The spectral matching strategy of MS2 fragment spectrograms serves as a ubiquitous method for compound characterization within the matrix. Nevertheless, challenges arise due to the deficiency of distinctions in spectra across instruments caused by coelution peak-derived fragments and incompleteness of the current spectral reference database, leading to dilemma of multidimensional omics annotation. The graph attention model embedded with long short-term memory was proposed as an optimized approach involving integrating similar MS2 spectra into molecular networks according to the isotopic ion peak cluster spacing features to collapse diverse ion species and expand the spectral reference library, which efficiently evaluated the substance capture capacity to 123.1% than classic substance perception tactics. The versatility and utility of the established annotation procedure were showcased in a study on the stimulation of pork mediated by 2,2-bis(4-hydroxyphenyl)propane and enabled the global metabolite annotation from knowns to unknowns at metabolite-lipid-protein level. On the spectra for which in silico extended spectral library search provided a group truth, 83.5–117.1% accuracy surpassed 1.2–14.3% precision after manual validation. β-Ala-His dipeptidase was first evidenced as the critical node related to the transformation of α-helical (36.57 to 35.74%) to random coil (41.53 to 42.36%) mediated by 2,2-bis(4-hydroxyphenyl)propane, ultimately triggering an augment of catalytic performance, inducing a series of oxidative stress, and further intervening in the availability of animal-derived substrates. The integration of ionic fragment feature networks and long short-term memory models allows the effective annotation of recurrent unknowns in organisms and the deciphering of unacquainted matter in multiomics.

中文翻译：

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3D-MPEA：一种图形注意力模型引导的计算方法，通过以质谱为重点的多层分子网络注释相互作用组学中的未知代谢物


MS2 片段谱图的光谱匹配策略是基质内化合物表征的普遍方法。然而，由于共洗脱峰衍生片段导致仪器之间光谱缺乏区分以及当前光谱参考数据库的不完整性，带来了挑战，导致多维组学注释的困境。提出了嵌入长短期记忆的图注意力模型作为一种优化方法，根据同位素离子峰簇间距特征将相似的MS2光谱集成到分子网络中，以折叠不同的离子种类并扩展光谱参考库，从而有效地评估了物质捕获能力比经典物质感知策略高 123.1%。所建立的注释程序的多功能性和实用性在一项关于 2,2-双（4-羟基苯基）丙烷介导的猪肉刺激的研究中得到了展示，并实现了代谢物-脂质-蛋白质水平上从已知到未知的全局代谢物注释。对于通过计算机扩展光谱库搜索提供的光谱，手动验证后，83.5–117.1% 的准确度超过了 1.2–14.3% 的准确度。 β-丙氨酸-组氨酸二肽酶首次被证明是2,2-双(4-羟苯基)丙烷介导的α-螺旋(36.57%至35.74%)向无规卷曲(41.53%至42.36%)转化相关的关键节点，最终引发催化性能增强，引发一系列氧化应激，并进一步干预动物源性底物的可用性。 离子片段特征网络和长短期记忆模型的集成可以有效注释生物体中反复出现的未知事物以及破译多组学中的未知物质。