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Protein Glycosylation

Protein Glycosylation

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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Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions

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Published on: January 20, 2022

GlyTrait: A Versatile Bioinformatics Tool for Glycomics Analysis.

Bin Fu^1,2, Guoli Wang², Chenxin Li²

¹Department of Chemistry and Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.

Journal of Proteome Research

|October 27, 2025

Summary

This summary is machine-generated.

GlyTrait is a new Python tool that simplifies glycomics analysis by calculating functional traits from N-glycan data. It helps uncover disease-related glycosylation patterns for better biomarker discovery.

Keywords:

N-glycans bioinformatics glycomics machine learning python

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Published on: March 8, 2016

Area of Science:

Glycomics
Computational Biology
Biotechnology

Background:

Glycomics analysis faces challenges in data interpretability and biological relevance.
Current methods often focus on glycan abundance rather than functional properties.

Purpose of the Study:

To introduce GlyTrait, a Python framework for automated derivation and interpretation of functional traits from N-glycan data.
To enable custom trait calculation without coding knowledge.
To facilitate statistical and machine learning analyses for disease-associated glycosylation patterns.

Main Methods:

Development of GlyTrait, a Python-based framework.
Automated calculation of functional glycan traits (e.g., branching, fucosylation).
Implementation of a formula grammar for user-defined trait creation.
Application of statistical and interpretable machine learning models.

Main Results:

GlyTrait successfully derives biologically significant traits from N-glycan data.
Demonstrated efficacy through reanalysis of published data (glycoengineered CHO cells, visceral leishmaniasis).
Pilot study for hepatocellular carcinoma (HCC) N-glycan biomarker discovery showed promising results.

Conclusions:

GlyTrait enhances glycomics analysis by focusing on functional glycan properties.
The framework supports custom trait derivation and advanced statistical analysis.
GlyTrait is poised to become a valuable tool for glycomics research and biomarker discovery.