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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

13.2K
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 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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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...
4.4K
Protein-protein Interfaces02:04

Protein-protein Interfaces

13.3K
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...
13.3K
Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

305
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...
305
Protein Networks02:26

Protein Networks

4.1K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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相关实验视频

Updated: Sep 12, 2025

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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使用密度矩阵碎片化和基于物理的机器学习分散潜力,准确和快速排名蛋白质 - 连接物结合的亲缘关系.

Ka Un Lao1, Danyang Wang1

  • 1Department of Chemistry, Virginia Commonwealth University, Richmond, 23284, VA, USA.

Chemphyschem : a European journal of chemical physics and physical chemistry
|August 4, 2025
PubMed
概括

密度矩阵 (GMBE-DM) 的通用化多体扩展和机器学习校正的分散潜力 (D3-ML) 准确地排列蛋白质-联体结合亲缘关系. 在药物发现查中,D3-ML表现出了卓越的速度和准确性.

关键词:
分散的分散性分散.碎片化 碎片化 碎片化机器学习是机器学习.蛋白质连接和结合量子化学是一种量子化学.

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科学领域:

  • 计算化学计算化学
  • 药物发现 药物发现 药物发现
  • 分子建模分子建模

背景情况:

  • 准确地预测蛋白质 - 配体结合亲缘关系对于药物发现至关重要.
  • 现有的方法经常面临效率,准确性或在各种化学系统中可转移性方面的挑战.

研究的目的:

  • 评估用于构建密度矩阵 (GMBE-DM) 的通用化多体扩张的性能,以及用于排名蛋白质-联体结合亲缘关系的物理信息,机器学习校正的分散潜力 (D3-ML).
  • 在准确性,效率和可转移性方面,将这些方法与深度学习模型 (Sfcnn) 进行比较.

主要方法:

  • 应用GMBE-DM方法,在单体水平上使用净化方案进行切断.
  • 开发和应用D3-ML潜力,结合机器学习纠正分散相互作用.
  • 对循环素依赖激酶2 (CDK2) 和雅努斯激酶1 (JAK1) 的数据集进行测试,共计28个配体.

主要成果:

  • GMBE-DM与实验性的结合自由能量 (R2 =0.84) 实现了强烈的相关性,具有高效的运行时间 (每组合<5分钟).
  • D3-ML表现出优越的排名性能 (R2 =0.87),运行时间小于秒,突出了分散相互作用的重要性.
  • 深度学习模型Sfcnn在数据集之间具有较低的可转移性 (R 2 = 0.57).

结论:

  • GMBE-DM和D3-ML是强大的和可扩展的计算工具,用于排名蛋白质-连接体结合亲缘关系.
  • D3-ML提供了特殊的速度和准确性,使其非常适合用于药物发现中的高通量虚拟查.
  • 这项研究强调了分散相互作用的关键作用以及各种化学系统中广泛训练的神经网络的局限性.