In silico fragmentation for computer assisted identification of metabolite mass spectra

By Sebastian Wolf, Stephan Schmidt, Matthias Müller-Hannemann, Steffen Neumann. BMC Bioinformatics, 2010

659 citations recorded by the source corpus.

BackgroundMass spectrometry has become the analytical method of choice in metabolomics research. The identification of unknown compounds is the main bottleneck. In addition to the precursor mass, tandem MS spectra carry informative fragment peaks, but the coverage of spectral libraries of measured reference compounds are far from covering the complete chemical space. Compound libraries such as PubChem or KEGG describe a larger number of compounds, which can be used to compare their in silico fragmentation with spectra of unknown metabolites.ResultsWe created the MetFrag suite to obtain a candidate list from compound libraries based on the precursor mass, subsequently ranked by the agreement between measured and in silico fragments. In the evaluation MetFrag was able to rank most of the correct compounds within the top 3 candidates returned by an exact mass query in KEGG. Compared to a previously published study, MetFrag obtained better results than the commercial MassFrontier software. Especially for large compound libraries, the candidates with a good score show a high structural similarity or just different stereochemistry, a subsequent clustering based on chemical distances reduces this redundancy. The in silico fragmentation requires less than a second to process a molecule, and MetFrag performs a search in KEGG or PubChem on average within 30 to 300 seconds, respectively, on an average desktop PC.ConclusionsWe presented a method that is able to identify small molecules from tandem MS measurements, even without spectral reference data or a large set of fragmentation rules. With today's massive general purpose compound libraries we obtain dozens of very similar candidates, which still allows a confident estimate of the correct compound class. Our tool MetFrag improves the identification of unknown substances from tandem MS spectra and delivers better results than comparable commercial software. MetFrag is available through a web application, web services and as java library. The web frontend allows the end-user to analyse single spectra and browse the results, whereas the web service and console application are aimed to perform batch searches and evaluation.

Subfields: Analytical Chemistry, Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/17d4f2a996e8bfb2ae6d0458d4e8f0f7b11d03f2

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Citations in the corpus

MetFrag relaunched: incorporating strategies beyond in silico fragmentation (2016, methodology): “[11], also used in [6, 19], formed the first evaluation set.”
Non-target screening with high-resolution mass spectrometry: critical review using a collaborative trial on water analysis (2015, methodology): “Software used for fragmentation prediction included MetFrag [28], MetFusion [55], Mass Frontier [29], MassFragment in UNIFI and MassLynx (Waters [56]), SmartFormula 3D (Bruker Daltronics [57]), and Molecular Structure correlator) [58, 59].”
Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification (2013, methodology): “This current work does not compare against these methods empirically, however in at least one study they have been found to have been out-performed by MetFrag (Wolf et al. 2010), to which we do compare.”
Searching molecular structure databases with tandem mass spectra using CSI:FingerID (2015, background): “Our method reaches 66.7% identifications for fractional rank 7.62, compared with MetFrag (58.5) and MAGMa (63.1).”
The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching (2017, methodology): “Examples include jCompoundMapper [11], ScaffoldHunter [12, 13], OMG [14], PaDEL [15], Chem-Des [16], ReactPRED [17], SMSD [18–20], WhichCyp [21], MetaPrint2D [22], MetFrag [23], and the IUPHAR/BPS Guide to Pharmacology [24], BRENDA [25] and QSAR DataBank [26] databases.”
Computational mass spectrometry for small molecules (2013, methodology): “The most recent approach is MetFrag [179], a somewhat greedy heuristic to match molecular structures to measured spectra that makes no attempt to create a mechanistically correct prediction of the fragmentation processes.”
CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra (2014, background): “Programs directed towards MS / MS-based identification of such metabolites include MetFrag (13) and FingerId (21), both of which have web server interfaces to their meth-ods.”
Gut Microbiota-Derived Tryptophan Metabolites Modulate Inflammatory Response in Hepatocytes and Macrophages (2018, positive): “Each feature was searched against spectral libraries in METLIN (Smith et al., 2005) and HMDB (Wishart et al., 2007) and analyzed using in silico fragmentation tools MetFrag (Wolf et al., 2010) and CFM-ID (Allen et al., 2014).”