Quantifying Protein-Carbohydrate Interactions Using Liquid Sample Desorption Electrospray Ionization Mass Spectrometry
Bibliographic record
Abstract
The application of liquid sample desorption electrospray ionization mass spectrometry (liquid sample DESI-MS) for quantifying protein-carbohydrate interactions in vitro is described. Association constants for the interactions between lysozyme and β-D-GlcNAc-(1 → 4)-β-D-GlcNAc-(1 → 4)-D-GlcNAc and β-D-GlcNAc-(1 → 4)-β-D-GlcNAc-(1 → 4)-β-D-GlcNAc-(1 → 4)-D-GlcNAc, and between a single chain antibody and α-D-Galp-(1 → 2)-[α-D-Abep-(1 → 3)]-α-D-Manp-OCH3 and β-D-Glcp-(1 → 2)-[α-D-Abep-(1 → 3)]-α-D-Manp-OCH3 measured using liquid sample DESI-MS were found to be in good agreement with values measured by isothermal titration calorimetry and the direct ESI-MS assay. The reference protein method, which was originally developed to correct ESI mass spectra for the occurrence of nonspecific ligand-protein binding, was shown to reliably correct liquid sample DESI mass spectra for nonspecific binding. The suitability of liquid sample DESI-MS for quantitative binding measurements carried out using solutions containing high concentrations of the nonvolatile biological buffer phosphate buffered saline (PBS) was also explored. Binding of lysozyme to β-D-GlcNAc-(1 → 4)-β-D-GlcNAc-(1 → 4)-D-GlcNAc in aqueous solutions containing up to 1× PBS was successfully monitored using liquid sample DESI-MS; with ESI-MS the binding measurements were limited to concentrations less than 0.02 X PBS.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".