Computational mass spectrometry for small-molecule fragmentation

Hufsky, Franziska; Scheubert, Kerstin; Böcker, Sebastian

doi:10.1016/j.trac.2013.09.008

Cited by 97 publications

(92 citation statements)

References 48 publications

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“…In more detail, our fragmentation trees are predicted from MS/MS data by an automated computational method such that peaks in the MS/MS spectrum are annotated with molecular formulas of the corresponding fragments, and fragments are connected via assumed losses. Clearly, there exist other approaches with the broad aim of identifying metabolites, such as network-based methods (24)(25)(26) and combined approaches (27); see Hufsky et al (28) for a review of computational methods in MS-based metabolite identification.…”

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confidence: 99%

Searching molecular structure databases with tandem mass spectra using CSI:FingerID

Dührkop

Shen

Meusel

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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829

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Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem MS to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. We present a method for searching molecular structure databases using tandem MS data of small molecules. Our method computes a fragmentation tree that best explains the fragmentation spectrum of an unknown molecule. We use the fragmentation tree to predict the molecular structure fingerprint of the unknown compound using machine learning. This fingerprint is then used to search a molecular structure database such as PubChem. Our method is shown to improve on the competing methods for computational metabolite identification by a considerable margin.mass spectrometry | small compound identification | metabolomics | bioinformatics | machine learning M etabolites, small molecules that are involved in cellular reactions, can provide detailed information about cellular state. Untargeted metabolomic studies may use NMR or MS technologies, but liquid chromatography followed by MS (LC/MS) can detect the highest number of metabolites from minimal amounts of sample (1, 2). Untargeted metabolomics comprehensively compares the mass spectral intensities of metabolite signals (peaks) between two or more samples (3, 4). Advances in MS instrumentation allow us to simultaneously detect thousands of metabolites in a biological sample. Identification of these compounds relies on tandem MS (MS/MS) data, produced by fragmenting the compound and recording the masses of the fragments. Structural elucidation remains a challenging problem, in particular for compounds that cannot be found in any spectral library (1): In total, all available spectral MS/MS libraries of pure chemical standards cover fewer than 20,000 compounds (5). Growth of spectral libraries is limited by the unavailability of pure reference standards for many compounds.In contrast, molecular structure databases such as PubChem (6) and ChemSpider (7) contain millions of compounds, with PubChem alone having surpassed 50 million entries. Searching in molecular structure databases using MS/MS data is therefore considered a powerful tool for assisting an expert in the elucidation of a compound. This problem is considerably harder than the fundamental analysis step in the shotgun proteomics workflow, namely, searching peptide MS/MS data in a peptide sequence database (8): Unlike proteins and peptides, metabolites show a large structural variability and, consequently, also large variations in MS/MS fragmentation. Computational approaches for interpreting and predicting MS/MS data of small molecules date back to the 1960s (9): Due to the unavailability of molecular structure databases at that time, structure libraries were combinatorially generated and then "searched" using the experimental MS/MS data. "Modern" methods for this question have been developed since mid-2000. Particular progress has been made for restricted metabolite cl...

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confidence: 99%

Searching molecular structure databases with tandem mass spectra using CSI:FingerID

Dührkop

Shen

Meusel

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

840

829

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“…Fragmentation spectra have been used to partially overcome this problem (4)(5)(6). Most tools compare individual fragmentation spectra to reference spectra (5, 7) stored in public databases, for example, MassBank (8) or Human Metabolome Database (9), and are thus constrained by the limited number of reference spectra (10)(11)(12).…”

