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Kendrick Mass Defect Approach Combined to NORINE Database for Molecular Formula Assignment of Nonribosomal Peptides

  • Mickaël Chevalier
  • Emma Ricart
  • Emeline Hanozin
  • Maude Pupin
  • Philippe Jacques
  • Nicolas Smargiasso
  • Edwin De Pauw
  • Frédérique Lisacek
  • Valérie Leclère
  • Christophe FlahautEmail author
Research Article

Abstract

The identification of known (dereplication) or unknown nonribosomal peptides (NRPs) produced by microorganisms is a time consuming, expensive, and challenging task where mass spectrometry and nuclear magnetic resonance play a key role. The first step of the identification process always involves the establishment of a molecular formula. Unfortunately, the number of potential molecular formulae increases significantly with higher molecular masses and the lower precision of their measurements. In the present article, we demonstrate that molecular formula assignment can be achieved by a combined approach using the regular Kendrick mass defect (RKMD) and NORINE, the reference curated database of NRPs. We observed that irrespective of the molecular formula, the addition and subtraction of a given atom or atom group always leads to the same RKMD variation and nominal Kendrick mass (NKM). Graphically, these variations translated into a vector mesh can be used to connect an unknown molecule to a known NRP of the NORINE database and establish its molecular formula. We explain and illustrate this concept through the high-resolution mass spectrometry analysis of a commercially available mixture composed of four surfactins. The Kendrick approach enriched with the NORINE database content is a fast, useful, and easy-to-use tool for molecular mass assignment of known and unknown NRP structures.

Keywords

Kendrick map Mass defect Molecular formula Nonribosomal peptides NORINE 

Abbreviations

ESI

Electrospray ionization

FT

Fourier transform

ICR

Ion cyclotron resonance

KMD

Kendrick mass defect

NRPs

Nonribosomal peptides

NRPS

Nonribosomal peptide synthetase

NKM

Nominal Kendrick mass

MALDI

Matrix-assisted laser desorption/ionization

Notes

Acknowledgements

This work has been carried out in the framework of Alibiotech project which is financed by the European Union, French State, and the French Region of Hauts-de-France. Authors would like to thank the European Union funding through the INTERREG Va FWVL BioScreen/SmartBioControl Project. This work has been carried out thanks to the support provided by the Ministries of Europe and Foreign Affairs (MEAE) and Higher Education, Research and Innovation (MESRI) through the program Hubert Curien, Germaine de Staël. Emma Ricart is supported by the SIB Swiss Institute of Bioinformatics Fellowship program. We are grateful to Dr. Areski Flissi for his technical assistance and help in updating the NORINE database.

Supplementary material

13361_2019_2314_MOESM1_ESM.docx (442 kb)
ESM 1 (DOCX 442 kb)

