Integrating Peptide-MHC Stability with Computational Predictions to Enhance T Cell Epitope Discovery 9197

Abstract Description The prediction of peptide binding to Major Histocompatibility Complex (MHC) molecules using computational tools has significantly advanced epitope discovery. However, measuring peptide-MHC (pMHC) stability through peptide dissociation rates provides a vital additional layer of information for identifying immunogenic epitopes. The stability of pMHC complexes, defined by their half-lives, strongly correlates with immunodominance and frequencies of CD8+ T cells. Longer pMHC half-lives enhance antigen presentation and increase the likelihood of triggering a T cell response suggesting that stability is a better predictor of immunogenicity compared to peptide-MHC affinity alone. Our high-throughput, real-time assay quantifies pMHC stability by measuring peptide dissociation under physiological conditions. This robust and reproducible method uses natural HLA allomorphs and detects b2m dissociation, delivering precise insights into the longevity of peptide presentation. Incorporating pMHC stability data refines the prioritization of predicted peptide binders and assists in the selection of epitopes with higher immunogenic potential. By combining computational binding predictions with experimental pMHC stability measurements, this approach enhances T cell epitope discovery for vaccine development and immunotherapy; it advances the selection of candidates capable of eliciting strong immune responses, particularly for combating infectious diseases and cancer. Topic Categories Technological Innovations in Immunology (TECH)