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Consensus Structure Elucidation Combining GC/EI-MS, Structure Generation, and Calculated Properties

机译:结合GC / EI-MS,结构生成和计算属性的共识结构解析

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This article explores consensus structure elucidation on the basis of GC/EI-MS, structure generation, and calculated properties for unknown compounds. Candidate structures were generated using the molecular formula and substructure information obtained from GC/EI-MS spectra. Calculated properties were then used to score candidates according to a consensus approach, rather than filtering or exclusion. Two mass spectral match calculations (MOLGEN-MS and MetFrag), retention behavior (Lee retention index/boiling point correlation, NIST Kovat's retention index), octanol-water partitioning behavior (log K_(ow)), and finally steric energy calculations were used to select candidates. A simple consensus scoring function was developed and tested on two unknown spectra detected in a mutagenic subfraction of a water sample from the Elbe River using GC/EI-MS. The top candidates proposed using the consensus scoring technique were purchased and confirmed analytically using GC/EI-MS and LC/MS/MS. Although the compounds identified were not responsible for the sample mutagenicity, the structure-generation-based identification for GC/EI-MS using calculated properties and consensus scoring was demonstrated to be applicable to real-world unknowns and suggests that the development of a similar strategy for multidimensional high-resolution MS could improve the outcomes of environmental and metabolomics studies.
机译:本文根据GC / EI-MS,结构生成和未知化合物的计算性质探索共有结构的阐明。使用分子式和从GC / EI-MS光谱获得的亚结构信息生成候选结构。然后使用计算出的属性根据共识方法对候选人进行评分,而不是过滤或排除。使用两次质谱匹配计算(MOLGEN-MS和MetFrag),保留行为(李保留指数/沸点相关性,NIST Kovat保留指数),辛醇-水分配行为(log K_(ow)),最后使用空间位能计算选择候选人。开发了一个简单的共识评分功能,并使用GC / EI-MS在易北河水样的诱变亚组分中检测到的两个未知光谱上进行了测试。购买了使用共识评分技术提出的最佳候选人,并使用GC / EI-MS和LC / MS / MS进行了分析确认。尽管鉴定出的化合物与样品的致突变性无关,但已证明使用计算出的性质和共识评分对GC / EI-MS进行基于结构世代的鉴定可应用于现实世界中的未知数,并表明开发了类似的策略用于多维高分辨率MS可以改善环境和代谢组学研究的结果。

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