RESEARCH ARTICLE SUMMARY◥

ORGANIC CHEMISTRY

Unequivocal determination ofcomplex molecular structures usinganisotropic NMR measurementsYizhou Liu, Josep Saurí, Emily Mevers, Mark W. Peczuh, Henk Hiemstra, Jon Clardy,Gary E. Martin,* R. Thomas Williamson*

INTRODUCTION: Single-crystal x-ray diffrac-tion studies represent the gold standard forunequivocal establishment of molecular struc-ture and configuration. For molecules that willnot crystallize or that form poorly-diffractingcrystals, alternativemethodsmust beused. Crys-talline sponges and atomic force microscopyare techniques with increasing potential, al-thoughnuclearmagnetic resonance (NMR) spec-troscopy methods provide the primary viablealternativemeans to determinemolecular struc-tures.However,misinterpretationofNMRdata—as a result of poor data quality, inappropriate

experiment selection, or investigator bias—hasled to burgeoning numbers of structure revisionreports. Clearly, the development of amethod tomore effectively use NMR data and simulta-neously quell reports of incorrect structureswould be highly beneficial.

RATIONALE: Combining computer-assistedstructureelucidation (CASE)algorithmsandden-sity functional theory (DFT) calculations withmeasured anisotropic NMR parameters, spe-cifically residual dipolar coupling (RDC), andresidual chemical shift anisotropy (RCSA) holds

strongpromise as an effective alternativemeansof assigning three-dimensional (3D) molecularstructures. Anisotropic NMRdata provide a spa-tial view of the relative orientations betweenbonds (RDCs) and chemical shielding tensors(RCSAs), regardless of the separation betweenthe bonds and atoms, respectively.Hence, thesedata are sensitive reporters of global structuralvalidity. The combination ofDFT calculations andanisotropic NMR data represents an orthog-onal approach to conventional NMRdata inter-pretation that isnot subject to the interpretationalbiases of human investigators and, as such, mit-igates the risk of incorrect structure assignments.

RESULTS: Anisotropic NMR data can be useddirectly to evaluate the validity of investigator-proposed structures or can be combined witha CASE program in conjunction with DFT cal-

culations forboth structuralproposalandvalidation.TheRDC data are typically usedto structurally define C-Hbond vectors, whereas theRCSA data report on thechemical shift tensors of

both protonated and nonprotonated carbons,the latter only accessible by long-range RDCdata that are difficult to measure and inter-pret. These data are used to evaluate a givenstructureproposal on thebasis of the agreementbetween the experimentallymeasured data andtheoretical values calculated for the correspond-ing 3DDFTmodels.When structures generatedby a CASE program are being considered, themethodonly requires amultidimensionalNMRdata set of sufficient quality and sophisticationto allow the CASE program to generate a set ofproposals that contains the correct structure ofthemolecule.Themoleculesbeingstudiedshouldalso be amenable tomodernDFT calculations for3D model building. The CASE program output issorted on the basis of cumulative error betweenexperimental and calculated 13C data for theensemble of structures generated, and the best-fitting molecules are subsequently subjected toDFT calculation for analysis. Results obtainedusing the proposed method demonstrate itsapplicability to a diverse range of complex mol-ecules, each of which challenged the inves-tigators originally reporting the structures.

CONCLUSION: The technique described hererepresents a potential paradigm shift fromconventional NMR data interpretation andcan provide an unequivocal and unbiased con-firmation of interatomic connectivity and rel-ative configuration for organic and naturalproduct structures.▪

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Liu et al., Science 356, 43 (2017) 7 April 2017 1 of 1

The list of author affiliations is available in the full article online.*Corresponding author. Email: gary.martin2@merck.com(G.E.M.); robert.williamson2@merck.com (R.T.W.)Cite this article as Y. Liu et al., Science 356, eaam5349(2017). DOI: 10.1126/science.aam5349

The principle of residual dipolar coupling (RDC)–based model differentiation is shown usingaquatolide as an example.The revised structure of aquatolide is shown on the top left, with theoriginally reported structure shown on the bottom left. The selected C-H bond vectors in the twostructures have different orientations, as is evident after translating them to the same origin in themiddle diagrams.Theoretical RDC values associated with these vectors can be calculated for eachmodel on the basis of the experimentally determined alignment tensor. Correlation data are shownfor only the four highlighted CH groups, although the alignment tensor was actually determinedusing all available data. The originally proposed (incorrect) structure clearly shows poorer agree-ment between the calculated and experimental data.

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RESEARCH ARTICLE◥

ORGANIC CHEMISTRY

Unequivocal determination ofcomplex molecular structures usinganisotropic NMR measurementsYizhou Liu,1 Josep Saurí,2 Emily Mevers,3 Mark W. Peczuh,4 Henk Hiemstra,5

Jon Clardy,3 Gary E. Martin,6* R. Thomas Williamson6*

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

Single-crystal x-ray diffraction is unquestion-ably the gold standard in structure elucida-tion. However, a crystal that diffracts wellis not always easily obtained, and manycompounds, especially natural products,

are isolated and purified in quantities too smallfor conventional crystallization screening. Forsomemolecules that will not crystallize directly,crystalline sponges offer an exciting possibility,although currently, the sample-soaking step re-quires careful optimization and serendipity on aper-molecule basis (1). On adifferent front, atomicforce microscopy with atomic resolution (2) hasreached thebreakthroughpoint of enabling visual-ization of individual atoms, but the current state ofthe art requires that the molecule under studyadopt a planar structure for quality resolution (3,4).In the realm of spectroscopicmethods, nuclear

magnetic resonance (NMR) is the primary tech-nique for full structure elucidation. However, con-ventional NMR has inherent limitations subjectto the interpretational biases of the investigator.

