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ScienceDirect Recent advances and new strategies in the NMR-based identification of natural products Maria Halabalaki, Konstantina Vougogiannopoulou, Emmanuel Mikros and Alexios Leandros Skaltsounis Nature comprises an untapped pool of unique compounds with high structural uniqueness and exceptional properties. At the core of natural products (NPs) discovery is the identification procedure and NMR remains the most efficient method. Technical improvements such as miniaturized and crycogenic NMR probes along with hyphenation capabilities and computational support are at the center of evolution. Concepts such as dereplication and metabolomics are increasingly adopted in NPs using the power of databases, currently fragmented. The introduction and utilization of these technical and computational implements could lead NPs research to more comprehensive structure identification and new holistic perspectives. Addresses Division of Medicinal and Natural Products Chemistry, Department of Pharmacy, University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece Corresponding author: Mikros, Emmanuel ([email protected])
Current Opinion in Biotechnology 2014, 25:1–7 This review comes from a themed issue on Analytical biotechnology Edited by Frank L Jaksch and Savas¸ Tay
0958-1669/$ – see front matter, # 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.copbio.2013.08.005
Introduction Nature-derived small molecules continue to comprise an exceptional cluster of chemical entities of high interest and exploitation potentials [1]. Currently, natural products (NPs) are advancing a dynamic comeback in the modern drug discovery [2]. Much effort is given to overcome the bottlenecks of the entire process such as the laborious and repeated isolation steps, the re-discovery of known compounds, the quantity limitations and the lack of systematic registration of origin organisms and derived compounds. The last decade, NPs discovery pipeline has become significantly technology-driven with nuclear magnetic resonance (NMR) spectroscopy occupying a vital position especially in identification and structure elucidation. Technical improvements, mainly related to the new www.sciencedirect.com
generation of miniaturized and cryogenic probes results to increase of sensitivity and speed. Computational support is penetrating into the data mining processes. Dereplication and metabolomic approaches are more and more implemented leading NPs research to more holistic and integrated perceptions. These advances are gradually redirecting the NPs’ research to the identification of mixtures and extracts than to single compounds which is currently an open discussion (Figure 1) [2]. Under the aforementioned prism, this review will focus on the latest advances in NPs identification related to NMR spectroscopy. Specifically, improvements in NMR hardware, pulse sequences, hyphenation capabilities as well as new structural elucidation tools and intelligent software are presented. Finally, the role of NMR in modern dereplication and metabolomic approaches is discussed.
NMR instrumentation NMR is an indispensible analytical method in all structure elucidation protocols. Up-to-date challenges and key targets are the enhancement of reliability and speed in NPs research. Several advances have taken place concerning the inherent capabilities of NMR apparatuses, able to reduce experiment times and increase sensitivity toward more efficient analyses of natural materials available in microgram quantities [3]. In the timeline of probe advances, sample volumes are diminished and analysis is accelerated: from the conventional 5 mm probe (600 mL), to the 3 mm probes (140 mL), to the microvolume probes of 1.0–1.7 mm (approx. 10 mL), to the microcoil flow probes (1.5–2.5 mL of active volume) [4,5]; similarly, operational temperatures decrease: from the room temperature to the cryogenic probes, an approximate 3.5 increase in signal-to-noise ratio (S/N) is observed [4]. The new trend is the combination of micro-properties and cryo-properties into a single powerful probe, such as the 1.7 mm TXI MicroCryoProbeTM (Bruker), implemented in integrated NMR platforms and hyphenated systems [6,7]. Lately, the development of high resolution magic angle spin NMR (HR-MAS NMR) probes is giving the possibility to analyze intact tissues. However, while MAS is incorporated in food chemistry where both primary and secondary metabolites are of importance, it has not been yet widely introduced in NPs [8,9]. Current Opinion in Biotechnology 2014, 25:1–7
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Figure 1
Data Mining / Databases Spectra simulation / Structure Elucidation / Matching and learning algorithms / Data manipulation microvolume capillary cryogenic
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NMR-based advances in the timeline of contemporary NPs discovery.
Experiments The swift implementation of the progress made in NMR hardware is not followed by a proportional response in NMR experiments related to NPs chemistry and structure elucidation. Even nowadays, 1D (1H, 13C 15N), 2D (COSY, TOCSY, NOESY, HSQC, HMBC, and HNMBC) and various combinations thereof are basically used in routine analysis [10,11]. However, crucial complications in structure elucidation can arise from the inability of routine experiments to determine the exact skeletal connectivity. The evolution of the INADEQUATE experiment, 1,1-ADEQUATE is introduced for the accurate determination of 1JC–C connectivity, which is decisive for the elucidation of demanding structures, such as molecules with low degree of protonation [12,13]. Although 1,1-ADEQUATE is still considered as time consuming despite the advances in terms of instrumentation, a structure revision of the natural antibiotic coniothyrione was achieved. In this case, the acquisition time was approximately 17 h for 1.2 mg of sample, even by using the 1.7 mm MicroCryoProbeTM (Figure 2) [14]. The determination of the relative configuration of NPs is still a major concern especially among those of marine origin. In NPs, configurational analysis (Murata’s method) through NMR is typically performed with the measurement of both H–H and C–H coupling constants through a Current Opinion in Biotechnology 2014, 25:1–7
combination of experiments such as COSY-DQF, E.COSY, HETLOC, HSQC-HECADE, HSQMBC, IPAP-HSQMBC, and J-resolved methodologies [10]. Recently, selective pulse sequences like JselHSQMBC-IPAP allow the simultaneous measurement of H–H and long-range C–H coupling constants (nJCH, n > 1) [15]. Likewise, a slightly modified HSQC-TOCSY experiment reported in 2012 appears very robust for the determination of small heteronuclear coupling constants important in the structural and configurational determination of NPs [16]. Similarly, the significance of certain parameters like residual dipolar couplings (RDCs) is not yet appreciated for the extraction of valuable information involved in conformation analysis. Alignment media (liquid crystals, gels, paramagnetic tags) are used to manipulate the order of the molecule and render RDCs measurable. Through RDCs and in combination with theoretical calculations, a restricted number of conformers validated through NOE data could be proposed for both rigid and flexible molecules [17]. Apart from addressing the question of structural clarity, much attention has been given lately on parallel acquisition NMR strategies with multiple receivers operating in parallel, and tuned to different nuclei. Fast Proton and nitrogen and carbon et alia (PANACEA) and Parallel ‘‘Ultrafast’’ Spectroscopy (PUFSY) pulses allow the combination of www.sciencedirect.com
NMR-based advances in natural products identification Halabalaki et al.
