InfluProto: Predicting Cross-Species Transmission of Influenza A Virus via Prototype Learning

Abstract

Zoonotic influenza A viruses pose constitute a significant persistent threat to global public health, yet quantitative approaches for assessing their cross-species transmission potential remain limited. Here, we present InfluProto, a deep learning model that introduces prototype learning into host prediction for influenza A virus, framing conceptualizing host adaptation as a continuous process rather than a discrete outcome. InfluProto embeds viral sequences into a continuous representation space anchored by human, swine, and avian prototypes, using cosine distances as a quantitative measure of host adaptation. InfluProto accurately discriminated among the three host classes, achieving recall and precision exceeding 0.97, and substantially outperformed conventional classification models models--particularly on challenging zoonotic strains, including the 2009 pandemic H1N1 (pdm09) lineage. Beyond host classification, the learned representations captured biologically meaningful host-adaptive signatures,; notably, with pdm09 viruses occupying occupied an intermediate position between human and swine clusters, consistent with their evolutionary origin. Analysis of simulated reassortment further revealed that the introduction of human-origin segments into swine influenza viruses reduced their distance to the human prototype, indicating that InfluProto can detect reassortment-associated shifts in host adaptation. To facilitate broad public useaccess, we have developed an interactive web service for InfluProto. Collectively, our work establishes a continuous framework for characterizing host adaptation and evaluating zoonotic risk in influenza A viruses, with potential implications for pandemic surveillance and preparedness.