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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
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 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
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

13.0K
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:
13.0K
Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

75
Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
75
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.6K
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...
12.6K

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相关实验视频

Updated: Jul 23, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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用水网增强的两态模型来预测蛋白质 - 配体结合的亲和力.

Xiaoyang Qu1, Lina Dong1, Ding Luo1

  • 1State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China.

Journal of chemical information and modeling
|July 11, 2023
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概括

这项研究引入了一种新的深度学习模型,该模型解释了蛋白质-连接体结合过程中水网络的变化. 这种方法提高了药物发现的评分函数的准确性,特别是在具有挑战性的绑定口袋中.

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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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A Protocol for Computer-Based Protein Structure and Function Prediction
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科学领域:

  • 计算化学的计算化学
  • 结构生物学 结构生物学
  • 机器学习在药物发现中的作用

背景情况:

  • 蛋白质 - 配体相互作用对于药物发现至关重要.
  • 现有的机器学习评分功能往往忽视了水网的动态作用.
  • 水分子在未结合和结合状态之间的重新排列显著影响结合亲和力.

研究的目的:

  • 开发一种深度学习模型,将来自连接体无结合状态和连接体结合状态的水网络信息纳入其中.
  • 提高基于机器学习的评分函数的准确性和稳定性.
  • 加强虚拟查和药物设计过程.

主要方法:

  • 将扩展连接互动功能集成到图形表示中.
  • 图形变压器操作员用于特征提取的应用.
  • 开发一个水网增强的双状态模型 (ECIF图::HM-Holo-Apo).

主要成果:

  • ECIFGraph::HM-Holo-Apo模型在CASF-2016基准上的评分,排名,对接和选任务中表现出强的表现.
  • 该模型在使用DEKOIS2.0数据集的大规模虚拟选测试中取得了卓越的结果.
  • 包括水网信息在内显著提高了模型的准确性.

结论:

  • 用水网增强的两态模型是增强机器学习评分功能的有效策略.
  • 这种方法提高了评分函数的稳定性和适用性,特别是对于具有含水性或暴露于溶剂的结合口袋的目标.
  • 开发的模型提供了更现实的蛋白质 - 配体结合相互作用的表现.