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REQUIMTE, Departamento de Quımica

Universidade do Porto, 687, Rua do Ca

E-mail: hari.moorthy@fc.up.pt; hari.nmoor

+351-220 402 506

† Electronic supplementary informa10.1039/c3ra43670e

Cite this: RSC Adv., 2013, 3, 23409

Received 15th July 2013Accepted 18th September 2013

DOI: 10.1039/c3ra43670e

www.rsc.org/advances

This journal is ª The Royal Society of

Combined ligand and structure based binding modeanalysis of oxidosqualene cyclase inhibitors†

N. S. Hari Narayana Moorthy,* Nuno M. F. S. A. Cerqueira, Maria J. Ramosand Pedro A. Fernandes

In this present investigation, computational analysis was performed on a data set of OSC inhibitors in order

to perform its binding mode analysis and lead identification. The pharmacophore analysis and QSAR

studies revealed that the radius of the Aro/Hyd and/or Hyd properties and the polar properties are

important for the interaction and activity elicitation. The docking results derived from the analysis also

explain that the aromatic amino acids predominantly present in the active site and the polar groups are

separated by the aromatic/hydrophobic regions. The significant aromatic amino acids Phe696, Phe444

and His232 make p–p stacking or CH–p interactions with the functional groups (aromatic or aliphatic)

present in the inhibitors. The polar groups present in the compounds interact with Asp455 or Trp581 or

Trp387 or Ile338 or Gly380 (all through water) or with Glu532. The natural products analyzed on this

target provided significant docking scores and its mode of interactions are similar to the OSC inhibitors,

which can be used for the treatment of obesity.

Introduction

2,3-Oxidosqualene cyclase-lanosterol synthase (OSC, EC5.4.99.7) is one of the most intensively studied enzymes of sterolbiosynthesis.1 It plays a pivotal role in the life of most eukaryoticorganisms by catalyzing the conversion of an acyclic 2,3-oxi-dosqualene (OS) to the cyclic lanosterol (cyclic 2,3-oxidosqua-lene).2–4 Subsequently, lanosterol is converted into biologicallyimportant sterols (including cholesterol). Inhibition of thisenzyme decreases the synthesis of cholesterol and 24(S),25-epoxycholesterol, which affect important biochemical path-ways, such as protein prenylation and ubiquinone synthesis.4–6

Hence, OSC is an attractive target for novel anticholesteremicdrugs development. This enzyme acts downstream fromimportant branching points in the cholesterol pathway andinhibition of OSC avoiding by this way the accumulation ofsteroidal intermediates in the pathway.

Several inhibitors for OSC and squalene-hopene cyclase (SHC)(its bacterial counterpart) have been reported as irreversibleinhibitors, reversible inhibitors (substrate or transition stateanalogues) and non-terpenoid inhibitors.7–10 Pharmaceuticalcompanies are attempting to identify OSC inhibitors as anti-cholesteremic drugs because this class of drugs is expected to befree from the adverse effects (myopathy) of statins. The Ro48-8071

e Bioquımica, Faculdade de Ciencias,

mpo Alegre, 4169-007 Porto, Portugal.

thy@gmail.com; pafernan@fc.up.pt; Tel:

tion (ESI) available. See DOI:

Chemistry 2013

(Fig. 1) is an inhibitor that shows inhibitory activity against OSCand SHC, but till date it has not yet been approved as a clinicaldrug for the treatment of hypercholesterolemia. Failure of theclinical use of this drug may be caused by the toxicological issuessuch as skin and epididymidal changes and cataract forma-tion.11–14 It is noted that OSC inhibitors are not associate with theside effects that exhibited by the HMG CoA reductase inhibitors,such as gall bladder, testicular and neurological changes.15

U18666A (3b-(2-diethylaminoethoxy)-androst-5-en-17-onehydrochloride) is probably the rst reported OSC inhibitor,known to induce cataracts (Fig. 1).16,17 Hence, the modicationsin the structures of the existing OSC inhibitors can decrease theimpact of adverse effect (cataract formation) associated withOSC inhibitors.18 Recent researches revealed that the followingmicroorganisms include Pneumocystis carinii (pneumonia),Trypanosoma brucei (African sleeping sickness), Trypanosomacruzi (Chagas disease) and Leishmania sp. (leishmaniasis) havesynthesized sterols with the help of SHC enzyme. Hence, thesterol biosynthetic inhibitors (OSC inhibitors) can also be usedfor the treatment for these diseases.19

In silico based drug design studies on existing OSC inhibitorsprovide information on the active site binding features and thephysicochemical properties of the OSC inhibitors required forthe inhibitory activity. The reported literature revealed that onlylimited works have been performed for the binding modeanalysis of OSC inhibitors to the eukaryotic OSC enzyme and itsbacterial counterpart SHC.14,20,21 Hence, extensive studies(computational analysis) are needed to investigate the bindingmode feature of different inhibitors against the OSC enzyme. Inour laboratory, last few years in silico based structural analysis

RSC Adv., 2013, 3, 23409–23422 | 23409

Fig. 1 Structure of cholesterol and some reference OSC inhibitors.

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of different inhibitors on anticancer and antiviral targets isundergoing.22,23 Hence, in the present investigation, somecomputational analysis especially, pharmacophore analysis,quantitative structure activity relationships (QSAR) and dockingstudies (virtual screening) were performed on a data set of OSCinhibitors that were obtained from the literature. Thesecombined ligand and structure based drug design analysis ofthe data set provide useful information for the identicationand development of new lead compounds.

