Synergistic Combination of CASE Algorithms and DFT Chemical Shift

Article pubs.acs.org/jnp

Synergistic Combination of CASE Algorithms and DFT Chemical Shift Predictions: A Powerful Approach for Structure Elucidation, Verification, and Revision Alexei V. Buevich*,† and Mikhail E. Elyashberg*,‡ †

Department of Discovery and Preclinical Sciences, Process Research and Development, NMR Structure Elucidation, Merck & Co., Inc., Kenilworth, New Jersey 07033, United States ‡ Advanced Chemistry Development (ACD/Laboratories), Akademik Bakulev Street 6, 117513 Moscow, Russian Federation S Supporting Information *

ABSTRACT: Structure elucidation of complex natural products and new organic compounds remains a challenging problem. To support this endeavor, CASE (computer-assisted structure elucidation) expert systems were developed. These systems are capable of generating a set of all possible structures consistent with an ensemble of 2D NMR data followed by selection of the most probable structure on the basis of empirical NMR chemical shift prediction. However, in some cases, empirical chemical shift prediction is incapable of distinguishing the correct structure. Herein, we demonstrate for the first time that the combination of CASE and density functional theory (DFT) methods for NMR chemical shift prediction allows the determination of the correct structure even in difficult situations. An expert system, ACD/Structure Elucidator, was used for the CASE analysis. This approach has been tested on three challenging natural products: aquatolide, coniothyrione, and chiral epoxyroussoenone. This work has demonstrated that the proposed synergistic approach is an unbiased, reliable, and very efficient structure verification and de novo structure elucidation method that can be applied to difficult structural problems when other experimental methods would be difficult or impossible to use.

S

Perhaps the most comprehensive and universally employed structural method is NMR spectroscopy. NMR spectra carry a wealth of structural information about the nature of atoms, functional groups, and their arrangement within the molecule. This information is revealed through nuclear shielding (chemical shifts) and internuclear interactions (spin−spin couplings, NOEs) of a given magnetically active nuclei (1H, 13 C, 15N, 19F, etc.). Thus, the combination of NMR and HRMS data can be considered as a primary source of structural information. Application of spectroscopic methods to natural products research is discussed in a series of reviews.3−7 However, a comprehensive literature analysis showed8−12 that even when equipped with advanced spectroscopic methods, structure elucidation methodology is still not free from errors and, hence, the development of better, more robust methods remains in high demand. As has been shown,13−15 the problem of structure elucidation belongs to the class of inverse problems. The intrinsic property of all inverse problems is to concede a number of possible solutions and then to select the most probable one by successive imposition of additional constraints that were not used at the previous stages of problem solving.16−18 In structure elucidation based on NMR and HRMS data, a typical, fundamental set of experimental data consists of the molecular

tructure elucidation of isolated new natural products and novel organic compounds remains one of the most challenging tasks.1 To date, more than 110 million compounds are registered in CAS,2 and the number is growing rapidly. For instance, in 2014 there were more structures added to the CAS registry than the sum of all structures deposited between 1965 and 1990.2 Spectroscopic methods have a pivotal role in structure elucidation.1 Optical spectroscopy (UV, IR, and Raman) is capable of revealing valuable information about the presence or absence of certain functional groups, for instance, OH, NH, NH2, SH, CO, CC, CN, NO2, and Ar, but it affords scant information about the arrangement of these groups in the molecule. Vibrational circular dichroism (VCD), electronic CD (ECD), and Raman optical activity (ROA) have gained popularity over the past decade, but their applications are primarily focused on stereochemical problems. X-ray crystallography obviously provides the most detailed and unambiguous structural information, but is often impeded by a limited amount of the isolated sample and/or the inability of a molecule to form a suitable single crystal, which limits the scope of its applications. High-resolution mass spectrometry (HRMS) can provide the mass of molecular ions, which makes it possible to determine a molecular formula, a key parameter in structure elucidation. HRMS can also provide considerable valuable information via MS−MS analysis, but the extraction of structural details can be very complicated and some structural features are not amenable to analysis by mass spectrometric methods. © XXXX American Chemical Society and American Society of Pharmacognosy

Received: September 1, 2016

A

DOI: 10.1021/acs.jnatprod.6b00799 J. Nat. Prod. XXXX, XXX, XXX−XXX

Journal of Natural Products

Article

formula, chemical shifts (primarily 1H and 13C), and 2D NMR correlations observed in COSY, HSQC, and HMBC spectra. On the basis of spectral features detected in NMR spectra, a set of initial structural constraints is produced, which leads to the construction of a set of isomeric structures, where all are equiprobable and satisfy the constraints imposed. Therefore, the strategy of structure elucidation reduces to the generation of a list of possible structures (models) and then the selection of the best one by using additional (not previously employed) structural constraints; hence the method is consistent with the general strategy of inverse problem solving.17,18 The latter constraints are usually extracted from available spectroscopic data, such as 1H−1H, 1H−13C, and 13C−13C J-couplings19,20 and by conducting additional experiments (NOESY, LRHSQMBC,21 INADEQUATE,22 1,1- or 1,n-ADEQUATE,23,24 etc.). To exhaustively consider all plausible structures and to exclude the possibility that some would be missed or overlooked,8−12 it is necessary to have a strict logical tool capable of managing this task. Such a tool was realized in a series of computer-assisted structure elucidation (CASE) expert systems (see reviews25−27 and monographs13,28). One of the most advanced CASE expert system,13,14,27 ACD/ Structure Elucidator,29−31 is capable of deducing all (without any exceptions) corollaries (structures) following a wellestablished set of rules regarding interconnections between molecular structures and NMR spectra.13,25,32 This system is based on 1D and 2D NMR spectral information and the molecular formula determined from HRMS. The standard algorithm of structure generation in ACD/Structure Elucidator is a so-called “strict structure generation” algorithm, which implies only standard two- and three-bond HMBC correlations. However, ACD/Structure Elucidator is also capable of solving structural problems even when an undefined number of HMBC and COSY correlations of unknown length n (n > 3) is present in the 2D NMR data. Solving structures with correlations over an undetermined number of bonds is achieved via a so-called “fuzzy structure generation” algorithm.13,33 The CASE program output contains all plausible structures for which 13C and 1H (optional) chemical shifts are calculated by three empirical methods: HOSE code, neural networks, and incremental methods (additivity rules).13,14,25 All three chemical shift prediction methods were developed to predict NMR chemical shifts based on large databases. For instance, reference structures used for HOSE code calculations contain over 3 million experimental 13C chemical shifts.14 The empirical methods of chemical shift predictions are quite fast, and at the same time their accuracy is often sufficient to distinguish the most probable structures of unknowns. For example, the fastest of the algorithms, the incremental approach, calculates about 30 000 13C chemical shifts per second (3.4 GHz PC with 16 GB of RAM) with prediction accuracy that can be as high as 1.6−1.8 ppm.34,35 Following chemical shift prediction, structures in the CASE output file are ranked in order of increased discrepancy between experimental and predicted NMR chemical shifts; the top-ranked structure(s) is/are considered the most probable. The system also provides the possibility of investigating the final solution for its uniqueness and stability, which is achieved by employing the fuzzy structure generation algorithm.13,33 Structure Elucidator was initially tested on approximately 300 published structure elucidation studies done by other methods. In the overwhelming majority of cases, the correct structure was placed at the first position by the ranking procedure.14,36 Since

