# Quantitative Characterization of Glycopeptide Isomers > **NIH NIH R01** · TEXAS TECH UNIVERSITY · 2020 · $300,714 ## Abstract Because of the pressing needs to comprehensively understand the biological attributes of glycosylation in many critical biological functions such as the immune response, cell development, cellular differentiation/adhesion and host-pathogen interactions, glycoproteomics continues to be a highly dynamic research area. Moreover, aberrant glycosylation for decades has been recognized as the attribute of many mammalian diseases, including osteoarthritis, cystic fibrosis, and cancer. The diverse biological roles of glycans and their implications in diseases have created a demand for reliable MS-based glycoproteomic approaches, permitting sensitive monitoring of glycoproteins in biological systems. We are proposing here three specific aims: Aim 1. Isomeric separation and structural identification of glycopeptides by porous graphitic columns at high temperatures coupled to post-column enzyme reactor; Aim 2. Double metabolic Stable Isotope Labeling of Glycoproteins in Cell cultures (DSILGC); and Aim 3. Develop software tools for automated identification and quantitation of glycopeptides. The outcome of these aims will be technologies that facilitate the unequivocal quantitative assessment of the isomeric microheterogenieties of glycoproteins associated with biological samples. The innovations of this proposal originate from the uniqueness of the proposed analytical methods and software. Isomeric separation of glycopeptides on PGC at high temperatures, a highly innovative method developed in our laboratory, permits the separation of all glycan isomers associated with protein glycosylation. Although enzyme reactors have been demonstrated by others, they usually used for protein digestion or glycan releasing. To our best knowledge, it is the first time a post-column reactor interfacing with MS for the characterization of isomeric glycopeptide structures is proposed (Aim 1). The simultaneous double stable isotope labeling of both proteins and glycans that we propose here will enable simultaneous analysis of glycomics, proteomics, and glycoproteomics with more accurate and effective quantitation (Aim 2). From the bioinformatics point of view, our project has several novel aspects (Aim 3). Our glycan sequencing algorithm based on HCD/CID spectra of glycopeptides reports the whole (or partial when some fragment ions are missing in the MS/MS spectra) topology of glycan (except linkages) instead of only the monosaccharide compositions that abovementioned methods can elucidate. Additionally, our algorithm can potentially identify new glycan structures, since it does not rely on previously known glycan structures. More importantly, the glycan sequencing algorithm provides complementary information to peptide identification (e.g., from CID spectra of de-glycosylated peptides or the ETD. The deliverables of this proposal are (i) analytical methods that are readily available, adaptable, and affordable to quantitively characterize and separate glycopeptide isomers and (... ## Key facts - **NIH application ID:** 9858366 - **Project number:** 5R01GM130091-02 - **Recipient organization:** TEXAS TECH UNIVERSITY - **Principal Investigator:** Yehia Mechref - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2020 - **Award amount:** $300,714 - **Award type:** 5 - **Project period:** 2019-02-01 → 2023-01-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/9858366 ## Citation > US National Institutes of Health, RePORTER application 9858366, Quantitative Characterization of Glycopeptide Isomers (5R01GM130091-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9858366. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*