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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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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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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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Introduction to Enzymes01:22

Introduction to Enzymes

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The use of enzymes by humans dates to 7000 BCE. Humans first used enzymes to ferment sugars and produce alcohol without knowing that this was an enzyme-catalyzed reaction. Wilhelm Kuhne coined the term 'enzyme' in 1877 from the Greek words ‘en’ meaning ‘in’ or ‘within’ and ‘zyme’ meaning ‘yeast.’
Most enzymes are proteins that speed up biochemical reactions without being consumed. Enzymes contain one or more active sites that...
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Multi-enzyme Screening Using a High-throughput Genetic Enzyme Screening System
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机器学习辅助酶工程的机遇和挑战

Jason Yang1, Francesca-Zhoufan Li2, Frances H Arnold1,2

  • 1Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.

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概括
此摘要是机器生成的。

机器学习 (ML) 通过帮助发现酶起点并优化其性能来增强酶工程. 这种方法加速了具有改进或全新的催化功能的新酶的发展.

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科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 蛋白质工程是指蛋白质工程.

背景情况:

  • 酶工程通过氨基酸序列修改优化了蛋白质的特性,如稳定性和效率.
  • 传统方法涉及有针对性的进化,由于巨大的蛋白质搜索空间,这可能会耗时.
  • 机器学习 (ML) 正在成为实证酶工程的补充工具.

研究的目的:

  • 解释ML如何补充传统的酶工程.
  • 讨论ML在推动酶工程成果方面的未来潜力.
  • 突出ML在起始点发现和健身优化中的作用.

主要方法:

  • ML模型用于已知的蛋白质序列的功能注释.
  • ML用于生成具有所需功能的新型蛋白序列.
  • 基于ML的导航蛋白质健身景观通过学习序列-健身关系.

主要成果:

  • ML有助于识别合适的酶起点.
  • ML有助于优化特定应用的酶适应性.
  • 机器学习模型可以预测或生成具有增强属性的序列.

结论:

  • ML显著补充和加速酶工程.
  • 机器学习为发现和优化具有新功能酶提供了强大的工具.
  • 机器学习的整合有望释放改进的工程成果,并扩大酶能力.