ExperimentalQSAR study

QSAR study was carried out on the data set comprised of N,N0-diphenyl methyl piperazine, pyrrole and 1,2,3-triazole basedderivatives (series A), 4-piperidinopyridine derivatives (series B),(2E,6E)-10-(dimethylamino)-3,7-dimethyl-2,6-decadien-1-ol ether

23410 | RSC Adv., 2013, 3, 23409–23422

derivatives (series C) and some structurally varied compounds(series D and E)1,4,14,18,24 (Table S1a†). Over all, the data set hasmade up of 59 compounds and 36 compounds in the data sethave dened OSC inhibitory activity and rest of the compoundsdid not exhibit activity (considered as experimentally inactive).However, those 36 active compounds consist of structurallydifferent parent structures and varied activities, which were usedfor the QSAR analysis (Table S1a†). Initially, the OSC inhibitoryactivity of the compounds were converted as –log IC50 or log 1/IC50 or pIC50 to correlate the physicochemical properties.

The Molecular Operating Environment (MOE) soware25 wasused to perform the computational studies. The semi-empiricalMOPAC program with Hamiltonian Austin Model 1 (AM1) forceeld with 0.05 RMS gradients of MOE soware was used tooptimize the molecules. The physicochemical descriptors for thecompounds were calculated to dene the structural properties ofthe molecules26 needed to perform QSAR analysis (Table S2†).

This journal is ª The Royal Society of Chemistry 2013

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The data set (with dened inhibitory activity) was dividedrandomly (care was taken to distribute varied structures andactivities in both sets) as training set (75%) and test set (25%) forthe correlation analysis using the Statistica 8.0 soware.27

Among the calculated descriptors (>300), those descriptorspossessed zero correlation to the dependent variable as well asdescriptors showed intercorrelation superior to 0.5 were dis-carded. Forward and backward stepwise regression analysis wasperformed on the data set to select the appropriate descriptorsfor multiple linear regression (MLR) models development. Thesignicant models selected were further undergone validationstudy by internal (leave-one-out (LOO), leave-many-out (LMO)and bootstrapping (BS)) and external methods.28,29 The LMOvalidation analysis was carried out as LOO method but in thismethod, the training set compounds were divided into groups(blocks) of N compounds. The BS is a kind of validationmethods, in which the data set compounds (training set) wasrandomly split several test sets and training sets. The activities oftest set compounds were calculated using the model developedwith the training sets. The external validation study (test set)performed with two sets of compounds (Test-1 and Test-2). TheTest-1 contains 25% of compounds from the data set which wasused to develop QSAR models (only 75% compounds were usedto construct QSAR models) and the Test-2 made of a set of 28compounds from different series (Series-F in Table S1b†).10 TheTest-2 compounds were not considered for the QSAR modeldevelopment and were only used to evaluate the predictiveability of the models. The training set, Test-1 and Test-2 containstructurally dissimilar compounds. The rule of thumb wasadapted to select number of descriptors in the models (three tosix times the number of parameters under consideration).30

Multicollinearity and serial autocorrelations of the descrip-tors and the models were tested with variance ination factor(VIF) and Durbin–Watson (DW) values. VIF is an index whichmeasures the collinearity of ith independent variable with othervariables in the analysis and is connected directly to the vari-ance of a coefficient (square of the standard deviation) associ-ated with this independent variable. It can be calculated asfollows.

VIF ¼ 1/1 � R2 or 1/tolerance

The DW statistics is useful for evaluating the presence orabsence of a serial correlation of residuals (i.e. whether or notresidual for adjacent cases are correlated, indicating that theobservations or cases in the data le are not independent) or theregression models assume that the error deviations areuncorrelated.30,31

Pharmacophore analysis

The pharmacophore analysis was performed with ligand basedand complex based (protein–ligand) pharmacophore methods,to investigate the features responsible for the interactions andfurther screening of the data set for HITs identication. Thepharmacophore analysis of the data set (above mentioned) wascarried out using MOE soware.25 The conformers of the data

This journal is ª The Royal Society of Chemistry 2013

set were developed by stochastic search covering maximumnumber of conformers generated to 250, superimposed RMSDto 0.15 and the fragment strain limit of 4 kcal mol�1. The lowestenergy conformers obtained from the stochastic search wasaligned using ligand exibility on MMFF94x force eld with theenergy cutoff of 10 for non-bonded interactions. The followingproperties have been calculated for the aligned structures: thestrain energy (U), the mutual similarity score (F) and the value ofthe objective function (S) of each alignment. The alignedstructures exhibit lower U, F and S values were considered forthe pharmacophore query development and further virtualscreening analysis.32 In this study, three kinds of pharmaco-phore query structures were used for pharmacophore baseddocking analysis of the data set.

1. Three reference compounds such as Ro48-8071, ZD-9720and BIBB-515 (Fig. 1) were exialigned to generate the phar-macophore query (Pharmacophore 1). Low energy conformersof ZD-9720 and BIBB-515 and the conformer of Ro48-8071,obtained from the X-ray structure were used for theexialignment.

2. Flexialigned highly active compounds in the data set(different parent structure were used to create pharmacophorequery) included the compounds such as A-1, A-2, A-4, A-8 (N,N0-diphenyl methyl piperazine, pyrrole and 1,2,3-triazole basedderivatives), B-3 (4-piperidinopyridine derivatives), C-2, C-6((2E,6E)-10-(dimethylamino)-3,7-dimethyl-2,6-decadien-1-olether derivatives), D-2, D-4, D-5, D-6 (structurally dissimilarcompounds), E-5, E-6, E-11 and E-12 (structurally dissimilarcompounds) (Pharmacophore 2).1,4,14,18,24 Structures of thecompounds are provided in ESI (Table S1†).

3. In complex based pharmacophore analysis, the complexstructure with the pdb code entry 1W6J was used (data retrievedfrom the protein data bank), which has the OSC inhibitor, Ro48-8071 bounded in the active site. The pharmacophore query wasgenerated on the crystallographic conformer of the molecule.The contact between the molecule and the protein was ana-lysed, subsequently, and the pharmacophore query was gener-ated (Pharmacophore 3).

