Protein oxidation of fucose environments (POFE) reveals fucose-protein interactions

Molecular mechanism of enhancing antitumor activity through the interaction between monosaccharides and human serum albumin

This study investigated the molecular mechanisms of the interactions between three antitumor active monosaccharides and human serum albumin (HSA) using spectroscopic and electrochemical analyses, supplemented by molecular docking simulations. The antitumor efficacy of these monosaccharides can be significantly enhanced by covalent drug coupling, while HSA, with its long half-life and low immunogenicity, provides new opportunities for the development of advanced antitumor drug delivery systems. The results showed that these monosaccharides effectively burst the fluorescence of HSA. Thermodynamic analysis revealed that Fucose undergoes a spontaneous, exothermic process that decreases entropy, while the binding of Mannose and Galactose is entropy-driven. Notably, the addition of these three monosaccharides slightly compacts the structure of HSA, stabilizing its transport and delivery in vivo, with the binding strength categorized as Fucose > Mannose > Galactose. These variations in binding constants explain the differences in efficacy and potential side effects in antitumor therapy. Further studies have shown that the interaction between monosaccharides and HSA improves drug stability and targeting, thereby enhancing antitumor activity. An in-depth study of these interactions may provide new insights into the design and optimization of antitumor drugs and the further development of novel antitumor therapies.

Metabolic Control of Glycosylation Forms for Establishing Glycan-Dependent Protein Interaction Networks

Protein-protein interactions (PPIs) provide essential insights into the complex molecular mechanisms and signaling pathways within cells that regulate development and disease-related phenotypes. However, for membrane proteins, the impact of various forms of glycosylation has often been overlooked in PPI studies. In this study, we introduce a novel approach, glycan-dependent affinity purification followed by mass spectrometry (GAP-MS), to assess variations in PPIs for any glycoprotein of interest under different glycosylation conditions. As a proof of principle, we selected four glycoproteins-BSG, CD44, EGFR, and SLC3A2-as baits to compare their co-purified partners across five metabolically controlled glycan conditions. The findings demonstrate the capability of GAP-MS to identify PPIs influenced by altered glycosylation states, establishing a foundation for systematically exploring the Glycan-Dependent Protein Interactome (GDPI) for other glycoproteins of interest.

Development and application of GlycanDIA workflow for glycomic analysis

Glycans modify protein, lipid, and even RNA molecules to form the regulatory outer coat on cells called the glycocalyx. The changes in glycosylation have been linked to the initiation and progression of many diseases. Thus, while the significance of glycosylation is well established, a lack of accessible methods to characterize glycans has hindered the ability to understand their biological functions. Mass spectrometry (MS)-based methods have generally been at the core of most glycan profiling efforts; however, modern data-independent acquisition (DIA), which could increase sensitivity and simplify workflows, has not been benchmarked for analyzing glycans. Herein, we developed a DIA-based glycomic workflow, termed GlycanDIA, to identify and quantify glycans with high sensitivity and accuracy. The GlycanDIA workflow combined higher energy collisional dissociation (HCD)-MS/MS and staggered windows for glycomic analysis, which facilitates the sensitivity in identification and the accuracy in quantification compared to conventional data-dependent acquisition (DDA)-based glycomics. To facilitate its use, we also developed a generic search engine, GlycanDIA Finder, incorporating an iterative decoy searching for confident glycan identification and quantification from DIA data. The results showed that GlycanDIA can distinguish glycan composition and isomers from N-glycans, O-glycans, and human milk oligosaccharides (HMOs), while it also reveals information on low-abundant modified glycans. With the improved sensitivity, we performed experiments to profile N-glycans from RNA samples, which have been underrepresented due to their low abundance. Using this integrative workflow to unravel the N-glycan profile in cellular and tissue glycoRNA samples, we found that RNA-glycans have specific forms as compared to protein-glycans and are also tissue-specific differences, suggesting distinct functions in biological processes. Overall, GlycanDIA can provide comprehensive information for glycan identification and quantification, enabling researchers to obtain in-depth and refined details on the biological roles of glycosylation.