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相关概念视频

Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
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...
12.5K
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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Protein Complexes with Interchangeable Parts01:57

Protein Complexes with Interchangeable Parts

2.5K
Groups of proteins may form a complex where each protein in this complex has a different role in the overall execution of the complex’s function. Often some of the proteins in the complex can be replaced by a closely related variant to give a complex that contains many of the same components yet is functionally distinct.
The SCF ubiquitin ligase is a protein complex of five individual proteins. This complex attaches ubiquitin to other target proteins to mark them for degradation. In order...
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Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
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Protein-Protein Interfaces02:04

Protein-Protein Interfaces

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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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相关实验视频

Updated: Jul 1, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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图形转换器CPI:用于预测化合物-蛋白相互作用的图形转换器.

Jun Ma1,2, Zhili Zhao3, Tongfeng Li3,4

  • 1School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China. maj19@lzu.edu.com.

Interdisciplinary sciences, computational life sciences
|March 8, 2024
PubMed
概括
此摘要是机器生成的。

新的深度学习框架GraphsformerCPI准确预测化合物-蛋白相互作用 (CPI) 并提高可解释性. 这种方法通过分析分子结构和关系来改善药物设计,以便更好地预测.

关键词:
注意力机制注意力机制消费者价格指数预测预测深度学习是一种深度学习.分子图谱 分子图表

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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相关实验视频

Last Updated: Jul 1, 2025

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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科学领域:

  • 计算化学是一种计算化学.
  • 生物信息学是一种生物信息学.
  • 机器学习是机器学习.

背景情况:

  • 预测化合物-蛋白相互作用 (CPI) 对药物设计至关重要.
  • 越来越多的数据需要高效和可解释的预测模型.
  • 现有的深度学习方法往往缺乏透明度.

研究的目的:

  • 引入GraphsformerCPI,这是一个端到端的深度学习框架,用于改进CPI预测.
  • 在化合物-蛋白相互作用预测中增强模型解释性.
  • 用空间结构和注意力机制来利用深层分子表征.

主要方法:

  • 图形构造器CPI将化合物和蛋白质视为结构化的节点序列.
  • 利用结构增强的自我注意力来整合分子特征.
  • 采用双重注意力机制来提取原子残留关系特征.
  • 将变压器功能扩展到空间结构,以增强学习.

主要成果:

  • 图表表格CPI表现优于基线模型对分类CPI数据集的表现.
  • 在回归CPI数据集上实现竞争性表现.
  • 在基准数据集上显示了AUC,精度和回忆的显著改善.
  • 在KIBA数据集上显示了对应指数 (CI) 和平均平方误差 (MSE) 的显著增长.
  • 分子对接揭示了结合机制和相互作用的洞察力.

结论:

  • 在CPI预测中,GraphsformerCPI提供了卓越的性能和可解释性.
  • 该框架为药物设计和发现提供了实际意义.
  • 识别了关键的分子成分,并增强了对结合机制的理解.
  • 推进生物应用的可解释深度学习领域.