Paper
Accurate de novo sequencing of the modified proteome with OmniNovo
Authors
Yuhan Chen, Shang Qu, Zhiqiang Gao, Yuejin Yang, Xiang Zhang, Sheng Xu, Xinjie Mao, Liujia Qian, Jiaqi Wei, Zijie Qiu, Chenyu You, Lei Bai, Ning Ding, Tiannan Guo, Bowen Zhou, Siqi Sun
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.