Protein Language Models Expose Viral Immune Mimicry.

Viruses have evolved sophisticated solutions to evade host immunity. One of the most pervasive strategies is molecular mimicry, whereby viruses imitate the molecular and biophysical features of their hosts. This mimicry poses significant challenges for immune recognition, therapeutic targeting, and vaccine development. In this study, we leverage pretrained protein language models (PLMs) to distinguish between viral and human proteins. Our model enables the identification and interpretation of viral proteins that most frequently elude classification. We characterize these by integrating PLMs with explainable models. Our approach achieves state-of-the-art performance with ROC-AUC of 99.7%. The 3.9% of misclassified sequences are signified by viral proteins with low immunogenicity. These errors disproportionately involve human-specific viral families associated with chronic infections and immune evasion, suggesting that both the immune system and machine learning models are confounded by overlapping biophysical signals. By coupling PLMs with explainable AI techniques, our work advances computational virology and offers mechanistic insights into viral immune escape. These findings carry implications for the rational design of vaccines, and improved strategies to counteract viral persistence and pathogenicity.