References

  1. 1.
    Caboche, S., Pupin, M., Leclère, V., Fontaine, A., Jacques, P., Kucherov, G.: NORINE: a database of nonribosomal peptides. Nucleic Acids Res. 36, D326–D331 (2007)Google Scholar
  2. 2.
    Flissi, A., Dufresne, Y., Michalik, J., Tonon, L., Janot, S., Noé, L., Jacques, P., Leclére, V., Pupin, M.: Norine, the knowledgebase dedicated to non-ribosomal peptides, is now open to crowdsourcing. Nucleic Acids Res. 44, D1113–D1118 (2016)CrossRefGoogle Scholar
  3. 3.
    Schrader, M., Schulz-Knappe, P., Fricker, L.D.: Historical perspective of peptidomics. EuPA Open Proteomics. 3, 171–182 (2014)CrossRefGoogle Scholar
  4. 4.
    Nicholson, J.K., Lindon, J.C.: Metabonomics. 455, 1054–1056 (2008)Google Scholar
  5. 5.
    Nicholson, J.K., Lindon, J.C., Holmes, E.: “Metabonomics”: understanding the metabolic responses of living systems to path physiological stimul via multivariate statistical analysis of biological NMR spectroscopic data. xenobiotica. 29, 1181–1189 (1999)CrossRefGoogle Scholar
  6. 6.
    Fiehn, O.: Combining geonomics, metabolome analysis, and biochemical modelling to understand metabolic networks. Comp. Funct. Genom. 2, 155–168 (2001)CrossRefGoogle Scholar
  7. 7.
    Hubert, J., Nuzillard, J.M., Renault, J.H.: Dereplication strategies in natural product research: how many tools and methodologies behind the same concept? Phytochem. Rev. 16, 55–95 (2017)CrossRefGoogle Scholar
  8. 8.
    Marston, A., Hostettmann, K.: Natural product analysis over the last decades. Planta Med. 75, 672–682 (2009)CrossRefGoogle Scholar
  9. 9.
    Cho, Y., Ahmed, A., Annana, I., Kim, S.: Developments in FT-ICR MS instrumentation, ionization techniques, and data interpretation methods for petroleomics. Mass Spectrom. Rev. 221–235 (2014).  https://doi.org/10.1002/mas.21438
  10. 10.
    Glish, G.L., Burinsky, D.J.: Hybrid mass spectrometers for tandem mass spectrometry. J. Am. Soc. Mass Spectrom. 19, 161–172 (2008)CrossRefGoogle Scholar
  11. 11.
    Kind, T., Fiehn, O.: Seven golden rules for heuristic filtering of molecular formulas obtained by accurate mass spectrometry. BMC Bioinformatics. 8, 105 (2007)CrossRefGoogle Scholar
  12. 12.
    Rogers, S., Scheltema, R.A., Girolami, M., Breitling, R.: Probabilistic assignment of formulas to mass peaks in metabolomics experiments. Bioinformatics. 25, 512–518 (2009)CrossRefGoogle Scholar
  13. 13.
    Werner, E., Heilier, J.-F., Ducruix, C., Ezan, E., Junot, C., Tabet, J.-C.: Mass spectrometry for the identification of the discriminating signals from metabolomics: current status and future trends. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 871, 143–163 (2008)CrossRefGoogle Scholar
  14. 14.
    Grange, A.H., Genicola, F.A., Sovocool, G.W.: Utility of three types of mass spectrometers for determining elemental compositions of ions formed from chromatographically separated compounds. Rapid Commun. Mass Spectrom. 16, 2356–2369 (2002)CrossRefGoogle Scholar
  15. 15.
    Grange, A.H., Winnik, W., Ferguson, P.L., Sovocool, G.W.: Using a triple-quadrupole mass spectrometer in accurate mass mode and an ion correlation program to identify compounds. Rapid Commun. Mass Spectrom. 19, 2699–2715 (2005)CrossRefGoogle Scholar
  16. 16.
    Böcker, S., Letzel, M.C., Lipták, Z., Pervukhin, A.: SIRIUS: decomposing isotope patterns for metabolite identification. Bioinformatics. 25, 218–224 (2009)CrossRefGoogle Scholar
  17. 17.
    Dittwald, P., Burzykowski, T., Valkenborg, D., Gambin, A.: BRAIN: a universal tool for high-throughput calculations of the isotopic distribution for mass spectrometry. Anal. Chem. (2013).  https://doi.org/10.1021/ac303439m
  18. 18.
    Meija, J., Coplen, T.B., Berglund, M., Brand, W.A., De Bièvre, P., Gröning, M., Holden, N.E., Irrgeher, J., Loss, R.D., Walczyk, T., Prohaska, T.: Atomic Weights of the Elements 2013 (IUPAC Technical Report) (2016).  https://doi.org/10.1515/pac-2015-0305
  19. 19.
    Patiny, L., Borel, A.: ChemCalc: a building block for tomorrow ’ s chemical infrastructure. J. Chem. Inf. Model. 1–21 (2013).  https://doi.org/10.1021/ci300563h
  20. 20.
    Sleno, L.: The use of mass defect in modern mass spectrometry. 226–236 (2012).  https://doi.org/10.1002/jms.2953
  21. 21.
    Kendrick, E.: A mass scale based on CH 2 = 14.0000 for high resolution mass spectrometry of organic compounds. Anal. Chem. 35, 2146–2154 (1963)CrossRefGoogle Scholar
  22. 22.
    Fouquet, T.N.J., Cody, R.B., Ozeki, Y., Kitagawa, S., Ohtani, H., Sato, H.: On the Kendrick mass defect plots of multiply charged polymer ions: splits, misalignments, and how to correct them. J. Am. Soc. Mass Spectrom. 1–16 (2018).  https://doi.org/10.1007/s13361-018-1972-4
  23. 23.
    Roach, P.J., Laskin, J.: And, Laskin, a.: higher-order mass defect analysis for mass spectra of complex organic mixtures. Anal. Chem. 83, 4924–4929 (2011)CrossRefGoogle Scholar
  24. 24.
    Fouquet, T., Sato, H.: Improving the resolution of Kendrick mass defect analysis for polymer ions with fractional base units. Mass Spectrom. 6, A0055–A0055 (2017)CrossRefGoogle Scholar
  25. 25.
    Ohno, T., Parr, T.B., Gruselle, M.-C.I., Fernandez, I.J., Sleighter, R.L., Hatcher, P.G.: Molecular composition and biodegradability of soil organic matter: a case study comparing two New England forest types. Environ. Sci. Technol. 48, 7729–7236 (2014)CrossRefGoogle Scholar
  26. 26.
    Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., Wang, J., Yu, B., Zhang, J., Bryant, S.H.: PubChem substance and compound databases. Nucleic Acids Res. 44, D1202–D1213 (2016)CrossRefGoogle Scholar
  27. 27.
    Yang, J.Y., Sanchez, L.M., Rath, C.M., Liu, X., Boudreau, P.D., Bruns, N., Glukhov, E., Wodtke, A., De Felicio, R., Fenner, A., Wong, W.R., Linington, R.G., Zhang, L., Debonsi, H.M., Gerwick, W.H., Dorrestein, P.C.: Molecular networking as a dereplication strategy. J. Nat. Prod. 76, 1686–1699 (2013)CrossRefGoogle Scholar
  28. 28.
    Editorial: ChemSpider – a tool for Natural Products research, (2015).  https://doi.org/10.1039/c5np90022k

