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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

12.7K
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...
12.7K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
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...
4.2K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.8K
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
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.8K
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...
4.8K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.4K
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...
12.4K
Cooperative Allosteric Transitions01:58

Cooperative Allosteric Transitions

7.8K
Cooperative allosteric transitions can occur in multimeric proteins, where each subunit of the protein has its own ligand-binding site. When a ligand binds to any of these subunits, it triggers a conformational change that affects the binding sites in the other subunits; this can change the affinity of the other sites for their respective ligands. The ability of the protein to change the shape of its binding site is attributed to the presence of a mix of flexible and stable segments in the...
7.8K

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相关实验视频

Updated: Jun 1, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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使用多实例学习与对接结构来预测蛋白质 - 配体结合的亲和力.

Hyojin Kim1, Heesung Shim2, Aditya Ranganath1

  • 1Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA, United States.

Frontiers in pharmacology
|January 20, 2025
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度学习方法,用于使用多个分子对接姿势预测蛋白质-连接体结合亲和力. 这种方法通过不需要共晶结构来增强药物发现,使其适用于更广泛的蛋白质标.

关键词:
3D原子图表表示的3D原子图表表示.人工智能驱动的药物开发.注意力机制注意力机制分子对接的分子对接.多级别的学习多级别的学习.蛋白质 - 配体相互作用基于结构的机器学习虚拟高通量选 虚拟高通量选

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A Protocol for Computer-Based Protein Structure and Function Prediction
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科学领域:

  • 计算化学计算化学
  • 结构生物学 结构生物学
  • 机器学习 机器学习

背景情况:

  • 深度学习模型在预测药物发现的蛋白质-连接体结合亲和力方面表现有前途.
  • 目前的方法通常依赖于共同晶体结构,这些结构并不总是可用.
  • 来自分子对接的不准确预测结构可以降低机器学习模型的准确性.

研究的目的:

  • 开发一种新的基于结构的推理方法来预测结合亲和力.
  • 为了克服在结合亲和力预测中需要共同晶体结构的局限性.
  • 为了利用多个分子对接姿势来提高预测准确度.

主要方法:

  • 开发了一种新的基于结构的推理方法.
  • 该方法利用每个复合体的多个分子对接姿势.
  • 使用多实例学习与注意网络来预测绑定亲和力.

主要成果:

  • 拟议的方法使用PDBbind和SARS-CoV-2主要蛋白酶数据集进行了验证.
  • 结合亲和力预测性能与最先进的方法相竞争.
  • 该方法使用对接姿势而不是共同晶体结构证明了有效性.

结论:

  • 开发的方法可以在不需要共晶结构的情况下进行结合亲和力预测.
  • 这种方法显著扩大了绑定亲和力预测模型的适用性.
  • 该方法为药物发现中的虚拟高通量查提供了有价值的工具.