Development of a High Throughput Screen for the Identification of Inhibitors of Peptidoglycan O-Acetyltransferases, New Potential Antibacterial Targets
Bibliographic record
Abstract
The O-acetylation of peptidoglycan occurs in many Gram-negative and most Gram-positive pathogens and this modification to the essential wall polymer controls the lytic activity of the autolysins, particularly the lytic transglycosylases, and inhibits that of the lysozymes of innate immunity systems. As such, the peptidoglycan O-acetyltransferases PatA/B and OatA are recognized as virulence factors. In this study, we present the high throughput screening of small compound libraries to identify the first known inhibitors of these enzymes. The fluorometric screening assay developed involved monitoring the respective O-acetyltransferases as esterases using 4-methylumbelliferylacetate as substrate. Pilot screens of 3921 compounds validated the usefulness of the HTS protocol. A number of potential inhibitors were identified amongst a total of 145,000 low molecular-weight compounds, some of which were common to both enzymes, while others were unique to each. After eliminating a number of false positives in secondary screens, dose response curves confirmed the apparent specificity of a benzothiazolyl-pyrazolo-pyridine as an inhibitor of Neisseria gonorrhoeae PatB, and several coumarin-based compounds as inhibitors of both this PatB and OatA from Staphylococcus aureus. The benzothiazolyl-pyrazolo-pyridine was determined to be a non-competitive inhibitor of PatB with a Ki of 126 µM. At 177 µg/mL and close to its solubility limit, this compound caused a 90% reduction in growth of N. gonorrhoeae, while growth of Escherichia coli, a bacterium that lacks PatB and, hence, does not produce O-acetylated peptidoglycan, was unaffected. These data provide preliminary proof of concept that peptidoglycan O-acetyltransferases would serve as useful antibacterial targets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".