then, the high efficiency of CASE methodology has been confirmed through its use in solving a significant number of structure elucidation problems, primarily related to structure elucidation of new natural products, their degradants, etc. (see examples in refs 13 and 36−44). However, there are structural problems where CASE programs have failed to distinguish the correct structure. The two kinds of such challenges were observed in our practice: (a) the correct structure is on the first position, but its average deviations of chemical shifts are too large (5−6 ppm); (b) the correct structure is either first or among several top-ranked structures with acceptable but very similar deviations. The first challenge is likely due to the lack of relevant structural motifs in the reference database, whereas the second may be due to a high degree of similarity between the top ranked structures. These shortcomings of empirical methods of NMR chemical shift predictions can be circumvented by the utilization of higher accuracy quantum-mechanical (QM) calculations. Recent progress in these calculations, specifically by the density functional theory (DFT) methods, has opened the opportunity to apply them to medium-sized (20 or more heavy atoms) natural products and synthetic organic molecules with very moderate computational costs. The accuracy of the prediction of chemical shifts by DFT methods has constantly improved over the past decade (see a comprehensive review45 on this subject and references contained therein). Specifically, for natural products and small organic molecule applications, about 250 different DFT methods, which include a diverse set of functionals, basis sets, and solvent models, have been tested by Rablen’s, Bally’s, and Tantillo’s group.46−48 The standard deviations of the chemical shift predictions attained of less than 0.1 ppm for protons and less than 2 ppm for carbons provide a sufficient degree of accuracy for the majority of structural problems. In less routine situations, such as in the case of molecules with heavy atoms, more rigorous approaches, including relativistic and electron correlation effects, can be applied to further improve the accuracy of chemical shift predictions.45 However, DFT methods cannot compete in speed with empirical methods, and their routine application in CASE algorithms seems to be unfeasible at the present time.49 Thus, for an average organic molecule the time required to calculate a single set of chemical shifts by DFT is measured by tens of minutes to hours depending on the level of theory used and available computational resources. In contrast, the number of structures generated during CASE analysis is measured by thousands or even millions,13,36 and, consequently, the DFT methods of predicting chemical shifts cannot replace empirical methods within CASE systems. It appears logical to suggest that in those cases when CASE empirical calculations fail to definitively converge to a single structure, application of QMbased chemical shift prediction on a limited number of topranked structures could potentially help to solve this problem. In this case, the selection of structures to be calculated by DFT methods would be done strictly by computer-assisted logical analysis and not by a potentially biased investigator, which ensures that the complete set of all possible isomers satisfying 1D spectra and 2D correlations will be rigorously tested. To the best of our knowledge, such a combination of CASE and DFT methods has not yet been explored. The goal of this study was to verify whether the DFT calculations could improve the empirical CASE structure elucidation algorithm particularly in those challenging cases B



Article

Table 1. Spectroscopic NMR Data for Aquatolide (2)51

when the latter approach had difficulties in determining a single structure. To check this hypothesis, three previously determined natural products for which CASE methods could not define a unique, single isomer have been chosen as a test set. The correctness of the analyzed structures has been independently verified by different combinations of other experimental methods (direct synthesis, X-ray crystallography, comprehensive NMR data analysis, DFT calculations, ECD spectroscopy). Examples include aquatolide, coniothyrione, and epoxyroussoenone. All three cases represent rather challenging structural problems: the structures of the first two molecules were originally misassigned, and the third structure, in addition to being a proton-deficient molecule, has four chiral centers.

■

RESULTS AND DISCUSSION Aquatolide. The first example used to examine our proposed approach was the structural analysis of aquatolide (C15H18O3). Aquatolide is a humulane-derived sesquiterpenoid lactone isolated from Asteriscus aquaticus. The structure of aquatolide (1), originally proposed on the basis of 1D and 2D NMR analysis,50 contained an extremely rare ladderane substructure.51

Lodewyk and co-workers51 found significant discrepancies between experimental and DFT-predicted chemical shift values for structure 1. The revised structure of aquatolide 2 was subsequently suggested as an alternative photocyclization product of the potential precursor of aquatolide isolated from the same plant.51 To further confirm the proposed structure 2, the authors51 tested 60 different possible alternative structures, largely based on other related compounds found in the same plant. The most probable structure was selected based on DFT calculations of 13C and 1H chemical shifts and associated coupling constants for all candidates. As a result of these extensive calculations, it was proven that the true structure of aquatolide was 2, which was subsequently confirmed by X-ray crystallography51 and by the total synthesis.52 Experimental 13C and 1H NMR data acquired for aquatolide by Lodewyk and co-workers51 gave us an opportunity to study the effectiveness of the proposed combination of CASE and DFT methods, as well as a chance to verify that the set of possible structures that have been considered in prior investigations was complete and did not contradict any of the NMR data. In this study, the ACD/Structure Elucidator14,29 CASE program was used. Since CASE analysis is still a relatively unknown method, a brief stepwise description of the CASE analysis is illustrated below on the example of aquatolide. A more complete description of CASE analysis of aquatolide can be found elsewere.14,53 CASE analysis was initiated with the assortment and systematization of available 1D proton and carbon and 2D NMR COSY, HSQC, and HMBC data (see Table 1 and Supporting Information Table 1S). The spectroscopic data were then used by the ACD/ Structure Elucidator program to produce a molecular connectivity diagram (MCD), shown in Figure 1.

label

δCexp

CHn

δHexp

JHH, Hz

HMBC (H to C)

C1 C2 C3 C4 C4

84.2 54.54 62.83 22.15 22.15

CH CH C CH2 CH2

4.48 3.26

t (2.2) dd (7.3, 2.5)

C12, C15, C3, C10, C14 C11, C8, C3, C10

1.96 2.52

m m

C5 C5 C6

28.63 28.63 131.1

CH2 CH2 CH

2.35 2.03 5.85

m m ddt (4.7, 3.1, 1.5)

C7 C8 C9

135.08 211.94 54.45

C C CH

2.92

s

C10

62.59

CH

2.64

dd (7.3, 1.8)

C11 C12 C13 C14 C15

41.86 177.5 22.22 22.62 22.84

C C CH3 CH3 CH3

1.87 1.05 1.19

q(2.0) s s

C2, C6, C10, C12, C3, C5

C4, C13, C8

C8, C7, C1, C10, C2, C3, C11 C11, C1, C15, C8, C2, C9, C14, C4

C 7, C 8, C 6 C 15, C 1, C 11, C 10 C 11, C 14

Figure 1. Molecular connectivity diagram for aquatolide (C15H18O3). Carbon atoms hybridized as sp3 and sp2 are marked by blue and violet colors, respectively. HMBC connectivities are displayed by green arrows. Labels ob (obligatory) and fb (forbidden) indicate the admissibility of a neighbor heteroatom. Three oxygen heteroatoms are shown in the lower right corner of the diagram for consistency with the molecular formula of aquatolide.

