π-SPECFormer: A Mass Spectrometry-based Foundation Model for Decoding the Dark Proteome

In mass-spectrometry-based proteomics, a substantial proportion of acquired spectra remain unidentified, concealing a vast reservoir of biological variations commonly termed the "dark proteome", potentially obscuring sequence mutations, non-canonical peptides and post-translational modifications. Existing deep-learning methods largely rely on supervised training with peptide-spectrum matches (PSMs), limiting their ability to learn from the much larger pool of unannotated spectra. Here we introduce {pi}-SPECFormer, a mass-spectrometry foundation model pre-trained on 291 million spectra through a two-stage framework that combines self-supervised and PSMs-guided representation learning. {pi}-SPECFormer transfers effectively to de novo peptide sequencing, spectrum clustering and modification detection, consistently outperforming task-specific baselines. When applied to proteomic profiles from 103 individuals with lung adenocarcinoma, the model identified nine high-confidence mutated peptides, including six that were absent from the reference databases. {pi}-SPECFormer thus enables unbiased decoding of molecular complexity beyond the boundaries of the canonical proteome.