A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity

T cell receptor (TCR) antigen-specific recognition is essential for the adaptive immune system. However, building a TCR-antigen interaction map has been challenging due to the staggering diversity of TCRs and antigens. Accordingly, highly multiplexed dextramer-TCR binding assays have been recently developed, but the utility of the ensuing large datasets is limited by the lack of robust computational methods for normalization and interpretation. Here, we present a computational framework comprising a novel method, ICON (Integrative COntext-specific Normalization), for identifying reliable TCR-pMHC (peptide-major histocompatibility complex) interactions and a neural network-based classifier TCRAI that outperforms other state-of-the-art methods for TCR-antigen specificity prediction. We further demonstrated that by combining ICON and TCRAI, we are able to discover novel subgroups of TCRs that bind to a given pMHC via different mechanisms. Our framework facilitates the identification and understanding of TCR-antigen-specific interactions for basic immunological research and clinical immune monitoring.

Automated summary

This study introduces a computational framework with a novel method ICON for identifying reliable TCR-pMHC interactions and a neural network-based classifier TCRAI for predicting TCR-antigen specificity, which outperforms other state-of-the-art methods. The combination of ICON and TCRAI enables the discovery of novel subgroups of TCRs binding to a given pMHC via different mechanisms, facilitating the identification and understanding of TCR-antigen-specific interactions for basic immunological research and clinical immune monitoring.