No manual edits of the aquatolide MCD were made, and strict structure generation14 was performed, which gave only three possible structures within 0.05 s (k = 3, tg = 0.05 s).53 These structures ranked along with their 13C average deviations dA, dN, and dI are shown in Figure 2. The maximum 13C deviations are also given for each structure. As seen from Figure 2, the first ranked structure #1 is identical to the revised structure 2, though all its average deviation values were significantly higher than those typically observed in similar analyses (dn < 3 ppm, n = A, N, I).13,14 This result is a consequence of the very unusual skeleton of 2, for which there is a lack of associated structures in the reference databases. It is interesting to note that if the empirical 13C chemical shift predictions were done for the originally proposed ladderane structure, 1,50 the structure would have been conclusively rejected due to a large max_dA value (26.5 ppm, see Figure 1S). In this case, the suggestion of an incorrect structural hypothesis would be prevented. As seen in Figure 2 Structure Elucidator allowed the identification of the correct structure of aquatolide in a fully automatic mode without detailed analysis of J-couplings in NMR spectra.51 However, the preference of structure #1 over C



Article

Figure 2. Candidate structures for aquatolide ranked with dA values. Notations of average deviations depending on a method of prediction: dA, HOSE code; dN, neural networks. dI, incremental approach. Here, the correct structure is first in the rank-ordered output list.

Table 2. Complete Set of Experimental and DFT-Calculated Carbon Chemical Shifts for Aquatolide Candidates

experimental

structure #1

structure #2

structure #3

label

δC

δCcalc

δCcalc

δCcalc

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 RMSD, ppm max_δ, ppm

84.2 54.54 62.83 22.15 28.63 131.1 135.08 211.94 54.45 62.59 41.86 177.5 22.22 22.62 22.84

83.28 54.53 63.17 22.80 30.69 135.07 137.29 211.99 54.97 64.91 44.91 177.40 22.94 20.63 20.89 1.82 3.97

87.38 69.43 54.56 36.46 33.12 160.44 132.61 197.42 55.04 79.42 48.70 172.69 13.69 19.75 25.47 11.38 29.34

85.33 53.46 45.07 22.29 32.50 146.18 138.82 201.88 49.13 50.35 48.82 176.12 16.92 18.37 27.87 7.65 17.76

As is clear from the data presented in Table 2, the DFTpredicted 13C chemical shifts unequivocally define structure #1 (2) as the most probable among the three candidate structures. Thus, the root-mean-square deviation (RMSD) and maximum deviation of 13C chemical shifts for structure #1 were 1.82 and 3.97 ppm, respectively. These values are more than 4 times smaller than those of the closest second structure #3, 7.65 and 17.76 ppm, respectively. For completeness of the investigation, it was interesting to learn why the original structure 1 was not generated by the Structure Elucidator program. The structure of 1 was rechecked by the program using HMBC data, and it was found that it had three 4JHC “nonstandard” correlations. Because all HMBC

other structures displayed in Figure 2 is only slightly better when judged by the average 13C deviation values. Selection of the most probable structure #1 obviously needs a better confirmation, which prompted us to employ the QM calculations. It is noteworthy that the QM calculations were needed only for three plausible molecules, rather than 60 as in the study leading to the revision of the structure of aqutolide.51 QM calculations of 13C chemical shifts were performed at the mPW1PW91/6-311+G(2d,p) level of theory on optimized geometries at the B3LYP/6-31+G(d,p) level. The summary of QM calculations for three possible structures of aquatolide determined by CASE study is shown in Table 2. D



Article

correlations were assumed to be of a “standard” three-bond length, the structure of 1 could not be generated in the “strict generation” mode.13,25 To generate this structure along with the correct one, the fuzzy structure generation mode33 should be used. As was mentioned above, this mode provided within the ACD/Structure Elucidator allows solving a problem even if an unknown number of “nonstandard” correlations of “nonstandard” length are present in the 2D NMR data. Recently, aquatolide appeared in another method development study. Pauli and co-workers54 have shown that the exhaustive extraction of information carried by chemical shifts and scalar coupling constants in the 800 MHz 1H NMR spectrum combined with QM prediction of these parameters and spectrum simulation confidently confirmed the validity of the revised structure 2 over the original structure, 1. The full analysis of proton NMR spectra was indeed a very popular tool until the late 1980s, when it fell out of favor with the development of modern 2D NMR spectroscopy, high-field magnets, and high-sensitivity NMR probes. The progress of NMR spectroscopy over the last three decades significantly expanded the scope of available and structurally relevant NMR parameters. Some of the data now routinely available include heteronuclear couplings and chemical shifts, homo- and heteronuclear NOEs, carbon−carbon correlation experiments at natural abundance, and even anisotropic NMR parameters such as residual dipolar couplings55 and residual chemical shift anisotropy.56 In turn, the availability of these data has led to the development of new structure elucidation protocols that are more robust and possess higher predictive and diagnostic power than those based on proton NMR spectroscopy alone. In our experience, proton NMR data can be used for structure verification, but are less applicable for de novo structure elucidation. For instance, ACD/Structure Elucidator calculations showed that the number of aquatolide structural isomers that can be constructed, if rather obvious CO (211.94 ppm) and O−CO (177.5 ppm) fragments were added manually to MCD (Figure 1) and all HMBC connectivities were omitted, was about 9 × 109. Therefore, it is practically impossible to discriminate these structures based on proton NMR data alone. Second, a proton-centric method cannot be applied to protondeficient molecules. In combination, these two factors significantly limit the scope of application of 1D proton NMR spectroscopy for structure elucidation of natural products. The amount of structural information carried by heteronuclear 2D NMR spectroscopy is significantly greater than that contained in 1D 1H NMR spectra,57 and the extraction of structurally relevant information from 2D NMR data is often much easier in comparison with the full analysis of a complex 1H NMR spectrum. Hence, it is difficult to conceive that a 1H spectra analysis alone will be used widely as the primary basis for structure elucidations, while 2D NMR data will serve only for structure confirmation as commented by Pauli and co-workers.54 In summary, the method we are proposing is not only more efficient but also more general and can be applied to the verification or de novo determination of proton-deficient molecular structures (see the other two examples below), and even for stereochemical analysis (see epoxyroussoenone example below). Coniothyrione. The second challenging example was the structural analysis of coniothyrione, with a molecular formula of C14H9ClO6, where the ratio of the number of hydrogen to heavy atoms is 0.4. On the basis of Crews’ rule the ratio of hydrogen to heavy atoms less than 2 is indicative of an

especially challenging structure elucidation.58 The original structure of coniothyrione, 3, was proposed based on the analysis of the 1H and 13C NMR data and HMBC correlations59 summarized in Table 2S.

