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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...
12.8K
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

12.8K
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:
12.8K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

684
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
684
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
Drug Discovery: Overview01:26

Drug Discovery: Overview

7.7K
Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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通过分子对接和自我注意力推进生物活性预测.

Yueming Yin, Hilbert Yuen In Lam, Yuguang Mu

    IEEE journal of biomedical and health informatics
    |August 23, 2024
    PubMed
    概括

    这项研究引入了一种新的深度学习模型,即药物向相互作用图神经网络 (DTIGN),通过整合药物向相互作用来改善生物活性预测. DTIGN显著提高了候选药物的预测准确性.

    科学领域:

    • 计算化学是一种计算化学.
    • 药理学 药理学是指药理学的学科.
    • 生物信息学是一种生物信息学.

    背景情况:

    • 生物活性预测对于药物发现至关重要,传统上依赖量化结构-活性关系 (QSAR) 模型.
    • 现有的深度学习方法主要集中在连接体结构上,忽视了其他关键因素,如药物向相互作用.
    • 生物活性受到连接体结构,药物标结合,信号通路和生物环境的复杂相互作用的影响.

    研究的目的:

    • 开发一种先进的深度学习模型,集成药物向相互作用,以便更准确地预测生物活性.
    • 通过改善潜在活性分子的识别,增强早期候选药物查.
    • 建立一个新的基准数据集,用于评估蛋白质-连接体复合体的背景下生物活性预测模型.

    主要方法:

    • 设计了一种药物向相互作用图神经网络 (DTIGN) 模型,将原子间力量纳入分子间图中.
    • 在DTIGN中利用多头自我注意,从分子对接数据中识别最佳的结合口袋和姿势.
    • 使用来自晶体数据库的有限本地结构的半监督学习来改进生物活性预测.

    主要成果:

    • 与现有方法相比,DTIGN模型在生物活性预测方面表现优越.
    • 在9个领先的基于深度学习的生物活性预测技术中,实现了平均27.03%的性能改善.

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  • 建立了一个新的基准数据集,以促进对蛋白质-连接体复合体的生物活性预测模型的评估.
  • 结论:

    • 整合药物向相互作用可以显著提高生物活性预测模型的准确性.
    • DTIGN模型为有效和准确的候选药物查提供了一种强大的新方法.
    • 开发的基准数据集将推动计算药物发现和个性化医学的研究.