Applications of a Catch and Release Electrospray Ionization Mass Spectrometry Assay for Carbohydrate Library Screening
Bibliographic record
Abstract
Applications of a catch and release electrospray ionization mass spectrometry (CaR-ESI-MS) assay for screening carbohydrate libraries against target proteins are described. Direct ESI-MS measurements were performed on solutions containing a target protein (a single chain antibody, an antigen binding fragment, or a fragment of a bacterial toxin) and a library of carbohydrates containing multiple specific ligands with affinities in the 10(3) to 10(6) M(-1) range. Ligands with moderate affinity (10(4) to 10(6) M(-1)) were successfully detected from mixtures containing >200 carbohydrates (at concentrations as low as 0.25 μM each). Additionally, the absolute affinities were estimated from the abundance of free and ligand-bound protein ions determined from the ESI mass spectrum. Multiple low affinity ligands (~10(3) M(-1)) were successfully detected in mixtures containing >20 carbohydrates (at concentrations of ~10 μM each). However, identification of specific interactions required the use of the reference protein method to correct the mass spectrum for the occurrence of nonspecific carbohydrate-protein binding during the ESI process. The release of the carbohydrate ligands, as ions, was successfully demonstrated using collision-induced dissociation performed on the deprotonated ions of the protein-carbohydrate complexes. The use of ion mobility separation, performed on deprotonated carbohydrate ions following their release from the complex, allowed for the positive identification of isomeric ligands.
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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) | 0.001 | 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".