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Updated: May 22, 2025

Glycopeptide Capture for Cell Surface Proteomics
Published on: May 9, 2014
Hydrophilic Sulfonate Covalent Organic Frameworks for Serum Glycopeptide Profiling
Shishu Yang1, Yuheng Jiang1, Shijie Jiang1
1Key Laboratory of Green Chemical Media and Reactions, Ministry of Education, Henan Key Laboratory of Organic Functional Molecule and Drug Innovation, School of Chemistry and Chemical Engineering, Henan Normal University, Xinxiang 453007, China.
Abstract:
Aberrant protein glycosylation is closely associated with a number of biological processes and diseases. However, characterizing the types of post-translational modifications (PTMs) from the complex biological samples is challenging for comprehensive glycoproteomic analysis. The development of high-performance enrichment materials and strategies during the sample pretreatment process is a prerequisite to glycoproteome research. Here in this work, a sulfonate-rich covalent organic framework (COF) called TpPa-(SO3H)2 (referred to as SCOF-2) was synthesized using the Schiff base reaction for the identification of glycopeptides. Benefiting from high hydrophilicity and abundant sulfonate affinity, a total of 28 and 16 glycopeptides could be efficiently detected from the standard glycoproteins of horseradish peroxidase (HRP) and immunoglobulin G (IgG) tryptic digest, respectively. Moreover, the as-prepared sulfonate-rich SCOF-2 has an ultralow detection limit (0.01 fmol μL-1), excellent enrichment selectivity (molar ratio HRP:BSA = 1:5000), satisfactory recovery rate (89.1%), high adsorption capacity (150 mg g-1) and good reusability in the individual enrichment. Meanwhile, by using the SCOF-2 adsorbent, 196 and 194 endogenous glycopeptides in the serum of ovarian cancer patients and healthy people among triplicates were successfully enriched and identified, respectively, using combined nanoLC-MS/MS technology. It demonstrated its great application potential in glycoproteomics research and provided a novel insight for the design of affinity materials.

