Boeckaerts, DimitriStock, MichielFerriol-González, CeliaOteo-Iglesias, JesusSanjuán, RafaelDomingo-Calap, PilarDe Baets, BernardBriers, Yves2025-03-242025-03-242024-05-22Boeckaerts 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/20.500.12105/26553Phages 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.engVoRhttp://creativecommons.org/licenses/by/4.0/BacteriophagesComputer SimulationHost SpecificityKlebsiellaMachine LearningAttribution 4.0 International38778023151435510.1038/s41467-024-48675-62041-1723Nature communicationsopen access