Protein Recognition of Glycosphingolipids in Membranes: Mechanistic and Quantitative Insights
Bibliographic record
Abstract
Recognition of glycosphingolipids (GSLs) in cell membranes by glycan-binding proteins (GBPs) is essential for diverse biological processes. However, owing to deficiencies in available analytical methods, the thermodynamics of GBP-GSL interactions remains poorly characterized. Native mass spectrometry (nMS) analysis performed using soluble GSL-containing model membranes provides a direct readout of the identity and stoichiometry of bound GSL ligands and, under certain conditions, can inform on affinity. Yet, for multivalent GBPs capable of engaging multiple model membranes simultaneously, data analysis relies on untested assumptions, which has limited adoption of the assay. Here, we apply mass photometry to quantify a series of high-affinity interactions between glycolipids in soluble model membranes (nanodiscs) and mono- and multivalent GBPs and compare with binding data acquired with nMS. Remarkably, the mass photometry results indicate that glycolipids are distributed nonstatistically across the lipid bilayer and engage in clustering that is sensitive to GBP binding. Moreover, the affinities and stoichiometries (of bound nanodiscs) measured for multivalent GBPs are strongly modulated by glycolipid clustering, which can overwhelm avidity gains from multivalent binding. After normalization for the number of GBP binding sites and glycolipid content, the affinities from mass photometry are found to be, overall, in good agreement with native nMS-derived affinities. Collectively, the findings of this study provide critically needed affinity and stoichiometry benchmarks for assay validation and significant new insights into the mechanisms of GBP recognition of GSLs in model membranes, which serve as a foundation for understanding binding in natural 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".