Pharmacophore Mapping, In Silico Screening and Molecular Docking to Identify Selective ...

DOI: 10.1002/minf.201300023

Pharmacophore Mapping, In Silico Screening andMolecular Docking to Identify Selective Trypanosomabrucei Pteridine Reductase InhibitorsDivya Dube,[a] Sujata Sharma,[a] Tej P. Singh,[a] and Punit Kaur*[a]

1 Introduction

Trypanosoma brucei, a flagellate protozoan parasite, is thecausative agent of dreaded Human African trypanosomiasis(HAT), generally known as sleeping sickness. This diseaseposes a major health risk as it infects a large population inthe sub-Sahara region. It is transmitted due to the bite ofthe parasite, the tsetse fly. The treatment of HAT in ad-vanced stages is extremely difficult, especially if the para-sites cross the blood brain barrier resulting in the involve-ment of the central nervous system. The disease is progres-sive and can prove fatal if not treated timely. The presentdrug regime is proving to be inadequate due to the emer-gence of resistant strains.[1] Moreover, not only is the cur-rent treatment long drawn and expensive with limited effi-cacy but also requires parenteral administration. This makesit imperative to discover and develop novel therapeuticswhich not only reduce the span of treatment but are simul-taneously safer, inexpensive, orally administrable and pos-sess higher efficacy. Not much progress has been made inthis direction though enormous efforts are on for the de-velopment of newer drugs. The ongoing investigations foridentification of newer candidates include the glutathionederivatives,[2] the prodrug DB289,[3] and its azo analogs[4]

and diamidines.[5] In our quest for the identification ofnovel scaffolds as lead compounds, we have focussed onpteridine reductase 1 (PTR1) which has recently emergedas a promising drug target.[6]

PTR1 from Trypanosoma brucei (T. brucei) is essential forits growth, survival and virulence as observed from micegene knockout and knockdown studies.[7] PTR1 is a shortchain NADPH-dependent oxidoreductase enzyme of thesalvage pathway. It catalyzes in two steps the reduction ofbiopterin into active cofactors 5,6,7,8-dihydrobiopterin andthen subsequently to 5,6,7,8-tetrahydrobiopterin. PTR1 isalso involved in the conversion of dihydrofolate to tetrahy-dofolate. It acts in conjugation with the enzymes dihydro-folate reductase�thymidylate synthase (DHFR-TS) which areinvolved in the reduction of folates. The trypanosomatids,unlike the human hosts, are auxotrophic in nature for thenecessary pterins and folates as they lack the enzymes fortheir de novo synthesis in their genome.[8] Consequently,they have evolved a complex pathway for acquiring andsalvaging the essential extracellular pteridines for theirgrowth. As these enzymes are important for the existenceof the protozoa, they form potential targets for drug thera-py. The PTR1 exhibits broad-spectrum activity against both

[a] D. Dube, S. Sharma, T. P. Singh, P. KaurDepartment of Biophysics, All India Institute of Medical SciencesNew Delhi�110029, India*e-mail : [email protected]

Supporting Information for this article is available on the WWWunder http://dx.doi.org/10.1002/minf.201300023.

Abstract : Trypanosoma brucei Pteridine reductase (TbPTR1)is of vital importance and is an established drug target fordreaded Human African trypanosomiasis (HAT). Pharmaco-phore perception strategy has been employed to identifykey chemical features responsible for the biological activityfor TbPTR1. The findings suggest that three different phar-macophore features can be associated with T. brucei anti-PTR1 activity namely: H-bond donors (D), Hydrophobic aro-matic (H) and Ring aromatic (R). The resulting hypothesis isable to predict the activity of other existing TbPTR1 inhibi-tors with a correlation coefficient (r) of 0.89. An in silico da-tabase screening, based on the best hypothesis, has been

used to identify some potential nanomolar range TbPTR1inhibitors. These compounds were then checked by molec-ular docking and subjected to ADMET analysis. Further, a de-tailed comparison of the pharmacophore behavior and dif-ferential analysis of binding pockets of T. brucei and L.major was made which revealed subtle differences in termsof their shape and charge properties. This investigation canform the basis for tweaking the specificity of compoundsfor generating new improved species specific inhibitor mol-ecules for Pteridine reductase in these different parasiticprotozoans.

