On the impact of local protein structure features on prediction of major histocompatibility complex class I and II antigen presentation

Antigen presentation by major histocompatibility complex (MHC) molecules is a complex pathway essential for T cell-mediated immunity. The pathway involves unfolding and processing of the antigen protein structure, yet limited work has been made evaluating the potential influence of local protein structure on the prediction of antigen processing and presentation. Here, we investigated this by integrating local structural features-disorder score, relative surface accessibility, and the probabilities of α-helix, β-sheet, and coil-into an NNAlign-based framework for predicting MHC class I and HLA-DR antigen presentation. Large-scale eluted ligand datasets were used to train and validate our models, demonstrating that for MHC class I, the addition of structural features yielded marginal, nonsignificant improvements in performance. In contrast, for HLA-DR ligands, models incorporating positional structural information showed a significant yet limited performance boost. Post-hoc analysis revealed no clear amino acid enrichment patterns associated with structural propensities. Rather the HLA-specific gain in performance was found to be linked to the number of positive instances seen in training. Stratification by cellular localisation indicated that peptides from endolysosomal proteins benefited more from structural integration than those from cytosolic sources. Our comprehensive benchmark shows that incorporating local protein structural features improves epitope prediction for MHC class II ligands.