Startseite Computational methods for NMR and MS for structure elucidation II: database resources and advanced methods
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Computational methods for NMR and MS for structure elucidation II: database resources and advanced methods

  • Marilia Valli EMAIL logo , Helena Mannochio Russo , Alan Cesar Pilon , Meri Emili Ferreira Pinto , Nathalia B. Dias , Rafael Teixeira Freire , Ian Castro-Gamboa und Vanderlan da Silva Bolzani EMAIL logo
Veröffentlicht/Copyright: 31. August 2019
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Abstract

Technological advances have contributed to the evolution of the natural product chemistry and drug discovery programs. Recently, computational methods for nuclear magnetic resonance (NMR) and mass spectrometry (MS) have speeded up and facilitated the process of structural elucidation even in high complex biological samples. In this chapter, the current computational tools related to NMR and MS databases and spectral similarity networks, as well as their applications on dereplication and determination of biological biomarkers, are addressed.

Acknowledgements

The authors acknowledge Fundação de Amparo à Pesquisa do Estado de São Paulo(FAPESP) grants#2013/07600-3 (CIBFar-CEPID), #2014/50926-0 (INCT BioNat CNPq/FAPESP), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Termo de Execução Descentralizado Arbocontrol #74/2016 for grant support and research fellowships. Authors acknowledge scholarships: MV (CNPQ #167874/2014-4 and #152243/2016-0; Finatec #120/2017), HMR (CNPQ #142014/2018-4), ACP (Fapesp #2016/13292-8), MEFP (Fapesp #2017/17098-4).

References

[1] Duarte DF. Opium and opioids: a brief history. Rev Bras Anestesiol. 2005;55:135–46.10.1590/S0034-70942005000100015Suche in Google Scholar PubMed

[2] Dunn WB, Broadhurst DI, Atherton HJ, Goodacre R, Griffin JL. System level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear resonance spectroscopy. Chem Soc Rev. 2011;40:387–426.10.1039/B906712BSuche in Google Scholar PubMed

[3] Batista JM, Jr, Blanch EW, Bolzani VS. Recent advances in the use of vibrational chiroptical spectroscopy methods for stereochemical characterization of natural products. Nat Prod Rep. 2015;32:1280–302.10.1039/C5NP00027KSuche in Google Scholar PubMed

[4] Pilon AC, Valli M, Dametto AC, Pinto MEF, Freire RT, Castro-Gamboa I, et al. NuBBEDB: an updated database to uncover chemical and biological information from Brazilian biodiversity. Sci Rep. 2017;7:7215.10.1038/s41598-017-07451-xSuche in Google Scholar PubMed

[5] Griffiths J. A brief history of mass spectrometry. Anal Chem. 2008;80:5678–83.10.1021/ac8013065Suche in Google Scholar PubMed

[6] Pilon AC, Paez-Garcia A, Pavarini DP, Scotti MT. Chemical biology databases in mass spectrometry. Iin: chemical biology: evolving applications. Cambridge, UK: Royal Society of Chemistry, 2018:221–63.Suche in Google Scholar

[7] Whisart DS. Computational strategies for metabolite identification in metabolomics. Bioanalysis. 2009;1:1579–96.10.4155/bio.09.138Suche in Google Scholar PubMed

[8] Fayyad U, Stolorz P. Data mining and KDD: promise and challenges. Fut Gen Comp Syst. 1997;13:99–115.10.1016/S0167-739X(97)00015-0Suche in Google Scholar

[9] Capra F. The web of life: a new scientific understanding of living systems. New York-USA: Anchor Books, 1996:1–347.Suche in Google Scholar

[10] O’Boyle NM, Banck M, James C, Morley C, Vandermeersch T, Hutchison GR. Open Babel: a chemical toolbox. J Cheminform. 2011;3:33.10.1186/1758-2946-3-33Suche in Google Scholar PubMed PubMed Central

