Bibliographic record
Abstract
Bacterial genomes encode a collection of small peptides that are deleterious to their hosts when overexpressed. The physiological relevance of the majority of these peptides is unknown at present, although many of them have been implicated in regulatory processes important for cell survival and adaptability. One peptide that is of particular interest to us is a 19-amino acid proteic toxin, coined IbsC, whose production is repressed by SibC, an RNA antitoxin. Together, IbsC and SibC constitute a type I toxin-antitoxin (TA) pair. To better understand the function of IbsC and to decipher the sequence determinants for its toxic phenotype, we carried out extensive sequence analyses of the peptide. We generated a series of truncation and single amino acid deletion mutants to determine the minimal sequence required for toxicity. We further probed into functionally relevant amino acids with a comprehensive set of IbsC mutants produced using a systematic sequence randomization strategy. We found that IbsC remained toxic in the presence of multiple deletions and single amino acid substitutions, despite being well-conserved in Escherichia coli and across other Gram-negative bacteria. The toxicity of this peptide was determined to be dependent on a stretch of highly hydrophobic residues near its center. Our results defined sequence-function relationship of IbsC and offered additional insights into properties common to membrane-targeting type I toxins in E. coli and related species.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.128 | 0.099 |
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".