A Computational Strategy for the Rapid Identification and Ranking of Patient‐Specific T Cell Receptors Bound to Neoantigens

• Published in: Macromolecular rapid communications. 2024-12, Vol.45 (24), p.e2400225-n/a
• Main Authors: Rollins, Zachary A., Curtis, Matthew B., George, Steven C., Faller, Roland
• Format: Article
• Language: English
• Subjects: Adaptive immunity; Antigens, Neoplasm - chemistry; Antigens, Neoplasm - immunology; Antigens, Neoplasm - metabolism; Automation; Balancing; Colorectal carcinoma; Colorectal Neoplasms - immunology; Computational Biology; Configurations; Dynamic structural analysis; Humans; Hydrogen bonding; Hydrogen bonds; Immune response; Immune system; Immunogenicity; Immunotherapy; Lymphocytes; Lymphocytes T; Machine learning; Major histocompatibility complex; Major Histocompatibility Complex - immunology; Molecular dynamics; Molecular Dynamics Simulation; Neoantigens; Peptides; Probability learning; Protein Binding; Protein structure; Proteins; Ranking; Receptors; Receptors, Antigen, T-Cell - chemistry; Receptors, Antigen, T-Cell - immunology; T cell receptors; T cells
• ISSN: 1022-1336; 1521-3927; 1521-3927
• DOI: 10.1002/marc.202400225

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. A pipeline to evaluate patient‐specific, sequence‐based TCRs to a target pMHC is presented. Using the three most frequently expressed TCRs from 16 colorectal cancer patients, the protein–protein structure of the TCRs to the target CEA peptide–MHC is predicted using Modeller and ColabFold. TCR‐pMHC structures are compared using automated equilibration and successive analysis. ColabFold generated configurations require an ≈2.5× 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. TCR ranking criteria that may prioritize TCRs for evaluation of in vitro immunogenicity are identified, and this ranking is validated by comparing to state‐of‐the‐art machine learning‐based methods trained to predict the probability of TCR‐pMHC binding. Process flow diagram for the protein–protein structure prediction of proteins (here TCRs) to a target protein (here pMHC) is presented. The process begins with single‐cell sequencing from patients. Then, protein–protein structure prediction of proteins sequenced is performed. Finally, molecular dynamics simulations equilibrate the structure and rank proteins based on the number of interactions at equilibrium.