Keywords: Docking · Inhibitors · Pharmacophore modeling · Pteridine reductase · QSAR · Structure-based drug design · Trypanosomabrucei

� 2014 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim Mol. Inf. 2014, 33, 124 – 134 124

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pteridines and folates and thus provides a metabolicbypass for DHFR inhibition. As a result the treatment of try-panosomiasis by the antifolates is compromised as theknown DHFR inhibitors are inactive against it. Since PTR1 isinvolved in both biopterin and folate reduction, it has beenestablished as an excellent target for the development ofnovel scaffolds for treatment against T. brucei infections.

The structure elucidation of PTR1 complexes with bothsubstrate and inhibitors from T. brucei have revealed itsmodes of binding and molecular recognition principles.[9–12]

Several structure-based drug design approaches includingvirtual screening have been employed to identify inhibi-tors[12–15] against T. brucei PTR1 (TbPTR1). However, till dateno compound is available with greater potency, efficacyand tolerability than the existing regime. The current studyexplores the binding preferences of the inhibitory mole-cules of TbPTR1 in terms of structure-activity relationship.The 3D-QSAR pharmacophore models have been derivedbased on molecular alignment of existing TbPTR1 inhibitorsusing HypoGen algorithm.[16] The pharmacophore features:hydrogen bond donor, aromatic and hydrophobic aromaticwere found to be the key features associated with the se-lectivity and potency of TbPTR1 inhibitors. This identifiedpharmacophore hypothesis was later used to scan theligand database to identify compounds with similar molec-ular features. Subsequently virtual screening has been car-ried out where the ligands were docked into TbPTR1 to pre-dict the binding affinities of the compounds. This strategypredicted compounds better than the existing class of in-hibitors. The comparison of predictive pharmacophoremodels amongst the two different species Leishmaniamajor (L. major) and T. brucei as well as a detailed structuralcomparison of both the enzymes and has provided usefulprospects for the development of selective inhibitorsagainst them.

2 Experimental

2.1 Collection and Preparation of Molecules

The selection of compounds is an important step for phar-macophore identification. T. brucei inhibitory moleculeswith experimentally determined Ki values were collectedfrom the literature searches. A total of 45 inhibitory mole-cules comprising four different scaffolds with varying sub-stituents were included in this study. The data collectedwas separated into two independent sets, training set fordeveloping the pharmacophore model and test set for vali-dation of the generated pharmacophore hypothesis.[12–15]

These sets were planned so as to cover a wide-range of theactivity values and structural diversity. The T. brucei trainingset consisted of 38 molecules with their Ki values rangingfrom 0.007 to 288 mM (over five orders of magnitude)(Table 1) and test set consisted of 7 molecules (Ki valuesranging from 0.15 to 35 mM) (Table 2). The three-dimension-al coordinates of the compounds were generated using the

Discovery Studio (DS) 2D/3D visualizer.[17] For each com-pound, the geometries were corrected, atoms were typedand energy minimization was performed based on themodified CHARMm force field.[18,19] The various protocols inthe molecular modeling package, Discovery Studio v2.0(DS) were utilized for 3D-QSAR modeling, virtual screeningand molecular docking studies.

