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

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
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
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
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 Networks02:26

Protein Networks

4.0K
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,...
4.0K
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

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

Pushing the Limits of One-Dimensional NMR Spectroscopy for Automated Structure Elucidation Using Artificial Intelligence.

Journal of chemical information and modeling·2026
Same author

Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations.

Journal of chemical theory and computation·2026
Same author

Mapping the avoid-ome: a systematic open-science approach to predictive ADMET.

Nature communications·2026
Same author

Large-Scale Collaborative Assessment of Binding Free Energy Calculations for Drug Discovery Using OpenFE.

Journal of chemical information and modeling·2026
Same author

Developing and Benchmarking Sage 2.3.0 with the AshGC Neural Network Charge Model.

Journal of chemical theory and computation·2026
Same author

Efficient simulation of optical spectra via machine learning and physical decomposition of environmental effects.

The Journal of chemical physics·2026
Same journal

Advancing Biochemical Molecule Registration, Representation and Search for New Drug Modalities.

Journal of chemical information and modeling·2026
Same journal

A Unified Molecular Graph and Protein Language Model Framework for Predicting Human Drug-Hormone Receptor Interactions with Structure-Aware Validation.

Journal of chemical information and modeling·2026
Same journal

Intricate Role of Cholesterol in Membrane Fusion.

Journal of chemical information and modeling·2026
Same journal

tmGNN-XAI: An Explainable Graph Neural Network Tool for Predicting Electronic Properties of Transition Metal Complexes from SMILES.

Journal of chemical information and modeling·2026
Same journal

QSAR in the Browser: An Interactive Cheminformatics Web Application.

Journal of chemical information and modeling·2026
Same journal

FoldDoF: Utilizing the Primary Degrees of Freedom of Protein Backbone for Geometric Modeling and Generation.

Journal of chemical information and modeling·2026
查看所有相关文章

相关实验视频

Updated: Jul 2, 2025

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

使用神经网络潜力增强蛋白质 - 连接物结合亲和力预测.

Francesc Sabanés Zariquiey1,2, Raimondas Galvelis1,2, Emilio Gallicchio3

  • 1Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), C Dr. Aiguader 88, 08003 Barcelona, Spain.

Journal of chemical information and modeling
|February 20, 2024
PubMed
概括
此摘要是机器生成的。

机器学习潜力增强了蛋白质 - 配体结合亲和力预测. 一种混合神经网络潜力和分子力学 (NNP / MM) 方法显示了与传统分子力学力场相比的显著改进.

更多相关视频

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
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K

相关实验视频

Last Updated: Jul 2, 2025

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
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
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.1K

科学领域:

  • 计算化学是一种计算化学.
  • 分子建模分子建模
  • 药物发现 药物发现

背景情况:

  • 准确预测蛋白质 - 配体结合亲和力对于药物发现至关重要.
  • 传统的分子力学 (MM) 力场往往在结合亲和力计算中的准确性方面扎.
  • 分子动力学 (MD) 模拟为研究分子相互作用提供了强大的框架.

研究的目的:

  • 为了提高蛋白质 - 配体结合亲和力预测的准确性.
  • 引入和评估一种新的混合机器学习潜力和分子力学 (NNP/MM) 方法.
  • 根据已建立的基准来评估NNP/MM方法的性能.

主要方法:

  • 使用分子动力学 (MD) 模拟.
  • 采用混合神经网络潜力和分子力学 (NNP/MM) 方法.
  • 使用化学转移方法计算相对结合的自由能量.

主要成果:

  • 通过NNP/MM方法论,在预测蛋白质 - 配体结合亲和关系方面取得了显著的改进.
  • 开发的方法优于传统的MM力场,如GAFF2.
  • 根据已建立的基准验证证实了NNP/MM方法的可靠性和更好的性能.

结论:

  • 混合NNP / MM方法代表了计算药物发现的重大进步.
  • 这种方法提供了一种更准确,更可靠的方法来预测蛋白质-连接体结合亲和关系.
  • 这些发现为制药研究中更高效,更有效的优化铺平了道路.