Screening Oligosaccharide Libraries against Lectins Using the Proxy Protein Electrospray Ionization Mass Spectrometry Assay
Bibliographic record
Abstract
An electrospray ionization mass spectrometry (ESI-MS) assay for screening carbohydrate libraries against lectins is described. The assay is based on the proxy protein ESI-MS method, which combines direct ESI-MS protein-ligand binding measurements and competitive protein binding, to simultaneously detect and quantify protein-carbohydrate interactions. Specific interactions between components of the library and the target protein (PT) are identified from changes in the relative abundances (as measured by ESI-MS) of the carbohydrate complexes of a proxy protein (Pproxy), which binds to all components of the library with known affinity, upon addition of PT to the solution. The magnitude of the change in relative abundance of a given Pproxy-ligand complex provides a quantitative measure of the affinity of the corresponding PT-ligand interaction. A mathematical framework for the implementation of the method in the case of monovalent (single binding site) Pproxy and monovalent and multivalent (multiple equivalent and independent binding sites) PT is described. The application of the method to screen small libraries of oligosaccharides, on the basis of human histo-blood group antigens and milk oligosaccharides, against an N-terminal fragment of the family 51 carbohydrate-binding module, a fucose-binding lectin from Ralstonia solanacearum, and human norovirus VA387 P particle (24-mer of the protruding domain of the capsid protein), serves to demonstrate the reliability and versatility of the assay.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".