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相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

5.0K
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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Conserved Binding Sites01:49

Conserved Binding Sites

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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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Protein Organization01:24

Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
The primary structure of a protein is its amino acid sequence....
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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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相关实验视频

Updated: Jan 8, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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SSIF-Affinity:对精确的蛋白质-蛋白质结合关系预测的序列结构特征的多模式深度学习.

Xinyi Xu1,2, Haotian Zhang1,2, Qi Liu1,2

  • 1Center for Biomedical-photonics and Molecular Imaging, Advanced Diagnostic-Therapy Technology and Equipment Key Laboratory of Higher Education Institutions in Shaanxi Province, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China.

Journal of chemical information and modeling
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概括

这项研究介绍了SSIF亲和力,这是一种用于预测蛋白质与蛋白质结合亲和力的深度学习模型. 它整合了结构和序列数据以进行高精度的预测,推动了抗体药物发现.

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科学领域:

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

背景情况:

  • 准确预测蛋白质-蛋白质结合亲和力对于理解生物过程和开发向疗法至关重要.
  • 测量结合亲和力的实验方法昂贵且耗时,需要计算方法.
  • 深度学习为高通量和精确的绑定亲和力预测提供了一个强大的替代方案.

研究的目的:

  • 开发一个创新的多式联络深度学习框架,SSIF亲和,用于高精度预测蛋白质-蛋白质复合体结合亲和.
  • 通过整合结构和序列信息来克服单模式方法的局限性.
  • 为人工智能驱动的抗体药物发现提供新的战略.

主要方法:

  • SSIF-affinity识别了绑定接口,并构建了几何上受约束的区域以提取原子级相互作用特征.
  • 一个结构引导的交叉模式注意模块融合了关键残留物的结构和序列特征.
  • 卷积神经网络 (CNN) 和长短期记忆 (LSTM) 网络提取全长序列特征,捕获本地和远程依赖.
  • 多层感知器 (MLP) 回归预测基于集成的多层特征的结合亲和力.

主要成果:

  • 该框架通过采用区域选择策略,有效地减少冗余计算和噪音.
  • 通过对序列和结构特征的协作表示,SSIF亲和度克服了单模式限制.
  • 该模型平衡了接口和远程交互的贡献,以获得准确的预测.
  • 对抗体-抗原复合体的案例研究表明该模型具有强大的概括能力.

结论:

  • SSIF-affinity提供了一种新的多式联络深度学习策略,用于准确的蛋白质-蛋白质结合亲和力预测.
  • 该框架为加速人工智能驱动的抗体药物发现提供了一个新的范式.
  • 这种方法增强了对蛋白质-蛋白质相互作用的理解,并促进了治疗开发.