Structural analysis of 23S rRNA methylating enzyme Cfr reveals RNA binding determinants for methylation regiospecificity and antibiotic resistance
Bibliographic record
Abstract
Abstract The 23S rRNA methylating enzyme Cfr, found in pathogens including Staphylococcus aureus, Clostridium difficile, Escherichia coli and Klebsiella pneumoniae , confers resistance to phenicols, lincosamides, oxazolidinones (including linezolid), pleuromutilins, and streptogramins A (the PhLOPS A phenotype). Cfr catalyses methylation of the C8 position of the A2503 base in 23S rRNA; the recognition site of the above antibiotic classes. Along with RlmN housekeeping enzyme, Cfr can also promote methylation of the C2 position of the same base. The molecular and structural basis of Cfr’s dual substrate specificity is not known, which hinders our ability to design Cfr-targeting inhibitors necessary to curb the PhLOPS A resistance. Here, we present the first crystal structure of Cfr and a detailed analysis of its possible interactions with rRNA. Using structure-guided mutagenesis, mass spectrometry analysis of in-cellulo 23S rRNA methylated species, and in-cellulo resistance studies, we identify the key amino acids essential for Cfr methylation and multi-drug resistance activity. In particular, we found that Cfr’s Q329 residue is important for C8-specific methylation. These data provide a framework for further studies of the biochemistry, structure and inhibition of this important resistance determinant.
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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".