Quantitative Structure–Activity Relationshipof Organosulphur Compounds as Soybean15-Lipoxygenase Inhibitors Using CoMFAand CoMSIA

Julio Caballero1,*, Michael Fernandez2 andDeysma Coll3

1Centro de Bioinform�tica y Simulaci�n Molecular, Facultad deIngenier�a en Bioinform�tica, Universidad de Talca, 2 Norte 685,Casilla 721, Talca, Chile2Department of Bioscience and Bioinformatics, Kyushu Institute ofTechnology (KIT), Kawazu, Iizuka, Japan3Facultad de Qu�mica, Pontificia Universidad Cat�lica de Chile,Santiago, Chile*Corresponding author: Julio Caballero, jcaballero@utalca.cl;jmcr77@yahoo.com

Three-dimensional quantitative structure–activityrelationship studies were carried out on a series of28 organosulphur compounds as 15-lipoxygenaseinhibitors using comparative molecular field analy-sis and comparative molecular similarity indicesanalysis. Quantitative information on structure–activity relationships is provided for furtherrational development and direction of selectivesynthesis. All models were carried out over a train-ing set including 22 compounds. The best compara-tive molecular field analysis model only includedsteric field and had a good Q2 = 0.789. Comparativemolecular similarity indices analysis overcame thecomparative molecular field analysis results: thebest comparative molecular similarity indices anal-ysis model also only included steric field and had aQ2 = 0.894. In addition, this model predicted ade-quately the compounds contained in the test set.Furthermore, plots of steric comparative molecularsimilarity indices analysis field allowed conclusionsto be drawn for the choice of suitable inhibitors. Inthis sense, our model should prove useful in future15-lipoxygenase inhibitor design studies.

Key words: CoMFA, CoMSIA, garlic, organosulphur compounds,quantitative structure–activity relationships, soybean 15-lipoxygenase

Received 22 March 2010, revised 8 June 2010 and accepted for publi-cation 6 September 2010

Lipoxygenases (LOs) constitute a heterogeneous family of non-hemeiron-containing enzymes that catalyse the stereoselective dioxygen-

ation of polyunsaturated fatty acids to their corresponding hydroper-oxy derivatives and can be isolated from animals, higher plants andfungi (1,2). LOs contribute to the eicosanoid pathway (3) by thehydroperoxidation of linoleic and arachidonic acids. These enzymeproducts are intermediates for the inflammatory mediators that areinvolved in a variety of human diseases such as arthritis, bronchialasthma, psoriasis and cancer (4–7) and thus are attractive pharma-ceutical targets. In this sense, discovery of new and selective LOinhibitors appears to be relevant for the treatment of the above-mentioned diseases.

Allium sativum L., commonly known as garlic, is a species in theonion family Alliaceae. Garlic has been used worldwide throughoutrecorded history for both culinary and medicinal purposes. It has acharacteristic spicy flavour that mellows and sweetens with cook-ing. Otherwise, its profitable health-related biological effects havebeen ascribed to its peculiar organosulphur compounds (8).

Phytochemicals from plant-rich diets, including organosulphur com-pounds such as alliin, diallylsulfides and allicin (9), provide protec-tion against oxidant damage (10). The oxidant injury caused byreactive oxygen species is linked to various pathologies includingthe development of cardiovascular disease. Garlic extracts contain-ing organosulphur compounds protect endothelial cells by increas-ing levels of superoxide dismutase, catalase, and, consequently,they reduce the production of O��2 and H2O2 (11). In addition, theorganosulphur compounds have beneficial effects in blood circula-tory system: garlic extract components improve peripheral circula-tion (12), reduce plasma lipids (13) and alter platelet function(14).

