TAPAS: Learned integration of AlphaFold3 confidence and geometric features for TCR-pMHC binding prediction

Abstract

Motivation Recent advances in biomolecular structure prediction, exemplified by AlphaFold3, have opened new opportunities for the prediction of TCR-pMHC binding specificity. Although individual AlphaFold3 confidence metrics provide informative binding signals, their predictive performance varies across datasets, highlighting the need to combine complementary signals rather than rely on any single metric. Results We present TAPAS, a tabular learning framework that integrates AlphaFold3-derived interface confidence and structural geometry with sequence embeddings. Although no single zero-shot metric was the strongest across all benchmarks, TAPAS was consistently the top-ranked method on VDJdb and two external benchmarks, matching or exceeding the strongest zero-shot AlphaFold3 metric. Feature group ablation showed that the contributions of sequence, confidence, and geometric features varied across evaluation settings, and that combining them resulted in the best overall performance. These results highlight the value of integrating complementary structural and sequence features within a unified framework for robust TCR-pMHC binding prediction.