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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
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
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

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

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相关实验视频

Updated: Jul 16, 2025

Achieving Efficient Fragment Screening at XChem Facility at Diamond Light Source
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Published on: May 29, 2021

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探索基于机器学习的虚拟选模型的能力,以确定负责绑定功能的功能组.

Thomas E Hadfield1, Jack Scantlebury1, Charlotte M Deane2

  • 1Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK.

Journal of cheminformatics
|September 19, 2023
PubMed
概括

本研究引入了一种合成数据方法,用于评估用于药物发现的机器学习模型. 它表明深度学习模型在识别关键绑定组方面比传统方法更有效,强调需要解决数据集偏差.

关键词:
可以解释性 解释性机器学习 机器学习基于结构的虚拟选.

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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

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Last Updated: Jul 16, 2025

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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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科学领域:

  • 计算化学是一种计算化学.
  • 机器学习在药物发现中的作用
  • 生物信息学是一种生物信息学.

背景情况:

  • 基于结构的虚拟选 (SBVS) 模型通常依赖于数据集偏差,而不是真正的绑定交互.
  • 对于有效的SBVS,准确识别关键的功能组对于结合至关重要.

研究的目的:

  • 开发一种用于评估机器学习 (ML) 模型识别关键绑定功能组的能力的新方法.
  • 创建一个合成数据生成框架,用于SBVS模型的基准测试.

主要方法:

  • 使用确定性绑定规则和随机的药点云生成合成蛋白质 - 配体复合物的数据.
  • 量化了合成复合体中每个原子的重要性.
  • 与地面真相相比较ML模型衍生的特征属性.

主要成果:

  • 与随机森林模型相比,深度学习模型PointVS在识别重要的功能组方面显示出39%的更高效率.
  • 数据集中的干特异性偏差显著阻碍了所有测试的ML模型的性能.
  • 合成数据生成框架已公开提供.

结论:

  • 提出的合成数据方法有效地评估了ML模型识别真实绑定相互作用的能力.
  • 深度学习模型在识别关键功能组的绑定方面表现出卓越的表现.
  • 解决数据集偏差对于提高SBVS模型的概括性至关重要.