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相关概念视频

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.6K
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...
10.6K
Conserved Binding Sites01:49

Conserved Binding Sites

4.1K
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: May 12, 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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M-DeepAssembly:增强的DeepAssembly基于多目标多域蛋白质构成样本采样.

Xinyue Cui1, Yuhao Xia1, Minghua Hou1

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

BMC bioinformatics
|May 5, 2025
PubMed
概括
此摘要是机器生成的。

M-DeepAssembly通过使用一种新的多目标形态采样算法来改善多域蛋白质结构预测. 这种方法提高了准确性,特别是对于具有弱进化信号或大结构的蛋白质.

关键词:
形状采样采样 形状采样多域蛋白质组件组件多域蛋白质组件多目标能源模式多目标能源模式蛋白质结构预测 蛋白质结构预测

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相关实验视频

Last Updated: May 12, 2025

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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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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
  • 生物物理学的生物物理.

背景情况:

  • 蛋白质结构预测对于理解功能和药物设计至关重要.
  • 深度学习已经推进了单域预测,但多域预测仍然具有挑战性.
  • 软弱的进化信号和大的蛋白质大小阻碍了准确的多域结构预测.

研究的目的:

  • 开发一种用于准确预测多域蛋白质结构的新型计算协议.
  • 解决大型多域蛋白质和具有弱域间进化信号的蛋白质结构预测方面的局限性.

主要方法:

  • 提出了M-DeepAssembly,这是一个使用多目标蛋白质构成采样算法的协议.
  • 使用DeepAssembly和AlphaFold2.2提取了域间相互作用和序列距离特征.
  • 构建了一个多目标能量模型,并使用抽样算法来生成构造集.
  • 使用内部模型质量评估算法进行最终结构选择.

主要成果:

  • 在一个由164个多域蛋白质组成的测试组中,M-DeepAssembly的TM平均得分比AlphaFold2高15.4%,比DeepAssembly高2.0%.
  • 集合包含了具有明显更高准确度的模型,其性能高于基线方法高达20.3%.
  • 在CASP15多域目标上表现出比AlphaFold2的性能优势.

结论:

  • M-DeepAssembly为多域蛋白质组装提供了一种新的方法,克服了弱进化信号和大结构的挑战.
  • 该方法通过多目标形状采样产生多样化的集合,有助于预测复杂的蛋白质结构.
  • 为探索多域蛋白功能做出贡献,并提供了对具有多个构造状态的蛋白质的见解.