How Choice of Model Membrane Affects Protein–Glycosphingolipid Interactions: Insights from Native Mass Spectrometry
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".