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相关概念视频

Eukaryotic Transcription Activators02:42

Eukaryotic Transcription Activators

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Transcription activators are proteins that promote the transcription of genes from DNA to RNA. In most cases, these proteins contain two separate domains ‒ a domain that binds to DNA and a domain for activating transcription; however, in some cases, a single domain is responsible for both binding and activation of transcription, as seen in the glucocorticoid receptor and MyoD.
The binding domains are capable of recognizing and interacting with regulatory sequences on the DNA. These...
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Co-activators and Co-repressors02:04

Co-activators and Co-repressors

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Gene transcription is regulated by the synergistic action of several proteins that form a complex at a gene regulatory site. This is observed in eukaryotes, where the regulation of gene expression is a complex process. Regulatory proteins in eukaryotes can broadly be classified into two types – regulators that bind directly to specific DNA sequences and co-regulators that associate with regulatory proteins but cannot directly bind to the DNA. These co-regulators are further divided into...
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Transcription Factors02:16

Transcription Factors

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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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RNA Polymerase II Accessory Proteins02:36

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Proteins that regulate transcription can do so either via direct contact with RNA Polymerase or through indirect interactions facilitated by adaptors, mediators, histone-modifying proteins, and nucleosome remodelers. Direct interactions to activate transcription is seen in bacteria as well as in some eukaryotic genes. In these cases, upstream activation sequences are adjacent to the promoters, and the activator proteins interact directly with the transcriptional machinery. For example, in...
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Master Transcription Regulators02:23

Master Transcription Regulators

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Master transcription regulators are regulatory proteins that are predominantly responsible for regulating the expression of multiple genes. Often these genes work in concert to drive a  complex process. Activation of a master transcription regulator can lead to a cascade of transcriptional activation necessary for that outcome. These regulators can directly bind to the regulatory sequences of the various genes involved, or they can indirectly regulate transcription by binding to regulatory...
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Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
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相关实验视频

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
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使用图形神经网络预测转录激活域函数.

Farhanaz Farheen1, Bradley K Broyles2, Yuanyuan Zhang1

  • 1Department of Computer Science, Purdue University, West Lafayette, IN, USA.

bioRxiv : the preprint server for biology
|May 20, 2024
PubMed
概括
此摘要是机器生成的。

图形神经网络通过分析残留物和原子结构特征,准确地预测转录激活域,优于传统方法. 功能域的特点是特定的氨基酸特性,如酸度和芳香度.

关键词:
激活域名的激活域名图形神经网络的神经网络逻辑回归的逻辑回归二级结构是二级结构的二次结构.

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
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科学领域:

  • 分子生物学分子生物学
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 由于序列多样性和结构混乱,预测转录激活域 (TAD) 是一个挑战.
  • 现有的方法,如物流回归和CNN等,与复杂的模式和结构特征作斗争.
  • TADs的结构性质提供了提高预测准确性的潜力.

研究的目的:

  • 开发一种更准确的方法来预测转录激活域的功能.
  • 探索图形神经网络 (GNN) 在分析TAD结构特征中的实用性.
  • 确定确定TAD功能的关键序列和结构性质.

主要方法:

  • 利用图形神经网络 (GNN) 用残留物或原子作为节点来建模TAD结构.
  • 实验了各种特征组合,包括氨基酸类型,位置,物理化学性质和二次结构.
  • 开发了一个物流回归模型来分析特征的重要性并比较性能.

主要成果:

  • 结合氨基酸类型,位置,物理化学性质和二次结构的残留水平GNN模型,实现了97.9%的准确性,71%的F1得分和97.1%的AUROC.
  • 这种GNN模型在测试数据集上表现优于现有的文献方法.
  • 后勤回归确定了氨基酸频率作为主要特征,酸性和芳香性残留物表明功能性,而基本残留物表明非功能性.

结论:

  • 图形神经网络通过捕捉复杂的结构关系来预测TAD功能是一个强大的方法.
  • 具体的残留物质,如酸度和芳香度,是TAD功能的关键决定因素.
  • 调查结果强调了整合结构信息的重要性,以便更好地预测监管要素.