Screening Glycolipids Against Proteins in Vitro Using Picodiscs and Catch-and-Release Electrospray Ionization-Mass Spectrometry
Bibliographic record
Abstract
This work describes the application of the catch-and-release electrospray ionization-mass spectrometry (CaR-ESI-MS) assay, implemented using picodiscs (complexes comprised of saposin A and lipids, PDs), to screen mixtures of glycolipids (GLs) against water-soluble proteins to detect specific interactions. To demonstrate the reliability of the method, seven gangliosides (GM1, GM2, GM3, GD1a, GD1b, GD2, and GT1b) were incorporated, either individually or as a mixture, into PDs and screened against two lectins: the B subunit homopentamer of cholera toxin (CTB5) and a subfragment of toxin A from Clostridium difficile (TcdA-A2). The CaR-ESI-MS results revealed that CTB5 binds to six of the gangliosides (GM1, GM2, GM3, GD1a, GD1b, and GT1b), while TcdA-A2 binds to five of them (GM1, GM2, GM3, GD1a, and GT1b). These findings are consistent with the measured binding specificities of these proteins for ganglioside oligosaccharides. Screening mixtures of lipids extracted from porcine brain and a human epithelial cell line against CTB5 revealed binding to multiple GM1 isoforms as well as to fucosyl-GM1, which is a known ligand. Finally, a comparison of the present results with data obtained with the CaR-ESI-MS assay implemented using nanodiscs (NDs) revealed that the PDs exhibited similar or superior performance to NDs for protein-GL binding measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".