The Effect of Molecular Conformation on the Accuracy of Theoretical ¹H and ¹³C Chemical Shifts Calculated by Ab Initio Methods for Metabolic Mixture Analysis

Abstract

NMR spectroscopy is a powerful method for analyzing metabolic mixtures. The information obtained from an NMR spectrum is in the form of physical parameters, such as chemical shifts, and construction of databases for many metabolites will be useful for data interpretation. To increase the accuracy of theoretical chemical shifts for development of a database for a variety of metabolites, the effects of sets of conformations (structural ensembles) and the levels of theory on computations of theoretical chemical shifts were systematically investigated for a set of 29 small molecules in the present study. For each of the 29 compounds, 101 structures were generated by classical molecular dynamics at 298.15 K, and then theoretical chemical shifts for 164 (1)H and 123 (13)C atoms were calculated by ab initio quantum chemical methods. Six levels of theory were used by pairing Hartree-Fock, B3LYP (density functional theory), or second order Møller-Plesset perturbation with 6-31G or aug-cc-pVDZ basis set. The six average fluctuations in the (1)H chemical shift were ±0.63, ± 0.59, ± 0.70, ± 0.62, ± 0.75, and ±0.66 ppm for the structural ensembles, and the six average errors were ±0.34, ± 0.27, ± 0.32, ± 0.25, ± 0.32, and ±0.25 ppm. The results showed that chemical shift fluctuations with changes in the conformation because of molecular motion were larger than the differences between computed and experimental chemical shifts for all six levels of theory. In conclusion, selection of an appropriate structural ensemble should be performed before theoretical chemical shift calculations for development of an accurate database for a variety of metabolites.

Structure Elucidation of Unknown Metabolites in Metabolomics by Combined NMR and MS/MS Prediction

“…Each prediction takes about 10 s on a desktop computer. To get more accurate chemical shift values, one could use quantum chemical calculations at the expense of calculation time [ 31 , 32 ]. To make NMR parameters even more selective, one could include additional NMR parameters, in particularly the connectivity information retrieved from 2D 1 H- 1 H TOCSY [ 23 ], 2D 13 C- 1 H HSQC-TOCSY [ 33 ] or 2D 13 C- 1 H HMBC [ 24 ].…”

Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods

“…Identification of unknowns clearly remains one of the biggest challenges of present day metabolomics. The combined use of MS and NMR methods to the same sample,[43, 44] along with advanced statistical,[45–48] cheminformatics[27],[35] and spectral prediction methods[49,50], promises to considerably shorten the arduous, but rewarding path to the structure determination of unknowns. All these scientific and technological advances provide rich and urgently needed information about the metabolic make-up of life.…”

NMR-TS: de novo molecule identification from NMR spectra

“…To date, predicting the molecules in a sample from its NMR spectrum has mainly been performed based on databases [8][9][10]. For example, to identify the metabolites in a sample, methods for accurately calculating chemical shifts [11,12] and for predicting molecules using specific databases [13][14][15][16] have been proposed. However, these methods are not effective for unknown molecules whose spectra are not registered in the database.…”