2.2 Pharmacophore Generation and Validation

The 3D QSAR Pharmacophore Generation protocol with theCatalyst HypoGen[16] algorithm was used to derive SARpharmacophore models from a given set of inhibitors withknown biological activity. The pharmacophore model andconformers were developed by using the same procedureadopted for L. major inhibitors.[20] A maximum of 250 di-verse conformational models were generated for each com-pound using an energy constraint of 20 kcal/mol. InLmPTR1, H-bond donor, H-bond acceptor and hydrophobicinteractions have been established to play a key role in pro-tein-ligand recognition. Therefore, four pharmacophoricfeatures HB_donor, HB_acceptor, Hydrophobic_aromaticand Ring_aromatic were selected to construct predictivepharmacophore models. The activity of each training setcompound was estimated using regression parameters. Thedeveloped pharmacophore models were verified using mul-tiple cross-validation procedure. The cross validation is car-ried out by calculating cost analysis, root mean square de-viation (rmsd) and Best Fit values concurrently. The overallcost of a hypothesis is calculated by summing over threecost factors: weight cost, error cost, and configuration cost.Cross validation was also performed by using the Fischer’srandomization test where the biological activity data arerandomized within a fixed chemical data set and the Hypo-Gen process is initiated to explore possibilities of other hy-potheses with good predictive values. A statistically rele-vant pharmacophoric model requires that the hypothesisgenerated prior to scrambling should be better than therest. The pharmacophore models were finally evaluated foraccuracy by using the test set of compounds with Ligandpharmacophore mapping protocol. Only the best mappingfor each ligand was allowed using the ‘Catalyst’ protocolwhich identifies the extent of ligand matching with thepharmacophore.

2.3 Database Query

The three-dimensional similarity searches were carried outwith the best pharmacophore model, which scored theleast rmsd value, high value for both ‘Best fit’ and cost dif-ference and could simultaneously predict the test set witha high correlation. The Screen library protocol was em-ployed to query around 80 000 ‘drug-like’ molecules fromthe 13 million structurally diverse small drug-like com-pounds comprising the ZINC chemical database to retrievenovel and potent molecules. The compounds obtained



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from the database search were further filtered out for theirdrug-like properties by applying various ADME (Adsorption,Distribution, Metabolism, Excretion) filters including toxicity.

These filters implemented in DS include aqueous solubility,blood brain barrier penetration, hepatotoxicity, CYP2D6binding and plasma protein binding. Additionally, those

Table 1. Training set molecules. R/R1, R2 and R3 are the side chains of the scaffolds.

Scaffold PDB-ID [a] R/R1 R2 R3/X Ki (mM) T.brucei

3JQ6[13] CH(CH3)2 CH(CH3)2 – 3.3

3JQ7[13] C6H5 NH2 – 1.23JQ8[13] CH3 (CH3)2 – >35

3JQB[13] H CH2 CH2C6H5 O 0.96

3JQC[13] Br CN O 3.93JQD[13] C6H5 CN O 0.713JQE[13] C6H4OCH3 CN O 0.363JQ9[13] C7H5O2 CN O 0.40J10[13] H H O >35J11[13] H CN O 5.8J12[13] C6H4 CH2CH2 CN O 0.50

3JQF[13] – NH2 >35

3BMN[13] C-C3H5 – NH >353BMO[13] C6H4CH3 – S 5.43BMQ[13] CH2C6H5 – S 3.2

3GN1[14] H 288

2WD7[14] H Cl 10.62WD8[14] C6H4(Cl)2 C6H5 0.007J1[13] 21.1J2[13] 141J5[13] C6H5CH3 9.8J6[13] CH2CH2C6H5 23.9J8[13] OCH2CH2CH3 CH2C6H4(Cl)2 0.51J9[13] C6H5 CH2C6H4(Cl)2 0.047A10[15] CH2CH2C6H5 H 24A11[15] CH2-(4-bromobenzene) H 3.1A12[15] CH2-(3,5-dichlorobenzene) H 8.8A13[15] CH2-(2-chlorobenzene) H 10A15[15] 3,4-dichlorobenzyl 4-Cl 2.3A16[15] 3,4-dichlorobenzyl 4-O(CH2)2CH3 0.31A17[15] 3,4-dichlorobenzyl 4-OBn 16A18[15] 3,4-dichlorobenzyl 4-Ph >60A19[15] 3,4-dichlorobenzyl 4-OMe 0.46A21[15] 3,4-dichlorobenzyl 7-O(CH2)2CH3 0.047A22[15] 3,4-dichlorobenzyl 7-OBn 0.098A23[15] 3,4-dichlorobenzyl 7Ph 0.007A24[15] 3,4-dichlorobenzyl 7-OMe 0.65A25[15] 3,4-dichlorobenzyl 5,6-dimethyl 3.9

