MHCXGraph: a graph-based approach to detecting T-cell receptor cross-reactivity

The T-cell receptor (TCR) recognition of multiple peptides presented by the major histocompatibility complex (MHC) is a key natural phenomenon, enabling the T-cell repertoire to respond to a broad array of antigens. Despite its importance to the immune response, T-cell cross-reactivity poses a major challenge for the development of novel T cell-based therapies. In this study, we present MHCXGraph, a graph-based computational approach for identifying conserved and immunologically relevant regions across multiple structures of peptides bound to MHC molecules (pMHC). Our approach provides three operational modes: Multiple, Pairwise, and Screening, with user-defined parameters, allowing flexible configuration according to specific scientific needs while delivering fully interpretable results through user-friendly interfaces. We evaluated MHCXGraph across several case studies, including peptides bound to classical MHC Class I, MHC Class II, unbound human leukocyte antigen (HLA) alleles, and pMHCs containing noncanonical amino acids, demonstrating its ability to capture conserved structural determinants beyond sequence similarity. By integrating structural information with efficient graph-based analysis, MHCXGraph addresses key limitations of sequence-based methods while maintaining computational scalability. Collectively, these results indicate that MHCXGraph can be readily integrated into computational pipelines for T-cell cross-reactivity discovery, especially in the context of $\textit{de novo}$ pMHC engager design and T cell-based vaccine development.