13c nmr spectroscopy of monoterpenoids - iq usp · 13c nmr spectroscopy of monoterpenoids m.j.p....

13C NMR spectroscopy of monoterpenoids

M.J.P. Ferreiraa,*, V.P. Emerencianoa, G.A.R. Liniaa, P. Romoffb,P.A.T. Macarib, G.V. Rodriguesc

aInstituto de Quımica, Universidade de Sao Paulo, Caixa Postal 26.077, 05599-970 Sao Paulo, BrazilbFaculdade de Ciencias Exatas e Experimentais, Universidade Mackenzie, 01239-902 Sao Paulo, BrazilcDepartamento de Quımica, Universidade Federal de Minas Gerais, 30161-000 Belo Horizonte, Brazil

Received 20 March 1998

Keywords: Monoterpenoids; 13C NMR; Heuristics; Structure determination; Expert system

1. Introduction

Structure elucidations of monoterpenoids attractwidespread interest because they represent an impor-tant group of naturally occurring substances, andmany of them exhibit pharmacological properties[1]. Since its first application 13C NMR spectroscopyhas become a popular technique for structure elucida-tion by providing a way of characterizing substancesand yielding information about their configuration and

substitution patterns. With the advent of powerfulexperimental methods in 13C NMR, and their subse-quent development over the years, structure elucida-tion of natural products has become common practice.As a consequence, several new isolated terpenoids,including mono-, di-, tri- and sesquiterpenes, havehad their structure established. Today studies onthousands of terpenoids have been reported but theirchemical shift data are still scattered in the literature.

Much of the impetus for the study of the 13C NMRspectra came from the combination of the computerand the 13C NMR spectrometer. This combination

Progress in Nuclear Magnetic Resonance Spectroscopy 33 (1998) 153–206

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* Corresponding author. E-mail: [email protected]

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1532. Brief overview of SISTEMAT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

2.1. Determination of carbon atom type—TIPCARB . . . . . . . . . . . . . . . . . . . . . 1542.2. Evaluation of chemical shift range—PICKUP . . . . . . . . . . . . . . . . . . . . . . 1552.3. Estimation of chemical shift range—PICKREV . . . . . . . . . . . . . . . . . . . . . 158

3. Structural data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1584. Tables of 13C NMR shifts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1585. Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1776. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

opened the way for progress in the structural analysisof complex organic compounds, especially in the fieldof natural products. Moreover the use of computersleads to ever more sophisticated information beingdrawn from the spectrometer, and also contributes tosuch tasks as interpreting the spectra and using thespectral information. There are several rules-basedsystems in existence that can aid chemists in deter-mining the structure of an unknown compound usingdata obtained from chemical and 13C NMR studies.There are also a variety of procedures used to predictstructural and chemical properties which depend onextracting the best possible structural informationfrom 13C NMR data. There have been several differ-ent implementations used in the structure elucidationprocess, but here we focus only on the use of SISTE-MAT, the program developed by our group [2,3].

The main purpose of this paper is to provide acollection of the 13C NMR chemical shifts of mono-terpenoids, and to show how SISTEMAT can be usedin evaluating the chemical shift range of isolatedsubstances in monoterpenes according to their plantclassifications.

SISTEMAT is a rules-based system that allows thedetermination of carbon atom types, evaluation of 13CNMR chemical shift ranges and predictions of struc-tures. In our approach we propose the division ofcompounds into skeletal types where the identificationof each skeletal type must be associated with someplant classification. The concept of skeletal typesduring the structural generation step helps to reducethe search space using the application of Bayes’ the-orem as a mathematical tool to avoid combinatoryexplosion [4]. The inference machine permits inter-actions between rules so that information may beobtained even in the presence or absence of skeletonsthat are not explicitly known to the system.

We limit our discussion to the determination ofcarbon atom types and the evaluation of the chemicalshifts of isolated substances found in monoterpenes. Asurvey of the literature allowed data from 900 13CNMR spectra of 873 substances to be compiled. Themultiplicity and chemical shift data from this com-pilation were used as input in the rules-based system.In evaluating the reliability of the data from theliterature, the general criteria adopted was to searchrecent issues of journals in which new monoterpenetypes are frequently reported. For the 900 samples

investigated, SISTEMAT was found to achieve bothhigh reliability and good performance when applied tostructural elucidations and chemical shift evaluations.Other classes of natural products, like sesquiterpenes,diterpenes, triterpenes and flavonoids, have also beenstudied with the aid of SISTEMAT [5–8].

