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%.

MAIVeSS: streamlined selection of antigenically matched, high-yield viruses for seasonal influenza vaccine production

“…Machine learning models have been shown to be effective in identifying antigenicity associated features in protein sequences from different subtypes of influenza A viruses 30 – 35 , We developed machine learning models to identify the specific sequence features in HA proteins that determine three important phenotypes: antigenicity, yield in cells and eggs, and receptor binding. To achieve this, we trained our models on large datasets of HA protein sequences and associated phenotype information.…”

Predicting Insolvency of Insurance Companies in Egyptian Market Using Bagging and Boosting Ensemble Techniques

“…In this context, machine learning algorithms are powerful tools that can learn through experience and through the use of data without being explicitly programmed. Therefore, machine learning algorithms are widely used in different realworld applications, such as email filtering [35], sequence-tosequence modeling [36], and computer vision [37].…”