Chemoenzymatic Synthesis of Genetically-Encoded Multivalent Liquid N-glycan Arrays
Bibliographic record
Abstract
Abstract A hallmark of cellular glycosylation is its chemical complexity and heterogeneity, which can be challenging to capture synthetically. Using chemoenzymatic synthesis on M13 phage, we produce a genetically-encoded liquid glycan array (LiGA) of biantennary complex type N-glycans. Ligation of azido-functionalized sialylglycosyl-asparagine derived from egg yolk to phage functionalized with 50–1000 copies of dibenzocyclooctyne produced divergent intermediate that can be trimmed by glycosidases and extended by glycosyltransferases to yield a library of phages with different N-glycans. Post-reaction analysis by MALDI-TOF MS provided a rigorous approach to confirm N-glycan structure and density, both of which were encoded in the bacteriophage DNA. The binding of this N-glycan LiGA by ten lectins, including CD22 or DC-SIGN expressed on live cells, uncovered an optimal structure/density combination for recognition. Injection of the LiGA into mice identified glycoconjugates with structures and avidity necessary for enrichment in specific organs. This work provides an unprecedented 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 | 0.001 |
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".