Accurate de novo sequencing of the modified proteome with OmniNovo
This addresses the challenge of comprehensively analyzing the modified proteome for researchers in proteomics and biology, representing a novel method rather than an incremental improvement.
The researchers tackled the problem of identifying post-translational modifications (PTMs) in proteomics, which is limited by combinatorial search spaces in standard methods, and introduced OmniNovo, a deep learning framework that achieved state-of-the-art accuracy by identifying 51% more peptides than standard approaches at a 1% false discovery rate.
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.