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相关概念视频

Nucleic Acid Structure01:25

Nucleic Acid Structure

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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA...
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RNA Stability01:53

RNA Stability

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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
33.9K
Nucleic Acids02:43

Nucleic Acids

45.8K
Nucleic acids are the most important macromolecules for the continuity of life. They carry the cell's genetic blueprint and carry instructions for its functioning.
DNA and RNA
The two main types of nucleic acids are deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). DNA is the genetic material in all living organisms, ranging from single-celled bacteria to multicellular mammals. It is in the nucleus of eukaryotes and in the organelles, chloroplasts, and mitochondria. In prokaryotes,...
45.8K
Bacterial RNA Polymerase00:43

Bacterial RNA Polymerase

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Unlike eukaryotes, bacteria use a single RNA Polymerase (RNAP) to transcribe all genes. The different subunits of bacterial RNAPhave distinct functions. The multisubunit structure of the bacterial RNAP helps the enzyme to maintain catalytic function, facilitate assembly, interact with DNA and RNA, and self-regulate its activity.
In most genes, the transcription site is a single base present upstream of the coding sequence. Though RNAP is a catalytically efficient enzyme, it does not recognize...
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Nucleic acids02:43

Nucleic acids

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Nucleic acids are the most important macromolecules for the continuity of life. They carry the cell's genetic blueprint and carry instructions for its functioning.
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The two main types of nucleic acids are deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). DNA is the genetic material in all living organisms, ranging from single-celled bacteria to multicellular mammals. It is in the nucleus of eukaryotes and in the organelles, chloroplasts, and mitochondria. In prokaryotes,...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

11.9K
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...
11.9K

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RiNALMo:通用RNA语言模型可以很好地概括结构预测任务.

Rafael Josip Penić1, Tin Vlašić2, Roland G Huber3

  • 1Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia.

Nature communications
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概括

研究人员开发了最大的RNA语言模型RiNALMo,用于解码RNA序列. 这种先进的模型提取隐藏的知识,并预测RNA结构,超过现有的方法在看不见的RNA家族.

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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:

  • 核糖核酸 (RNA) 正成为小分子药物的关键标,需要对其结构和功能有更深入的了解.
  • 由测序技术产生的大量未标记的RNA序列数据,为生物洞察提供了大量尚未开发的潜力.
  • 现有的深度学习方法难以将RNA二次结构预测推广到新型RNA家族.

研究的目的:

  • 介绍Ribonucleic Acid语言模型 (RiNALMo),这是迄今为止开发的最大的RNA语言模型.
  • 为了利用蛋白质语言模型的进步来分析RNA序列.
  • 从大型RNA数据集中提取隐性结构信息和隐藏的知识.

主要方法:

  • 在来自不同数据库的3600万个非编码RNA序列的数据集上预训练RiNALMo,一个650M参数模型.
  • 使用基于变压器的架构,类似于成功的蛋白质语言模型.
  • 评估RiNALMo在各种下游任务上的表现,包括二次结构预测.

主要成果:

  • RiNALMo在多个与RNA相关的下游任务中实现了最先进的性能.
  • 与现有的深度学习模型相比,展示了优越的概括能力,特别是用于预测未见的RNA家族的二次结构.
  • 成功捕获嵌入RNA序列中的隐性结构信息.

结论:

  • RiNALMo在分析RNA序列和理解它们的功能方面取得了重大进展.
  • 该模型对新RNA家族的概括能力解决了目前用于RNA结构预测的深度学习方法的关键局限性.
  • RiNALMo释放了大型未标记RNA数据集的潜力,用于药物发现和生物研究.