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

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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https://hal.univ-reims.fr/hal-01692926
Contributor : Jean-Hugues Renault <>
Submitted on : Thursday, January 25, 2018 - 3:40:24 PM
Last modification on : Thursday, September 10, 2020 - 4:12:48 PM

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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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