End-to-end antigenic variant generation for H1N1 influenza HA protein using sequence to sequence models

Abstract

The growing risk of new variants of the influenza A virus is the most significant to public health. The risk imposed from new variants may have been lethal, as witnessed in the year 2009. Even though the improvement in predicting antigenicity of influenza viruses has rapidly progressed, few studies employed deep learning methodologies. The most recent literature mostly relied on classification techniques, while a model that generates the HA protein of the antigenic variant is not developed. However, the antigenic pair of influenza virus A can be determined in a laboratory setup, the process needs a tremendous amount of time and labor. Antigenic shift and drift which are caused by changes in surface protein favored the influenza A virus in evading immunity. The high frequency of the minor changes in the surface protein poses a challenge to identifying the antigenic variant of an emerging virus. These changes slow down vaccine selection and the manufacturing process. In this vein, the proposed model could help save the time and efforts exerted to identify the antigenic pair of the influenza virus. The proposed model utilized an end-to-end learning methodology relying on deep sequence-to-sequence architecture to generate the antigenic variant of a given influenza A virus using surface protein. Employing the BLEU score to evaluate the generated HA protein of the antigenic variant of influenza virus A against the actual variant, the proposed model achieved a mean accuracy of 97.57%.

Fig 1. LSTM cell.

Fig 2. Sequence to sequence architecture.

Fig 3. Attention with sequence to sequence architecture.

Fig 4. Training vs validation.

(a) Training vs validation accuracy for Deep_BI_GRU Model. (b) Training vs validation loss for Deep_BI_GRU Model.

Fig 5. Accuracy distribution.

(a) BLEU-1 accuracy distribution. (b) BLEU-4 accuracy distribution.