The pharmacophore contours and the radius of the contoursused in the query structures are provided in Table 1. Thepharmacophore queries created were used for docking studiesand to investigate the HITs from the data set (seriesA–E).1,4,10,14,18,24,33

Normal and pharmacophore based dockings

Pharmacophore based docking analysis was performed on thecrystallographic structure of the protein-drug (Ro48-8071)complex (pdb: 1W6J). Initially the sequence was pruned andprotonated. Partial charges of the proteins residues were calcu-lated. Energy of the complex was rened using the force eldsMMFF94x and AMBER with the potential set to R-eld (reactioneld). In order to generate the pharmacophore query, the activesite was viewed and its molecular surface was created. In order tovalidate the study, the docking was performed on the complexmolecule (Ro48-8071) and its pharmacophore contours weregenerated. The pharmacophore based docking was performed

RSC Adv., 2013, 3, 23409–23422 | 23411

Table 1 Details of the pharmacophore contours and the radius used in the pharmacophore and docking analyses

Pharmacophore 1 Pharmacophore 2 Pharmacophore 3 Docking

Contour name Radius (A) Contour name Radius (A) Contour name Radius (A) Contour name Radius (A)

Acc (F1) 2 Acc (F2) 2 Acc (F1) 2 Acc (F4) 1Hyd (F2) 2.5 Hyd (F3) 2.5 Hyd (F2) 2.5 Hyd (F2) 2Hyd (F3) 1 Hyd (F3) 2 Hyd (F3) 2Acc2 (F4) 1 Acc2 (F5) 1 Acc2 (F4) 1Don (F5) 2 Don (F5) 2Aro|Hyd (F6) 2 Aro|Hyd (F1) 2 Aro|Hyd (F6) 2 Aro|Hyd (F1) 1Aro|Hyd (F7) 2.5 Aro|Hyd|Acc|Don (F4) 2 Aro|Hyd (F7) 2.5Same atoms F[1,4] (C1) Same atoms F[2,5] (C1) Same atoms F[1,4] (C1)Exterior volume (+V1 H) 2.5 Exterior volume (+V1 H) 2.5 Excluded volume (+V1 H) 1.5

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with the presence of the pharmacophore contours provided inTable 1. The docking analysis was also performed without thepharmacophore contours. In both of the cases, the presence andthe absence of water molecule in the active site was considered.The compounds obtained from the literature was considered forthis docking analysis (Table S1a–c†).1,4,10,14,18,24,33

Results and discussionsQSAR

In the present investigation, we have developed three best QSARmodels to explain the structural features important for the OSCinhibitory activity (Table 2). The selected signicant modelsexhibited acceptable statistical parameters such as correlationcoefficient (R) is >0.9, F values and ttest values are 99% and99.5% signicance respectively with respect to the tabulatedvalues. Standard error of estimate (SEE) has propertiesanalogues to those of standard deviation. The SEE values of themodels are <0.5, which are greater difference from the meanbiological activity values. These statistical parameters revealthat the selected models possessed signicant goodness of tfor activity prediction. Hence, those models were validated toinvestigate its predictive ability. The validation results of thedeveloped models provided better Q2 values (Q2 > 0.7) for LOO,

Table 2 QSAR models selected from the studies with its statistical parameters

Model no. Models

Model 1 pIC50 ¼ 0.4814(�0.0488) KierA2�12.1618(�2.1300) E_oop �0.0082(�0.0021DASA �0.0234(�0.0076) S log P_VSA0 +4.0511(�0.4699)

Model 2 pIC50 ¼ �0.1060(�0.0130) E_tor�0.1666(�0.0260) vsurf_Wp6-1.1115(�0.195std_dim2-13.0465(�3.7540) vsurf_HL1 +10.1315(�0.3865)

Model 3 pIC50 ¼ 0.5191 (�0.0436) KierFlex �0.0371(�0.0080) S log P_VSA2-0.9811 (�0.1259)lip_don + 0.9828 (�0.1753) a_nCl + 5.2783(�0.3675)

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LMO and BS methods. The external validation study (test set)performed with two sets of compounds (Test-1 and Test-2) (25%of the compounds from the data set which was used to developQSAR models (Test-1) and 28 compounds from different series(Series-F in Table S1†) (Test-2)), which yielded good Q2 values($0.6). The Q2 values of these test set compounds arementioned as QTest-1

2 and QTest-22 for Test-1 and Test-2 respec-

tively. The Rpred2 values for the test set compounds (Test-1 and

Test-2) denoted as Rpred(Test-1)2 and Rpred(Test-2)

2 respectively and arealso$0.6 for all three models. The Rpred(Test-1)

2 and Rpred(Test-2)2 of

the models are comparatively lower than the R2 values of thetraining compounds. The Rpred

2 values of the models providesome sort of condence on the predictive ability of the selectedmodels. These results suggested that the selected models havesignicant predictive power (Tables S3 and S4;† Fig. 2). But thestability and predictive power of any multiple regressionmodelsdepend upon the multicollinearity and autocorrelations. TheDW and VIF values were calculated to test the serial autocor-relations and multicollinearity and behavior of the models. AVIF value greater than 10 is an indication of potential multi-collinearity problems (inated standard errors of regressioncoefficients)29,30,33 and in this analysis, the models 1–3 have theVIF values between 1 and 1.5. The calculated DW values for themodels are between 2 and 2.7. The tabulated upper and lower

Statistical parameters

)N¼ 27, R¼ 0.9319, R2 ¼ 0.8685, AdjR2 ¼ 0.8446,QLOO

2 ¼ 0.7498, QLMO2 ¼ 0.8202, QBS

2 ¼ 0.8168,QTest-1

2 ¼ 0.6662, QTest-22 ¼ 0.7029, Rpred(Test-1)

2

¼ 0.6680, Rpred(Test-2)2¼ 0.7043, F(4,22)¼ 36.3150,

SEE ¼ 0.4623, t(22) ¼ 8.6219, p ¼ 0.0000

9)N¼ 27, R¼ 0.9520, R2 ¼ 0.9063, AdjR2 ¼ 0.8893,QLOO

2 ¼ 0.8636, QLMO2 ¼ 0.8221, QBS

2 ¼ 0.8256,QTest-1

2 ¼ 0.7219, QTest-22 ¼ 0.6321, Rpred(Test-1)

