Detecting Protein–Glycolipid Interactions Using CaR-ESI-MS and Model Membranes: Comparison of Pre-loaded and Passively Loaded Picodiscs
Bibliographic record
Abstract
Catch-and-release electrospray ionization mass spectrometry (CaR-ESI-MS), implemented using model membranes (MMs), is a promising approach for the discovery of glycolipid ligands of glycan-binding proteins (GBPs). Picodiscs (PDs), which are lipid-transporting complexes composed of the human sphingolipid activator protein saposin A and phospholipids, have proven to be useful MMs for such studies. The present work compares the use of conventional (pre-loaded) PDs with passively loaded PDs ( PL PDs) for CaR-ESI-MS screening of glycolipids against cholera toxin B subunit homopentamer (CTB 5 ). The pre-loaded PDs were prepared from a mixture of purified glycolipid and phospholipid or a mixture of lipids extracted from tissue, while the PL PDs were prepared by incubating PDs containing only phospholipid with glycolipid-containing lipid mixtures in aqueous solution. Time-dependent changes in the composition of the PL PDs produced by incubation with glycomicelles of the ganglioside GM1 were monitored using collision-induced dissociation of the gaseous PD ions and from the extent of ganglioside binding to CTB 5 measured by ESI-MS. GM1 incorporation into PDs was evident within a few hours of incubation. At incubation times ≥ 10 days, GM1 binding to CTB 5 was indistinguishable from that observed with pre-loaded PDs produced directly from GM1 at the same concentration. Comparison of ganglioside binding to CTB 5 measured for pre-loaded PDs and PL PDs prepared from glycolipids extracted from pig and mouse brain revealed that the PL PDs allow for the detection of a greater number of ganglioside ligands. Together, the results of this study suggest PL PDs may have advantages over conventionally prepared PDs for screening glycolipids against GBPs using CaR-ESI-MS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".