In silico prediction and in-depth analysis of phage receptor binding protein structures, enzymatic activities and host receptor interactions

Machine learning (ML) approaches are reshaping the functional annotation of bacteriophage proteins, particularly receptor-binding proteins (RBPs). Traditional sequence-based bioinformatic methods frequently fall short in reliably characterizing RBPs and are not able to predict their potential enzymatic activity or specific interactions with bacterial host receptors. Here, we present a computational pipeline integrating ML-based functional annotation and structural modelling to characterize RBPs from phage genomes. The pipeline combines protein structure prediction and in-depth comparative analyses against datasets of known phage protein structures and enzymes. Specifically, active sites within RBPs are computationally identified by leveraging comparisons of geometrical spatial arrangements of protein residues, providing insights into reaction mechanisms. Additionally, the pipeline incorporates methods for predicting potential interactions between identified RBPs and bacterial surface receptors, offering valuable hypotheses for experimental validation. This integrative approach of existing novel tools streamlines the prediction of phage-host interactions, enabling high-throughput analyses with implications for phage therapy, bioengineering and microbial ecology. Our results suggest that AI-enhanced structural analyses substantially improve the functional annotation and biophysical understanding of phage receptor-binding mechanisms compared to conventional methodologies, accelerating their characterization.