Abstract: The rapid evolution of influenza virus hemagglutinin protein has led to the continuous production of new virus strains,which may cause seasonal influenza and even global influenza outbreaks.Timely detection of antigen variants is essential for vaccine screening and design.Therefore,a robust predictive model of antigenicity is an effective method to deal with the challenge of vaccines.Various end-to-end feature learning tools provide good feature representation methods for proteomics,but the existing influenza A prediction models cannot effectively extract and utilize features in amino acid sequences.In this paper,a gated two-tower model is designed based on the transformer.By inputting the amino acid sequence of the influenza A virus hemagglutinin protein,two parallel encoders are used to capture the antigenic characteristics from the time and space dimensions of the hemagglutinin protein amino acid sequence,and learn the nonlinear relationship between features and prediction results.In order to reduce the noise in the data,when fusing the features in the time dimension and the space dimension,the weights that measure their relative importance are adaptively obtained through the gate mechanism for selective fusion,and finally the fusion features are used to predict the H1N1 influenza antigen variants.Experimental results on the H1N1 data set show that the use of the model’sexcellent non-linear feature learning ability improves the predictive performance of antigenic variation,and at the same time has good robustness.

Key words: Influenza A, H1N1, Antigenicity prediction, Transformer, Two-tower mode, Gate mechanism