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Structure of retrorsine and C–C correlations extracted from the 60 Hz optimized 1,1-ADEQUATE spectrum shown on the right. NMR analysis was performed with a 1.6 mg sample of retrorsine dissolved 30 mL of DMSO-d6, with the aid of an 600 MHz apparatus equipped with a 1.7 mm MicroCryoProbe. Adapted with permission from Martin et al. [12]. Copyright 2011 American Chemical Society.
standard sequences into a single entity, allowing the structure of small molecules to be determined in a single measurement. In the case of fast-PANACEA, INADEQUATE, HSQC, and HMBC pulses are combined, while measurements are accelerated by exciting individual 13C sites by selective radiofrequency pulses acting on double-quantum coherence [18]. On the other hand, PUFSY aims to provide a simultaneous homonuclear and heteronuclear COSY correlations by using a phase modulated encoding for the homonuclear portion, followed by a pair of hard 908 read pulses acting on the heteronuclei [19]. The significance of the synergy between NMR hardware and innovative pulse sequences is greatly demonstrated in the work performed by Queiroz Ju´nior et al. This is the first time that an ultrafast COSY pulse sequence is applied to a hyphenated LC–NMR separation of a mixture of three natural flavonoids (naringin, epicatechin, and naringenin). In 2D ultrafast COSY, the detection is based on echo-planar imaging; thus every H–H correlation can be observed basically in one scan. Through this methodology, analytes elute from the chromatographic column, while COSY spectra are acquired every 12 s, making the characterization of each molecule possible, even when overlapping occurs. The detection volume was only www.sciencedirect.com
60 mL, while two scans have proven sufficient to obtain spectra with optimized resolution and sensitivity. This application portrays the generality of ultrafast methodologies in NPs, placing LC–NMR as an analytical routine methodology [20]. Since the tendency in NPs chemistry is the analysis of mixtures, there is a need for experiments offering high resolving power in both chemical shifts and coupling constants [21]. The diffusion ordered spectroscopy (DOSY) experiment, the refined OneShot45 DOSY and the matrix-assisted DOSY are implemented for the virtual separation of the components of a mixture, offering greater peak dispersion and more accurate metabolite assignment [22,23]. Recently, Evans et al. have proposed an alternative model employing the DOSY-calculated diffusion coefficients, through which, the molecular weight of several analytes in a range of solvents can be proposed via a modified Stoke–Einstein equation [24]. This method remains to be tested by applications in real samples such as extracts and if successful, this could be an interesting progress in the role of NMR in NPs chemistry.
LC–NMR dereplication The ‘‘rediscovery’’ of known structures in natural extracts is a major setback in NPs chemistry, practically Current Opinion in Biotechnology 2014, 25:1–7
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invalidating the entire discovery procedure. NMR-based dereplication approaches place the identification of known compounds early in the discovery line and also enable the targeted isolation of unknown NPs. Several apparatuses are combined with HR-NMR, and hyphenation is realized at a technological (on-line) or at a data mining (off-line) level. Nowadays, the most technologically complete platform facilitating the successful dereplication in terms of time, ease, automation, quality, and identification yield is LC–DAD-HRMS-SPE-NMR equipped with microvolume, cryogenic, flow probes, or combinations thereof [25,26]. Since dereplication is completely consistent with high throughput mentality, peripheral devices such as sample changers and robotic solvent preparation managers are strongly promoted in such modern operational platforms (e.g. NMRbot–Bruker) [27]. Because of the expected impact of hyphenation in NMR platforms in the future, several related research papers are progressively published and excellent reviews discuss technical characteristics and applications [28]. Recently, supplementary advancements have been also proposed, promising to assist in more demanding issues of NPs’ structural elucidation in combined systems. For instance, the hyphenation of NMR with CD devices aims to answer stereochemistry issues [29], or GC–NMR setups address the facile identification of volatiles [30]. To our opinion, technical advances to be evolved in the future, leading to hyper-hyphenated NMR platforms will
point the way to more efficient identification strategies of original NPs.
