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相关概念视频

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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Protein-protein Interfaces02:04

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
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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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使用InterLabelGO+用于准确的蛋白质语言基于模型的功能预测.

Chengxin Zhang1, Quancheng Liu2, Lydia Freddolino3,4

  • 1CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.

Methods in molecular biology (Clifton, N.J.)
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PubMed
概括
此摘要是机器生成的。

InterLabelGO+是一个新的深度学习模型,用于使用基因本体学 (GO) 术语预测蛋白质功能. 它通过整合序列特征和同类数据,在CAFA5挑战中取得了最佳表现.

关键词:
深度学习是一种深度学习.基因本体学是基因的本体学.蛋白质功能的预测和预测蛋白质语言模型的模型序列的同质性 序列的同质性

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相关实验视频

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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
  • 基因组学就是基因组学.

背景情况:

  • 蛋白质序列的大型语言模型的出现加速了深度学习方法的发展,用于预测蛋白质功能,主要是通过基因本体学 (GO) 术语.
  • 准确的蛋白质功能预测对于理解生物系统和疾病机制至关重要.

研究的目的:

  • 介绍InterLabelGO+,一种基于深度学习的新型模型,用于蛋白质GO术语预测.
  • 为了证明InterLabelGO+在功能注释的批判性评估 (CAFA5) 挑战中的有效性.
  • 为蛋白质功能预测和模型再培训提供可访问的工具 (web服务器和独立包).

主要方法:

  • 利用ESM2蛋白语言模型提取基于序列的特征.
  • 开发了一种深度学习模型,使用专门的损失函数进行训练,以计算间期关系.
  • 集成的深度学习预测与GO术语来源于序列同质性搜索的共识预测.

主要成果:

  • 在CAFA5挑战中,InterLabelGO+在蛋白质功能预测方面取得了最佳表现.
  • 该模型有效地利用序列特征和同质信息来准确地分配GO术语.
  • 开发的方法为预测蛋白质功能提供了一个强大的框架.

结论:

  • InterLabelGO+代表了基于深度学习的蛋白质功能预测的重大进展.
  • InterLabelGO+网络服务器和软件包的可访问性有助于更广泛的研究应用.
  • 能够重新训练模型的能力确保其与不断变化的生物数据的持续相关性.