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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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Physiological Pharmacokinetic Models: Assumption with Protein Binding01:13

Physiological Pharmacokinetic Models: Assumption with Protein Binding

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Physiological models with protein binding in pharmacokinetics offer a sophisticated approach to understanding drug disposition. These models consider drug-protein interactions, enabling them to effectively predict drug concentrations in different organs and tissues. This precision aids in accurate drug dosing, providing a significant advantage over conventional models. A key process within these models is equilibration, which ensures that drug concentrations achieve a steady state within the...
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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...
930
Protein-protein Interfaces02:04

Protein-protein Interfaces

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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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Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

493
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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The Two-State Receptor Model01:29

The Two-State Receptor Model

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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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相关实验视频

Updated: May 29, 2025

Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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GS-DTA:集成图形和序列模型来预测药物标结合亲和力.

Junwei Luo1, Ziguang Zhu1, Zhenhan Xu1

  • 1School of Software, Henan Polytechnic University, Jiaozuo, 454000, China.

BMC genomics
|February 5, 2025
PubMed
概括

这项研究引入了GS-DTA,这是一种用于预测药物标结合亲和力 (DTA) 的新型图形和序列模型. 通过更好地捕捉复杂的分子结构和蛋白质相互作用,GS-DTA提高了准确性,推动了药物发现工作.

关键词:
药物标结合亲和力 药物标结合亲和力图形神经网络是一个神经网络.变压器变压器变压器

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科学领域:

  • 计算化学计算化学
  • 生物信息学是一种生物信息学.
  • 药物发现 药物发现 药物发现

背景情况:

  • 药物标结合亲和力 (DTA) 的预测对于药物发现和重新定位至关重要.
  • 现有的方法难以处理复杂的分子结构和长距离的蛋白质相互作用.
  • 需要改进DTA预测模型,以捕捉复杂的分子和蛋白质特征.

研究的目的:

  • 开发一种新的图形和基于序列的方法,GS-DTA,用于准确的DTA预测.
  • 解决当前模型在分析重要的分子节点和蛋白质结构信息方面的局限性.
  • 改进复杂药物分子内部以及远处的氨基酸片段之间的关系的探索.

主要方法:

  • 对于药物,GS-DTA使用简化分子输入线输入系统 (SMILES),对于蛋白质,使用氨基酸序列.
  • 药物特征是使用图表注意网络版本2-图表卷积网络 (GATv2-GCN) 和三层GCN提取的.
  • 使用卷积神经网络 (CNN),双向长期短期记忆 (Bi-LSTM) 和变压器网络的组合提取蛋白质特征.

主要成果:

  • GS-DTA将药物模拟为图形,GATv2-GCN专注于重要的原子节点,GCN捕捉层次特征.
  • 蛋白质框架从氨基酸序列中提取全面的上下文和结构信息.
  • 药物和蛋白质特征向量通过完全连接的层集成用于DTA预测.

结论:

  • 在戴维斯和KIBA数据集上,GS-DTA表现出强的表现,这是有利的MSE,CI和r2值所表明的.
  • 该方法提高了药物标结合亲缘关系预测的准确性.
  • GS-DTA为计算药物发现和开发提供了一个有前途的方法.