ArticleProceedings of the National Academy of Sciences of the United States of America2025
Metabolic control of glycosylation forms for establishing glycan-dependent protein interaction networks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- GlycoDiveR: A Modular R Framework to Analyze and Visualize Highly Dimensional Glycoproteomics Data.ACS measurement science au · 2026Article
- A Bimodal Graph Neural Network with Transfer Learning and Contrastive Learning for Protein-Protein Interaction Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- GlycoDiveR: a modular R framework to analyze and visualize highly dimensional glycoproteomics data.bioRxiv : the preprint server for biology · 2026Article
- The impact of N-glycan conformational entropy on the binding affinity of Fc γ receptor IIIa/CD16a.Structure (London, England : 1993) · 2026Article
- Metabolic control of glycosylation forms for establishing glycan-dependent protein interaction networks.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
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Abstract
Protein-protein interactions (PPIs) are crucial for comprehending the molecular mechanisms and signaling pathways underlying diverse biological processes and disease progression. However, investigating PPIs involving membrane proteins is challenging due to the complexity and heterogeneity of glycosylation. To tackle this challenge, we developed an approach termed glycan-dependent affinity purification coupled with mass spectrometry (GAP-MS), specifically designed to characterize changes in glycoprotein PPIs under varying glycosylation conditions. GAP-MS integrates metabolic control of glycan profiles in cultured cells using small molecules referred to as glycan modifiers with affinity purification followed by mass spectrometry analysis (AP-MS). Here, GAP-MS was applied to characterize and compare the interaction networks under five different glycosylation states for four bait glycoproteins: BSG, CD44, EGFR, and SLC3A2. This analysis identified a network comprising 156 interactions, of which 131 were determined to be glycan dependent. Notably, the GAP-MS analysis of BSG provided distinct information regarding glycosylation-influenced interactions compared to the commonly used glycosylation site mutagenesis approach combined with AP-MS, emphasizing the unique advantages of GAP-MS. Collectively, GAP-MS presents distinct insights over existing methods in elucidating how specific glycosylation forms impact glycoprotein interactions. Additionally, the glycan-dependent interaction networks generated for these four glycoproteins serve as a valuable resource for guiding future functional investigations and therapeutic developments targeting the glycoproteins discussed in this study.
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