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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

14.4K
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...
14.4K
Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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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,...
4.5K
Protein Networks02:26

Protein Networks

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2.8K
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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相关实验视频

Updated: Jan 9, 2026

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling
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Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

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基于图形的深度学习方法用于高通量蛋白质-DNA相互作用评分.

Yi-Hao Zhao1, Ying Wang1, Chao Shen2

  • 1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.

Acta pharmacologica Sinica
|December 1, 2025
PubMed
概括
此摘要是机器生成的。

PDIScore是一种新的深度学习工具,通过模拟核酸灵活性,准确地预测蛋白质-DNA相互作用 (PDIs). 它在选,对接和排名方面表现优于现有的方法,有助于生物研究和药物设计.

关键词:
深度学习是一种深度学习.机器学习是机器学习.分子对接的分子对接.蛋白质-DNA相互作用虚拟选是虚拟的选.

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

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相关实验视频

Last Updated: Jan 9, 2026

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling
11:19

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

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Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens
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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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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
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 精确量化蛋白-DNA相互作用 (PDIs) 对于理解生物过程和药物设计至关重要.
  • 核酸的灵活性对结构确定和训练预测模型提出了挑战.
  • 现有的评分函数 (SFs) 与PDI复合体的复杂性作斗争.

研究的目的:

  • 开发一种新的基于深度学习的评分函数 (SF),用于预测蛋白质-DNA相互作用 (PDI).
  • 为了解决当前处理核酸灵活性和大型相互作用接口的方法的局限性.
  • 为研究和治疗设计中PDI预测创建一个强大的和可通用的工具.

主要方法:

  • 开发了PDIScore,这是一个深度学习的SF,使用了用于核酸灵活性的全面图形表示.
  • 采用可扩展的GraphGPS架构与BigBird线性全球关注的大型接口.
  • 综合混合物密度网络 (MDN) 用于模拟残留-核酸距离分布.
  • 在约7000个蛋白质核酸复杂结构的数据集上进行训练.

主要成果:

  • 与现有方法相比,PDIScore在选,对接和排名任务中表现出卓越的性能.
  • 取得了卓越的选功率 (例如,AUROC=0.82) 和高的对接成功率 (48.94%顶1).
  • 案例研究强调了PDIScore阐明生物机制和识别关键相互作用地点的能力.

结论:

  • PDIScore是一个强大的和可泛化的深度学习工具,用于预测蛋白质-DNA相互作用.
  • 它显著提升了PDI量化能力,有助于生物研究.
  • 通过提高PDI预测准确度,为加速治疗设计提供了潜力.