A Computational Strategy for the Rapid Identification and Ranking of Patient-Specific T cell Receptors Bound to Neoantigens.

T Cell Receptor (TCR) recognition of a peptide-major histocompatibility complex (pMHC) is crucial for adaptive immune response. The identification of therapeutically relevant TCR-pMHC protein pairs is a bottleneck in the implementation of TCR-based immunotherapies. The ability to computationally design TCRs to target a specific pMHC requires automated integration of next-generation sequencing, protein-protein structure prediction, molecular dynamics, and TCR ranking. We present a 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 predict the protein-protein structure of the TCRs to the target CEA peptide-MHC using Modeller and ColabFold. TCR-pMHC structures are compared using automated equilibration and successive analysis. ColabFold generated configurations require a ∼2.5X reduction in equilibration time of TCR-pMHC structures compared to Modeller. The structural differences between Modeller and ColabFold are demonstrated by root mean square deviation (∼0.20 nm) between clusters of equilibrated configurations, which impact the number of hydrogen bonds and Lennard-Jones contacts between the TCR and pMHC. We identify TCR ranking criteria that may prioritize TCRs for evaluation of in vitro immunogenicity and validate this ranking by comparing to state-of-the-art machine learning based methods trained to predict the probability of TCR-pMHC binding. This article is protected by copyright. All rights reserved.