[a] PDB IDs have been provided where available. In other cases user defined assignment has been done. J1-9 and A10-25 has been as-signed to molecules where crystal structures are not available in references 13 and 15 respectively



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molecules which survived this filter were checked for theLipinski’s rule of 5. A molecule which fitted all the featuresof the predicted pharmacophore model and survived thesepharmacokinetic property filters was then retrieved as a hitmolecule and taken up for further docking with the recep-tor molecule.

2.4 Molecular Docking and Validation

The X-ray crystal structure for the receptor protein TbPTR1(PDB ID: 3JQD)[13] was used for docking. The receptor mole-cule was prepared by extracting all non-protein atomsexcept the co-factor NADP/NADPH from the available crys-tal structure. Subsequently the receptor was typed by ap-plying CHARMm Forcefield (version c28),[18,19] hydrogenatom positions added and optimized with the conjugategradient algorithm. The cofactor was retained throughoutthe molecular docking analysis as it has been found to bepresent in all crystal structures of TbPTR1 receptor-ligandcomplexes. This was corrected for its valency, bond orderas well as alternate conformations before docking. Similarly,as the inhibitor MTX has been observed in crystal structuresof both TbPTR1 and L. major PTR1 (LmPTR1), it was initiallydefined as the ligand. The energy-optimized MTX was ini-tially docked into the active site of the protein to validatethe docking procedure. The docking algorithm[21] is verifiedby control docking where the rmsd between the superim-posed docked and crystallographic conformations is deter-mined. The validation of the docking algorithm is necessaryto ascertain that both docking and its subsequent scoring

are able to accurately reproduce the experimentally deter-mined ligand conformational pose within the allowed limitsof deviation (2 �). The lower the rmsd values, the better isthe pose prediction by the docking algorithm. The molecu-lar docking of the known and identified pteridine reductaseinhibitors was carried out with docking engine to furtherstrengthen our predictions. The docked poses of the gener-ated ligands were scored with nine different functionswhere the Ludi III scoring function gave the best correla-tion values for docking with MTX. These parameters ob-tained were subsequently validated employing the sameprocedure by control docking with the inhibitor DX7 (PDBID: 3JQD) where again Ludi III emerged as the best scoringfunction. Hence, the Ludi III scoring function was taken asthe scoring function of choice for subsequent moleculardocking studies with the different scaffolds obtained fromthe database screening.

3 Results and Discussion

3.1 Pharmacophore Mapping and Validation

The generated three-dimensional pharmacophore modelscomprising distinct scaffolds utilized the well definedchemical features as well as the structure of ligands andtheir interaction with TbPTR1 from the training set. The de-tails of the top ten ranked 3D-QSAR pharmacophore hy-potheses derived for T. brucei inhibitors using the HypoGenalgorithm and the relevant statistics are provided inTable 3. Almost all the generated pharmacophore models

Table 2. Test set molecules. R/R1, R2 and R3 are the side chains of the scaffolds.

Scaffold PDB-ID [a] R/R1 R2 R3/X Ki (mM) T.brucei

2C7V[12] CH2N(CH3)C6H5CONHCH(COOH)CH2CH2COOH H – 0.15

3JQA[13] H H S >35

J13[13] C6H4CHO(meta) CN O 0.29

3JQG[13] CH2C6H4OCH3(para) – S 18

3GN2[14] C6H4(Cl)2 – – 0.4

A14[15] CH2-(3,4-chlorobenzene) H – 0.4A20[15] 3,4-dichlorobenzyl – 7-Cl 0.51

[a] PDB IDs have been provided where available. In other cases user defined assignment has been done. J1-9 and A10-25 has been as-signed to molecules where crystal structures are not available in references 13 and 15 respectively



