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相关概念视频

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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Protein Networks02:26

Protein Networks

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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,...
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G Protein-coupled Receptors01:15

G Protein-coupled Receptors

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G Protein-Coupled Receptors or GPCRs are membrane-bound receptors that transiently associate with heterotrimeric G proteins and induce an appropriate response to sensory stimuli such as light, odors, hormones, cytokines, or neurotransmitters.
GPCRs are also called heptahelical, 7TM, or serpentine receptors, and consist of seven (H1-H7) transmembrane alpha-helices that span the bilayer to form a cylindrical core. The transmembrane helices are connected by three extracellular loops and three...
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Protein Glycosylation01:25

Protein Glycosylation

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Glycosylation, the most common post-translational modification for proteins, serves diverse functions. Adding sugars to proteins makes the proteins more resistant to proteolytic digestion. Glycosylated proteins can act as markers and receptors to promote cell-cell adhesion. Additionally, they have many essential quality control functions in the cell, such as correct protein folding and facilitating transport of misfolded proteins to the cytosol, which can be degraded.
Glycosylation occurs in...
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一个基于神经网络的稳健和可解释的图谱协议,用于预测p-glycoprotein基底.

Kuang-Cheng Hsu1, Pei-Hua Wang2, Bo-Han Su3

  • 1Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Da'an Dist., Taipei City 106319, Taiwan.

Briefings in bioinformatics
|August 3, 2025
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概括
此摘要是机器生成的。

这项研究引入了一个图形神经网络模型来预测P-glycoprotein (P-gp) 基质,这对药物开发至关重要. 该模型准确地识别了与P-gp相互作用的药物分子,有助于评估中枢神经系统透率.

关键词:
P-葡萄糖蛋白 (P-gp) 是一种蛋白质.注意力机制注意力机制深度学习是一种深度学习.可解释的人工智能 (XAI)图形神经网络 (GNN) 是一个图形神经网络.集成梯度 (IG) 是指集成的梯度 (IG) 是指集成的梯度.

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科学领域:

  • 药理学 药理学是指药理学的学科.
  • 计算化学计算化学
  • 生物技术是生物技术.

背景情况:

  • P-glycoprotein (P-gp) 是一种ATP结合盒载体,对于药物吸收和分发至关重要.
  • 血脑屏障中P-gp的存在需要了解中枢神经系统透的药物相互作用.
  • 目前的P-gp研究经常强调抑制剂超过基质,突出显示基质预测中的差距.

研究的目的:

  • 开发一个强大的计算模型来预测P-glycoprotein (P-gp) 基质.
  • 加强对候选药物透中枢神经系统的能力的评估.
  • 确定与P-gp基质活性相关的关键分子亚结构.

主要方法:

  • 使用图形神经网络方法,包括图形卷积网络和AttentiveFP.
  • 在1995年药物分子 (1202个基质,793个非基质) 的数据集上训练并验证了模型.
  • 采用集成梯度分析来解释模型预测和识别关键子结构.

主要成果:

  • 专注FP模型实现了0.848的ROC-AUC和0.815的精度,超过了传统方法.
  • 确定了20个与P-gp基质分类有显著关联的关键子结构.
  • 发现了四个子结构,赋予了>70%的基质分类概率,使得快速评估.

结论:

  • 开发的图形神经网络框架为预测P-gp基质提供了一种有效和可解释的方法.
  • 这种方法可以通过改善对中枢神经系统透率的评估来显著帮助药物开发.
  • 关键子结构的识别为早期药物查和设计提供了有价值的工具.