2. Brief overview of SISTEMAT

Structural elucidation in general is viewed as aprocess consisting of various stages [9]. It involvescollecting and interpreting the results of chemicaland spectroscopic studies. SISTEMAT, which simu-lates the routine used by NMR spectroscopists tosome extent, has many useful functions for the pre-diction and assignment of 13C NMR chemical shifts.At the collecting stage, SISTEMAT uses a relationaldatabase built on a foundation of botanic information(i.e. family, genus, species and natural substancessuch as alkaloids and terpenes) and 13C NMR data(i.e. chemical shifts and multiplicity). The databaseis formed by a code and a spectral set of multiplicityand chemical shift signals for each substance. Themolecular structures are represented by a vectorwhich includes topological information. At the inter-pretation stage it uses a knowledge base consisting ofa set of defined rules relating spectral information tothe presence or absence of a given substructure. Theprogram automatically searches and identifies thematch occurrences of each rule with the correspond-ing skeleton. When this information is available,further information can be found by interactionswith other rules: for example, molecular weight,chemical formula and the type of carbon atom in aspecific skeleton according to its biogenetic label. Theheuristic modulus consists of a three-track spectruminterpretation procedure (TIPCARB PICKUP andPICREV) which we describe below. Fig. 1 showsthe flow chart of the SISTEMAT procedure. SISTE-MAT is available at the ftp site address143.107.53.186/PUB.

2.1. Determination of carbon atom type—TIPCARB

The first track, TIPCARB [6], was designed todetermine the type of carbon atom in a specificskeletal type according to its biogenetic label. This

154 M.J.P. Ferreira et al. / Progress in Nuclear Magnetic Resonance Spectroscopy 33 (1998) 153–206

procedure is possible because in this stage all struc-tural information and the biogenetic label for eachskeletal type is already stored in the database. Withthe help of the interaction of rules, it is possible todiscover the skeletal type where substitution mostoften takes place, as well as which substituents aremost common in these positions.

Carbon atoms which are not subject to substitutioncan also be identified. To exemplify this feature, thebornane skeletal types shown in Fig. 2 were exam-ined. The determination of carbon atom type is carriedout after the activation of menu point (skeletal type)assignment which asks for input from all atomicgroupings available in SISTEMAT. Then the programlists the carbon atom type. The resulting assignment ispresented in Table 1. From examination of Table 1 wenotice that the carbon atom 2 in the substitutedskeleton occurs 20 times as a CH– and 13 times asCy. This means that the presence of substituents likehydroxylic, acetate, glucosidic and carbonyl groupsare common in this position. With this information

in hand the characteristic 13C NMR shift range foreach skeleton can be determined with the secondtrack: PICKUP.

2.2. Evaluation of chemical shift range—PICKUP

PICKUP [5] acts as a heuristic function andsearches for the spectral patterns of each skeletaltype. The substructure and atomic grouping used byPICKUP to obtain the characteristic shift range arepresented in Tables 2 and 3, where structures and

Fig. 1. Flow chart of the SISTEMAT procedure for structural elucidation.

Fig. 2. Bornane skeleton.

155M.J.P. Ferreira et al. / Progress in Nuclear Magnetic Resonance Spectroscopy 33 (1998) 153–206

Table 1Carbon atom type of bornane skeleton

Atom no. CH3 CH2 CH C CH2y CHy Cy

1 0 0 0 51 0 0 02 0 18 20 0 0 0 133 0 49 1 0 0 0 14 0 0 50 1 0 0 05 0 36 13 1 0 0 16 0 34 9 0 0 0 87 0 0 0 51 0 0 08 47 4 0 0 0 0 09 51 0 0 0 0 0 010 50 1 0 0 0 0 0

Fig. 3. Alicyclic skeletons.


atomic groupings are shown, respectively. Theapproach used in producing the set of structural build-ing units, is a three-step process. The first step consistsof identifying certain structural features to allow thecharacterization of a given skeleton or substructure.The second step consists of choosing from the menu

the substructures (Table 2) and atomic grouping(Table 3), in order to estimate the 13C NMR shiftranges for a particular substance. The PICKUP mod-ulus then works with this information to sort theexperimental 13C chemical shifts according to theirsize for each structure.

Table 2The substructure sets used by PICKUP program


Table 3Atomic grouping used by PICKUP program

2.3. Estimation of chemical shift range—PICKREV

The chemical shifts of a characteristic structurepredicted by PICKUP are now requested as inputinto PICKREV. A user-friendly routine guides theentry to PICKREV. These input statements are inter-preted by the program which sorts the experimental13C chemical shifts according to their sizes in order toestimate the 13C NMR shift ranges for a particularsubstructure in comparison with those already storedwithin a database. In summary, PICKREV checks theevaluated pattern against the experimentally deter-mined one.

