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Glucose Transporters

Glucose Transporters

Glucose transporters facilitate the transport of glucose across the cell membrane. In addition to glucose, some glucose transporters can also aid the movement of other hexoses such as fructose, mannose, and galactose.
Facilitated diffusion-glucose transporters (GLUTs) are encoded by the solute-linked carrier (SLC) family 2, subfamily A gene family, or SLC2A. The 14 GLUT protein members are distributed into three classes:

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Updated: May 7, 2026

A Quantitative Glycomics and Proteomics Combined Purification Strategy

A Quantitative Glycomics and Proteomics Combined Purification Strategy

Published on: March 8, 2016

Expanding Glycopeptide Identification with Match-Between-Glycans in FragPipe.

Jiechen Shen¹, Daniel A Polasky¹, Shelley Jager^2,3

¹Department of Pathology, University of Michigan, Ann Arbor, MI, USA.

Biorxiv : the Preprint Server for Biology

|February 27, 2026

Summary

This summary is machine-generated.

Match-Between-Glycans (MBG) enhances glycopeptide identification by utilizing MS1 signals, expanding the discovery of complex glycosylation patterns. This method improves quantitative profiling of glycosylation, even for low-abundance glycopeptides.

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Area of Science:

Biochemistry
Proteomics
Glycobiology

Background:

Glycosylation is a critical post-translational modification influencing protein function and disease.
Mass spectrometry-based glycoproteomics is vital for studying glycosylation in health and disease.
Current methods struggle to identify low-abundance or complex glycopeptides due to challenging MS2 spectra.

Purpose of the Study:

To introduce a novel method, Match-Between-Glycans (MBG), for expanding glycopeptide identification.
To improve the comprehensive analysis of glycosylation patterns in complex biological samples.
To enable the identification of previously unassigned glycopeptides and novel glycan structures.

Main Methods:

Development of the Match-Between-Glycans (MBG) algorithm.
Utilizing MS1 signal analysis to infer glycopeptide presence.
Integrating MBG with existing glycoproteomics workflows and target-decoy false discovery rate (FDR) control.

Main Results:

MBG successfully expands the identification of glycopeptides, including those lacking sufficient MS2 data.
The method accurately identifies novel glycan structures with modifications or adducts.
MBG provides a more complete quantitative profile of glycosylation at each glycosite.

Conclusions:

MBG significantly enhances glycopeptide identification capabilities in glycoproteomics.
This method offers a more comprehensive understanding of glycosylation in physiological and pathological processes.
MBG is seamlessly integrated into FragPipe for user-friendly, advanced glycoproteomic analysis.