Rationally Designed Self-Derived Peptides Kill <i>Escherichia coli</i> by Targeting BamA and BamD Essential for Outer Membrane Protein Biogenesis
Bibliographic record
Abstract
There is an urgent need to develop antibiotics with new mechanisms of action for combating antibiotic-resistant bacteria, particularly against Gram-negative pathogens that severely threaten human health. Here, we introduce the rational design and comprehensive characterization of self-derived antibacterial peptides that specifically target Escherichia coli BamA and BamD, vital components of the β-barrel assembly machine (BAM) for the folding and membrane integration of outer membrane proteins (OMPs) in Gram-negative bacteria. Among the three BamA-targeted peptides, BamA 543–551, which corresponds to an extracellular loop of BamA, exhibits remarkable bactericidal activity against OM-permeabilized E. coli cells. Similarly, among four BamD-targeted peptides, BamD 163–187 corresponding to a BamA-interacting α-helix exhibits potent bactericidal activity. Notably, both BamA 543–551 and BamD 163–187 are able to kill other OM-permeabilized Gram-negative pathogens but not Gram-positive ones, and fusion with a cell membrane-penetrating peptide enabled them to directly kill intact E. coli cells. Further, both of them significantly change the cell membrane integrity of E. coli, induce the accumulation of misfolded OmpF, and reduce the level of folded OmpF. In particular, in vivo photo-cross-linking analysis indicates that BamA 543–551 disrupts the direct interaction between BamA and periplasmic chaperone SurA in living E. coli cells, thus offering insights into their mode of action. Collectively, our findings confirm the potential of BamA and BamD as promising antibiotic targets and suggest that BamA- and BamD-derived peptides can be candidates for antibiotic development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".