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by the HypoGen module basically possessed three chemicalfeatures comprising hydrogen bond donor (D), ring aromat-ic (R) and hydrophobic aromatic (H). The best pharmaco-phore hypothesis obtained from numerous possibilities byapplying a cost analysis for T. brucei inhibitors consisted offour chemical features: one hydrogen bond donor (D), tworing aromatic (R) and one hydrophobic aromatic (H). All thehigh activity inhibitors from the training set were super-posed onto this pharmacophore hypothesis (Figure 1). This,thus described the chemical features required for the PTR1inhibition in T. brucei enzyme.

These pharmacophore models were further assessed bythe known standard parameters comprising cost analysis

values, rmsd and ‘Best Fit’ values. The hypothesis where thecost is closest to the fixed cost rather than the null cost isconsidered to be statistically significant. The difference be-tween the null cost of top 10 hypotheses (162.8) and fixedcost (116.8) is 46 and the configuration cost value for theHypo1 is less than 17 (11.5) indicating that this is the mostsignificant pharmacophore model. All 10 hypotheses havetotal cost close to the fixed cost and the rmsd lies withinthe acceptable range from 0.9–1.2�. ‘Best Fit’ values also in-dicate the overall goodness of fit for all the training setcompounds. Hypo1 conformed to the best fit value of 6.1,least rmsd of 0.9� and highest correlation of 0.83 signifyingthat this had the best correlation data.

Table 3. Statistical values of the top ten pharmacophore hypotheses generated from the training set by the 3D QSAR Pharmacophore Gen-eration protocol for T. brucei inhibitors. ~cost = null cost – total cost; null cost = 162.8; fixed cost = 116.8; configuration cost = 11.5; D: hy-drogen bond donor, H: hydrophobic aromatic, R: aromatic.

Hypo no. Features Best fit value Total cost ~cost RMSD (�) Correlation (r)

1 DHRR 6.1 121.0 41.8 0.9 0.832 DHRR 6.1 121.2 41.6 1.0 0.833 DHR 5.5 123.4 39.4 1.1 0.804 DRR 5.4 123.9 38.9 1.1 0.805 DRR 5.7 125.1 37.7 1.1 0.806 DHRR 5.4 128.4 34.4 1.1 0.797 DHRR 5.6 128.6 34.2 1.1 0.798 HRR 5.8 129.8 33.0 1.2 0.789 DRR 5.1 130.3 32.5 1.2 0.77

10 DHR 5.2 131.3 31.5 1.2 0.76

Figure 1. Hypo1 consists of a Hydrophobic aromatic (H, light grey), Ring aromatic (R, black) and H-bond donors (D, grey) features. Thehigh activity T. brucei inhibitors are shown superposed onto Hypo I.



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The statistical relevance of the hypothesis was also esti-mated by performing the Fischer’s randomization test with95 % confidence level. The aim here is to verify the correla-tion between the biological activity and the hypothesisgenerated. This involves the generation of pharmacophoremodels by randomly scrambling and reassigning the bio-logical activities amongst the training set molecules. 19random hypotheses were generated by the catScrambleprogram using exactly the same conditions as used in gen-erating the pharmacophore hypothesis. Out of the total 19random hypotheses generated, only 18 scramble runs pro-duced a valid hypotheses. The original cost and total costof random pharmacophore models obtained from theFischer’s randomization test (Figure 2) indicate that the ran-domly produced hypotheses do not have a predictive valuesimilar to that of original hypothesis and that Hypo1 exist-ed with a statistical significance level of 95 %. Thus, Hypo1is not generated by chance as it is far more superior tothose produced randomly. The Hypo1 is able to predict theactivity of the training set compounds with a correlationvalue of 0.83 (Figure 3) and the test set comprising an ex-ternal set of compounds with a correlation value of 0.89(Figure 4). This further validated the statistical significanceof Hypo1 as the hypothesis of choice for new lead com-pound search. Hence, for identification of novel T. brucei in-hibitory scaffolds, Hypo1 was chosen as the best hypothe-sis and subsequently used as the template for small mole-cule database search in the virtual screening experiments.Hypo1 conformed to all the four features: one H-bonddonors (D), one Hydrophobic aromatic (H) and two Ring ar-omatic (R) which predicted the test set compounds witha high correlation and best fit value.