[11] Valli M, Dos Santos RN, Figueira LD, Nakajima CH, Castro-Gamboa I, Andricopulo AD, et al. Development of a natural products database from the biodiversity of Brazil. J Nat Prod. 2013;76:439–4410.1021/np3006875Suche in Google Scholar PubMed

[12] Weber RJM, Lawson TN, Salek RM, Ebbels TMD, Glen RC, Goodacre R, et al. Computational tools and workflows in metabolomics: an international survey highlights the opportunity for harmonisation through Galaxy. Metabolomics. 2017;13:12.10.1007/s11306-016-1147-xSuche in Google Scholar PubMed PubMed Central

[13] Lewis IA, Schommer SC, Markley JL. rNMR: open source software for identifying and quantifying metabolites in NMR spectra. Mag Res Chem. 2009;47:123–6.10.1002/mrc.2526Suche in Google Scholar PubMed PubMed Central

[14] Plainchont B, Nuzillard JM, Rodrigues GV, Ferreira MJ, Scotti MT, Emerenciano VP. New improvements in automatic structure elucidation using the LSD (Logic for Structure Determination) and the SISTEMAT expert systems. Nat Prod Comm. 2010;5:763–70.10.1177/1934578X1000500517Suche in Google Scholar

[15] Steinbeck C. Recent developments in automated structure elucidation of natural products. Nat Prod Rep. 2004;21:512–18.10.1039/b400678jSuche in Google Scholar PubMed

[16] Robinette SL, Zhang F, Brüschweiler-Li L, Brüschweiler R. Web server based complex mixture analysis by NMR. Anal Chem. 2008;80:3606–11.10.1021/ac702530tSuche in Google Scholar PubMed

[17] Xia J, Bjorndahl TC, Tang P, Wishart DS. MetaboMiner–semi-automated identification of metabolites from 2D NMR spectra of complex biofluids. BMC Bioinformatics. 2008;28:507.10.1186/1471-2105-9-507Suche in Google Scholar PubMed PubMed Central

[18] Tulpan D, Léger S, Belliveau L, Culf A, Cuperlović-Culf M. MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures. BMC Bioinformatics. 2011;14:400.10.1186/1471-2105-12-400Suche in Google Scholar PubMed PubMed Central

[19] Freire RT, Castro-Gamboa I São Paulo State University, assignee. “Method of analysis’ pattern recognition and computer program”. Patent WIPO WO2016183647A1 PCT/BR2015/000075. 2015.Suche in Google Scholar

[20] Gaudêncio SP, Pereira F. Dereplication: racing to speed up the natural products discovery process. Nat Prod Rep. 2015;32:755–878.10.1039/C4NP00134FSuche in Google Scholar PubMed

[21] Hubert J, Nuzillard JM, Renault JH. Dereplication strategies in natural product research: how many tools and methodologies behind the same concept? Phytochem Rev. 2017;16:55–95.10.1007/s11101-015-9448-7Suche in Google Scholar

[22] Hanka LJ, Kuentzel SL, Martin DG, Wiley PF, Neil GL. Detection and assay of antitumor antibiotics. In: Carter SK, Umezawa H, Douros J, Sakurai Y, editor(s). Antitumor antibiotics. Berlin, Heidelberg: Springer Berlin Heidelberg, 1978:69–76.10.1007/978-3-642-81219-4_7Suche in Google Scholar PubMed

[23] Beutler JA, Alvarado AB, Schaufelberger DE, Andrews P, McCloud TG. Dereplication of phorbol bioactives: lyngbya majuscula and croton cuneatus. J Nat Prod. 1990;53:867–74.10.1021/np50070a014Suche in Google Scholar PubMed

[24] Yang JY, Sanchez LM, Rath CM, Liu X, Boudreau PD, Bruns N, et al. Molecular networking as a dereplication strategy. J Nat Prod. 2013;76:1686–99.10.1021/np400413sSuche in Google Scholar PubMed

