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Triquinanes and Related Sesquiterpenes Revisited Computationally: Structure Corrections of Hirsutanols B and D, Hirsutenol E, Cucumin B, Antrodins C−E, Chondroterpenes A and H, Chondrosterins C and E, Dichrocephone A, and Pethybrene Andrei G. Kutateladze* and Dmitry M. Kuznetsov Department of Chemistry and Biochemistry, University of Denver, Denver, Colorado 80208, United States S Supporting Information *

ABSTRACT: NMR data for 90+ natural sesquiterpenes possessing triquinane cores were examined with the help of a relatively fast parametric/DFT hybrid computational method, DU8+. Thirteen of these compounds, i.e., approximately 14% of the sample, required structure correction. This rate of misassignment is similar to the percentage of misassigned halogenated sesquiterpenes reported previously.

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INTRODUCTION Natural sesquiterpenes possessing triquinane cores draw considerable interest because of their biological activity and the synthetic challenges they pose. Some of these structures have been unambiguously elucidated by X-ray crystallography or confirmed by total synthesis. However, the majority of them were characterized by solution NMR. Given that substituted cyclopentanes are particularly challenging for NMR-based stereochemical assignment,1 we analyzed the reported NMR data for 90+ natural triquinanes utilizing our relatively fast parametric/DFT hybrid computational method, DU8+.2 As a result of this highthroughput in silico screening for inconsistencies in reported NMR spectra, we have determined that 13 of these compounds, i.e., approximately 14% of the sample, require structure correction. This rate of misassignment is similar to the percentage of misassigned halogenated sesquiterpenes reported previously.2 A number of these compounds are purported to possess useful pharmacological properties, so it is critical to ensure the correct structure assignment. As more accurate computations of NMR spectra become more practical, they nicely augment the experimental methods of structure elucidation. Computations of 13C chemical shifts have been used most often,3 with many leading to notable structure revisions.4 Nuclear spin−spin coupling constants (SSCCs) contain more structural information but are difficult to compute. Recently, however, practical parametric methods are emerging for fast and accurate evaluation of SSCCs: Bally and Rablen’s work on linear scaling of readily computed Fermi contacts5 and our own related method for parametric NBO-aided6 evaluations of nuclear spin−spin coupling, which we termed relativistic force f ield (rf f).7,8 These advances also depend on the availability of reliable experimental © 2017 American Chemical Society

data. Modern methods for full spin analysis, including HiFSA (1H iterative Full Spin Analysis),9 are essential for adequate reporting and interpretation of experimental data. With all these recent developments, practical computational tools are now available to practitioners in the field of organic chemistry to expedite structure elucidation of new organic compounds or revise misassigned structures. In the world of natural products, these computational tools are becoming a must for successful structure assignment.10 The DU8+ method combines computations of structure and NMR properties of organic molecules at a light level of DFT theory and is implemented with the following components: (a) structure optimization, B3LYP/6-31G(d);11 (b) magnetic shielding, ωB97xD/6-31G(d); (c) Fermi contacts, B3LYP/ DU8; (d) rf f scaling of the computed Fermi contacts according to ref 7b,c to obtain spin−spin coupling constants; and (e) scaling of isotropic magnetic shielding values according to ref 2 to obtain chemical shifts. The parametric corrections to remedy the imperfections of these fast DFT computations are applicable to a broad range of structural features and atom types. In this paper, we report that DU8+ works well for triquinanes allowing for expedited structure validations or revisions.

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RESULTS AND DISCUSSION Natural products in this study were the authors’ personal selection12 and included both linear and angular triquinanes. Their structures were preoptimized with the force field Received: August 10, 2017 Published: September 8, 2017 10795

DOI: 10.1021/acs.joc.7b02018 J. Org. Chem. 2017, 82, 10795−10802

The Journal of Organic Chemistry

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MMFF9413 as implemented in OpenBabel. For structures with freely rotatable groups, conformers were generated using OpenBabel’s confab, whereas the conformers resulting from conformational changes in cyclic cores were generated manually using Chem3D. As parametrization for DU8+ was accomplished on a training set of 1H and 13C NMR spectra recorded in CDCl3,7a fast gas-phase computations were used for such cases. For experimental data obtained in DMSO-d6 or methanol-d4 we utilized a PCM model14 with additional linear scaling of chemical shifts to match experimental data. The details of our computational method are described in ref 2. All DFT computations were carried out at a relatively light level of theory, which is reflected in short total computational “wall-clock” times per structure; see the histogram in Figure 1.

