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相关概念视频

Mutations01:39

Mutations

77.5K
Overview
77.5K
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

10.6K
Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
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Covalently Linked Protein Regulators02:04

Covalently Linked Protein Regulators

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Proteins can undergo many types of post-translational modifications, often in response to changes in their environment. These modifications play an important role in the function and stability of these proteins. Covalently linked molecules include functional groups, such as methyl, acetyl, and phosphate groups, and also small proteins, such as ubiquitin. There are around 200 different types of covalent regulators that have been identified.
These groups modify specific amino acids in a protein....
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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...
8.5K
Translation01:31

Translation

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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...
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Mismatch Repair01:20

Mismatch Repair

4.7K
Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
The Mutator Protein Family Plays a Key Role in DNA Mismatch Repair
The human genome has more than 3 billion base pairs of DNA per cell. Prior to cell division, that vast amount of genetic...
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相关实验视频

Updated: May 16, 2025

In Vivo Modeling of the Morbid Human Genome using Danio rerio
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In Vivo Modeling of the Morbid Human Genome using Danio rerio

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[基于蛋白质语言模型的突变效应预测研究进展]

Liang Zhang1, Pan Tan2,3, Liang Hong1,2,3,4

  • 1School of Physics and Astronomy, Shanghai Jiao Tong University, Shanghai 200240, China.

Sheng wu gong cheng xue bao = Chinese journal of biotechnology
|April 2, 2025
PubMed
概括
此摘要是机器生成的。

蛋白质语言模型 (PLM) 通过改进对蛋白质突变效应的预测来彻底改变生物信息学. 本综述涵盖了序列,结构和组合模型,讨论了挑战和未来的方向.

关键词:
深度学习是一种深度学习.多式联络融合多式联络融合突变效应的预测和预测蛋白质语言模型的模型序列模型的模型.结构模型 结构模型监督学习学习监督学习没有监督的学习学习.

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相关实验视频

Last Updated: May 16, 2025

In Vivo Modeling of the Morbid Human Genome using Danio rerio
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In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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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
  • 蛋白质工程是指蛋白质工程.

背景情况:

  • 预测蛋白质突变效应对于生物信息学和蛋白质工程至关重要.
  • 深度学习,特别是蛋白质语言模型 (PLM),提供了新的方法.

研究的目的:

  • 审查PLM在预测蛋白质突变效应中的应用.
  • 分析不同的PLM类型,它们的原理和局限性.

主要方法:

  • 专注于基于序列的,基于结构的和混合的PLM.
  • 在PLM培训中讨论无监督和监督学习.
  • 分析数据质量和数据噪声等挑战.

主要成果:

  • 在预测蛋白质突变效应方面,PLM显著有前途.
  • 不同的模型架构提供不同的优势和局限性.
  • 当前的挑战包括数据采集和噪音处理.

结论:

  • PLM是预测蛋白质突变效应的强大工具.
  • 未来的研究应该探索多式联络融合和少量学习.
  • 本综述为推进该领域提供了一个全面的视角.