A-P.16: Ensembling Structure Model Outputs for Nearly Free Performance Gains in TCR-pMHC Interaction Prediction
TCR-pMHC interactions play a central role in adaptive immune recognition. Much recent work has focused on modeling these interactions using deep neural networks, trained to classify a TCR-pMHC pair as binding or non-binding. However, benchmark studies show that these models fail to generalize to unseen epitopes, exhibiting near-random performance. Conversely, several recent works leveraging protein structure models show that simple 0-shot predictors using confidence metrics or Rosetta binding energy can achieve higher performance on the binding prediction task, motivating further exploration. Here, we assess both zero-shot structure model outputs and ensembles of confidence metrics and Rosetta binding energy values on three different TCR-pMHC interaction prediction tasks, with ensembles achieving superior performance over zero-shot outputs. We further identify strong baseline ensembles for each task and demonstrate their superior performance on TCR-pMHC binding prediction for unseen epitopes compared to supervised predictive models.
Co-authors: Heewook Lee
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