Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens.
basic_science · Level V
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- Record sourced from PubMed, PMID 42337259.
- Also identified by DOI 10.1038/s41467-026-74236-0.
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Abstract
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<sub>58-66</sub>-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.