Copyright information

© American Society for Mass Spectrometry 2019

Authors and Affiliations

  • Mickaël Chevalier
    • 1
  • Emma Ricart
    • 2
  • Emeline Hanozin
    • 3
  • Maude Pupin
    • 4
    • 5
  • Philippe Jacques
    • 6
  • Nicolas Smargiasso
    • 3
  • Edwin De Pauw
    • 3
  • Frédérique Lisacek
    • 2
  • Valérie Leclère
    • 1
  • Christophe Flahaut
    • 1
    Email author
  1. 1.Univ. Lille, INRA, ISA, Univ. Artois, Univ. Littoral Côte d’Opale, EA 7394-Institut Charles Viollette (ICV)LilleFrance
  2. 2.Proteome informatics Group, SIB Swiss Institute of Bioinformatics (SIB), and Computer Science DepartmentUniversity of GenevaGenevaSwitzerland
  3. 3.Mass Spectrometry Laboratory, Molecular Systems - MolSys Research UnitUniversity of LiègeLiègeBelgium
  4. 4.Univ. Lille, CNRS, Centrale Lille, UMR 9189 - CRIStAL - Centre de Recherche en Informatique Signal et Automatique de LilleLilleFrance
  5. 5.Inria-Lille Nord Europe, Bonsai teamVilleneuve d’Ascq CedexFrance
  6. 6.TERRA Research Centre, Microbial Processes and Interactions (MiPI), Gembloux Agro-Bio Tech University of LiègeGemblouxBelgium

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