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相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

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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...
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Quantitative Aspects of Drug-Receptor Interaction01:30

Quantitative Aspects of Drug-Receptor Interaction

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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...
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Ligand Binding Sites02:40

Ligand Binding Sites

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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
Drug-Receptor Interactions01:29

Drug-Receptor Interactions

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Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
Several parameters, such as the drug's affinity for its receptor and its efficacy, which is its ability to activate the receptor, determine the drug's effect on the tissue....
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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...
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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...
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相关实验视频

Updated: Jul 5, 2025

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

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预测药物标结合亲和力与交叉尺度图的对比学习学习.

Jingru Wang1,2,3, Yihang Xiao1,2, Xuequn Shang1,2,3

  • 1School of Computer Science, Northwestern Polytechnical University, Xi'an, 710072, China.

Briefings in bioinformatics
|January 15, 2024
PubMed
概括

我们开发了CSCo-DTA,这是一种用于预测药物向相互作用的新计算方法. 这种跨尺度图形对比学习方法整合了分子和网络数据,以提高药物发现和重新定位的准确性.

关键词:
这是一个跨度尺度的跨度尺度.发现药物的发现.药物目标结合亲缘关系图表对比的学习学习.

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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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科学领域:

  • 计算化学是一种计算化学.
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 准确的药物标结合亲和力预测对于药物发现和重新定位至关重要.
  • 现有的计算方法经常单独分析分子结构或网络相互作用,限制了全面的特征捕获.
  • 整合分子规模和网络规模的信息可以提高预测质量.

研究的目的:

  • 引入CSCo-DTA,一种新的跨尺度图形对比学习方法,用于药物标结合 afinity 预测.
  • 为了有效地结合分子和网络特征,以提供强大的药物向相互作用表示.
  • 提高计算药物发现模型的准确性和可靠性.

主要方法:

  • 开发了一种新的跨尺度图形对比学习框架 (CSCo-DTA).
  • 集成的分子结构特征与药物向双方网络信息.
  • 采用对比式学习,从多尺度数据中捕捉当地和全球的视角.

主要成果:

  • 在两个基准数据集上,CSCo-DTA显著超过了现有的最先进的方法.
  • 废除研究证实了多尺度特征和跨尺度对比学习的有效性.
  • 成功预测了Erlotinib的新型潜在标,通过分子对接分析验证.

结论:

  • CSCo-DTA提供了一种强大的新方法,通过整合各种数据尺度来预测药物标结合亲和力.
  • 该模型利用分子和网络信息的能力提高了预测性能.
  • 这种方法有望加速药物发现和重新定位努力.