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相关概念视频

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 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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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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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 Dynamics in Living Cells01:19

Protein Dynamics in Living Cells

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Different fluorescence-based techniques are used to study the protein dynamics in living cells. These techniques include FRAP, FRET, and PET.
Fluorescent recovery after photobleaching (FRAP) is a fluorescent-protein-based detection technique used to quantify protein movement rates within the cell. This method exposes a small portion of the cell to an intense laser beam. The laser beam causes permanent photobleaching of the fluorophore-tagged proteins in the exposed region. As the bleached...
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Protein Complex Assembly02:41

Protein Complex Assembly

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相关实验视频

Updated: May 29, 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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使用深度学习方法对蛋白质复合体的物理意识模型准确度估计.

Haodong Wang1, Meng Sun1, Lei Xie1

  • 1College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.

Computational and structural biotechnology journal
|February 7, 2025
PubMed
概括
此摘要是机器生成的。

DeepUMQA-PA是一种新的深度学习方法,使用物理意识特征准确评估蛋白质复杂模型质量. 它的性能优于现有的方法,特别是对于像纳米体抗原这样的灵活蛋白质.

关键词:
估计模型准确性的估计.蛋白质复合体结构预测和预测采用单一模型方法的方法.

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相关实验视频

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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科学领域:

  • 结构生物学 结构生物学
  • 计算生物学 计算生物学
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 在AlphaFold2的成功之后,蛋白质结构预测的重点已经从单体转移到复合体.
  • 对于蛋白质复杂模型来说,独立于预测方法的准确质量估计至关重要.
  • 现有的方法需要改进,以评估预测的蛋白质复杂结构的准确性.

研究的目的:

  • 开发一种新的物理感知深度学习方法,用于评估蛋白质复合体模型的残留智能质量.
  • 提高蛋白质复杂结构的质量评估的准确性,特别是灵活的蛋白质相互作用.

主要方法:

  • 开发了DeepUMQA-PA,这是一种物理意识的深度学习方法,用于对蛋白质复合体模型的残留智能质量评估.
  • 基于残留物构建的接触面积和方向特征,使用Voronoi图形来表示物理相互作用.
  • 整合基于几何学的特征,蛋白质语言模型嵌入,以及基于知识的潜力,融入一个融合网络 (图形神经网络和ResNet).

主要成果:

  • 在CASP15测试中,DeepUMQA-PA在3.69% (皮尔森) 和3.49% (斯皮尔曼) 的表现优于最先进的DeepUMQA3.
  • 在纳米体抗原评估方面取得了显著的改进:16.8% (皮尔森) 和15.5% (斯皮尔曼).
  • 与AlphaFold-Multimer和AlphaFold3自我评估相比,在大多数目标上表现出更高的平均绝对误差 (MAE) 分数.

结论:

  • 物理意识的特征,包括接触区域和方向,有效地捕获蛋白质中的序列结构质量关系.
  • 在评估灵活蛋白质复合物的质量方面,DeepUMQA-PA特别有前途.
  • 开发的方法为评估预测的蛋白质复杂结构的准确性提供了有价值的工具.