Abstract
<jats:p>We present FoxNovo, a hybrid deep learning-combinatorial framework for de novo sequencing of immunopeptides trained on a large-scale HLA-I immunopeptidomics dataset assembled and reprocessed from public mass spectrometry (MS) repositories. This integration achieved >90% peptide accuracy on the reported benchmarks while enabling repository-scale analysis at ~2,800 spectra per second—more than 100-fold faster than the evaluated beam-search baseline under the reported benchmark conditions. To mimic the heterogeneous spectral quality encountered in experimental MS analyses, we constructed controlled peak-removal stress tests, in which FoxNovo retained higher accuracy than the evaluated methods at different simulation levels. We subsequently re-analyzed 168 million spectra from all collected 4,423 MS raw files in only 18 hours on a single GPU, equivalent to ~245 raw files per hour. This repository-scale application yielded score-filtered canonical and putative ncORF-mapped peptide predictions and recovered 41 of 42 non-canonical HLA-I peptides previously validated by targeted MS. FoxNovo demonstrates the potential of integrating AI with combinatorial decoding for scalable immunopeptidomics. The source code is available at https://github.com/fennomix/fennomix.novo.</jats:p>