Abstract
SIRIUS 4 is the best-in-class computational tool for metabolite identification from high-resolution tandem mass spectrometry data. It offers de novo molecular formula annotation with outstanding accuracy. When searching fragmentation spectra in a structure database, it reaches over 70% correct identifications. A predicted fingerprint, which indicates the presence or absence of thousands of molecular properties, helps to deduce information about the compound of interest even if it is not contained in any structure database. Here, we present best practices and describe how to leverage the full potential of SIRIUS 4, how to incorporate it into your own workflow, and how it adds value to the analysis of mass spectrometry data beyond spectral library search.
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References
Allen F, Greiner R, Wishart D (2015) Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification. Metabolomics 11(1):98–110. https://doi.org/10.1007/s11306-014-0676-4
Böcker S (2017) Searching molecular structure databases using tandem MS data: are we there yet? Curr Opin Chem Biol 36:1–6. https://doi.org/10.1016/j.cbpa.2016.12.010. https://authors.elsevier.com/a/1UF-u4sz6LvFfY
Böcker S, Dührkop K (2016) Fragmentation trees reloaded. J Cheminform 8:5. https://doi.org/10.1186/s13321-016-0116-8. http://www.jcheminf.com/content/8/1/5
Caspi R, Altman T, Billington R, Dreher K, Foerster H, Fulcher CA, Holland TA, Keseler IM, Kothari A, Kubo A, Krummenacker M, Latendresse M, Mueller LA, Ong Q, Paley S, Subhraveti P, Weaver, DS, Weerasinghe D, Zhang P, Karp PD (2014) The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res 42(D1):D459–D471. https://doi.org/10.1093/nar/gkt1103. http://nar.oxfordjournals.org/content/42/D1/D459.abstract
da Silva RR, Dorrestein PC, Quinn RA (2015) Illuminating the dark matter in metabolomics. Proc Natl Acad Sci U S A 112(41):12549–12550. https://doi.org/10.1073/pnas.1516878112
Djoumbou-Feunang Y, Fiamoncini J, Gil-de-la Fuente A, Greiner R, Manach C, Wishart DS (2019) BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification. J Cheminf 11(1):2
Dührkop K, Shen H, Meusel M, Rousu J, Böcker S (2015) Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proc Natl Acad Sci U S A 112(41):12580–12585. https://doi.org/10.1073/pnas.1509788112
Dührkop K, Lataretu MA, White WTJ, Böcker S (2018) Heuristic algorithms for the maximum colorful subtree problem. In: Proceedings of workshop on algorithms in bioinformatics (WABI 2018). Leibniz international proceedings in informatics (LIPIcs), vol 113. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, Dagstuhl, pp 23:1–23:14. https://doi.org/10.4230/LIPIcs.WABI.2018.23. http://drops.dagstuhl.de/opus/volltexte/2018/9325
Dührkop K, Fleischauer M, Ludwig M, Aksenov AA, Melnik AV, Meusel M, Dorrestein PC, Rousu J, Böcker S (2019) Sirius 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nat Methods. https://doi.org/10.1038/s41592-019-0344-8
Fonger GC, Hakkinen P, Jordan S, Publicker S (2014) The National Library of Medicine’s (NLM) Hazardous Substances Data Bank (HSDB): background, recent enhancements and future plans. Toxicology 325:209–216. https://doi.org/10.1016/j.tox.2014.09.003
Gu J, Gui Y, Chen L, Yuan G, Lu HZ, Xu X (2013) Use of natural products as chemical library for drug discovery and network pharmacology. PLoS One 8(4):1–10
Hastings J, Owen G, Dekker A, Ennis M, Kale N, Muthukrishnan V, Turner S, Swainston N, Mendes P, Steinbeck C (2016) ChEBI in 2016: improved services and an expanding collection of metabolites. Nucleic Acids Res 44(D1):D1214–D1219. https://doi.org/10.1093/nar/gkv1031. http://europepmc.org/articles/PMC4702775
Heinonen M, Shen H, Zamboni N, Rousu J (2012) Metabolite identification and molecular fingerprint prediction via machine learning. Bioinformatics 28(18):2333–2341. https://doi.org/10.1093/bioinformatics/bts437
Hoffmann N, Rein J, Sachsenberg TT, Hartler J, Haug K, Mayer G, Alka O, Dayalan S, Pearce JTM, Rocca-Serra P et al (2019) mzTab-M: a data standard for sharing quantitative results in mass spectrometry metabolomics. Anal Chem 91(5):3302–3310. https://doi.org/10.1021/acs.analchem.8b04310
