AI predicted TCR-pMHC structures differentiate immune interactions

Abstract

The T Cell Receptor (TCR) is a highly variable component of the T cell immune response that recognizes unique epitopes presented on MHC molecules (pMHC). Random genetic recombination limits the ability for sequence homology to predict epitope specificity, which is more dependent on the strength of the TCR-pMHC binding interaction. Structures for understanding this interaction only exist for well characterized positive interactors, and there is no information available about the physical interaction of non-specific TCR-pMHCs. In this study, we explore the ability for structural prediction algorithms to generate interacting and non-interacting multimeric TCR-pMHC structures, then, examine features that can predict immune interaction. AlphaFold2 shows more consistent multimeric structure prediction compared to other deep learning structure generators or template based algorithms. Poor structure generation does not correlate with immune interaction, and non-interacting structures show similar structural properties to interacting structures. However, this results in less energetically stable conformations in non-interacting structures. Molecular dynamic simulation supports this finding and reveals a novel structural conformation that contributes mechanistically to proper immune synapse. We show that structural and physical features extracted from generated structures are more predictive of interaction than sequence based features. To support researchers in the prediction of TCR-epitope specificity we have made our structural prediction models available through an accessible notebook based webserver: https://github.com/RobbenLab/TCRSIP.