Poster presented at the Analytical Tools for Cutting-edge Metabolomics meeting in London, 30 April 201

Jacques Vervoort

Justin J.J. van der Hooft

Lars Ridder

Raoul J. Bino

Ric C.H. de Vos

Stefan Verhoeven

English

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Automatic metabolite annotation in complex LC-MS(n ≥ 2) data using MAGMa Lars Ridder*,  Justin J.J. van der Hooft, Stefan Verhoeven, Ric C. H. de Vos, Raoul J. Bino, Jacques Vervoort *Laboratory of Biochemistry, Wageningen University, The Netherlands (lars.ridder@wur.nl)    Problem  • The manual annotation of unknown compounds in complex LC-MSn datasets is time-consuming and requires specific knowledge of the detected compound classes and their fragmentation patterns in the mass spectrometer.   Objective:  develop algorithms and tools (MAGMa) to • automatically interpret multistage MSn spectral trees based on substructures of candidate molecules • systematically process untargeted LC-MSn datasets for comprehensive compound annotation  • predict candidate molecules not present in chemical databases Application: urinary metabolites of compounds in green tea  Methods                         Schematic overview of MAGMa Conclusions • MAGMa succesfully prioritizes correct candidate molecules based on (multistage) MSn spectral data,  and automatically assigns relevant substructures to multiple levels of MS fragments. • Application to untargeted LC-MSn profile of green tea assisted putative identification of new compounds. • The combination with in silico biotransformation lead to annotation of novel urinary metabolites. • MAGMa makes chemical interpretation of LC-MSn data more systematic and faster.   Computational approach  Recognize fragment ions Reconstruct molecules Rationalize biochemistry Explain fragment ions Match data & molecules Model biochemistry Manual approach  Datafile, e.g. mzXMLExtract MSnspectral treesGenerate substructuresAssign (sub)structures to observed ionsScore and rank candidate moleculesUser interfaceIdentified moleculesPubChemDatabaseCompound databaseSelect candidate moleculesApply metabolism rules• Four interactive panels: candidates, chromatogram, MSn spectra and substructures:  • Select a scan and a candidate to show the fragment annotation • In silico reaction  rules for gut metabolism and  human phase I and II biotransformations are encoded as SMIRKS  • Rules are applied following a multistage “scenario” involving different types of subsequent metabolism • Recursively split molecules by removing atoms • Bitwise representation of substructures (fast processing) • Substructure penalty score depends on disconnected bonds:      • Example: buspirone substructures  versus expert (literature) • Rearrangements are not included   • Recursive algorithm matches multistage MSn data with hierarchical trees of in silico generated substructures • Substructure assignments at each MS level take the assignments of the precursor as well as subsequent fragmentations into account • Total score is √(intensity) weighted average of substructure scores MS4 MS3 MS2 MS1 LC-MSn LC-MSn PubChem candidates MAGMa MAGMa Predict candidate metabolites 71 knowns: median rank 3.5 26 new assignments 77% not in PubChem  Evaluation of candidate ranking • MS/MS of 100 drugs, ramp 10-50 eV - On average 248 candidates per dataset                        a Wolf et al. BMC Bioinformatics 2010, 11, 148.     b Hill et al. Anal. Chem 2008, 80, 5574  • CASMI 2013       a Candidates were  filter by refscore > 5. b MAGMa results generated retrospec- tively.  ranking statistics selected spectra merged spectra median rank 3rd quartile rank median rank 3rd quartile rank MAGMa, NBD = 2 4 35 4 17 MAGMa, NBD = 3 3 17.5 3 11 MAGMa, NBD = 4 3 14.5 3 9 Hill et al. b 4a 17.5 a MetFraga 4.5 a 11.75 a # Compound # candidates rank 1 Feruloyl tyramine 1084a 1 2 Feruloyl putrescine 631a 3 3 N2-Acetyl glutaminyl leucinamide 370 17 4 Dihydrochalcone 825 78 5 Isoprothiolane 350 2 6 Phosphatidyl-6-acetyl-glucose 7 1 7 Cinnamtannin A3 17 1 8 Prodelphinidin C2 1 1 9 Chlorpyrifos 113 1 10 VAL-HIS-LEU-THR-PRO-VAL-GLU-LYS 20 1 11 Demethoxycurcumin  906a,b 6 11 Demethoxycurcumin (tautomer 1)  906a,b 4 12 Baicalein  813b 271 13 EST; Aloxistatin 207 42 14 Tetrahydroalstonine 1583a 5 15 2-(Perfluorooctyl)ethanol  720b  2 16 Ofloxacin  998a,b  18 𝑆𝑢𝑏𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒 𝑠𝑐𝑜𝑟𝑒 =  ℎ𝑏𝑝𝑏𝑏∈𝑑𝑖𝑠𝑐𝑜𝑛𝑛 . 𝑏𝑜𝑛𝑑𝑠  1 Bond type Value Single bond p=1 Double bond p=2 Triple or aromatic bond p=3 Carbon-carbon bonds h=2 Bonds involving non-carbon atoms h=1 m/z Assigned substructure score Literature 122.0712  2  150.1024  2  152.1067  3  222.1479  1   References • Van der Hooft et al. Anal. Chem. 2011, 83, 409 • Ridder et al. Rapid Commun. Mass Spectrom. 2012, 26, 2461. • Van der Hooft et al. Anal. Chem. 2012, 84, 7263 • Ridder et al. Anal. Chem. 2013, 85, 6033. • Ridder et al. Anal. Chem. 2014, DOI: 10.1021/ac403875b  Flavan-3-olsFlavonolsDehydroxylationα-oxidationβ-oxidation++Conversions in the gutEsteraseGlycosidaseDecarboxylationReductionPhase II conjugationsGlucuronidationSulfationMethylationGlycination5-phenyl-γ-valerolactones5-phenyl-valeric acidsC-ring fissionhydrolysisC-ring fission01002003004005006007000 10 20 30 40 50 60 70 80#candidates, rankAnnotated spectral trees in order of decreasing #candidates85 knowns: median rank 3.5 24 new assignments  

Automatic metabolite annotation in complex LC-MS(n ≥ 2) data using MAGMa

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Automatic metabolite annotation in complex LC-MS(n ≥ 2) data using MAGMa

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