Horai H, Arita M, Kanaya S, Nihei Y, Ikeda T, Suwa K, Ojima Y, Tanaka K, Tanaka S, Aoshima K, Oda Y, Kakazu Y, Kusano M, Tohge T, Matsuda F, Sawada Y, Hirai MY, Nakanishi H, Ikeda K, Akimoto N, Maoka T, Takahashi H, Ara T, Sakurai N, Suzuki H, Shibata D, Neumann S, Iida T, Tanaka K, Funatsu K, Matsuura F, Soga T, Taguchi R, Saito K, Nishioka T (2010) MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom 45(7):703–714. https://doi.org/10.1002/jms.1777
Irwin JJ, Sterling T, Mysinger MM, Bolstad ES, Coleman RG (2012) ZINC: a free tool to discover chemistry for biology. J Chem Inf Model 52(7):1757–1768
Jeffryes JG, Colastani RL, Elbadawi-Sidhu M, Kind T, Niehaus TD, Broadbelt LJ, Hanson AD, Fiehn O, Tyo KEJ, Henry CS (2015) MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics. J Cheminform 7:44. https://doi.org/10.1186/s13321-015-0087-1
Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M (2016) KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44(D1):D457–D462
Keseler IM, Mackie A, Santos-Zavaleta A, Billington R, Bonavides-Martínez C, Caspi R, Fulcher C, Gama-Castro S, Kothari A, Krummenacker M, Latendresse M, Muñiz-Rascado L, Ong Q, Paley S, Peralta-Gil M, Subhraveti P, Velázquez-Ramírez DA, Weaver D, Collado-Vides J, Paulsen I, Karp PD (2017) The EcoCyc database: reflecting new knowledge about Escherichia coli k-12. Nucleic Acids Res 45:D543–D550
Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, Wang J, Yu B, Zhang J, Bryant SH (2016) PubChem substance and compound databases. Nucleic Acids Res 44:D1202–D1213. https://doi.org/10.1093/nar/gkv951
Klekota J, Roth FP (2008) Chemical substructures that enrich for biological activity. Bioinformatics 24(21):2518–2525. https://doi.org/10.1093/bioinformatics/btn479
Larson EA, Hutchinson CP, Lee YJ (2018) Gas chromatography-tandem mass spectrometry of lignin pyrolyzates with dopant-assisted atmospheric pressure chemical ionization and molecular structure search with CSI:FingerID. J Am Soc Mass Spectrom 29(9):1908–1918. https://doi.org/10.1007/s13361-018-2001-3
Ludwig M, Dührkop K, Böcker S (2018) Bayesian networks for mass spectrometric metabolite identification via molecular fingerprints. Bioinformatics 34(13):i333–i340. https://doi.org/10.1093/bioinformatics/bty245. Proceedings of Intelligent Systems for Molecular Biology (ISMB 2018)
Meusel M, Hufsky F, Panter F, Krug D, Müller R, Böcker S (2016) Predicting the presence of uncommon elements in unknown biomolecules from isotope patterns. Anal Chem 88(15):7556–7566. https://doi.org/10.1021/acs.analchem.6b01015
Mohimani H, Gurevich A, Shlemov A, Mikheenko A, Korobeynikov A, Cao L, Shcherbin E, Nothias LF, Dorrestein PC, Pevzner PA (2018) Dereplication of microbial metabolites through database search of mass spectra. Nat Commun 9(1):4035. https://doi.org/10.1038/s41467-018-06082-8
Nelson SJ, Johnston WD, Humphreys BL (2001) Relationships in medical subject headings. In: Bean CA, Green R (eds) Relationships in the organization of knowledge. Kluwer Academic Publishers, Dordrecht, pp 171–184. http://www.nlm.nih.gov/mesh/meshrels.html
Pluskal T, Castillo S, Villar-Briones A, Oresic M (2010) MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinf 11:395. https://doi.org/10.1186/1471-2105-11-395
Ramirez-Gaona M, Marcu A, Pon A, Guo AC, Sajed T, Wishart NA, Karu N, Djoumbou Feunang Y, Arndt D, Wishart DS (2017) YMDB 2.0: a significantly expanded version of the yeast metabolome database. Nucleic Acids Res 45:D440–D445
Rasche F, Svatoš A, Maddula RK, Böttcher C, Böcker S (2011) Computing fragmentation trees from tandem mass spectrometry data. Anal Chem 83(4):1243–1251. https://doi.org/10.1021/ac101825k
Ridder L, van der Hooft JJJ, Verhoeven S, de Vos RCH, Bino RJ, Vervoort J (2013) Automatic chemical structure annotation of an LC-MS(n) based metabolic profile from green tea. Anal Chem 85(12):6033–6040. https://doi.org/10.1021/ac400861a
