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相关概念视频

Leaky Scanning02:28

Leaky Scanning

5.1K
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...
5.1K
Initiation of Translation02:33

Initiation of Translation

32.4K
Initiating translation is complex because it involves multiple molecules. Initiator tRNA, ribosomal subunits, and eukaryotic initiation factors (eIFs) are all required to assemble on the initiation codon of mRNA. This process consists of several steps that are mediated by different eIFs.
First, the initiator tRNA must be selected from the pool of elongator tRNAs by eukaryotic initiation factor 2 (eIF2). The initiator tRNA (Met-tRNAi) has conserved sequence elements including modified bases at...
32.4K
From DNA to Protein03:06

From DNA to Protein

18.3K
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...
18.3K
Improving Translational Accuracy02:07

Improving Translational Accuracy

10.0K
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...
10.0K
Ribosome Profiling02:24

Ribosome Profiling

3.5K
Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique...
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The Central Dogma01:25

The Central Dogma

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Overview
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相关实验视频

Updated: Jun 22, 2025

Synthesis and Characterization of mRNA-Loaded PolyBeta Aminoesters Nanoparticles for Vaccination Purposes
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对于mRNA疫苗的CodonBERT大语言模型.

Sizhen Li1, Saeed Moayedpour1, Ruijiang Li1

  • 1Digital R&D, Sanofi, Cambridge, Massachusetts 02141, USA.

Genome research
|July 1, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了CodonBERT,这是一个用于信息RNA (mRNA) 序列优化的新型大语言模型 (LLM). 科登伯特 (CodonBERT) 改进了mRNA设计,以获得更好的表达,稳定性和免疫性,优于现有方法.

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Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems
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Measurement of Specific Mycobacterial Mistranslation Rates with Gain-of-function Reporter Systems

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Bacterial Artificial Chromosomes: A Functional Genomics Tool for the Study of Positive-strand RNA Viruses
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Bacterial Artificial Chromosomes: A Functional Genomics Tool for the Study of Positive-strand RNA Viruses

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科学领域:

  • 生物技术是生物技术.
  • 计算生物学 计算生物学
  • 基因组学就是基因组学.

背景情况:

  • 使者RNA (mRNA) 技术对于疫苗和治疗来说越来越重要.
  • 优化mRNA序列对于增强表达,稳定性和免疫性至关重要.
  • 大量的mRNA序列甚至可以编码小蛋白质,需要高效的设计工具.

研究的目的:

  • 为优化mRNA序列开发一种先进的大型语言模型 (LLM).
  • 创建一个工具,在mRNA序列中捕捉复杂的生物概念.
  • 改善用于治疗和疫苗开发的mRNA特性预测.

主要方法:

  • 开发了CodonBERT,这是一个LLM,它利用编码子作为增强表示学习的输入.
  • 训练有素的CodonBERT对来自各种生物的1000多万种不同的mRNA序列进行了训练.
  • 评估CodonBERT在预测mRNA特性方面的表现,并将其与现有方法进行比较.

主要成果:

  • CodonBERT有效地从mRNA序列中学习表征,使用编码子作为输入.
  • 该模型展示了从mRNA数据中捕获重要的生物见解的能力.
  • 与以前的方法相比,CodonBERT在mRNA属性预测方面取得了更好的表现,包括在新型流感疫苗数据集上.

结论:

  • 在mRNA序列设计和优化方面,CodonBERT代表了重大进步.
  • 该LLM提供了一个强大的工具,以加强基于mRNA的疫苗和治疗方法的开发.
  • 进一步应用CodonBERT可以扩展到预测各种mRNA相关的生物特性.