The lack of a three-bond HMBC correlation from the olefinic proton, H4 (δH 7.20 ppm), to the carbonyl group (C1) in the HMBC spectrum was interpreted by the authors59 as evidence that the olefinic proton was connected to the carbon C4 and the chlorine was attached to carbon C3. However, strictly speaking, the absence of an HMBC correlation between a given proton and carbon nuclei does not necessarily mean that the number of bonds between them is larger than three.31 Kong and co-workers60 noted that the lack of the aforementioned three-bond HMBC correlation could be due to an unfavorable dihedral angle between the olefinic proton and the methoxycarbonyl group (C1). The dihedral angle was found to be approximately 70° by MM2 force field calculation. The change of the olefinic proton position from C4 to C3 in structure 4 did not violate any HMBC correlations described in the article.59 The revision of the structure of coniothyrione to 4 by Kong and co-workers60 was based on empirical chemical shift arguments and a speculative biosynthetic pathway absent of any experimental data. Subsequently, Martin and coworkers61 undertook a more rigorous experimental and DFTbased theoretical investigation of coniothyrione. The structure of coniothyrione as 4 was confirmed with much stronger proof, which included 1,1-ADEQUATE experiments and DFT analysis of JCC, JHC couplings and carbon chemical shifts.61

The NMR spectroscopic data for coniothyrione presented in Table 2S were analyzed by ACD/Structure Elucidator with a slightly edited MCD as shown in Figure 3. The following edits were made to MCD: four carbons with 13C chemical shifts in the range between 160.6 and 176.6 ppm were labeled as ob,

Figure 3. Molecular connectivity diagram for coniothyrione. E



Article

Figure 4. Six top-ranked structures from CASE study of coniothyrione.

which indicated that at least one heteroatom must be a neighbor of the corresponding carbon atom. Note that the carbon atom at 127.2 ppm did not have any connectivities to other atoms, so a great number of generated structures was expected. There were no contradictions detected in the HMBC data, and the strict structure generation algorithm gave the following results: k = 157 803 → 36 590 → 14 986, tg = 1 min, where k is the number of generated structures (157 803) from which 36 590 passed spectral and structural filtering,14 while 14 986 structures were finally saved after removing duplicates, and tg is the time used for structure generation. Thus, there were approximately 15 000 structures that satisfied both the constraints displayed in MCD and system knowledge. It is worth noting that when the ACD/Structure Elucidator solutions to more than 400 problems were examined (solutions to about 100 new problems were described in ref 14), it was found that the correct structure was usually ranked as the first or at least among the top three candidates.13,36 Only once was the correct structure as low as fifth. These previous results justified the selection of the six top-ranked structures for further analysis (Figure 4). CASE analysis of coniothyrione showed that the revised structure 4 was the second-ranked structure #2 shown in Figure 4; average and maximum deviations of the top six structures were too close to make a final decision regarding the correct structure of coniothyrione based on those criteria. Note that each predicted structure in Figure 4 from #3 to #6 is expected to have a relatively large three-bond J-coupling between the isolated vinyl proton (H4) and one of the low-field carbonyl/ carboxyl atoms. This contention is justified by the fact that these atoms and those separating them are coplanar. Since this correlation was not experimentally detected (Figure 3), these structures could be discarded from further analysis. Interestingly, the original structure, 3, was placed in the 18th position

by the ranking procedure (see Figure 2S). Its average deviations and max_dA = 25.55 ppm allowed the structure to be confidently rejected from consideration. It is noteworthy that the top two structures shown in Figure 4, #1 and #2, differ only by the orientation of the two wellcharacterized but nearly independent subunits. Neither structure had any violations with the MCD, and both fully satisfied all 2D NMR correlation data used in the structure revision study61 and, therefore, should be considered as viable candidates. Differentiation of structures #1 and #2 from the other four structures from CASE analysis with the DFT approach was undertaken. The summary of DFT calculations for the top six highest ranked by CASE structures is shown in Table 3. RMSD and max_δ for structure #2 were significantly lower than those of other candidate structures (Table 3), which confirmed the revised structure 4 of coniothyrione.60,61 Even though the structure 4 can be convincingly assigned based on DFT predictions of carbon chemical shifts, it is worth noting that the RMSD and max_δ of structure #2 (4) can be further improved by inclusion of relativistic corrections for carbon C345 or by applying the WC04 functional, as previously described.61 It has also been suggested45 that the chemical shift of the C3 carbon can be excluded from the analysis, and then the RMSD and, in particular, max_δ parameter for structure #2 (4) would drop down to more acceptable values of 1.67 and 3.33 ppm, respectively. Further differentiation of structures #1 and #2 (4) of coniothyrione could also be done by the heteronuclear Jcoupling analysis. Thus, the DFT-calculated J(H3,C5) and J(H3,C14) coupling constants for structure #2 (4) (12.7 and 4.1 Hz, respectively) were in nearly perfect agreement with experimental values of 12.1 and 4.4 Hz,61 whereas the predicted J-couplings for structure #1 of 8.6 and 8.0 Hz were inconsistent with experimental values. F



Article

Table 3. Experimental and DFT-Calculated Carbon Chemical Shifts (ppm) for the Six Top-Ranked Candidate Structures for Coniothyrione Shown in Figure 4

exptl

structure #1

structure #2 (4)

structure #3

structure #4

structure #5

label

δC

δCcalc

δCcalc

δCcalc

δCcalc

δCcalc

structure #6 δCcalc

C1 C2 C3 C4 C5 C7 C8 C9 C10 C11 C12 C13 C14 C15 RMSD, ppm max_δ, ppm

168.7 79.8 127.2 143.1 164.5 155.7 108.1 135.8 112.7 160.6 110.7 176.0 120.8 52.9

171.3 81.6 142.7 125.4 171.0 154.6 105.3 134.8 111.9 160.5 109.8 173.9 117.2 54.1 6.75 17.71