Röst HL, Sachsenberg T, Aiche S, Bielow C, Weisser H, Aicheler F, Andreotti S, Ehrlich HC, Gutenbrunner P, Kenar E, Liang X, Nahnsen S, Nilse L, Pfeuffer J, Rosenberger G, Rurik M, Schmitt U, Veit J, Walzer M, Wojnar D, Wolski WE, Schilling O, Choudhary JS, Malmström L, Aebersold R, Reinert K, Kohlbacher O (2016) OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Methods 13(9):741–748. https://doi.org/10.1038/nmeth.3959
Ruttkies C, Schymanski EL, Wolf S, Hollender J, Neumann S (2016) MetFrag relaunched: incorporating strategies beyond in silico fragmentation. J Cheminform 8:3. https://doi.org/10.1186/s13321-016-0115-9
Schymanski EL, Ruttkies C, Krauss M, Brouard C, Kind T, Dührkop K, Allen FR, Vaniya A, Verdegem D, Böcker S, Rousu J, Shen H, Tsugawa H, Sajed T, Fiehn O, Ghesquière B, Neumann S (2017) Critical assessment of small molecule identification 2016: automated methods. J Cheminf 9:22. https://doi.org/10.1186/s13321-017-0207-1
Shinbo Y, Nakamura Y, Altaf-Ul-Amin M, Asahi H, Kurokawa K, Arita M, Saito K, Ohta D, Shibata D, Kanaya S (2006) KNApSAcK: a comprehensive species-metabolite relationship database. In: Saito K, Dixon RA, Willmitzer L (eds) Plant metabolomics. Biotechnology in agriculture and forestry, vol 57. Springer, Berlin, pp 165–181
Steinbeck C, Han Y, Kuhn S, Horlacher O, Luttmann E, Willighagen E (2003) The Chemistry Development Kit (CDK): an open-source Java library for chemo- and bioinformatics. J Chem Inf Comput Sci 43:493–500
Tautenhahn R, Cho K, Uritboonthai W, Zhu Z, Patti GJ, Siuzdak G (2012) An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol 30(9):826–828. https://doi.org/10.1038/nbt.2348
Tsugawa H, Kind T, Nakabayashi R, Yukihira D, Tanaka W, Cajka T, Saito K, Fiehn O, Arita M (2016) Hydrogen rearrangement rules: computational ms/ms fragmentation and structure elucidation using MS-FINDER software. Anal Chem 88(16):7946–7958. https://doi.org/10.1021/acs.analchem.6b00770
Wang R, Fu Y, Lai L (1997) A new atom-additive method for calculating partition coefficients. J Chem Inf Comput Sci 37(3):615–621. https://doi.org/10.1021/ci960169p
Wang R, Gao Y, Lai L (2000) Calculating partition coefficient by atom-additive method. Perspect Drug Discov Des 19(1):47–66. https://doi.org/10.1023/A:1008763405023
Wang Y, Kora G, Bowen BP, Pan C (2014) MIDAS: a database-searching algorithm for metabolite identification in metabolomics. Anal Chem 86(19):9496–9503. https://doi.org/10.1021/ac5014783
Wang M et al (2016) Sharing and community curation of mass spectrometry data with Global Natural Products Social molecular networking. Nat Biotechnol 34(8):828–837. https://doi.org/10.1038/nbt.3597
Weber RJM, Li E, Bruty J, He S, Viant MR (2012) MaConDa: a publicly accessible mass spectrometry contaminants database. Bioinformatics 28(21):2856–2857. https://doi.org/10.1093/bioinformatics/bts527
Willighagen EL, Mayfield JW, Alvarsson J, Berg A, Carlsson L, Jeliazkova N, Kuhn S, Pluskal T, Rojas-Chertó M, Spjuth O, Torrance G, Evelo CT, Guha R, Steinbeck C (2017) The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching. J Cheminf 9(1):33. http://dx.doi.org/10.1186/s13321-017-0220-4
Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vázquez-Fresno R, Sajed T, Johnson D, Li C, Karu N, Sayeeda Z, Lo E, Assempour N, Berjanskii M, Singhal S, Arndt D, Liang Y, Badran H, Grant J, Serra-Cayuela A, Liu Y, Mandal R, Neveu V, Pon A, Knox C, Wilson M, Manach C, Scalbert A (2018) HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res 46(D1):D608–D617. http://dx.doi.org/10.1093/nar/gkx1089
Wolf S, Schmidt S, Müller-Hannemann M, Neumann S (2010) In silico fragmentation for computer assisted identification of metabolite mass spectra. BMC Bioinf 11:148. https://doi.org/10.1186/1471-2105-11-148
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Ludwig, M., Fleischauer, M., Dührkop, K., Hoffmann, M.A., Böcker, S. (2020). De Novo Molecular Formula Annotation and Structure Elucidation Using SIRIUS 4. In: Li, S. (eds) Computational Methods and Data Analysis for Metabolomics. Methods in Molecular Biology, vol 2104. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0239-3_11
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