PepPr: De Novo peptide sequencing with peptide priors

Abstract

Mass spectrometry-based peptide sequencing underpins proteomics. Database search confines to predefined candidates, whereas de novo peptide sequencing (DNPS) struggles with incomplete spectra. Existing hybrid methods use databases only for post hoc correction or retrieve previously identified spectra to guide decoding. We instead combine DNPS with a peptide prior: a language model fitted to a reference proteome, tolerating variants yet uncertain on strongly divergent sequences. Relative to prior-free DNPS, our implementation, PepPr, improved reference peptide identification and improved rather than suppressed variant identification. Using a related proteome as prior, it matched or outperformed homology-driven database search, and its gain over prior-free DNPS grew with relatedness. In antibody sequencing, it improved recovery of peptides spanning the variable-constant boundary, deamidation calls, and assembly, while preserving recall of hypervariable regions inaccessible to database search. Peptide priors thus bring reference knowledge into DNPS without limiting discovery.