Absolute Affinities from Quantitative Shotgun Glycomics Using Concentration-Independent (COIN) Native Mass Spectrometry
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Native mass spectrometry (nMS) screening of natural glycan libraries against glycan-binding proteins (GBPs) is a powerful tool for ligand discovery. However, as the glycan concentrations are unknown, affinities cannot be measured directly from natural libraries. Here, we introduce Co ncentration- In dependent (COIN)-nMS, which enables quantitative screening of natural glycan libraries by exploiting slow mixing of solutions inside a nanoflow electrospray ionization emitter. The affinities ( K d ) of detected GBP–glycan interactions are determined, simultaneously, from nMS analysis of their time-dependent relative abundance changes. We establish the reliability of COIN-nMS using interactions between purified glycans and GBPs with known K d values. We also demonstrate the implementation of COIN-nMS using the catch-and-release (CaR)-nMS assay for glycosylated GBPs. The COIN-CaR-nMS results obtained for plant, fungal, viral, and human lectins with natural libraries containing hundreds of N -glycans and glycopeptides highlight the assay’s versatility for discovering new ligands, precisely measuring their affinities, and uncovering “fine” specificities. Notably, the COIN-CaR-nMS results clarify the sialoglycan binding properties of the SARS-CoV-2 receptor binding domain and establish the recognition of monosialylated hybrid and biantennary N -glycans. Moreover, pharmacological depletion of host complex N -glycans reduces both pseudotyped virions and SARS-CoV-2 cell entry, suggesting that complex N -glycans may serve as attachment factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".