Computational support and NMR databases Generally, in dereplication studies computational support is required regarding data handling, processing and structure elucidation. Even though user-friendly and sophisticated software packages are accessible for efficacious data mining, they are not widely employed for dereplication purposes in NPs (Figure 3) [6]. This task is indeed rather challenging mainly due to NPs’ unique and unexpected spectral patterns and the residual complexity frequently observed. For instance, prediction and simulation software such as PERCH, in combination with 1H iterative full spin analysis (HiFSA approach), provide an accurate distinction of NPs with nearly identical NMR spectra. Even though proposed by the authors as a tool for puzzling qNMR analyses, it could be an alternative source of dereplication data [31]. In addition, computer-assisted structural elucidation (CASE) is a methodology that allows users to input their NMR data, and through matching algorithms the molecule is hopefully identified. For this purpose, software used are mainly the Structure Elucidator by ACD Labs [32], StrucEluc [33], and CCASA [34], the latter being open to public use [35]. However, the success of these approaches is dependent on the quality of the spectra to be processed and the efficacy of the algorithms used. Additionally, the software used
Figure 3
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Schematic representation of the multiple aspects of computational support on modern NPs chemistry. Current Opinion in Biotechnology 2014, 25:1–7
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On the right, the three major triterpenes identified in a complex mixture that resulted from the fractionation procedure of the ethyl acetate extract of Actaea racemosa (E2). On the left, the classification of all Actaea triterpenes based on Canonical Discriminal Analysis with the use of the methyl groups’ dH. (a & b) Adapted with permission from Qiu et al. [41]. Copyright 2012 American Chemical Society.
present an inherent dependence on the databases from which data are extracted. Thus, NPs databases are of high priority and importance for structure search, matching and identification (Figure 3). Unfortunately, NMR databases dedicated to NPs appear as in-house, fragmented attempts, or are chemical group/organism/NMR experiment/solvent, among others, specific. For instance, MarinLit, and AntiBase, specialize in marine, fungal and microorganism NPs, NAPROC-13 is based on 13C resonances [36], while recently compiled TOCCATA uses 13C-labeled NPs [37]. Commercial NMR databases are limited to few vendors, like the SpecInfo database of Wiley and Bruker’s NMR database [38,39]. Overall, the synergy of copious NMR set-ups with sophisticated algorithms in conjunction with pattern recognition NMR databases, promise excellent dereplication studies. Such an HPLC-PDA-HRMS-SPENMR (600 MHz) based approach was suggested by Johansen et al. [40]. In this work, a cryogenically cooled 1.7 mm TCI probe was employed accompanied with a robotic liquid handler for SPE elution and capillary NMR tubes filling as well as an automated sample changer. Two different extracts of approx. 600 and 50 mg were analyzed by a single injection. Following online isolation, the acquired spectra were matched against an in-house database using an algorithm developed and operating under Matlab. For both extracts analyzed the matching was found successful with low rate of false structures while due to the relative www.sciencedirect.com
flexibility of the algorithm the method was also considered robust. Similarly, addressing a restricted however structurally more demanding group of NPs is the approach suggested by Qiu and coworkers (Figure 4) [41]. In this work the hyphenation factor is not exploited but much attention is given to the construction of a triterpenes database (Actaea triterpenes) and the development of computer-aided tools for dereplication. Moreover, qNMR is utilized pointing out the important problem of purity (residual complexity) afflicting NPs’ structure elucidation and bioevaluation. Using methyl chemical shifts as main structural indicators and descriptors, classification models (canonical discriminant analysis – CDA, classification binary trees – CBT) were generated and applied to the in-house library resulting to a high matching rate. For the measurements, a 700 MHz 1.7 mm microcryoprobe equipment was used. However, the implementation rhythm of both hyphenation and computation is relatively slow, comprising so far occasional and circumstantial attempts. The use of approaches that exploit the potency of hyphenation and advanced instrumentation along with the power of computational assisted data manipulation and databases needs to be more systematic.
Metabolomics Despite the unequivocal value of metabolomics, the father of NMR-based metabolomics J. Nicholson said Current Opinion in Biotechnology 2014, 25:1–7
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‘‘Metabolomics has about 20 published definitions, conflicting but all analytical, all about measuring some stuff in some other stuff’’ [42]. This apparent confusion has been also imported in the so-called ‘‘plant metabolomics’’ or in broader terms ‘‘NPs metabolomics’’. Since 2000 with the first relevant publications, several other related definitions arose (e.g. fingerprinting, profiling). Currently, in NPs there is an array of metabolomics applications from chemotaxonomy, quality control, flux analysis to new NPs discovery [43,44]. Interestingly, the latter area still remains unexplored considering the low yield of new chemical entities resulting from metabolomics. Thus, several metabolomic concepts are successfully applied satisfying the demands of analysis, data mining, and unmasking significant compounds. However, it is rather common for those key metabolites to remain structurally uncharacterized. Interestingly, recognizing this situation, the metabolomics standard initiative (MSI) set specific protocols for reporting chemical analysis introducing different levels for metabolite identification (MI) ranking from MI-1 (completely identified structures) to MI-4 (unknown metabolites exhibiting NMR signals or patterns) [45].
algorithms enabling structure or spectra prediction will give recourses for the structure elucidation of NPs in complicated mixtures. Comprehensive and qualified open access NPs databases will be able to support considerably not only the accelerated identification of unknown molecules but also more importantly the dereplication process. Finally, metabolomics if integrated and harmonized with both traditional and contemporary approaches could effectively strengthen the high throughput manner of identification and discovery in NPs chemistry.
According to our view, the dissociation of metabolomics from NPs discovery is due to the relative absence of wide and open-access NPs spectral databases and especially NMR-based for facile structure elucidation; the employment of rather traditional NMR experiments in metabolomic analysis routine; the conservative employment of sophisticated algorithms and statistical tools in data manipulation and mining; and probably the general perception that NPs chemistry and metabolomics address different objectives. For instance, NMR experiments such as DOSY and JRES could be rather useful also for unraveling new structures. Moreover, decisive structural information could be derived from statistical interpretation methods applied in metabonomics such as statistical heterospectroscopy (SHY) [46], statistical total correlation spectroscopy (STOCSY) [47], SubseT optimization by reference matching (STORM) [48] and cluster analysis statistical spectroscopy (CLASSY) [49]. Such implements will be more than welcomed in the toolbox of plant metabolomics, as they could assist considerably to the targeted identification of significant metabolites and open new directions in the interpretation of their role.
