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相关概念视频

Protein Networks02:26

Protein Networks

3.9K
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,...
3.9K
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
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
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 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
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

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相关实验视频

Updated: Jun 24, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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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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敏感:对小分子与上下文解释网络的结合有可解释的洞察力

Roshni Bhatt1,2, David Ryan Koes1, Jacob D Durrant2

  • 1Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States.

Journal of chemical information and modeling
|June 7, 2024
PubMed
概括

我们开发了CENsible,这是一种可解释的深度学习方法,用于预测小分子结合亲和力. 这种方法有助于确定影响结合的关键因素,帮助药物发现和优化.

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.
  • 机器学习在药物发现中的作用

背景情况:

  • 准确预测小分子结合亲和力对于药物发现至关重要.
  • 现有的机器学习模型往往缺乏可解释性,阻碍了优化工作.

研究的目的:

  • 介绍CENsible,一种新且可解释的深度学习方法,用于评估蛋白质/配体结合亲和力.
  • 为了证明CENsible能够区分活性与非活性化合物的能力.

主要方法:

  • 使用上下文解释网络 (CEN),一个深层卷积神经网络架构.
  • 预测预先计算的项对蛋白质/配体复合体的整体结合亲和力的贡献.

主要成果:

  • 在各种系统中,CENsible有效地区分了活性与非活性化合物.
  • 该模型通过确定每个术语对最终预测的贡献来提供可解释性.

结论:

  • CENsible为现有的机器学习评分功能提供了一个可解释的替代方案.
  • CENsible的可解释性对指导药物开发中的优化有直接影响.

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