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相关概念视频

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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Protein and Protein Structure02:15

Protein and Protein Structure

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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.
A protein's shape is critical to its function. For example, an enzyme...
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Protein Families02:47

Protein Families

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Protein families are groups of homologous proteins; that is, they have similarities in amino acid sequences and three-dimensional structures. Protein families usually occur because of gene duplication, where an additional copy of a gene is inserted into the genome of an organism.   Mutations that change the amino acids but still allow the protein to be properly synthesized, will lead to new protein family members.   If these new proteins contain similar amino acids in key...
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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 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 and Protein Structures02:15

Protein and Protein Structures

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TAWFN:一种用于蛋白质功能预测的深度学习框架.

Lu Meng1, Xiaoran Wang1

  • 1College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, 110000, China.

Bioinformatics (Oxford, England)
|September 23, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新的双模型自适应重量融合网络 (TAWFN),用于使用蛋白质结构预测蛋白质功能. TAWFN有效地结合了卷积神经网络 (CNN) 和图形卷积网络 (GCN),以提高预测准确度.

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科学领域:

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

背景情况:

  • 蛋白质功能预测对于生物研究和应用至关重要.
  • 高通量技术产生大量的蛋白质序列数据,但功能性注释仍然具有挑战性.
  • 现有的基于结构的方法经常独立使用卷积神经网络 (CNN) 或图形卷积网络 (GCN).

研究的目的:

  • 为蛋白质功能预测开发一个综合框架,利用蛋白质结构信息.
  • 将CNN和GCN的优势结合到一个统一的模型中,以提高预测准确度.
  • 引入双模型适应性重量融合网络 (TAWFN) 用于预测蛋白质功能.

主要方法:

  • 从蛋白质结构中提取的氨基酸接触图和序列.
  • 采用了自适应图卷积网络 (AGCN) 和多层卷积神经网络 (MCNN) 模块.
  • 开发了一个自适应式重量计算网络,将AGCN和MCNN的预测合并为最终分类.

主要成果:

  • 在PDBset和AFset数据集上,TAWFN取得了有前途的表现.
  • 精度回调曲线 (AUPR) 下的实现面积值为0.718 (分子功能),0.385 (生物过程) 和0.488 (细胞组成部分).
  • 在实验评估中表现优于现有的蛋白质功能预测方法.

结论:

  • 拟议的TAWFN框架有效地整合了CNN和GCN用于蛋白质功能预测.
  • 通过TAWFN模型利用蛋白质结构,比基于序列的方法提供了显著的进步.
  • TAWFN展示了卓越的性能和生物信息学实际应用的潜力.