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相关概念视频

Translation01:31

Translation

Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Translation01:31

Translation

Lesson: Translation
Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
Improving Translational Accuracy02:07

Improving Translational Accuracy

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...
Translation01:31

Translation

Translation is the process of synthesizing proteins from the genetic information carried by messenger RNA (mRNA). Following transcription, it constitutes the final step in the expression of genes. This process is carried out by ribosomes, complexes of protein and specialized RNA molecules. Ribosomes, transfer RNA (tRNA), and other proteins produce a chain of amino acids—the polypeptide—as the end product of translation.
Translation Produces the Building Blocks of Life
Proteins are called the...

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相关实验视频

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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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人工智能转化后翻译修改研究研究

Doo Nam Kim1, Tianzhixi Yin2, Tong Zhang1

  • 1Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA 99352, USA.

Bioengineering (Basel, Switzerland)
|January 24, 2025
PubMed
概括
此摘要是机器生成的。

人工智能 (AI) 有助于理解翻译后修饰 (PTM),这是关键的蛋白质变化. 本综述探讨了用于PTM站点预测和功能分析的AI应用,并引入了用于AI培训的数据管道.

关键词:
后翻译修改 后翻译修改人工智能的人工智能是人工智能.深度学习是一种深度学习.机器学习是机器学习.

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科学领域:

  • 生物化学和分子生物学
  • 计算生物学和生物信息学

背景情况:

  • 翻译后修改 (PTMs) 是合成后蛋白质的基本共价变化.
  • PTMs显著影响蛋白质功能,细胞过程和分子结构.
  • 了解PTM对于进化研究和理解生物复杂性至关重要.

研究的目的:

  • 探索人工智能 (AI) 在翻译后修改 (PTM) 研究中的应用.
  • 讨论在PTM功能分析中使用人工智能的优势和理由.
  • 为了比较各种深度学习架构来预测PTM站点和监管功能.

主要方法:

  • 对深度学习架构和用于PTM站点预测的程序进行审查和比较.
  • 讨论PTM研究中语言模型的最新应用.
  • 用于人工智能培训和预测的高通量PTM数据生成管道的描述.

主要成果:

  • 人工智能,特别是深度学习和语言模型,在预测PTM网站方面显示出巨大的潜力.
  • 人工智能工具可以阐明各种后翻译修改的调节功能.
  • 一个新的数据管道促进了用于PTM分析的AI模型开发.

结论:

  • 人工智能为推进研究翻译后修改提供了强大的工具.
  • 未来的AI模型可以得到改进,进一步为PTM生物工程做出贡献.
  • 这项工作突出了AI在解读蛋白质修饰复杂格局中的作用.