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相关概念视频

Oligosaccharide Assembly01:24

Oligosaccharide Assembly

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Protein glycosylation starts in the ER lumen and continues in the Golgi apparatus. Glycosyltransferases catalyze the addition of sugar molecules or glycosylation of proteins. Usually, these enzymes add sugars to the hydroxyl groups of selected serine or threonine residues to form O-linked glycans or the amino groups of asparagine residues to form N-linked glycans. Different positions on the same polypeptide chain can contain differently linked glycans.
Multiple sugar molecules that may or may...
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Protein Glycosylation01:25

Protein Glycosylation

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Glycosylation, the most common post-translational modification for proteins, serves diverse functions. Adding sugars to proteins makes the proteins more resistant to proteolytic digestion. Glycosylated proteins can act as markers and receptors to promote cell-cell adhesion. Additionally, they have many essential quality control functions in the cell, such as correct protein folding and facilitating transport of misfolded proteins to the cytosol, which can be degraded.
Glycosylation occurs in...
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Proteoglycans01:05

Proteoglycans

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Glycans, a class of complex heterogeneous molecules, can be covalently attached to proteins to form glycosylated proteins that regulate various physiological and pathological processes. Glycosylated proteins or glycoproteins comprise N-linked and O-linked oligosaccharides. O-glycosylation is the most common type of protein glycosylation. Here, glycans attach to the oxygen atom of the hydroxyl groups of Serine or Threonine residues. O-linked glycosylation occurs later in protein processing,...
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The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides
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通过机器学习镜头对糖化酶的异构选择性

Natasha Videcrantz Faurschou1, Victor Friis1, Priyanka Raghavan2

  • 1Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen, Denmark.

Journal of the American Chemical Society
|September 25, 2025
PubMed
概括

机器学习模型现在可以预测糖化立体选择性,包括异构比率. 这一突破有助于碳水化合物化学,

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科学领域:

  • 碳水化合物化学
  • 计算化学
  • 机器学习

背景情况:

  • 在糖化反应中预测立体选择性仍然是碳水化合物化学中的一个重大挑战.
  • 了解和控制异构选择性对于合成复杂碳水化合物至关重要.

研究的目的:

  • 开发能够预测糖分化立体选择性的机器学习模型.
  • 创建一个公开的工具,GlycoPredictor,整合这些预测模型.
  • 分析糖化趋势,并建立管理立体选择性的规则等级.

主要方法:

  • 对糖化反应的文献数据的统计分析.
  • 开发和整合三种机器学习模型来预测主要异构体,小异构体存在和异构体比率.
  • 通过新的糖化方法验证预测趋势.

主要成果:

  • 成功构建了准确预测糖化结果的机器学习模型.
  • 该GlycoPredictor工具提供主要异位数,小异位数和异位数比率的预测.
  • 确定了控制糖化立体选择性的规则等级,揭示了新的趋势.

结论:

  • 机器学习提供了一种强大的方法来预测和理解糖化立体选择性.
  • 作为化学家的宝贵资源,GlycoPredictor补充了专家的知识.
  • 通过提供预测见解和指导合成策略, 这些发现推动了碳水化合物化学领域的发展.