Nanodiscs and Electrospray Ionization Mass Spectrometry: A Tool for Screening Glycolipids Against Proteins
Bibliographic record
Abstract
Electrospray ionization-mass spectrometry (ESI-MS) is extensively employed to detect and quantify protein-carbohydrate interactions in vitro and is increasingly used to screen carbohydrate libraries against target proteins. However, current ESI-MS methods are limited to carbohydrate ligands that are relatively soluble in water and are, therefore, not generally suitable for studying protein interactions with glycolipids, an important class of cellular receptors. Here, we describe a catch-and-release (CaR)-ESI-MS assay, which exploits nanodiscs (NDs) to solubilize glycolipids and mimic their natural cellular environment, suitable for screening libraries of glycosphingolipids (GSL) against proteins to identify specific interactions and to rank their relative affinities. Using the B subunit homopentamers of cholera toxin and heat labile toxin as model GSL-binding proteins, the CaR-ESI-MS was applied to NDs containing mixtures of gangliosides. The results demonstrate that the CaR-ESI-MS assay can simultaneously detect both high and low affinity GSL ligands using either a library of NDs that each contains one GSL or incorporating a mixture of GSLs into a single ND. Moreover, the relative abundances of the released ligands appear to reflect their relative affinities in solution. Application of the CaR-ESI-MS assay using NDs containing gangliosides extracted from porcine brain led to the discovery of a neolacto GSL as a cholera toxin ligand, highlighting the power of the assay for identifying specific protein-glycolipid interactions from biologically relevant mixtures.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".