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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

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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 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.
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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
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相关实验视频

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A Protocol for Computer-Based Protein Structure and Function Prediction
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具有对比学习的等级图形变压器用于蛋白质功能预测.

Zhonghui Gu1, Xiao Luo2, Jiaxiao Chen3

  • 1Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.

Bioinformatics (Oxford, England)
|June 27, 2023
PubMed
概括

一个新的深度学习模型,分层图形转换器与对比学习 (HEAL),准确地预测蛋白质功能. HEAL利用结构信息并优于现有方法,即使使用预测的蛋白质结构.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 结构生物学是结构生物学.

背景情况:

  • 高通量测序提供了大量的蛋白质数据,但功能性注释是缓慢和昂贵的.
  • 计算模型对于加速蛋白质功能预测至关重要.
  • 现有的图形神经网络在长距离的结构相关性和确定关键残余方面扎.

研究的目的:

  • 引入一种新的深度学习模型,分层图形转换器与对比学习 (HEAL),用于增强蛋白质功能的预测.
  • 解决捕捉结构语义和蛋白质图中长距离依赖性的局限性.
  • 为了提高计算蛋白质功能预测的准确性和效率.

主要方法:

  • 开发了HEAL,一个分层图形变压器模型,包含超级节点来模仿功能动图.
  • 采用图形对比学习来优化网络和改进图形表示.
  • 集成的AlphaFold2预测了结构,以增强缺乏实验数据的蛋白质的预测.

主要成果:

  • 在训练数据较少的情况下,HEAL-PDB实现了与最先进的方法可比的性能.
  • 在使用AlphaFold2预测结构时,HEAL在PDBch测试集上的表现明显优于DeepFRI.
  • 与使用预测结构的DeepFRI和DeepGOPlus相比,HEAL在AFch测试集上表现优异.
  • 类激活映射使HEAL能够识别蛋白质中的功能位点.

结论:

  • HEAL为蛋白质功能预测提供了一种强大而准确的方法,特别是当实验结构不可用时.
  • 该模型捕捉层次结构语义和利用预测结构的能力代表了显著的进步.
  • 在后基因组时代,HEAL为加速功能注释提供了一个有价值的工具.