Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra
Deciphering complex metabolite mixtures by unsupervised and supervised substructure discovery and semi-automated annotation from MS/MS spectra
复制标题
通过无监督和监督的子结构发现以及 MS/MS 谱图的半自动注释来破译复杂的代谢物混合物
DOI：
10.1101/491506
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Rogers S
中科院分区：
文献类型：
--
作者：
Rogers S
复杂的代谢物混合物很难解开。质谱（MS）是用于获得复杂混合物的结构信息的广泛使用和灵敏的技术。然而，仅仅知道混合物成分的分子量几乎总是不足以确定相关化学结构的归属。结构信息可以通过MS碎片化实验来增强，其中检测到的代谢物被碎片化，产生MS/MS光谱。然而，我们如何才能最大限度地提高我们从碎片光谱中获得的结构信息？我们最近提出了一种基于子结构的策略，通过将代谢物视为我们可以通过质谱裂解方法检测到的（生物）化学相关部分的总和，来增强复杂混合物的代谢物注释。我们的MS 2LDA工具允许我们发现-无监督-质量片段和/或中性损失的组，称为Mass 2 Motifs，通常对应于子结构。手动注释后，这些Mass 2 Motifs可用于新数据集的后续MS 2LDA分析，从而为光谱数据库中不存在的许多分子提供结构注释。在这里，我们描述了其他策略如何利用（i）实验质量特征与候选分子的子结构的组合计算机匹配，以及（ii）分子的自动机器学习分类，可以促进子结构的半自动注释。我们展示了我们的方法如何加速Mass 2 Motif注释过程，从而拓宽了特征图案所跨越的化学空间。我们用于对碎片光谱进行分类的机器学习模型学习了碎片光谱与化学特征之间的关系。这些特征上的分类预测可以针对有助于特定Mass 2 Motif和指导Mass 2 Motif注释的所有分子进行聚合。为了使注释的Mass 2 Motifs可供社区使用，我们还介绍了MotifDB：一个开放的Mass 2 Motifs数据库，可以通过应用程序编程接口（API）以编程方式浏览和访问。MotifDB集成在ms2lda.org中，允许用户在自己的实验中有效地搜索特征化的基序。我们希望通过不断增长的数据库提供越来越多的Mass 2 Motif注释，我们可以更快地了解复杂混合物的成分。这将允许对新的或意想不到的化学物质进行优先级排序，并更快地识别已知的生物化学结构单元。
Complex metabolite mixtures are challenging to unravel. Mass spectrometry (MS) is a widely used and sensitive technique for obtaining structural information of complex mixtures. However, just knowing the molecular masses of the mixture’s constituents is almost always insufficient for confident assignment of the associated chemical structures. Structural information can be augmented through MS fragmentation experiments whereby detected metabolites are fragmented, giving rise to MS/MS spectra. However, how can we maximize the structural information we gain from fragmentation spectra? We recently proposed a substructure-based strategy to enhance metabolite annotation for complex mixtures by considering metabolites as the sum of (bio)chemically relevant moieties that we can detect through mass spectrometry fragmentation approaches. Our MS2LDA tool allows us to discover – unsupervised – groups of mass fragments and/or neutral losses, termed Mass2Motifs, that often correspond to substructures. After manual annotation, these Mass2Motifs can be used in subsequent MS2LDA analyses of new datasets, thereby providing structural annotations for many molecules that are not present in spectral databases. Here, we describe how additional strategies, taking advantage of (i) combinatorial in silico matching of experimental mass features to substructures of candidate molecules, and (ii) automated machine learning classification of molecules, can facilitate semi-automated annotation of substructures. We show how our approach accelerates the Mass2Motif annotation process and therefore broadens the chemical space spanned by characterized motifs. Our machine learning model used to classify fragmentation spectra learns the relationships between fragment spectra and chemical features. Classification prediction on these features can be aggregated for all molecules that contribute to a particular Mass2Motif and guide Mass2Motif annotations. To make annotated Mass2Motifs available to the community, we also present MotifDB: an open database of Mass2Motifs that can be browsed and accessed programmatically through an Application Programming Interface (API). MotifDB is integrated within ms2lda.org, allowing users to efficiently search for characterized motifs in their own experiments. We expect that with an increasing number of Mass2Motif annotations available through a growing database, we can more quickly gain insight into the constituents of complex mixtures. This will allow prioritization towards novel or unexpected chemistries and faster recognition of known biochemical building blocks.
登录
查看更多内容
组织样品稳定性：对多器官样品的解冻效果
影响因子：
3.6
作者：
Frida Torell;K. Bennett;S. Cereghini;S. Rännar;K. Lundstedt;T. Moritz;C. Haumaître;J. Trygg;T. Lundstedt
通讯作者：
T. Lundstedt
DOI：
10.1073/pnas.1203689109
发表时间：
2012-06-26
影响因子：
11.1
作者：
Watrous, Jeramie;Roach, Patrick;Dorrestein, Pieter C.
通讯作者：
Dorrestein, Pieter C.
影响因子：
3.6
作者：
J. J. Hooft;R. D. Vos;L. Ridder;J. Vervoort;R. Bino
通讯作者：
R. Bino
DOI：
--
发表时间：
2006
期刊：
--
影响因子：
--
作者：
Iroon Polytechniou-
通讯作者：
Iroon Polytechniou-
MetCirc：在高分辨率 MS/MS 代谢组学数据中导航质谱相似性
影响因子：
5.8
作者：
Thomas Naake;Emmanuel Gaquerel
通讯作者：
Emmanuel Gaquerel