172.0 79.6 135.6 144.9 163.8 154.7 105.5 134.3 112.0 160.5 110.3 172.9 120.6 53.7 2.76 8.39

165.9 69.6 146.1 134.1 160.8 158.8 105.3 125.3 106.5 145.3 110.2 159.1 121.1 53.8 9.46 18.94

165.6 85.1 147.1 135.8 155.7 160.3 101.5 126.4 108.9 150.2 108.8 173.1 124.7 53.2 7.86 19.87

164.0 84.4 140.9 136.1 163.8 159.6 103.8 126.7 109.1 148.9 108.6 180.5 122.7 51.7 6.40 13.67

160.4 102.9 156.3 125.7 164.1 154.7 105.7 134.1 111.8 160.8 110.1 172.3 117.3 52.0 11.29 29.07

In the present study, the NMR data set for epoxyroussoenone was used to further evaluate our approach. Considering that epoxyroussoenone is a proton-deficient molecule (C15H14O7), and experimental methods used for its structure elucidation were mainly based on heteronuclear proton−carbon correlations (HSQC, HMBC), it is somewhat surprising that only two structural isomers of epoxyroussoenone, 5 and 6, were considered by the authors.62

In summary, application of the CASE algorithm in the original study of coniothyrione would have prevented the publication of the erroneous structure while suggesting a very plausible alternative structure that has not been tested before, but should have been due to its full conformity with experimental data. Only the subsequent application of DFT calculations has confirmed that the revised structure 4 was indeed the correct structure. Epoxyroussoenone. Honmura and co-workers62 isolated two natural products, epoxyroussoenone and epoxyroussoedione, from a culture broth of Roussoella japanensis KT1651. Structures of the compounds were determined using 1D and 2D NMR spectroscopy supported by DFT calculations of 13C chemical shifts and ECD spectra. Comparison of experimental chemical shifts with theoretical values calculated at the EDF2/ 6-31G* level of theory and analysis of NOE data allowed the authors62 to identify the most plausible structures for each of the two isolated natural products. Their structures and absolute configurations were then confirmed by the ECD spectral analysis, which was based on TD-DFT calculations at the BHandHLYP/TZVP level of theory. It turned out that employing X-ray analysis for further structure confirmations was impossible since the authors62 were unable to obtain single crystals of sufficient quality.

For each of the candidate structures, 5 and 6, four stereoisomers were produced and 13C and 1H chemical shifts were calculated for each stereoisomer by DFT methods.62 These predictions, NOE data, and ECD spectra analysis allowed the authors62 to define structure 5 as the most probable geometrical and stereoisomer of epoxyroussoenone. To verify this hypothesis, the molecular formula and spectroscopic NMR data (Table 3S) for epoxyroussoenone62 were submitted to ACD/Structure Elucidator, and an MCD was created by the program (Figure 5). G



Article

ranked structures for epoxyroussoenone, shown in Figure 6, were selected for further analysis. Interestingly, the CASE analysis showed that the correct structure, 5 (#2), was again not the highest ranking structure, while the alternative structure 6 was not even present among the final 13 scaffolds. Note that structures #1 and #2 in Figure 6 are very similar. The difference between the two structures is in the relative orientation of the two large subunits separated by a proton-deficient region. The lack of experimental NMR data, such as long-range proton−carbon (LR-HSQMBC) or carbon−carbon (1,n-ADEQUATE) correlation data, which would likely differentiate one molecule from the other, necessitated the use of alternative methods. Thus, this example clearly showed that if structure elucidation of epoxyroussoenone was supported by CASE, the alternative structure 6 would be rejected at earlier stages of analysis, but instead at least six plausible structural isomers would need to be examined. Further differentiation of the six top-ranked structures of epoxyroussoenone was done by DFT calculations. As in the original work,62 four possible stereoisomers for each candidate structure notated with A, B, C, and D indices were considered (see Figures 7 and 3S). Only the lowest energy conformations were considered in this study. RMSD and maximum carbon chemical shift deviations (max_δ) calculated by DFT for six candidate structures for epoxyroussoenone are shown in Figure 8 (see the complete set of carbon chemical shifts in Table 4S). As seen from Figure 8 and Table 4S, the smallest RMSD (2.22 ppm) was indeed found for structure #2A, which is identical to structure 5 proposed by Honmura and co-

Figure 5. Molecular connectivity diagram for epoxyroussoenone (C15H14O7) based on HSQC and HMBC correlation spectra. Atom properties were set in accordance with the authors’ assumptions.62 1H signal multiplicities were not included in the MCD input.

Strict structure generation for epoxyroussoenone accompanied by 13C chemical shift prediction by an incremental approach took about 1 s and produced 1500 structures. Only 26 structures passed spectral filtering14 (verifying structures for agreement with characteristic chemical shifts in 13C and 1H spectra) and the 4 ppm average deviation threshold. Eventually 13 candidate structures were stored for further analysis after removing the duplicates (k = 1500 → 26 →13, tg = 1 s). The 13C chemical shifts of the filtered structures were calculated using HOSE code and neural network-based approaches, after which the structures were ranked in increasing order of dA values. As in the case of coniothyrione, only six top-

Figure 6. Top-ranked structures for epoxyroussoenone. H



Article

Figure 7. Six top-ranked candidate structures and their stereoisomers (notated with A, B, C, and D indices) for epoxyroussoenone.

Figure 8. RMSD and max_δ between experimental and DFT-calculated δ(13C) for six top-candidate structures of epoxyroussoenone and their stereoisomers A, B, C, and D (Figure 7). I



Article

workers.62 Even though all stereoisomers of structures #3, #4, #5, and #6 can be easily excluded from further analysis due to the large RMSDs, the RMSD of the correct isomer, #2A, is only a fraction better than those of #1B and #2B, 2.27 and 2.24 ppm, respectively. Moreover, the smallest maximum deviation is also observed for structure #2B. As noted above, structures #1 and #2 are very similar. Therefore, to distinguish one structure from the other, it is logical to focus only on those carbons (or other nuclei, such as protons, see below) that are more sensitive to the differences of the two types of scaffolds. Such an approach allows the amplification of discrepancies between the structures that would otherwise be reduced by being averaged out in a complete set of calculated differences. Furthermore, when the comparison is done for relative differences of chemical shifts, it also helps to minimize systematic errors concomitant with chemical shift calculations. Thus, in the case of candidate structures #1 and #2 for epoxyroussoenone the largest difference between them is expected for carbons C5 and C10, situated in the central section of the molecule, where the effect of the relative orientation of the two substructures (O1−C2−C3−C4−C5a− C10a and C6−C7−C8−C9−C9a−C6a) would be most noticeable. The same carbons are also associated with the two chiral centers, and, therefore, they should help to distinguish stereoisomers #2A from #2B as well. In Figure 9

between the 10-OH hydroxy and the carbonyl-4 oxygen would be expected for structure #1. To verify this hypothesis, proton chemical shifts for the three hydroxy groups (5-OH, 6-OH, and 10-OH) were calculated with DFT at the same level of theory as carbon chemical shifts (Table 5S). As seen in Figure 10,

