Literature DB >> 26963288

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

Eisuke Chikayama1,2, Yudai Shimbo3, Keiko Komatsu1, Jun Kikuchi1,4,5.   

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.

Mesh:

Year:  2016        PMID: 26963288     DOI: 10.1021/acs.jpcb.5b12748

Source DB:  PubMed          Journal:  J Phys Chem B        ISSN: 1520-5207            Impact factor:   2.991


  4 in total

Review 1.  Knowns and unknowns in metabolomics identified by multidimensional NMR and hybrid MS/NMR methods.

Authors:  Kerem Bingol; Rafael Brüschweiler
Journal:  Curr Opin Biotechnol       Date:  2016-08-20       Impact factor: 9.740

2.  Exploratory machine-learned theoretical chemical shifts can closely predict metabolic mixture signals.

Authors:  Kengo Ito; Yuka Obuchi; Eisuke Chikayama; Yasuhiro Date; Jun Kikuchi
Journal:  Chem Sci       Date:  2018-09-10       Impact factor: 9.825

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

Authors:  Rene M Boiteau; David W Hoyt; Carrie D Nicora; Hannah A Kinmonth-Schultz; Joy K Ward; Kerem Bingol
Journal:  Metabolites       Date:  2018-01-17

4.  NMR-TS: de novo molecule identification from NMR spectra.

Authors:  Jinzhe Zhang; Kei Terayama; Masato Sumita; Kazuki Yoshizoe; Kengo Ito; Jun Kikuchi; Koji Tsuda
Journal:  Sci Technol Adv Mater       Date:  2020-07-30       Impact factor: 8.090

  4 in total

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