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- Title
Prediction of Klebsiella phage-host specificity at the strain level.
- Authors
Boeckaerts, Dimitri; Stock, Michiel; Ferriol-González, Celia; Oteo-Iglesias, Jesús; Sanjuán, Rafael; Domingo-Calap, Pilar; De Baets, Bernard; Briers, Yves
- Abstract
Phages are increasingly considered promising alternatives to target drug-resistant bacterial pathogens. However, their often-narrow host range can make it challenging to find matching phages against bacteria of interest. Current computational tools do not accurately predict interactions at the strain level in a way that is relevant and properly evaluated for practical use. We present PhageHostLearn, a machine learning system that predicts strain-level interactions between receptor-binding proteins and bacterial receptors for Klebsiella phage-bacteria pairs. We evaluate this system both in silico and in the laboratory, in the clinically relevant setting of finding matching phages against bacterial strains. PhageHostLearn reaches a cross-validated ROC AUC of up to 81.8% in silico and maintains this performance in laboratory validation. Our approach provides a framework for developing and evaluating phage-host prediction methods that are useful in practice, which we believe to be a meaningful contribution to the machine-learning-guided development of phage therapeutics and diagnostics. Bacterial viruses (phages) are promising alternatives to treat antibiotic-resistant bacterial infections, but finding matching phages against bacteria of interest is challenging. Here, Boeckaerts et al. present a machine learning approach that predicts phage-bacteria pairs at the strain level for Klebsiella pathogens.
- Subjects
KLEBSIELLA; BACTERIAL proteins; PROTEIN receptors; BACTERIAL diseases; BACTERIOPHAGES; MACHINE learning
- Publication
Nature Communications, 2024, Vol 15, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-024-48675-6