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相关概念视频

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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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Pleiotropy01:33

Pleiotropy

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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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Nonsense-mediated mRNA Decay02:27

Nonsense-mediated mRNA Decay

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The Upf proteins that carry out nonsense-mediated decay (NMD) are found in all eukaryotic organisms, including humans. Each protein has an individual role, but they need to work in collaboration. Upf1 is an ATP-dependent RNA helicase that unwinds the RNA helix. Because Upf1 can unwind any RNA, Upf2 and Upf3 are required to help Upf1 discriminate between nonsense and normal mRNAs.
Usually, Upf3 binds to an Exon Junction Complex (EJC) at mRNA splice sites. If a ribosome fully translates the mRNA,...
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Mutations01:39

Mutations

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Overview
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Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

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Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
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Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

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A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
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相关实验视频

Updated: Jun 29, 2025

In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila
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In Vivo Functional Study of Disease-associated Rare Human Variants Using Drosophila

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通过使用VariPred的蛋白质语言模型增强误解变体病原性预测.

Weining Lin1, Jude Wells2, Zeyuan Wang3

  • 1Division of Biosciences, Institute of Structural and Molecular Biology, University College London, London, UK.

Scientific reports
|April 7, 2024
PubMed
概括
此摘要是机器生成的。

VariPred是一种新的计算工具,使用蛋白质序列准确预测遗传变异的病原性. 这种方法通过利用先进的蛋白质语言模型而优于现有方法,而不需要复杂的特征工程.

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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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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相关实验视频

Last Updated: Jun 29, 2025

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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科学领域:

  • 基因组学和生物信息学
  • 计算生物学 计算生物学
  • 分子遗传学 分子遗传学

背景情况:

  • 预测遗传变异的致病性对于了解疾病机制和临床影响至关重要.
  • 传统方法依赖于手工制作的特征,通常需要复杂的数据预处理,如结构或进化分析.
  • 深度学习和大型蛋白质语言模型的出现为变异性病原性预测提供了新的途径.

研究的目的:

  • 介绍VariPred,这是一个用于预测遗传变异致病性的新框架.
  • 利用预先训练的蛋白质语言模型进行端到端的变体影响预测.
  • 为了证明VariPred的性能优于现有的最先进的方法,仅使用蛋白质序列数据.

主要方法:

  • 开发了VariPred,这是一个端到端的深度学习模型,使用预训练的蛋白质语言模型 (ESM-1b).
  • 输入要求仅限于蛋白质序列,消除了对结构或多个序列对齐特征的需求.
  • 评估了VariPred在六个已建立的变体影响预测基准上的表现.

主要成果:

  • 与3Cnet,Polyphen-2,REVEL,MetaLR,FATHMM和ESM等已知预测器相比,VariPred的表现相当或优越.
  • 该模型在多个基准中实现了强大的分类准确性.
  • 简化的输入要求 (仅蛋白质序列) 简化了预测过程.

结论:

  • VariPred提供了一种强大而高效的新工具,用于预测变种病原性.
  • 该框架强调了蛋白质语言模型在基因组变异解释中的潜力.
  • 这种基于序列的方法简化了病原性预测,使研究人员更容易获得.