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相关概念视频

Protein Folding01:25

Protein Folding

7.8K
Proteins are chains of amino acids linked together by peptide bonds. Upon synthesis, a protein folds into a three-dimensional conformation, critical to its biological function. Interactions between its constituent amino acids guide protein folding, and hence the protein structure is primarily dependent on its amino acid sequence.
Protein Structure Is Critical to Its Biological Function
Proteins perform a wide range of biological functions such as catalyzing chemical reactions, providing...
7.8K
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
Protein Denaturation01:28

Protein Denaturation

4.0K
The function of proteins depends on their native three-dimensional structure, which is dictated by the amino acid sequence of the specific protein. Folding of the polypeptide chain takes place under specific conditions that energetically favor the folded conformation. In contrast, protein denaturation occurs spontaneously under unfavorable conditions that disrupt the integrity of the folded conformation. Thus, the chemical and physical environment of a protein, such as significant changes in pH...
4.0K
Protein and Protein Structure02:15

Protein and Protein Structure

78.8K
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...
78.8K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.8K
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...
10.8K
Molecular Chaperones and Protein Folding03:00

Molecular Chaperones and Protein Folding

17.8K
The native conformation of a protein is formed by interactions between the side chains of its constituent amino acids. When the amino acids cannot form these interactions, the protein cannot fold by itself and needs chaperones. Notably, chaperones do not relay any additional information required for the folding of polypeptides; the native conformation of a protein is determined solely by its amino acid sequence. Chaperones catalyze protein folding without being a part of the folded protein.
The...
17.8K

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Updated: Jun 11, 2025

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web
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使用大规模生物物理采样探索蛋白质形态变化 增强深度学习策略

Yao Hu1, Hao Yang2, Mingwei Li1

  • 1Department of Physics, University of Science and Technology of China, Hefei, Anhui, 230026, China.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|October 10, 2024
PubMed
概括
此摘要是机器生成的。

研究人员开发了一种深度学习模型,以预测蛋白质结构变化. 这种在广泛的模拟数据上训练的模型准确地预测了蛋白质过渡,并揭示了诸如全调节等生物机制.

关键词:
形状的变化 形状的变化深度学习是一种深度学习.蛋白质是一种蛋白质.过渡路径的过渡路径

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科学领域:

  • 生物物理学的生物物理.
  • 计算生物学 计算生物学
  • 结构生物学 结构生物学

背景情况:

  • 深度学习模型已经进行了先进的静态蛋白质结构预测.
  • 由于训练数据有限,预测动态蛋白质构造变化仍然是一个重大挑战.
  • 了解蛋白质动态对于破译生物功能至关重要.

研究的目的:

  • 开发一种通用的深度学习模型,用于预测蛋白质构造转变路径.
  • 通过创建一个大规模的模拟数据库来解决蛋白质动力学训练数据的稀缺问题.
  • 验证模型的预测能力及其在生物发现中的应用.

主要方法:

  • 通过将分子动力学模拟与2635种蛋白质的增强采样方法相结合,生成了一个大规模的数据库.
  • 模拟每个蛋白质在两个已知的稳定状态之间发生的构造变化.
  • 开发并应用了一种通用的深度学习模型来预测蛋白质过渡途径.

主要成果:

  • 深度学习模型在不同长度的蛋白质和各种形状变化的蛋白质中表现出强度.
  • 预测显示与多个测试系统中的实验数据有很强的一致性.
  • 该模型成功地确定了人类β-心脏肌肉酶的新型全调节.

结论:

  • 开发的深度学习模型对于预测蛋白质构造变化是有效的.
  • 将模拟与深度学习相结合的方法为研究蛋白质动态提供了强大的工具.
  • 这项工作促进了我们对蛋白质构造转变及其生物学影响的理解.