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相关概念视频

Gene Evolution - Fast or Slow?02:05

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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
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The flow of genetic information in cells from DNA to mRNA to protein is described by the central dogma, which states that genes specify the sequence of mRNAs, which in turn specify the sequence of amino acids making up all proteins. The decoding of one molecule to another is performed by specific proteins and RNAs. Because the information stored in DNA is so central to cellular function, it makes intuitive sense that the cell would make mRNA copies of this information for protein synthesis...
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相关实验视频

Updated: Jun 7, 2025

In Vitro Directed Evolution of a Restriction Endonuclease with More Stringent Specificity
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通过使用EVOLVEpro的蛋白质语言模型进行快速的基导向进化

Kaiyi Jiang1,2,3,4, Zhaoqing Yan1,2,3, Matteo Di Bernardo5

  • 1Department of Medicine Division of Engineering in Medicine Brigham and Women's Hospital Harvard Medical School, Boston, MA, USA.

Science (New York, N.Y.)
|November 21, 2024
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概括

通过结合蛋白质语言模型 (PLM) 和主动学习来加速蛋白质工程. 这种方法迅速增强了蛋白质的活性,克服了当前计算和实验方法的局限性.

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Mutagenesis and Functional Selection Protocols for Directed Evolution of Proteins in E. coli
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In Vitro Directed Evolution of a Restriction Endonuclease with More Stringent Specificity

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科学领域:

  • 生物技术
  • 计算生物学
  • 蛋白质工程

背景情况:

  • 定向蛋白质进化对于生物医学应用至关重要,但面临复杂性和低效优化等挑战.
  • 使用蛋白质语言模型 (PLM) 的现有in silico方法对蛋白质活动的概括和映射有限.

研究的目的:

  • 使用最少的实验数据开发一种快速改善蛋白质活性的新框架.
  • 克服当前蛋白质工程和指导方法的局限性.

主要方法:

  • 推出了EVOLVEpro,一个简单的积极学习框架.
  • 综合蛋白质语言模型 (PLM) 与适应性景观指导的回归模型.
  • 用最少的数据对各种蛋白质工程任务应用框架.

主要成果:

  • 在所需的蛋白质特性方面实现了多达100倍的改善.
  • 在RNA生产,基因组编辑和抗体结合方面证明了六种蛋白质的有效性.
  • 展示了少量激活学习对零射击预测的优势.

结论:

  • 通过人工智能指导的蛋白质工程显著加速.
  • 该框架为优化生物和医学中的蛋白质活动提供了强有力的方法.
  • 突出了对蛋白质设计有限的实验数据的积极学习的优势.