Development of Antibiotics That Dysregulate the <i>Neisserial</i> ClpP Protease
Bibliographic record
Abstract
Evolving antimicrobial resistance has motivated the search for novel targets and alternative therapies. Caseinolytic protease (ClpP) has emerged as an enticing new target since its function is conserved and essential for bacterial fitness, and because its inhibition or dysregulation leads to bacterial cell death. ClpP protease function controls global protein homeostasis and is, therefore, crucial for the maintenance of the bacterial proteome during growth and infection. Previously, acyldepsipeptides (ADEPs) were discovered to dysregulate ClpP, leading to bactericidal activity against both actively growing and dormant Gram-positive pathogens. Unfortunately, these compounds had very low efficacy against Gram-negative bacteria. Hence, we sought to develop non-ADEP ClpP-targeting compounds with activity against Gram-negative species and called these activators of self-compartmentalizing proteases (ACPs). These ACPs bind and dysregulate ClpP in a manner similar to ADEPs, effectively digesting bacteria from the inside out. Here, we performed further ACP derivatization and testing to improve the efficacy and breadth of coverage of selected ACPs against Gram-negative bacteria. We observed that a diverse collection of Neisseria meningitidis and Neisseria gonorrhoeae clinical isolates were exquisitely sensitive to these ACP analogues. Furthermore, based on the ACP-ClpP cocrystal structure solved here, we demonstrate that ACPs could be designed to be species specific. This validates the feasibility of drug-based targeting of ClpP in Gram-negative bacteria.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".