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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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Conserved Binding Sites01:49

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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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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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相关实验视频

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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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在深度学习中利用角形几何学来预测蛋白质-配体结合亲和力.

Julia Rahman1, M A Hakim Newton2, Jiffriya Mohamed Abdul Cader3

  • 1Griffith University, 170 Kessels Rd, Nathan, 4111, QLD, Australia; Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.

Computer methods and programs in biomedicine
|February 15, 2026
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概括
此摘要是机器生成的。

这项研究引入了角度几何特征,用于预测蛋白质-连接体结合亲和力,实现了最先进的结果. 角度感知预测器 (AAP) 为药物发现提供了一种轻量级且有效的方法.

关键词:
角度地图的角度地图.绑定亲和力预测预测深度学习是一种深度学习.二面角的角度是二面体的角度.连接物 连接物 连接物蛋白质蛋白质是一种蛋白质.

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科学领域:

  • 计算化学是一种计算化学.
  • 结构生物学是结构生物学.
  • 药物发现 药物发现

背景情况:

  • 在基于结构的药物设计中,蛋白质 - 配体结合性亲和力预测至关重要.
  • 当前的深度学习模型通常依赖于资源密集的3D网格或分子图.
  • 这些方法可能无法完全捕捉绑定所必需的特定定向相互作用.

研究的目的:

  • 引入新的角度几何特征作为绑定相互作用的描述符.
  • 开发一种有效和轻量级的模型,用于结合亲和力预测.

主要方法:

  • 提取蛋白质和配体原子之间的七种二面角类型,以编码导向和几何.
  • 开发一个完全连接的整体网络,即角度感知预测器 (AAP),以整合这些角度特征.

主要成果:

  • 在CASF-2016基准指标上,AAP取得了最先进的表现,R=0.872,RMSE=1.072,MAE=0.817,SD=1.077,CI=0.845.
  • 在四个额外的基准数据集上观察到持续的性能改进,范围从0.3%到36%.
  • 该模型展示了有效性,稳定性和计算效率.

结论:

  • 角形几何特征是有效的,轻量级的,强大的描述符,用于绑定亲和力预测.
  • 角形几何学代表了未来基于结构的药物发现的有希望的方向.
  • 该AAP程序和数据是公开可用的,用于进一步的研究和应用.