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

The Equilibrium Binding Constant and Binding Strength

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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
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
Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

181
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...
181

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相关实验视频

Updated: Jul 2, 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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用数据驱动的对连接体解结路径的分类.

Dhiman Ray1, Michele Parrinello1

  • 1Simulations Research Line, Italian Institute of Technology, Via Enrico Melen 83, Genova GE 16152, Italy.

Proceedings of the National Academy of Sciences of the United States of America
|February 27, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种使用动态时间曲线分析分子轨迹的自动化方法,使得能够精确地分类连接体解离路径,并计算出口路径特定的药物发现动力学.

关键词:
动态时间扭曲.运动学的动力学.机器学习是机器学习.分子动力学分子动力学罕见事件 罕见事件

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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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科学领域:

  • 计算化学和生物物理学
  • 分子动力学模拟的模拟.
  • 药物的发现和开发.

背景情况:

  • 了解连体受体结合机制对于识别小分子点至关重要.
  • 分子动力学 (MD) 模拟可以计算结合的自由能量和动力学,但只能提供定性途径分析.
  • 手动分析MD轨迹是劳动密集型的,限制了大规模的药物发现应用.

研究的目的:

  • 开发一种自动化的方法来分析分子过渡路径,特别是蛋白质-连接体解离.
  • 克服手动分析在表征连接体结合/解结合途径方面的局限性.
  • 为了提高药物发现,使解离路径的准确分类和动态分析成为可能.

主要方法:

  • 引入基于动态时间曲折算法用于分子轨迹分析的自动化方法.
  • 使用诸如接触或距离等通用描述符对分子轨迹的分类.
  • 计算出口路径特定的配体解离动力学.

主要成果:

  • 自动化方法准确地分类分子轨迹,优于人工分类.
  • 该方法成功地在已识别的途径中区分了并行解离通道.
  • 沿着最快的路径计算的无约束时间表显示与实验停留时间一致.

结论:

  • 开发的数据驱动协议提供了一种物理解释和准确的方法来分析连接体解离.
  • 这种技术有助于探索联体解离路径,并计算路径特定的热力学和运动性质.
  • 与增强的采样算法集成可以显著推进早期药物发现和属性计算.