A recent SciFinder search under the keywords“structure revision” revealed >1200 reports, in-cluding 39 in 2016, with five of these examplesappearing in a single week inOrganic Letters andthe Journal of Organic Chemistry (5–9). Clearly,a more objective and robust protocol for NMRstructural determination not hampered by inter-pretational difficulties is highly desirable.TraditionalNMRstructural elucidation is essen-

tially a reverse-engineering process—i.e., one thatdeduces the actual structure in a puzzle-solvingfashion, starting from various pieces of exper-imental data. For example, proton and carbonchemical shifts are first measured, providinginformation on the numbers of atoms and typesof functional groups. Next, experiments based onscalar (J ) couplings, such as homonuclear corre-lation spectroscopy (COSY) and heteronuclearmultiple-bond correlation (HMBC), are used toestablish connectivities between these groups.Distance constraints afforded by nuclear Over-hauser effect spectroscopy (NOESY) and rotat-ing frameOverhauser effect spectroscopy (ROESY)can also be employed to further facilitate the as-sembly of connectivity networks and to define athree-dimensional (3D) configuration. Unfortu-nately, prejudices of the investigator can intrudeat any point in this process.Here we describe an orthogonal check on the

validity ofmolecular structure assignments basedon anisotropic NMRparameters, namely residualdipolar coupling (RDC) (10–12) and residualchemical shift anisotropy (RCSA) (13–16). Thesedata provide 3D information on relative orienta-tions of different bonds and chemical shieldingtensors in the molecule. Because RDC and RCSAdata are insensitive to the distances between thebonds and atoms, respectively, they reveal angular

relationships of various structural elements fromall positions of a molecule and therefore reflectthe correct overall structure without being subjectto investigator bias.

Fundamental principles of combiningCASE and DFTcalculations with RDCand RSCA measurements

Over the past decade, advances in computer algo-rithms (17–19) and quantum chemistry computa-tionalmethods (20–23) have underpinned a trendin structural elucidation that could be termed“forward engineering.” Rather than striving toassemble the correct structure in a single strokeusing all available information, all possible struc-tures consistent with the ensemble of availableexperimentaldataare first assembledbyacomputer-assisted structure elucidation (CASE) algorithmusing homo- and heteronuclear shift correlationdata (17–19). Next, theoretical values associatedwith critical measurements (i.e., 1H and 13C NMRchemical shifts) are calculated from each of theproposed structural candidates through densityfunctional theory (DFT). Structures generated bythe program are finally sorted on the basis of thecongruence of the experimental and calculated13C chemical shift data, leading to the selection ofthe best candidate or candidates.Clearly, the CASE software undertakes some of

the reverse-engineering task in its black-box suiteof algorithms.However, in the approach presentedhere, we are not so concerned with obtaining theexact structure, but more with covering sufficientchemical space so that the correct structure iscontained in the ensemble of structural proposalsgenerated by the program. Inclusion of additionallong-range 1H-13C correlations absent in conven-tional HMBC experiments but available fromnewer NMR experiments such as LR-HSQMBC(long-range heteronuclear single quantummultiple-bond correlation) (24), and 1/n-bond 13C-13C correla-tions available from 1,1/n-HD-ADEQUATE (adequatesensitivity double-quantum spectroscopy) experi-ments (25–27), can greatly facilitate candidate gen-eration or narrow down the range of candidates inthe CASE output (28, 29).As an alternative to using a CASE algorithm,

candidate structures deduced by an investigatorcan also be examined using anisotropic NMRdata. Examples of bothCASE-generated structuresand those deduced by an investigator will bediscussed.Typically, the top candidates from the CASE

programor those deduced by the investigator aresubjected to DFT geometry optimization to obtain3D structures. These calculations can be a rate-limiting step if too many candidates are to beevaluated; however, on modern computationalclusters, they can often be completed in a matterof hours, and optimizationwith an extensive con-former search can be completed for a dozen can-didates within 2 to 3 days.With the 3Dmodels inhand, experimentally observable parameters suchas chemical shifts, scalar (J) couplings, NOE andROE patterns, and vibrational circular dichroism(VCD) and/or infrared spectra can be calculated.Although thesedata canpotentially serve as critical

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1Structure Elucidation Group, Process and AnalyticalResearch and Development, Merck & Co., Inc., 2000Galloping Hill Road, Kenilworth, NJ 07033, USA. 2StructureElucidation Group, Process and Analytical Research andDevelopment, Merck & Co., Inc., 33 Avenue Louis Pasteur,Boston, MA 02115, USA. 3Department of Biological Chemistryand Molecular Pharmacology, Harvard Medical School, 240Longwood Avenue, Boston, MA 02115, USA. 4Department ofChemistry, University of Connecticut, 55 North EaglevilleRoad, Unit 3060, Storrs, CT 06269, USA. 5Van’t HoffInstitute for Molecular Sciences, University of Amsterdam,Science Park 904, 1098 XH Amsterdam, Netherlands.6Structure Elucidation Group, Process and AnalyticalResearch and Development, Merck & Co., Inc., 126 EastLincoln Avenue, Rahway, NJ 07065, USA.*Corresponding author. Email: gary.martin2@merck.com (G.E.M.);robert.williamson2@merck.com (R.T.W.)