The presence of two hydrophobic features in the QSARhypothesis emphasizes the already established importanceof these interactions in PTR1 molecular recognition and cat-

Figure 2. Fischer’s validation performed at 95 % confidence level for T. brucei inhibitors. The difference in costs between HypoGen runsand the randomly scrambled runs obtained from Fischer’s randomization are depicted.

Figure 3. Correlation plot between experimental and predicted ac-tivities of Hypo1 for T. brucei inhibitors training set.

Figure 4. Correlation plot between experimental and predicted ac-tivities of T. brucei inhibitors for the test set.



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alysis.[20] Our group has earlier reported 3D-QSAR pharmo-cophore hypothesis derived from the LmPTR1.[20] Four phar-macophore features, namely: two H-bond donors (D), oneHydrophobic aromatic (H) and one Ring aromatic (R) havebeen shown to be responsible for biological activity againstLmPTR1. The difference in the pharmacophore hypothesisbetween the two protozoan species lies in the presence ofone extra Ring Aromatic feature in place of a bond donor(D) in the TbPTR1 3D-QSAR hypothesis when compared tothat of LmPTR1.

3.2 Database Screening

The T. brucei predictive 3D-QSAR pharmacophore Hypo1was used to screen the ZINC database drug-like molecules.The initial query criteria for filtration comprising all the fourfeatures of the T. brucei. Hypo1 filtered out a total of 284molecules from ZINC database. The screened molecules ob-tained were subjected to Prepare ligand protocol, which ledto generation of total 970 3D-poses for these ligands. Thisprotocol generates the 3-D structures and filters out thenon-drug molecules based on the Lipinski’s rule of 5. Thecontrol docking was performed using TbPTR1 inhibitor DX7co-complex bound with 0.7 mM binding affinity (PDB ID:3JQD). The pose of DX7 with the least rmsd value of 0.6 �as compared to experimentally determined crystal struc-ture, was selected as the best pose. The predicted bindingaffinity of 0.9 mM was in good agreement with the reportedexperimental binding affinity of the complex. The 970poses were then docked into the same TbPTR1 binding sitepocket as DX7. The docking for all the compounds ob-tained by virtual screening was performed using the sameparameters as that of the control docking experiment withDX7. The pose in which the ligand did not form any stack-ing interaction, particularly with the co-factor NADP, wasexcluded. Poses with maximum stacking interactions anda good docking score were given a higher priority. Thedocking results were thus ranked based on both the Ludi-III docking scores and the molecular interactions. Two ofthe best inhibitors identified in this manner from the ZINCdatabase (Table 4). Both these inhibitors occupy the bind-ing pocket of the enzyme and interact through several mo-lecular interactions. The docked pose obtained for the bestderived inhibitor ZINC33025157 into the TbPTR1 bindingsite is indicated (Figure 5). The 2-methoxynaphthaline ringof ZINC33025157 forms stacking interactions with the nico-tinamide moeity of the co-factor NADP on one side andPhe 97 and Tyr 174 on the other end. Additionally it formshydrogen bonded interactions with NADP, Tyr 174 and Ser95. It also forms a number of van der Waals interactionswith the surrounding amino acid residues in the bindingcleft. The inhibitor ZINC20149815 was found to make stack-ing interacitons with NADP and is surrounded by the hy-drophobic area of the enzyme comprising of residues Phe71, Pro 210, Phe 171, Met 213 and Trp 221.

