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相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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From DNA to Protein03:06

From DNA to Protein

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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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Transfer RNA Synthesis02:36

Transfer RNA Synthesis

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One of the unique features of tRNA is the presence of modified bases. In some tRNAs, modified bases account for nearly 20% of the total bases in the molecule. Altogether, these unusual bases protect the tRNA from enzymatic degradation by RNases.
Each of these chemical modifications is carried by a specific enzyme, post-transcription. All of these enzymes have unique base and site-specificity. Methylation, the most common chemical modification, is carried by at least nine different enzymes, with...
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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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Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Leaky Scanning02:28

Leaky Scanning

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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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编码BERT:基于BERT的架构,专门用于使用交叉注意力机制进行编码器优化.

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  • 1Changchun Veterinary Research Institute, Chinese Academy of Agricultural Sciences, State Key Laboratory of Pathogen and Biosecurity, Key Laboratory of Jilin Province for Zoonosis Prevention and Control, Changchun 130122, China.

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概括
此摘要是机器生成的。

基于BERT的新模型CodonBERT优化了信使RNA (mRNA) 疫苗序列,以改善蛋白质表达. 它有效地捕捉了长期的编码子依赖性,提高了mRNA疫苗的设计.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 分子生物学分子生物学

背景情况:

  • 对mRNA疫苗的有效性至关重要,因为它会影响蛋白质的稳定性和表达.
  • 对于mRNA的庞大的序列空间对in silico优化方法提出了挑战.
  • 现有的深度学习方法与长期的编码子依赖性作斗争.

研究的目的:

  • 开发一个先进的深度学习模型来优化mRNA编码子.
  • 为了解决当前机器翻译启发方法的局限性.
  • 为了改善稳定和高度表达的mRNA序列的in silico预测.

主要方法:

  • 开发了基于BERT的架构CodonBERT,该架构利用交叉注意力来进行编码子优化.
  • 采用掩盖的子序列 (键/值) 和氨基酸序列 (查询) 方法.
  • 在人类蛋白质图谱中的高表达性转录上训练了CodonBERT.

主要成果:

  • 编码器BERT有效地捕捉了编码器和氨基酸之间的长期依赖.
  • 证明了该模型作为针对特定优化目标的定制培训框架的能力.
  • 该模型显示了提高mRNA疫苗设计的前景.

结论:

  • 在mRNA疫苗中,CodonBERT提供了一种新且有效的方法来优化mRNA疫苗的密码子.
  • 该模型处理长期依赖的能力超过了现有方法.
  • CodonBERT为设计更稳定和高度表达的mRNA疗法提供了有价值的工具.