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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".