MATCHMAKERS - SOLVING TCR RECOGNITION AND DESIGN VIA INTEGRATED HIGH-THROUGHPUT SCREENING, STRUCTURAL, FUNCTIONAL, AND COMPUTATIONAL APPROACHES - Abstract Understanding how T cell receptors (TCRs) see tumor antigens presented by MHCs is necessary to fully understand how the immune system recognizes tumor antigens, and to reap the full potential of antigen-specific immunotherapy. To achieve this goal, a quantum leap forward is required in which the revolutionary advances in machine learning are combined with a large volume of structure, function, data on matched TCR-pMHC pairs. The development of accurate predictors of TCR-antigen recognition will be dependent on the creation and integration sequencing-based datasets with high-throughput structural and functional insights. Our proposal, submitted as a CRUK/NCI Grand Challenge team (MATCHMAKERS) will combine researchers with expertise in immunology, methods development, structural biology, and computation to enable generalized prediction and design of TCR recognition. This work will be spread across four Work Packages (WPs): WP1: Large-scale generation of TCR-pMHC pairs from naturally occurring sources. We will build datasets of naturally occurring TCR-pMHC pairs. Our team will use an array of approaches to collect these datasets, from humans and from mouse models, and in the context of both cancer and immunity more generally. WP2: Ultra-high throughput TCR-pMHC matching using molecular engineering. Efforts to create general models will require a broader array of data than feasible to collect from natural TCR systems. We will use an array of synthetic approaches developed by our team to comprehensively match TCRs with pMHCs to train computational models. WP3: Large-scale structural and biochemical analyses of TCR-pMHC interactions. A key to our team’s vision is to match interaction datasets with high throughput structural and functional insights. A deep understanding of how the TCR contacts with MHC helices control function and orientation will be essential for training and testing computational models. WP4 AI-based prediction and design of TCR-pMHC interactions. We will integrate our data to train next- generation algorithms capable of generally predicting and designing TCR-pMHC interactions. These predictions will proceed through a reiterative testing and feedback circuits for further model optimization.
