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Record W797819430

Characterization of Erythromycin Esterases: A Genomic Enzymology Approach to Macrolide Resistance

2010· dissertation· en· W797819430 on OpenAlexfundno aff
Kate Pengelly

Bibliographic record

VenueMacSphere (McMaster University) · 2010
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBiochemical and Molecular Research
Canadian institutionsnot available
FundersMcMaster University
KeywordsErythromycinComputational biologyResistance (ecology)BiologyGeneticsAntibioticsEcology
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.008
GPT teacher head0.215
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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