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measurements, all havewell-known limitations. Forexample, structural information from chemicalshifts, scalar (J) couplings, and NOE and ROE

correlations are inherently local, as these phenom-ena arisemostly through short-range interactions;the readout fromVCD is a cumulative effect from

all stereogenic centers in a molecule, so that dia-stereomeric differentiation canbe very challengingfor compounds possessing multiple chiral centers.For the reasons above, we propose here an al-

ternative, orthogonalmethod that provides a checkon the global structure in an objective manner.Anisotropic NMR parameters, RDCs (10–12), andRCSAs (13–16)—used here for critical structureand configuration assessment—arise from well-known NMR phenomena. RDCs and RCSAs areobserved onlywhen amolecule is partially aligned,usuallywith the aid of an alignmentmedium suchas a constrained polymeric gel. A schematic ofhow a molecule might orient with a polymericgel stretched along the B0 axis of the magneticfield is shown in Fig. 1. Experimentally, RDC valuesare extracted from the difference of two mea-surements that yield the heteronuclear 1JCHcoupling constant in isotropic solution and thetotal coupling constant 1JCH+ 1DCH inananisotropicmedium, respectively. The 1DCH component of thetotal coupling constantmeasured in an anisotropicmedium is the RDC, which can be either positiveor negative—that is, the total coupling constantmeasured in anisotropicmedia canbe either largeror smaller than the value of 1JCH. The commonlymeasured one-bond 13C-1H RDC data, for exam-ple, report on the relative orientations of differentC-Hbonds in themolecule. This information formsthe basis of RDC-based structural differentiation.The corresponding bond-bond orientation rela-tionships are invariably different between thecorrect and incorrect structures. Whereas thetheoretical RDC values based on a correct struc-turemust agreewith experimentalmeasurements,the same is not true for an incorrect structure.Such agreement can be qualitatively assessed bya theoretical versus experimental correlation plotor numericallymeasured by the quality (Q) factor(30). A low Q factor indicates good agreementbetween theory and experiment.A complementary anisotropic NMR parameter,

RCSA, has the advantage of providing structuralinformation for carbons that are not bonded tohydrogen. Structural information for these carbonswas previously only obtainable with challenging-to-acquire long-range RDCmeasurements (31–33).Due to difficulties in the elimination of isotropiccontributions to theRCSAmeasurement and otherissues, theRCSAmeasurementswere only recentlyapplied to small molecules (13–16). In the mostcurrent report, two alternative methods for suc-cessfullymeasuringRCSAs in stretched and com-pressed gels have been described, along with themeans of eliminating any isotropic chemicalshift change that otherwise contaminates RCSAmeasurement (16). As the measurement of RCSAis operationally somewhatmore demanding thanRDCs, any satisfactoryRCSA-enabling techniquesusually facilitate RDC measurements as well,although the reverse is not always true. As RCSAand RDC can provide complementary structuralinformation, the measurement of both is advis-able when RCSA data are being acquired. UnlikeRDCs, which inform on relative bond orienta-tions, RCSAs report on the relative orientationsof different chemical shielding tensors in the

Liu et al., Science 356, eaam5349 (2017) 7 April 2017 2 of 7

Fig. 1. Illustration of partial molecular ordering at the gel polymer surface. The polymer structure(left) represents a poly(methyl methacrylate) (PMMA) filament. The filament is displayed along B0,demonstrating the excess projection along this direction in the actual NMR experimental setup. Therotation of the analyte (center) is restricted at the polymer surface due to steric occlusion, leading topartial molecular alignment. The experimentally determined principal axis frame of the alignment tensor,a particular molecular frame in which the alignment tensor is diagonalized, is displayed as the yellowdashed frame on the far right. The most ordered axis, Pz, can frequently be understood intuitively on thebasis of the molecular shape. For example, in aquatolide, the most ordered molecular axis Pz is, asexpected, roughly perpendicular to the planar envelope of this nearly flat molecule.

Fig. 2. The principle of residual chemical shift anisotropy (RCSA)–based model differentiationshown using aquatolide as an example. The chemical shielding anisotropy tensors are shown inmagenta and green for two sp2 carbons for which residual dipolar coupling (RDC) data are unavailable. Inthe revised (top) and the original (bottom) structures, these two anisotropy tensors have differentorientations relative to each other, as clearly seen here, and relative to anisotropy tensors of the othercarbons, which are not displayed for visual clarity.The revised structure is clearly favored by much betteragreement between experimental RCSA data and calculated values predicted using alignment-tensorparameters determined from all data, including both RCSA and RDC. Correlations for carbons whosechemical shielding anisotropy tensors are not displayed are shown in tan.

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molecular structure. In terms of their applica-tion in structure elucidation, RCSAs share a sim-ilar utility to that of RDCs.The principle for structural differentiation

based on RCSAs is illustrated in Fig. 2 and iscomparable to RDC-based analysis, except thatthe bond vectors are now replaced by chemicalshielding tensors that can be accurately calcu-lated by gauge-invariant atomic orbital (GIAO)DFT (20–23). Simultaneously using both RDCand RCSA data also leads to more reliable de-termination of the alignment tensor, consequent-ly enhancing the robustness of overall structuredifferentiation.Recently, we reported the application of a com-

bination of RDC and RCSA measurements inestablishing the stereochemistry of the naturalproduct homodimericinA (29). During the courseof that work, it became obvious that the combina-tion of RDCs and RCSAs provides a powerful andorthogonal means of confirming not only therelative configuration of a given stereocenter butalso the overall molecular structure and atomicconnectivity of the molecule under study. Com-bined analysis of RDC and RCSA data providesan independent means of confirming or refutinga proposed structure or choosing among alter-native structures generated by a CASE program.The confluence of capabilities embodied by CASEmethods, DFT calculations, and now the relativelyfacile measurement of anisotropic NMR parame-ters has facilitated the development of a generallyapplicable method for the definition of molecularstructure and configuration that should help toaddress the growing and general problem of struc-tural mischaracterization.

