Nα-acetyl-L-ornithine deacetylase from Escherichia coli and a ninhydrin-based assay to enable inhibitor identification
Bibliographic record
Abstract
Bacteria are becoming increasingly resistant to antibiotics, therefore there is an urgent need for new classes of antibiotics to fight antibiotic resistance. Mammals do not express Nɑ -acetyl-L-ornithine deacetylase (ArgE), an enzyme that is critical for bacterial survival and growth, thus ArgE represents a promising new antibiotic drug target, as inhibitors would not suffer from mechanism-based toxicity. A new ninhydrin-based assay was designed and validated that included the synthesis of the substrate analog N5, N5-di-methyl Nα-acetyl-L-ornithine (kcat/Km = 7.32 ± 0.94 × 104 M−1s−1). This new assay enabled the screening of potential inhibitors that absorb in the UV region, and thus is superior to the established 214 nm assay. Using this new ninhydrin-based assay, captopril was confirmed as an ArgE inhibitor (IC50 = 58.7 μM; Ki = 37.1 ± 0.85 μM), and a number of phenylboronic acid derivatives were identified as inhibitors, including 4-(diethylamino)phenylboronic acid (IC50 = 50.1 μM). Selected inhibitors were also tested in a thermal shift assay with ArgE using SYPRO Orange dye against Escherichia coli ArgE to observe the stability of the enzyme in the presence of inhibitors (captopril Ki = 35.9 ± 5.1 μM). The active site structure of di-Zn EcArgE was confirmed using X-ray absorption spectroscopy, and we reported two X-ray crystal structures of E. coli ArgE. In summary, we describe the development of a new ninhydrin-based assay for ArgE, the identification of captopril and phenylboronic acids as ArgE inhibitors, thermal shift studies with ArgE + captopril, and the first two published crystal structures of ArgE (mono-Zn and di-Zn).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".