Figure 2. Comparison of DP4 and DU8+. Figure 1. Wall-clock time distribution histogram for DFT calculations of structures, reported in this study, on a 16-core node of a Linux cluster (including geometry optimization, computations of Fermi contacts, and isotropic magnetic shielding values).

{−3.72 1.34}. Other carboxylic acids in the table, including all penarins, exhibit similar behavior, with the absolute deviation of the carboxylate’s chemical shifts varying broadly, presumably depending on concentration and KD’s for dissociation of dimers. This phenomenon is recognized in work of others, including a recent report from the Tantillo group.15 Generally, inter- or intramolecular hydrogen bonds may notably perturb the carbonyls’ chemical shifts. Luckily these effects are mostly localized on the carbonyl carbons and do not propagate into the rest of the molecule. Another limitation of the light/fast DFT approach is that the Boltzman populations and, by extension, the conformer ratios based on the B3LYP/6-31G(d) energies should be interpreted with caution when the differences in calculated energies are small, on the order of 1 kcal/mol or less; for details see ref 2. This is especially true for the cases where the conformational equilibrium occurs on shallow potential energy surfaces (PES), for example, “floppy” enones with extended conjugation. These cases are easier to identify and resolve when the proton spin− spin coupling constants (or chemical shifts) for the two nearly degenerate conformers have a considerable dynamic range, such that one can rely on the SSCC averaging for accurate evaluation of conformers content at the conformational equilibrium. Instructive examples for this are methyl O-methylcantabrenolate and methyl O-methyl-5-epicantabrenolate.31 For both compounds,

As the computations for individual conformers could be distributed over the necessary number of nodes on a Linux cluster, in the majority of studied cases the overall structure validation or revision per triquinane took under 30 min. The majority of triquinane structures corroborated in this work gave good agreement between the calculated and experimental NMR data; see Table 1. Analysis of cases with large absolute deviations of 13C chemical shifts revealed that such deviations are mostly attributable to carbonyl carbons. Besides the obvious point that the magnitude of carbonyl’s chemical shifts is inherently large, there are a few other factors which require additional consideration. Dimerization of free carboxylic acids in CDCl3 solutions presents a challenge as the carboxyl chemical shifts become strongly concentration dependent (while NMR concentrations are rarely reported in the original papers). A representative example for this are chondrosterins I and J. For chondrosterin J, exclusion of the carboxyl improved the rmsd of 13C chemical shift prediction from 1.55 to 0.95 ppm, with maximum negative and positive deviations {−0.78 1.86}. A similar trend is observed for chondrosterin I: exclusion of the carboxy carbon improved the rmsd from 1.91 to 1.16 ppm; 10796



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Table 1. Structures of Triquinanes Validated in This Worka (Original Authors’ Numbering/Nomenclature and Drawings Are Preserved Where Possible)

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Table 1. continued

a

Data presented in this table for each structure: overall rmsd(δC) with the number of reported chemical shifts, /N, used for statistical analysis; largest negative and positive deviations of δC are shown in braces. bThe R epimer at CH−OH is assigned on the basis of 13C NMR chemical shifts comparison. cOne or more carbons are omitted because of potential concentration dependence, as for carboxylic acids in CDCl3, or a possible typo/ misassignment in the original report; for details, see the Supporting Information. 10798



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Table 2. Structures of Triquinanes Revised in This Work (Relative Stereochemistry)

a

Original or most recent assignment. bConfiguration at C4 was not clearly defined in the original report; see the Supporting Information. cOne or more carbons are omitted because of a possible typo/misassignment in the original report; for details, see the Supporting Information.

