Identification of the target and mode of action for the prokaryotic nucleotide excision repair inhibitor ATBC
Bibliographic record
Abstract
In bacteria, nucleotide excision repair (NER) plays a major role in repairing DNA damage from a wide variety of sources. Therefore, its inhibition offers potential to develop a new antibacterial in combination with adjuvants, such as UV light. To date, only one known chemical inhibitor of NER is 2-(5-amino-1,3,4-thiadiazol-2-yl)benzo(f)chromen-3-one (ATBC) exists and targets Mycobacterium tuberculosis NER. To enable the design of future drugs, we need to understand its mechanism of action. To determine the mechanism of action, we used in silico structure-based prediction, which identified the ATP-binding pocket of Escherichia coli UvrA as a probable target. Growth studies in E. coli showed it was nontoxic alone, but able to impair growth when combined with DNA-damaging agents, and as we predicted, it reduced by an approximately 70% UvrA's ATPase rate. Since UvrA's ATPase activity is necessary for effective DNA binding, we used single-molecule microscopy to directly observe DNA association. We measured an approximately sevenfold reduction in UvrA molecules binding to a single molecule of dsDNA suspended between optically trapped beads. These data provide a clear mechanism of action for ATBC, and show that targeting UvrA's ATPase pocket is effective and ATBC provides an excellent framework for the derivation of more soluble inhibitors that can be tested for activity.
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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 | 0.001 | 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".