2.
Conclusion The scrutiny of unknown structures still lies in the heart of NPs research and NMR persistently continues leading this procedure. Even today, when important technological advances occur the unambiguous identification remains a challenging task. On the other hand, the current orientation of NPs research to the elaboration of mixtures aggravates further the identification process. Toward this direction, computational tools might hold a key role in the future. Expert systems equipped with versatile Current Opinion in Biotechnology 2014, 25:1–7
Acknowledgement This work was supported by the Commission of the European Community through the INsPiRE project (EU-FP7-REGPOT-2011-1, proposal 284460).
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12. Martin GE, Hilton BD, Blinov KA: HSQC-1,1-ADEQUATE and HSQC-1,n-ADEQUATE: enhanced methods for establishing adjacent and long-range 13C–13C connectivity networks. J Nat Prod 2011, 74:2400-2407. 13. Martin GE, Blinov KA, Williamson RT: HMBC-1,n-ADEQUATE spectra calculated from HMBC and 1,n-ADEQUATE spectra. Magn Reson Chem 2013, 51:299-307. 14. Martin GE, Buevich AV, Reibarkh M, Singh SB, Ondeyka JG, Williamson RT: Coniothyrione: anatomy of a structure revision. Magn Reson Chem 2013. [Epub ahead of print]. 15. Saurı´ J, Parella T: Simultaneous measurement of J(HH) and two different nJ(CH) coupling constants from a single multiply edited 2D cross-peak. Magn Reson Chem 2013, 51:397-402. 16. Saurı´ J, Espinosa JF, Parella T: A definitive NMR solution for a simple and accurate measurement of the magnitude and the sign of small heteronuclear coupling constants on protonated and non-protonated carbon atoms. Angew Chem Int Ed 2012, 51:3919-3922. 17. Kummerlo¨we G, Luy B: Residual dipolar couplings as a tool in determining the structure of organic molecules. Trends Anal Chem 2009, 28:483-493. 18. Kupcˇe E, Freeman R: Molecular structure from a single NMR sequence (fast-PANACEA). J Mag Reson 2010, 206:147-153. 19. Donovan KJ, Kupcˇe E, Frydman L: Multiple parallel 2DNMR acquisitions in a single scan. Angew Chem Int Ed 2013, 52:4152-4155. 20. Queiroz Ju´nior LHK, Queiroz DPK, Dhooghe L, Ferreira AG, Giraudeau P: Real-time separation of natural products by ultrafast 2D NMR coupled to on-line HPLC. Analyst 2012, 137:2357-2361. In this article the first application of ultrafast COSY on an online hyphenated LC–NMR platform is described. Specifically, it regards the analysis of a mixture of flavonoids (naringin, epicatechin and naringenin) as a proof of concept of the application of this technique in routine analysis. 21. Ludwig C, Viant MR: Two-dimensional J-resolved NMR spectroscopy: review of a key methodology in the metabolomics toolbox. Phytochem Anal 2010, 21:22-32. 22. Adams RW, Aguilar JA, Cassani J, Morris GA, Nilsson M: Resolving natural product epimer spectra by matrix-assisted DOSY. Org Biomol Chem 2011, 9:7062-7064. 23. Botana A, Aguilar JA, Nilsson M, Morris GA: J-modulation effects in DOSY experiments and their suppression: the Oneshot45 experiment. J Magn Reson 2011, 208:270-278. 24. Evans R, Deng Z, Rogerson AK, McLachlan AS, Richards JJ, Nilsson M, Morris GA: Quantitative interpretation of diffusionordered NMR spectra: Can we rationalize small molecule diffusion coefficients? Angew Chem Int Ed 2013, 52:3199-3202. 25. Wubshet SG, Johansen KT, Nyberg NT, Jaroszewski JW: Direct 13 C NMR detection in HPLC hyphenation mode: analysis of Ganoderma lucidum Terpenoids. J Nat Prod 2012, 75:876-882. 26. Johansen KT, Ebild SJ, Christensen SB, Godejohann M, Jaroszewski JW: Alkaloid analysis by high-performance liquid chromatography-solid phase extraction-nuclear magnetic resonance: new strategies going beyond the standard. J Chromatogr A 2012, 1270:171-177. 27. Clos LJ II, Jofre MF, Ellinger JJ, Westler WM, Markley JL: NMRbot: Python scripts enable high-throughput data collection on current Bruker BioSpin NMR spectrometers. Metabolomics 2013, 9:558-563. 28. Sturm S, Seger C: Liquid chromatography–nuclear magnetic resonance coupling as alternative to liquid chromatographymass spectrometry hyphenations: curious option or powerful complementary routine tool? J. Chromatogr A 2012, 1259:50-61. 29. Tokunaga T, Okamoto M, Tanaka K, Tode C, Sugiura M: Chiral liquid chromatography–circular dichroism–NMR for estimating separation conditions of chiral HPLC without authentic samples. Anal Chem 2010, 82:4293-4297. 30. Ku¨hnle M, Kreidler D, Holtin K, Czesla H, Schuler P, Schurig V, Albert K: Online coupling of enantioselective capillary gas www.sciencedirect.com
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chromatography with proton nuclear magnetic resonance spectroscopy. Chirality 2010, 22:808-812. 31. Napolitano JG, Lankin DC, Graf TN, Brent Friesen J, Chen SN, McAlpine JB, Oberlies NH, Pauli GF: HiFSA fingerprinting applied to isomers with near-identical NMR Spectra: The silybin/isosilybin case. J Org Chem 2013, 78:2827-2839. 32. Elyashberg M, Blinov K, Molodtsov S, Williams AJ: Structure revision of asperjinone using computer-assisted structure elucidation methods. J Nat Prod 2013, 76:113-116. 33. Elyashberg M, Williams AJ, Blinov K: Structural revisions of natural products by computer-assisted structure elucidation (CASE) systems. Nat Prod Rep 2010, 27:1296-1328. 34. Plainchont B, Nuzillard J-M: Structure verification through computer-assisted spectral assignment of NMR spectra. Magn Reson Chem 2013, 51:54-59. 35. http://eos.univ-reims.fr/LSD//JmnSoft/CASA/. 36. Lo´pez-Pe´rez JL, Thero´n R, del Olmo E, Dı´az D: NAPROC-13: a database for the dereplication of natural product mixtures in bioassay-guided protocols. Bioinformatics 2007, 23:3256-3257. 