Targeted Chemoenzymatic Synthesis of Sugar Nucleotide Probes Reveal an Inhibitor of the GDP-D-Mannose Dehydrogenase from Pseudomonas Aeruginosa
Bibliographic record
Abstract
Sufferers of the autosomal recessive genetic disorder cystic fibrosis are at extremely high risk for contracting chronic lung infections. Over their lifetime one bacterial strain in particular, Pseudomonas aeruginosa , becomes the dominant pathogen. Bacterial strains incur loss-of-function mutations in the mucA gene that lead to a phenomenon known as mucoid conversion, resulting in copious secretion of alginate, a carbohydrate exopolysaccharide. Strategies that can stop the production of alginate in mucoid Pseudomonas aeruginosa infections are therefore of paramount importance. To aid in this we developed a series of sugar nucleotide chemical tools to probe an enzyme critical to alginate biosynthesis, guanosine diphosphate mannose dehydrogenase (GMD). This enzyme catalyses the irreversible formation of the alginate sugar nucleotide building block, guanosine diphosphate mannuronic acid. Using a chemoenzymatic strategy we accessed a series of modified sugar nucleotides, identifying a C6-amide derivative of the native substrate as a micromolar inhibitor of GMD. This discovery will provide a framework for wider inhibition strategies against GMD to be developed.
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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) | 0.001 | 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".