Structural Analysis of the Tobramycin and Gentamicin Clinical Resistome Reveals Limitations for Next-generation Aminoglycoside Design
Bibliographic record
Abstract
Widespread use and misuse of antibiotics has allowed for the selection of resistant bacteria capable of avoiding the effects of antibiotics. The primary mechanism for resistance to aminoglycosides, a broad-spectrum class of antibiotics, is through covalent enzymatic modification of the drug, waning their bactericidal effect. Tobramycin and gentamicin are two medically important aminoglycosides targeted by several different resistance factors, including aminoglycoside 2″-nucleotidyltransferase [ANT(2″)], the primary cause of aminoglycoside resistance in North America. We describe here two crystal structures of ANT(2″), each in complex with AMPCPP, Mn(2+), and either tobramycin or gentamicin. Together these structures outline ANT(2″)'s specificity for clinically used substrates. Importantly, these structures complete our structural knowledge for the set of enzymes that most frequently confer clinically observed resistance to tobramycin and gentamicin. Comparison of tobramycin and gentamicin binding to enzymes in this resistome, as well as to the intended target, the bacterial ribosome, reveals surprising diversity in observed drug-target interactions. Analysis of the diverse binding modes informs that there are limited opportunities for developing aminoglycoside analogs capable of evading 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".