Asymmetry-Aware Representation Learning for TCR-Epitope binding prediction

T cell receptors (TCRs) play a vital role in immune recognition by binding specific epitopes. Accurate prediction of TCR–epitope binding specificity is fundamental for advancing immunology research. Existing methods commonly formulate TCR–epitope interaction prediction as a dual-encoder matching problem, in which TCRs and epitopes are independently encoded and subsequently aligned in a shared representation space. However, this formulation overlooks the representational asymmetry between structurally complex TCRs and lower-complexity epitopes, introducing unnecessary modeling overhead and hindering effective alignment. To address this issue, we propose an asymmetry-aware representation learning method that combines structured TCR encoding with discrete epitope modeling, reducing joint modeling complexity and recasting heterogeneous alignment as a discriminative mapping to the candidate epitope space. Specifically, the model uses a frozen protein language model to extract contextualized CDR3 representations from full-length $\alpha$- and $\beta$-chain sequences. These representations are then encoded by weight-shared multiscale convolutions and independent global branches to capture cross-chain local patterns and chain-specific context, respectively. Candidate epitopes are represented as discrete categories in a finite prediction space. The model is optimized with complementary supervision to learn the global TCR-to-epitope mapping while refining the relative distinction between target and competing epitopes. Comparative experiments against multiple baseline methods on several benchmark datasets demonstrate the effectiveness of the proposed method.