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The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

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The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
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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.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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相关实验视频

Updated: May 28, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

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对于模型接的连接体复合体的物理灵感精度估计器.

Byung-Hyun Bae1,2, Jungyoon Choi1,3, Chaok Seok2

  • 1Biomedical Research Division, Korea Institute of Science and Technology, Seoul 02792, Republic of Korea.

Journal of chemical theory and computation
|February 10, 2025
PubMed
概括
此摘要是机器生成的。

深度神经网络DENOISer通过在模型对接中准确得分蛋白质连接体姿势来改善药物发现. 它克服了传统评分函数的局限性,提高了识别正确解决方案的准确性.

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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
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相关实验视频

Last Updated: May 28, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry
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Analyzing Protein Architectures and Protein-Ligand Complexes by Integrative Structural Mass Spectrometry

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科学领域:

  • 计算化学是一种计算化学.
  • 结构生物学是结构生物学.
  • 人工智能在药物发现中的作用

背景情况:

  • 人工智能驱动的蛋白质结构预测推进了药物发现的模型对接.
  • 传统的对接分数函数与轻微的结构不准确性作斗争,无法识别正确的连接体姿势.

研究的目的:

  • 开发一个深度神经网络,DENOISer,以解决模型对接中的评分挑战.
  • 为了提高识别正确蛋白质连接体复杂结构的准确性.

主要方法:

  • 提出了DENOISer,一个具有本地相似性和绑定能量预测子网络的深度神经网络.
  • 纳入物理知识作为诱导偏差,用于增强姿势歧视和噪音耐受性.
  • 组合的DENOISer与罗塞塔GALigandDock采样. 采样时间

主要成果:

  • 在模型对接和交叉对接的基准指标上,DENOISer的性能优于现有的对接工具.
  • 基于物理的组件和共识排名被确定为成功的关键因素.
  • 对小型接口结构噪声的证明耐受性.

结论:

  • DENOISer有效地解决了模型对接中的评分挑战,提高了准确性.
  • 该方法通过提供可靠的结构模型,有望帮助药物发现.
  • 未来的药物发现工作可以从DENOISer增强的姿势排名能力中受益.