1 research outputs found
Fragment Assembly Approach Based on Graph/Network Theory with Quantum Chemistry Verifications for Assigning Multidimensional NMR Signals in Metabolite Mixtures
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 <sup>13</sup>C-labeled standards. Application
to a <sup>13</sup>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