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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.
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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The Two-State Receptor Model01:29

The Two-State Receptor Model

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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.
The binding affinity of a drug determines its interaction with...
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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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Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

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Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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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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Molecular Models02:00

Molecular Models

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Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
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相关实验视频

Updated: May 24, 2025

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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DTF-扩散:基于连接体-目标信息融合的3D等价扩散生成模型.

Jianxin Wang1, Yongxin Zhu1, Yushuang Liu2

  • 1School of Data Science, Qingdao University of Science and Technology, Qingdao 266061, China.

Computational biology and chemistry
|February 28, 2025
PubMed
概括

这项研究介绍了DTF-扩散,这是一种用于药物发现的新型深度学习模型,该模型融合了3D连接体和目标信息. 它通过结合相互作用数据和化学规则来产生更多化学有效的药物分子.

关键词:
化学规则约束的化学规则约束深度学习是一种深度学习.扩散模型是一个扩散模型.药物分子生成 药物分子生成药物目标信息的融合是药物信息的融合.

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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
  • 分子建模和模拟分子模型

背景情况:

  • 药物发现的深度学习模型旨在产生与标蛋白结合的分子.
  • 三维 (3D) 分子结构在药物发现中比2D模型提供更高的性能.
  • 当前的3D深度生成模型往往忽略了关键的连接体-目标相互作用信息和化学知识,导致不现实的分子结构.

研究的目的:

  • 提出DTF-扩散,一种新的3D等价扩散模型用于药物分子生成.
  • 通过整合配体-标相互作用信息和化学规则来解决现有模型的局限性.
  • 提高在中生成的药物分子的合理性和有效性.

主要方法:

  • DTF-扩散使用一个扩散模型框架.
  • 一个多式特征融合模块集成了连接体和目标的3D位置特征,从连接体原子和目标序列信息中提取先进的隐藏特征.
  • 化学规则区分模块用于学习和强制执行生成的分子结构中的化学规则.

主要成果:

  • 与基线方法相比,DTF扩散在CrossDock2020数据集上显示出更高的性能.
  • 与之前的最佳模型相比,该模型实现了药物相似性指数的3.85%增加和药物有效性指数的4.34%增加.
  • 广泛的生成实验证实了DTF扩散的出色性能和潜力.

结论:

  • DTF-扩散有效地融合了连接体-目标相互作用信息和化学知识,以改善3D分子生成.
  • 拟议的模型显著提高了产生的分子的药物相似性和有效性.
  • 在加速药物发现过程中,DTF扩散显示出有前途的应用前景.