ImmSET: Sequence-Based Predictor of TCR-pMHC Specificity at Scale

T cells are a critical component of the adaptive immune system, playing a role in infectious disease, autoimmunity, and cancer. T cell function is mediated by the T cell receptor ({TCR}) protein, a highly diverse receptor targeting specific peptides presented by the major histocompatibility complex ({pMHC}s). Predicting the specificity of {TCR}s for their cognate {pMHC}s is central to understanding adaptive immunity and enabling personalized therapies. However, accurate prediction of this protein–protein interaction remains challenging due to the extreme diversity of both {TCR}s and {pMHC}s. Here, we present {ImmSET} (Immune Synapse Encoding Transformer), a novel sequence-based architecture designed to model interactions among sets of variable-length biological sequences. We train this model across a range of dataset sizes and compositions and study the resulting models’ generalization to {pMHC} targets. We describe a failure mode in prior sequence-based approaches that inflates previously reported performance on this task and show that {ImmSET} remains robust under stricter evaluation. In systematically testing the scaling behavior of {ImmSET} with training data, we show that performance scales consistently with data volume across multiple data types and compares favorably with the pre-trained protein language model {ESM2} fine-tuned on the same datasets. Finally, we demonstrate that {ImmSET} can outperform AlphaFold2 and AlphaFold3-based pipelines on {TCR}-{pMHC} specificity prediction when provided sufficient training data. This work establishes {ImmSET} as a scalable modeling paradigm for multi-sequence interaction problems, demonstrated in the {TCR}-{pMHC} setting but generalizable to other biological domains where high-throughput sequence-driven reasoning complements structure prediction and experimental mapping.