{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":["Huot M"],"funding":["NIGMS NIH HHS"],"pubmed_abstract":["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 domai"],"journal":["bioRxiv : the preprint server for biology"],"pagination":["2025.10.26.684604"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12636330"],"repository":["biostudies-literature"],"pubmed_title":["Constrained Evolutionary Funnels Shape Viral Immune Escape."],"pmcid":["PMC12636330"],"funding_grant_id":["R35 GM139571"],"pubmed_authors":["Monasson R","Shakhnovich E","Wang D","Cocco S","Huot M"],"additional_accession":[]},"is_claimable":false,"name":"Constrained Evolutionary Funnels Shape Viral Immune Escape.","description":"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 domai","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-06-30T03:24:17.78Z","creation":"2026-06-30T03:20:10.773Z"},"accession":"S-EPMC12636330","cross_references":{"pubmed":["41278698"],"doi":["10.1101/2025.10.26.684604"]}}