深度学习G蛋白结合受体的动态调节
Hung N Do1, Jinan Wang1, Yinglong Miao1
1Computational Biology Program and Department of Molecular Biosciences, University of Kansas, Lawrence, Kansas 66047, United States.
JACS Au
|November 30, 2023
概括
G-蛋白结合受体 (GPCRs) 在全调节器结合后动态选择构造. 这项研究表明,负和正基调节剂 (NAM和PAM) 稳定特定的GPCR状态,指导药物设计.
科学领域:
- 生物化学和结构生物学.
- 计算化学和药物发现
背景情况:
- G-蛋白结合受体 (GPCR) 是一种主要的药物标类,其中基调节剂提供了更好的选择性.
- 尽管有众多的静态结构研究,但GPCR全调节的动态机制仍然不太清楚.
研究的目的:
- 系统地绘制GPCRs的动态自由能量景观,以响应全调节器结合.
- 阐明GPCRs中动态基调节的机制.
主要方法:
- 在44个GPCR系统中利用高斯加速分子动力学 (GaMD) 在66μs内.
- 在GLOW工作流程中应用深度学习 (DL) 和免费能源计算.
- 分析了形状变化和自由能量景观,有和没有全调节器.
主要成果:
- 阿洛斯特基调节器结合显著减少了GPCR的动态波动和形态空间.
- 负基调节剂 (NAMs) 稳定了不活跃的GPCR状态,而正基调节剂 (PAMs) 稳定了活跃的G蛋白结合状态.
- GPCR全ostery通过动态的"符合性选择"机制运作,对非同源受体亚型的合作性降低.
结论:
- 在基调节器结合后,GPCRs会动态选择特定的形状,而不是仅仅采用先前存在的状态.
- 采用一组GPCR形态组合进行对接,而不是单个结构,对于改进基于结构的新型全性GPCR药物的设计至关重要.
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