Fragment Assembly Approach Based on Graph/Network Theory with Quantum Chemistry Verifications for Assigning Multidimensional NMR Signals in Metabolite Mixtures

ACS Chemical Biology
Kengo ItoJun Kikuchi

Abstract

The abundant observation of chemical fragment information for molecular complexities is a major advantage of biological NMR analysis. Thus, the development of a novel technique for NMR signal assignment and metabolite identification may offer new possibilities for exploring molecular complexities. We propose a new signal assignment approach for metabolite mixtures by assembling H-H, H-C, C-C, and Q-C fragmental information obtained by multidimensional NMR, followed by the application of graph and network theory. High-speed experiments and complete automatic signal assignments were achieved for 12 combined mixtures of (13)C-labeled standards. Application to a (13)C-labeled seaweed extract showed 66 H-C, 60 H-H, 326 C-C, and 28 Q-C correlations, which were successfully assembled to 18 metabolites by the automatic assignment. The validity of automatic assignment was supported by quantum chemical calculations. This new approach can predict entire metabolite structures from peak networks of biological extracts.

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Citations

Aug 24, 2016·Current Opinion in Biotechnology·Kerem Bingol, Rafael Brüschweiler
Sep 18, 2020·Science and Technology of Advanced Materials·Jinzhe ZhangKoji Tsuda
Feb 7, 2018·Progress in Nuclear Magnetic Resonance Spectroscopy·Jun KikuchiYasuhiro Date
Oct 8, 2018·Analytica Chimica Acta·Taiga AsakuraJun Kikuchi

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