD v5.0. Finally, the obtained Hits were consequently represented to be ideal lead candidates that were escalated to the MD simulations and binding free energy calculations. Additionally, the gene-disease association was performed to delineate the associated disease caused by CYP19A1.

摘要

乳腺癌是全球女性死亡的主要原因之一。最近,最成功的治疗方法是使用芳香化酶抑制剂(AIs)。在当前的研究中,采用了一种双向方法来识别新的先导化合物。评估了 81 种化学化合物,以了解它们对芳香酶的潜在作用,以及四种已知药物。对接使用 Discovery Studio(DS v4.5)上可用的 CDOCKER 协议进行。依西美坦在已知药物候选物中显示出较高的对接评分,并被标记为参考。在 81 种配体中,有 14 种显示出比参考更高的对接评分。在第二种方法中,这些 14 种化合物被用于生成药效团。经过验证的四特征药效团随后被允许筛选 Chembridge 数据库,并在对它们进行 Lipinski 的五规则和 ADMET 性质筛选后获得潜在的命中物。随后,获得的 3050 个命中物被利用 GOLD v5.0 进行分子对接。最后,获得的命中物被表示为理想的先导化合物,这些化合物被提升到 MD 模拟和结合自由能计算中。此外,还进行了基因-疾病关联,以描绘 CYP19A1 引起的相关疾病。

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