Novel Class of Antibiotics Disassembles the Lipopolysaccharide Transport Bridge
Bibliographic record
Abstract
Abstract The rapid rise of multi-resistant bacteria poses a significant threat to our health system. The development of drugs against novel targets is urgently needed to replenish the clinical arsenal with effective antibiotics. The naturally occurring peptide thanatin kills Gram-negative bacteria by targeting the periplasmic protein bridge, in particular the lipopolysaccharide transport protein A (LptA). Using the thanatin scaffold together with phenotypic medicinal chemistry and a target-focused approach, we developed potent antimicrobial macrocyclic peptides with drug-like properties that exhibited low frequencies for development of resistance both in vitro and in vivo. Binding affinities to Escherichia coli LptA of the thanatin analogs correlated well with minimal inhibitory concentrations for both parent and thanatin-resistant E. coli strains. Mode of action studies revealed that the antimicrobial activity of the antibiotics involves the specific disruption of the periplasmic Lpt protein bridge. Our studies validate the Lpt protein bridge as a novel target for macrocyclic peptide antibiotics that can avoid current mechanisms of drug resistance. The peptides warrant further preclinical and clinical studies addressing WHO priority 1 carbapenem-resistant Enterobacteriaceae, in particular for Klebsiella pneumoniae lung infections.
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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".