Protein-level prediction of Klebsiella phage adsorption identifies conserved receptor-binding motifs.

Bacteriophage therapy offers a potential route to treat antibiotic-resistant Klebsiella pneumoniae infections, but its use is limited by the narrow specificity of phage-host interactions. In Klebsiella, adsorption is largely determined by receptor-binding proteins (RBPs) that recognize bacterial capsular polysaccharides, yet current machine learning approaches often represent whole phages rather than the individual proteins that mediate recognition. Here, we ask whether adsorption can be predicted at the level of single RBPs and whether the resulting models can identify the molecular features responsible for host specificity. Using experimentally validated Klebsiella phage-host interactions, we extended the PhageHostLearn framework from averaged phage-level representations to individual RBP-level predictions. We found that single-RBP models recover the predictive performance of strain-level models when host capsule identity is explicitly represented. However, models trained only on interaction-level labels did not reliably distinguish motif-bearing RBPs from other viral proteins, indicating that protein-level inputs alone are insufficient for mechanistic interpretability. To resolve this ambiguity, we identified serotype-specific conserved motifs among RBPs from phages infecting the same capsular type. Structural modelling showed that these motifs localize to exposed regions of RBPs and resemble carbohydrate-binding modules. Incorporating motif information into a relabelled training scheme improved prioritization of motif-bearing RBPs while preserving interaction-level predictive power. We further identified a candidate multi-motif RBP from phage S8c that may recognize multiple capsular serotypes. Together, these results support a modular model of Klebsiella phage adsorption in which conserved sub-protein elements drive capsule recognition. More broadly, this work shows how protein-level machine learning combined with biological constraints can move beyond accurate phage-host prediction toward mechanistic identification of host-range determinants.