Transporter Protein-Guided Genome Mining for Head-to-Tail Cyclized Bacteriocins
Bibliographic record
Abstract
Head-to-tail cyclized bacteriocins are ribosomally synthesized antimicrobial peptides that are defined by peptide backbone cyclization involving the N- and C- terminal amino acids. Their cyclic nature and overall three-dimensional fold confer superior stability against extreme pH and temperature conditions, and protease degradation. Most of the characterized head-to-tail cyclized bacteriocins were discovered through a traditional approach that involved the screening of bacterial isolates for antimicrobial activity and subsequent isolation and characterization of the active molecule. In this study, we performed genome mining using transporter protein sequences associated with experimentally validated head-to-tail cyclized bacteriocins as driver sequences to search for novel bacteriocins. Biosynthetic gene cluster analysis was then performed to select the high probability functional gene clusters. A total of 387 producer strains that encode putative head-to-tail cyclized bacteriocins were identified. Sequence and phylogenetic analyses revealed that this class of bacteriocins is more diverse than previously thought. Furthermore, our genome mining strategy captured hits that were not identified in precursor-based bioprospecting, showcasing the utility of this approach to expanding the repertoire of head-to-tail cyclized bacteriocins. This work sets the stage for future isolation of novel head-to-tail cyclized bacteriocins to serve as possible alternatives to traditional antibiotics and potentially help address the increasing threat posed by resistant pathogens.
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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".