PhageAI: a new approach to predicting the lifestyle of bacteriophages using proteinBERT and convolutional neural networks

Abstract Bacteriophages are viruses that infect bacteria, including temperate, virulent and chronic phages. In the current times of increasing resistance to antibiotics (AMR), it is necessary to quickly find phages and determine their lifecycle, therefore computational methods in the first phase of composing phage preparations are necessary to find such sequences. The challenge in building such tools is the lack of good quality and correctly labeled data from multiple reference databases, which are necessary to select sequences to create cocktails that should contain complete and replication-capable phages. Current published models also do not support chronic phages, which constitute a small but significant group of phages. Another problem is explainability, current models are black-box models where we cannot observe what actually influences the model’s predictions, this may result in the model overlooking features that are truly associated with the phage life cycle. In the presented tool, however, these issues have been explicitly addressed. The presented tool was also compared to other lifecycle prediction models: Phatyp, PhagePred, Bacphlip and Phacts.