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bioRxiv · 10.1101/2025.01.20.633936

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

Gauthier, J.·Kukavica-Ibrulj, I.·Levesque, R. C.
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Abstract

MotivationAntibiotic 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. ResultsWe 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 ImplementationSource 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. Contactjeff.gauthier.1@ulaval.ca; rclevesq@ibis.ulaval.ca Supplementary information[add Supp. Mat. URL here when available]

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Gauthier, J., Kukavica-Ibrulj, I., Levesque, R. C.. 2025-01-22. PDP-Miner: an AI/ML tool to detect prophage tail proteins with depolymerase domains across thousands of bacterial genomes. https://doi.org/10.1101/2025.01.20.633936

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