[25] Queiroz MMF, Queiroz EF, Zeraik ML, Ebrahimi SN, Marcourt L, Cuendet M, et al. Chemical composition of the bark of Tetrapterys mucronata and identification of acetylcholinesterase inhibitory constituents. J Nat Prod. 2014;77:650–6.10.1021/np401003pSuche in Google Scholar PubMed

[26] Zhang J-G, Huang X-Y, Ma Y-B, Zhang X-M, Chen -J-J, Geng C-A. Dereplication-guided isolation of a new indole alkaloid triglycoside from the hooks of Uncaria rhynchophylla by LC with ion trap time-of-flight MS. J Sep Sci. 2018;41:1532–8.10.1002/jssc.201701175Suche in Google Scholar PubMed

[27] Nielsen KF, Smedsgaard J. Fungal metabolite screening: database of 474 mycotoxins and fungal metabolites for dereplication by standardised liquid chromatography-UV-mass spectrometry methodology. J Chrom A. 2003;1002:111–36.10.1016/S0021-9673(03)00490-4Suche in Google Scholar

[28] Fraige K, Dametto AC, Zeraik ML, de Freitas L, Saraiva AC, Medeiros AI, et al. Dereplication by HPLC-DAD-ESI-MS/MS and screening for biological activities of Byrsonima Species (Malpighiaceae). Phytochem Anal. 2017;29:196–204.10.1002/pca.2734Suche in Google Scholar PubMed

[29] Brkljača R, Göker ES, Urban S. Dereplication and chemotaxonomical studies of marine algae of the ochrophyta and rhodophyta phyla. Mar Drugs. 2015;13:2714–31.10.3390/md13052714Suche in Google Scholar PubMed PubMed Central

[30] Dos Santos VS, Macedo FA, Vale JS, Silva DB, Carollo CA. Metabolomics as a tool for understanding the evolution of Tabebuia sensu lato. Metabolomics. 2017;13:72.10.1007/s11306-017-1209-8Suche in Google Scholar

[31] Quinn RA, Nothias LF, Vining O, Meehan M, Esquenazi E, Dorrestein PC. Molecular networking as a drug discovery, drug metabolism, and precision medicine strategy. Trend Pharm Sci. 2017;38:143–54.10.1016/j.tips.2016.10.011Suche in Google Scholar PubMed

[32] Da Silva RR, Dorrestein PC, Quinn RA. Illuminating the dark matter in metabolomics. Proc Nat Acad Sci USA. 2015;112:12549–50.10.1073/pnas.1516878112Suche in Google Scholar PubMed PubMed Central

[33] Dunkel M, Fullbeck M, Neumann S, Preissner R. SuperNatural: a searchable database of available natural compounds. Nucleic Acids Res. 2006;34:D678–83.10.1093/nar/gkj132Suche in Google Scholar PubMed PubMed Central

[34] Banerjee P, Erehman J, Gohlke BO, Wilhelm T, Preissner R, Dunkel M. Super Natural II-a database of natural products. Nucleic Acids Res. 2015;43:D935–39.10.1093/nar/gku886Suche in Google Scholar PubMed PubMed Central

[35] Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: asoftware environment for integrated models of biomolecular interaction networks. Gen Res. 2003;13:2498–504.10.1101/gr.1239303Suche in Google Scholar PubMed PubMed Central

[36] Horai H, Arita M, Kanaya S, Nihei Y, Ikeda T, Suwa K, et al. MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spec. 2010;45:703–14.10.1002/jms.1777Suche in Google Scholar PubMed

[37] Sawada Y, Nakabayashi R, Yamada Y, Suzuki M, Sato M, Sakata A, et al. RIKEN tandem mass spectral database (ReSpect) for phytochemicals: A plant-specific MS/MS-based data resource and database. Phytochemistry. 2012;82:38–45.10.1016/j.phytochem.2012.07.007Suche in Google Scholar PubMed

[38] Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N, Peng Y, et al. Sharing and community curation of mass spectrometry data with global natural products social molecular networking. Nature Biotech. 2016;34:828–37.10.1038/nbt.3597Suche in Google Scholar PubMed PubMed Central

