2026-04-29T02:37:31Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/265532025-12-18T13:01:17Zcom_20.500.12105_2052com_20.500.12105_2051col_20.500.12105_19609

00925njm 22002777a 4500 dc Boeckaerts, Dimitri author Stock, Michiel author Ferriol-González, Celia author Oteo-Iglesias, Jesus author Sanjuán, Rafael author Domingo-Calap, Pilar author De Baets, Bernard author Briers, Yves author 2024-05-22 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. Boeckaerts D, Stock M, Ferriol-González C, Oteo-Iglesias J, Sanjuán R, Domingo-Calap P, De Baets B, Briers Y. Prediction of Klebsiella phage-host specificity at the strain level. Nat Commun. 2024 May 22;15(1):4355. https://hdl.handle.net/20.500.12105/26553 38778023 10.1038/s41467-024-48675-6 2041-1723 Nature communications Prediction of Klebsiella phage-host specificity at the strain level