3. Structural data

For identification purposes and for the structuralelucidation of new compounds it is useful to haveaccess to an extensive list of skeletons and theirstructural data. Table 4 shows a collection of the

structural data of monoterpenes collected from theliterature. The literature covered in the present com-pilation includes most of the papers published up to1996 [10–231]. The data are subdivided by skeletaltype, trivial names, substituents, stereochemistry andreferences. From Table 4 it is possible to build up thestructures of any substances belonging to the terpenesclass.

4. Tables of 13C NMR shifts

The experimental chemical shifts and multiplicityof 873 isolated monoterpene substances are shown inTable 5. From Table 5 it is also possible to build upthe structures because the chemical shifts of differentfunctional groups fall into well-defined ranges. Forexample, carbonyl carbons of ionane skeletonsresonate in the range between 170.5 and 211.7 ppm,and hydroxyl carbons resonate between 58.7 and71.5 ppm.


Table 4Structural information of monoterpenoids


Table 4 (continued )


13C shielding data are not very sensitive to solventchanges, but as solvent–solute interactions are solventdependent, some small changes in chemical shift cantake place. Table 5 also includes information aboutthe solvent used during the extraction process.

5. Results and discussion

With recent publication of many papers describingthe use of 13C NMR data for the structural elucidationof terpenoids, it seems appropriate to present a newreview dealing with the 13C NMR data of monoter-penes. The Tables contain only data from the litera-ture which can be definitely related to a compound.Assignments which are incorrect and difficult to carryout have been discarded. These tables have beenincluded because the information can be used in thestructural elucidation process as discussed.

To obtain the characteristic 13C NMR shift range ofany skeleton using the computer-assisted method, it isnecessary first to identify certain features which char-acterize each skeleton under study and its substruc-tures. Our carbon type determination procedure wasestablished on the basis of a learning database of 1233compounds with diverse structures. From PICKUPand PICKREV output it is possible to evaluate thecomputer-assisted chemical shifts and compare themwith experimentally determined values. Table 6shows the percentage of recognition (j)1 for the

bornane, carane, pinane, thujane, isocamphane,ionane, menthane and myrcane skeletons. Percentageof recognition analysis for the thujane skeleton listedin the fourth column of Table 6 shows that if a newisolated substance in the monoterpene class presents13C chemical shifts in the same range of those experi-mentally determined, this new isolated substancemust include the thujane skeleton in its molecularstructure with a percentage of recognition equal to1.0. This means that SISTEMAT can carry out thestructure elucidation of substances having thujaneskeletons with 100% accuracy. Although Table 6gives reasonably good 13C chemical shift values formany compounds, there still are a number of com-pounds showing some deviations. This can be seenin the pinane case, where SISTEMAT can predictthe molecular structure of substances with pinane ske-leton types with only 0.714 accuracy. This means thatother skeletons also appear as structural candidates,but with low statistical significance. Table 7 shows theSISTEMAT output for the pinane skeleton. It is clearthat this result has no influence on the results of thegeneral analysis, so whether or not to drop this struc-tural candidate is not a difficult decision for theresearcher. The above results show that all experimen-tal 13C resonances of the set chosen for analysis can beassigned confidently. Therefore, the chemical shiftassignment procedure applied in this study proved tobe useful and fast.

The use of substituent induced chemical shifts forthe prediction of 13C chemical shifts of differentclasses of compounds is well known. The estimationof 13C chemical shifts for substituted myrcane, ionaneand pinane skeletons (see Figs. 3 and 4) was carried

1 The number of substances that have a specific skeleton dividedby the total number of substances in a specific 13C NMR absorptionrange.



