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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
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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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:
12.8K
Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

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Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
Indirect methods involve isolating the bound drug from its free form in biological samples such as blood, serum, or plasma. These techniques aim to measure the percentage of drugs bound to proteins. Equilibrium dialysis is a commonly used method where the free drug concentration at equilibrium is measured by separating the bound...
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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Updated: Jun 14, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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从静态结构到动态结构:通过基于图形的深度学习改进绑定亲和度预测.

Yaosen Min1, Ye Wei1, Peizhuo Wang1,2

  • 1Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China.

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|August 29, 2024
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概括
此摘要是机器生成的。

这项研究介绍了Dynaformer,这是一种使用分子动力学模拟来预测蛋白质 - 连接体结合亲和力的深度学习模型. Dynaformer 实现了最先进的性能,通过识别有前途的药物化合物来加速药物发现.

关键词:
结合性亲和力是一种结合性亲和力.发现药物的发现.图形变压器 图形变压器分子动力学分子动力学

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科学领域:

  • 计算化学和化学信息学
  • 结构生物学和药物设计.
  • 在分子建模中的人工智能.

背景情况:

  • 准确预测蛋白质 - 配体结合亲缘关系对于基于结构的药物设计至关重要.
  • 目前的数据驱动方法受到依赖静态蛋白质结构的限制,忽视了动态结合组合.
  • 分子动力学 (MD) 模拟提供了一种方法来近似这些动态组合.

研究的目的:

  • 开发一种深度学习模型,利用MD模拟进行增强的蛋白质 - 连接体结合亲缘关系预测.
  • 评估模型在基准数据集和虚拟选应用中的性能.
  • 通过改进的计算预测,加速药物发现的早期阶段.

主要方法:

  • 策划了一个MD数据集,包含3218个蛋白质 - 配体复合体.
  • 开发了基于图形的深度学习模型Dynaformer,以从MD轨迹中学习.
  • 应用Dynaformer对CASF-2016基准数据集进行评分和排名评估.
  • 对热冲击蛋白90 (HSP90) 和实验验证的打击化合物进行虚拟选.

主要成果:

  • 在CASF-2016基准上,Dynaformer展示了最先进的评分和排名能力.
  • 该模型在结合亲缘关系预测方面表现优于之前报告的方法.
  • 虚拟查确定了20个HSP90候选化合物,其中12个被验证为命中,包括新的支架.

结论:

  • 通过整合MD模拟和深度学习,Dynaformer可以有效地预测蛋白质-连接体结合亲和力.
  • 该模型显示了加速虚拟药物查和早期药物发现过程的重大前景.
  • 这种方法提供了一个强大的计算工具,用于识别新药候选药物.