O-methylcantabrenolate, the calculated chemical shifts for C7 deviates by 5 ppm from the experimental value, while for methyl O-methyl-5-epicantabrenolate this deviation is 6 ppm. Still, we hypothesize that the compounds are not misassigned. Fitting the conformer ratio to the experimental NMR data produced an rmsd of 1.25 ppm and required a very small (below 1 kcal/mol) adjustment of the conformers’ relative energies to reconcile the

seven to eight conformers were identified with Boltzman populations greater than 1% according to their DFT energies. The chemical shift for their β-carbons (C7) exhibited considerable dynamic range of the calculated 13C chemical shifts, which amplifies the effect of small energy errors. For chloroform solutions, Table 1 presents the data where conformers’ populations are evaluated on the basis of their DFT energies. For methyl 10799



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potentially exceeding several kcal/mol, the intramolecularly H-bonded structures gain unrealistic advantage in the DFT/ PCM computations. Under these circumstances the only sensible approach is to fit the computed and experimental data by varying the conformer ratios. In the case of chondroterpene A, such free fitting produces the best match for the epi-2,4,6- (which is equivalent to epi-1,8- relative stereochemistry), rmsd 2.01 ppm. Other candidate stereoisomeric structures did not produce a comparable fit. Pethybrene is another instructive example where significant improvement of the rmsd value (from 3.79 to 0.92 ppm) was achieved by the revision of the entire molecular scaffold. This case demonstrates that one often could use mechanistic considerations to propose candidate structures when the originally proposed structure is too different from the actual. Pethybrene was shown to rearrange into α-isocomene upon acid catalysis. However, the mechanism of this rearrangement from the originally proposed structure of pethybrene, i.e., the shown angular triquinane, required quite an extended carbocationic cascade involving five 1,2-alkyl shifts, Figure 3.51 It is evident that the

computed and the experimental data. For example, for methyl O-methylcantabrenolate, such fitting brings the calculated chemical shift for atom C7 within 2 ppm of its experimental value. We suggest that practitioners in the field will have to continue using their chemical intuition in deciding whether there is a rational explanation for any given deviation, or the structure under consideration is actually misassigned. Very often in our experience, if a structure is truly misassigned, no additional fitting would reconcile the calculated and experimental data. For compounds devoid of the complications described above, DU8+ allows for ready differentiation of the candidate structures. For example, Ikeda and co-workers reported two new triquinanes, isohirsut-1-ene (compound 8) and isohirsut-4-ene (compound 9), Figure 2, with established connectivity but no stereochemical assignment.35a Employing the DP449 method, Tantillo analyzed the complete set of stereoisomeric candidate structures and proposed their stereoassignment (Tantillo’s relative stereochemistry for these two triquinanes is shown in Figure 2 and Table 1).35b We used this opportunity to compare the performance of our method. Two top-matching stereoisomers for isohirsut-1-ene (8) were identified by Tantillo as 8D and 8F. DP4 allows for confident differentiation of these candidate structures, except for the proton shifts, which favor 8F. As shown in Figure 2, DU8+ supports the choice of 8D as the correct structure for isohirsut-1-ene (0.68 ppm vs 1.92 ppm for 8F). The match for proton chemical shifts is shown as two values; the second is with an additional linear scaling of calculated chemical shifts to match the experimental values. This is an accepted practice because proton chemical shifts exhibit much greater sensitivity to media effects.50 Again, the accuracy of the match for calculated proton chemical shifts showed slight preference for 8D. In contrast, the calculated proton spin−spin coupling constants are very similar for these two candidate structures and do not allow for differentiation in this case as both rmsd values, 0.36 and 0.25 Hz, are too close to the inherent accuracy of the method, 0.28 Hz. Comparison of δ(13C) rmsd’s for isohirsut-4-ene (9) also favor the same candidate structure 9D (1.18 ppm) over 9B (2.62 ppm), as predicted by Tantillo.35b

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MISASSIGNED STRUCTURES Our new computational tool allows for fast identification of problematic structures where computed NMR parameters contradict the experimental data. As a result, we have identified and corrected the misassigned structures of 13 naturally occurring triquinanes. The ability to expediently compute both chemical shifts and nuclear spin−spin coupling constants in a synergistic fashion imparted confidence that the structures described below are indeed misassigned; see Table 2. In all of these cases, we were able to propose the corrected structures by postulating several alternative plausible candidates and subsequently matching their computed spectra to the experimental data. For the majority of these structure revisions, the correct structures produced a significantly better match of the calculated and experimental data. However, the challenges presented by both intra- and intermolecular hydrogen bonding described above remain. An instructive example is chondroterpene A,29 which is a tetraol recorded in methanol-d4. We would argue that due to various competing inter- and intramolecular hydrogen bonding opportunities in this compound, the calculated DFT energieseven with the inclusion of a PCM modeldo not allow for accurate estimates of relative conformer energies. With contributions from individual hydrogen bonds to the solvent

Figure 3. Acid-catalyzed rearrangement of pethybrene into α-isocomene.

