Proteolytically activated antibacterial toxins inhibit the growth of diverse gram-positive bacteria
Bibliographic record
Abstract
Many species of bacteria produce small-molecule antibiotics that enter and kill a wide range of competitor microbes. However, diffusible antibacterial proteins (ABPs) that share this broad-spectrum activity are not known to exist. Here, we report a family of proteins widespread in gram-positive bacteria that display potent antibacterial activity against a diverse range of target organisms. Upon entering susceptible cells, these ABPs enzymatically degrade essential cellular components including DNA, transfer ribonucleic acid (tRNA), and ribosomal ribonucleic acid (rRNA). Unlike previously characterized bactericidal proteins, which require a specific cell surface receptor and therefore display a narrow spectrum of activity, we find that ABPs act in a receptor-independent manner and consequently kill bacteria spanning multiple phyla. Target cell entry by ABPs requires proteolytic activation by a cognate, coexported serine protease, and the liberated toxin component of the cleaved ABP is driven across the target cell membrane by the proton motive force. By examining representative ABPs from diverse pathogenic, commensal, and environmental bacteria, we show that broad-spectrum antibacterial activity is a conserved property of this protein family. Collectively, our work demonstrates that secreted proteins can act as broad-spectrum antibiotics, suggesting that ABPs represent one of potentially many such families produced in nature.
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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".