Characterization of Erythromycin Esterases: A Genomic Enzymology Approach to Macrolide Resistance
Bibliographic record
Abstract
Enzymatic inactivation of antibiotics is a successful strategy employed by antibiotic resistant clinical pathogens. Genes coding for the antibiotic inactivating enzyme erythromycin esterase, or Ere, have been found in a variety of bacterial species, from clinical isolates to environmental soil-dwelling organisms. In order to better understand this family of proteins and this mode of macrolide resistance, four erythromycin esterases (EreA from Providencia stuartii, EreB from E. coli, and two putative Ere's from Saccharopolyspora erythraea and Bacillus cereus) were purified and characterized using a genomic enzymology approach. A robust quantitative enzyme assay was developed, and kinetic parameters for these different enzymes were compared. In the absence of a crystal structure for EreA or EreB, a model for EreB was developed based on the existing structure of the putative esterase from B. cereus. The importance of some conserved and potentially catalytic residues was examined using site-directed mutagenesis. Combined with mutagenesis, inhibitors, pH studies and solvent isotope effects were used to investigate potential enzyme mechanisms and two potential mechanisms were proposed for this family of proteins. A greater understanding of the function and mechanisms of antibiotic resistance elements such as the erythromycin esterases may provide valuable insights to aid in the ongoing struggle against resistant organisms in clinical settings.
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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".