Chemoenzymatic synthesis of genetically-encoded multivalent liquid N-glycan arrays
Bibliographic record
Abstract
Cellular glycosylation is characterized by chemical complexity and heterogeneity, which is challenging to reproduce synthetically. Here we show chemoenzymatic synthesis on phage to produce a genetically-encoded liquid glycan array (LiGA) of complex type N-glycans. Implementing the approach involved by ligating an azide-containing sialylglycosyl-asparagine to phage functionalized with 50-1000 copies of dibenzocyclooctyne. The resulting intermediate can be trimmed by glycosidases and extended by glycosyltransferases yielding a phage library with different N-glycans. Post-reaction analysis by MALDI-TOF MS allows rigorous characterization of N-glycan structure and mean density, which are both encoded in the phage DNA. Use of this LiGA with fifteen glycan-binding proteins, including CD22 or DC-SIGN on cells, reveals optimal structure/density combinations for recognition. Injection of the LiGA into mice identifies glycoconjugates with structures and avidity necessary for enrichment in specific organs. This work provides a quantitative evaluation of the interaction of complex N-glycans with GBPs in vitro and in vivo.
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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) | 0.000 | 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 | 0.001 |
| 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".