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Updated: Sep 10, 2025
Phage Phenomics: Physiological Approaches to Characterize Novel Viral Proteins
Published on: June 11, 2015
PDP-Miner: an AI/ML tool to detect prophage tail proteins with depolymerase domains across thousands of bacterial
Jeff Gauthier1,2, Irena Kukavica-Ibrulj1,2, Roger C Levesque1,2
1Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, QC, G1V 0A6, Canada.
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 1294 Pseudomonas genomes available on the International Pseudomonas Consortium Database while also accurately reporting depolymerases in known phage genomes, similarly to other software like PhageDPO or DepoScope.
Availability And Implementation:
Source code, test datasets and documentation are freely available for download at http:///www.github.com/jeffgauthier/pdpminer. This software is free and open source under the GNU General Public License v3.0.
Insights
Phage tail proteins are a promising alternative to antibiotics. PDP-Miner identifies potential depolymerase genes in Pseudomonas genomes, aiding in the fight against antibiotic resistance.
Area of Science:
- Microbiology
- Bioinformatics
- Genomics
Background:
- Antibiotic resistance is a growing global health crisis, with no new major antibiotic classes approved since 1987.
- Phage tail proteins, particularly depolymerases targeting bacterial outer membrane polysaccharides, represent a promising alternative therapeutic strategy.
- Pathogenic bacteria often harbor prophages, providing known molecular targets for phage-derived therapies.
Purpose of the Study:
- To develop a computational tool for identifying phage depolymerase genes.
- To annotate and detect depolymerase activity in phage tail proteins.
- To validate potential depolymerase candidates by analyzing protein domains.
Main Methods:
- Developed PDP-Miner, a wrapper for the machine learning-based Depolymerase-Predictor tool.
- Annotated phage tail proteins ab initio and detected depolymerase activity.
- Performed post-hoc validation by annotating protein domains for depolymerase indicators.
Main Results:
- Identified 10 high-confidence phage depolymerase gene candidates in 1,294 Pseudomonas genomes from the International Pseudomonas Consortium Database.
- Accurately reported depolymerases in known phage genomes.
- Demonstrated comparable performance to existing tools like PhageDPO and DepoScope.
Conclusions:
- PDP-Miner is an effective tool for identifying novel phage depolymerase candidates.
- The tool aids in the discovery of potential antimicrobial agents to combat antibiotic resistance.
- Facilitates the investigation of phage proteins for therapeutic applications.
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