Immunopeptidomics is used to identify novel
epitopes for (therapeutic) vaccination strategies in
cancer and
infectious disease. Various false discovery rates (FDRs) are applied in the field when converting liquid chromatography-tandem mass spectrometry (LC-MS/MS) spectra to
peptides. Subsequently, large efforts have recently been made to rescue
peptides of lower confidence. However, it remains unclear what the overall relation is between the FDR threshold and the percentage of obtained HLA-binders. We here directly evaluated the effect of varying FDR thresholds on the resulting immunopeptidomes of HLA-eluates from human
cancer cell lines and primary hepatocyte isolates using HLA-binding algorithms. Additional
peptides obtained using less stringent FDR-thresholds, although generally derived from poorer spectra, still contained a high amount of HLA-binders and confirmed recently developed tools that tap into this pool of otherwise ignored
peptides. Most of these
peptides were identified with improved confidence when cell input was increased, supporting the validity and potential of these identifications. Altogether, our data suggest that increasing the FDR threshold for
peptide identification in conjunction with data filtering by HLA-binding prediction, is a valid and highly potent method to more efficient exhaustion of immunopeptidome datasets for
epitope discovery and reveals the extent of
peptides to be rescued by recently developed algorithms.