HATIS: Hierarchical Atomistic Graph Transfer and Interface-Aware Stable Learning for TCR–pMHC Recognition under Distribution Shifts

Candidate screening for T-cell immunotherapy requires accurate prediction of T cell receptor (TCR) recognition of peptide–MHC (pMHC) complexes. Such screening often involves peptides and TCRs not represented in the training data, requiring models to generalize under shifts in peptide and TCR distributions. Despite recent advances, generalization across peptide distributions remains challenging, particularly when test peptides differ substantially from those used for training. A key limitation underlying this challenge is the limited coverage of TCR–pMHC interactions in training data, which restricts the learning of transferable recognition patterns. Models may also exploit spurious correlations arising from the frequent co-occurrence of molecular patterns across peptides, MHC molecules, and TCRs in training data, learning predictive shortcuts that fail under distribution shifts. We therefore propose HATIS, a framework integrating hierarchical atomistic graph transfer and interface-aware stable learning. To alleviate limited triplet supervision, we progressively transfer molecular representations and interaction knowledge across monomer, pairwise, and triadic levels to support TCR–pMHC recognition. To reduce reliance on spurious interaction correlations, we introduce interface-aware stable learning at both pairwise and triadic levels, using the molecular composition of interaction clusters to guide sample reweighting and interface-gated pooling. For systematic evaluation, we construct a benchmark covering peptide-only, TCR-only, and joint peptide–TCR distribution shifts, complemented by evaluation on an external test set. HATIS improves AUPR over the strongest baseline by 12.86–13.98 percentage points under peptide-only and joint shifts and by 4.65 points on the external test set, while remaining competitive under TCR-only shifts.