Influence of Solution and Gas Phase Processes on Protein−Carbohydrate Binding Affinities Determined by Nanoelectrospray Fourier Transform Ion Cyclotron Resonance Mass Spectrometry
Bibliographic record
Abstract
The influence of solution pH, analyte concentration and in-source dissociation on the measurement of the association constant for a single chain variable fragment of a monoclonal antibody (scFv) and its native trisaccharide ligand by nanoelectrospray-Fourier transform ion cyclotron resonance mass spectrometery has been systematically investigated. From the results of this study, experimental conditions that preserve the original distribution of bound and unbound protein in solution into the gas phase, such that the nanoES mass spectrum provides a quantitative measure of the solution composition, were identified. These include the use of short spray durations (<10 min) to minimize pH changes, equimolar concentrations of protein and ligand to minimize the formation of nonspecific complexes, and short accumulation times (<2 s) in the hexapole of the ion source to avoid collisional heating and dissociation of the gaseous complex. Application of this methodology to the scFv and a series of carbohydrate ligands yields results that are in agreement with values previously determined by isothermal titration calorimetry. Competitive binding experiments performed on solutions containing the scFv and a mixture of carbohydrate ligands were also found to yield accurate association constants.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".