EpiTune: An Accurate Epitope Prediction Model with Mechanistic Insights

Authors

Calin, C., Nguyen, D.-T., Perrin, B. S.

Abstract

The accurate prediction of b-cell epitopes facilitates vaccine development by identifying known antibodies for an antigen. Multiple epitope prediction models use protein language models to enable more accurate predictions with modest results. Here, we present EpiTune, a b-cell epitope prediction model that fine-tunes the underlying protein language model to deliver best-in-class predictions of linear epitopes and competitive predictions for confirmational epitopes. EpiTune achieves this performance from antigen sequence alone, and utilizes ESM-2's RoPE architecture to fine-tune and infer on sequences longer than other sequence-based models currently available in the literature. EpiTune's single-model architecture allows the model to determine the meaningfulness of sequence features for epitope prediction. This avoids the need for assigning importance to intermediates such as structure-based information, while still allowing a high degree of model interpretability.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 19 Aug 2026.

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