Figure 4. Single 1,2-shift in protonated (revised) pethybrene leading to protonated α-isocomene. ZPE-corrected DFT B3LYP/6-311+G(d,p) relative energies. 10800



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5218. (c) Kutateladze, A. G.; Mukhina, O. A. J. Org. Chem. 2015, 80, 10838. (8) For structure revisions utilizing rf f, see: (a) Reddy, D. S.; Kutateladze, A. G. Org. Lett. 2016, 18, 4860. (b) Kutateladze, A. G. J. Org. Chem. 2016, 81, 8659. (c) Reddy, D. S.; Kutateladze, A. G. Tetrahedron Lett. 2016, 57, 4727. (d) Mukhina, O. A.; Koshino, H.; Crimmins, M. T.; Kutateladze, A. G. Tetrahedron Lett. 2015, 56, 4900. (9) (a) Napolitano, J. G.; Gödecke, T.; Rodríguez-Brasco, M. F.; Jaki, B. U.; Chen, S.-N.; Lankin, D. C.; Pauli, G. F. J. Nat. Prod. 2012, 75, 238. (b) Napolitano, J. G.; Lankin, D. C.; McAlpine, J. B.; Niemitz, M.; Korhonen, S.-P.; Chen, S.-N.; Pauli, G. F. J. Org. Chem. 2013, 78, 9963. (c) 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. (d) Gao, W.; Napolitano, J. G.; Lankin, D. C.; Kim, J.-Y.; Jin, Y.-Y.; Lee, H.; Suh, J.-W.; Chen, S.-N.; Pauli, G. F. Magn. Reson. Chem. 2017, 55, 239. (10) Nguyen, Q. N. N.; Tantillo, D. J. Chem. - Asian J. 2014, 9, 674. (11) (a) Becke, A. D. J. Chem. Phys. 1993, 98, 5648. (b) Lee, C.; Yang, W.; Parr, R. G. Phys. Rev. B: Condens. Matter Mater. Phys. 1988, 37, 785. (c) Vosko, S. H.; Wilk, L.; Nusair, M. Can. J. Phys. 1980, 58, 1200. (d) Stephens, P. J.; Devlin, F. J.; Chabalowski, C. F.; Frisch, M. J. J. Phys. Chem. 1994, 98, 11623. (12) While the selection was “personal”, we believe that the conclusions of this work are representative, if not statistically significant: the selection was based only on the availability of 13C and 1H NMR data reported in the literature; once a reported triquinane was added to the set, it was “carried through” and never discarded. (13) For the description of MMFF94, see: Halgren, T. A. J. Comput. Chem. 1996, 17, 616 and references therein. (14) For PCM (polarized continuum model), see: Tomasi, J.; Mennucci, B.; Cammi, R. Chem. Rev. 2005, 105, 2999. (15) Saunders, C. M.; Tantillo, D. J. Mar. Drugs 2017, 15, 8. (16) Geum, S.; Lee, H.-Y. Org. Lett. 2014, 16, 2466. (17) Paquette, L. A.; Galemmo, R. A., Jr.; Caille, J.-C.; Valpey, R. S. J. Org. Chem. 1986, 51, 686. (18) (a) Mizuno, H.; Domon, K.; Masuya, K.; Tanino, K.; Kuwajima, I. J. Org. Chem. 1999, 64, 2648. (b) Van Hijfte, L.; Little, R. D.; Petersen, J. L.; Moeller, K. D. J. Org. Chem. 1987, 52, 4647. (19) Takazawa, H.; Kashino, S. Chem. Pharm. Bull. 1991, 39, 555. (20) San Feliciano, A.; Medarde, M.; Gordaliza, M.; Del Olmo, E.; Del Corral, J. M. J. Nat. Prod. 1988, 51, 1153. (21) Schmitz, R.; Frahm, A. W.; Kating, H. Phytochemistry 1980, 19, 1477. (22) Hellwig, V.; Dasenbrock, J.; Schumann, S.; Steglich, W.; Leonhardt, K.; Anke, T. Eur. J. Org. Chem. 1998, 1998, 73. (23) Li, H.-J.; Xie, Y.-L.; Xie, Z.-L.; Chen, Y.; Lam, C.-K.; Lan, W.-J. Mar. Drugs 2012, 10, 627. (24) Li, H.-J.; Jiang, W.-H.; Liang, W.-L.; Huang, J.-X.; Mo, Y.-F.; Ding, Y.-Q.; Lam, C.-K.; Qian, X.-J.; Zhu, X.-F.; Lan, W.-J. Mar. Drugs 2014, 12, 167. (25) Huang, L.; Lan, W.-J.; Deng, R.; Feng, G.-K.; Xu, Q.-Y.; Hu, Z.-Y.; Zhu, X.-F.; Li, H.-J. Mar. Drugs 2016, 14, 157. (26) Yun, B.-S.; Lee, I.-K.; Cho, Y.; Cho, S.-M.; Yoo, I.-D. J. Nat. Prod. 2002, 65, 786. (27) Yoo, N.-H.; Kim, J.-P.; Yun, B.-S.; Ryoo, I.-J.; Lee, I.-K.; Yoon, E.S.; Koshino, H.; Yoo, I.-D. J. Antibiot. 2006, 59, 110. (28) Wang, G.-Y.-S.; Abrell, L. M.; Avelar, A.; Borgeson, B. M.; Crews, P. Tetrahedron 1998, 54, 7335. (29) Hsiao, G.; Chi, W.-C.; Pang, K.-L.; Chen, J.-J.; Kuo, Y.-H.; Wang, Y.-K.; Cha, H.-J.; Chou, S.-C.; Lee, T.-H. J. Nat. Prod. 2017, 80, 1615. (30) (a) NMR data are from the total synthesis: Vo, N. H.; Snider, B. B. J. Org. Chem. 1994, 59, 5419. (b) The original 13C NMR data from ref 32 (San Feliciano et al.) has a typographical error (according to Snider, the peak reported at 107.02 should be corrected to 157.02 ppm). (31) Marco, J. A.; Sanz-Cervera, J. F.; Morante, M. D.; Garcia-Lliso, V.; Valles-Xirau, J.; Jakupovic, J. Phytochemistry 1996, 41, 837. (32) San Feliciano, A.; Del Corral, J. M.; Caballero, E.; Alvarez, A.; Medarde, M. J. Nat. Prod. 1986, 49, 845.

