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相关概念视频

Protein Networks02:26

Protein Networks

3.9K
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,...
3.9K
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
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
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...
4.2K
Protein Families02:47

Protein Families

15.3K
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...
15.3K
Protein Organization01:24

Protein Organization

6.3K
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....
6.3K
Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
12.8K

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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使用统计信息的图形网络准确预测蛋白质功能.

Yaan J Jang1,2, Qi-Qi Qin3,4, Si-Yu Huang3,5,6

  • 1Department of Biochemistry, University of Oxford, Oxford, UK. yaan.jang@gmail.com.

Nature communications
|August 3, 2024
PubMed
概括

从单独的序列预测蛋白质功能现在可以使用 PhiGnet,这是一种新的深度学习方法. 这种方法分析进化特征以识别功能部位,提高准确性并缩小未表征蛋白质的序列功能差距.

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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.
  • 蛋白质科学中的深度学习

背景情况:

  • 了解蛋白质功能对于医学和生物技术至关重要.
  • 超过2亿种蛋白质没有表征,阻碍了生物研究.
  • 当前的计算方法通常依赖于蛋白质结构,这种结构并不总是可用.

研究的目的:

  • 开发一种仅从氨基酸序列预测蛋白质功能的方法.
  • 为了利用进化特征用于功能性网站识别.
  • 为了克服基于结构的预测方法的局限性.

主要方法:

  • 使用统计信息图形网络 (PhiGnet).
  • 嵌入进化签名以评估残留物显著性.
  • 开发了一种基于序列的函数预测的深度学习方法.

主要成果:

  • 与现有方法相比,PhiGnet表现出优越的性能.
  • 该方法在没有结构信息的情况下成功预测了蛋白质功能.
  • 通过分析进化数据,在残留水平上确定了功能部位.

结论:

  • 应用在进化数据上的深度学习可以从序列中准确预测蛋白质功能.
  • PhiGnet缩小了序列功能差距,有助于对未研究的蛋白质进行表征.
  • 这种方法为解释蛋白质特性和发现研究和生物医学中的新功能提供了宝贵的见解.