Title: Accelerating MHC-II Epitope Discovery via Multi-Scale Prediction in Antigen Presentation
Primary area: datasets and benchmarks
Keywords: MHC-II Epitope Discovery; Multi-scale Antigen Presentation Prediction

Abstract: Antigenic epitope presented by major histocompatibility complex II (MHC-II) proteins plays an essential role in immunotherapy. However, compared to the more widely studied MHC-I in computational immunotherapy, the study of MHC-II antigenic epitope poses significantly more challenges due to its complex binding specificity and ambiguous motif patterns. Consequently, existing datasets for MHC-II interactions are  smaller and less standardized than those available for MHC-I. To address these challenges, we present a well-curated dataset derived from the Immune Epitope Database (IEDB) and other public sources. It not only extends and standardizes existing peptide–MHC-II datasets, but also introduces a novel antigen–MHC-II dataset with richer biological context. Leveraging this dataset, we formulate three major machine learning (ML) tasks of peptide binding, peptide presentation, and antigen presentation, which progressively capture the broader biological processes within the MHC-II antigen presentation pathway. We further employ a multi-scale evaluation framework to benchmark our best approach against existing methods, along with comprehensive ablations covering input configurations, model architectures, training strategies, and task formulations. Overall, this dataset serves as a valuable resource for advancing computational immunotherapy, providing a foundation for future research in ML guided epitope discovery and predictive modeling of immune responses.

TL;DR: We present a comprehensive dataset on MHC-II antigen presentation to accelerate research in computational immunotherapy.