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相关概念视频

Protein Organization01:24

Protein Organization

6.0K
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....
6.0K
Globular and Fibrous Proteins02:21

Globular and Fibrous Proteins

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Many proteins can be classified into two distinct subtypes - globular or fibrous. These two types differ in their shapes and solubilities.
Globular proteins are also known as spheroproteins and typically are approximately round in shape. They contain a mix of amino acid types and contain differing sequences in their primary structures. Globular proteins have many different functions, such as enzymes, cellular messengers, and molecular transporters. These roles often require the proteins to be...
43.0K
Protein and Protein Structure02:15

Protein and Protein Structure

77.5K
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...
77.5K
Protein Families02:47

Protein Families

15.2K
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...
15.2K
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 and Protein Structures02:15

Protein and Protein Structures

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Updated: May 15, 2025

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
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Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

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使用结构图嵌入式的快速蛋白质结构搜索.

Joe G Greener1, Kiarash Jamali1

  • 1Medical Research Council Laboratory of Molecular Biology, Cambridge, CB2 0QH, United Kingdom.

Bioinformatics advances
|April 8, 2025
PubMed
概括
此摘要是机器生成的。

我们开发了Progres,这是一种使用图形神经网络快速搜索蛋白质结构的方法. 它可以有效地比较和分类蛋白质域,有助于远程同质检测和功能注释.

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A Protocol for Computer-Based Protein Structure and Function Prediction
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相关实验视频

Last Updated: May 15, 2025

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A Protocol for Computer-Based Protein Structure and Function Prediction
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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科学领域:

  • 结构生物信息学 结构生物信息学
  • 计算生物学是一种计算生物学.
  • 机器学习在生物学中的应用

背景情况:

  • 独立于序列的蛋白质结构的比较对于理解蛋白质的功能和进化至关重要.
  • 蛋白质结构数据库的快速增长需要高效的搜索工具.
  • 现有的方法在进行大规模结构比较时面临着速度和准确性的挑战.

研究的目的:

  • 开发一种快速准确的方法来搜索和比较蛋白质结构.
  • 使用图形神经网络创建蛋白质域的低维嵌入.
  • 为了促进远程同质检测,功能注释和蛋白质分类.

主要方法:

  • 使用监督对比学习来训练图形神经网络 (GNN).
  • 学习蛋白质域的低维嵌入.
  • 实现Progres方法作为软件和Web服务器.

主要成果:

  • 实现了与最先进的方法可比的准确性.
  • 证明了在AlphaFold数据库中快速搜索蛋白质域的能力.
  • 启用搜索TED域名在CPU上每次查询的十分之一秒内.

结论:

  • 进步在蛋白质结构搜索速度和准确性方面取得了重大进展.
  • 该方法对于探索大型结构数据集 (如AlphaFold) 是有价值的.
  • 进步支持关键的生物信息任务,如同质检测和功能注释.