Counterfactual Peptide Editing for Causal TCR--pMHC Binding Inference

• URL: http://arxiv.org/abs/2604.13256v1
• Date: Tue, 14 Apr 2026 19:40:46 GMT
• Title: Counterfactual Peptide Editing for Causal TCR--pMHC Binding Inference
• Abstract summary: Neural models for TCR-pMHC binding prediction are susceptible to shortcut learning.<n>We introduce emphCounterfactual Invariant Prediction (CIP), a training framework that generates biologically constrained counterfactual peptide edits.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Neural models for TCR-pMHC binding prediction are susceptible to shortcut learning: they exploit spurious correlations in training data -- such as peptide length bias or V-gene co-occurrence -- rather than the physical binding interface. This renders predictions brittle under family-held-out and distance-aware evaluation, where such shortcuts do not transfer. We introduce \emph{Counterfactual Invariant Prediction} (CIP), a training framework that generates biologically constrained counterfactual peptide edits and enforces invariance to edits at non-anchor positions while amplifying sensitivity at MHC anchor residues. CIP augments the base classifier with two auxiliary objectives: (1) an invariance loss penalizing prediction changes under conservative non-anchor substitutions, and (2) a contrastive loss encouraging large prediction changes under anchor-position disruptions. Evaluated on a curated VDJdb-IEDB benchmark under family-held-out, distance-aware, and random splits, CIP achieves AUROC 0.831 and counterfactual consistency (CFC) 0.724 under the challenging family-held-out protocol -- a 39.7\% reduction in shortcut index relative to the unconstrained baseline. Ablations confirm that anchor-aware edit generation is the dominant driver of OOD gains, providing a practical recipe for causally-grounded TCR specificity modeling.

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