2

¼ 0.7234, Rpred(Test-2)2¼ 0.6339, F(4,22)¼ 53.2200,

SEE ¼ 0.3901, t(22) ¼ 26.2120, p ¼ 0.0000N¼ 27, R¼ 0.9492, R2 ¼ 0.9009, AdjR2 ¼ 0.8829,QLOO

2 ¼ 0.8558, QLMO2 ¼ 0.8203, QBS

2 ¼ 0.8338,QTest-1

2 ¼ 0.5927, QTest-22 ¼ 0.6144, Rpred(Test-1)

2

¼ 0.5949, Rpred(Test-2)2¼ 0.6163, F(4,22)¼ 50.0020,

SEE ¼ 0.4013, t(22) ¼ 14.3630, p ¼ 0.0000

This journal is ª The Royal Society of Chemistry 2013

Fig. 2 Graphical representation of predicted activities of the test set compoundswith the observed activities.

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bound values of DW are considered to test the hypothesis ofzero autocorrelation against the positive and negative autocor-relation, which reveal that the DW values are signicant at 5%level (Table S5†). These results explained that the selectedmodels are having signicant stability and predictive capacity.

In these models, the following descriptors such as the shapeand exibility descriptors (Kier2 and KierFlex), the potentialenergy descriptors (std_dim2, E_oop and E_tor), the van derWaals (vdW) surface area descriptors (DASA, vsurf_Wp6 andvsurf_HL-1) and the polar and hydrophobicity descriptors(S log P_VSA2, lip_don and a_nCl) have contributed for the OSCinhibitory activity prediction (Fig. 3). In model 1, the topological,the potential energy and the vdW surface area descriptors suchas KierA2, E_oop, DASA and S log P_VSA0 have contributed. TheKierA2 describes the second alpha modied shape index of themolecule and E_oop explains the out of plane potential energy.The vdW surface area descriptor DASA describes the absolutevalue of the difference between the water accessible surface areaof all atoms with positive partial charge (strictly greater than 0)and negative partial charge (strictly less than 0). The subdividedsurface area descriptor, S log P_VSA0 reveals that the contribu-tion to log P(o/w) for atom i on the vdW surface area calculatedwith the Li # �0.4 (Li denotes the contribution to log P(o/w) foratom i).33 The contributions of these descriptors in the model

Fig. 3 Contribution of physicochemical descriptors for the activity prediction.

This journal is ª The Royal Society of Chemistry 2013

suggest that the exibility in the molecules allow an easy rota-tion of the substituents or bonds in the molecules in order toimprove the pharmacophores toward the active site. Thedecreased (towards positive values) negative partial chargepotential on the vdW surface of the molecule makes the mole-cule with less absolute water accessible surface area.

The descriptors such as E_tor, vsurf_Wp6, vsurf_HL1 andstd_dim2 are present in the model 2. The volume, shape andsurface area descriptors (vsurf_) signify the hydrophilic–lipho-philic balance (HL) and the polar volume of the molecules. Thecontributions in the model suggest that the values for the HLshould be small and the polar volume (Wp) of the moleculeshould also be small for better interaction.23,26,29,34 It suggeststhat the molecule should have some hydrophobic/aromaticproperties for better interaction. The torsion energy of thebonds and the volume of the molecule (shape) should be low forbetter interactions. This model suggests that the polar volumein the vdW surface of the molecule should be low with someexibility in the bond to reduce the potential energy (torsionenergy) needed for the interactions. The model 3 also explainsthe molecular exibility (KierFlex), the partition coefficient onthe subdivided surface area (S log P_VSA2) and the atom countdescriptors (lip_don and a_nCl) are important for the OSCinhibitory activity. The atom count descriptors contributedpositively in the model suggest that the presence of chlorineatom, hydroxyl and NH groups in the molecules favourable forthe inhibitory activity. It shows that these groups can formhydrogen bonding with the polar amino acid residues or watermolecules present in the active site.

The selected models 1–3 explain that the exibility of themolecules are important for the interaction and the vdWsurface area of the molecule should have some hydrophobic/aromatic groups to interact with hydrophobic/aromatic aminoacid residues located in the active site. The contributions ofthese descriptors on the activity prediction show that the exi-bility of the molecules, hydrophobicity/aromaticity and lesspolar charge on the vdW surface of the molecule is importantfor the interactions.

In order to support the QSAR results, the effect of substitu-ents on the molecules for the inhibitory activity were analyzed.Those compounds have lengthy aliphatic or highly lipophilicside chain (cyclic or acyclic) exhibit less inhibitory activity thanthose compounds substituted with polar groups and aromaticrings. These compounds also have signicant calculated physi-cochemical descriptor values for the activity prediction. Thepresence of halogen and/or halogenated methyl groupsprovided good inhibitory activity. Furthermore, the substitu-tions of fused ring system or rigid structures in their moleculeshave low activity, because of its less exibility and more poten-tial energy (torsion energy) needed for the exibility. However,benzimidazole/indole/benzthiazole substituted compoundshave less inhibitory activity than the quinoline or quinazolinesubstituted derivatives. The aromatic rings (phenyl) connectedthrough SO2 group have signicant inhibitory activity than thosecompounds connected with CO or CH2 groups. In this case, theincreased number of halogen atoms substituted in the phenylor any aromatic rings do not have signicant effect on the

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activity. The compounds substituted with dimethyl aminegroups have less activity than diethyl or ethyl propyl or olinicamine groups substituted compounds.

This structure activity relationship results of the moleculesis comparable with the QSAR results and this support theresults derived from QSAR study. This conrm that the pres-ence of aromatic rings, bond exibility and polar properties onthe molecules play important role for the OSC inhibitoryactivity.

