Univ-flu: A structure-based model of influenza A virus hemagglutinin for universal antigenic prediction

Abstract

The rapid mutations on hemagglutinin (HA) of influenza A virus (IAV) can lead to significant antigenic variance and consequent immune mismatch of vaccine strains. Thus, rapid antigenicity evaluation is highly desired. The subtype-specific antigenicity models have been widely used for common subtypes such as H1 and H3. However, the continuous emerging of new IAV subtypes requires the construction of universal antigenic prediction model which could be applied on multiple IAV subtypes, including the emerging or re-emerging ones. In this study, we presented Univ-Flu, series structure-based universal models for HA antigenicity prediction. Initially, the universal antigenic regions were derived on multiple subtypes. Then, a radial shell structure combined with amino acid indexes were introduced to generate the new three-dimensional structure based descriptors, which could characterize the comprehensive physical–chemical property changes between two HA variants within or across different subtypes. Further, by combining with Random Forest classifier and different training datasets, Univ-Flu could achieve high prediction performances on intra-subtype (average AUC of 0.939), inter-subtype (average AUC of 0.771), and universal-subtype (AUC of 0.978) prediction, through independent test. Results illustrated that the designed descriptor could provide accurate universal antigenic description. Finally, the application on high-throughput antigenic coverage prediction for circulating strains showed that the Univ-Flu could screen out virus strains with high cross-protective spectrum, which could provide in-silico reference for vaccine recommendation.

Keywords

• Influenza virus
• Hemagglutinin
• Antigenic prediction
• In-silico model