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相关概念视频

Ribosome Profiling02:24

Ribosome Profiling

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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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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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Initiation of Translation02:33

Initiation of Translation

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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...
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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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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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Translation in Prokaryotes01:29

Translation in Prokaryotes

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Prokaryote translation is a complex, highly coordinated process that converts genetic information from mRNA into functional proteins. It involves three stages: initiation, elongation, and termination, each facilitated by specific molecular components.Initiation of TranslationThe process begins with the assembly of the ribosomal subunits and initiation factors on the mRNA. In bacteria, the 30S ribosomal subunit recognizes the Shine-Dalgarno sequence in the mRNA, a conserved region upstream of...
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相关实验视频

Updated: Sep 16, 2025

Rapid, Enzymatic Methods for Amplification of Minimal, Linear Templates for Protein Prototyping using Cell-Free Systems
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UTRGAN:学习生成5' UTR序列,以优化翻译效率和基因表达.

Sina Barazandeh1,2, Furkan Ozden3, Ahmet Hincer4

  • 1Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States.

Bioinformatics advances
|July 7, 2025
PubMed
概括

一个新的生成对抗网络模型UTRGAN设计了合成的5'未翻译区域 (UTR) 来增强蛋白质表达. 这种由人工智能驱动的方法显著提高了合成生物学应用的翻译效率和核糖体负载.

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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相关实验视频

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科学领域:

  • 合成生物学 合成生物学
  • 生物信息学是一种生物信息学.
  • 分子生物学分子生物学

背景情况:

  • mRNA的5'未翻译区域 (UTR) 对于控制蛋白质表达水平和稳定性至关重要.
  • 优化UTR序列对于合成生物系统中高稳定的蛋白质生产至关重要.
  • 现有的UTR序列通常是专利的,需要开发新的高性能替代品.

研究的目的:

  • 开发一种计算模型,用于生成具有改进性质的新型5' UTR 序列.
  • 优化生成的UTR序列,以提高目标基因表达,核糖体负载和翻译效率.
  • 为设计用于生物应用的合成UTR提供一个公开的工具.

主要方法:

  • 使用了一个名为UTRGAN的生成对抗网络 (GAN) 框架来生成5' UTR序列.
  • 实施了一种优化程序,以提高预测的蛋白质表达,核糖体负载和翻译效率.
  • 通过比较它们的预测性质和实验翻译率与已知的UTRs来验证生成的UTR序列.

主要成果:

  • 与初始序列相比,UTRGAN生成的UTR显示出高达5倍的预测表达和34倍的预测翻译效率.
  • 序列与已知的调节动机的相似性增加,包括内部核糖体进入点和Kozak序列.
  • 在体外实验证实,使用UTRGAN设计的UTRs与高容量的β-环球蛋白5'UTR相比,TNF-α蛋白的翻译率更高.

结论:

  • UTRGAN提供了一种有效的AI驱动方法,用于设计具有显著增强翻译特性的合成5' UTR.
  • 该模型模拟自然UTR特征并优化特定表达指标的能力为合成生物学提供了有价值的工具.
  • 开源发布的UTRGAN及其数据集有助于进一步研究和应用优化蛋白质表达系统.