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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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Extracting glycan motifs using a biochemicallyweighted kernel.

Hao Jiang, Kiyoko F Aoki-Kinoshita, Wai-Ki Ching

|February 21, 2012

Summary

This summary is machine-generated.

A new method, the biochemically-weighted tree kernel (BioLK-method), accurately identifies biologically important glycan motifs. This advances the analysis of complex carbohydrate structures for biomarker discovery.

Keywords:

internal transcribed spacer metacestode polymerase chain reaction ribosomal DNA rodent

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Glycan Node Analysis: A Bottom-up Approach to Glycomics

Glycan Node Analysis: A Bottom-up Approach to Glycomics

Published on: May 22, 2016

Area of Science:

Carbohydrate chemistry and bioinformatics
Glycomics and computational biology
Biomolecular structure analysis

Background:

Carbohydrates (glycans) are crucial biomolecules with diverse biological roles, yet their structural complexity hinders analysis compared to DNA and proteins.
Understanding glycan structure is vital for deciphering biological processes like signaling and cellular recognition.
Existing kernel methods for glycan analysis, such as the weighted q-gram (LK-method), show promise in classification but struggle with extracting biologically meaningful features.

Purpose of the Study:

To develop an advanced kernel method that incorporates biochemical information for improved glycan structure classification and feature extraction.
To enhance the identification of biologically relevant glycan motifs and potential biomarkers.
To overcome the limitations of previous methods in extracting meaningful features from complex glycan data.

Main Methods:

Development of a biochemically-weighted tree kernel (BioLK-method) utilizing a glycan similarity matrix.
Integration of biochemical information of individual q-grams into the kernel matrix construction.
Application of the BioLK-method with a Support Vector Machine (SVM) for glycan motif classification on public datasets.

Main Results:

The BioLK-method successfully detected biologically important motifs, outperforming the LK-method in this aspect.
Classification performance of the BioLK-method was comparable to the existing LK-method.
The method was validated on three glycan datasets from CFG and KEGG GLYCAN, with results aligning with existing literature.

Conclusions:

The novel BioLK-method demonstrates flexibility and capability in feature extraction by incorporating biochemical information.
This approach aids in the accurate detection of biologically significant glycan motifs.
The BioLK-method shows potential for predicting glycan biomarkers, advancing glycomics research.