Prospects for circumventing aminoglycoside kinase mediated antibiotic resistance
Bibliographic record
Abstract
Aminoglycosides are a class of antibiotics with a broad spectrum of antimicrobial activity. Unfortunately, resistance in clinical isolates is pervasive, rendering many aminoglycosides ineffective. The most widely disseminated means of resistance to this class of antibiotics is inactivation of the drug by aminoglycoside-modifying enzymes (AMEs). There are two principal strategies to overcoming the effects of AMEs. The first approach involves the design of novel aminoglycosides that can evade modification. Although this strategy has yielded a number of superior aminoglycoside variants, their efficacy cannot be sustained in the long term. The second approach entails the development of molecules that interfere with the mechanism of AMEs such that the activity of aminoglycosides is preserved. Although such a molecule has yet to enter clinical development, the search for AME inhibitors has been greatly facilitated by the wealth of structural information amassed in recent years. In particular, aminoglycoside phosphotransferases or kinases (APHs) have been studied extensively and crystal structures of a number of APHs with diverse regiospecificity and substrate specificity have been elucidated. In this review, we present a comprehensive overview of the available APH structures and recent progress in APH inhibitor development, with a focus on the structure-guided strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".