Deep learning has led to a new generation of de novo sequencing tools, with promising applications in immunopeptidomics . An important consideration is that due to the different peptide and spectral characteristics of immunopeptidomics data, depending on the initial training strategy, such deep learning-based de novo sequencing tools typically need to be optimized to handle immunopeptides, for example to avoid a tryptic bias in their results .