Quantifying Protein Interactions with Isomeric Carbohydrate Ligands Using a Catch and Release Electrospray Ionization-Mass Spectrometry Assay
Bibliographic record
Abstract
The application of a catch-and-release electrospray ionization mass spectrometry (CaR-ESI-MS) assay to quantify interactions between proteins and isomeric carbohydrate ligands is described. Absolute affinities for each ligand are determined from the abundance ratio of ligand-bound to free protein measured directly by ESI-MS and the relative abundances of the individual isomeric ligands, which are established by releasing the ligands, in their deprotonated form, from the protein using collision-induced dissociation (CID) and subjecting them to ion mobility separation (IMS) or another stage of CID to fragment the ions. Using Gaussian functions to represent the contributions of individual ligands to the arrival time distributions (ATDs) measured by IMS, the relative abundance of each ligand bound to the protein can be established. A modification of this method, suitable for cases where nonspecific ligand-protein binding occurs during the ESI process, is also described. In cases where the ATDs are not sufficiently different to distinguish the isomeric ligands, CID can establish the relative abundance of each ligand bound to the protein from the relative abundance of the resulting fragment ions. The implementation and reliability of the CaR-ESI-MS assay for the analysis of isomeric carbohydrate ligands is demonstrated using three carbohydrate-binding proteins, a single chain antibody, an antigen binding fragment, and a fragment of a bacterial toxin, and their interactions with isomeric carbohydrate ligands with affinities ranging from 10(3) to 10(5) M(-1).
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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".