Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

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
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...
12.8K
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
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.3K
VSEPR Theory for Determination of Electron Pair Geometries
34.3K
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
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

3.1K
3.1K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Multiscale Neural Network Potential with Anisotropic Message Passing for the Fast and Accurate Simulation of Protein Dynamics and Enzymatic Reactions.

Journal of the American Chemical Society·2026
Same author

Balancing Data Quantity and Quality: Evaluating Curation Strategies for Bioactivity Prediction in Lead Optimization.

Journal of chemical information and modeling·2026
Same author

How well do classical and multiscale QM/MM molecular dynamics simulations capture stereoelectronic effects? A comparative study on atropisomerism.

The Journal of chemical physics·2026
Same author

Single-position ligand modifications tune CB<sub>2</sub>R activity by targeting the toggle switch.

Chemical science·2026
Same author

Structures of ALG3/9/12 reveal the assembly logic of the N-glycan oligomannose core.

Nature chemical biology·2026
Same author

Unraveling Torsional Preferences: Comparative Analysis of Torsion Motif Torsional-Angle Distributions across Different Environments.

Journal of chemical information and modeling·2025

相关实验视频

Updated: Jul 4, 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

2.5K

探索用基于电子密度的几何深度学习来预测蛋白质 - 配体结合的亲和力.

Clemens Isert1, Kenneth Atz1, Sereina Riniker1

  • 1ETH Zurich, Department of Chemistry and Applied Biosciences Vladimir-Prelog-Weg 4 8093 Zurich Switzerland gisbert@ethz.ch +41 44 633 73 27.

RSC advances
|February 5, 2024
PubMed
概括
此摘要是机器生成的。

这项研究探索了使用电子密度键关键点来预测蛋白质-连接体结合亲和力. 虽然显示出希望,但这种方法并没有显著超过现有的计算药物设计方法.

更多相关视频

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

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

Published on: July 19, 2024

1.4K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

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

Published on: January 26, 2024

1.8K

相关实验视频

Last Updated: Jul 4, 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

2.5K
Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
09:30

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

Published on: July 19, 2024

1.4K
Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
06:50

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

Published on: January 26, 2024

1.8K

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.
  • 药物发现 药物发现

背景情况:

  • 基于结构的药物设计需要准确预测蛋白质-连接体结合亲和力.
  • 当前的深度学习方法可能无法完全捕捉物理交互,或者可能具有偏见.
  • 电子密度提供了分子相互作用的基本物理表征.

研究的目的:

  • 研究来自电子密度的键关键点的实用性,用于预测结合亲和力.
  • 通过使用这些点对现有方法进行几何深度学习模型的基准测试.
  • 批判性地分析电子密度在药物设计的深度学习中的作用.

主要方法:

  • 使用了几何深度学习模型.
  • 从蛋白质 - 连接体复合体的电子密度获得的内置键关键点.
  • 在PDBbind和PDE10A数据集上评估模型性能.
  • 分析了电子密度和结合亲和力之间的相关性.

主要成果:

  • 模型实现了1.4-1.8日志单位 (PDBbind) 和1.0-1.7日志单位 (PDE10A) 的根平均平方误差.
  • 性能与基准方法相当,没有显著的优势.
  • 对于一些目标,在电子密度和结合亲和力之间观察到皮尔森相关系数 (r > 0.7).
  • 发现电子密度对深度学习模型的实用性取决于上下文.

结论:

  • 电子密度键关键点为蛋白质-连接体相互作用分析提供了物理基础的方法.
  • 在深度学习中直接应用绑定亲和力预测显示了取决于上下文的实用性.
  • 需要进一步的研究,以优化电子密度特征的集成到计算药物设计模型中.