revised structure of pethybrene helps revise this excessively complex mechanism and allows for protonated pethybrene to reach the isocomene framework in a single protonation-initiated 1,2-shift. Figure 4 shows ZPE-corrected relative energies for this 1,2-shift calculated at the B3LYP/6-311+G(d,p) level of DFT theory. It is easy to see that this Wagner−Meerwein rearrangement step has a very small energy barrier of 1.1 kcal/mol with the product, i.e., protonated α-isocomene lying 4.1 kcal/mol below the initial carbocation.

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CONCLUSIONS High-throughput in silico structure validation and revision was carried out on a set of 90+ natural products possessing the triquinane structures, utilizing a recently developed hybrid parametric/DFT method DU8+. As the method relies on fast and accurate computations of both NMR chemical shifts and spin−spin coupling constants, it offers a synergistic and reliable double-criterion tool for expedient structure validation or revision based on ubiquitous 1D NMR data. Thirteen prominent triquinane structures were revised, which is in keeping with the 13−15% rate of misassignment reported by us previously.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.joc.7b02018. Computational details (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. ORCID

Andrei G. Kutateladze: 0000-0003-3066-517X Dmitry M. Kuznetsov: 0000-0002-7956-1833 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research is supported by the NSF, CHE-1665342. REFERENCES