Application to cryptospirolepine

The structure proposed for cryptospirolepine, 1,first reported in 1993, was based solely on COSY,1H-13Cheteronuclearmultiple-quantumcoherence,and optimized 8-Hz 1H-13C HMBC data (34). In2002, the original NMR sample was examinedchromatographically and found to contain 26components with none of 1 remaining. The twomajor components, 2 and 3, were isolated andfully characterized (35). Identification of themajordegradants strongly suggested that the originalstructure report was incorrect (Fig. 3). Finally,in 2015, with the use of 1.7-mmMicroCryoProbetechnology and the newly developed 1,1-HD-ADEQUATE experiment (27), it was possible torevise the structure to 4.Although cryptospirolepine, 4, is racemic, the

molecule provides a useful sample for assessingthe structural validation capability of RDC andRCSA data, which are measured in achiral align-ment media that are insensitive to absolute con-figuration. The comparison is presented in Fig. 4.Even a cursory inspection reveals that the corre-lation between theoretical and experimentalvalues for the revised structure, 4 (panel B), isconsiderably stronger than for the original struc-ture, 1 (panel A). This difference is also reflectedin a much lower Q value of 0.122 for 4, in com-parison with the value of 0.245 for 1. There arethree strongly coupled pairs of resonances in the

500-MHz 1HNMR spectrum of 4 that can poten-tially undermineRDCmeasurement accuracy (36).After removing the correspondingRDCdata, theQvalue for 4 decreased to 0.082, whereas that for1 was 0.217, still providing ample basis for differ-entiating the correct structure, 4, from the erro-neous original structure, 1 (see supplementarymaterials).In this particular case (Fig. 5), the bottom half

of the structure (green) is actually identical instructures 1 and 4 (correlation Q factors of 0.16and 0.13, respectively), but the top half (red) is sub-stantially different (correlation Q factors of 0.29and 0.12 for 1 and 4, respectively). The fact thatthe poor agreement in 1 is localized to the tophalf further confirms the location of the struc-tural assignment mistake.

Application to spiroketalrearrangement products

We recently collaborated on a study that involvedthe rearrangement of a spiroketal molecule trig-gered by an enol-ether epoxidation (37). Becauseof the indeterminatenature of thenumber of bondsspanned byHMBC correlations, it was not possibleto assemble the structure from the normal ensem-ble of NMR data [COSY, heteronuclear single-quantum coherence (HSQC), andHMBC]. Resortingto computer-assisted structure elucidation reducedthe number of structures consistent with the datato the two best choices (based on cumulative errorbetween the experimental versus calculated 13Cshift data), represented by 5 and 6. The correctstructure was readily identified as 5 after theacquisitionof a40-Hzoptimized1,1-HD-ADEQUATE

spectrum (27). In the event that an investigatordid not have either a working knowledge of theADEQUATE experiments or access to a spectrom-eter equipped with a cryoprobe with which toacquire those data, the differentiation of 5 from6 afforded another opportunity for structure as-signment using RDC and RCSA data.Density functional theory calculations per-

formed on both 5 and 6 suggested multiple con-formers of comparable thermal energies, but theRDC and RCSA data instead suggested a singlemajor conformer (see supplementary materials).Plotting theexperimental versus theback-calculatedRDC and RCSA data as above produced the re-sults shown in Fig. 6. Although it was readilypossible to identify the correct structure betweenthe two choices using the 1,1-HD-ADEQUATEdata, clearly the RDC and RCSA data indepen-dently identify the correct choice as 5 as well,based on the significant difference in the Q =0.20 for 5 versus Q = 0.55 for 6.

Application to aquatolide

For a final example, we chose the natural prod-uct aquatolide (38–42). The originally proposedstructure, 7, incorporated a very rare ladderanemoiety in the molecular framework (38). Reiso-lation followed by extensive NMR, quantumme-chanical calculations, and x-ray crystallographyled to the revision of the structure, 8 (39), whichwas followed in 2015 by the total synthesis re-ported by Saya et al. (41). A more recent studyby Buevich and Elyashberg examined the use ofDFT calculation of chemical shifts as a means ofchoosing between alternative structures suggested

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Fig. 3. Evolution of the cryptospirolepine structure. The originally reported structure, 1 (34), wasfound to degrade to two major compounds, 2 and 3, in 2002 (35). Although the formation of 2 could bemechanistically rationalized, the formation of 3 could not, which suggests that the originally reportedstructure, 1, was likely incorrect. Using 1.7-mmMicroCryoProbe capabilities in conjunction with the recentlyreported 1,1-HD-ADEQUATE experiment allowed revision of the structural assignment to 4 (27). The for-mation of both 2 and 3 can be mechanistically rationalized from 4.

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by a CASE program (42). In addition to the re-vised structure, 8, the CASE program outputcontained two additional structures, 9 and 10,that were considered to be potentially viable al-ternatives on the basis of the cumulative errorbetween the experimentally observed and cal-culated 13C chemical shifts for the structures.As is readily apparent from theRDC andRCSA

data for aquatolide in dimethyl sulfoxide (DMSO)–d6 plotted in Fig. 7, the best fit is obtained for therevised structure (39, 41) for which the data areshown in panel A (Q = 0.12). In stark contrast,the fit for the originally proposed structure withthe ladderanemoiety is extremely poor, withQ =0.72. The alternative structures, 9 and 10, hadsuccessively poorer correlation plotswithQ=0.23and 0.59, respectively. Hence, as is shown in thetwo previous examples, a correlation plot betweenthe experimental and back-calculated RDC andRCSAdata for themodel structures readily estab-lishes which structure is in best agreement withthe data.

Outlook

The suite of model compounds in this report,all of which have occasioned either incorrectstructure reports or structural assignment diffi-culties, illustrate the power of combining modernCASE algorithms and DFT calculations with RDCand RCSA data to simultaneously define chemicalconnectivity and configuration as well as 3Dstructure. The best candidates in our exampleshave RDC- and RCSA-combined Q values of ~0.1to ~0.2. A Q value in this range is typically asso-ciated with high-resolution crystallographic anal-ysis (43). A 3D structure of this accuracy providesvaluable insights into drug-target interactionsand structure-activity relationships. On the otherhand, a successful execution of this approachhas some prerequisites when a CASE algorithmis employed to generate structures. First, theinitial input for the program must contain suf-ficient information to allow for candidate gener-ation within a reasonable amount of time. Inmany cases, proton and carbon chemical shiftsand HMBC correlations suffice, but in more chal-lenging cases, such as that of proton-deficientcompounds like homodimericin A (29), long-range H-C (24) correlation and C-C correlationdata will be required. Fortunately, these dataare now available, even for submilligram samples,owing to new NMR pulse sequences and devel-opments in spectrometer hardware (27). Second,the candidates generated by CASE must containthe correct structure. Our experience with theCASE program indicates that this requirementis met in most instances. In fact, a good computeralgorithm has been reported to outperform ahuman expert, with respect to deep explorationof all structural possibilities (17–19). Third, theproposed chemical structures should be approach-able by DFT geometry optimization. In this work,DFT geometry optimization was conducted inGaussian 09 at the B3LYP/6-31G(d,p) level. B3LYPworks well with a small basis set such as thatused in this work, and employing a large basisset causes virtually no difference in the final

