High-Throughput Label- and Immobilization-Free Screening of Human Milk Oligosaccharides Against Lectins
Bibliographic record
Abstract
The intense interest in the mechanisms responsible for the beneficial effects of breast-feeding on infant health has created a significant need for analytical methods capable of rapidly identifying interactions between human milk oligosaccharides (HMOs) and their protein receptors. Currently, there are no established, high-throughput assays for the screening libraries of free (unmodified) HMOs against lectins. The present work describes a rapid and label- and immobilization-free assay, based on catch-and-release electrospray ionization mass spectrometry (CaR-ESI-MS), capable of simultaneously screening mixtures of free HMOs of known concentration for binding to lectins in vitro . Ligand identification relies on the molecular weights (MWs), ion mobility separation arrival times, and collision-induced dissociation fingerprints of HMO anions released from the target protein in the gas phase. To establish the reliability of the assay, a library of 31 free HMOs, ranging in size from tri- to octasaccharide, was screened against three human galectin (hGal) proteins (a stable mutant of hGal1 (hGal-1), a C-terminal fragment of hGal-3 (hGal-3C) and hGal-7), with known HMO affinities. When implemented using an equimolar concentration library, the CaR-ESI-MS assay identified 100% of ligands with affinities >500 M –1 and ≥93% of all HMO ligands (hGal-1–31 of 31 ligands; hGal-3C–25 of 25; hGal-7–28 of 30); no false positives were detected. The assay also successfully identified the majority of the highest affinity HMO ligands (or isomer sets that contain the highest affinity ligands) in the library for each of the three hGal. Notably, for each lectin, CaR-ESI-MS screening required <1 h to complete and consumed <5 ng of each HMO and <0.5 μg of protein.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".