Metabolomic database annotations via query of elemental compositions: Mass accuracy is insufficient even at less than 1 ppm

By Tobias Kind, Oliver Fiehn. BMC Bioinformatics, 2006

605 citations recorded by the source corpus.

BackgroundMetabolomic studies are targeted at identifying and quantifying all metabolites in a given biological context. Among the tools used for metabolomic research, mass spectrometry is one of the most powerful tools. However, metabolomics by mass spectrometry always reveals a high number of unknown compounds which complicate in depth mechanistic or biochemical understanding. In principle, mass spectrometry can be utilized within strategies of de novo structure elucidation of small molecules, starting with the computation of the elemental composition of an unknown metabolite using accurate masses with errors <5 ppm (parts per million). However even with very high mass accuracy (<1 ppm) many chemically possible formulae are obtained in higher mass regions. In automatic routines an additional orthogonal filter therefore needs to be applied in order to reduce the number of potential elemental compositions. This report demonstrates the necessity of isotope abundance information by mathematical confirmation of the concept.ResultsHigh mass accuracy (<1 ppm) alone is not enough to exclude enough candidates with complex elemental compositions (C, H, N, S, O, P, and potentially F, Cl, Br and Si). Use of isotopic abundance patterns as a single further constraint removes >95% of false candidates. This orthogonal filter can condense several thousand candidates down to only a small number of molecular formulas. Example calculations for 10, 5, 3, 1 and 0.1 ppm mass accuracy are given. Corresponding software scripts can be downloaded from http://fiehnlab.ucdavis.edu. A comparison of eight chemical databases revealed that PubChem and the Dictionary of Natural Products can be recommended for automatic queries using molecular formulae.ConclusionMore than 1.6 million molecular formulae in the range 0–500 Da were generated in an exhaustive manner under strict observation of mathematical and chemical rules. Assuming that ion species are fully resolved (either by chromatography or by high resolution mass spectrometry), we conclude that a mass spectrometer capable of 3 ppm mass accuracy and 2% error for isotopic abundance patterns outperforms mass spectrometers with less than 1 ppm mass accuracy or even hypothetical mass spectrometers with 0.1 ppm mass accuracy that do not include isotope information in the calculation of molecular formulae.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

View Paper: https://www.semanticscholar.org/paper/cce59554089095a82162b3e7249a81a96e48e224

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Citations in the corpus

Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles (2009, methodology): “Although CE-TOF-MS provides accurate molecular mass at the milli m/z level, the m/z alone is seldom successful to identify the metabolite (Kind and Fiehn 2006, 2007).”
Computational mass spectrometry for small molecules (2013, methodology): “Decomposing alone is not suﬃcient to exclude enough possible molecular formulas in higher mass regions even with very high mass accuracy [117].”
MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis (2018, background): “This process can often generate a number of false positives, due to redundancies in masses or the lack of unique MS spectral signatures for many small compounds (28,29).”
Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry (2007, methodology): “However, data acquired even by ultrahigh mass accuracy and mass resolution are insufficient for calculating unique elemental compositions without information about isotope ratios [10].”
Untargeted metabolomics strategies – Challenges and Emerging Directions (2016, background): “Kind and Fiehn demonstrated that high mass accuracy measurements (<1 ppm error) were inadequate for determining the elemental composition of numerous metabolites [19]; notably, the authors later showed that isotope ratio measurements were more important than mass accuracy for determining the most pr”
SIRIUS: decomposing isotope patterns for metabolite identification (2008, methodology): “To resolve the sum formula, Kind and Fiehn (2006) suggest to proceed as follows: first, compute all sum formulas that share monoisotopic mass with the input mass spectrum.”
Metabolomics for functional genomics, systems biology, and biotechnology. (2010, background): “Thus, structural information— including elemental composition deduced from high-resolution mass data as well as a similarity of mass and tandem mass (MSn) spectroscopic data—are often employed for the elucidation of metabolite structures (13, 100, 128).”
Predicting Network Activity from High Throughput Metabolomics (2013, background): “Computational prediction of metabolite identity, based on m/z alone, is deemed inadequate as a single m/z feature can match multiple metabolites even with high instrumental accuracy [33,34], and”