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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月23日

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  • Zahra S. Ghoreyshi,
  • Noah Tubo,
  • Luca Zammataro,
  • Xizeng Mao,
  • Ho Ngai,
  • Duncheng Wang,
  • Yibin Chen,
  • Qiuming He,
  • Eduardo Cisneros de la Rosa,
  • Shoudan Liang,
  • Priya J. Koppikar,
  • Xingcheng Lin,
  • Jeffrey J. Molldrem,
  • Jason T. George

摘要

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 M158-66-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.