Work

PhageDPO: A machine-learning based computational framework for identifying phage depolymerases

Year: 2025

Type: article

Abstract: Bacteriophages are the most predominant and genetically diverse biological entities on Earth. They are bacterial viruses which encode numerous proteins with potential antibacterial activity. However, most bacteriophage-encoded proteins have no assigned function, hindering the discovery of novel antibacterial agents. In particular, there has been a growing interest in exploring recombinant bacteriophage depolymerases from the fundamental standpoint, but mostly for biotechnological applications to control bacterial pathogens. Due to the lack of efficient identification tools, we developed PhageDPO , the first developed tool that predicts depolymerases in bacteriophage genomes using machine learning methods. Availability and implementation PhageDPO was integrated into a Galaxy framework available online at: bit.ly/phagedpo.

Source: Computers in Biology and Medicine

DOI: 10.1016/j.compbiomed.2025.109836

Authors: M. Fernanda Vieira, José Duarte, Rita Domingues, Hugo Oliveira, Oscar Dias

Institutions: University of Minho, Labbels

Language: English

Topic: Bacteriophages and microbial interactions

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,080

Funders: “la Caixa” Foundation, Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona, Fundação para a Ciência e a Tecnologia