Figure 10. RMSD(OH) and max_δ between experimental and calculated by DFT for 5-OH, 6-OH, and 10-OH protons of epoxyroussoenone’s six top-ranked candidate structures and their stereoisomers A, B, C, and D (Figure 7).

where RMSD(OH) and maximum chemical shift deviations for hydroxy protons 5-OH, 6-OH, and 10-OH for all 24 studied isomers of epoxyroussoenone are shown, the lowest RMSD(OH) and max_δ were indeed found for the correct structure #2A (5), and all stereoisomers of structure #1 had much higher RMSD(OH) and max_δ than those of #2A (5). Again, we have shown that if the epoxyroussoenone structure elucidation was done or supplemented with CASE program results, the alternative structure, 6, would have been rejected in the early stages of data analysis. Importantly, the set of plausible structures delivered by CASE (13 structures) was much more diverse than those originally examined (two items).62 Nevertheless, the DFT calculations of δ(13C) and δ(1H) were able to determine the correct structural isomer with correct relative configuration. For completeness of investigation we repeated CASE structure generation under less stringent conditions, thus creating a wider pool of structures that included the candidate structure 6. This has been accomplished by switching off the spectral filtering and raising the threshold of 13C chemical shift during structure generation. The following results were obtained: k = 1500 → 816 → 382, tg = 1 s. It turned out that the candidate 6 occupied the 124th position in accordance with average deviation dA, while deviations calculated with all three empirical methods mentioned above are very large, 6.5− 7.5 ppm (see Figure 4S).

Figure 9. RMSD(C5,C10) and ΔΔδ(C5,C10) between experimental and those calculated by DFT for C5 and C10 carbons of epoxyroussoenone’s six top-ranked candidate structures and their stereoisomers A, B, C, and D (Figure 7). ΔΔδ(C5,C10) = |(Δδ(C5,C10)exp − Δδ(C5,C10)calc|.

(Table 4S) the RMSD(C5,C10) calculated only for two carbons, C5 and C10, and relative differences between the C5 and C10 carbon chemical shifts (ΔΔδ(C5,C10)) for all stereoisomers of the six top-ranked candidates of epoxyroussoenone are shown. As is clear from Figure 9, the correct structure #2A (5) of epoxyroussoenone can be easily differentiated by the conspicuously smallest RMSD(C5,C10) and ΔΔδ(C5,C10) among all 24 candidates. Another set of NMR parameters that was expected to be capable of differentiating structures #1 and #2 were the proton chemical shifts of the three hydroxy groups. In the case of structure #2 the two hydroxy, 5-OH and 6-OH, and carbonyl-4 oxygen are likely interconnected via hydrogen bonds. This structural motif is conserved in all four stereoisomers of #2, whereas it is absent in structure #1. Instead, the hydrogen bond

■

SUMMARY In summary, we have demonstrated that the application of the CASE expert system provided the most complete sets of structural hypotheses, which were much more diverse and relevant in all three shown examples than those in original studies. Moreover, the use of CASE analysis in many cases would have prevented the publication of erroneous structures.12,50,59,63 In those difficult situations, when CASEsuggested candidates had almost identical 13C chemical shift J



Article

average deviations, the correct structures were still determined based on DFT calculations. Because of DFT calculations, not only were the correct structures elucidated for the structural challenges explored, but the most probable stereoisomer was also distinguished. Currently the approach has been tested only on rigid molecules or on a molecule with very limited flexibility (epoxyroussoenone). For all of the examples considered, a single conformation approximation was assumed. The results obtained justified this assumption handily. However, for more flexible molecules, one would need to do an additional step of the chemical shift averaging for the ensemble of conformations. Usually the conformational analysis is done by using a stochastic generation algorithm, cluster analysis of conformations, and DFT optimization of electronic and/or free Gibbs free energies, which would be used to calculate weights of conformations based on the Boltzmann distribution. We and others have successfully applied this approach,64−66 and we are planning to extend the present study in this direction. It should also be noted that the applied level of DFT methods in the current study was determined by the type of molecules under investigation. For molecules having heavier atoms, it would be advisable to use DFT methods that include relativistic corrections and electron correlation effects.45 Such a precaution would ensure higher accuracy of the DFT methods, which could be needed if the accuracy of traditional methods was insufficient. As the relativistic effects of heavy atoms are generally localized and manifest primarily on the directly bound carbons, investigators could also exclude these carbons from the analysis,45 providing, of course, that the rest of the data are sufficient to support the resolution of the problem. Thus, the present work has demonstrated that the synergistic combination of CASE and DFT methods can serve as an unbiased, reliable, and efficient de novo structure elucidation method and can potentially be applied to those difficult situations when molecular systems are chiral or conformationally flexible and/or when other experimental methods (ADEQUATE, X-ray crystallography, etc.) would be difficult or impossible to use.

■

EXPERIMENTAL SECTION

■

ASSOCIATED CONTENT

■

Experimental and calculated NMR data for aquatolide, coniothyrione, and epoxyroussoenone (PDF)

AUTHOR INFORMATION

Corresponding Authors

*Tel (A. V. Buevich): +1-908-740-3990. Fax: +1-908-740-4042. E-mail: [email protected]. *Tel (M. E. Elyashberg): +7-495-438-2153. Fax: +7-495-4382874. E-mail: [email protected]. ORCID

Alexei V. Buevich: 0000-0002-5968-9151 Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS The authors would like to thank Drs. G. E. Martin and R. T. Williamson for helpful discussions and Prof. M. Hashimoto for sharing details of conformational analysis of epoxyroussoenone.

