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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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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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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...
8.2K
Enzymes02:34

Enzymes

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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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Induced-fit Model01:13

Induced-fit Model

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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...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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相关实验视频

Updated: Jul 9, 2025

Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System
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Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System

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通过机器学习提高酶适应性

David Patsch1,2, Rebecca Buller3

  • 1Institute of Chemistry and Biotechnology, Zurich University of Applied Sciences, CH-8820 Wädenswil. patc@zhaw.ch.

Chimia
|December 4, 2023
PubMed
概括

机器学习预测蛋白质序列和功能,加速酶工程. 这种方法优化了酶特性,如活性和稳定性,从而使可持续化学的成本效益高的生物催化剂开发成为可能.

关键词:
生物信息学是一种生物信息学.酶工程是什么?酶工程是什么?原酶是一种基酶.工业生物催化剂的工业生物催化剂机器学习是机器学习.

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GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization
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科学领域:

  • 生物技术和生物化学
  • 计算生物学 计算生物学
  • 酶工程是什么? 酶工程是什么?

背景情况:

  • 机器学习 (ML) 越来越多地应用于酶工程,因为蛋白质序列和功能之间的复杂关系.
  • 预测性ML模型旨在通过识别有希望的蛋白质序列来减少酶工程中的实验工作量.
  • 本综述侧重于算法辅助的蛋白质工程,强调成功和计算方法.

研究的目的:

  • 审查机器学习在酶工程中的成功应用.
  • 讨论用于改善酶特性的计算方法.
  • 强调ML在开发可持续合成生物催化剂中的作用.

主要方法:

  • 对蛋白质工程中的机器学习应用现有文献的审查.
  • 讨论用于预测和增强酶特性的计算技术.
  • 来自NCCR Catalysis的案例研究说明了算法辅助的酶设计.

主要成果:

  • 已经确定了ML驱动酶工程的成功例子.
  • 计算方法可以有效地提高酶的酶选择性,区域选择性,活性和稳定性.
  • ML加速了优化酶序列的识别.

结论:

  • 机器学习是推动酶工程的强大工具.
  • 计算方法的持续应用将产生改进的生物催化剂.
  • 基于ML的酶工程对于可持续的化学合成至关重要.