Mass-spec sequencing stops relying on spectra alone: PepPr adds a proteome prior and lifts median peptide recall by 15.8 points across nine species
PepPr uses a peptide language model trained on a reference proteome as a prior, fused with spectral evidence at every step of autoregressive decoding rather than applied as post hoc correction. On the nine-species dataset with Casanovo as the base model, median peptide recall rose by 15.8 percentage points, improving all nine species; on single-amino-acid variants and antibodies, variant identification was not suppressed, and recall of peptides spanning the variable-constant boundary rose from 0.23 to 0.68.