A novel bioinformatic method for the identification of antimicrobial peptides in metagenomes
Bibliographic record
Abstract
AIMS: This study aimed to develop a new bioinformatic approach for the identification of novel antimicrobial peptides (AMPs), which did not depend on sequence similarity to known AMPs held within databases, but on structural mimicry of another antimicrobial compound, in this case an ultrashort, synthetic, cationic lipopeptide (C12-OOWW-NH2). METHODS AND RESULTS: When applied to a collection of metagenomic datasets, our outlined bioinformatic method successfully identified several short (8-10aa) functional AMPs, the activity of which was verified via disk diffusion and minimum inhibitory concentration assays against a panel of 12 bacterial strains. Some peptides had activity comparable to, or in some cases, greater than, those from published studies that identified AMPs using more conventional methods. We also explored the effects of modifications, including extension of the peptides, observing an activity peak at 9-12aa. Additionally, the inclusion of a C-terminal amide enhanced activity in most cases. Our most promising candidate (named PB2-10aa-NH2) was thermally stable, lipid-soluble, and possessed synergistic activity with ethanol but not with a conventional antibiotic (streptomycin). CONCLUSIONS: While several bioinformatic methods exist to predict AMPs, the approach outlined here is much simpler and can be used to quickly scan huge datasets. Searching for peptide sequences bearing structural similarity to other antimicrobial compounds may present a further opportunity to identify novel AMPs with clinical relevance, and provide a meaningful contribution to the pressing global issue of AMR.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| 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) | 0.003 | 0.002 |
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".