ArticleViruses2026
Multi-View Transformers for Structure-Aware HA-NA Drift Risk Scoring and Mutation Hotspot Mapping.
Article in Viruses, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Seasonal influenza A evolves quickly through mutations in haemagglutinin (HA) and neuraminidase (NA), which can reduce vaccine match and lower protection. Many sequence-only models do not link codon-level mutations to three-dimensional (3D) protein context and long-term evolutionary signals within one scoring framework. This study presents TRIAD-Influenza (TRIAD: Token-Residue-Integrated Architecture for Drift), a multi-view transformer that combines (i) codon- and residue-level sequence representations, (ii) structure-derived residue interaction features from predicted HA/NA models, and (iii) an embedding-space phylogeny that captures cluster and drift context. The pipeline curates more than 3×105 paired HA/NA coding sequences from the NCBI Virus resource (2010-2024) using strict quality control and codon-aware alignment and predicts 3D structures for nearly all unique HA and NA proteins to build contact graphs and surface/stability descriptors. TRIAD-Influenza outputs a continuous, structure-aware risk score for each HA/NA pair and produces interpretable mutation hotspot maps using gradient saliency and a contact-weighted mutation risk index (CMRI). On rolling-origin temporal cross-validation and for a temporally held-out internal test window with strong class imbalance (∼3.4% high-risk), the model shows strong ranking performance (AUROC ≈0.89; AUPRC ≈0.44; Brier score =0.069) while operating at surveillance speed (median latency ≈1.6 ms per HA/NA pair). External validation on independent GISAID/Nextstrain cohorts (2023-2024; 5000 isolates) preserves discrimination (AUROC ≈0.85-0.86). Predicted risk scores correlate with experimental haemagglutination inhibition (HI) antigenic distances (Spearman ρ up to ≈0.82 at the virus-aggregated level), and CMRI hotspots enrich known epitope and deep mutational scanning escape residues (odds ratios ≈2.7-3.6). Overall, token-residue-phylogeny coupling enables rapid, structure-aware prioritisation of emerging influenza A HA/NA sequences and delivers compact hotspot maps for expert review and targeted experiments.
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