HIFT-TCR: Interface-Structured TCR Library Screening under Epitope-Family Shift

Emerging pathogen variants and tumor neoantigens motivate screening characterized T-cell receptor (TCR) libraries against new peptide families. We introduce the Hierarchical Interface Field Transformer for T-cell Receptor screening (HIFT-TCR), a structure-based graph Transformer for modeled interfaces between TCRs and peptide–major histocompatibility complexes (pMHCs). It combines typed local interactions, molecular hierarchy, and separate TCR–peptide and TCR–MHC compatibility. Five-fold cross-validation on the STAG pan-peptide benchmark holds out complete peptide components defined at 75% sequence identity. Across 25 matched seed–fold runs, HIFT-TCR achieves 0.5874 AUROC, 0.2679 AUPRC, and 0.5112 standardized partial AUROC at a false-positive rate of 0.1, leading six same-split sequence, language-model, and structure baselines across all three mean metrics. Performance is higher without an exact paired-CDR3 training match. Retrained ablations support the combined objective, interface topology, and hierarchy, while interventions identify TCR residues and TCR–peptide edges as consistent support for positive predictions. These findings support interface-structured receptor prioritization for experimental binding validation under peptide-family shift.