Protein–Glycolipid Interactions Studied in Vitro Using ESI-MS and Nanodiscs: Insights into the Mechanisms and Energetics of Binding
Bibliographic record
Abstract
Electrospray ionization-mass spectrometry (ESI-MS) analysis combined with the use of nanodiscs (NDs) to solubilize glycolipids (GLs) has recently emerged as a promising analytical method for detecting protein-GL interactions in vitro and, when applied to libraries of GLs, ranking their affinities. However, there is uncertainty regarding the mechanism(s) of complex formation in solution and the extent to which the relative abundances of protein-glycolipid complexes observed by ESI-MS reflect the relative concentrations in solution. Here, we describe the results of a systematic ESI-MS study aimed at elucidating the processes that influence binding of water-soluble proteins to GLs incorporated into NDs and to exploit these insights to quantify the binding energetics. The interactions between the cholera toxin B subunit homopentamer (CTB5) and its native ganglioside receptor, β-D-Gal-(1 → 3)-β-D-GalNAc-(1 → 4)-[α-D-Neu5Ac-(2 → 3)]-β-D-Gal-(1 → 4)-β-D-Glc-ceramide (GM1), and between a recombinant fragment of family 51 carbohydrate-binding module (CBM), originating from S. pneumoniae, with a synthetic B type 2 neoglycolipid, α-D-Gal-(1 → 3)-[α-L-Fuc-(1 → 2)]-β-D-Gal-(1 → 4)-β-D-GlcNAc-1,2-di-O-dodecyl-sn-glycero (B2NGL) served as model protein-GL complexes for this study. The results of the ESI-MS measurements reveal that proteins bind reversibly to ND-bound GLs and that proteins possessing multiple ligand binding sites are able to interact with GLs originating from different NDs. Experimental evidence suggests that the diffusion of GLs between NDs is rapid and influences the nature of the protein-GL complexes that are detected. Using a newly developed ESI-MS assay, the proxy ligand method, the association constants for the CBM-B2NGL and CTB5-GM1 interactions were quantified and found to be slightly smaller than those for the corresponding oligosaccharides in solution.
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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) | 0.000 | 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 | 0.000 | 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".