TEAM: A time-enhanced attention-based model for virus mutation prediction

The evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during the coronavirus disease 2019 (COVID-19) pandemic highlights the critical need for predicting virus mutations in order to stay ahead of infectious diseases. Here, we present the time-enhanced attention-based model (TEAM), which combines phylogenetic sampling with deep learning to improve virus mutation prediction accuracy. TEAM introduces a novel time-enhanced phylogenetic sampling strategy that preserves both evolutionary and temporal sequence relationships, enhancing its ability to predict site-specific mutations as a multi-class classification task. The framework leverages evolutionary scale modeling embeddings and a dual-attention mechanism to capture sequence-level patterns and temporal dynamics. Experiments on the SARS-CoV-2 spike protein dataset show that TEAM substantially outperforms existing methods. Further evaluations on membrane protein and H1N1 datasets confirm its robustness and generalizability. In practice, TEAM provides a scalable and interpretable solution for mutation prediction, offering valuable insights for evolutionary research and public health planning.