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相关概念视频

Enzymes02:34

Enzymes

81.3K
Inside living organisms, enzymes act as catalysts for many biochemical reactions involved in cellular metabolism. The role of enzymes is to reduce the activation energies of biochemical reactions by forming complexes with its substrates. The lowering of activation energies favor an increase in the rates of biochemical reactions.
Enzyme deficiencies can often translate into life-threatening diseases. For example, a genetic abnormality resulting in the deficiency of the enzyme G6PD...
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Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

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For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes...
8.0K
Induced-fit Model01:13

Induced-fit Model

80.7K
Most chemical reactions in cells require enzymes—biological catalysts that speed up the reaction without being consumed or permanently changed. They reduce the activation energy needed to convert the reactants into products. Enzymes are proteins, that usually work by binding to a substrate—a reactant molecule that they act upon.
Enzymes exhibit substrate specificity, meaning that they can only bind to certain substrates. This is mainly determined by the shape and chemical...
80.7K
Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

3.9K
The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
Most enzymes...
3.9K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.8K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.8K
Allosteric Proteins-ATCase01:19

Allosteric Proteins-ATCase

5.7K
Binding sites linkages can regulate a protein's function.  For example, enzyme activity is often regulated through a feedback mechanism where the end product of the biochemical process serves as an inhibitor.
Aspartate transcarbamoylase (ATCase) is a cytosolic enzyme that catalyzes the condensation of L-aspartate and carbamoyl phosphate to  N-carbamoyl-L-aspartate. This reaction is the first step in pyrimidine biosynthesis. UTP and CTP, the end products of the pyrimidine synthesis...
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相关实验视频

Updated: Jun 18, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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GloEC:一种具有层次意识的全球模型,用于预测酶功能.

Yiran Huang1,2,3, Yufu Lin1, Wei Lan1,2,3

  • 1School of Computer, Electronics and Information, Guangxi University, Nanning 530004, China.

Briefings in bioinformatics
|July 29, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了GloEC,这是通过利用酶层次来预测酶功能的新型模型. GloEC通过考虑全球和双向的酶标签依赖性来提高准确性.

关键词:
酶委员会号码 酶委员会号码图表 卷积网络 卷积网络终端到终端的终端.酵素酶是一种酶.

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相关实验视频

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Modeling an Enzyme Active Site using Molecular Visualization Freeware
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科学领域:

  • 生物技术是生物技术.
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 酶功能注释对于工业生物技术和理解病理学至关重要.
  • 现有的计算方法很难模拟酶标签的等级性质及其层次间的相互作用.
  • 准确的酶功能预测需要对酶标签依赖性的全球视角.

研究的目的:

  • 开发一种用于预测酶功能的新型计算模型,解决现有方法的局限性.
  • 为了有效地建模酶标签的等级结构和相互依赖.
  • 提高酶功能预测的准确性和范围,包括异酶识别.

主要方法:

  • 形成的酶标签层次结构作为一个定向的酶图.
  • 提出了一个基于层次的图形卷积网络 (GCN) 编码器,用于全球依赖性建模.
  • 开发了一个名为GloEC的端到端层次意识的全球模型.
  • 在等级-GCN编码器中实现双向计算,用于自下而上的和自上而下的分析.

主要成果:

  • 与现有方法相比,GloEC在三个基准数据集中实现了优异的预测性能.
  • 该模型通过案例研究证明了其在预测异酶功能的有效性.
  • 层次意识嵌入和特征的演融合增强了预测准确性.

结论:

  • 通过结合层次信息,GloEC在酶功能预测方面取得了重大进展.
  • 层次-GCN编码器中的双向计算方法捕获了新的酶标签相关性.
  • 该模型为计算生物学中的酶功能注释提供了强大而准确的解决方案.