用深度学习直接从质谱数据解码蛋白质修饰，发现更多未知蛋白变化。
Accurate de novo sequencing of the modified proteome with OmniNovo
- 基于深度学习的统一模型，无需参考数据库直接解析肽段序列。
- 在1%错误发现率下比传统方法多识别51%的肽段。
- 能发现训练时未见的修饰位点，适合探索未知蛋白质调控机制。
翻译后修饰（PTMs）是调控蛋白质功能的动态化学语言，但现有蛋白质组学方法仍无法检测大量修饰蛋白。标准数据库搜索算法因搜索空间呈组合爆炸式增长，难以识别未表征或复杂修饰。本文提出OmniNovo，一种统一的深度学习框架，可直接从串联质谱中进行无参考的未修饰与修饰肽段从头测序。与仅限特定修饰类型工具不同，OmniNovo学习通用断裂规律，在单一模型中解析多种修饰。通过结合质量约束解码算法与严格的错误发现率估计，OmniNovo达到当前最佳准确率，在1%错误发现率下比标准方法多识别51%的肽段。关键的是，该模型可泛化至训练时未见的生物位点，揭示了蛋白质组中的“暗物质”，实现细胞调控的无偏全面分析。
原文摘要 · Abstract (English)
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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