PREDAC-CNN: predicting antigenic clusters of seasonal influenza A viruses with convolutional neural network

作者：Jing Meng, Jingze Liu, Wenkai Song, Honglei Li, Jiangyuan Wang, Le Zhang, Yousong Peng, Aiping Wu, Taijiao Jiang · 发表于：Briefings in Bioinformatics · 年份：2024 · DOI：10.1093/bib/bbae033 · 被引用次数：14 · 研究领域：Influenza Virus Research Studies、vaccines and immunoinformatics approaches、RNA and protein synthesis mechanisms

Vaccination stands as the most effective and economical strategy for prevention and control of influenza. The primary target of neutralizing antibodies is the surface antigen hemagglutinin (HA). However, ongoing mutations in the HA sequence result in antigenic drift. The success of a vaccine is contingent on its antigenic congruence with circulating strains. Thus, predicting antigenic variants and deducing antigenic clusters of influenza viruses are pivotal for recommendation of vaccine strains. The antigenicity of influenza A viruses is determined by the interplay of amino acids in the HA1 sequence. In this study, we exploit the ability of convolutional neural networks (CNNs) to extract spatial feature representations in the convolutional layers, which can discern interactions between amino acid sites. We introduce PREDAC-CNN, a model designed to track antigenic evolution of seasonal influenza A viruses. Accessible at http://predac-cnn.cloudna.cn, PREDAC-CNN formulates a spatially oriented representation of the HA1 sequence, optimized for the convolutional framework. It effectively probes interactions among amino acid sites in the HA1 sequence. Also, PREDAC-CNN focuses exclusively on physicochemical attributes crucial for the antigenicity of influenza viruses, thereby eliminating unnecessary amino acid embeddings. Together, PREDAC-CNN is adept at capturing interactions of amino acid sites within the HA1 sequence and examining the collective impact of point mutations on antig...