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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.
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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.
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Anomeric Selectivity of Glycosylations through a Machine Learning Lens.

Natasha Videcrantz Faurschou1, Victor Friis1, Priyanka Raghavan2

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|September 25, 2025
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Summary
This summary is machine-generated.

Machine learning models can now predict glycosylation stereoselectivity, including anomeric ratios. This breakthrough aids carbohydrate chemistry by offering a tool called GlycoPredictor for predicting glycosylation outcomes.

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Area of Science:

  • Carbohydrate Chemistry
  • Computational Chemistry
  • Machine Learning

Background:

  • Predicting stereoselectivity in glycosylation reactions remains a significant challenge in carbohydrate chemistry.
  • Understanding and controlling anomeric selectivity is crucial for synthesizing complex carbohydrates.

Purpose of the Study:

  • To develop machine learning models capable of predicting glycosylation stereoselectivity.
  • To create a publicly available tool, GlycoPredictor, integrating these predictive models.
  • To analyze glycosylation trends and establish a hierarchy of rules governing stereoselectivity.

Main Methods:

  • Statistical analysis of literature data on glycosylation reactions.
  • Development and integration of three machine learning models to predict major anomer, minor anomer presence, and anomeric ratio.
  • Validation of predicted trends through novel glycosylation methods.

Main Results:

  • Successfully built machine learning models that accurately predict glycosylation outcomes.
  • The GlycoPredictor tool provides predictions for major anomer, minor anomer, and anomeric ratio.
  • Identified a hierarchy of rules governing glycosylation stereoselectivity, revealing new trends.

Conclusions:

  • Machine learning offers a powerful approach to predict and understand glycosylation stereoselectivity.
  • GlycoPredictor serves as a valuable resource for chemists, complementing expert knowledge.
  • The findings advance the field of carbohydrate chemistry by providing predictive insights and guiding synthetic strategies.