According to the report of Block et al. (15), a complex mixture ofacyclic and heterocyclic polysulfides in the essential oil of garlic isformed as a result of the action of heat during the steam distilla-tion process on the allicin and diallyl disulfide forming thioacroleinand allyldithio radicals. The culinary procedure that exposes garlicor garlic-spiced food to heat generates these types of compoundswith health benefits associated with the antioxidant or LO inhibitoryactivity. These authors reported the inhibitory activities of severalorganosulphur compounds against soybean 15-lipoxygenase (15-LO)and proposed that activity of these compounds may be attributedprimarily to lipophilic interactions. In a recent work, Camargo et al.(16) employed quantitative structure–activity relationship (QSAR)

511

Chem Biol Drug Des 2010; 76: 511–517

Research Article

ª 2010 John Wiley & Sons A/S

doi: 10.1111/j.1747-0285.2010.01039.x

analysis to evaluate inhibitor–enzyme interactions for the organosul-phur compounds reported by Block et al. (15) by using multiple lin-ear regression and partial least-squares (PLS) methods. Authorsdescribed the molecules by physicochemical parameters and severalnon-empirical descriptors, such as topological, geometrical andquantum chemical indices. In this paper, we investigated the three-dimensional (3D) structural requirements of these compounds forhaving a high inhibitory activity using comparative molecular fieldanalysis (CoMFA) (17) and comparative molecular similarity indicesanalysis (CoMSIA) (18).

Materials and Methods

The primary structures and activities of 28 organosulphur com-pounds were taken from the literature (15). The activities were col-lected and transformed in log(106 ⁄ IC50) values. IC50 valuesrepresent the molar concentration of compounds required to achieve50% of inhibition of 15-LO. Organosulphur compounds and biologi-cal activities used in this study are summarized in Table 1.

All the molecules were sketched using the HYPERCHEM software.a

First, we sketched atoms denoted as 1, 2 and 3 in Table 1, andthen hydrogen atoms were added. This part of the molecule wasoptimized by using molecular mechanics optimization with Polak-Ribiere as the minimization algorithm. Afterwards, rest of the mole-cule was sketched, selected and optimized again. This process wascarried out for all compounds; keeping atoms denoted by 1, 2 and3 in same place for all compounds; thus, the alignment of themolecules was guaranteed. Gasteiger–Marsili partial atomic charges(19) were assigned when each molecule was saved in mol2 formatusing OPEN BABEL 2.0.0 (20).b For a stronger evaluation of modelapplicability for prediction on new chemicals, data set wasdivided into two subdata sets. Six compounds were chosen ran-domly as a test set and were used for external validation of the3D-QSAR models; the training sets included all the remaining 22compounds.

QSAR modelling was performed using the SYBYL 7.3 software of Tri-pos.c Firstly, molecules were placed in a rectangular grid, and theinteraction energies between a probe atom and all compoundswere computed at the surrounding points, using a volume-depen-dent lattice with 2.0 � grid spacing. Then, standard Sybyl parame-ters were used for a PLS analysis. The number of components inthe PLS models were optimized by using a Q 2 value, obtained fromthe leave-one-out (LOO) cross-validation procedure, with theSAMPLS (21) sampling method. The number of components wasincreased until additional components did not increase Q 2 by atleast 5% per added component. The CoMFA models were gener-ated by using steric and electrostatic probes with standard30 kcal ⁄ mol cutoffs. In the CoMSIA analyses, similarity is expressedin terms of steric occupancy, electrostatic interactions, local hydro-phobicity and H-bond donor and acceptor properties, using a 0.3attenuation factor.

The modelling capability (goodness of fit) was judged by the corre-lation coefficient squared, R 2. The prediction capability (goodnessof prediction) was indicated by the cross-validated R 2 (Q 2). For a

stronger evaluation of model applicability for prediction on newchemicals, the activities of the compounds of the test set wereevaluated by using the best QSAR model.

