Rapid Assessment of T-Cell Receptor Specificity of the Immune Repertoire. Academic Article uri icon


  • Accurate assessment of TCR-antigen specificity at the whole immune repertoire level lies at the heart of improved cancer immunotherapy, but predictive models capable of high-throughput assessment of TCR-peptide pairs are lacking. Recent advances in deep sequencing and crystallography have enriched the data available for studying TCR-p-MHC systems. Here, we introduce a pairwise energy model, RACER, for rapid assessment of TCR-peptide affinity at the immune repertoire level. RACER applies supervised machine learning to efficiently and accurately resolve strong TCR-peptide binding pairs from weak ones. The trained parameters further enable a physical interpretation of interacting patterns encoded in each specific TCR-p-MHC system. When applied to simulate thymic selection of an MHC-restricted T-cell repertoire, RACER accurately estimates recognition rates for tumor-associated neoantigens and foreign peptides, thus demonstrating its utility in helping address the large computational challenge of reliably identifying the properties of tumor antigen-specific T-cells at the level of an individual patient's immune repertoire.

published proceedings

  • Nat Comput Sci

author list (cited authors)

  • Lin, X., George, J. T., Schafer, N. P., Chau, K. N., Birnbaum, M. E., Clementi, C., Onuchic, J. N., & Levine, H.

complete list of authors

  • Lin, Xingcheng||George, Jason T||Schafer, Nicholas P||Chau, Kevin Ng||Birnbaum, Michael E||Clementi, Cecilia||Onuchic, José N||Levine, Herbert

publication date

  • May 2021