Identification of a novel <scp>DNA</scp> repair inhibitor using an <i>in silico</i> driven approach shows effective combinatorial activity with genotoxic agents against multidrug‐resistant <i>Escherichia coli</i>
Bibliographic record
Abstract
Increasing antimicrobial drug resistance represents a global existential threat. Infection is a particular problem in immunocompromised individuals, such as patients undergoing cancer chemotherapy, due to the targeting of rapidly dividing cells by antineoplastic agents. We recently developed a strategy that targets bacterial nucleotide excision DNA repair (NER) to identify compounds that act as antimicrobial sensitizers specific for patients undergoing cancer chemotherapy. Building on this, we performed a virtual drug screening of a ~120,000 compound library against the key NER protein UvrA. From this, numerous target compounds were identified and of those a candidate compound, Bemcentinib (R428), showed a strong affinity toward UvrA. This NER protein possesses four ATPase sites in its dimeric state, and we found that Bemcentinib could inhibit UvrA's ATPase activity by ~90% and also impair its ability to bind DNA. As a result, Bemcentinib strongly diminishes NER's ability to repair DNA in vitro. To provide a measure of in vivo activity we discovered that the growth of Escherichia coli MG1655 was significantly inhibited when Bemcentinib was combined with the DNA damaging agent 4-NQO, which is analogous to UV. Using the clinically relevant DNA-damaging antineoplastic cisplatin in combination with Bemcentinib against the urological sepsis-causing E. coli strain EC958 caused complete growth inhibition. This study offers a novel approach for the potential development of new compounds for use as adjuvants in antineoplastic therapy.
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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) | 0.000 | 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) | 0.002 | 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".