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相关概念视频

Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
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Protein Denaturation01:28

Protein Denaturation

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The function of proteins depends on their native three-dimensional structure, which is dictated by the amino acid sequence of the specific protein. Folding of the polypeptide chain takes place under specific conditions that energetically favor the folded conformation. In contrast, protein denaturation occurs spontaneously under unfavorable conditions that disrupt the integrity of the folded conformation. Thus, the chemical and physical environment of a protein, such as significant changes in pH...
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相关实验视频

Updated: Jun 29, 2025

A New Screening Method for the Directed Evolution of Thermostable Bacteriolytic Enzymes
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通过深度进化的高温耐受性蛋白质工程.

Huanyu Chu1,2, Zhenyang Tian1,2,3, Lingling Hu1,2,4

  • 1Key Laboratory of Engineering Biology for Low-carbon Manufacturing, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, P. R. China.

Biodesign research
|April 4, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了DeepEvo,这是一种新的深度学习策略,可以有效地设计蛋白质的温度耐受性. 这种人工智能驱动的方法显著加速了耐热蛋白的发现,克服了传统的劳动密集型方法.

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Engineering 'Golden' Fluorescence by Selective Pressure Incorporation of Non-canonical Amino Acids and Protein Analysis by Mass Spectrometry and Fluorescence
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Engineering 'Golden' Fluorescence by Selective Pressure Incorporation of Non-canonical Amino Acids and Protein Analysis by Mass Spectrometry and Fluorescence

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科学领域:

  • 计算生物学 计算生物学
  • 蛋白质工程是指蛋白质工程.
  • 人工智能的人工智能

背景情况:

  • 传统的蛋白质工程提高温度耐受性是劳动密集型,依赖于代突变和高通量查.
  • 开发具有更好的热稳定性的蛋白质对于各种生物技术应用至关重要.

研究的目的:

  • 开发一种高效,人工智能驱动的策略,用于设计蛋白质高温耐受性.
  • 为了减少与发现耐温度蛋白质相关的劳动和时间.

主要方法:

  • 开发了一个深度进化 (DeepEvo) 策略,集成蛋白质序列生成和选择的深度学习模型.
  • 利用蛋白质语言模型作为选择器,在潜在序列空间中施加进化压力.
  • 使用生成对抗网络 (GAN) 作为变异生成器来创建功能蛋白序列.

主要成果:

  • 在模型蛋白质甘 3-酸盐脱酶中成功设计了高温耐受性.
  • 从仅30个生成的序列中获得了8个耐高温的变体.
  • 在识别功能变异方面取得了惊人的成功率,超过26%.

结论:

  • 该DeepEvo战略在工程蛋白质高温耐受性方面表现出高效率.
  • 深度学习模型可以有效地指导蛋白质工程,加速发现具有所需功能特征的蛋白质.