Pharmacophore analysis

In pharmacophore analysis, three queries such as the ex-ialigned structure of the reference compounds (Pharmacophore1), the exialigned structure of the active compounds in thedata set (Pharmacophore 2) and the protein–ligand complex(Pharmacophore 3) were used to illustrate the propertiesimportant for the OSC inhibitory activity and to virtual screenthe data set. The pharmacophore contour features and theirvolume (radius) used in these analyses are provided in Table 1.Importantly, the hydrophobic (hyd), hydrogen bond acceptor(Acc), projected hydrogen bond acceptor (Acc2), aromatic/hydrophobic (Aro/Hyd) and hydrogen bond donor (Don)features were considered as query pharmacophore contours. Inthese queries, the exterior volume (2.5 A) for the pharmaco-phore 1 and the pharmacophore 2 and the excluded volume(1.5 A) for the pharmacophore 3 were also considered for theanalysis.

In pharmacophore 1, the reference compounds such asRo48-8071, ZD-9720 and BIBB-515 were used for the querygeneration, which provided seven pharmacophore contourfeatures, one constraint (made between Acc and Acc2 contours)and an exterior volume. The pharmacophore 2 yielded vepharmacophore contour features, one constraint and an exte-rior volume. In pharmacophore 3, seven contours, oneconstraint and an excluded volume are included. In all thepharmacophore queries (pharmacophore 1–3), the constraintwas kept in the same contours features (between Acc and Acc2)with the radius of 2 A and 1 A for Acc and Acc2, respectively. Thedistance between the contour sites have been calculated for allthe queries, which are graphically provided in Fig. 4. Thedistance between the constraint features (Acc and Acc2) is 2.75–2.80 A in pharmacophores 2 and 3, while in pharmacophore 1,this value is greater than 3 A (3.30 A). The distances between thecontours located in each end of the molecule are greater than 10A and the hyd/aro contours are placed between these distances.But the polar contours such as Acc and Don are connectedthrough hyd/aro contours by the following distances, 8.17 A,14.59 A and 12.03 A for pharmacophore 1, 2 and 3 respectively.This bridged hyd/aro contours located within 5 A radius. Thesepharmacophore analyses show that the hyd/aro properties inthe molecules play an important role and the polar propertiesare also needed for the interactions and to elicit the inhibitoryactivity. The reported pharmacophore analysis of the referencecompound Ro48-8071 supports our present results that thearomatic/hydrophobicity properties in the molecules makeinteraction with the aromatic amino acids in the active site

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through p–p or p–CH interactions.18 The calculated distancebetween the contours sites revealed that the linearity in themolecular connections is an important feature for the interac-tion with the active site.

The generated pharmacophores queries (1–3) have beenused to undergo screening, in order to derive HITs moleculesfrom the data set and the studies identied totally, 15, 25 and 14HITs through pharmacophore 1, 2 and 3 respectively. Thedetails of the obtained HITs are provided in Table S6† and thesignicant HITs are given in Table 3. The compounds C-3, E-9and G-10 have been considered as signicant HITs, becauseamong the 3 pharmacophores queries, 2 queries have selectedthese compounds as signicant HIT with the RMSD values lessthan 1 A. Some compounds such as A-1, D-4 and G-5 have theRMSD values little above 1 A. Also the compounds D-3, D-5, F-5,F-25 and G-9 possessed the RMSD values less than 1 A for onepharmacophore query and higher than 1 A against other phar-macophore queries (Table 4).

Docking studies

In this analysis, we have used pharmacophore based dockingand normal docking methods to investigate the active sitefeatures of the enzymes responsible for the interactions. Thedocking study was carried out on the protein-Ro48-8071complex (pdb: 1W6J). Initially, the docking study was validatedusing the same ligand on the OSC protein. The obtained resultsshowed that the docked conformers have low RMSD values andthe crystallographic structure of the protein showed some watermolecules present in the active site, which are needed for thehydrogen bonding interactions. Hence, in this investigation, wehave performed the docking studies with and without thepresence of water molecules, to nd out the effect of watermolecules for the inhibitory activity of different OSC inhibitors.The pharmacophore query was generated on the complex ligandfor the pharmacophore based docking study. The followingcontours such as Aro/Hyd, Hyd, Hyd and Acc have been used inthis docking study. The specied radius of the contours such as2 A for Hyd contours and 1 A for the remaining contours (Aro/Hyd and Acc) were used. The pharmacophore based dockingperformed on the data set (series A–G) identied 29 and 26molecules as HIT compounds in the presence and the absenceof water molecules in the active site respectively (Table S6†).Comparing the results obtained from the pharmacophoreanalysis with the pharmacophore based docking explain thatthe compounds such as D-3, E-9 and F-25 possessed betterdocking score and lower RMSD values (around #1 A for bothpharmacophore and docking analysis). The HITs identiedfrom the docking studies (with and without water molecules)described that least number of compounds have good dockingscore and the RMSD values#1 A and the remaining compoundshave the RMSD values between 1 and 2 A. Compounds such asD-3, D-5, E-9, E-10, E-11, F-3, F-5, F-25 and G-7 have beenidentied as signicant HITs through the pharmacophorebased docking (presence and absence of water) and at least onepharmacophore analysis method.

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Fig. 4 Pharmacophore query contours and its distances used for the pharmacophore and docking analyses.

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In order to conrm the signicance of the HITs identiedthrough the analyses (pharmacophore and pharmacophore baseddocking analysis), further study was performed on the same dataset with normal docking studies (presence and absence of watermolecules). The compounds have highest docking score from thedocking with and without water molecules are provided in TableS6.† Comparing these results, the study without water providedgreater docking score than the study with water. But the later one(with water) provided structurally different compounds (parentstructure) as signicantly scored compounds. Other compounds,which have high docking score possessed the RMSD values are>1.5 A. The details of the results are provided in Tables S6, S7†and 3. Among the 9 signicant HITs identied through thepharmacophore studies (docking also), the following compoundssuch as D-3, E-9 and F-25 qualied as better HITs with gooddocking score and low RMSD values (around 1 A). The compoundF-15 is structural analogue to the reference compound Ro48-8071and the compounds G-5 to G-10 possessed signicant results with

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the normal docking. These structural analogues are identied asHITs through the pharmacophore analysis also.

