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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...
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Catalytically Perfect Enzymes01:07

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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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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.
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A New Screening Method for the Directed Evolution of Thermostable Bacteriolytic Enzymes
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Seq2Topt:一种基于序列的深度学习预测酶最佳温度的预测器.

Sizhe Qiu1, Bozhen Hu2,3, Jing Zhao4,5

  • 1Department of Engineering Science, University of Oxford, Parks Road, OX1 3PJ, Oxford, United Kingdom.

Briefings in bioinformatics
|March 13, 2025
PubMed
概括

一个新的深度学习模型Seq2Topt,只使用蛋白质序列,准确地预测酶的最佳温度. 这个工具有助于酶挖掘和计算酶设计.

关键词:
注意力机制注意力机制深度学习是一种深度学习.酶的最佳温度是最佳温度.基于序列的预测.热友蛋白质是一种热友蛋白质.

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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
  • 酶工程是什么? 酶工程是什么?

背景情况:

  • 酶的最佳温度 (Topt) 对于催化活性至关重要.
  • 准确预测Topt对于酶应用是必不可少的.
  • 现有的模型在Topt预测准确度上有局限性.

研究的目的:

  • 使用蛋白质序列开发Topt酶的深度学习预测器.
  • 增强酶开采和内酶设计.
  • 为酶特性创建一个多功能预测平台.

主要方法:

  • 开发了Seq2Topt,这是一个利用蛋白质序列的深度学习模型.
  • 雇佣了多个头的注意力,以确定Topt.的关键蛋白质区域.
  • 通过对热友酶和突变效应的案例研究验证了Seq2Topt.

主要成果:

  • 在Topt预测中,Seq2Topt实现了卓越的准确性 (RMSE = 12.26°C,R2 = 0.57).
  • 该模型确定了影响Topt.的关键蛋白质区域.
  • 开发了酶最佳pH值 (Seq2pHopt) 和化温度 (Seq2Tm) 的准确预测器.

结论:

  • Seq2Topt是一个有前途的计算工具,用于酶的发现和设计.
  • 模型架构可以扩展到预测其他酶特性.
  • 这项工作为全面的酶性质预测平台奠定了基础.