The spectral database-based mass spectrometry (MS) matching strategy is versatile for structural annotating in ingredient fluctuation profiling mediated by external interferences. However, the systematic variability of MS pool attributable to aliasing peaks and inadequacy of present spectral database resulted in a substantial metabolic feature depletion. An amended procedure termed multiple-charges overlap peaks extraction algorithm (MCOP) was proposed involving identifying collision-trigged dissociation precursor ions through iteratively matching mass features of fragmentations to expand the spectral reference library. We showcased the versatility and utility of established strategy in an investigation centered on the stimulation of milk mediated by diphenylolpropane (BPA). MCOP enabled efficient unknown annotations at metabolite-lipid-protein level, which elevated the accuracy of substance annotation to 85.3% after manual validation. Arginase and α-amylase (|r| > 0.75, < 0.05) were first identified as the crucial issues via graph neural network-based virtual screening in the abnormal metabolism of urea triggered by BPA, resulting in the accumulation of arginine (original: 1.7 μg kg 1.7 times) and maltodextrin (original: 6.9 μg kg 2.9 times) and thus, exciting the potential dietary risks. Conclusively, MCOP demonstrated generalisation and scalability and substantially advanced the discovery of unknown metabolites for complex matrix samples, thus deciphering dark matter in multi-omics.

中文翻译：

---

通过多层分子网络对物质进行高可信度结构注释揭示了受二酚丙烷干扰的牛奶中的全系统成分变化

基于光谱数据库的质谱 (MS) 匹配策略可用于外部干扰介导的成分波动分析中的结构注释。然而，由于混叠峰和现有光谱数据库的不足，导致 MS 池的系统变异性导致代谢特征严重缺失。提出了一种称为多电荷重叠峰提取算法（MCOP）的修正程序，涉及通过迭代匹配碎片的质量特征来识别碰撞触发的解离前体离子，以扩展光谱参考库。我们在一项以二苯酚丙烷 (BPA) 介导的乳汁刺激为中心的调查中展示了既定策略的多功能性和实用性。 MCOP实现了代谢物-脂质-蛋白质水平的高效未知注释，在手动验证后将物质注释的准确性提高到85.3%。通过基于图神经网络的虚拟筛选，首次发现精氨酸酶和α-淀粉酶（|r| > 0.75，< 0.05）是BPA引发的尿素代谢异常导致精氨酸积累的关键问题（原文：1.7） μg·kg 1.7倍）和麦芽糖糊精（原：6.9μg·kg 2.9倍），因此，令人兴奋的潜在饮食风险。最后，MCOP 展示了泛化性和可扩展性，并大大推进了复杂基质样品未知代谢物的发现，从而破译了多组学中的暗物质。