# High-Throughput De Novo Glycan Sequencing

> **NIH NIH R01** · BOSTON UNIVERSITY MEDICAL CAMPUS · 2020 · $447,308

## Abstract

Glycosylation fulfills important physiological functions, including protein folding, embryogenesis, cell adhesion,
pathogen recognition, and immune response. The multifaceted roles glycosylation plays derive from the
presence of a range of glycan epitopes, where a small structural variation can have a profound impact on
functions. Further, a glycome consists of many closely related structures, with their relative amounts determined
by metabolic conditions in a cell- and growth-specific manner. Altered glycosylation is linked to many diseases,
including cardiovascular, pulmonary, neurological and autoimmune disorders, and cancer. Thus, there is a clear
need for analytical methods that can rapidly identify and quantify the many glycoforms in a glycome from different
health and disease states. Finally, no genome-predicted glycan database exists due to the unscripted nature of
glycan biosynthesis, and discovery of new glycan structures must be achieved by de novo methods.
Although tandem mass spectrometry-based biopolymer sequencing has been the major catalyst to the recent
rapid advance of 'omics, the prevailing collisionally activated dissociation method often fails to provide sufficient
glycan structural detail at the MS2 level, whereas the MSn approach lacks the speed, sensitivity, and quantitative
potential for high-throughput glycome analysis. We have recently developed an electronic excitation dissociation
(EED) method that can yield rich structural information in a single stage of MS/MS analysis. However, the impact
of EED on glycomics research is currently limited by its poor accessibility, insufficient coupling to on-line glycan
separation methods, and difficulty in interpretation of complex glycan EED tandem mass spectra.
Here, we propose to develop an integrated approach that combines EED with on-line liquid chromatography (LC)
separation and a novel bioinformatics tool to achieve high-throughput, de novo, and comprehensive glycome
characterization. We will explore the potential of EED for analysis of glycans in various derivatized forms, study
their fragmentation behaviors, and establish fragmentation rules for the development of bioinformatics software.
We will optimize conditions for efficient coupling of EED to reversed-phase, and porous graphitic carbon LC, and
develop an LC-EED-MS/MS approach for simultaneous characterization and quantitation of glycan mixtures. We
will implement EED on a Q-TOF instrument to improve its access to the glycoscience community. Finally, we will
develop and rigorously test the performance of a novel bioinformatics software that can rapidly and accurately
determine each glycan's structure from its tandem MS spectra. The proposed algorithm is fundamentally different
from most existing software, in that it no longer relies solely on glycosidic and cross-ring fragments for topology
and linkage analysis, but rather adopts a machine learning approach that considers the contexts of various types
of fragment pea...

## Key facts

- **NIH application ID:** 10000171
- **Project number:** 5R01GM132675-02
- **Recipient organization:** BOSTON UNIVERSITY MEDICAL CAMPUS
- **Principal Investigator:** Pengyu Hong
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $447,308
- **Award type:** 5
- **Project period:** 2019-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10000171

## Citation

> US National Institutes of Health, RePORTER application 10000171, High-Throughput De Novo Glycan Sequencing (5R01GM132675-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10000171. Licensed CC0.

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