Binding mode analysis

The docking studies performed on the data set provided someinteresting results, which are used here to elucidate thebinding mode analysis of different OSC inhibitors on theenzymes. The graphical representations of the interactions ofdifferent compounds on the active site are provided in theFig. S1–S3,† 5 and 6. The amino acid residues, which are takingpart in the interactions of the compounds D-3, E-9, F-5, F-25and G-7 are provided in Table S8.† The compounds D-3, E-9and F-25 are the best HITs identied through all the pharma-cophore and docking studies. F-5 is structural analogue to thereference compound Ro48-8071, also it possessed signicantresults on all the docking studies. The compound G-7 is one ofthe signicant representative HIT of the G-5 to G-10 series.

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Table 3 Significant HITs identified by pharmacophore and docking analyses

S. no. Comp code

Pharmacophore Pharmacophore based docking Normal docking

1 2 3 Without water With water Without water With water

RMSD RMSD RMSD Score RMSD Score RMSD Score RMSD Score RMSD

1 D-3 — 0.474 1.584 �7.792 0.943 �8.742 1.071 �8.458 0.923 �8.258 0.9912 E-9 — 0.670 0.771 �12.230 1.002 �10.092 1.078 �12.210 0.928 �9.971 1.1603 F-15 1.315 — 0.674 �12.380 1.628 �10.184 1.721 �12.590 1.711 �10.336 1.6034 F-25 0.984 — 0.782 �10.225 1.040 �10.657 1.065 �10.312 1.146 �10.792 1.0955 G-5 1.082 0.705 — — — �10.312 1.361 �8.147 1.218 �10.342 0.9636 G-6 — 1.281 — — — �9.636 1.191 �8.757 0.955 �10.133 1.0367 G-7 — 1.247 — �7.012 1.383 �8.396 0.921 �8.690 1.348 �10.371 1.0418 G-8 — 0.976 — — — �9.981 1.542 �9.673 1.182 �8.978 1.0449 G-9 — 0.945 1.380 — — �7.228 0.881 �9.542 1.112 �10.576 0.81710 G-10 — 0.758 0.990 — — �10.109 0.965 �9.822 0.731 �10.817 0.61111 Nat-1 1.178 — — �8.040 1.186 �4.810 1.315 �10.154 1.284 �10.371 1.73912 Nat-2 — 0.862 — — — �8.530 2.652 �10.980 2.069 �11.465 1.75013 Nat-3 — 0.926 — �9.344 1.758 �8.367 1.960 �10.165 2.457 — —14 Nat-4 — 0.931 — �9.278 1.959 �7.870 1.639 �10.723 1.342 �10.520 1.10715 Nat-5 — 0.835 — �7.426 1.898 �7.575 1.773 �10.848 1.779 �10.524 2.433

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Here, we are discussing the binding interactions of thesemolecules with the active site residues of the enzyme. Fig. S1–S3,† 5 and 6 show that some amino acid such Phe696, Trp581,Asp455, Phe444, Trp387 and His232 play an important role inthe binding of inhibitors to the active site. In the presence ofwater molecules, some electronegative functional groupsespecially oxygen or sulphur atoms in ketones or sulfoxide areforming hydrogen bonds with Ile338 and/or Gly380 throughwater bridges.

The interactions of the compound E-9 with the OSC enzymeis provided in the Fig. S1 and S2.† These gures show that NH+

functional group (as –NH+(CH3)2) in the molecule interacts withthe Asp455 by charged hydrogen bonding, the methyl groupsattached with the NH+ groups (–NH+(CH3)2) interact with theTrp387 and Trp581 through CH–p interactions. The phenyl ringlocated adjacent to the –NH+(CH3)2 group in the moleculemakes p–p stacking interaction with Phe444. The piperazinering also makes p–p stacking interaction with the Phe696 andCH–p bonding interactions observed on His232. Cys456 hasweak bonding with CH2 group adjacent to the –NH+(CH3)2group. The residues such as Tyr98, Tyr503, Trp192, Asn697 andIle521 are also exposed in the active site for hydrogen bonding orp–p or CH–p interactions. The interactions shown above arecommon in all the docking results (pharmacophore based andnormal dockings with and without water), but some specicinteractions are also seen in different active site environmentand methods. The interaction between Asp455 and NH+ isobserved extensively in the presence or in the absence of watermolecule and is one of the strongest interactions. The maindifferences between the interactions that were observed betweenthe pharmacophore based docking and the normal dockingmethod are related with the interaction between the His232 withthe phenyl and piperazine rings. In normal docking it is mostlyp–p stacking (with water molecules), while in pharmacophorebased docking (without water molecules), Phe444 has taken partfor the interaction. In the absence of water, Trp387 and Trp581

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have signicant effect for the interactions with the methylgroups attached with the NH+ group.

The interactions observed in compound D-3 are compara-tively similar to the compound E-9. In this compound, Asp455has charged hydrogen bonding (metal contact) interaction withthe NH+ and hydrogen bonding with the CH2 groups adjacent tothe –NH+(CH3)2 group of the molecule. In the presence of waterin normal docking, the interaction (with Asp455) is very weak,but the Trp387 has strong interaction with the NH+ by chargedhydrogen bonding. Trp581 also has signicant interaction withthe NH+ or CH groups in the molecules in any environmentalcondition. In the absence of water, one of the acidic amino acidsGlu532 present in the active site orient towards the oxygen atomin ketone for hydrogen bonding. As compound D-3, Phe444makes p–p stacking interaction with the phenyl ring in theabsence and the presence of water. In this compound, theresidues His232 and Phe696 have weak p–p or CH–p interac-tions. Some other residues such as Tyr98, Asn697 and Ile338 areexposed in the active site for the interaction with the hydrogenbond donor and acceptor groups and the aromatic rings presentin the molecule.

