Small Changes of Active Site Residues of an Acinetobacter Aminoglycoside Acetyltransferase Affect Antibiotic Susceptibility
Bibliographic record
Abstract
Antibiotics are drugs used to treat bacterial infections by inhibiting growth and proliferation of microorganisms. Commonly prescribed broad‐spectrum antibiotics include aminoglycosides, but many bacteria have developed resistance to these drugs by acquiring enzymes that acetylate them. We previously reported the structural and kinetic characterization of two of these enzymes from Acinetobacter. However, several outstanding questions regarding the role of specific residues in the active site of these proteins remain. In particular, we observed an active site residue that was different between these two enzymes and appeared to alter the aminoglycoside binding pocket. It has been previously shown that active site mutations within aminoglycoside acetyltransferase enzymes can adjust their ability to modify aminoglycosides and therefore their susceptibility. Since we cannot predict which point mutations will ultimately enhance their activity and therefore resistance to aminoglycosides, we performed saturation mutagenesis of this residue in the Acinetobacter haemolyticus AAC(6′)‐Ig enzyme and tested the antibiotic susceptibility of Escherichia coli to four different aminoglycosides when these mutants were overexpressed. We found the identity of the amino acid at this position was critical for altering the susceptibility of E. coli to these antibiotics. Additionally, these mutants exhibited different susceptibility patterns depending upon the identity of aminoglycoside tested. Our results show how small changes of active site residues of aminoglycoside acetyltransferase can drastically affect bacterial susceptibility to aminoglycosides, and may provide insight to which antibiotics could be effective for treating Acinetobacter infections. Support or Funding Information Research reported in this work was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number R35GM133506 (to MLK).
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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".