Prediction of Klebsiella phage-host specificity at the strain level

Dimitri Boeckaerts, Michiel Stock, Celia Ferriol-González, Jesús Oteo-Iglesias, Rafael Sanjuán, Pilar Domingo-Calap, Bernard Baets and Yves Briers ()
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Dimitri Boeckaerts: Ghent University
Michiel Stock: Ghent University
Celia Ferriol-González: Universitat de Valencia-CSIC
Jesús Oteo-Iglesias: Instituto de Salud Carlos III
Rafael Sanjuán: Universitat de Valencia-CSIC
Pilar Domingo-Calap: Universitat de Valencia-CSIC
Bernard Baets: Ghent University
Yves Briers: Ghent University

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-48675-6

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