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相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

5.0K
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...
5.0K
Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
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...
14.4K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

14.8K
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:
14.8K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

9.9K
9.9K
Ligand Binding Sites02:40

Ligand Binding Sites

14.8K
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...
14.8K
Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

594
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...
594

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相关实验视频

Updated: Jan 9, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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人工智能预测的复合体可以教机器学习计算药物结合亲和力吗?

Wei-Tse Hsu1, Savva Grevtsev2, Anna M Herz3

  • 1Structural Bioinformatics and Computational Biochemistry, Department of Biochemistry, University of Oxford, South Parks Road, Oxford, OX1 3QU, U.K.

Journal of chemical information and modeling
|December 10, 2025
PubMed
概括
此摘要是机器生成的。

同折叠模型可以增强基于机器学习的评分函数 (MLSFs) 的数据. 合成数据的性能增长取决于结构质量,指导未来的数据增强策略.

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A Protocol for Computer-Based Protein Structure and Function Prediction
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相关实验视频

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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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A Protocol for Computer-Based Protein Structure and Function Prediction
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科学领域:

  • 计算化学是一种计算化学.
  • 结构生物学是结构生物学.
  • 机器学习 机器学习

背景情况:

  • 基于机器学习的评分函数 (MLSFs) 对于预测绑定亲和关系至关重要.
  • 合成数据增强是一种有前途的方法来提高MLSF的性能.
  • 同折叠模型提供了合成结构数据的潜在来源.

研究的目的:

  • 评估在MLSF培训中使用共折叠模型进行合成数据增强的可行性.
  • 确定增强数据质量对MLSF性能的影响.
  • 开发用于识别高质量的共同折叠预测数据增强的方法.

主要方法:

  • 利用共同折叠模型来生成合成蛋白质复杂结构.
  • 训练有素的MLSF使用实验和共同折叠衍生的数据.
  • 开发和应用启发式分析来评估共同折叠预测的结构质量.
  • 使用不同的数据增强策略比较MLSF性能.

主要成果:

  • 通过共同折叠数据增强获得的性能收益高度依赖于预测的结构质量.
  • 已建立的简单启发式可以有效地识别高质量的共同折叠预测,而不需要实验结构.
  • 当根据质量进行过时,共同折叠的预测可以成功地替代MLSF培训中的实验结构.

结论:

  • 共同折叠模型是一个可行的,虽然依赖于质量的,在MLSF培训中合成数据增强的来源.
  • 质量控制的启发式分析对于使用共同折叠模型的成功数据增强至关重要.
  • 这项工作为利用共同折叠模型提供了一个框架,通过数据增强来增强绑定亲和力预测.