Enzymatic synthesis and properties of glycoconjugates with legionaminic acid as a replacement for neuraminic acid
Bibliographic record
Abstract
In addition to sialic acid, bacteria produce several other nonulosonic acids, including legionaminic acid (Leg). This has exactly the same stereochemistry as sialic acid, with the added features of 9-deoxy and 7-amino groups. In order to explore the biological effects of replacing sialic acid residues (Neu5Ac) in glycoconjugates with Leg in its diacetylated form, diacetyllegionaminic acid (Leg5Ac7Ac), we tested CMP-Leg5Ac7Ac as a donor substrate with a selection of bacterial and mammalian sialyltransferases. The CMP-Leg5Ac7Ac was synthesized in vitro by means of cloned enzymes from the bacillosamine portion of the Campylobacter jejuni N-glycan pathway and from the Leg pathway of Legionella pneumophila. Using fluorescent derivatives of lactose, Galβ1,4GlcNAcβ and T-antigen (Galβ1,3GalNAcα) as acceptors, we tested eight different sialyltransferases and found that the Pasteurella multocida PM0188h and porcine ST3Gal1 sialyltransferases were significantly active with CMP-Leg5Ac7Ac, showing ∼60% activity when compared with CMP-Neu5Ac. The Photobacterium α2,6 sialyltransferase was weakly active, with ∼6% relative activity. The Leg5Ac7Ac-α-2,3-lactose product was then tested as a substrate with six sialidases of viral, bacterial and mammalian origin. All showed much lower activities than with the corresponding sialic acid substrate, with the influenza virus N1 being the most active and human NEU2 being the least active. These results show the feasibility of producing glycoconjugates with Leg5Ac7Ac residues as the terminal sugars, which should display novel biological properties.
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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 | 0.000 |
| 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 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".