Proteomics

Dataset Information

0

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


ABSTRACT: 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.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human) Severe Acute Respiratory Syndrome Coronavirus 2

SUBMITTER: Peter Bruno  

LAB HEAD: Stephen Elledge

PROVIDER: PXD036939 | Pride | 2022-10-13

REPOSITORIES: Pride

Dataset's files

Source:
Action DRS
MSB-8926_Harvard_Bruno_030222_5000_only.xlsx Xlsx
MSB5000A.raw Raw
checksum.txt Txt
sdrf-EpiScan.tsv Tabular
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