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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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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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Allosteric Proteins-ATCase01:19

Allosteric Proteins-ATCase

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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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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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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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Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
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数据驱动的蛋白质工程用于改善催化活性和选择性.

Yu-Fei Ao1,2,3, Mark Dörr1, Marian J Menke1

  • 1Department of Biotechnology and Enzyme Catalysis, Institute of Biochemistry, University of Greifswald, Felix-Hausdorff-Str. 4, 17487, Greifswald, Germany.

Chembiochem : a European journal of chemical biology
|November 29, 2023
PubMed
概括

机器学习通过预测酶性能来帮助蛋白质工程,克服了传统方法的局限性. 这种数据驱动的方法指导了生物催化剂酶活性和选择性的优化.

关键词:
生物催化剂是一种生物催化剂.催化活动的催化活动.机器学习是机器学习.蛋白质工程工程 蛋白质工程选择性的选择性

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科学领域:

  • 生物催化剂是一种生物催化剂.
  • 蛋白质工程是指蛋白质工程.
  • 计算生物学 计算生物学

背景情况:

  • 传统的蛋白质工程方法,如定向进化和理性设计,在选广的蛋白质突变空间时面临着挑战.
  • 优化酶基质范围,催化活性和选择性对于生物催化剂应用至关重要.
  • 机器学习 (ML) 提供了一种强大的替代方案,可以近似蛋白质健身景观,并识别催化模式.

研究的目的:

  • 审查用于评估酶-基质-催化关系的机器学习模型.
  • 突出ML在指导数据驱动蛋白质工程运动中的作用.
  • 为了展望未来的ML在酶优化中的发展.

主要方法:

  • 审查现有的机器学习模型,应用于酶工程.
  • 基于有限的实验数据,分析ML在预测酶性能方面的能力.
  • 探索ML以了解酶-基质-催化相互作用.

主要成果:

  • 机器学习模型可以有效地近似蛋白质健身景观.
  • ML促进了催化模式的识别,指导了蛋白质工程的努力.
  • 使用ML的数据驱动方法可以改善酶活性和选择性.

结论:

  • 机器学习为蛋白质工程提供了一种新且高效的策略.
  • 以ML为指导的酶优化对于促进生物催化剂的发展至关重要.
  • 未来的ML开发将为设计具有所需性质的酶提供增强的工具.