(1) (a) Lipnick, R. L. J. Am. Chem. Soc. 1974, 96, 2941. (b) Constantino, M. G.; Junior, V. L.; da Silva, G. V. J. Magn. Reson. Chem. 2003, 41, 641. (c) Lacerda, V., Jr.; da Silva, G. V. J.; Constantino, M. G.; Tormena, C. F.; Williamson, R. T.; Marquez, B. L. Magn. Reson. Chem. 2006, 44, 95. (2) Kutateladze, A. G.; Reddy, D. S. J. Org. Chem. 2017, 82, 3368. (3) For reviews, see: (a) Lodewyk, M. W.; Siebert, M. R.; Tantillo, D. J. Chem. Rev. 2012, 112, 1839. (b) Bifulco, G.; Dambruoso, P.; GomezPaloma, L.; Riccio, R. Chem. Rev. 2007, 107, 3744. (c) Wiitala, K. W.; Cramer, C. J.; Hoye, T. R. Magn. Reson. Chem. 2007, 45, 819. (4) (a) Rychnovsky, S. D. Org. Lett. 2006, 8, 2895. (b) Braddock, D. C.; Rzepa, H. S. J. Nat. Prod. 2008, 71, 728. (c) Saielli, G.; Nicolaou, K. C.; Ortiz, A.; Zhang, H.; Bagno, A. J. Am. Chem. Soc. 2011, 133, 6072. (d) 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. (5) Bally, T.; Rablen, P. R. J. Org. Chem. 2011, 76, 4818. (6) (a) Foster, J. P.; Weinhold, F. J. Am. Chem. Soc. 1980, 102, 7211. (b) Reed, A. E.; Weinhold, F. J. Chem. Phys. 1983, 78, 4066. (c) Reed, A. E.; Weinstock, R. B.; Weinhold, F. J. Chem. Phys. 1985, 83, 735. (7) (a) Kutateladze, A. G.; Mukhina, O. A. J. Org. Chem. 2014, 79, 8397. (b) Kutateladze, A. G.; Mukhina, O. A. J. Org. Chem. 2015, 80, 10801



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(33) Amouzou, E.; Ayer, W. A.; Browne, L. M. J. Nat. Prod. 1989, 52, 1042. (34) Chen, Z.-M.; Chen, H.-P.; Wang, F.; Li, Z.-H.; Feng, T.; Liu, J.-K. Fitoterapia 2015, 102, 61. (35) (a) Yamada, Y.; Arima, S.; Nagamitsu, T.; Johmoto, K.; Uekusa, H.; Eguchi, T.; Shin-ya, K.; Cane, D. E.; Ikeda, H. J. Antibiot. 2015, 68, 385. (b) Nguyen, Q. N. N.; Tantillo, D. J. J. Antibiot. 2016, 69, 534. (36) Weyerstahl, P.; Marschall, H.; Seelmann, I.; Jakupovic, J. Eur. J. Org. Chem. 1998, 1998, 1205. (37) Reina, M.; Nold, M.; Santana, O.; Orihuela, J. C.; GonzalezColoma, A. J. Nat. Prod. 2002, 65, 448. (38) Otto, A.; Porzel, A.; Schmidt, J.; Wessjohann, L.; Arnold, N. Phytochemistry 2014, 108, 229. (39) NMR data are from: Willmore, N. D.; Goodman, R.; Lee, H.-H.; Kennedy, R. M. J. Org. Chem. 1992, 57, 1216. (40) Liermann, J. C.; Schüffler, A.; Wollinsky, B.; Birnbacher, J.; Kolshorn, H.; Anke, T.; Opatz, T. J. Org. Chem. 2010, 75, 2955. (41) Abate, D.; Abraham, W.-R. J. Antibiot. 1994, 47, 1348. (42) Giannetti, B. M.; Steffan, B.; Steglich, W. Tetrahedron 1986, 42, 3587. (43) Mehta, G.; Murthy, A. S. K. Tetrahedron Lett. 2003, 44, 5243. (44) Pierre, M.-C.; Tenaglia, A.; Santelli, M. Tetrahedron 1998, 54, 14803. (45) (a) Lee, H.; Kang, T.; Lee, H.-Y. Angew. Chem., Int. Ed. 2017, 56, 8254. (b) Clarke, D. B.; Hinkley, S. F. R.; Weavers, R. T. Tetrahedron Lett. 1997, 38, 4297. (46) Tian, X.; Li, L.; Hu, Y.; Zhang, H.; Liu, Y.; Chen, H.; Ding, G.; Zou, Z. RSC Adv. 2013, 3, 7880. (47) (a) Reyes-Trejo, B.; Joseph-Nathan, P. Phytochemistry 1999, 51, 75. (b) Bohlmann, F.; Jakupovic, J. Phytochemistry 1980, 19, 259. (48) Fitjer, L.; Majewski, M.; Monzo-Oltra, H. Tetrahedron 1995, 51, 8835. (49) Smith, S. G.; Goodman, J. M. J. Am. Chem. Soc. 2010, 132, 12946. (50) As an example, such additional linear scaling worked well for Rychnovsky in the high-profile revision of hexacyclinol; see ref 4a. (51) Saritas, Y.; von Reuβ, S. H.; König, W. A. Phytochemistry 2002, 59, 795.

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