Liu et al., Science 356, eaam5349 (2017) 7 April 2017 4 of 7

Fig. 4. RDC and RSCA analysis for structural assignment of cryptospirolepine. (A) Plot of the experi-mental versus calculated RDC (red) and RCSA (blue) data for the incorrect structure of cryptospirolepinereported in 1993 (34). Q = 0.245. (B) Plot of the experimental versus calculated RDC and RCSA data forthe revised structure of cryptospirolepine (4) reported in 2015 (27). Q = 0.122.

Fig. 5. Division of RDC and RCSA data on the basis of their structural locations narrows the struc-tural error in 1 to the top half.The correlation associated with the common green part is nearly identicalbetween 1 (A) and 4 (B), whereas the correlation associated with the diverging red part is a substantiallypoorer fit in 1 than in 4, which indicates that the red part in 1 contains the structural error. Blue denotesnitrogen atoms.

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structure in our tests. All DFT calculations wereperformed in vacuum without solvent modeling,although the actual samples were analyzed inDMSO. Our previous experience shows that theinclusion of solvent effectsminimally changes theoptimized structure but does alter its calculatedenergy, which should be considered when accu-rate Boltzmann distribution weighting is neededamongmultiple rotamers of comparable energies.For compounds in this study, only a single lowest-energy rotamer was predicted, except for thespiroketal molecule. In that case, as describedin the supplementarymaterials, anisotropic NMRdata agreed best with a major rotamer that isactually not the lowest-energy rotamer accordingto DFT in vacuum. This discrepancy may reflectan effect of DMSO that was not taken into ac-count, although including the implicit effect ofDMSO by a polarizable continuum model didnot lower the relative energy of this rotamer.Although for some compounds even simple

molecular mechanics calculations can yield 3Dstructures of high accuracy, it can still be quitechallenging to obtain useful results from DFTcalculation for other compounds—for instance,those whose structures are stabilized mostlythrough intra- or intermolecular hydrogen bonds,such as polypeptides. However, other structure-prediction tools, such as CS-ROSETTA (chemicalshift ROSETTA), are better tailored to these needs(30). For GIAO chemical shielding calculation,the mpw1pw91 functional and the 6-31G(d,p) basisset were used, which consistently produced slightlybetter RCSA Q factors than the B3LYP/6-31G(d,p)combination in all tested cases. The RCSA-basedanalysis is more robust against GIAO-DFT in-accuracy than the chemical shift–based analysis,because for the former only the size of the pre-diction error relative to the overall shielding ac-tually affects the analysis, whereas for the latterthe absolute prediction error directly influencesthe analysis. Once a reasonable 3D model as-sociated with each candidate is generated, whethervia computational methods or investigator deduc-tion, and chemical shielding tensors are calcu-lated by DFT based on this 3D model, RDC andRCSA data can be employed as a sensitive criticalmeasure to evaluate the validity of the structuralassignment. The possibility of a false-positivedetermination—that is, agreement of RDC andRCSA data with an incorrect structure—is sub-stantially lower (44) than that in an analysisusing only conventional NMR data, especiallywhen both RDC and RCSA are jointly used. Thesedata can serve as a convenient NMR litmus testof structure and stereochemical validity. As such,the method described in this work has considerablepotential to be widely applied, which could helpto quell the flow of incorrect structures appearingin the literature.

Materials and methodsPreparation of poly-(2-hydroxylethylmethacrylate) (poly-HEMA) gel

The preparation of EGDMA (ethylene glycol di-methylacrylate) cross-linked poly-HEMA gel fol-lowed a published protocol (45), but the HEMA

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Fig. 6. RDC and RSCA analysis for spiroketal rearrangement. (A) Plot of the RDC (red) and RCSA(blue) data for the structure of the enol-epoxide rearrangement product confirmed with 1,1-HD-ADEQUATE data (27) as 5. The Q value was 0.20. (B) Correlation plot of the RDC and RCSA data for thealternative structure, 6, suggested by CASE, associated with a Q value of 0.55. RDC and RCSA dataalone can clearly differentiate between the two structures.

Fig. 7. Plots of the calculated versus experimental RDC (red) and RCSA (blue) data for aquatolidecandidate structures. (A) The revised structure, 8; (B) the originally proposed incorrect structure ofaquatolide, 7; and (C and D) two alternative structures, 9 and 10, respectively. Clearly there is a vastdifference in the Q value for the correct structure, 8, and the originally reported structure of aquatolide, 7.The alternative structures generated by CASE, 9 and 10, had intermediary Q values of 0.23 and 0.59,respectively. A twofold difference in the Q value between the correct structure, 8, and the best alternativestructure from the CASE program, 9, still allowed an ample basis for choosing between the structures.

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monomer concentration during polymerizationand the cross-linking ratio were optimized spe-cifically for use with the gel-stretching device tofinal values of 60% (v/v) and 0.07% (v/v), respec-tively. Polymerization was carried out in 1/8′′ IDFEP tubing (Cole-Parmer) at 50°Cwith 0.06% (v/v)V70 (Wako Chemicals USA) as the radical initiator.The polymerized gel was cut into 2-cm segmentsand washed three times inmethanol over a periodof 2 days. The gel sticks were dried on a glass sur-face before use.

