用深度学习破解复杂质谱数据，提升从头肽段测序准确率。
Disentangling the Complex Multiplexed DIA Spectra in De Novo Peptide Sequencing
- 提出DIANovo模型，深度解析共洗脱肽段的复合质谱图。
- 在Orbitrap Astral仪器上，DIA比DDA序列匹配率提升显著。
- 明确DIA适用条件：窄隔离窗+高信噪比，适合蛋白组学研究者。
数据无关采集（DIA）通过覆盖所有肽段而非仅高丰度峰，提升了质谱灵敏度。然而，由于共洗脱肽段、高噪声和数据质量波动，DIA在从头肽段测序中的实用性尚不明确。本文提出深度学习方法DIANovo，有效应对上述挑战，显著优于现有系统。研究还比较了DIA与数据依赖采集（DDA）在从头与数据库搜索模式下的表现：在旧型仪器上窄窗口有优势，但宽窗口下性能下降；而在Orbitrap Astral仪器上，因支持窄窗模式，DIA始终优于DDA。论文进一步从理论上解释该现象，强调信噪比对从头测序成功的关键作用。
原文摘要 · Abstract (English)
Data-Independent Acquisition (DIA) was introduced to improve sensitivity to cover all peptides in a range rather than only sampling high-intensity peaks as in Data-Dependent Acquisition (DDA) mass spectrometry. However, it is not very clear how useful DIA data is for de novo peptide sequencing as the DIA data are marred with coeluted peptides, high noises, and varying data quality. We present a new deep learning method DIANovo, and address each of these difficulties, and improves the previous established systems by a large margin, via equipping the model with a deeper understanding of coeluted DIA spectra. This paper also provides criteria about when DIA data could be used for de novo peptide sequencing and when not to by providing a comparison between DDA and DIA, in both de novo and database search mode. We find that while DIA excels with narrow isolation windows on older-generation instruments, it loses its advantage with wider windows. However, with Orbitrap Astral, DIA consistently outperforms DDA due to narrow window mode enabled. We also provide a theoretical explanation of this phenomenon, emphasizing the critical role of the signal-to-noise profile in the successful application of de novo sequencing.
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