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相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

9.7K
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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Next-generation Sequencing03:00

Next-generation Sequencing

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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features....
88.5K
RNA Editing02:23

RNA Editing

9.0K
RNA editing is a post-transcriptional modification where a precursor mRNA (pre-mRNA) nucleotide sequence is changed by base insertion, deletion, or modification. The extent of RNA editing varies from a few hundred bases, in mitochondrial DNA of trypanosomes, to a just single base, in nuclear genes of mammals. Even a single base change in the pre-mRNA can convert a codon for one amino acid into the codon for another amino acid or a stop codon. This type of re-coding can significantly affect the...
9.0K
Proofreading01:43

Proofreading

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Overview
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Mismatch Repair01:36

Mismatch Repair

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Overview
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Lagging Strand Synthesis01:59

Lagging Strand Synthesis

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相关实验视频

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A Nonsequencing Approach for the Rapid Detection of RNA Editing
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使用代字符串编辑模型进行回复合成预测.

Yuqiang Han1,2, Xiaoyang Xu3, Chang-Yu Hsieh4

  • 1College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China.

Nature communications
|July 30, 2024
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概括
此摘要是机器生成的。

这项研究引入了一种新的AI方法用于逆合成,将其重新定义为分子字符串编辑任务. 这种方法提高了前体预测的准确性和药物发现的多样性.

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Efficient PAM-Less Base Editing for Zebrafish Modeling of Human Genetic Disease with zSpRY-ABE8e
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In Vitro Directed Evolution of a Restriction Endonuclease with More Stringent Specificity
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科学领域:

  • 有机化学 有机化学
  • 计算化学计算化学
  • 药物发现 药物发现 药物发现

背景情况:

  • 复合对于药物发现和有机合成至关重要.
  • 目前的人工智能方法使用代币对代币解码,导致性能不佳和多样性有限.
  • 化学反应导致局部分子变化,这意味着反应物和产物之间的重叠.

研究的目的:

  • 开发一种人工智能模型,用于改进单步逆合成预测.
  • 为了解决现有的代币对代币解码方法的局限性.
  • 为了提高预测前体化合物的准确性和多样性.

主要方法:

  • 重构复合作为一个分子串编辑任务.
  • 使用基于片段的生成编辑模型与明确的序列编辑操作.
  • 实现一个推断模块,采用重置采样和序列增强.

主要成果:

  • 拟议的模型产生高质量和多样化的前体化合物.
  • 在USPTO-50K基准数据集上达到60.8%的top-1准确度.
  • 与现有方法相比,表现出优越的性能.

结论:

  • 分子链编辑方法对于单步回复合成是有效的.
  • 开发的AI模型显著提高了预测准确性和多样性.
  • 这项工作为人工智能驱动的药物发现和有机合成提供了有希望的进步.