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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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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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Predicting Molecular Geometry02:27

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VSEPR Theory for Determination of Electron Pair Geometries
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Ligand Binding Sites02:40

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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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A Protocol for Computer-Based Protein Structure and Function Prediction
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用几何学学习和预训策略预测蛋白质与蛋白质结合部位.

Wenhong Xu1, Yuqian Xie1, Huasen Jiang1

  • 1College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China.

Journal of chemical information and modeling
|October 24, 2025
PubMed
概括
此摘要是机器生成的。

通过先进的几何图形学习,GeoPPIS准确地预测了蛋白质与蛋白质相互作用的地点. 这种方法克服了现有的计算工具的局限性,为药物开发提供了可靠的in silico解决方案.

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科学领域:

  • 计算生物学 计算生物学
  • 结构生物信息学 结构生物信息学
  • 药物发现 药物发现 药物发现

背景情况:

  • 蛋白与蛋白相互作用 (PPI) 对生物过程至关重要.
  • 实验性识别PPI站点是资源密集的.
  • 当前的计算方法在3D结构,有限的数据和性能差异方面扎.

研究的目的:

  • 开发一种先进的计算方法,准确地预测蛋白质与蛋白质相互作用部位 (PPIS).
  • 解决现有方法的局限性,包括不充分的结构特征建模和过度装配.
  • 提高PPIS预测的稳定性和稳定性.

主要方法:

  • 介绍了GeoPPIS,一种利用几何图形学习模块的几何意识方法.
  • 采用了两阶段混合转移学习策略,以减轻过度装配.
  • 实现基于密度的聚类和集体学习,用于预测后的改进.

主要成果:

  • 在两个基准数据集 (>370种蛋白质) 上,GeoPPIS实现了最先进的性能.
  • 与11个基线模型相比,实现了高预测准确度 (0.857和0.860).
  • 证明了3D结构特征的卓越建模,并减少了预测方差.

结论:

  • GeoPPIS提供了一个可靠的in silico工具,用于稳健的蛋白质-蛋白质相互作用地点预测.
  • 结合几何信息可以提高预测的准确性和稳定性.
  • 该方法促进了精准医学和药物开发的进步.