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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

12.9K
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...
12.9K
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
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

12.9K
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:
12.9K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K

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相关实验视频

Updated: Jul 6, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
10:21

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

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利用深度学习进行增强的连接对接.

Xujun Zhang1, Chao Shen1, Chang-Yu Hsieh2

  • 1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China; Hangzhou Carbonsilicon AI Technology Co., Ltd, Hangzhou 310018, Zhejiang, China.

Trends in pharmacological sciences
|December 30, 2023
PubMed
概括
此摘要是机器生成的。

深度学习 (DL) 可以提高连接器对接 (LD) 的准确性和速度,用于蛋白质-连接器结合的预测. 这项技术提供了一种有前途的方法来加强药物发现中的虚拟查 (VS).

关键词:
深度学习是一种深度学习.带对接对接器评分功能是一个得分函数.虚拟选 虚拟选 虚拟选

更多相关视频

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
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Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

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Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

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相关实验视频

Last Updated: Jul 6, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

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Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
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Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

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Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
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Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

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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.
  • 生物信息学是一种生物信息学.

背景情况:

  • 干对接 (LD) 对于预测蛋白质-干 (PL) 结合至关重要.
  • 目前的LD方法在准确性和速度方面存在局限性.
  • 虚拟查 (VS) 在很大程度上依赖于高效的LD技术.

研究的目的:

  • 探索深度学习 (DL) 在应对深度学习挑战方面的潜力.
  • 审查最近的DL在LD的进步.
  • 预测DL在计算药物发现中的未来趋势.

主要方法:

  • 审查关于在联结对接中深度学习应用的现有文献.
  • 对最近的进展和方法的分析.
  • 讨论未来的研究方向和潜在影响.

主要成果:

  • 深度学习模型在提高 LD 准确性方面表现有前途.
  • DL技术可能会加速LD过程.
  • 预计DL的整合将提高虚拟查效率.

结论:

  • 深度学习提供了一种改造性的方法来增强连接器对接.
  • DL的进步是克服当前LD速度和准确性的局限性的关键.
  • 虚拟查的未来可能将涉及深度学习的重大整合.