Reconstruction of HMBC Correlation Networks: A Novel NMR-Based Contribution to Metabolite Mixture Analysis.
Fiche publication
Date publication
février 2018
Journal
Journal of chemical information and modeling
Auteurs
Membres identifiés du Cancéropôle Est :
Dr NUZILLARD Jean-Marc
Tous les auteurs :
Bakiri A, Hubert J, Reynaud R, Lambert C, Martinez A, Renault JH, Nuzillard JM
Lien Pubmed
Résumé
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 H or C 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.).
Mots clés
Algorithms, Biological Products, chemistry, Carbon-13 Magnetic Resonance Spectroscopy, Computer Simulation, Databases, Chemical, Magnetic Resonance Spectroscopy, methods, Metabolomics, Picea, chemistry, Proof of Concept Study, Proton Magnetic Resonance Spectroscopy
Référence
J Chem Inf Model. 2018 Feb 26;58(2):262-270