Predicting bacteriophage infection of specific bacterial strains promises advancements in phage therapy and microbial ecology. Whether the dynamics of well-established phage–host model systems generalize to the wide diversity of microbes is currently unknown. Here we show that we could accurately predict the outcomes of phage–bacteria interactions at the strain level in natural isolates from the genus Escherichia using only genomic data (area under the receiver operating characteristic curve (AUROC) of 86%). We experimentally established a dataset of interactions between 403 diverse Escherichia strains and 96 phages. Most interactions are explained by adsorption factors as opposed to antiphage systems which play a marginal role. We trained predictive algorithms and pinpoint poorly predicted interactions to direct future research efforts. Finally, we established a pipeline to recommend tailored phage cocktails, demonstrating efficiency on 100 pathogenic E. coli isolates. This work provides quantitative insights into phage–host specificity and supports the use of predictive algorithms in phage therapy.

预测特定细菌菌株的噬菌体感染有望在噬菌体治疗和微生物生态学方面取得进展。目前尚不清楚成熟的噬菌体-宿主模型系统的动力学是否推广到广泛的微生物多样性。在这里，我们表明，我们可以仅使用基因组数据 （受试者工作特征曲线下面积 （AUROC） 为 86%） 准确预测埃希氏菌属天然分离株中噬菌体-细菌相互作用在菌株水平上的结果。我们实验建立了 403 种不同埃希菌菌株和 96 种噬菌体之间相互作用的数据集。大多数相互作用是由吸附因子解释的，而不是由抗噬菌体系统解释的，后者起着边际作用。我们训练了预测算法并精确定位预测不佳的交互作用，以指导未来的研究工作。最后，我们建立了一个推荐定制噬菌体混合物的管道，证明了对 100 种致病性大肠杆菌分离株的效率。这项工作提供了对噬菌体-宿主特异性的定量见解，并支持在噬菌体治疗中使用预测算法。