Research Article

Unequivocal determination of complex molecular structures using anisotropic NMR measurements

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Science  07 Apr 2017:
Vol. 356, Issue 6333, eaam5349
DOI: 10.1126/science.aam5349

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Picking structures out of a lineup

Pharmaceutical research relies critically on determining the correct structures of numerous complex molecules. When well-ordered crystals are not available for x-ray analysis, nuclear magnetic resonance (NMR) spectroscopy is the most common structure-elucidation method. However, sometimes it is hard to distinguish isomers with similar spectra. Liu et al. showcase a protocol that combines computer modeling with anisotropic NMR data acquired using gel-aligned samples. Because of its uniform sensitivity to relative bond orientations across the whole molecular framework, the method overcomes common pitfalls that can lead to invalid structure assignments.

Science, this issue p. eaam5349

Structured Abstract


Single-crystal x-ray diffraction studies represent the gold standard for unequivocal establishment of molecular structure and configuration. For molecules that will not crystallize or that form poorly-diffracting crystals, alternative methods must be used. Crystalline sponges and atomic force microscopy are techniques with increasing potential, although nuclear magnetic resonance (NMR) spectroscopy methods provide the primary viable alternative means to determine molecular structures. However, misinterpretation of NMR data—as a result of poor data quality, inappropriate experiment selection, or investigator bias—has led to burgeoning numbers of structure revision reports. Clearly, the development of a method to more effectively use NMR data and simultaneously quell reports of incorrect structures would be highly beneficial.


Combining computer-assisted structure elucidation (CASE) algorithms and density functional theory (DFT) calculations with measured anisotropic NMR parameters, specifically residual dipolar coupling (RDC), and residual chemical shift anisotropy (RCSA) holds strong promise as an effective alternative means of assigning three-dimensional (3D) molecular structures. Anisotropic NMR data provide a spatial view of the relative orientations between bonds (RDCs) and chemical shielding tensors (RCSAs), regardless of the separation between the bonds and atoms, respectively. Hence, these data are sensitive reporters of global structural validity. The combination of DFT calculations and anisotropic NMR data represents an orthogonal approach to conventional NMR data interpretation that is not subject to the interpretational biases of human investigators and, as such, mitigates the risk of incorrect structure assignments.


Anisotropic NMR data can be used directly to evaluate the validity of investigator-proposed structures or can be combined with a CASE program in conjunction with DFT calculations for both structural proposal and validation. The RDC data are typically used to structurally define C-H bond vectors, whereas the RCSA data report on the chemical shift tensors of both protonated and nonprotonated carbons, the latter only accessible by long-range RDC data that are difficult to measure and interpret. These data are used to evaluate a given structure proposal on the basis of the agreement between the experimentally measured data and theoretical values calculated for the corresponding 3D DFT models. When structures generated by a CASE program are being considered, the method only requires a multidimensional NMR data set of sufficient quality and sophistication to allow the CASE program to generate a set of proposals that contains the correct structure of the molecule. The molecules being studied should also be amenable to modern DFT calculations for 3D model building. The CASE program output is sorted on the basis of cumulative error between experimental and calculated 13C data for the ensemble of structures generated, and the best-fitting molecules are subsequently subjected to DFT calculation for analysis. Results obtained using the proposed method demonstrate its applicability to a diverse range of complex molecules, each of which challenged the investigators originally reporting the structures.


The technique described here represents a potential paradigm shift from conventional NMR data interpretation and can provide an unequivocal and unbiased confirmation of interatomic connectivity and relative configuration for organic and natural product structures.

The principle of residual dipolar coupling (RDC)–based model differentiation is shown using aquatolide as an example.

The revised structure of aquatolide is shown on the top left, with the originally reported structure shown on the bottom left. The selected C-H bond vectors in the two structures have different orientations, as is evident after translating them to the same origin in the middle diagrams. Theoretical RDC values associated with these vectors can be calculated for each model on the basis of the experimentally determined alignment tensor. Correlation data are shown for only the four highlighted CH groups, although the alignment tensor was actually determined using all available data. The originally proposed (incorrect) structure clearly shows poorer agreement between the calculated and experimental data.


Assignment of complex molecular structures from nuclear magnetic resonance (NMR) data can be prone to interpretational mistakes. Residual dipolar couplings and residual chemical shift anisotropy provide a spatial view of the relative orientations between bonds and chemical shielding tensors, respectively, regardless of separation. Consequently, these data constitute a reliable reporter of global structural validity. Anisotropic NMR parameters can be used to evaluate investigators’ structure proposals or structures generated by computer-assisted structure elucidation. Application of the method to several complex structure assignment problems shows promising results that signal a potential paradigm shift from conventional NMR data interpretation, which may be of particular utility for compounds not amenable to x-ray crystallography.

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