Actionable prediction of Klebsiella phage-host specificity at the subspecies level

Abstract

Abstract Phages are increasingly considered as 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. As of yet, current computational tools do not accurately predict interactions at the subspecies level in a way that is relevant and properly evaluated for practical use. We present PhageHostLearn, a machine learning system that predicts subspecies-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 83.0% 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 machine-learning-guided development of phage therapeutics and diagnostics.