NMR sample preparationand experiments

The NMR experiments for RDC and RCSA mea-surements are relatively straightforward andwell documented (16, 29). The experimental timeclosely depends on the material availability. Forexample, J-resolved HSQC and 13C{1H} spectra ofexcellent quality can be obtained in about 15 hourseach, for 1 mg of aquatolide in an unstretchedHEMA gel on a 500-MHz magnet equipped witha Prodigy probe. Under the gel-stretched state,longer experiments with 50% more transients areadvised to account for the reduction in active sam-ple volume in the narrower segment of the tube,as previously described (16, 29). The compoundsused in our current work, which ranged from 1 to3mg in quantity, took ~2 to ~3.5 days of analysistime per sample. The DMSO-compatible gel hasan advantage over the chloroform-compatiblegel for dilute samples in that longer experimentscan be run without solvent evaporation.The samples used for this study were either iso-

lated and purified or synthesized as described intheprimary references for the individual compounds.Resonance assignments of cryptospirolepine (34)were adopted from a previous publication (27).Samples of the spiroketal (1 mg) (37) and aqua-tolide (1 mg) (41) were first dissolved in 150 mlDMSO-d6 for resonance assignment prior to RDCand RCSA measurements, as the original chem-ical shift assignments were carried out in differ-ent solvents. NMR resonance assignments werecompleted using a combination of 1H, 13C, COSY,1H-13C HSQC, and 1H-13C HMBC experiments.To prepare the gel sample, the test compound

was dissolved in 350 ml DMSO-d6 with the addi-tion of 5 ml tetramethylsilane for carbon chem-ical shift referencing, and a gel stick was addedin a horizontal position to swell for a period of3 days. The fully swollen gel was then transferredto a gel-stretching device with inner diameters of4.2 and 3.2mm for thewide and narrow sections,respectively, as described previously (16). For RDCmeasurements, the J-resolved BIRD-HSQC exper-iment with F2 homonuclear decoupling (HD-J-HSQC) (46) was utilized with an F1 acquisitiontime ranging from 256 to 312 ms, an F2 acquisi-tion time of 120ms, and a recycling delay of 1.5 s.For the spiroketal and aquatolide, some signalsoverlapped with signals from the gel polymer, sothe F2-coupled CLIP-HSQC experiment (47) wasemployed to obtain coupling data for those over-lapped resonances, as well as couplings of indi-vidual C-H vectors of methylene groups. (Notethat the HD-J-HSQC experiment only provides

the sum of two C-H couplings for anisochronousprotons of a methylene group.) An F1 acquisi-tion time of 12 ms, an F2 acqusition time of500ms, and a recycling delay of 1.5 s were usedin the F2-coupled CLIP-HSQC experiment; thearomatic regions were folded in the F1 dimen-sion to conserve spectrometer time. All NMR ex-periments were conducted at 25°C on a Bruker500-MHz spectrometer equipped with a Prodigyprobe.

REFERENCES AND NOTES

1. Y. Inokuma et al., X-ray analysis on the nanogram tomicrogram scale using porous complexes. Nature 495,461–466 (2013). doi: 10.1038/nature11990; pmid: 23538828

2. L. Gross, F. Mohn, N. Moll, P. Liljeroth, G. Meyer, The chemicalstructure of a molecule resolved by atomic force microscopy.Science 325, 1110–1114 (2009). doi: 10.1126/science.1176210;pmid: 19713523

3. K. Ø. Hansen et al., A combined atomic force microscopy andcomputational approach for the structural elucidation ofbreitfussin A and B: Highly modified halogenated dipeptidesfrom Thuria breitfussi. Angew. Chem. Int. Ed. 51, 12238–12241(2012).

4. L. Gross et al., Organic structure determination usingatomic-resolution scanning probe microscopy. Nat. Chem. 2,821–825 (2010). doi: 10.1038/nchem.765;pmid: 20861896

5. D. S. Reddy, A. G. Kutateladze, Structure revision of an acoranesesquiterpene cordycepol A. Org. Lett. 18, 4860–4863(2016). doi: 10.1021/acs.orglett.6b02341; pmid: 27611774

6. A. Ribaucourt, D. M. Hodgson, Total synthesis andstructural revision of the cytotoxin aruncin B. Org. Lett.18, 4364–4367 (2016). doi: 10.1021/acs.orglett.6b02120;pmid: 27529438

7. I. Shin, D. Lee, H. Kim, Substrate-controlled asymmetrictotal synthesis and structure revision of (–)-bisezakyneA. Org. Lett. 18, 4420–4423 (2016). doi: 10.1021/acs.orglett.6b02239; pmid: 27551943

8. M. Inman, C. Carvalho, W. Lewis, C. J. Moody, Synthesis ofthe reported pyranonaphthoquinone structure of the indoleamine-2,3-dioxygenase inhibitor annulin B by regioselectiveDiels–Alder reaction. J. Org. Chem. 81, 7924–7930 (2016).doi: 10.1021/acs.joc.6b01622; pmid: 27513176

9. A. Ghosh, D. T. Bainbridge, L. M. Stanley, Enantioselectivemodel synthesis and progress toward the putative structure ofyuremamine. J. Org. Chem. 81, 7945–7951 (2016).doi: 10.1021/acs.joc.6b01730; pmid: 27494137

10. N. Tjandra, A. Bax, Direct measurement of distances andangles in biomolecules by NMR in a dilute liquid crystallinemedium. Science 278, 1111–1114 (1997). doi: 10.1126/science.278.5340.1111; pmid: 9353189

11. G. Kummerlöwe, B. Luy, Residual dipolar coupling for theconfigurational and conformation analysis of organicmolecules. Annu. Rep. NMR Spectrosc. 68, 193–232 (2009).doi: 10.1016/S0066-4103(09)06804-5

12. R. R. Gil, C. Griesinger, A. Navarro-Vázquez, H. Sun, inStructure Elucidation in Organic Chemistry: The Search for theRight Tools, M.-M. Cid, J. Bravo, Eds. (Wiley-VCH, 2015),pp. 279–323.

