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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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Contributor : Jean-Hugues Renault Connect in order to contact the contributor
Submitted on : Monday, September 13, 2021 - 10:46:51 AM
Last modification on : Tuesday, January 4, 2022 - 5:06:28 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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