Structurally Informed Fitness Landscapes for Surveillance of Emerging PRRSV Variants

Antibodies play a central role in neutralizing pathogens through direct interference with viral entry and recruitment of effector immune cells. However, viruses employ sophisticated escape mechanisms to evade these defenses, primarily through mutations in surface glycoproteins that reduce antibody binding affinity or alter critical functional domains. Antibody escape remains a formidable challenge in drug design efforts for Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), a pathogen notorious for its rapid evolution and structural plasticity. Here we present EscaPRRS (Escape scoring for PRRS virus), a Bayesian variational autoencoder trained on ESM-2 protein language model embeddings to predict the fitness landscape and escape propensities of 52,622 mutants (recorded over 10 years) of the immunodominant GP5 glycoprotein encoded by PRRSV ORF5 gene. Unlike conventional models that estimate escape propensity only from sequence information, EscaPRRS circumvents the need for extensive alignments, integrating contributions from surface accessibility and biochemical dissimilarity at the binding interface. Our escape propensity scores demonstrate reliable structural and biological fidelity, with EscaPRRS scores correlating with binding affinities on seven different porcine receptor proteins (Pearson r = 0.74). Notably, EscaPRRS captures seasonal trends in immune evasion, highlighting its applicability in forecasting and surveillance of emerging/re-emerging PRRSV variants.