Reconstruction of HMBC Correlation Networks: A Novel NMR-based Contribution to Metabolite Mixture Analysis

Ali Bakiri 1, 2 Jane Hubert 1 Romain Reynaud 2 Carole Lambert 2 Agathe Martinez 1 Jean-Hugues Renault 1 Jean-Marc Nuzillard 1
1 ICMR - Institut de Chimie Moléculaire de Reims - UMR 7312
2 Givaudan ACI, Soliance
Abstract : A new in silico method is introduced for the dereplication of natural metabolite mixtures based on HMBC and HSQC spectra that inform about short-range and long-range H–C correlations occurring in the carbon skeleton of individual chemical entities. Starting from the HMBC spectrum of a metabolite mixture, an algorithm was developed in order to recover individualized HMBC footprints of the mixture constituents. The collected H–C correlations are represented by a network of NMR peaks connected to each other when sharing either a 1H or 13C chemical shift value. The network obtained is then divided into clusters using a community detection algorithm, and finally each cluster is tentatively assigned to a molecular structure by means of a NMR chemical shift database containing the theoretical HMBC and HSQC correlation data of a range of natural metabolites. The proof of principle of this method is demonstrated on a model mixture of 3 known natural compounds and then on a real-life bark extract obtained from the common spruce (Picea abies L.).
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Journal articles
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https://hal.univ-reims.fr/hal-01692926
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Submitted on : Thursday, January 25, 2018 - 3:40:24 PM
Last modification on : Tuesday, January 7, 2020 - 11:33:50 AM

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Ali Bakiri, Jane Hubert, Romain Reynaud, Carole Lambert, Agathe Martinez, et al.. Reconstruction of HMBC Correlation Networks: A Novel NMR-based Contribution to Metabolite Mixture Analysis. Journal of Chemical Information and Modeling, American Chemical Society, 2018, 58 (2), pp.262-270. ⟨10.1021/acs.jcim.7b00653⟩. ⟨hal-01692926⟩

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