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<p>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 <i>Salmonella enterica</i> and 3 <i>Escherichia coli</i> bacteriophages. The performance of prediction models differed among bacteriophages, ranging from 78 to 92% of accuracy in the case of <i>Salmonella</i> and 84–94% in <i>Escherichia</i> phages, with the highest accuracy (94%) achieved for <i>E. coli</i> phage CBDS-07. Results demonstrated the effectiveness of using PPI as a feature to design ML models for phage-bacteria phenotype prediction.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月31日

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  • Pamela Yael Camejo,
  • Felipe Rojas,
  • Antonio Ossa,
  • Rodrigo Hurtado,
  • Daniel Tichy,
  • Christian Pieringer,
  • Michael Pino,
  • Paola Mora-Uribe,
  • Soledad Ulloa,
  • Rodrigo Norambuena,
  • Eduardo Tobar-Calfucoy,
  • Matías Aguilera,
  • Victoria Rojas-Martínez,
  • Onix Cifuentes,
  • Andrea Sabag,
  • Nicolas Cifuentes,
  • Daniel San Martín,
  • Claudia Infante,
  • Pablo Cifuentes,
  • Hans Pieringer,
  • Luis E. León

摘要

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.