Results and Discussion

QSAR is a ligand-based approach that is applicable (in general)when the structure of the receptor site is unknown, but when aseries of compounds have been identified that exert the activity ofinterest. In recognition site mapping such as CoMFA and CoMSIA,an attempt is made to identify a pharmacophore, which is a tem-plate derived from the structures of these compounds. In CoMFAand CoMSIA, it is represented as a collection of isopleths in 3Dspace. In our current approach, we used extremely flexible mole-cules. We did not search or propose bioactive conformations,because it is impossible to determine them (according to the lackof knowledge regarding the structure of the receptor). Instead, wepreferred to ensure that all compounds were properly aligned. Thisassumption does not intend to suggest that the obtained conforma-tions are the bioactive conformers, but takes the view that all com-pounds interact with the receptor in the same way.

Figure 1 shows the aligned molecules within the grid box (gridspacing 2.0 �) used to generate the CoMFA and CoMSIA columns.The stepwise development of CoMFA and CoMSIA models usingdifferent fields (22,23) is presented in Table 2. The predictability ofthe models is the most important criterion for assessment of bothmethods. The CoMFA model describing 15-LO inhibition that usedonly steric field (model CoMFA-S) had a Q 2 value of 0.789 usingtwo components. After both fields were considered (model CoMFA-SE), a CoMFA model with lesser statistical significance (Q 2 = 0.734)was obtained. On the other hand, when only electrostatic field wasused (model CoMFA-E), the obtained model was statistically unac-ceptable (Q 2 < 0.5). These results reveal that steric features of thestudied molecules have a major influence on the 15-LO inhibitoryactivity, while electrostatic features do not contain relevant infor-mation for this structure–activity relationship.

In comparison with CoMFA, CoMSIA methodology has the advan-tage of exploring more fields. Particularly, H-bond donor field had 0values in the entire grid, because not any H-bond donor groups arein the molecules of the data set. The best model CoMSIA-S over-came the results achieved by CoMFA analysis (the statistical param-eters are highlighted in Table 2). The best model (CoMSIA-S) has aQ 2 value of 0.894 using five components and includes only the ste-ric field. The model explains 98.2 of the variance, has a low stan-dard deviation (s = 0.121) and a high Fischer ratio (F = 175.65). Theremaining CoMSIA models that only include one field showed lessreliable statistics. The addition of other fields to CoMSIA-S modeldid not produce an improvement in the internal validation. Thismeans that steric field is enough for describing the relationshipbetween the structure of organosulphur compounds and their inhibi-tory activities. The predictions of log(106 ⁄ IC50) values for the 22studied compounds in the training set using CoMSIA-S model areshown in Table 1. The correlations between the calculated andexperimental values of log(106 ⁄ IC50) (from training and LOO cross-validation) are shown in Figure 2.

Caballero et al.

512 Chem Biol Drug Des 2010; 76: 511–517

Table 1: Experimental and predicted inhibitory activities of organosulphur compounds (log(106 ⁄ IC50)) using model CoMSIA-S

Compounda Experimental log (106 ⁄ IC50) Predicted log (106 ⁄ IC50)

Training set1 S1

23 2.745 2.854

2 SS

12

3 3.289 3.273

3 SS

S12

3 3.539 3.735

4

SS

S12

3

4.509 4.459

5 SS S

1 23 4.538 4.624

6 SS

S12

3 4.538 4.569

7 SS

S

12

3 4.432 4.379

8S

SS

S1

23

4.553 4.612

9 SS S

S12

3 5.097 4.856

10

SS

SS

12

3

4.292 4.261

11

SS

SS

S1 23

4.699 4.814

12

SS

SS1

23

4.854 4.861

13 S S1 2 3 4.367 4.216

14 SS

SS

12

3 4.602 4.618

15S

S S

O

12

34.678 4.791

16

SS S

12

3

5.046 5.025

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Chem Biol Drug Des 2010; 76: 511–517 513

We also used model CoMSIA-S to predict the inhibitory activities ofthe test set compounds. The identified model was able to describethe test set variance with R 2 = 0.907. The test set predicted valuesare given in Table 1 and correlation between the predictions andexperimental values are represented in Figure 2. This analysisreveals that the proposed model also predicted adequately all thecompounds in the test set.