13. F. Hallwass et al., Residual chemical shift anisotropy (RCSA):A tool for the analysis of the configuration of small molecules.Angew. Chem. Int. Ed. 50, 9487–9490 (2011). doi: 10.1002/anie.201101784; pmid: 21915963

14. G. Kummerlöwe et al., Variable angle NMR spectroscopy and itsapplication to the measurement of residual chemical shiftanisotropy. J. Magn. Reson. 209, 19–30 (2011). doi: 10.1016/j.jmr.2010.11.019; pmid: 21256060

15. Y. Liu, J. H. Prestegard, A device for the measurementof residual chemical shift anisotropy and residual dipolarcoupling in soluble and membrane-associated proteins.J. Biomol. NMR 47, 249–258 (2010). doi: 10.1007/s10858-010-9427-7; pmid: 20506033

16. N. Nath et al., Determination of relative configuration fromresidual chemical shift anisotropy. J. Am. Chem. Soc. 138,9548–9556 (2016). doi: 10.1021/jacs.6b04082;pmid: 27294984

17. M. E. Elyashberg, A. J. Williams, G. E. Martin, Computer-assisted structure verification and elucidation tools in NMR-based structure elucidation. Prog. Nucl. Magn. Reson.

Spectrosc. 53, 1–104 (2008). doi: 10.1016/j.pnmrs.2007.04.003

18. M. Elyashberg, A. Williams, K. Blinov, Contemporary Computer-Assisted Approaches to Molecular Structure Elucidation (RSCPublishing, 2012).

19. M. E. Elyashberg, A. J. Williams, Computer-Based StructureElucidation from Spectral Data (Springer, 2015).

20. G. Schreckenbach, T. Ziegler, Density functional calculationsof NMR chemical shifts and ESR g-tensors. Theor. Chem. Acc.99, 71–82 (1998). doi: 10.1007/s002140050306

21. D. J. Tantillo, Walking in the woods with quantum chemistry—Applications of quantum chemical calculations in naturalproducts research. Nat. Prod. Rep. 30, 1079–1086 (2013).doi: 10.1039/c3np70028c; pmid: 23793561

22. N. Grimblat, A. M. Sarotti, Computational chemistry to therescue: Modern toolboxes for the assignment of complexmolecules by GIAO NMR calculations. Chemistry 22,12246–12261 (2016). doi: 10.1002/chem.201601150;pmid: 27405775

23. A. Navarro-Vázquez, State of the art and perspectives in theapplication of quantum chemical prediction of 1H and 13Cchemical shifts and scalar couplings for structural elucidationof organic compounds. Magn. Reson. Chem. 55, 29–32(2017). doi: 10.1002/mrc.4502; pmid: 27531665

24. R. T. Williamson, A. V. Buevich, G. E. Martin, T. Parella,LR-HSQMBC: A sensitive NMR technique to probe very long-range heteronuclear coupling pathways. J. Org. Chem. 79,3887–3894 (2014). doi: 10.1021/jo500333u;pmid: 24708226

25. G. E. Martin, Using 1,1- and 1,n-ADEQUATE 2D NMR data instructure elucidation protocols. Annu. Rep. NMR Spectrosc. 74,215–291 (2011). doi: 10.1016/B978-0-08-097072-1.00005-4

26. G. E. Martin, M. Reibarkh, A. V. Buevich, K. A. Blinov,R. T. Williamson, Development of 1,n-ADEQUATE and modifiedvariants and their application to structure elucidation andspectral assignment problems. eMagRes 3, 215–234 (2014).doi: 10.1002/9780470034590.emrstm1370

27. J. Saurí et al., Homodecoupled 1,1- and 1,n-ADEQUATE: PivotalNMR experiments for the structure revision ofcryptospirolepine. Angew. Chem. Int. Ed. 54, 10160–10164(2015). doi: 10.1002/anie.201502540; pmid: 26178441

28. K. A. Blinov, A. V. Buevich, R. T. Williamson, G. E. Martin, Theimpact of LR-HSQMBC very long-range heteronuclearcorrelation data on computer-assisted structure elucidation.Org. Biomol. Chem. 12, 9505–9509 (2014). doi: 10.1039/C4OB01418A; pmid: 25268697

29. E. Mevers et al., Homodimericin A: A complex hexacyclic fungalmetabolite. J. Am. Chem. Soc. 138, 12324–12327 (2016).doi: 10.1021/jacs.6b07588; pmid: 27608853

30. Y. Shen, R. Vernon, D. Baker, A. Bax, De novo protein structuregeneration from incomplete chemical shift assignments.J. Biomol. NMR 43, 63–78 (2009). doi: 10.1007/s10858-008-9288-5; pmid: 19034676

31. N. Nath, E. J. d’Auvergne, C. Griesinger, Long-range residualdipolar couplings: A tool for determining the configuration ofsmall molecules. Angew. Chem. Int. Ed. 54, 12706–12710(2015). doi: 10.1002/anie.201504432; pmid: 26359945

32. P. Trigo-Mouriño, M. C. de la Fuente, R. R. Gil,V. M. Sánchez-Pedregal, A. Navarro-Vázquez, Conformationalanalysis of the anti-obesity drug lorcaserin in water: How totake advantage of long-range residual dipolar couplings.Chemistry 19, 14989–14997 (2013). doi: 10.1002/chem.201202509; pmid: 24105936

33. T. F. Molinski, B. I. Morinaka, Integrated approaches to theconfigurational assignment of marine natural products.Tetrahedron 68, 9307–9343 (2012). doi: 10.1016/j.tet.2011.12.070; pmid: 23814320

34. A. N. Tackie et al., Cryptospirolepine, a unique spiro-nonacyclicalkaloid isolated from Cryptolepis sanguinolenta. J. Nat. Prod.56, 653–670 (1993). doi: 10.1021/np50095a001

