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

However, diversity in peptide antigens and TCR sequences at the level of individual patient repertoires remains a formidable computational challenge. Here, we develop a joint experimental and computational approach for predicting the antigen specificity of clinically-derived TCR sequences.

Our model is trained on a combination of experimentally pre-identified and in silico-predicted TCR-pMHC structures using AlphaFold3. We apply our structural model in the clinical setting of hematopoietic stem cell transplant (HSCT) and demonstrate that our model is able to effectively discern the specificity of previously unseen donor and patient-derived TCR sequences against tumor associated and viral antigens. Model performance was further enhanced through the integration of sequence-based clustering and structurally diverse training templates.