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<p>Rapid antigenic evolution of influenza viruses complicates timely vaccine strain selection and renders prospective interpretation of serological assays—including neutralization (NT) and hemagglutination inhibition (HI)—increasingly challenging in the face of a continuously shifting antigenic landscape. Here, we propose CIST, a clade-informed deep-learning-based framework for predicting influenza antigenicity directly from paired virus–vaccine or virus–antiserum hemagglutinin (HA) sequences. CIST leverages masked-language-model pre-training on 70,425 influenza A HA sequences to learn context-aware residue representations and integrates explicit evolutionary clade embeddings reinforced by a contrastive feature enhancement objective. The resulting sequence representations are processed by a local–global dual-attention encoder to regress both NT values and HI titers. Across two H3N2 antigenicity prediction tasks, CIST outperformed traditional machine-learning baselines and a standard Transformer, achieving a mean squared error (MSE)/mean absolute error (MAE)/root mean squared error (RMSE) of 0.0014/0.0141/0.0374 for NT prediction compared with 0.0029/0.0210/0.0538 for the Transformer, and 0.0059/0.0378/0.0768 for HI prediction, corresponding to error reductions of up to 51.7% in MSE for NT and 11.9% for HI over the Transformer baseline. Pearson correlation coefficients of 0.97 (NT) and 0.82 (HI) further confirm the quantitative agreement between predicted and observed titers. Attention-based interpretability analyses further revealed that high-contribution residues were enriched in known antigenic regions of HA, including classical antigenic sites A–E, supporting the biological plausibility of the learned features. Additional zero-shot temporal external validation on emerging K subclade viruses (<i>n</i> = 36 virus–antiserum pairs, absent from training) showed that CIST achieved the lowest prediction error among the evaluated methods (MSE = 0.092, Pearson <i>r</i> = 0.62), whereas RF showed limited correlation with observed titers (<i>r</i> = 0.25), supporting the potential utility of clade-informed deep learning for antigenic surveillance of emerging strains. Together, these results demonstrate that clade-informed deep learning enables accurate and interpretable computational assessment of influenza antigenicity, providing a practical complement to laboratory assays for antigenic surveillance and vaccine evaluation.</p>

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CIST: A clade-informed sequence transformer framework for predicting influenza virus antigenicity

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月27日

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  • Kang Hu,
  • Yongshan Zhu,
  • Qingchuan Zhang,
  • Jian Li

摘要

Rapid antigenic evolution of influenza viruses complicates timely vaccine strain selection and renders prospective interpretation of serological assays—including neutralization (NT) and hemagglutination inhibition (HI)—increasingly challenging in the face of a continuously shifting antigenic landscape. Here, we propose CIST, a clade-informed deep-learning-based framework for predicting influenza antigenicity directly from paired virus–vaccine or virus–antiserum hemagglutinin (HA) sequences. CIST leverages masked-language-model pre-training on 70,425 influenza A HA sequences to learn context-aware residue representations and integrates explicit evolutionary clade embeddings reinforced by a contrastive feature enhancement objective. The resulting sequence representations are processed by a local–global dual-attention encoder to regress both NT values and HI titers. Across two H3N2 antigenicity prediction tasks, CIST outperformed traditional machine-learning baselines and a standard Transformer, achieving a mean squared error (MSE)/mean absolute error (MAE)/root mean squared error (RMSE) of 0.0014/0.0141/0.0374 for NT prediction compared with 0.0029/0.0210/0.0538 for the Transformer, and 0.0059/0.0378/0.0768 for HI prediction, corresponding to error reductions of up to 51.7% in MSE for NT and 11.9% for HI over the Transformer baseline. Pearson correlation coefficients of 0.97 (NT) and 0.82 (HI) further confirm the quantitative agreement between predicted and observed titers. Attention-based interpretability analyses further revealed that high-contribution residues were enriched in known antigenic regions of HA, including classical antigenic sites A–E, supporting the biological plausibility of the learned features. Additional zero-shot temporal external validation on emerging K subclade viruses (n = 36 virus–antiserum pairs, absent from training) showed that CIST achieved the lowest prediction error among the evaluated methods (MSE = 0.092, Pearson r = 0.62), whereas RF showed limited correlation with observed titers (r = 0.25), supporting the potential utility of clade-informed deep learning for antigenic surveillance of emerging strains. Together, these results demonstrate that clade-informed deep learning enables accurate and interpretable computational assessment of influenza antigenicity, providing a practical complement to laboratory assays for antigenic surveillance and vaccine evaluation.