Compound F-5 is structurally analogous to compound Ro48-8071, and the mode of interaction is the same as that reportedin the literature.14 Here, the amino acid Phe696 makes p–p

interaction with the phenyl ring in all the docking studies andPhe444 interacts with the CH]CH group by CH–p interaction.The ketonic oxygen exhibited a hydrogen bonding interaction toIle338 through a water molecule. The charged hydrogenbonding interaction has observed with Asp455. In the absenceof water molecules, Phe444 has strong CH–p interaction withCH]CH group. Trp581, Tyr98, Tyr503, His232 and Gly380 arealso exposed for the interaction with the Br and oxygen atomsand the aromatic rings in the molecule.

The binding mode of compound F-25 showed that the NH+

in the piperidine ringmakes charged hydrogen bonding (acidic)with the Asp455 amino acid. In normal docking in the presence

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Table 4 Structure of the HITs obtained through the computational analyses

Comp. code Structure

D-3

E-9

F-15

F-25

G-5

G-6

G-7

G-8

G-9

G-10

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of water molecules, the charged hydrogen bonding interactionwith Asp455 is weak, but Trp581 has strong interactions. Thephenyl ring located adjacent to the piperidine ring has aromaticinteraction with the Phe444 and Phe696 residues either bystrong or weak way in the presence or absence of water mole-cule. Gly380 also interact with the methyl group present in theN-methyl by CH–CH bonding. The other residues Glu532, anacidic amino acid has exposure for the side chain donor prop-erty and possessed weak interaction with the polar substituentspresent in the molecule. Trp581 and His232 are the receptorexposure residues provide some p–p or hydrophobic interac-tion with the aromatic rings present in the molecule. Trp192,Tyr98, Tyr503 and Ile338 also play important role for thisinteractions. In the presence of water molecules, Trp581 inter-acts with the NH+ ion instead of Asp455 and the electronegativeatoms (oxygen) make hydrogen bonds with the Ile338 throughthe water molecule.

The compounds G-7 is one of the signicant HITs amongthe series G (G-5 to G-10). The binding mode of compounds G-5to G-10 (compounds have lowest RMSD values) suggested thatthe phenyl group bond with His432, NH+ with Cys456, CH ofthe quinuclidine with Asp455, ketonic oxygen form hydrogenbond with water molecules and Ile338. NH+ and CH of thequinuclidine make CH–p interaction with the Trp387 andTrp581 respectively. Trp581, Phe444, Trp230, Tyr98, Tyr381and Tyr503 are present as important residues for the interac-tion, while Asp455, Phe696 is not present for the interaction(Fig. 6). In compound G-7, Asp455 has strong interaction,which was observed in pharmacophore based docking, but it isweak in normal docking. In contrast to other compounds dis-cussed earlier (D-3, E-9, F-5 and F-25), the Phe696 and Phe444have weak bonding with the aromatic ring or the CH groups.Here, His232 is one of the important p–p stacking residues forthe interaction with or without water molecules. Thecompound G-6 is one of the high scored compounds has p–p

bonding interaction between phenyl ring and Phe696 residue,CH of quinuclidine forms CH–p interaction with the Phe444and the halogenated phenyl ring has p–CH bonding withTrp192.

The binding mode of high docking scored compoundsobtained from the normal docking with water moleculeexplains that the molecules containing phenyl rings interactwith His232 or Phe696 or Phe444 and the NH+ or the methylgroups attached to the amino group interact with Asp455 orTrp581 or Trp387 or Cys456 or Cys533 residues. The presence ofelectronegative atoms such as oxygen, sulphur or sulfoxide orhalogen atoms/groups interact with the water molecules andthe Ile338 or Gly380. The heterocyclic rings such as imidazole,benzimidazole and benzpyrazole present in themolecules makehydrogen bonds with the Ile338 through water molecule and p–

p stacking interaction with the Phe696 or His232. The Phe444makes p–p stacking or CH–p bonding with aromatic, cyclic oraliphatic groups in the molecules. The hydroxyl groups presentin the compounds make bonding with water molecule, Asp455and Cys533.

The docking study performed in the absence of watermolecules (normal) provided as the reference compound is one

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Fig. 5 Superimposed HITs (pink) derived from the docking studies on reference compound (green).

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of the highest scoring compounds (F-20; Ro48-8071). Thecompounds from F series possessed signicant docking scorein the study. The compound F-4 is the best scored compoundand is structurally similar to reference compound (as F-5). Inthis compound, the –NH+(CH3)2 in the terminal is replaced byN-methyl-N-cyclopropane moiety. Hence, it has considerabledifferences in the interactions with the residues present in theactive site than the reference compound. Generally, the methyl

Fig. 6 Superimposed docked structure of the representative compounds (G-7) of

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group attached to the amine group makes CH–p bonding withTrp387 and NH+ interact with Trp581 through cation–p inter-action. Also the CH groups in the cyclopropane and the linkergroups (aliphatic chain) possessed CH–p binding with Trp581residue. The phenyl group attached with uorine atom makesp–p stacking interaction with the Phe696. In the absence ofwater molecules, the heterocyclic rings such as benzopyrazole,isoquinoline, benzoxazole, imidazole, benzimidazole rings

the identified lead compound.

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make p–p stacking interaction with Phe696. Some heteroaromatic/aromatic rings exhibited double interaction (phenyland heterocyclic ring) with His232 and Phe696 or Phe696 alone.The terminal NH+ and CH3 groups interact with the Trp581 andAsp455 residues.

