Details

Autor(en) / Beteiligte

• Boeckaerts, Dimitri
• Stock, Michiel
• Ferriol-González, Celia
• Oteo-Iglesias, Jesús
• Sanjuán, Rafael
• Domingo-Calap, Pilar
• De Baets, Bernard
• Briers, Yves

Titel

Prediction of Klebsiella phage-host specificity at the strain level

Ist Teil von

• Nature communications, 2024-05, Vol.15 (1), p.4355-4355, Article 4355

Ort / Verlag

England: Nature Publishing Group

Erscheinungsjahr

2024

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• 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.