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相关概念视频

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...
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Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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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...
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Ligand Binding and Linkage00:49

Ligand Binding and Linkage

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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...
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The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

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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:
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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...
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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: Jun 23, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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OLB-AC:通过深度图形学习和活动悬崖来优化连接体生物活性.

Yueming Yin1,2, Haifeng Hu1, Jitao Yang1

  • 1School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.

Bioinformatics (Oxford, England)
|June 18, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的深度图形学习方法,以优化活动悬崖附近的药物分子,改善生物活性预测并产生新的有效化合物. 该方法通过识别具有改进性质的优化配体来增强药物发现.

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科学领域:

  • 计算化学和化学信息学
  • 药物的发现和开发.
  • 在化学领域的机器学习和人工智能.

背景情况:

  • 深度图形学习 (DGL) 对于基于连接体的虚拟选至关重要.
  • 活动悬崖 (ACs) 是一个挑战,因为微小的分子变化会大大改变生物活性.
  • 现有的DGL模型改善了在AC附近的预测,但优化机会仍未得到充分探索.

研究的目的:

  • 开发一种新的深度图形学习方法,用于同时预测和优化活动悬崖附近的联体生物活性.
  • 引入一种直接优化连接物分子的方法,为增强生物活性提供参考.
  • 探索活动悬崖在药物发现中优化连接体生物活性的潜力.

主要方法:

  • 提出了一种名为OLB-AC (优化活动悬崖附近的基生物活性) 的新方法,利用深度图形学习.
  • 开发了一个细心的图形重建神经网络来重建和优化连接体.
  • 采用从生物活性预测梯度获得的对抗性表示来进行连接物优化.

主要成果:

  • OLB-AC成功优化了667个分子,在训练数据集之外识别了49个已知的高活性/抑制剂/无毒配体.
  • 产生了27个新的分子对,其中的转换在训练集中不存在.
  • 在生物活性预测方面取得了最先进的表现,在27/33数据集上显示了最佳的皮尔森相关系数 (r2),改善了7.2%-22.9%.

结论:

  • OLB-AC方法有效地优化了活动悬崖附近的联体生物活性,在药物发现方面显示出显著的潜力.
  • 这种方法产生了新的分子转换,并提高了生物活性预测的准确性.
  • 代码和数据集是公开的,这有助于进一步研究DGL用于药物优化.