37. Bingol K, Zhang F, Bruschweiler-Li L, Bru¨schweiler R: TOCCATA: a customized carbon total correlation spectroscopy NMR metabolomics database. Anal Chem 2012, 84:9395-9401. 38. Blunt JW, Munro MHG: Data, 1H-NMR databases, data manipulation. Phytochem Rev 2012:1-13. 39. Spraul M, Schu¨z B, Humpfer E, Mo¨rtter M, Scha¨fer H, Koswig S, Rinke P: Mixture analysis by NMR as applied to fruit juice quality control. Magn Reson Chem 2009, 47:S130-S137. 40. Johansen KT, Wubshet SG, Nyberg NT: HPLC-NMR revisited: using time-slice high-performance liquid chromatographysolid-phase extraction-nuclear Magnetic resonance with database-assisted dereplication. Anal Chem 2013, 85:3183-3189. 41. Qiu F, Imai A, McAlpine JB, Lankin DC, Burton I, Karakach T, Farnsworth NR, Chen S-N, Pauli GF: Dereplication, residual complexity, and rational naming: the case of the Actaea triterpenes. J Nat Prod 2012, 75:432-443. 42. Hunter P: Reading the metabolic fine print. EMBO Rep 2009, 10:20-23. 43. Wolfender J-L, Rudaz S, Hae Choi Y, Kyong Kim H: Plant metabolomics: from holistic data to relevant biomarkers. Curr Med Chem 2013, 20:1056-1090. 44. Yuliana N, Jahangir M, Verpoorte R, Choi Y: Metabolomics for the rapid dereplication of bioactive compounds from natural sources. Phytochem Rev 2013:1-12. 45. Hooft JJ, Vos RH, Ridder L, Vervoort J, Bino R: Structural elucidation of low abundant metabolites in complex sample matrices. Metabolomics 2013:1-10. 46. Crockford DJ, Holmes E, Lindon JC, Plumb RS, Zirah S, Bruce SJ, Rainville P, Stumpf CL, Nicholson JK: Statistical heterospectroscopy, an approach to the integrated analysis of NMR and UPLC-MS data sets: application in metabonomic toxicology studies. Anal Chem 2005, 78:363-371. 47. Maher AD, Fonville JM, Coen M, Lindon JC, Rae CD, Nicholson JK: Statistical total correlation spectroscopy scaling for enhancement of metabolic information recovery in biological NMR spectra. Anal Chem 2011, 84:1083-1091. 48. Posma JM, Garcia-Perez I, De Iorio M, Lindon JC, Elliott P, Holmes E, Ebbels TMD, Nicholson JK: Subset optimization by reference matching (STORM): an optimized statistical approach for recovery of metabolic biomarker structural information from 1H NMR spectra of biofluids. Anal Chem 2012, 84:10694-10701. 49. Robinette SL, Bruschweiler R, Schroeder FC, Edison AS: NMR in metabolomics and natural products research: two sides of the same coin. Acc Chem Res 2012, 45:288-297. In this review the recent advances regarding NMR-based metabolomics in NPs are presented. It highlights critical bottlenecks concerning the rapid and reliable identification of unknown biogenic small molecules. The role of analytical methods, databases and statistical tools is underlined. Current Opinion in Biotechnology 2014, 25:1–7
ScienceDirect Recent advances and new strategies in the NMR-based identification of natural products Maria Halabalaki, Konstantina Vougogiannopoulou, Emmanuel Mikros and Alexios Leandros Skaltsounis Nature comprises an untapped pool of unique compounds with high structural uniqueness and exceptional properties. At the core of natural products (NPs) discovery is the identification procedure and NMR remains the most efficient method. Technical improvements such as miniaturized and crycogenic NMR probes along with hyphenation capabilities and computational support are at the center of evolution. Concepts such as dereplication and metabolomics are increasingly adopted in NPs using the power of databases, currently fragmented. The introduction and utilization of these technical and computational implements could lead NPs research to more comprehensive structure identification and new holistic perspectives. Addresses Division of Medicinal and Natural Products Chemistry, Department of Pharmacy, University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece Corresponding author: Mikros, Emmanuel ([email protected])
Current Opinion in Biotechnology 2014, 25:1–7 This review comes from a themed issue on Analytical biotechnology Edited by Frank L Jaksch and Savas¸ Tay
0958-1669/$ – see front matter, # 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.copbio.2013.08.005
Introduction Nature-derived small molecules continue to comprise an exceptional cluster of chemical entities of high interest and exploitation potentials [1]. Currently, natural products (NPs) are advancing a dynamic comeback in the modern drug discovery [2]. Much effort is given to overcome the bottlenecks of the entire process such as the laborious and repeated isolation steps, the re-discovery of known compounds, the quantity limitations and the lack of systematic registration of origin organisms and derived compounds. The last decade, NPs discovery pipeline has become significantly technology-driven with nuclear magnetic resonance (NMR) spectroscopy occupying a vital position especially in identification and structure elucidation. Technical improvements, mainly related to the new www.sciencedirect.com
generation of miniaturized and cryogenic probes results to increase of sensitivity and speed. Computational support is penetrating into the data mining processes. Dereplication and metabolomic approaches are more and more implemented leading NPs research to more holistic and integrated perceptions. These advances are gradually redirecting the NPs’ research to the identification of mixtures and extracts than to single compounds which is currently an open discussion (Figure 1) [2]. Under the aforementioned prism, this review will focus on the latest advances in NPs identification related to NMR spectroscopy. Specifically, improvements in NMR hardware, pulse sequences, hyphenation capabilities as well as new structural elucidation tools and intelligent software are presented. Finally, the role of NMR in modern dereplication and metabolomic approaches is discussed.
