Identifying Carbohydrate Ligands of a Norovirus P Particle using a Catch and Release Electrospray Ionization Mass Spectrometry Assay
Bibliographic record
Abstract
Noroviruses (NoVs), the major cause of epidemic acute gastroenteritis, recognize human histo-blood group antigens (HBGAs), which are present as free oligosaccharides in bodily fluid or glycolipids and glycoproteins on the surfaces of cells. The subviral P particle formed by the protruding (P) domain of the NoV capsid protein serves as a useful model for the study NoV-HBGA interactions. Here, we demonstrate the application of a catch-and-release electrospray ionization mass spectrometry (CaR-ESI-MS) assay for screening carbohydrate libraries against the P particle to rapidly identify NoV ligands and potential inhibitors. Carbohydrate libraries of 50 and 146 compounds, which included 18 and 24 analogs of HBGA receptors, respectively, were screened against the P particle of VA387, a member of the predominant GII.4 NoVs. Deprotonated ions corresponding to the P particle bound to carbohydrates were isolated and subjected to collision-induced dissociation to release the ligands in their deprotonated forms. The released ligands were identified by ion mobility separation followed by mass analysis. All 13 and 16 HBGA ligands with intrinsic affinities >500 M(-1) were identified in the 50 and the 146 compound libraries, respectively. Furthermore, screening revealed interactions with a series of oligosaccharides with structures found in the cell wall of mycobacteria and human milk. The affinities of these newly discovered ligands are comparable to those of the HBGA receptors, as estimated from the relative abundance of released ligand ions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".