Structure-guided optimization of protein kinase inhibitors reverses aminoglycoside antibiotic resistance
Bibliographic record
Abstract
Activity of the aminoglycoside phosphotransferase APH(3')-Ia leads to resistance to aminoglycoside antibiotics in pathogenic Gram-negative bacteria, and contributes to the clinical obsolescence of this class of antibiotics. One strategy to rescue compromised antibiotics such as aminoglycosides is targeting the enzymes that confer resistance with small molecules. We demonstrated previously that ePK (eukaryotic protein kinase) inhibitors could inhibit APH enzymes, owing to the structural similarity between these two enzyme families. However, limited structural information of enzyme-inhibitor complexes hindered interpretation of the results. In addition, cross-reactivity of compounds between APHs and ePKs represents an obstacle to their use as aminoglycoside adjuvants to rescue aminoglycoside antibiotic activity. In the present study, we structurally and functionally characterize inhibition of APH(3')-Ia by three diverse chemical scaffolds, anthrapyrazolone, 4-anilinoquinazoline and PP (pyrazolopyrimidine), and reveal distinctions in the binding mode of anthrapyrazolone and PP compounds to APH(3')-Ia compared with ePKs. Using this observation, we identify PP derivatives that select against ePKs, attenuate APH(3')-Ia activity and rescue aminoglycoside antibiotic activity against a resistant Escherichia coli strain. The structures described in the present paper and the inhibition studies provide an important opportunity for structure-based design of compounds to target aminoglycoside phosphotransferases for inhibition, potentially overcoming this form of antibiotic resistance.
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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".