NMR instrumentation NMR is an indispensible analytical method in all structure elucidation protocols. Up-to-date challenges and key targets are the enhancement of reliability and speed in NPs research. Several advances have taken place concerning the inherent capabilities of NMR apparatuses, able to reduce experiment times and increase sensitivity toward more efficient analyses of natural materials available in microgram quantities [3]. In the timeline of probe advances, sample volumes are diminished and analysis is accelerated: from the conventional 5 mm probe (600 mL), to the 3 mm probes (140 mL), to the microvolume probes of 1.0–1.7 mm (approx. 10 mL), to the microcoil flow probes (1.5–2.5 mL of active volume) [4,5]; similarly, operational temperatures decrease: from the room temperature to the cryogenic probes, an approximate 3.5 increase in signal-to-noise ratio (S/N) is observed [4]. The new trend is the combination of micro-properties and cryo-properties into a single powerful probe, such as the 1.7 mm TXI MicroCryoProbeTM (Bruker), implemented in integrated NMR platforms and hyphenated systems [6,7]. Lately, the development of high resolution magic angle spin NMR (HR-MAS NMR) probes is giving the possibility to analyze intact tissues. However, while MAS is incorporated in food chemistry where both primary and secondary metabolites are of importance, it has not been yet widely introduced in NPs [8,9]. Current Opinion in Biotechnology 2014, 25:1–7
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Figure 1
Data Mining / Databases Spectra simulation / Structure Elucidation / Matching and learning algorithms / Data manipulation microvolume capillary cryogenic
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NMR-based advances in the timeline of contemporary NPs discovery.
Experiments The swift implementation of the progress made in NMR hardware is not followed by a proportional response in NMR experiments related to NPs chemistry and structure elucidation. Even nowadays, 1D (1H, 13C 15N), 2D (COSY, TOCSY, NOESY, HSQC, HMBC, and HNMBC) and various combinations thereof are basically used in routine analysis [10,11]. However, crucial complications in structure elucidation can arise from the inability of routine experiments to determine the exact skeletal connectivity. The evolution of the INADEQUATE experiment, 1,1-ADEQUATE is introduced for the accurate determination of 1JC–C connectivity, which is decisive for the elucidation of demanding structures, such as molecules with low degree of protonation [12,13]. Although 1,1-ADEQUATE is still considered as time consuming despite the advances in terms of instrumentation, a structure revision of the natural antibiotic coniothyrione was achieved. In this case, the acquisition time was approximately 17 h for 1.2 mg of sample, even by using the 1.7 mm MicroCryoProbeTM (Figure 2) [14]. The determination of the relative configuration of NPs is still a major concern especially among those of marine origin. In NPs, configurational analysis (Murata’s method) through NMR is typically performed with the measurement of both H–H and C–H coupling constants through a Current Opinion in Biotechnology 2014, 25:1–7
combination of experiments such as COSY-DQF, E.COSY, HETLOC, HSQC-HECADE, HSQMBC, IPAP-HSQMBC, and J-resolved methodologies [10]. Recently, selective pulse sequences like JselHSQMBC-IPAP allow the simultaneous measurement of H–H and long-range C–H coupling constants (nJCH, n > 1) [15]. Likewise, a slightly modified HSQC-TOCSY experiment reported in 2012 appears very robust for the determination of small heteronuclear coupling constants important in the structural and configurational determination of NPs [16]. Similarly, the significance of certain parameters like residual dipolar couplings (RDCs) is not yet appreciated for the extraction of valuable information involved in conformation analysis. Alignment media (liquid crystals, gels, paramagnetic tags) are used to manipulate the order of the molecule and render RDCs measurable. Through RDCs and in combination with theoretical calculations, a restricted number of conformers validated through NOE data could be proposed for both rigid and flexible molecules [17]. Apart from addressing the question of structural clarity, much attention has been given lately on parallel acquisition NMR strategies with multiple receivers operating in parallel, and tuned to different nuclei. Fast Proton and nitrogen and carbon et alia (PANACEA) and Parallel ‘‘Ultrafast’’ Spectroscopy (PUFSY) pulses allow the combination of www.sciencedirect.com
NMR-based advances in natural products identification Halabalaki et al.
