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

12.7K
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
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
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 Two-State Receptor Model01:29

The Two-State Receptor Model

1.9K
The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with...
1.9K
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
Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

931
The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
931

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

A Centralized AI Lakehouse Framework for Brain Tumor MRI Classification and Segmentation, University KPI Forecasting, and Water Potability Prediction.

Sensors (Basel, Switzerland)·2026
Same author

Smart optical biosensor for edible oil detection with machine learning integration.

Analytical biochemistry·2026
Same author

A novel electrochemical exfoliation route to tailor the graphene bandgap through silicon incorporation: semi-metallic to semiconducting transition.

Nanoscale advances·2026
Same author

Seed priming-induced enhancement in seed germination, Seedling vigor, and productivity of foxtail millet (Setaria italica L.) in winter and summer seasons under Bangladesh conditions.

PloS one·2026
Same author

Direct Synthesis of Exfoliated Carbon Nitride Mediated by Sodiated Cellulose Nanocrystals for Photocatalytic and Adsorbent Applications.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Machine learning-enhanced novel design and performance optimization of M<sub>3</sub>SbI<sub>3</sub> (M = Ba and Ca) based dual absorber perovskite solar cells.

RSC advances·2026

相关实验视频

Updated: May 31, 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

1.5K

代数扩展的原子类型基于图形的模型,用于精确的联体受体结合亲缘关系预测.

Farjana Tasnim Mukta1, Md Masud Rana2, Avery Meyer1

  • 1Department of Mathematics, University of Kentucky, Lexington, KY, 40506, USA.

Journal of cheminformatics
|January 23, 2025
PubMed
概括

一个新的评分功能,AGL-EAT-Score,增强了用于药物设计的联体受体结合亲和性预测. 它使用代数图理论和扩展的原子类型来提高对现有方法的准确性.

关键词:
代数图的学习图表学习.绑定亲和关系预测的预测.扩展原子类型的扩展原子类型.非冗余的培训套件蛋白质 - 配体相互作用类似性计算的计算方法

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

1.2K
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

2.3K

相关实验视频

Last Updated: May 31, 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

1.5K
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

1.2K
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

2.3K

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:

  • 准确预测连接体受体结合亲和力对于基于结构的药物设计至关重要.
  • 基于机器学习的评分函数具有先进的预测,但与复杂的分子相互作用作斗争.

研究的目的:

  • 介绍AGL-EAT-Score,这是一个新的评分功能,用于预测连接体-受体结合亲和力.
  • 提高药物设计工具的准确性和可靠性.

主要方法:

  • 整合扩展的原子类型的多尺度加权彩色子图与代数图形理论.
  • 利用图形拉普拉斯矩阵和相邻矩阵的自值和自向量.
  • 对蛋白质序列,连接体结构和结合部位进行全面的相似性分析.

主要成果:

  • 在基准数据集 (CASF-2016,CASF-2013,Cathepsin S) 上,AGL-EAT-Score显示出了显著的准确性.
  • 性能优于现有的传统和基于机器学习的评分功能.
  • 使用扩展原子类型有效捕获复杂的蛋白质-连接体相互作用.

结论:

  • AGL-EAT-Score在基于结构的药物设计中提供了显著的进步.
  • 提供了一个强大的和系统的工具来完善和加速药物发现过程.
  • 解决了数据集偏差和预测模型中的过度代表性的挑战.