A machine learning approach to predict strain-specific phage-host interactions

The use of bacteriophages for biological control of bacterial infections is a promising approach to combat antimicrobial resistant bacteria. Prediction of phage-bacteria interactions is key to identify sensitive bacterial strains to phage therapy. Since these interactions are governed by multiple biological mechanisms, it is not a simple task to predict the outcome of a phage infection, which varies even among strains from the same species. In this study, machine learning-based models capable of predicting the host range of phages from sequencing data were developed. Models were trained using phage-bacteria protein-protein interactions (PPI), predicted from PPI databases, and a host-range dataset obtained from experimental assays with 10 Salmonella enterica and 3 Escherichia coli bacteriophages. The performance of prediction models differed among bacteriophages, ranging from 78 to 92% of accuracy in the case of Salmonella and 84-94% in Escherichia phages, with the highest accuracy (94%) achieved for E. coli phage CBDS-07. Results demonstrated the effectiveness of using PPI as a feature to design ML models for phage-bacteria phenotype prediction.

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Publication Details

Journal
Scientific Reports
Published
2025-10-31
DOI
https://doi.org/10.1038/s41598-025-22075-2
Citations
3
Primary Topic
Bacteriophages and microbial interactions
Type
article
Field-Weighted Citation Impact
5.15
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article

A machine learning approach to predict strain-specific phage-host interactions

Hans Pieringer, Andrea Sabag, Michael Pino, Onix Cifuentes et al.
3 citations
Scientific Reports
Bacteriophages and microbial interactions
5.15
article

A machine learning approach to predict strain-specific phage-host interactions

Hans Pieringer, Andrea Sabag, Michael Pino, Onix Cifuentes, Victoria Rojas-Martínez, Felipe Suárez Rojas, Soledad Ulloa, Pamela Y. Camejo, Rodrigo Norambuena, Eduardo Tobar-Calfucoy, Alberto de la Ossa, Pablo Cifuentes, Christian Pieringer, Matías Aguilera, Luis León, Claudia Infante, Daniel San Martín, Daniel Tichy, Rodrigo Hurtado, Paola Mora-Uribe, Nicolas Cifuentes
article en
3 citations

Abstract

The use of bacteriophages for biological control of bacterial infections is a promising approach to combat antimicrobial resistant bacteria. Prediction of phage-bacteria interactions is key to identify sensitive bacterial strains to phage therapy. Since these interactions are governed by multiple biological mechanisms, it is not a simple task to predict the outcome of a phage infection, which varies even among strains from the same species. In this study, machine learning-based models capable of predicting the host range of phages from sequencing data were developed. Models were trained using phage-bacteria protein-protein interactions (PPI), predicted from PPI databases, and a host-range dataset obtained from experimental assays with 10 Salmonella enterica and 3 Escherichia coli bacteriophages. The performance of prediction models differed among bacteriophages, ranging from 78 to 92% of accuracy in the case of Salmonella and 84-94% in Escherichia phages, with the highest accuracy (94%) achieved for E. coli phage CBDS-07. Results demonstrated the effectiveness of using PPI as a feature to design ML models for phage-bacteria phenotype prediction.

Scientific ReportsVol. 15(1)
Hospital Luis Calvo Mackenna (CL), Production Development Corporation (CL)
Openalex Percentile: Top 5%
Bacteriophages and microbial interactions
5.15
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