Liu Xingyu, Yi Li, Lin Zongtao, Chen Siyu, Wang Shunyang, Sheng Ying, Lebrilla Carlito B, Garcia Benjamin A, Xie Yixuan
State Key Laboratory of Genetic Engineering, Greater Bay Area Institute of Precision Medicine (Guangzhou), School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai 200433, China.
Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis 63108, MO.
Proc Natl Acad Sci U S A. 2025 Jun 24;122(25):e2422936122. doi: 10.1073/pnas.2422936122. Epub 2025 Jun 18.
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.
蛋白质-蛋白质相互作用(PPIs)对于理解各种生物过程和疾病进展背后的分子机制及信号通路至关重要。然而,由于糖基化的复杂性和异质性,研究涉及膜蛋白的PPIs具有挑战性。为应对这一挑战,我们开发了一种称为聚糖依赖性亲和纯化结合质谱分析(GAP-MS)的方法,专门用于表征在不同糖基化条件下糖蛋白PPIs的变化。GAP-MS将使用称为聚糖修饰剂的小分子对培养细胞中聚糖谱的代谢控制与亲和纯化相结合,随后进行质谱分析(AP-MS)。在这里,GAP-MS被应用于表征和比较四种诱饵糖蛋白(BSG、CD44、EGFR和SLC3A2)在五种不同糖基化状态下的相互作用网络。该分析确定了一个包含156种相互作用的网络,其中131种被确定为聚糖依赖性的。值得注意的是,与常用的糖基化位点诱变方法结合AP-MS相比,对BSG的GAP-MS分析提供了关于糖基化影响的相互作用的独特信息,强调了GAP-MS的独特优势。总体而言,GAP-MS在阐明特定糖基化形式如何影响糖蛋白相互作用方面比现有方法提供了不同的见解。此外,为这四种糖蛋白生成的聚糖依赖性相互作用网络可作为一种有价值的资源,用于指导针对本研究中讨论的糖蛋白的未来功能研究和治疗开发。