Mechanistic Basis for the Translation Inhibition of Cutibacterium acnes by Clindamycin
Bibliographic record
Abstract
Inflammation and the Gram-positive anaerobic bacterium Cutibacterium acnes, which is implicated in acne pathogenesis and pilosebaceous-unit inflammation, are the main targets of antibiotic-based therapy against acne vulgaris (acne). The most widely used antibiotics in acne therapy are tetracyclines, macrolides, and lincosamides. Unfortunately, C. acnes bacteria over the past several decades have demonstrated increased resistance to these antibiotics, particularly to clindamycin. The precise knowledge of how antibiotics interact with their clinical target is needed to overcome this problem. Toward this goal, we determined the structure of clindamycin in complex with the ribosome of C. acnes at 2.53 Å resolution using cryogenic electron microscopy. The galactose sugar moiety of clindamycin interacts with nucleotides of the 23S ribosomal RNA directly or through a conserved network of water-mediated interactions. Its propyl pyrrolidinyl group interacts with the 23S ribosomal RNA through van der Waals forces. Clindamycin binding to the C. acnes ribosome interferes with both: proper orientation of the aminoacyl group of the A-site bound transfer RNA that is needed for peptide bond formation and with the extension of the nascent peptide. Our data are important for advancing the understanding of antibiotic resistance and development of narrow-spectrum antibacterial drugs, which is an urgent need for contemporary antibiotic stewardship.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".