Genome-predicted PBP profiling shows high agreement with phenotypic testing for pneumococcal beta-lactam susceptibilityGenome test predicts pneumococcal antibiotic resistance accurately

β-Lactams remain central to the treatment of pneumococcal infections, and whole-genome sequencing increasingly enables pneumococcal β-lactam susceptibility to be inferred from the combined transpeptidase-domain sequences of PBP1a, PBP2b, and PBP2x. PBP-profile lookup, statistical models, and integrated genomic pipelines can predict drug-specific minimum inhibitory concentrations with high overall agreement with phenotypic antimicrobial susceptibility testing. However, technical prediction accuracy does not by itself establish direct clinical use. Novel or sparsely represented PBP profiles, interspecies recombination within the mitis-group gene pool, lineage and geographical structure, non-PBP genetic effects, and uncertainty in reference MIC measurements can limit model transportability. Furthermore, a predicted MIC cannot be converted into a clinically meaningful susceptibility interpretation without considering the antimicrobial agent, infection site, dosing or exposure context, interpretive standard, and breakpoint version. This mini review summarizes the PBP-centered genetic architecture of pneumococcal β-lactam susceptibility, evaluates current approaches for genome-based MIC prediction, and examines the factors that constrain their generalizability. We propose a three-layer reporting framework that separates genomic findings, predicted phenotypes, and potential clinical interpretation, while explicitly communicating prediction confidence and identifying circumstances requiring confirmatory phenotypic MIC testing. Genome-based PBP profiling is already well positioned to strengthen pneumococcal surveillance and may inform potential clinical interpretation, but patient-level reporting will require continuously curated phenotype-linked databases, external validation in intended-use populations, and an uncertainty-aware interpretive layer connecting genomic evidence to treatment-specific breakpoints.