■

REFERENCES

(1) Structure Elucidation in Organic Chemistry: The Search for the Right Tools; Cid, M.-M.; Bravo, J., Eds.; Wiley-VCH: Weinheim, 2015. (2) https://www.cas.org. (3) Fukushi, E. Biosci., Biotechnol., Biochem. 2006, 70, 1803−1812. (4) Halabalaki, M.; Vougogiannopoulou, K.; Mikros, E.; Skaltsounis, A. L. Curr. Opin. Biotechnol. 2014, 25, 1−7. (5) Breton, R. C.; Reynolds, W. F. Nat. Prod. Rep. 2013, 30, 501− 524. (6) Reynolds, W. F.; Mazzola, E. P. In Progress in the Chemistry of Organic Natural Products. Methodology; Kinghorn, A. D.; Falk, Y.; Kobayashi, J., Eds.; Springer: Heidelberg, 2015; pp 223−310. (7) Přichystal, J.; Schug, K. A.; Lemr, K.; Novák, J.; Havlíček, V. Anal. Chem. 2016, 88, 10338−10346. (8) Nicolaou, K. C.; Snyder, S. A. Angew. Chem., Int. Ed. 2005, 44, 1012−1044. (9) Maier, M. E. Nat. Prod. Rep. 2009, 26, 1105−1124. (10) Amagata, T. In Comprehensive Natural Products II: Chemistry and Biology; Mander, L.; Liu, H.-W., Eds.; Elsevier, 2010; pp 581−621. (11) Suyama, T. L.; Gerwick, W. H.; McPhail, K. L. Bioorg. Med. Chem. 2011, 19, 6675−6701. (12) Elyashberg, M. E.; Williams, A. J.; Blinov, K. A. Nat. Prod. Rep. 2010, 27, 1296−1328. (13) Elyashberg, M. E.; Williams, A. J.; Blinov, K. A. Contemporary Computer-Assisted Approaches to Molecular Structure Elucidation; RSC: Cambridge, 2012. (14) Elyashberg, M. E.; Williams, A. J. Computer-Based Structure Elucidation from Spectral Data. The Art of Solving Problems; Springer: Heidelberg, 2015. (15) Elyashberg, M. E. TrAC, Trends Anal. Chem. 2015, 69, 88−97. (16) Tarantola, A. Nat. Phys. 2006, 2, 492−494. (17) Tarantola, A. Inverse Problem Theory and Methods for Model Parameter Estimation; SIAM: Philadelphia, 2005. (18) Denisov, A. M. Elements of the Theory of Inverse Problems; VSP: Utrecht, 1999. (19) Saurí, J.; Parella, T.; Williamson, R. T.; Martin, G. E. Magn. Reson. Chem., 2016, 54, DOI: 10.1002/mrc.4322. (20) Köver, K. E.; Forgó, P. J. Magn. Reson. 2004, 166, 47−52. (21) Blinov, K. A.; Buevich, A. V.; Williamson, R. T.; Martin, G. E. Org. Biomol. Chem. 2014, 12, 9505−9509. (22) Uhrín, D. In Annual Reports on NMR Spectroscopy; Webb, G. A., Ed.; Elsevier: London, 2010; Vol. 70, pp 1−34. (23) Martin, G. E. In Annual Reports on NMR Spectroscopy; Webb, G. A., Ed.; Elsevier: London, 2011; Vol. 74, pp 215−291. (24) Martin, G. E.; Reibarkh, M.; Buevich, A. V.; Blinov, K. A.; Williamson, R. T. eMagRes. 2014, 3, 215−234.

CASE Analysis. The commercially available ACD/Structure Elucidator program v.1429 was used for the CASE analysis. All calculations were performed on a standard 3.4 GHz PC with 16 GB of RAM. DFT Calculations. DFT calculations of chemical shifts were carried out at the mPW1PW91/6-311+G(2d,p) level of theory with inclusion of the polarizable continuum model for chloroform (scrf = (solvent = chloroform,smd)). Chemical shift values were then derived from calculated isotropic shielding constants by applying the following scaling coefficients: for 13C, −186.5242 (intercept) and −1.0533 (slope); for 1H, 31.8018 (intercept) and −1.0936 (slope).48 Prior to chemical shift calculations molecular geometries were optimized at the B3LYP/6-31+G(d,p) level of theory. DFT calculations of J-couplings were done at the B3LYP/6-311+G(d,p) level of theory (NMR = mixed), as previously described.67 All DFT calculations were done using the Gaussian 09 software package.68

