Accurate de novo sequencing of the modified proteome with OmniNovo

• URL: http://arxiv.org/abs/2512.12272v1
• Date: Sat, 13 Dec 2025 10:27:14 GMT
• Title: Accurate de novo sequencing of the modified proteome with OmniNovo
• Abstract summary: We introduce OmniNovo, a unified deep learning framework for reference-free sequencing of peptides directly from tandem mass spectra.<n>By integrating a mass-constrained decoding algorithm with rigorous false discovery rate estimation, OmniNovo achieves state-of-the-art accuracy, identifying 51% more peptides than standard approaches at a 1% false discovery rate.<n> Crucially, the model generalizes to biological sites unseen during training, illuminating the dark matter of the proteome and enabling unbiased comprehensive analysis of cellular regulation.
• Score: 39.43336836259004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Post-translational modifications (PTMs) serve as a dynamic chemical language regulating protein function, yet current proteomic methods remain blind to a vast portion of the modified proteome. Standard database search algorithms suffer from a combinatorial explosion of search spaces, limiting the identification of uncharacterized or complex modifications. Here we introduce OmniNovo, a unified deep learning framework for reference-free sequencing of unmodified and modified peptides directly from tandem mass spectra. Unlike existing tools restricted to specific modification types, OmniNovo learns universal fragmentation rules to decipher diverse PTMs within a single coherent model. By integrating a mass-constrained decoding algorithm with rigorous false discovery rate estimation, OmniNovo achieves state-of-the-art accuracy, identifying 51\% more peptides than standard approaches at a 1\% false discovery rate. Crucially, the model generalizes to biological sites unseen during training, illuminating the dark matter of the proteome and enabling unbiased comprehensive analysis of cellular regulation.

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