Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens

We develop and apply a dual experimental and computational framework to predict antigen specificity of TCR sequences in serial clinical samples. Our model integrates TCR primary sequences with previously reported and in silico-derived TCR-pMHC structural data. We apply this approach in the setting of hematopoietic stem cell transplant, focusing on a collection of HLA-A*02-restricted epitopes, including the Melan-A tumor associated antigen (ELAGIGILTV), Influenza A virus M1_58-66-derived peptide (GILGFVFTL), and human cytomegalovirus pp65-derived peptide (NLVPMVATV). We demonstrate accurate prediction of specificity for previously uncharacterized donor- and patient-derived TCRs, wherein model performance is enhanced through sequence-based clustering and incorporation of structurally diverse templates. Our results demonstrate that structure-guided learning enables robust specificity prediction from limited training data and can generalize across sequentially obtained patient samples. This framework provides a scalable strategy for TCR specificity prediction with potential applications in immunotherapy, vaccine design, and immune monitoring.