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努力发现强效 AMG3 大麻素类似物在到达大麻素受体的活性部位时的优先构象。

An effort to discover the preferred conformation of the potent AMG3 cannabinoid analog when reaching the active sites of the cannabinoid receptors.

机构信息

Institute for Biocomplexity and Informatics, Department of Biological Sciences, University of Calgary, Calgary, Alberta, Canada.

出版信息

Eur J Med Chem. 2012 Jan;47(1):44-51. doi: 10.1016/j.ejmech.2011.10.015. Epub 2011 Oct 15.

DOI:10.1016/j.ejmech.2011.10.015
PMID:22071252
Abstract

Most of current 3D-QSAR algorithms use alignments of compounds at the training set based on reference active ligands in the first step of the construction of the pharamacophore modeling. This first step mostly defines the success of constructed pharmacophore models. In this step, it is essential to find the bioactive conformation for solid and reliable 3D-QSAR models. Therefore, we have proceeded through different approaches for revealing the preferred conformations of Δ(8)-THC derivative AMG-3 at CB1 and CB2 receptors. In the first approach, we have applied conformational search methods in gas and in solvent phases for the ligand. The derived low energy conformers using these methodologies have been modeled through 3D-QSAR studies (first generation model). In the second approach, the low energy conformers derived from molecular docking studies have been used as input for 3D-QSAR studies (second generation model). In the current study, a new approach using MD calculations in a simulated biological environment, thus the CB receptors surrounded by a lipid bilayer environment has been used (third generation). The obtained results for different environments were compared and the approach deriving the highest statistic results was used for the generation of the novel AMG3 analogs for optimal and selective binding affinities at CB1 and CB2 receptors by the de novo drug design modeling.

摘要

大多数当前的 3D-QSAR 算法在构建药效团模型的第一步中使用基于参考活性配体的化合物对齐。这第一步在很大程度上定义了所构建药效团模型的成功。在这一步中,找到固体和可靠的 3D-QSAR 模型的生物活性构象是至关重要的。因此,我们已经通过不同的方法来揭示 Δ(8)-THC 衍生物 AMG-3 在 CB1 和 CB2 受体上的优先构象。在第一种方法中,我们对配体在气相和溶剂相中进行构象搜索方法。使用这些方法学得到的低能量构象已经通过 3D-QSAR 研究进行了建模(第一代模型)。在第二种方法中,从分子对接研究中得到的低能量构象被用作 3D-QSAR 研究的输入(第二代模型)。在当前的研究中,使用模拟生物环境中的 MD 计算的新方法(即,被脂双层环境包围的 CB 受体)被使用(第三代)。比较了不同环境下的结果,并使用产生最高统计学结果的方法为通过从头药物设计建模生成具有最佳和选择性结合亲和力的新型 AMG3 类似物提供了最佳和选择性结合亲和力。

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