Computational Pipeline for Targeted Integration and Variable Payload Expression in Bacteriophage Engineering

Bacteriophages offer a promising alternative to conventional antimicrobials, especially when such treatments fail. While natural phages are viable for therapy, advances in synthetic biology allow precise genome modifications to enhance their therapeutic potential. One approach involves inserting antimicrobial genetic payloads into the phage genome. These are typically placed behind late-expressed genes, such as the major capsid gene (cps). However, phages engineered with toxic payloads often fail to produce viable progeny due to premature host shutdown. To broaden the scope of viable genetic insertion sites, we developed a method to identify intergenic loci with favorable expression profiles using the machine learning-based promoter prediction tool, PhagePromoter. Guided by these predictions, we designed a computationally assisted engineering pipeline for targeted genomic payload integration. We validated this approach by engineering bioluminescent reporter genes into the genome of the strictly lytic Staphylococcus phage K at various predicted loci. Using homologous recombination, we generated three recombinant phages, each carrying the reporter at a distinct genomic location. These engineered phages exhibited expression levels consistent with computational predictions and demonstrated temporal expression patterns corresponding to early, middle, or late gene clusters. Our study highlights the power of combining computational tools with classical genome analysis to streamline phage engineering. This method supports rational design and enables high-throughput, automated phage modification, advancing the development of personalized phage therapy.