3.3 PTR1 in L. major and T. brucei

The protein PTR1 possesses the typical architecture ofshort-chain oxidoreductases (SDR) with the formation ofa a/b domain based on the Rossman fold. PTR1 is reportedto carry out common functions in both L. major and T.brucei, though studies in literature regarding its mechanismof action mainly focus on the protozoan L. major. The mul-tiple sequence alignment of the PTR1 carried out usingClustalW (http:/www.ebi.ac.uk/clustalW) showed only twomajor homologous regions within these species (Support-

Table 4. Virtual screening results of PTR1 inhibitors identified fromZINC Database (Zinc-ID).

Zinc-ID Structure Estimatedactivity

DX7 0.9 mM

ZINC33025157 0.03 nM

ZINC20149815 0.01 nM

Figure 5. Molecular interactions of the inhibitor within the PTR1binding pocket of T. brucei bound with ligand ZINC33025157 la-beled as A. Hydrogen bonds are marked by dashed lines. The in-hibitor is sandwiched between NADP (light gray) from one endand Phe97 and Tyr174 from other end in T. brucei PTR1.



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ing Information Figure S1A). Though, the overall foldingpattern of PTR1 is similar, the proteins from the two speciesshare a sequence homology of 51 %. with the T. bruceibeing shorter by 20 amino acid residues in the loop be-tween the beta sheet b3 and helix a3 in L. major (Figure 6).

Phylogenetic analysis was carried out using full-lengthsequences of PTR1 from the pathogenic species of Leish-mania and Trypanosoma obtained from UniProt. A topolog-ical overview of the PTR1 groupings and their phylogenticrelationship as a dendogram have been displayed in Sup-porting Figure S1B. Both the species cluster into two differ-ent clades with Leishmania belonging to one lineage andTrypanosoma to another. This shows that both the specieshave evolved separately as two distinct genotypes.

A least squares superimposition of the MTX co-complexstructures of PTR1 from T. brucei (PDB ID: 2C7V)[12] and L.major (PDB ID: 1E7W)[9] (Figure 7) yield differences in theloop regions. The region linking helix a3 with the b-sheetb3 constituting residues 64–68 and the conformation of theloop region corresponding to residues 205–214 existing be-tween the helix a7 and sheet b6 in TbPTR1 is significantlydifferent from that observed in LmPTR1. There is no evi-dence to suggest a direct role of the first loop in substratebinding and hence catalysis. However, the second loopregion is flexible and is found to be in direct contact withthe substrate or inhibitor moiety. Hence, the interactions ofthis loop region with ligands may dictate the possible ori-entation and conformation adopted by it. Moreover, thenature of the amino acid residues comprising this segment

possibly determine the substrate binding specificity. Theconformation of this loop also influences the orientation ofthe succeeding helix a7 which is inclined in TbPTR1 at anangle of 208 with respect to that in LmPTR1. On the wholethe conformation at the active site of the enzyme PTR1within these two species is comparable, though, subtle var-iations existing in the amino acid residues confer specificitymaking the development of scaffolds as lead moleculesa challenge.

3.4 Binding Site of L. major and T. brucei PTR1

Though the sequence homology between PTR1 from L.major and T. brucei is 51 %, the homology amongst theirbinding site residues is relatively high. The active siteregion encompasses a single chain which comprises resi-dues from both the C- and N-terminal regions. The overallshape and depth of the elongated tunnel-like active siteregion in TbPTR1 and LmPTR1 is by and large the same(Figure 7). The catalytic triad Ser-Tyr-Lys in SDRs is highlyconserved, however, the Ser is substituted by Asp in thecase of these two species. The co-enzyme NADP is essentialfor the binding of the inhibitor or substrate with PTR1. Thecoenzyme binding pattern TGxxxRhG is present with the re-placement of the residue Gly by Arg. Another motif NNASwhich is known to bind to NADP is present in both the spe-cies (Supporting Figure S1A). The NADP moiety adopts anextended conformation. It forms the base of the tunneland plays a crucial role in the binding of the inhibitor.

Figure 6. Superimposition of the overall structures of T. brucei (light grey) and L. major (dark grey) PTR1 along with the bound coenzymeNADP.



