Neoglycolipids as Glycosphingolipid Surrogates for Protein Binding Studies Using Nanodiscs and Native Mass Spectrometry
Bibliographic record
Abstract
Interactions between glycan-binding proteins (GBPs) and glycosphingolipids (GSLs) in the membranes of cells are implicated in a wide variety of normal and pathophysiological processes. Despite the critical biological roles these interactions play, the GSL ligands of most GBPs have not yet been identified. The limited availability of purified GSLs represents a significant challenge to the discovery and characterization of biologically relevant GBP–GSL interactions. The present work investigates the use of neoglycolipids (NGLs) as surrogates for GSLs for catch-and-release-electrospray ionization mass spectrometry (CaR-ESI-MS)-based screening, implemented with nanodiscs, for the discovery of GSL ligands. Three pairs of NGLs based on the blood group type A and B trisaccharides, with three different lipid head groups but all with “ring-closed” monosaccharide residue at the reducing end, were synthesized. The incorporation efficiencies (into nanodiscs) of the NGLs and their affinities for a fragment of family 51 carbohydrate-binding module (CBM) identified an amide-linked 1,3-di- O -hexadecyl-glycerol moiety as the optimal lipid structure. Binding measurements performed on cholera toxin B subunit homopentamer (CTB 5 ) and nanodiscs containing an NGL consisting of the optimal lipid moiety and the GM1 ganglioside pentasaccharide yielded affinities similar, within a factor of 2, to those of native GM1. Finally, nanodiscs containing the optimal A and B trisaccharide NGLs, as well as the corresponding NGLs of lactose, A type 2 tetrasaccharide, and the GM1 and GD2 pentasaccharides were screened against the family 51 CBM, human galectin-7, and CTB 5 to illustrate the potential of NGLs to accelerate the discovery of GSL ligands of GBPs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".