PHIStruct

PHIStruct predicts phage-host interactions by using structure-aware protein embeddings of receptor-binding proteins (RBPs) to determine host specificity among ESKAPEE bacterial genera.

Key Features:

• Structure-aware embeddings: Utilizes structure-aware embeddings generated by SaProt to incorporate structural information and capture host-specificity signals not evident from sequence alone.
• RBP focus: Targets receptor-binding proteins (RBPs) of bacteriophages as primary determinants of host specificity.
• Multilayer perceptron (MLP): Processes SaProt embeddings with an MLP architecture to predict interactions between phages and hosts, focusing on the ESKAPEE genera.
• Performance metrics: Maintains a high and stable F1 score across confidence thresholds and sequence similarity settings, balancing precision and recall.
• Low sequence similarity advantage: Improves class-averaged F1 scores compared to other machine learning tools and BLASTp for phages with low sequence similarity to known phages.
• Robustness across confidence thresholds: Shows a 7%–9% improvement over structure-non-informed machine learning tools and a 5%–6% increase over BLASTp at high-confidence thresholds (>50%).

Scientific Applications:

• Predicting Host Specificity: Accurately identifies potential hosts among the ESKAPEE genera by analyzing phage RBPs.
• Enhancing Phage Therapy Research: Facilitates identification of suitable phages for targeting specific bacterial pathogens.
• Broadening Computational Biology Scope: Integrates structural data into predictive models to advance computational methods in bioinformatics and molecular biology.

Methodology:

Generates structure-aware protein embeddings using SaProt and inputs them into a multilayer perceptron (MLP) to predict phage-host interactions from receptor-binding proteins, focusing on ESKAPEE bacterial genera.