Seven Golden Rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry

By Tobias Kind, Oliver Fiehn. BMC Bioinformatics, 2007

1,109 citations recorded by the source corpus.

BackgroundStructure elucidation of unknown small molecules by mass spectrometry is a challenge despite advances in instrumentation. The first crucial step is to obtain correct elemental compositions. In order to automatically constrain the thousands of possible candidate structures, rules need to be developed to select the most likely and chemically correct molecular formulas.ResultsAn algorithm for filtering molecular formulas is derived from seven heuristic rules: (1) restrictions for the number of elements, (2) LEWIS and SENIOR chemical rules, (3) isotopic patterns, (4) hydrogen/carbon ratios, (5) element ratio of nitrogen, oxygen, phosphor, and sulphur versus carbon, (6) element ratio probabilities and (7) presence of trimethylsilylated compounds. Formulas are ranked according to their isotopic patterns and subsequently constrained by presence in public chemical databases. The seven rules were developed on 68,237 existing molecular formulas and were validated in four experiments. First, 432,968 formulas covering five million PubChem database entries were checked for consistency. Only 0.6% of these compounds did not pass all rules. Next, the rules were shown to effectively reducing the complement all eight billion theoretically possible C, H, N, S, O, P-formulas up to 2000 Da to only 623 million most probable elemental compositions. Thirdly 6,000 pharmaceutical, toxic and natural compounds were selected from DrugBank, TSCA and DNP databases. The correct formulas were retrieved as top hit at 80–99% probability when assuming data acquisition with complete resolution of unique compounds and 5% absolute isotope ratio deviation and 3 ppm mass accuracy. Last, some exemplary compounds were analyzed by Fourier transform ion cyclotron resonance mass spectrometry and by gas chromatography-time of flight mass spectrometry. In each case, the correct formula was ranked as top hit when combining the seven rules with database queries.ConclusionThe seven rules enable an automatic exclusion of molecular formulas which are either wrong or which contain unlikely high or low number of elements. The correct molecular formula is assigned with a probability of 98% if the formula exists in a compound database. For truly novel compounds that are not present in databases, the correct formula is found in the first three hits with a probability of 65–81%. Corresponding software and supplemental data are available for downloads from the authors' website.

Subfields: Computational Theory and Mathematics, Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e29014280df418b29bdb77aeecf4c3839750f38b

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Citations in the corpus

Untargeted metabolomics strategies – Challenges and Emerging Directions (2016, background): “Potential inclusion of the Seven Golden Rules [20] may offer an approach to classify the legitimate candidate list for exclusion from the decoy elemental composition list.”
Metabolomics in pesticide research and development: review and future perspectives (2011, background): “…Scholz and Fiehn (2007) Seven Golden Rules Software http://fiehnlab.ucdavis.edu/projects/Seven_Golden_Rules/Software/ Kind and Fiehn (2007) SIRIOUS http://bio.informatik.uni-jena.de/sirius/download/index.html Böcker et al. (2009) TargetSearch…”
Computational mass spectrometry for small molecules (2013, methodology): “Kind and Fiehn [104] proposed “Seven Golden Rules” to ﬁlter molecular formulas based on chemical considerations.”
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).”
SIRIUS: decomposing isotope patterns for metabolite identification (2008, methodology): “In our evaluation, we have deliberately ignored some information such as prior probability of the elements or hetero-to-carbon ratio (Kind and Fiehn, 2007).”
A new approach to toxicity testing in Daphnia magna: application of high throughput FT-ICR mass spectrometry metabolomics (2009, methodology): “formulae with an absolute mass error of \0.75 ppm were recorded, and then all potential formulae were filtered using four heuristic rules reported by Kind and Fiehn (2007) , specifically: (1) restricted number of atoms per element, (2) Lewis and Senior rules, (3) H/C ratio, and (4) elemental ratio ”
Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics (2014, methodology): “• Annotation: a formula generator based on the HiRes (High Resolution) algorithm (Kind and Fiehn, 2007) is provided, in addition to several modules for public database query which allow the user to define specific annotation strategies (e.g. by searching from general to more specialized resources).”
Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics (2018, methodology): “…a Windows based GUI software aiding the structure elucidation process by in silico fragmentation of all predicted molecular formulas, determined from the accurate mass, isotope ratio, and product ion information [95], which are retrieved from 15 databases that are embedded into MS-FINDER [96,97].”
Metabolomics: a global biochemical approach to drug response and disease. (2008, methodology): “Structural identification is aided by a combination of three factors: high mass accuracy, ion fragmentation capability, and software designed to recognize the rules by which nature assembles compounds (57).”
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).”
Non-target screening with high-resolution mass spectrometry: critical review using a collaborative trial on water analysis (2015, background): “With highaccuracy data, isobaric substances often have the same molecular formula (but not always, because some can have the same exact mass within the instrumental accuracy [65]), and in environmental screening these can often be very similar substances structurally.”
The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching (2017, methodology): “To evaluate the performance of the CDK molecular formula generator, we compared its runtimes to those of the classic, full enumeration-based HR2 formula generator [62] and those of a recently developed Parallel Formula Generator (PFG) [63] (Table 1).”
Metabolomics for functional genomics, systems biology, and biotechnology. (2010, background): “In this context, it should be noted that searching a series of high-resolution mass and MSn spectra data against compound databases would produce false-positive hits (101).”