biostudies-literatureUnknownHuot MNIGMS NIH HHSUnderstanding how viral proteins adapt under immune pressure while preserving structural viability is crucial for anticipating the emergence of antibody-resistant variants. Here, we present a probabilistic framework that predicts the evolutionary trajectories of viral escape, revealing immune evasion is funneled through a remarkably small number of viable paths compared to total mutational space. These escape funnels arise from the combined constraints of protein viability and escape from antibodies, which we model using a generative model trained on structural homologs and deep mutational scanning data. We derive a mean-field approximation of evolutionary path ensembles, enabling us to quantify both the fitness and entropy of escape routes. Applied to the SARS-CoV-2 receptor binding domaibioRxiv : the preprint server for biology2025.10.26.684604https://www.ebi.ac.uk/biostudies/studies/S-EPMC12636330biostudies-literatureConstrained Evolutionary Funnels Shape Viral Immune Escape.PMC12636330R35 GM139571Monasson RShakhnovich EWang DCocco SHuot MfalseConstrained Evolutionary Funnels Shape Viral Immune Escape.Understanding how viral proteins adapt under immune pressure while preserving structural viability is crucial for anticipating the emergence of antibody-resistant variants. Here, we present a probabilistic framework that predicts the evolutionary trajectories of viral escape, revealing immune evasion is funneled through a remarkably small number of viable paths compared to total mutational space. These escape funnels arise from the combined constraints of protein viability and escape from antibodies, which we model using a generative model trained on structural homologs and deep mutational scanning data. We derive a mean-field approximation of evolutionary path ensembles, enabling us to quantify both the fitness and entropy of escape routes. Applied to the SARS-CoV-2 receptor binding domai2025-01-01T00:00:00Z2025 Oct2026-06-30T03:24:17.78Z2026-06-30T03:20:10.773ZS-EPMC126363304127869810.1101/2025.10.26.684604