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相关概念视频

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...
8.1K
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...
80.8K
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...
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GENPLAT: an Automated Platform for Biomass Enzyme Discovery and Cocktail Optimization
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EnTdecker - 一个基于机器学习的平台,用于指导能量转移催化中的基质发现

Leon Schlosser1, Debanjan Rana1, Philipp Pflüger1

  • 1Organisch-Chemisches Institut, University of Münster, Corrensstraße 36, 48149 Münster, Germany.

Journal of the American Chemical Society
|May 2, 2024
PubMed
概括

发现用于能量转移 (EnT) 催化物的新基质是具有挑战性的. EnTdecker平台使用机器学习对分子进行虚拟选,加速有效EnT催化剂的识别并提高实验成功率.

科学领域:

  • 计算化学
  • 催化剂
  • 机器学习

背景情况:

  • 由于目前的实验和计算方法的巨大化学空间和局限性,对能量转移 (EnT) 催化物的新基质的识别受到阻碍.
  • 在EnT催化中发现基质的现有策略往往耗时且昂贵,限制了该领域的快速发展.

研究的目的:

  • 在EnT催化中开发一种加速发现新基质的方法.
  • 引入EnTdecker平台,对潜在的EnT基板进行大规模的虚拟选.
  • 利用机器学习 (ML) 预测激发状态属性对于EnT催化至关重要.

主要方法:

  • 创建一个包含34000多个与EnT催化相关的综合数据集.
  • 使用此数据集来估计激发状态属性的预测机器学习模型的训练.
  • 通过重新发现已知的基质和基于发光的实验选来验证平台的有效性.

主要成果:

  • 通过基于ML的虚拟选,EnTdecker平台成功识别了EnT催化物的潜在基质.
  • 该平台通过从现有文献中重新发现已知的成功基板来证明其实用性.
  • 实验验证证了机器学习模型的预测能力,显示了性能预测的计算力度降低.

结论:

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  • EnTdecker平台显著提高了EnT催化基质选择的效率.
  • 预计该工具将增加EnT催化实验的成功率.
  • EnTdecker提供一个公开的网络应用程序 (entdecker.uni-muenster.de),以促进该领域的更广泛的研究.