Refining HLA Divergence Metrics Through TCR–Peptide Interaction Hotspots to Calculate Functional HLA Divergence.
Cedric Usureau
The human leukocyte antigen (HLA) system, shaped by evolutionary pressures, is essential for immune surveillance by presenting diverse peptides to T-cell receptors (TCRs). Although HLA evolutionary divergence (HED) metrics help quantify allele differences, these broad measures may overlook critical polymorphisms directly influencing peptide and TCR binding. To address this gap, we built a comprehensive dataset of experimentally determined and computationally modeled HLA–TCR–peptide complexes, focusing on key interaction hotspots. By refining divergence calculations to emphasize these pivotal residues, our approach aims to enhance predictions in infection control, cancer immunotherapy, and organ transplantation, bridging evolutionary and functional insights for improved clinical outcomes. We present a novel web server that allows researchers to identify and selectively analyze specific HLA residues directly involved in TCR–HLA–peptide binding. By focusing on these critical interaction hotspots rather than all polymorphic positions in exons 2 and 3, our refined metric captures functional divergence more precisely. In a proof-of-concept evaluation using HLA-A alleles, this targeted approach outperformed classical HLA evolutionary divergence (HED) in predicting immunotherapy responses. These findings underscore the importance of integrating structural and functional insights to enhance clinical applications such as cancer immunotherapy, vaccine design, and transplant compatibility.