MHC-IP MS of EpiScan A02 SARS-CoV-2 library

Identification of CD8+ T-cell epitopes is critical for the development of immunotherapeutics. Existing methods for MHC-I ligand discovery are time-intensive, specialized and unable to interrogate specific proteins on a large scale. Here we present EpiScan, which uses surface MHC-I levels as a readout for whether a genetically encoded peptide is an MHC-I ligand. Oligonucleotide synthesis permits facile screening for MHC-I ligands amongst predetermined starting pools comprising >100,000 peptides. We exploit this programmability of EpiScan to uncover an unappreciated role for cysteine that increases the number of predicted ligands by 12-21%, reveal affinity hierarchies by analysis of biased-anchor peptide libraries, and screen viral proteomes for MHC-I ligands. Using these data, we generate and iteratively refine peptide binding prediction predictions to create EpiScan Predictor, or ESP. ESP performed comparably to other state-of-the-art MHC-I peptide binding prediction algorithms while not suffering from underrepresentation of cysteine-containing peptides. Thus, targeted immunopeptidomics using EpiScan will accelerate CD8+ T-cell epitope discovery towards the goal of patient-specific immunotherapeutics.