The contour plots of the CoMSIA steric field (stedv*coeff) are pre-sented in Figure 3. For simplicity, the interaction between only the

most active compound and the contour map is shown. Contourplots show the requirements of the organosulphur chains forincreasing 15-LO inhibitory activity. The green and yellow polyhe-dra denote regions in which bulky groups favoured and disfa-voured the inhibitory activity, respectively. The plot in Figure 3shows a big favourable steric region (green isopleth) that extendsout from third atom from atom denoted as 3 in the acyclic chain.This feature indicates that larger linear compounds have a betterinhibitory activity. The most active compounds 17, 9, 16, 26, 27

and the hydrocarbure 21 contain atoms in this zone, while the

Table 1: (Continued)

Compounda Experimental log (106 ⁄ IC50) Predicted log (106 ⁄ IC50)

17 S SS S

12

3 5.699 5.707

18 S

S1

23 3.066 2.945

19 SS

S12

3 3.372 3.279

20

S SS

O

1 23

4.046 4.215

21 12

3 5.398 5.312

22

S

S

12

3

3.187 3.141

Test set

23

SS

12

3

3.496 3.850

24

SS

12

3

3.187 3.355

25 S SS

12

3 4.432 4.279

26 SS S

S12

3 5.000 5.420

27 SS S

S1 23 5.097 5.055

28 SS

SS

S1 23 4.699 5.133

CoMSIA, comparative molecular similarity indices analysis.aNumbers 1, 2 and 3 indicate the aligned atoms, see the text.

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514 Chem Biol Drug Des 2010; 76: 511–517

less active compounds 1, 18, 22, 2, 19 and 24 are shorter anddo not contain atoms there. In the other hand, there is a yellowisopleth located at atom denoted as 3. This isopleth indicates thatbulky groups at this position do not favour the inhibitory activity.In our data set, the majority of compounds contain –CH2- or –S-groups at this position. Despite S atom being bigger than C, thebonded hydrogens make the group CH2 bigger. In this sense, theyellow isopleth identified that compounds with –S- group are ingeneral better inhibitors than compounds with a –CH2- group atthis position.

According to previous QSAR analysis reported by Camargo et al.(16), the activities of the studied compounds depend on termsrelated to the molecular size (solvent-accessible surface area andaverage distance ⁄ distance degree descriptor), shape parametersand the lowest unoccupied molecular orbital. Despite these modelswere not evaluated for predictive purposes (authors did not predictsome test set), we observed that the statistic results were similar

in our CoMSIA model. More than one model can demonstrate agood fit to some data; however, a simpler model is desired follow-ing the principle of parsimony (24). Our CoMSIA approach demon-strated that only steric terms are able to describe the 15-LOinhibition of the studied compounds, and the use of other termscan be considered as redundant information.

The use of CoMSIA method to describe this data set gives newmodels with more practical application. CoMSIA is universallydistributed and is one of the most employed QSAR methods inthe world. In this sense, most authors should reproduce ourreported model and should use it to make predictions. Moreover,an interpretation of the CoMSIA fields makes it possible to drawconclusions concerning the most appropriate features for the ana-logues.

It is interesting that our model does not address to the role of thesulphur atoms and its chemical and biochemical interactions withreceptors that are mainly understood to be responsible for the vari-ous biological attributes of organosulphur compounds. CoMFA andCoMSIA methods just account for the difference between activeand inactive compounds. Because the majority of compounds havesulphur atoms, the developed methods did not identify the presenceof sulphur atoms as a relevant feature. In addition, one of the moreactive compounds in our data set is the tetradecane. Belman et al.(15) found that normal alkanes from octane to octadecane inhibitthe 15-soybean LO, and they reported that tetradecane has themaximum inhibition. Considering that the inhibitory site for theorganosulphur compounds is likely to be the same as that for thealkanes, it does not seem very unreasonable that potency of 15-LOinhibitors is regulated by steric factors.

12

3

Figure 1: Superposition used for 3D-quantitative structure–activ-ity relationship analysis. Atoms 1, 2 and 3 are indicated.

