Mining the <scp>UniProtKB</scp> /Swiss‐Prot database for antimicrobial peptides
Bibliographic record
Abstract
The ever-growing global health threat of antibiotic resistance is compelling researchers to explore alternatives to conventional antibiotics. Antimicrobial peptides (AMPs) are emerging as a promising solution to fill this need. Naturally occurring AMPs are produced by all forms of life as part of the innate immune system. High-throughput bioinformatics tools have enabled fast and large-scale discovery of AMPs from genomic, transcriptomic, and proteomic resources of selected organisms. Public protein sequence databases, comprising over 200 million records and growing, serve as comprehensive compendia of sequences from a broad range of source organisms. Yet, large-scale in silico probing of those databases for novel AMP discovery using modern deep learning techniques has rarely been reported. In the present study, we propose an AMP mining workflow to predict novel AMPs from the UniProtKB/Swiss-Prot database using the AMP prediction tool, AMPlify, as its discovery engine. Using this workflow, we identified 8008 novel putative AMPs from all eukaryotic sequences in the database. Focusing on the practical use of AMPs as suitable antimicrobial agents with applications in the poultry industry, we prioritized 40 of those AMPs based on their similarities to known chicken AMPs in predicted structures. In our tests, 13 out of the 38 successfully synthesized peptides showed antimicrobial activity against Escherichia coli and/or Staphylococcus aureus. AMPlify and the companion scripts supporting the AMP mining workflow presented herein are publicly available at https://github.com/bcgsc/AMPlify.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.013 |
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".