Screening Carbohydrate Libraries for Protein Interactions Using the Direct ESI-MS Assay. Applications to Libraries of Unknown Concentration
Bibliographic record
Abstract
A semiquantitative electrospray ionization mass spectrometry (ESI-MS) binding assay suitable for analyzing mixtures of oligosaccharides, at unknown concentrations, for interactions with target proteins is described. The assay relies on the differences in the ratio of the relative abundances of the ligand-bound and free protein ions measured by ESI-MS at two or more initial protein concentrations to distinguish low affinity (≤10(3) M(-1)) ligands from moderate and high affinity (>10(5) M(-1)) ligands present in the library and to rank their affinities. Control experiments were performed on solutions of a single chain antibody and a mixture of synthetic oligosaccharides, with known affinities, in the absence and presence of a 40-component carbohydrate library to demonstrate the implementation and reliability of the assay. The application of the assay for screening natural libraries of carbohydrates against proteins is also demonstrated using mixtures of human milk oligosaccharides, isolated from breast milk, and fragments of a bacterial toxin and human galectin 3.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".