4.2 Article

Enhancing computer-assisted structure elucidation with DFT analysis of J-couplings

Journal

MAGNETIC RESONANCE IN CHEMISTRY
Volume 58, Issue 6, Pages 594-606

Publisher

WILEY
DOI: 10.1002/mrc.4996

Keywords

H-1; C-13; DFT; natural products; NMR; J-couplings; structure elucidation

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Computer-assisted structure elucidation (CASE) is the class of expert systems that derives molecular structures primarily from one-dimensional and two-dimensional nuclear magnetic resonance data. Contemporary CASE systems, including Advanced Chemistry Development/Structure Elucidator (ACD/SE), consider cross-peaks in heteronuclear multiple bond coherence (HMBC) and correlation spectroscopy (COSY) spectra as two- or three-bond correlations by default. However, four and more bond correlations (nonstandard correlations [NSCs]) could be present in these spectra too. The indiscriminate addition of NSCs to the CASE computations is prohibitively expensive. To address this problem, the ACD/SE program performs a logical analysis of observed correlations and determines the minimum number of NSCs. Guided by this information, a more efficient fuzzy structure generation (FSG) algorithm is subsequently applied. Until now, the FSG algorithm was utilized without any verification of the reliability of found NSCs. Here, we report a verification method for NSCs based on the relationship between NSCs and J-couplings computed with high accuracy density functional theory (DFT) methods. We used the example of strychnine to show that 41 (32%) of 8-Hz HMBC cross-peaks were NSCs and were consistent with (4-6)J(CH) couplings greater than 0.3 Hz. This cutoff value was largely confirmed by the analysis of NSCs in 11 real-world natural products elucidated by ACD/SE. Additionally, utilizing the example of the CASE study of cleospinol A, we showed that the DFT-computed J-couplings of NSCs can distinctively differentiate the correct structure among six proposed isomers. The proposed approach of NSC verification should further improve the robustness of CASE analysis and can help reveal potential problems with reported experimental data.

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