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out by SISTEMAT via TIPCARB and PICKUP out-puts. We choose the myrcane, ionane and pinaneskeletons in this study because they occur frequentlyand are the most representative skeletons in our com-pilation data of the monoterpene class. The estimated

values for the absorption range of substitution patternson myrcane, ionane and pinane skeletons, respectively,are shown in Table 8. The assignment procedure forsubstituted myrcane, ionane and pinane can be used asa proof for the accuracy of SISTEMAT determining

Table 6Experimental 13C NMR shifts ranges for monoterpene skeletal types

Skeletal type Biogenetic label 13C NMR shifts range Multiplicity j/100(%)

Bornane 1 44.2–64.3 s 0.9534 39.2–86.0 d7 39.7–64.3 s

Carane 1 16.7–25.7 d 1.06 18.7–32.7 d7 16.7–22.5 s

Pinane 1 38.2–51.9 d 0.7145 36.5–57.6 d6 37.6–54.0 s7 25.7–40.8 t

Thujane 2 25.2–41.7 d 1.03 11.0–38.5 t4 27.8–43.5 s8 25.8–33.0 d

Isocamphane 1 39.7–55.0 d 0.9093 36.4–45.2 s4 48.3–50.5 d7 34.3–37.5 t

Ionane 1 23.1–42.9 s 0.8262 36.2–51.2 t6 33.0–90.1 s11 18.2–28.7 q12 20.0–28.3 q

Menthane 4 28.8–64.8 d 0.7358 24.3–41.5 d9 31.7–15.8 q10 28.8–15.1 q

Menthane 1 25.6–35.7 d 0.8062 27.3–51.5 t6 19.6–51.5 t7 17.2–23.1 q

Menthane 1 131.1–162.8 s 0.6052 27.2–39.0 t6 117.9–129.0 d7 22.2–25.2 q

Myrcane 3 118.0–152.0 d 1.04 25.6–28.1 t5 25.6–40.2 t6 111.0–169.3 s7 117.5–138.8 d

Myrcane 2 127.0–136.3 s 1.03 122.5–145.6 d4 22.5–25.1 t5 39.2–42.7 t6 72.0–84.4 s


Fig. 4.


the number of wrong assignments compared withthose which were experimentally determined.

6. Conclusions

The overall goal of this study has been to developan understanding of and an ability to predict the mole-cular structure of monoterpenes. In this case the inter-play between experimental measurements andcomputer-assisted determinations has led to a goodunderstanding of the processes of carbon atom typedetermination and chemical shift evaluation asdescribed in Sections 2.1–2.3. Accurate experimentaldeterminations of chemical shifts have been used totest our computer-assisted elucidation process. Com-putational methods, validated by experiments, havethen been used to examine the structural determina-tion of terpenoids. The chief virtue is that SISTEMATworks on the basis of botanical information whichmost strongly impacts the process under study.

In summary, we have shown here that chemicalshift values for monoterpene skeletons can be pre-dicted using SISTEMAT. The prediction requires asinput data only the knowledge of experimentalchemical shifts and the multiplicity of each isolated

substance structure. The resultant approach for mono-terpenes is chemically reasonable and consistent withbotanical classification. The use of SISTEMATshould thus prove to be a convenient approach forpredicting chemical shift and structure predictions inthe field of natural products. We notice that thepredictive ability and applicability of a 13C NMRchemical shift estimation procedure is stronglyaffected by the size of the learning database. In this

Fig. 4. Cyclic skeletons (continued ).

Table 7Percentual of recognition for pinane skeleton (SISTEMAT output)

Skeletal type j/100(%)

Pinane 0.714Isocamphane 0.143Bornane 0.085Ochtodane 0.029Ionane 0.029

Table 8Absorption range of substitution patterns of myrcane, ionane andpinane skeletons

Atom no. Substituent Absorption Multiplicity

min max

Myrcane skeleton6 –OH 72.6 78.4 s6 –OGly 75.0 84.4 s8 –OH 58.5 66.7 t8 –OGly 64.5 69.3 t2 D2 124.4 158.1 s3 118.0 154.5 d6 D6 131.6 169.3 s7 117.5 132.0 d7 D7 114.3 146.7 d8 110.9 119.2 tIonane skeleton3 –OH 64.6 71.5 d3 –OGly 71.5 82.5 d9 –OH 58.7 70.4 d9 –OGly 68.0 78.7 d3 –CyO 170.5 211.7 s9 –CyO 197.1 207.8 sPinane skeleton2 D2 128.1 155.3 s3 115.3 147.0 d2 D2(10) 120.6 155.4 s10 106.0 117.3 t


sense our literature compilation has come closest tobeing complete in relation to the accurate experimen-tal data available, although this is not very abundant.The results reported here indicate that SISTEMATrepresents an efficient guide for the skeletal identifi-cation of terpenoids.

Acknowledgements

This work was supported by grants from theFundacao de Amparo a Pesquisa do Estado de SaoPaulo (FAPESP) and by the Conselho Nacional deDesenvolvimento Cientıfico e Tecnologico (CNPq).The authors thank Josef W. Baader and JohnC. Carpenter for helpful discussion during thepreparation of the manuscript.

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