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jnatprod.6b00799. K



Article

(25) Elyashberg, M. E.; Williams, A. J.; Martin, G. E. Prog. Nucl. Magn. Reson. Spectrosc. 2008, 53, 1−103. (26) Nuzillard, J.-M. eMagRes. 2014, 3, 287−293. (27) Steinbeck, C. Nat. Prod. Rep. 2004, 21, 512−518. (28) Pretsch, E.; Tόth, G.; Munk, M. E.; Badertscher, M. ComputerAided Structure Elucidation; Wiley-VCH: New York, 2002. (29) ACD/Structure Elucidator, Version 14.0; Advanced Chemistry Development, Inc.: Ontario, Canada, 2014. http://www.acdlabs.com/ products/com_iden/elucidation/struc_eluc. (30) Elyashberg, M. E.; Blinov, K. A.; Molodtsov, S. G.; Williams, A. J.; Martin, G. E. J. Chem. Inf. Comput. Sci. 2004, 44, 771−792. (31) Elyashberg, M. E.; Williams, A. J.; Blinov, K. A. In Modern NMR Approaches to the Structure Elucidation of Natural Products, Instrumentation and Software; Williams, A.; Martin, G.; Rovnyak, D., Eds.; RSC: Cambridge, 2015; Vol. 1, Chapter 9, pp 187−242. (32) Elyashberg, M.; Blinov, K.; Molodtsov, S.; Smurnyy, Y.; Williams, A. J.; Churanova, T. J. Cheminf. 2009, 1, 3. (33) Elyashberg, M. E.; Blinov, K. A.; Williams, A. J.; Molodtsov, S. G.; Martin, G. E. J. Chem. Inf. Model. 2007, 47, 1053−1066. (34) Blinov, K. A.; Smurnyy, Y. D.; Churanova, T. S.; Elyashberg, M. E.; Williams, A. J. Chemom. Intell. Lab. Syst. 2009, 97, 91−97. (35) Smurnyy, Y. D.; Blinov, K. A.; Elyashberg, M. E.; Churanova, T. S.; Williams, A. J. J. Chem. Inf. Model. 2008, 48, 128−134. (36) Elyashberg, M. E.; Blinov, K. A.; Williams, A. J.; Molodtsov, S. G.; Martin, G. E. J. Chem. Inf. Model. 2006, 46, 1643−1656. (37) Martin, G. E.; Hadden, B. D.; Russell, C. E.; Kaluzny, D. J.; Guido, J. E.; Duholke, W. K.; Stiemsma, B. A.; Thamann, T. J.; Crouch, R. C.; Blinov, K. A.; Elyashberg, M. E.; Martirosian, E. R.; Molodtsov, S. G.; Williams, A. J.; Schiff, P. L., Jr. J. Heterocycl. Chem. 2002, 39, 1241−1250. (38) Blinov, K. A.; Elyashberg, M. E.; Martirosian, E. R.; Molodtsov, S. G.; Williams, A. J.; Sharaf, M. M. H.; Schiff, P. L., Jr.; Crouch, R. C.; Martin, G. E.; Hadden, C. E.; Guido, J. E.; Mills, K. A. Magn. Reson. Chem. 2003, 41, 577−584. (39) Elyashberg, M. E.; Blinov, K. A.; Molodtsov, S. G.; Williams, A. Magn. Reson. Chem. 2012, 50, 22−27. (40) Dufour, C.; Wink, J.; Kurz, M.; Kogler, H.; Olivan, H.; Sabl, S.; Heyse, W.; Gerlitz, M.; Toti, L.; Nusser, A.; Rey, A.; Couturier, C.; Bauer, A.; Brönstrup, M. Chem. - Eur. J. 2012, 18, 16123−16128. (41) Hollinshead, J. 6th Annual ACD/Labs UK user meeting, Old Windsor, UK, 2008. (42) Li, X.-N.; Ridge, C. D.; Mazzola, E. P.; Sun, J.; Gutierrez, O.; Moser, A.; DiMartino, J. C.; MacDonald, S. A., Chen, P. Magn. Reson. Chem. 2016, DOI 10.1002/mrc.4466. (43) Riveira, M. J.; Trigo-Mouriño, P.; Troche-Pesqueira, E.; Martin, G. E.; Navarro-Vázquez, A.; Mischne, M. P.; Gil, R. R. J. Org. Chem. 2015, 80, 7396−7402. (44) Codina, A.; Ryan, R. W.; Joyce, R.; Richards, D. S. Anal. Chem. 2010, 82, 9127−9133. (45) Lodewyk, M. W.; Siebert, M. R.; Tantillo, D. J. Chem. Rev. 2012, 112, 1839−1862. (46) Jain, R. J.; Bally, T.; Rablen, P. R. J. Org. Chem. 2009, 74, 4017− 4023. (47) Rablen, P. R.; Pearlman, S. A.; Finkbiner, J. J. Phys. Chem. A 1999, 103, 7357−7363. (48) http://cheshirenmr.info. (49) Elyashberg, M. E.; Blinov, K. A.; Smurnyy, Y. D.; Churanova, T. S.; Williams, A. Magn. Reson. Chem. 2010, 48, 219−229. (50) San Feliciano, A.; Medarde, M.; Miguel del Corral, J. M.; Aramburu, A.; Gordaliza, M.; Barrero, A. F. Tetrahedron Lett. 1989, 30, 2851. (51) Lodewyk, M. W.; Soldi, C.; Jones, P. B.; Olmstead, M. M.; Rita, J.; Shaw, J. T.; Tantillo, D. J. J. Am. Chem. Soc. 2012, 134, 18550− 18553. (52) Saya, J. M.; Vos, K.; Kleinnijenhuis, R. A.; van Maarseveen, J. H.; Ingemann, S.; Hiemstra, H. Org. Lett. 2016, 17, 3892−3894. (53) http://www.acdlabs.com/comm/elucidation/2013_10.php. (54) Pauli, G. F.; Niemitz, M.; Bisson, J.; Lodewyk, M. W.; Soldi, C.; Shaw, J. T.; Tantillo, D. J.; Saya, J. M.; Vos, K.; Kleinnijenhuis, R. A.;

Hiemstra, H.; Chen, S.-N.; McAlpine, J. B.; Lankin, D. C.; Friesen, J. B. J. Org. Chem. 2016, 81, 878−889. (55) Gil, R. R.; Griesinger, C.; Navarro-Vázquez, A.; Sun, H. In Structure Elucidation in Organic Chemistry−The Search for the Right Tools; Cid, M.-M.; Bravo, J., Eds.; Wiley-VCH: New York, 2015; p 279. (56) Nath, N.; Schmidt, M.; Gil, R. R.; Williamson, R. T.; Martin, G. E.; Navarro-Vázquez, A.; Griesinger, C.; Liu, Y. J. Am. Chem. Soc. 2016, 138, 9548−9556. (57) Keeler, J. Understanding NMR Spectroscopy; John Wiley and Sons, Ltd., 2005. (58) Molinski, T. F.; Morinaka, B. I. Tetrahedron 2012, 68, 9307− 9343. (59) Ondeyka, J. G.; Zink, D.; Basilio, A.; Vicente, F.; Bills, G.; Diez, M. T.; Motyl, M.; Dezeny, G.; Byrne, K.; Singh, S. B. J. Nat. Prod. 2007, 70, 668−670. (60) Kong, F.; Zhu, T.; Pan, W.; Tsao, R.; Pagano, T. G.; Nguyen, B.; Marquez, B. Magn. Reson. Chem. 2012, 50, 829−833. (61) Martin, G. E.; Buevich, A. V.; Reibarkh, M.; Singh, S. B.; Ondeyka, J. G.; Williamson, R. T. Magn. Reson. Chem. 2013, 51, 383− 390. (62) Honmura, Y.; Takekawa, H.; Tanaka, K.; Maeda, H.; Nehira, T.; Hehre, W.; Hashimoto, M. J. Nat. Prod. 2015, 78, 1505−1510. (63) Elyashberg, M. E.; Blinov, K. A.; Molodtsov, S. G.; Williams, A. J. J. Nat. Prod. 2013, 76, 113−116. (64) Willoughby, P. H.; Jansma, M. J.; Hoye, T. R. Nat. Protoc. 2014, 9, 643−660. (65) Sherer, E. C.; Lee, C. H.; Shpungin, J.; Cuff, J. F.; Da, C.; Ball, R.; Bach, R.; Crespo, A.; Gong, X.; Welch, C. J. J. Med. Chem. 2014, 57, 477−494. (66) Buevich, A. V. J. Org. Chem. 2016, 81, 485−501. (67) Williamson, R. T.; Buevich, A. V.; Martin, G. E. Org. Lett. 2012, 14, 5098−5101. (68) Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G. A.; Nakatsuji, H.; Caricato, M.; Li, X.; Hratchian, H. P.; Izmaylov, A. F.; Bloino, J.; Zheng, G.; Sonnenberg, J. L.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Vreven, T.; Montgomery, J. A., Jr.; Peralta, J. E.; Ogliaro, F.; Bearpark, M.; Heyd, J. J.; Brothers, E.; Kudin, K. N.; Staroverov, V. N.; Keith, T.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Rega, N.; Millam, J. M.; Klene, M.; Knox, J. E.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Zakrzewski, V. G.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Dapprich, S.; Daniels, A. D.; Farkas, O.; Foresman, J. B.; Ortiz, J. V.; Cioslowski, J.; Fox, D. J. Gaussian 09, Revision D.01; Gaussian, Inc.: Wallingford, CT, 2009.

L


Synergistic Combination of CASE Algorithms and DFT Chemical Shift

Recommend Documents