PDP-Miner: an AI/ML tool to detect prophage tail proteins with depolymerase domains across thousands of bacterial genomes

2025 · doi:10.1101/2025.01.20.633936
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The paper presents PDP-Miner, an AI/ML-based computational wrapper that identifies prophage tail protein candidates with depolymerase activity across large bacterial genome datasets, using an existing depolymerase prediction tool (Depolymerase-Predictor) followed by post-hoc protein domain annotation for validation. Applying this workflow to 1,294 Pseudomonas genomes in the International Pseudomonas Consortium Database, the authors report finding 10 high-confidence phage depolymerase gene candidates, with potential to uncover additional candidates in other genome repositories. A major limitation explicitly noted is that the approach is computational and relies on candidate prediction and domain-based validation rather than experimentally confirmed depolymerase function in this study. This paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Motivation Antibiotic resistance is predicted to become the leading cause of human mortality by 2050. Despite this, no other major antibiotic class has been approved for medical use since 1987. Nevertheless, phage tail proteins offer a promising alternative, given their depolymerase activity toward outer membrane polysaccharides. Several pathogenic bacteria harbor prophages, thus making these prophages’ molecular target already known. Results We therefore developed a wrapper for an existing machine learning-based phage depolymerase prediction tool (Depolymerase-Predictor), called PDP-Miner, which annotates phage tail proteins ab initio , detects depolymerase activity within this candidate protein subset, and then performs post-hoc validation by annotating protein domains thereby allowing the user to investigate for protein domains indicative of depolymerase activity. This tool allowed identification of 10 high confidence phage depolymerase gene candidates across all 1,294 Pseudomonas genomes available on the International Pseudomonas Consortium Database and could likely help detecting other candidates across other genome databases as well. Availability and Implementation Source code is freely available for download at http:///www.github.com/jeffgauthier/pdpminer . Implemented in Bash, supported on native Linux or WSL and supports submitting subtasks to a SLURM workload queue. Requires Miniconda3 to install dependencies. This software is free and open source under the GNU General Public License v3.0. Contact [email protected] ; [email protected] Supplementary information [add Supp. Mat. URL here when available]
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Abstract

Motivation Antibiotic resistance is predicted to become the leading cause of human mortality by 2050. Despite this, no other major antibiotic class has been approved for medical use since 1987. Nevertheless, phage tail proteins offer a promising alternative, given their depolymerase activity toward outer membrane polysaccharides. Several pathogenic bacteria harbor prophages, thus making these prophages’ molecular target already known.

Results

We therefore developed a wrapper for an existing machine learning-based phage depolymerase prediction tool (Depolymerase-Predictor), called PDP-Miner, which annotates phage tail proteins ab initio, detects depolymerase activity within this candidate protein subset, and then performs post-hoc validation by annotating protein domains thereby allowing the user to investigate for protein domains indicative of depolymerase activity. This tool allowed identification of 10 high confidence phage depolymerase gene candidates across all 1,294 Pseudomonas genomes available on the International Pseudomonas Consortium Database and could likely help detecting other candidates across other genome databases as well. Availability and Implementation Source code is freely available for download at http:///www.github.com/jeffgauthier/pdpminer. Implemented in Bash, supported on native Linux or WSL and supports submitting subtasks to a SLURM workload queue. Requires Miniconda3 to install dependencies. This software is free and open source under the GNU General Public License v3.0. Contact jeff.gauthier.1{at}ulaval.ca; rclevesq{at}ibis.ulaval.ca Supplementary information [add Supp. Mat. URL here when available] Competing Interest Statement The authors have declared no competing interest. Footnotes Complete List of Authors: Gauthier, Jeff; Université Laval, Institut de biologie intégrative et des systèmes (IBIS); Université Laval, Département de microbiologie, d’infectiologie et d’immunologie, Kukavica-Ibrulj, Irena; Université Laval, Institut de biologie intégrative et des systèmes (IBIS); Université Laval, Département de microbiologie, d’infectiologie et d’immunilogie, Levesque, Roger C.; Université Laval, Institut de biologie intégrative et des systèmes (IBIS); Université Laval, Département de microbiologie, d’infectiologie et d’immunologie

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