[39] Szabó LF. Rigorous biogenetic network for a group of indole alkaloids derived from strictosidine. Molecules. 2008;13:1875–96.10.3390/molecules13081875Suche in Google Scholar PubMed PubMed Central

[40] Fox Ramos AE, Alcover C, Evanno L, Maciuk A, Litaudon M, Duplais C, et al. Revisiting previously investigated plants: a molecular networking-based study of geissospermum leave. J Nat Prod. 2017;80:1007–1410.1021/acs.jnatprod.6b01013Suche in Google Scholar PubMed

[41] Lima JA, Costa TWR, Silva LL, Miranda ALP, Pinto AC. Antinociceptive and anti-inflammatory effects of a Geissospermum vellosii stem bark fraction. Anal Acad Bras Cien. 2016;88:237–48.10.1590/0001-3765201520140374Suche in Google Scholar PubMed

[42] Mbeunkui F, Grace MH, Lategan C, Smith PJ, Raskin I, Lila MA. In vitro antiplasmodial activity of indole alkaloids from the stem bark of Geissospermum vellosii. J Ethnopharm. 2012;139:471–7.10.1016/j.jep.2011.11.036Suche in Google Scholar PubMed

[43] Floros DJ, Jensen PR, Dorrestein PC, Koyama N. A metabolomics guided exploration of marine natural product chemical space. Metabolomics. 2016;12:145.10.1007/s11306-016-1087-5Suche in Google Scholar PubMed PubMed Central

[44] Crüsemann M, O’Neill EC, Larson CB, Melnik AV, Floros DJ, Da Silva RR, et al. Prioritizing natural product diversity in a collection of 146 bacterial strains based on growth and extraction protocols. J Nat Prod. 2017;80:588–97.10.1021/acs.jnatprod.6b00722Suche in Google Scholar PubMed PubMed Central

[45] Olivon F, Apel C, Retailleau P, Allard PM, Wolfender JL, Touboul D, et al. Searching for original natural products by molecular networking: detection, isolation and total synthesis of chloroaustralasines. Org Chem Front. 2018;5:2171–8.10.1039/C8QO00429CSuche in Google Scholar

[46] Pluskal T, Castillo S, Villar-Briones A, Orešič M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics. 2010;11:395.10.1186/1471-2105-11-395Suche in Google Scholar PubMed PubMed Central

[47] Nothias L-F-F, Nothias-Esposito M, Da Silva R, Wang M, Protsyuk I, Zhang Z, et al. Bioactivity-based molecular networking for the discovery of drug leads in natural product bioassay-guided fractionation. J Nat Prod. 2018;81:758–67.10.1021/acs.jnatprod.7b00737Suche in Google Scholar PubMed

[48] Esposito M, Nothias L-F, Nedev H, Gallard J-F, Leyssen P, Retailleau P, et al. Euphorbia dendroides latex as a source of jatrophane esters: isolation, structural analysis, conformational study, and Anti-CHIKV activity. J Nat Prod. 2016;79:2873–82.10.1021/acs.jnatprod.6b00644Suche in Google Scholar PubMed

[49] L-F N-S, Dumontet V, Neyts J, Roussi F, Costa J, Leyssen P, et al. LC-MS2-Based dereplication of Euphorbia extracts with anti-Chikungunya virus activity. Fitoterapia. 2015;105:202–9.10.1016/j.fitote.2015.06.021Suche in Google Scholar PubMed

[50] Kellogg JJ, Todd DA, Egan JM, Raja HA, Oberlies NH, Kvalheim OM, et al. Biochemometrics for natural products research: comparison of data analysis approaches and application to identification of bioactive compounds. J Nat Prod. 2016;79:376–86.10.1021/acs.jnatprod.5b01014Suche in Google Scholar PubMed PubMed Central

Published Online: 2019-08-31

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Heruntergeladen am 16.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/psr-2018-0167/pdf
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