Detecting Protein–Glycolipid Interactions Using Glycomicelles and CaR-ESI-MS
Bibliographic record
Abstract
This study reports on the use of the catch-and-release electrospray ionization mass spectrometry (CaR-ESI-MS) assay, combined with glycomicelles, as a method for detecting specific interactions between water-soluble proteins and glycolipids (GLs) in aqueous solution. The B subunit homopentamers of cholera toxin (CTB 5 ) and Shiga toxin type 1 B (Stx1B 5 ) and the gangliosides GM1, GM2, GM3, GD1a, GD1b, GT1b, and GD2 served as model systems for this study. The CTB 5 exhibits broad specificity for gangliosides and binds to GM1, GM2, GM3, GD1a, GD1b, and GT1b; Stx1B 5 does not recognize gangliosides. The CaR-ESI-MS assay was used to analyze solutions of CTB 5 or Stx1B 5 and individual gangliosides (GM1, GM2, GM3, GD1a, GD1b, GT1b, and GD2) or mixtures thereof. The high affinity interaction of CTB 5 with GM1 was successfully detected. However, the apparent affinity, as determined from the mass spectra, is significantly lower than that of the corresponding pentasaccharide or when GM1 is presented in model membranes such as nanodiscs. Interactions between CTB 5 and the low affinity gangliosides GD1a, GD1b, and GT1b, as well as GD2, which served as a negative control, were detected; no binding of CTB 5 to GM2 or GM3 was observed. The CaR-ESI-MS results obtained for Stx1B 5 reveal that nonspecific protein-ganglioside binding can occur during the ESI process, although the extent of binding varies between gangliosides. Consequently, interactions detected for CTB 5 with GD1a, GD1b, and GT1b are likely nonspecific in origin. Taken together, these results reveal that the CaR-ESI-MS/glycomicelle approach for detecting protein–GL interactions is prone to false positives and false negatives and must be used with caution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".