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Journal Articles Journal of Chemical Information and Modeling Year : 2018

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

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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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Dates and versions

hal-01692926 , version 1 (13-09-2021)

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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, 2018, 58 (2), pp.262-270. ⟨10.1021/acs.jcim.7b00653⟩. ⟨hal-01692926⟩
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