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confidence: 99%

Topic modeling for untargeted substructure exploration in metabolomics

Hooft

Wandy

Barrett

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

306

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The potential of untargeted metabolomics to answer important questions across the life sciences is hindered because of a paucity of computational tools that enable extraction of key biochemically relevant information. Available tools focus on using mass spectrometry fragmentation spectra to identify molecules whose behavior suggests they are relevant to the system under study. Unfortunately, fragmentation spectra cannot identify molecules in isolation but require authentic standards or databases of known fragmented molecules. Fragmentation spectra are, however, replete with information pertaining to the biochemical processes present, much of which is currently neglected. Here, we present an analytical workflow that exploits all fragmentation data from a given experiment to extract biochemically relevant features in an unsupervised manner. We demonstrate that an algorithm originally used for text mining, latent Dirichlet allocation, can be adapted to handle metabolomics datasets. Our approach extracts biochemically relevant molecular substructures ("Mass2Motifs") from spectra as sets of co-occurring molecular fragments and neutral losses. The analysis allows us to isolate molecular substructures, whose presence allows molecules to be grouped based on shared substructures regardless of classical spectral similarity. These substructures, in turn, support putative de novo structural annotation of molecules. Combining this spectral connectivity to orthogonal correlations (e.g., common abundance changes under system perturbation) significantly enhances our ability to provide mechanistic explanations for biological behavior.metabolomics | mass spectrometry | fragmentation | bioinformatics | topic modeling M ass spectrometry (MS)-based metabolomics aims to capture the entire small-molecule composition of biological systems. Analysis of MS metabolomics data are challenging as many molecules cannot be identified from their mass (e.g., isobaric molecules, and isomers) (1-3). Separation by liquid chromatography before MS (LC-MS) can add discriminatory information but does not solve the problem as isomers can exhibit similar chromatographic behavior, and chromatographic retention time is currently unpredictable.Fragmentation spectra have been used to partially overcome this problem (4-6). Most tools compare individual fragmentation spectra to reference spectra (5, 7) stored in public databases, for example, MassBank (8) or Human Metabolome Database (9), and are thus constrained by the limited number of reference spectra (10-12). Poor identification coverage can result in poor biochemical insight. We propose a method that analyzes all acquired fragmentation spectra to expose underlying biochemistry without relying on metabolite identification, inspired by machine-learning techniques developed initially for text processing (13).The paucity of techniques that share information across fragmentation spectra can be explained by the complexity of fragmentation data (14). One example, "Molecular Networking," clusters MS1 peaks by th...

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“…An implication of the fragmentation relationship between precursor ions and product ions is made before acquiring MS n data. Conversely, ion trees or mass spectral trees refer to the sequential stages and relationships of mass spectral acquisition in MS n processes, representing precursor and product ions as nodes and neutral losses as edges [25,26] (Fig. 2B).…”

Section: Fragmentation Trees and Mass Spectral Treesmentioning

confidence: 99%

Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

Vaniya

Fiehn

2015

TrAC Trends in Analytical Chemistry

126

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Identification of unknown metabolites is the bottleneck in advancing metabolomics, leaving interpretation of metabolomics results ambiguous. The chemical diversity of metabolism is vast, making structure identification arduous and time consuming. Currently, comprehensive analysis of mass spectra in metabolomics is limited to library matching, but tandem mass spectral libraries are small compared to the large number of compounds found in the biosphere, including xenobiotics. Resolving this bottleneck requires richer data acquisition and better computational tools. Multi-stage mass spectrometry (MSn) trees show promise to aid in this regard. Fragmentation trees explore the fragmentation process, generate fragmentation rules and aid in sub-structure identification, while mass spectral trees delineate the dependencies in multi-stage MS of collision-induced dissociations. This review covers advancements over the past 10 years as a tool for metabolite identification, including algorithms, software and databases used to build and to implement fragmentation trees and mass spectral annotations.

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Computational mass spectrometry for small-molecule fragmentation

Cited by 97 publications

References 48 publications

Searching molecular structure databases with tandem mass spectra using CSI:FingerID

Searching molecular structure databases with tandem mass spectra using CSI:FingerID

Topic modeling for untargeted substructure exploration in metabolomics

Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

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