Automated protein-protein structure prediction of the T cell receptor-peptide major histocompatibility complex

Abstract

T Cell Receptor (TCR) recognition of a peptide-major histocompatibility complex (pMHC) is a crucial component of the adaptive immune response. The identification of therapeutically relevant TCR-pMHC pairs is a significant bottleneck in the implementation of TCR-based immunotherapies but may be augmented by computational methodologies. The ability to computationally design TCRs to target a specific pMHC will require an automated integration of next-generation sequencing, protein-protein structure prediction, molecular dynamics (MD), and TCR ranking. We present a generic pipeline to evaluate patient-specific, sequence-based TCRs to a target pMHC. Using the three most frequently expressed TCRs from 16 colorectal cancer patients, we predicted the protein-protein structure of the TCRs to the target CEA peptide-MHC using Modeller and ColabFold. Then, these TCR-pMHC structures were compared by performing an automated molecular dynamics equilibration. ColabFold generates starting configurations that require, on average, a ~2.5X reduction in simulation time to equilibrate TCR-pMHC structures compared to Modeller. In addition, the structural differences between Modeller and ColabFold are demonstrated by an increase in root mean square deviation (~0.20 nm) between clusters of equilibrated configurations, which can impact the number of hydrogen bonds and Lennard-Jones contacts between the TCR and pMHC. Finally, we identify a TCR ranking criteria that may be used to prioritize TCRs for evaluation of in vitro immunogenicity.