Computational algorithm to predict interacting MHC alleles from TCR sequences

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Abstract Major histocompatibility complexes (MHC) guide immune response by presenting antigen fragments on a cell’s surface and interacting with T-cell receptors (TCRs). In recent years, many T-cell therapies have successfully engineered T-cells to target MHC-antigen complexes associated with cancers and other diseases. However, most T-cell therapies require identifying a TCR that interacts with an MHC-antigen complex of interest, a slow and expensive search process. Our proposal aims to speed up this search process through a computational algorithm that will predict whether a TCR will interact with an MHC allele of interest. Current screening assays for low frequency TCRs have high false positive rates. Researchers can use our tool to computationally filter TCR candidates for interaction with a specific MHC allele before running expensive validation experiments. In this proposal, we will first validate our approach through a prototype algorithm that we will train on public TCR-MHC interaction data. We will then conduct new tetramer staining experiments that address two major challenges for developing an algorithm across multiple MHC alleles: the lack of interaction data for alleles other than A*02, and the limited antigen diversity in existing public data. These experiments will provide TCR-MHC data across 800 antigens for four common MHC alleles: A*01:01, A*02:01, A*11:01, and B*07:02. Finally, we will construct and validate computational algorithms for each MHC allele and evaluate the importance of various TCR components (e.g., alpha or beta chan, CDR3) in predicting TCR-MHC interaction. Our work will result in the first computational tool to help T-cell therapy developers filter TCR candidates based on MHC specificity. Beyond cell therapies, this tool will also help researchers track T-cells in diseases where MHC alleles play a major role.