Accurate modeling of peptide-MHC structures with AlphaFold

Abstract

Major histocompatibility complex (MHC) proteins present peptides on the cell surface for Tcell surveillance. Reliable in silico prediction of which peptides would be presented and which Tcell receptors would recognize them is an important problem in structural immunology. Here, we introduce an AlphaFold-based pipeline for predicting the three-dimensional structures of peptide-MHC complexes for class I and class II MHC molecules. Our method demonstrates high accuracy, outperforming existing tools in class I modeling accuracy and class II peptide register prediction. We validate its performance and utility with new experimental data on a recently described cancer neoantigen/wild-type peptide pair and explore applications toward improving peptide-MHC binding prediction.