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相关概念视频

Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

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

Turnover Number and Catalytic Efficiency

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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....
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Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
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通过酶热优化改进代谢工程设计.

Wenqi Xu1, Jingyi Cai2, Wenjun Wu1

  • 1Tianjin University of Science & Technology, Tianjin, 300457, China; Biodesign Center, Key Laboratory of Engineering Biology for Low-Carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308, China.

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概括
此摘要是机器生成的。

ET-OptME通过将酶效率和热力学可行性整合到模型中来改善代谢工程. 这种增强的设计-构建-测试-学习循环导致对代谢标的更准确和生理上更现实的策略.

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科学领域:

  • 代谢工程是代谢工程.
  • 系统生物学 系统生物学
  • 计算生物学 计算生物学

背景情况:

  • 代谢工程中的经典石化计算法往往忽视了热力学可行性和酶成本.
  • 这种限制影响了设计-构建-测试-学习 (DBTL) 周期的预测准确度.
  • 需要改进的计算框架来增强代谢工程策略.

研究的目的:

  • 引入ET-OptME,这是代谢工程的一个新框架.
  • 系统地将酶效率和热力学可行性约束纳入基因组规模的代谢模型.
  • 提高代谢干预策略的预测性能和生理现实性.

主要方法:

  • 通过整合两个算法来解决经典方法的局限性,开发了ET-OptME.
  • 实施了逐步的约束分层方法来管理热力学瓶和酶使用.
  • 将框架应用于基因组规模的代谢模型,特别是评估Corynebacterium glutamicum中的目标.

主要成果:

  • ET-OptME与石化计,热力学和受酶约束方法相比,显著提高了精度和准确性.
  • 定量评估显示,五个产品目标的最小精度 (高达292%) 和准确性 (高达106%) 显著增加.
  • 该框架产生了更具生理现实的干预策略,与实验数据保持一致.

结论:

  • ET-OptME为代谢工程提供了更强大和更具预测性的方法.
  • 热力学和酶约束的整合提高了DBTL循环的有效性.
  • 这一框架为设计高效的代谢干预和优化微生物细胞工厂提供了有价值的工具.