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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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The Two-State Receptor Model01:29

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The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
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G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
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Updated: Mar 6, 2026

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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在口袋中的3D图表增强了在生成小分子创造中的干-标兼容性:多巴胺D2受体模型系统模型.

Seung-Gu Kang1,2, Jeffrey K Weber1, Joseph A Morrone1

  • 1Computational Biology, IBM Thomas J. Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, New York 10594, United States.

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概括
此摘要是机器生成的。

本研究介绍了一种基于3D图形的药物发现生成模型. 它通过结合蛋白质-配体相互作用来增强小分子生成,以获得更好的目标兼容性.

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科学领域:

  • 计算化学和化学信息学
  • 人工智能在药物发现中的作用
  • 结构生物学和分子建模.

背景情况:

  • 基于结构的药物发现依赖于蛋白质连接体复合体,但3D信息通常在生成模型中缺失.
  • 开发包含明确3D结构背景的生成模型对于改善药物设计至关重要.
  • 多巴胺D2受体 (DD2R) 作为一个相关的模型系统,用于评估蛋白质-配体相互作用.

研究的目的:

  • 介绍一种基于图形的新型生成建模技术,编码明确的3D蛋白质-连接体接触.
  • 用多巴胺D2受体 (DD2R) 作为模型来评估这种3D生成方法的有效性.
  • 为了证明如何结合结构上下文增强小分子生成在一个现实的结合环境.

主要方法:

  • 开发了一个基于图形的生成模型,使用条件变化自编码器来生成特定活动分子.
  • 集成的假定接触生成以预测目标结合口袋内的分子相互作用.
  • 根据对接分数,立体化学和化学数据库中的可恢复性评估生成的分子.

主要成果:

  • 使用3D方法生成的分子与2D方法相比,与DD2R结合口袋的兼容性更高.
  • 在商业数据库中,3D方法产生了更好的对接分数,预期的立体化学和更高的可恢复性.
  • 预测的蛋白质连接体接触经常是排名最高的对接姿势之一,复原率高.

结论:

  • 拟议的基于3D图形的生成模型有效地编码蛋白质-连接体接触,以增强药物发现.
  • 将蛋白质标的结构上下文纳入其中,可以显著改善用于现实的结合环境的小分子的生成.
  • 这种方法为利用深度学习推进基于结构的药物设计提供了一个有希望的方向.