Table 2: Results of the CoMFA and CoMSIA analyses using several different field combinationsa

NC R 2 s F Q 2 sCV

Fraction

Steric Electrostatic HydrophobicH-bondacceptor

H-bonddonor

CoMFA-S 2 0.941 0.202 150.30 0.789 0.380 1CoMFA-E 3 0.772 0.406 20.33 0.212 0.754 1CoMFA-SE 4 0.981 0.120 222.58 0.734 0.451 0.604 0.396CoMSIA-S 5 0.982 0.121 175.65 0.894 0.313 1

CoMSIA-E 2 0.790 0.379 35.81 0.341 0.671 1CoMSIA-H 6 0.995 0.069 458.09 0.717 0.416 1CoMSIA-D – – – – – – 1CoMSIA-A 1 0.488 0.577 19.05 0.128 0.753 1CoMSIA-SE 6 0.990 0.092 250.81 0.824 0.391 0.430 0.570CoMSIA-SH 6 0.995 0.069 455.60 0.863 0.345 0.479 0.521CoMSIA-SA 6 0.993 0.079 343.03 0.771 0.445 0.538 0.462CoMSIA-EH 6 0.986 0.110 175.28 0.565 0.614 0.581 0.419CoMSIA-EA 3 0.919 0.243 67.67 0.375 0.672 0.609 0.391CoMSIA-HA 5 0.989 0.095 283.38 0.630 0.548 0.590 0.410CoMSIA-SEH 6 0.993 0.080 332.25 0.784 0.433 0.299 0.420 0.281CoMSIA-SHA 6 0.995 0.063 544.15 0.800 0.416 0.351 0.362 0.287CoMSIA-SEHA 6 0.994 0.070 444.36 0.753 0.463 0.244 0.333 0.222 0.200

CoMFA, comparative molecular field analysis; CoMSIA, comparative molecular similarity indices analysis.aNC is the number of components from partial least-squares analysis, R 2 is the square of correlation coefficient, s is the standard deviation of the regression,F is the Fischer ratio, Q 2 and sCV are the correlation coefficient and standard deviation of the leave-one-out (LOO) cross-validation respectively. The best modelsare indicated in boldface.

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Chem Biol Drug Des 2010; 76: 511–517 515

Conclusions

Traditional CoMFA and CoMSIA approaches have been applied toderive quantitative relationships between the structure of organo-sulphur compounds and their 15-LO inhibitory activities. This studyindicates that the steric CoMSIA field is enough to describe fullythe studied activity. When using CoMFA method and other CoMSIAfields, statistically less relevant models were obtained. Thus, pre-diction of inhibitory activities with sufficient accuracy should bepossible. Moreover, an interpretation of the respective steric field

makes it possible to draw conclusions concerning the relevant char-acteristics of the organosulphur compounds.

Acknowledgment

This work was supported by 'Programa Bicentenario de Ciencia yTecnolog�a,' ACT ⁄ 24 (JC).

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3 4 5 6

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6Pr

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log(

106 /I

C50

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Figure 2: Scatter plot of the experimental activities versus pre-dicted activities for model comparative molecular similarity indicesanalysis-S: (d) training set predictions (s) LOO cross-validated pre-dictions (·) test set predictions.

Figure 3: Comparative molecular similarity indices analysis(CoMSIA) stdev*coeff contour maps for organosulphur compounds(CoMSIA-S model). Compound 17 is shown inside the fields. Greenisopleth (at the right) and yellow isopleth (at the left) indicateregions where bulky groups favoured and disfavoured the activity,respectively.

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Notes

aHYPERCHEM 7.0. (2002) Hypercube, Gainesville.bThe Open Babel Chemical File Format Conversion Package. http://sourceforge.net/projects/openbabel/ accessed Jul 2009.cSYBYL, version 7.3; Tripos Inc.: 1699 South Hanley Rd., St. Louis,MO 63144, USA.

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