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Structure of retrorsine and C–C correlations extracted from the 60 Hz optimized 1,1-ADEQUATE spectrum shown on the right. NMR analysis was performed with a 1.6 mg sample of retrorsine dissolved 30 mL of DMSO-d6, with the aid of an 600 MHz apparatus equipped with a 1.7 mm MicroCryoProbe. Adapted with permission from Martin et al. [12]. Copyright 2011 American Chemical Society.
standard sequences into a single entity, allowing the structure of small molecules to be determined in a single measurement. In the case of fast-PANACEA, INADEQUATE, HSQC, and HMBC pulses are combined, while measurements are accelerated by exciting individual 13C sites by selective radiofrequency pulses acting on double-quantum coherence [18]. On the other hand, PUFSY aims to provide a simultaneous homonuclear and heteronuclear COSY correlations by using a phase modulated encoding for the homonuclear portion, followed by a pair of hard 908 read pulses acting on the heteronuclei [19]. The significance of the synergy between NMR hardware and innovative pulse sequences is greatly demonstrated in the work performed by Queiroz Ju´nior et al. This is the first time that an ultrafast COSY pulse sequence is applied to a hyphenated LC–NMR separation of a mixture of three natural flavonoids (naringin, epicatechin, and naringenin). In 2D ultrafast COSY, the detection is based on echo-planar imaging; thus every H–H correlation can be observed basically in one scan. Through this methodology, analytes elute from the chromatographic column, while COSY spectra are acquired every 12 s, making the characterization of each molecule possible, even when overlapping occurs. The detection volume was only www.sciencedirect.com
60 mL, while two scans have proven sufficient to obtain spectra with optimized resolution and sensitivity. This application portrays the generality of ultrafast methodologies in NPs, placing LC–NMR as an analytical routine methodology [20]. Since the tendency in NPs chemistry is the analysis of mixtures, there is a need for experiments offering high resolving power in both chemical shifts and coupling constants [21]. The diffusion ordered spectroscopy (DOSY) experiment, the refined OneShot45 DOSY and the matrix-assisted DOSY are implemented for the virtual separation of the components of a mixture, offering greater peak dispersion and more accurate metabolite assignment [22,23]. Recently, Evans et al. have proposed an alternative model employing the DOSY-calculated diffusion coefficients, through which, the molecular weight of several analytes in a range of solvents can be proposed via a modified Stoke–Einstein equation [24]. This method remains to be tested by applications in real samples such as extracts and if successful, this could be an interesting progress in the role of NMR in NPs chemistry.
LC–NMR dereplication The ‘‘rediscovery’’ of known structures in natural extracts is a major setback in NPs chemistry, practically Current Opinion in Biotechnology 2014, 25:1–7
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invalidating the entire discovery procedure. NMR-based dereplication approaches place the identification of known compounds early in the discovery line and also enable the targeted isolation of unknown NPs. Several apparatuses are combined with HR-NMR, and hyphenation is realized at a technological (on-line) or at a data mining (off-line) level. Nowadays, the most technologically complete platform facilitating the successful dereplication in terms of time, ease, automation, quality, and identification yield is LC–DAD-HRMS-SPE-NMR equipped with microvolume, cryogenic, flow probes, or combinations thereof [25,26]. Since dereplication is completely consistent with high throughput mentality, peripheral devices such as sample changers and robotic solvent preparation managers are strongly promoted in such modern operational platforms (e.g. NMRbot–Bruker) [27]. Because of the expected impact of hyphenation in NMR platforms in the future, several related research papers are progressively published and excellent reviews discuss technical characteristics and applications [28]. Recently, supplementary advancements have been also proposed, promising to assist in more demanding issues of NPs’ structural elucidation in combined systems. For instance, the hyphenation of NMR with CD devices aims to answer stereochemistry issues [29], or GC–NMR setups address the facile identification of volatiles [30]. To our opinion, technical advances to be evolved in the future, leading to hyper-hyphenated NMR platforms will
point the way to more efficient identification strategies of original NPs.
Computational support and NMR databases Generally, in dereplication studies computational support is required regarding data handling, processing and structure elucidation. Even though user-friendly and sophisticated software packages are accessible for efficacious data mining, they are not widely employed for dereplication purposes in NPs (Figure 3) [6]. This task is indeed rather challenging mainly due to NPs’ unique and unexpected spectral patterns and the residual complexity frequently observed. For instance, prediction and simulation software such as PERCH, in combination with 1H iterative full spin analysis (HiFSA approach), provide an accurate distinction of NPs with nearly identical NMR spectra. Even though proposed by the authors as a tool for puzzling qNMR analyses, it could be an alternative source of dereplication data [31]. In addition, computer-assisted structural elucidation (CASE) is a methodology that allows users to input their NMR data, and through matching algorithms the molecule is hopefully identified. For this purpose, software used are mainly the Structure Elucidator by ACD Labs [32], StrucEluc [33], and CCASA [34], the latter being open to public use [35]. However, the success of these approaches is dependent on the quality of the spectra to be processed and the efficacy of the algorithms used. Additionally, the software used
Figure 3
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Schematic representation of the multiple aspects of computational support on modern NPs chemistry. Current Opinion in Biotechnology 2014, 25:1–7
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On the right, the three major triterpenes identified in a complex mixture that resulted from the fractionation procedure of the ethyl acetate extract of Actaea racemosa (E2). On the left, the classification of all Actaea triterpenes based on Canonical Discriminal Analysis with the use of the methyl groups’ dH. (a & b) Adapted with permission from Qiu et al. [41]. Copyright 2012 American Chemical Society.
