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相关概念视频

Experimental RNAi02:15

Experimental RNAi

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RNA interference (RNAi) is a cellular mechanism that inhibits gene expression by suppressing its transcription or activating the RNA degradation process. The mechanism was discovered by Andrew Fire and Craig Mello in 1998 in plants. Today, it is observed in almost all eukaryotes, including protozoa, flies, nematodes, insects, parasites, and mammals. This precise cellular mechanism of gene silencing has been developed into a technique that provides an efficient way to identify and determine the...
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Combinatorial Gene Control02:33

Combinatorial Gene Control

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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
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Updated: Jul 2, 2025

An Optimized Quantitative Pull-Down Analysis of RNA-Binding Proteins Using Short Biotinylated RNA
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An Optimized Quantitative Pull-Down Analysis of RNA-Binding Proteins Using Short Biotinylated RNA

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DERNA 能够实现帕雷托最佳的RNA设计.

Xinyu Gu1, Yuanyuan Qi1, Mohammed El-Kebir1,2

  • 1Department of Computer Science and University of Illinois Urbana-Champaign, Urbana, Illinois, USA.

Journal of computational biology : a journal of computational molecular cell biology
|February 28, 2024
PubMed
概括

设计用于疫苗的信使RNA (mRNA) 序列包括平衡最小自由能量 (MFE) 和密码体适应指数 (CAI). 我们的DERNA算法找到最佳的RNA设计,以提高稳定性和翻译效率.

科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 疫苗开发 疫苗开发

背景情况:

  • 设计信使RNA (mRNA) 序列对于疫苗开发至关重要,由于子退化的原因,有许多可能性.
  • RNA二次结构影响热力学稳定性 (最小自由能量,MFE) 和mRNA半衰期.
  • 编码使用偏差 (编码适应指数,CAI) 影响翻译效率,同时优化MFE和CAI是最近的重点.

研究的目的:

  • 为应对同时优化MFE和CAI用于RNA序列设计的挑战.
  • 介绍帕雷托最佳RNA设计问题和DEsignRNA (DERNA) 算法.
  • 为了确定RNA序列与热力学稳定性和翻译效率之间的最佳权衡.

主要方法:

  • 开发了DEsign RNA (DERNA) 算法,使用加权和法来计算帕雷托前线.
  • 采用动态编程来优化MFE和CAI目标的凸形组合.
  • 在基准数据集上对DERNA进行评估,并将其与CDSfold和LinearDesign进行比较.

主要成果:

  • DERNA确定了帕雷托最佳RNA序列,在MFE和CAI中没有更好的单一解决方案.
  • 与CDSfold (仅MFE优化) 相比,DERNA实现了相同的MFE,具有更高的CAI.
关键词:
的RNA序列设计.动态编程是动态的编程.多目标优化优化反向翻译和mRNA疫苗的设计.

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  • 德纳的解决方案质量与最近的双目标优化方法线性设计 (LinearDesign) 相匹配.

    • 结论:

    • 德纳算法有效地平衡MFE和CAI用于RNA序列设计.
    • 这种方法具有优化mRNA疫苗设计的巨大潜力,包括针对SARS-CoV-2尖端蛋白点.