How Choice of Model Membrane Affects Protein-Glycosphingolipid Interactions: Insights from Native Mass Spectrometry.

Interactions between glycan-binding proteins (GBPs) and glycosphingolipids (GSLs) are involved in numerous physiological and pathophysiological processes. Many model membrane systems are available for studying GBP-GSL interactions, but a systematic investigation has not been carried out on how the nature of the model membrane affects binding. In this work, we use electrospray ionization mass spectrometry (ESI-MS), both direct and competitive assays, to measure the binding of cholera toxin B subunit homopentamer (CTB 5 ) to GM1 ganglioside in liposomes, bilayer islands [styrene maleic acid lipid particles (SMALPs), nanodiscs (NDs), and picodiscs (PDs)], and micelles. We find that direct ESI-MS analysis of CTB 5 binding to GM1 is unreliable due to non-uniform response factors, incomplete extraction of bound GM1 in the gas phase, and nonspecific CTB 5 -GM1 interactions. Conversely, indirect proxy ligand ESI-MS measurements show that the intrinsic (per binding site) association constants of CTB 5 for PDs, NDs, and SMALPs are similar and comparable to the affinity of soluble GM1 pentasaccharide (GM1 os ). The observed affinity decreases with increasing GM1 content due to molecular crowding stemming from GM1 clustering. Unlike the smaller model membranes, the observed affinity of CTB 5 toward GM1 liposomes is ∼10-fold weaker than GM1 os and relatively insensitive to the GM1 content. GM1 glycomicelles exhibit the lowest affinity, ∼35-fold weaker than GM1 os . Together, the results highlight experimental design considerations for quantitative GBP-GSL binding studies involving multisubunit GBPs and factors to consider when comparing results obtained with different membrane systems. Notably, they suggest that bilayer islands with a low percentage of GSL, wherein clustering is minimized, are ideal for assessing intrinsic strength of GBP-GSL interactions in a membrane environment, while binding to liposomes, which is sub-optimal due to extensive clustering, may be more representative of authentic cellular environments.