Mapping the Global Antigenic Evolution of Human Influenza A/H3N2 Neuraminidase Based on a Machine Learning Model - 1968-2024.

Introduction: Human influenza A/H3N2 imposes a substantial global disease burden. Beyond hemagglutinin (HA), neuraminidase (NA) also plays a critical role in the antigenic evolution of influenza viruses. However, a comprehensive understanding of NA antigenic evolution remains lacking. Methods: NA inhibition (NAI) data were collected and structural epitopes for A/H3N2 NA were identified. A machine learning model was developed to accurately predict antigenic relationships by integrating four feature groups: epitopes, physicochemical properties, N-glycosylation, and catalytic sites. An antigenic correlation network (ACNet) was constructed and antigenic clusters were identified using the Markov clustering algorithm. Results: The best random forest model (PREDEC-N2) achieved an accuracy of 0.904 in cross-validation and 0.867 in independent testing. Eight main antigenic clusters were identified on the ACNet. Spatiotemporal analysis revealed the continuous replacement and rapid global spread of new antigenic clusters for human influenza A/H3N2 NA. Conclusions: This study developed a timely and accurate computational model to map the antigenic landscape of A/H3N2 NA, revealing both its relative antigenic conservation and continuous evolution. These insights provide valuable guidance for improved antigenic surveillance, vaccine recommendations, and prevention and control strategies for human influenza viruses.