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T-SCAPE: T-cell immunogenicity scoring via cross-domain aided predictive engine

Kim, Jeonghyeon1; Jung, Nuri1; Cho, Nam-Hyuk1; Lee, Jayyoon1; Noh, Jinsung2; Seok, Chaok1

Published Nov 20, 2025 on Dryad. https://doi.org/10.5061/dryad.s7h44j1k7

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Abstract

T-cell immunogenicity is a critical determinant of safety and efficacy for protein therapeutics and vaccines, but prediction is hampered by data scarcity. We present T-SCAPE, a multi-domain deep learning framework that uses adversarial domain adaptation to integrate diverse immunologically relevant data sources, including MHC presentation, peptide-MHC binding affinity, TCR-pMHC interaction, source organism information, and T-cell activation. Validated through rigorous leakage-controlled benchmarks, T-SCAPE shows exceptional performance in predicting T-cell activation for specific peptide-MHC pairs. Remarkably, it also accurately predicts the anti-drug antibody-inducing potential of therapeutic antibodies without MHC inputs, a success attributed to its biologically grounded pretraining. Confirmed by extensive case studies and ablation studies, T-SCAPE’s flexible architecture also supports broader tasks like molecular binding prediction. Its robust performance highlights its potential to advance the development of safer and more effective biologics.