Elusive Protein–Glycosphingolipid Interactions Revealed by Membrane Anchor-Assisted Native Mass Spectrometry
Bibliographic record
Abstract
Interactions between glycan-binding proteins (GBPs) and glycosphingolipids (GSLs) present in cell membranes are implicated in a wide range of biological processes. However, studying GSL binding is hindered by the paucity of purified GSLs and the weak affinities typical of monovalent GBP–GSL interactions. Native mass spectrometry (nMS) performed using soluble model membranes is a promising approach for the discovery of GBP ligands, but the detection of weak interactions remains challenging. The present work introduces ME mbrane AN chor-assisted nMS (MEAN-nMS) for the detection of low-affinity GBP–GSL complexes. The assay utilizes a membrane anchor, produced by covalent cross-linking of the GBP and a lipid in the membrane, to localize the GBP on the surface and promote GSL binding. Ligands are identified by nMS detection of intact GBP–GSL complexes (MEAN-nMS) or using a catch-and-release (CaR) strategy, wherein GSLs are released from GBP–GSL complexes upon collisional activation and detected (MEAN-CaR-nMS). To establish reliability, a library of purified gangliosides incorporated into nanodiscs was screened against human immune lectins, and the results compared with affinities of the corresponding ganglioside oligosaccharides. Without a membrane anchor, nMS analysis yielded predominantly false negatives. In contrast, all ligands were identified by MEAN-(CaR)-nMS, with no false positives. To highlight the potential of MEAN-CaR-nMS for ligand discovery, a natural library of GSLs was incorporated into nanodiscs and screened against human and viral proteins to uncover elusive ligands. Finally, nMS-based detection of GSL ligands directly from cells is demonstrated. This breakthrough paves the way for shotgun glycomics screening using intact cells.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".