Decoding TCR recognition via geometric deep learning of immunological fingerprints

T cell receptor (TCR) recognition of peptide-major histocompatibility complex (pMHC) molecules is the critical first step in adaptive immune activation, shaping immunity against pathogens and tumors, as well as tolerance to self. Despite extensive structural characterization of TCR-pMHC complexes, the molecular principles underlying this process remain incompletely understood, hindered by the inherent duality of TCR specificity and cross-reactivity. Traditional structural analyses often fall short in capturing the multidimensional features that govern TCR-pMHC engagement. Here, we introduce a multimodal geometric deep learning framework that systematically extracts and learns various physicochemical and spatial features from pMHC interfaces, which encode key immunological cues for TCR recognition. Applied to a curated dataset of human leukocyte antigens HLA-A*02-peptide-TCR crystal structures, our model robustly predicts TCR binding preferences and uncovers interfacial "immunological fingerprints" that inform receptor engagement. Through an integrated explainability module, we identify critical contact residues and interaction motifs, thus providing interpretable insights into the determinants of TCR specificity. We further demonstrate the model's generalizability by analyzing HLA-B*27-peptide complexes, revealing potential TCR cross-reactivity between self-derived and bacterial peptides-highlighting its utility in probing molecular mimicry. This work establishes a scalable, structure-based approach for decoding T cell recognition and offers a powerful tool for guiding antigen design, vaccine development, and TCR-based immunotherapies.