Pan-prediction of major histocompatibility complex class II-restricted epitopes across species via an AlphaFold-based quantification scheme.

The high polymorphism of histocompatibility complex class II (MHC-II) alleles and limited immunopeptidomic data hinder pan-species epitope prediction. In this study, leveraging the predictive power of AlphaFold (AF) and the conserved structural features of the core region of MHC-II-binding peptides, derived from a comprehensive analysis of MHC-II structure data in the PDB database, we developed a new tool, AF-prediction (AF-pred), with explicit quantitative criteria for MHC-II-restricted epitope prediction. We validated AF-pred across human, porcine, bovine, and bat MHC-II molecules through large-scale in silico analyses using known immunopeptidome datasets (1000 positive and 1000 negative antigenic peptides), together with in vitro binding assays and crystallographic characterization of newly predicted epitopes. Using uncharacterized bat MHC-II structures, we demonstrated that AF-pred's amino-acid interaction prediction underpins its pan-prediction capability and the underlying rationale of the method. Conversely, this characteristic limits the prediction of atypical MHC-II peptide-binding modes. Compared with sequence-based tools, AF-pred demonstrates enhanced cross-species MHC-II binding prediction, with higher accuracy and interpretability, and further reveals that iterative AF updates improve AF-pred performance. AF-pred has the potential to facilitate the development of novel T-cell epitope vaccines and advance the "One Health" initiative.