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Oligosaccharide Assembly

Oligosaccharide Assembly

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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Author Spotlight: MAPP Protocol – Advancing Glycan Analysis

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Published on: September 29, 2023

Deep structure-level N-glycan identification using feature-induced structure diagnosis integrated with a deep

Suideng Qin¹, Zhixin Tian²

¹School of Chemical Science & Engineering, Shanghai Key Laboratory of Chemical Assessment and Sustainability, Tongji University, Shanghai, 200092, China.

Analytical and Bioanalytical Chemistry

|August 30, 2024

Summary

This summary is machine-generated.

This study introduces a deep learning model for precise N-glycan structure identification, resolving complex isomers. The new method enhances N-glycoproteomics analysis by accurately distinguishing glycan structures.

Keywords:

FISD Featured motif N-Glycan structure N-Glycoproteomics Neural network

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Published on: November 2, 2021

Area of Science:

Biochemistry
Computational Biology
Proteomics

Background:

N-glycosylation is a crucial protein modification with complex structures.
Existing bioinformatics methods struggle to resolve N-glycan sequence and linkage isomers.

Purpose of the Study:

To develop a deep learning approach for high-resolution N-glycan structure identification.
To improve the distinction of challenging glycan isomers, including symmetric and linkage isomers.

Main Methods:

Integration of feature-induced structure diagnosis (FISD) with a deep learning model.
Training a neural network, including a convolutional autoencoder and multilayer perceptron, on N-glycoproteomics data.
Utilizing 23 motif features from MS/MS spectra for N-glycan motif prediction.

Main Results:

Achieved 0.8 prediction accuracy and 0.95 AUC for N-glycan motif identification.
Resolved 5701 previously unassigned N-glycan structures using diagnostic ions.
Identified two novel fragmentation features (m/z 674.25, 835.28) significant for specific N-glycan motifs.

Conclusions:

The developed deep learning model significantly enhances N-glycan structure identification accuracy.
FISD integrated with deep learning effectively resolves complex glycan isomers and discovers new spectral features.
This approach advances N-glycoproteomics by enabling deeper structural insights into N-glycans.