Domain−Domain Interactions in the Aminoglycoside Antibiotic Resistance Enzyme AAC(6‘)-APH(2‘ ‘)
Bibliographic record
Abstract
The most common determinant of aminoglycoside antibiotic resistance in Gram positive bacterial pathogens, such as Staphylococcus aureus, is a modifying enzyme, AAC(6')-APH(2' '), capable of acetylating and phosphorylating a wide range of antibiotics. This enzyme is unique in that it is composed of two separable modification domains, and although a number of studies have been conducted on the acetyltransferase and phosphotransferase activities in isolation, little is known about the role and impact of domain interactions on antibiotic resistance. Kinetic analysis and in vivo assessment of a number of N- and C-terminal truncated proteins have demonstrated that the two domains operate independently and do not accentuate one another's resistance activity. However, the two domains are structurally integrated, and mutational analysis has demonstrated that a predicted connecting alpha-helix is especially critical for maintaining proper structure and function of both activities. AAC(6')-APH(2' ') detoxifies a staggering array of aminoglycosides, where one or both activities make important contributions depending on the antibiotic. Thus, to overcome antibiotic resistance associated with AAC(6')-APH(2' '), aminoglycosides resistant to modification and/or inhibitors against both activities must be employed. Domain-domain interactions in AAC(6')-APH(2' ') offer a unique target for inhibitor strategies, as we show that their disruption simultaneously inhibits both activities >90%.
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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 | 0.001 |
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".