The results obtained from the docking studies (pharma-cophore based docking and normal docking in the presenceand absence water molecules) suggested that the types ofinteractions are common in any docking methods that werestudied. But the amino acid residues contributed for theinteractions may be different. In the normal docking withwater molecule, the interaction with Asp455 is weaker orabsent but with Trp387 or Trp581 it is stronger. In all thecases, the phenyl/hetero aromatic rings make p–p stacking

Fig. 7 Structure of novel compounds (natural products) identified through compu

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interactions with either Phe696 or Phe444 or His232 aminoacid. The interactions of electronegative atoms in the moleculestabilize the complex through the interaction with water andIle338 or Gly380 residues (Fig. 5). In consideration to theearlier binding mode analysis reported by Watanabe14 revealedthat the Trp192 and Trp230 have p–p interaction with thebromophenyl ring in the Ro48-8071. Phe696 and His232 alsohave p–p interaction with the aromatic ring present in themolecule. Asp455 has charged hydrogen bonding with thetertiary amine present in the molecule. CH–p interactionswith Phe444 and Trp581 contribute to the binding of themolecules additionally to the active site. The results derivedfrom our analysis also have coincided with these reportedresults. Studies performed on SHC-Ro48-8071 also showed

tational studies.

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that the Asp376 (as Asp455 in OSC) is important for thecharged bonding. Phe434 (as Phe696 in OSC) and Cys435 arealso important for the p–p stacking and hydrogen bondinginteractions respectively.20,21

The docking results derived from these studies showed thatthe aromatic amino acid residues such as Phe444 and Phe696interact with the aromatic ring or CH groups through p–p orCH–p interactions. In the absence of the above mentionedinteractions, the His232 makes p–p stacking interaction withthe aromatic rings. The Asp455 interacts with the cation or anyhydrogen donor atoms present in the molecules. In the pres-ence of water molecule, the residues Trp387 and/or Trp581actively participated for the interaction than Asp455. In thisstudy we have nd that the Glu532 plays an important role forhydrogen bonding interaction in the absence of water tostabilize the interaction on the opposite end of the Asp455region. The active site of the protein (body of the site) is mainlymade up of aromatic amino acids and the head and tail part ofthe active site possesses some polar amino acids and watermolecules for the interactions with the polar substituents inthe molecules. In the absence of water molecules some acidicamino acids play major role for the polar stabilization of thecomplex.

Binding interaction prediction of natural compounds

In order to validate the docking models and to predict thebinding score of some new compounds, we have used somenatural product compounds for the analysis. Totally 165 naturalproduct compounds were considered for the docking andpharmacophore based analysis and the compounds havesignicant docking scores are considered for the binding modeanalysis. We have selected 5 compounds (Nat-1 to Nat-5) assignicant compounds (Fig. 7) obsessed good docking scores

Fig. 8 Graphical representation of the binding mode of OSC inhibitors on the act

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with atleast three docking methods and one pharmacophoremethods studied. The docking scores and the RMSD vales forthe docking and the pharmacophore analysis are provided inTable 3. The compound Nat-1 has comparatively less dockingscore on pharmacophore based docking in the presence ofwater than other compounds. In comparison to the normaldocking results with and without water molecules, the phar-macophore based docking methods provided low docking scorefor all compounds. The binding interactions of all the naturalcompounds are similar to the reference compounds studied onthe target (Table 5 and Fig. S4†).

The pharmacophore based docking performed in the pres-ence of water showed that Nat-1 has bonding with Ser339through water molecule. Nat-2 has main interactions as theother reference OSC inhibitors studied earlier, but it also hassignicant interaction with Asn697 and Trp387. Nat-3 hasinteraction on Cys456 and water molecules. Nat-4 and Nat-5have similar interaction as other compounds and Nat-5 hasspecic interaction with Val695. In general, compounds Nat-1to Nat-5 have similar interactions with the residues such asPhe696 (p–p interactions), Trp581 (interaction with OH group),Asp455 (interaction with OH), Phe444 (p–p or CH–p or CH–CHinteractions), Cys456 (OH interaction), His232 (CH–CH or CH–

p interactions) and Tyr98 (p–p or CH–p interactions). Innormal docking without water, these compounds (Nat-1 to Nat-5) have weak interactions with the Asn697 through hydrogenbonding.

The results obtained from the virtual screening studies(pharmacophore based docking and normal docking in thepresence and absence water molecules) of the naturalcompounds suggested that the types of interactions arecommon in any docking methods that are studied. But theamino acid residues contributed for the interactions may belittle different. In all the docking methods studied, the strong

ive site.

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interactions have found with any one of the following residuessuch as Asp455 or Phe696 or Trp581 or Phe444 or His232 withthe aromatic/hetero aromatic rings through p–p stacking orCH–p interactions.

Conclusion

In the present work the binding mode of the OSC inhibitorswere analyzed with QSAR, pharmacophore analysis anddocking studies (pharmacophore based docking and normaldocking methods). The importance of the water moleculesfor the interactions reveals that these are needed for thehydrogen bonding interaction of electronegative atoms withthe Ile338 or Gly380. The obtained results are comparativelysimilar to the ones reported works in the literature and thepresent study provides more information for the interac-tions. The pharmacophore analysis and the QSAR studiessupported by the docking results provide a new interpreta-tion of the binding mode of the studied compounds and theirmode of interactions. These studies show the importance ofaromatic/hydrophobic interactions. The amino acids Phe696,Phe444 and His232 are responsible for p–p stacking inter-actions or CH–p interactions with the inhibitors. The polargroups present in the compounds are bonding with Asp455or Trp581 or Trp387 or Ile338 (through water) or Gly380(through water) or Glu532. The graphical representation ofthe binding mode of the OSC inhibitors with the consider-ation of the ligand and structure based analysis methods areprovided in Fig. 8. The HITs identied from the data set (D-3,E-9, F-25 and G series) showed that the presence of hetero-cyclic or aromatic rings in the central part of the molecule isimportant for the interactions. The natural productcompounds such as Nat-1 to Nat-5 have similar interactionsas the reference compounds. These compounds alsopossessed signicant docking scores reveals that it can beused as OSC inhibitors.

Acknowledgements

N.S.H.N. Moorthy is grateful to the Fundaçao para a Ciencia eTechnologia (FCT), Portugal for a Postdoctoral Grant (SFRH/BPD/44469/2008). The authors gratefully acknowledge FCT fornancial support for project PTDC/QUI-QUI/102760/2008.

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