35. G. E. Martin et al., Identificaiton of degradants of a complexalkaloid using NMR cryoprobe technology and ACD/structureelucidator. J. Heterocycl. Chem. 39, 1241–1250 (2002).doi: 10.1002/jhet.5570390619

36. B. Yu et al., More accurate 1JCH coupling measurement in thepresence of 3JHH strong coupling in natural abundance.J. Magn. Reson. 215, 10–22 (2012). doi: 10.1016/j.jmr.2011.09.037; pmid: 22227287

37. C. Lorenc et al., Turning spiroketals inside out: Arearrangement triggered by an enol ether epoxidation.ChemistryOpen 4, 577–580 (2015). doi: 10.1002/open.201500122; pmid: 26491634

Liu et al., Science 356, eaam5349 (2017) 7 April 2017 6 of 7

RESEARCH | RESEARCH ARTICLEon January 20, 2021

http://science.sciencemag.org/

Dow

nloaded from

38. A. San Feliciano et al., Aquatolide. A new type of humulane-related sesquiterpene lactone. Tetrahedron Lett. 30,2851–2854 (1989). doi: 10.1016/S0040-4039(00)99142-1

39. M. W. Lodewyk et al., The correct structure of aquatolide-experimental validation of a theoretically-predicted structuralrevision. J. Am. Chem. Soc. 134, 18550–18553 (2012).doi: 10.1021/ja3089394; pmid: 23101682

40. A. M. Sarotti, Successful combination of computationallyinexpensive GIAO 13C NMR calculations and artificial neuralnetwork pattern recognition: A new strategy for simple andrapid detection of structural misassignments. Org. Biomol.Chem. 11, 4847–4859 (2013). doi: 10.1039/c3ob40843d;pmid: 23779148

41. J. M. Saya et al., Total synthesis of aquatolide. Org. Lett. 17,3892–3894 (2015). doi: 10.1021/acs.orglett.5b01888;pmid: 26186274

42. A. V. Buevich, M. E. Elyashberg, Synnergistic combination ofCASE algorithms and DFT chemical shift predictions: Apowerful approach for structure elucidation, verification, andrevision. J. Nat. Prod. 79, 3105–3116 (2016). doi: 10.1021/acs.jnatprod.6b00799; pmid: 28006916

43. A. Bax, Weak alignment offers new NMR opportunities to studyprotein structure and dynamics. Protein Sci. 12, 1–16 (2003).doi: 10.1110/ps.0233303; pmid: 12493823

44. In an unpublished study on a nine-residue cyclized peptide,aureobasidin, out of 100,000 rotamers generated by a diversity-

orientated conformer search, three incorrectly folded structureswere identified that had Q factors of 0.33, 0.33, and 0.35,respectively. In contrast, comparison of 26 RDCs with the single-crystal x-ray structure [(48), Cambridge Crystallographic DataCenter entry QEFHUE] provided a significantly better Q factor of0.23. The average Q factor in the test set was 0.81, with astandard deviation of 0.10 (these results are illustrated graph-ically in fig. S10). Therefore, a false-positive rate in this case isestimated to be ~30 per million, even with a loose Q-factor cutoffof 0.35. This rate is expected to drop orders of magnitude furtherif carbonyl RCSA data are also included.

45. L. F. Gil-Silva, R. Santamaría-Fernández, A. Navarro-Vázquez,R. R. Gil, Collection of NMR scalar and residual dipolarcouplings using a single experiment. Chemistry 22, 472–476(2016). doi: 10.1002/chem.201503449;pmid: 26515991

46. L. Castañar et al., One-shot determination of residual dipolarcouplings: Application to the structural discrimination of smallmolecules containing multiple stereocenters. J. Org. Chem. 81,11126–11131 (2016). doi: 10.1021/acs.joc.6b02103;pmid: 27709936

47. A. Enthart, J. C. Freudenberger, J. Furrer, H. Kessler, B. Luy,The CLIP/CLAP-HSQC: Pure absorptive spectra for themeasurement of one-bond couplings. J. Magn. Reson. 192,314–322 (2008). doi: 10.1016/j.jmr.2008.03.009;pmid: 18411067

48. Y. In, T. Ishida, K. Takesako, Unique molecular conformation ofaureobasidin A, a highly amide N-methylated cyclicdepsipeptide with potent antifungal activity: X-ray crystalstructure and molecular modeling studies. J. Pept. Res. 53,492–500 (1999). doi: 10.1034/j.1399-3011.1999.00046.x;pmid: 10424343

ACKNOWLEDGMENTS

This work was funded, in part, by NIH grant GM086258.Experimental data are available in supplementary materials.We thank K. Lexa for providing the coordinates of 100,000peptide conformers for the false-positive rate estimation (44)and fig. S10.

SUPPLEMENTARY MATERIALS

www.sciencemag.org/content/356/6333/eaam5349/suppl/DC1Supplementary TextFigs. S1 to S10Tables S1 to S9References (49, 50)Data S1

7 December 2016; accepted 16 February 201710.1126/science.aam5349

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measurementsUnequivocal determination of complex molecular structures using anisotropic NMR

WilliamsonYizhou Liu, Josep Saurí, Emily Mevers, Mark W. Peczuh, Henk Hiemstra, Jon Clardy, Gary E. Martin and R. Thomas

DOI: 10.1126/science.aam5349 (6333), eaam5349.356Science 

, this issue p. eaam5349Sciencemethod overcomes common pitfalls that can lead to invalid structure assignments.samples. Because of its uniform sensitivity to relative bond orientations across the whole molecular framework, the

showcase a protocol that combines computer modeling with anisotropic NMR data acquired using gel-alignedet al.Liu most common structure-elucidation method. However, sometimes it is hard to distinguish isomers with similar spectra.When well-ordered crystals are not available for x-ray analysis, nuclear magnetic resonance (NMR) spectroscopy is the

Pharmaceutical research relies critically on determining the correct structures of numerous complex molecules.Picking structures out of a lineup

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