Discovery of leaderless bacteriocins through genome mining
Bibliographic record
Abstract
The emergence of antimicrobial resistance has prompted the search for antibiotic alternatives, and bacteriocins have emerged as promising alternatives to traditional antibiotics. Bacteriocins refer to a large family of antimicrobial peptides that are ribosomally synthesized by bacteria. The class of bacteriocins called leaderless bacteriocins is a unique group distinguished by the absence of a leader peptide and post-translational modifications, and hence their linear structures. Members of this class have been discovered by a traditional approach of screening bacterial isolates for antimicrobial activity and subsequently identifying the active molecule. This study presents the first extensive genome mining approach for leaderless bacteriocin discovery. In detail, we performed a precursor peptide-based genome mining to search for novel leaderless bacteriocins. Over 400 producer organisms with putative leaderless bacteriocins encoded in their genomes were identified, revealing the widespread occurrence of leaderless bacteriocins. Among the identified putative bacteriocins, 122 are unique peptide sequences. To validate our genome mining results, a novel bacteriocin we termed miticin, encoded in the genome of Streptococcus mitis, was obtained through chemical synthesis. Miticin was indeed found to be active, specifically against a wide range of Gram-positive bacteria. With this study, we provide the foundation for exploring a repertoire of potential novel antimicrobials identified through an innovative bioinformatics-guided approach, representing a significant leap compared to traditional screening and isolation methods for the discovery of bioactive molecules, specifically leaderless bacteriocins.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".