Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

4.0K
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...
4.0K
Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

8.1K
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...
8.1K
Turnover Number and Catalytic Efficiency01:19

Turnover Number and Catalytic Efficiency

10.1K
The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
Chymotrypsin is a pancreatic enzyme that breaks down proteins during digestion....
10.1K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A portable Cas6f-based system for multiplex translational repression in bacteria.

Nature communications·2026
Same author

Mind the Porins: Differential Effects of Porin Knockouts and Overexpression on Glucose and Xylose Uptake and Utilization in Pseudomonas putida.

Microbial biotechnology·2026
Same author

Cascading recombinase memory switch for programmable and stable gene expression in Pseudomonas putida.

Nucleic acids research·2026
Same author

Professional Academies: The Duty to Lead.

Microbial biotechnology·2026
Same author

Natural and Synthetic Metabolic Architectures.

Chembiochem : a European journal of chemical biology·2026
Same author

Adaptive evolution of <i>Pseudomonas putida</i> in the presence of fluoride exposes novel functions of a benzoate transporter.

Journal of bacteriology·2026

相关实验视频

Updated: Jun 28, 2025

Immobilization of Multi-biocatalysts in Alginate Beads for Cofactor Regeneration and Improved Reusability
09:27

Immobilization of Multi-biocatalysts in Alginate Beads for Cofactor Regeneration and Improved Reusability

Published on: April 22, 2016

17.4K

在体内自动化的酶工程加速了生物催化剂优化.

Enrico Orsi1, Lennart Schada von Borzyskowski2, Stephan Noack3

  • 1The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.

Nature communications
|April 24, 2024
PubMed
概括

开发稳定的生物催化剂对于生物基过程至关重要. 这项研究提出了机器学习引导的自动化工作流程,以加速发现优质酶,减少手工劳动和增加吞吐量.

更多相关视频

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

10.0K
Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation
09:28

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation

Published on: May 18, 2020

8.4K

相关实验视频

Last Updated: Jun 28, 2025

Immobilization of Multi-biocatalysts in Alginate Beads for Cofactor Regeneration and Improved Reusability
09:27

Immobilization of Multi-biocatalysts in Alginate Beads for Cofactor Regeneration and Improved Reusability

Published on: April 22, 2016

17.4K
Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
06:24

Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology

Published on: December 15, 2017

10.0K
Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation
09:28

Process Optimization using High Throughput Automated Micro-Bioreactors in Chinese Hamster Ovary Cell Cultivation

Published on: May 18, 2020

8.4K

科学领域:

  • 生物催化和酶工程 生物催化和酶工程
  • 合成生物学 合成生物学
  • 计算生物学 计算生物学

背景情况:

  • 具有成本效益的生物制造依赖于高效的生物催化剂.
  • 传统的酶工程方法是缓慢和劳动密集的.
  • 机器学习 (ML) 提供了扩大酶设计可能性的潜力.

研究的目的:

  • 为加速酶工程提供一个集成的,自动化的工作流.
  • 为了利用机器学习来引导酶进化.
  • 为工业应用增强优质生物催化剂的开发.

主要方法:

  • 开发 ML 引导的自动化工作流程.
  • 集成图书馆生成和超级突变系统.
  • 实验室进化和体内生长合选择的实施.

主要成果:

  • 拟议的自动化工作流显著增加了酶工程的吞吐量.
  • 机器学习指导着更广泛的酶设计空间的探索.
  • 可以实现稳定和选择性的生物催化剂的加速开发.

结论:

  • 集成的,ML引导的自动化工作流程对于快速的生物催化剂开发是有效的.
  • 这种方法克服了传统的低通量酶工程的局限性.
  • 该战略加快了向具有成本竞争力的生物基工艺的发展进程.