基于结构的虚拟查发现了强效和选择性的腺A1受体对手
Pierre Matricon1, Anh Tn Nguyen2, Duc Duy Vo1
1Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, SE-751 24, Uppsala, Sweden.
European journal of medicinal chemistry
|June 10, 2023
概括
基于结构的虚拟查确定了选择性的A1腺素受体 (A1R) 配体. 这种方法成功引导了强效和选择性的A1R抗剂的设计,为更安全的药物开发铺平了道路.
科学领域:
- 药用化学 医学化学
- 药理学 药理学是指药理学的学科.
- 计算机化药物发现技术
背景情况:
- G蛋白结合受体 (GPCRs) 是重要的药物标.
- 开发亚型选择性连接体对于有针对性的疗法和最小化副作用至关重要.
- 氨酸受体 (A1R和A2AR) 是密切相关的GPCRs,使亚型选择性具有挑战性.
研究的目的:
- 应用基于结构的虚拟选方法来设计针对A1腺素受体 (A1R) 和A2A腺素受体 (A2AR) 的亚型选择性连接体.
- 为了识别具有提高功效和选择性的新型A1R选择性抗剂.
主要方法:
- 利用分子对接对A1R和A2AR晶体结构进行选,对460万个化合物的库进行选.
- 专注于利用A1R绑定站点中的未保存的子口袋进行选择性.
- 设计和合成已识别的打击化合物的类似物,以优化功效和选择性.
主要成果:
- 从虚拟屏幕上预测了20个潜在的A1R选择性联体.
- 七种化合物显示出A1R的微分子对抗性,其中一些显示出轻微的亚型选择性.
- 经过优化后的类似物获得了纳米分子强度,A1R对A2AR的选择性高达76倍.
结论:
- 基于结构的虚拟查是发现亚型选择性GPCR配体的强大策略.
- 已确定的A1R抗剂代表了开发更安全的治疗方法的有希望的线索.
- 这种方法可以加速GPCR标的药物发现过程.
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