present an inherent dependence on the databases from which data are extracted. Thus, NPs databases are of high priority and importance for structure search, matching and identification (Figure 3). Unfortunately, NMR databases dedicated to NPs appear as in-house, fragmented attempts, or are chemical group/organism/NMR experiment/solvent, among others, specific. For instance, MarinLit, and AntiBase, specialize in marine, fungal and microorganism NPs, NAPROC-13 is based on 13C resonances [36], while recently compiled TOCCATA uses 13C-labeled NPs [37]. Commercial NMR databases are limited to few vendors, like the SpecInfo database of Wiley and Bruker’s NMR database [38,39]. Overall, the synergy of copious NMR set-ups with sophisticated algorithms in conjunction with pattern recognition NMR databases, promise excellent dereplication studies. Such an HPLC-PDA-HRMS-SPENMR (600 MHz) based approach was suggested by Johansen et al. [40]. In this work, a cryogenically cooled 1.7 mm TCI probe was employed accompanied with a robotic liquid handler for SPE elution and capillary NMR tubes filling as well as an automated sample changer. Two different extracts of approx. 600 and 50 mg were analyzed by a single injection. Following online isolation, the acquired spectra were matched against an in-house database using an algorithm developed and operating under Matlab. For both extracts analyzed the matching was found successful with low rate of false structures while due to the relative www.sciencedirect.com
flexibility of the algorithm the method was also considered robust. Similarly, addressing a restricted however structurally more demanding group of NPs is the approach suggested by Qiu and coworkers (Figure 4) [41]. In this work the hyphenation factor is not exploited but much attention is given to the construction of a triterpenes database (Actaea triterpenes) and the development of computer-aided tools for dereplication. Moreover, qNMR is utilized pointing out the important problem of purity (residual complexity) afflicting NPs’ structure elucidation and bioevaluation. Using methyl chemical shifts as main structural indicators and descriptors, classification models (canonical discriminant analysis – CDA, classification binary trees – CBT) were generated and applied to the in-house library resulting to a high matching rate. For the measurements, a 700 MHz 1.7 mm microcryoprobe equipment was used. However, the implementation rhythm of both hyphenation and computation is relatively slow, comprising so far occasional and circumstantial attempts. The use of approaches that exploit the potency of hyphenation and advanced instrumentation along with the power of computational assisted data manipulation and databases needs to be more systematic.
Metabolomics Despite the unequivocal value of metabolomics, the father of NMR-based metabolomics J. Nicholson said Current Opinion in Biotechnology 2014, 25:1–7
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‘‘Metabolomics has about 20 published definitions, conflicting but all analytical, all about measuring some stuff in some other stuff’’ [42]. This apparent confusion has been also imported in the so-called ‘‘plant metabolomics’’ or in broader terms ‘‘NPs metabolomics’’. Since 2000 with the first relevant publications, several other related definitions arose (e.g. fingerprinting, profiling). Currently, in NPs there is an array of metabolomics applications from chemotaxonomy, quality control, flux analysis to new NPs discovery [43,44]. Interestingly, the latter area still remains unexplored considering the low yield of new chemical entities resulting from metabolomics. Thus, several metabolomic concepts are successfully applied satisfying the demands of analysis, data mining, and unmasking significant compounds. However, it is rather common for those key metabolites to remain structurally uncharacterized. Interestingly, recognizing this situation, the metabolomics standard initiative (MSI) set specific protocols for reporting chemical analysis introducing different levels for metabolite identification (MI) ranking from MI-1 (completely identified structures) to MI-4 (unknown metabolites exhibiting NMR signals or patterns) [45].
algorithms enabling structure or spectra prediction will give recourses for the structure elucidation of NPs in complicated mixtures. Comprehensive and qualified open access NPs databases will be able to support considerably not only the accelerated identification of unknown molecules but also more importantly the dereplication process. Finally, metabolomics if integrated and harmonized with both traditional and contemporary approaches could effectively strengthen the high throughput manner of identification and discovery in NPs chemistry.
According to our view, the dissociation of metabolomics from NPs discovery is due to the relative absence of wide and open-access NPs spectral databases and especially NMR-based for facile structure elucidation; the employment of rather traditional NMR experiments in metabolomic analysis routine; the conservative employment of sophisticated algorithms and statistical tools in data manipulation and mining; and probably the general perception that NPs chemistry and metabolomics address different objectives. For instance, NMR experiments such as DOSY and JRES could be rather useful also for unraveling new structures. Moreover, decisive structural information could be derived from statistical interpretation methods applied in metabonomics such as statistical heterospectroscopy (SHY) [46], statistical total correlation spectroscopy (STOCSY) [47], SubseT optimization by reference matching (STORM) [48] and cluster analysis statistical spectroscopy (CLASSY) [49]. Such implements will be more than welcomed in the toolbox of plant metabolomics, as they could assist considerably to the targeted identification of significant metabolites and open new directions in the interpretation of their role.
2.
Conclusion The scrutiny of unknown structures still lies in the heart of NPs research and NMR persistently continues leading this procedure. Even today, when important technological advances occur the unambiguous identification remains a challenging task. On the other hand, the current orientation of NPs research to the elaboration of mixtures aggravates further the identification process. Toward this direction, computational tools might hold a key role in the future. Expert systems equipped with versatile Current Opinion in Biotechnology 2014, 25:1–7
Acknowledgement This work was supported by the Commission of the European Community through the INsPiRE project (EU-FP7-REGPOT-2011-1, proposal 284460).
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