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There exists a common set of inhibitors for both LmPTR1and TbPTR1 that bind with different specificities.[13] In orderto identify the likely cause of these parallel inhibitors withvariable specificities, the possible similarities and disparitiesin the binding site features of both the species have beenexplored. The active site residues in LmPTR1 and TbPTR1were compared using the SiteEngine webserver.[22] Thisserver is capable of comparing the features of the bindingsites based on the physico-chemical properties of both themain chain backbone as well as the side chain residues.The server compared 24 amino acids within the active siteregion with a search centered around the MTX binding siteon the complete surface of the structures with PDB IDs1E7W (LmPTR1) and 2C7V (TbPTR1). The mapping of thetwo protein surfaces revealed that 18 amino acid residuesout of 24 were identical while only 6 residues were dissimi-lar. The major differences are observed in the substitutionof amino acid residues Cys 168, Phe 171, Pro 210 and Trp221 from T. brucei by Leu188, Tyr 191, Asp 232 and His 241in L. major respectively. These substitutions result ina change of charge properties and profile of their respec-tive region of occurrence in the binding site pocket andthus impart specificity within the species. The major area of

differences in the binding site region are indicated inFigure 7.

The comparison of the 3D QSAR hypothesis generatedseparately for L. major from the recently reported series ofPTR1 inhibitors with that of T. brucei suggested that thereare three common pharmacophore feature amongst thetwo species which include the hydrogen bond donor, ringaromatic and hydrophobic aromatic.[20] The main differencelies in the presence of an extra Ring aromatic feature in T.brucei 3D QSAR pharmacophore (Figure 1). The differencesin the pharmacophore identified in both the species mightbe due to the variation in the outer region comprising resi-dues Asp 232 and His 241 in L. major (Figure 8A) emanatingfrom the ligand binding tunnel of the PTR1. In T. bruceia more tighter Pro 210 is present in place of Asp 232 in L.major while Trp 221 is present in place of His 241 (Fig-ure 8B). The increase in hydrophobicity in TbPTR1 can be at-tributed to the presence of (i) Pro 210 instead of the moreacidic Asp 232 and (ii) Phe171 from a more negativelycharged Tyr 191 in the binding site of LmPTR1. The iminogroup containing Pro residue restricts the conformation ofthis loop and results in a slight widening of the tunnel inthis region. Similarly the Trp 221 residue in TbPTR1 is com-

Figure 7. Superimposition of the methotrexate (MTX) binding site of T. brucei (light grey) and L. major (dark grey) PTR1. The major regionsin both the enzymes which are the likely cause of the difference in the ligand specificity in the MTX binding channel are depicted indotted grey arcs.



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paratively bulkier than the corresponding residue His 241in LmPTR1 and its aromatic side chain adopts a differentorientation in the binding pocket.

These alterations in the nature and charge of the resi-dues present at the outer part of the tunnel impart thisregion the ability to bind a variable set of inhibitors in thetwo species. Therefore, the presence of these hydrophobicresidues might be responsible for an additional Ring aro-matic feature in T. brucei predictive pharmacophores inplace of the H-bond donor. Due to the presence of threecommon pharmacophore features together with the activesite residues, the mode of ligand recognition is broadlyconserved in PTR1 of both species. Hence, it is generally ex-pected that the molecule inhibiting one will also tend to in-hibit PTR1 from other species. However, due to the pres-ence of a few differences in the outer region of the bindingsite of both the species, the inhibitors will tend to bindwith different specificities.

4 Conclusions

3D QSAR pharmacophore modeling approach used in com-bination with pharmacophore-based virtual screening hasled to the identification of key pharmacophoric featureswhich are responsible for the T. brucei Pteridine reductaseinhibitory activity. In addition to the identification of phar-macophores, the best pharmacophore hypothesis was thenused to identify similar molecular compounds from ZINCdatabase. The hits so obtained after similarity searcheswere further prioritized by molecular docking approachusing LigandFit[21] docking tool. The docking protocol andscoring functions were cross validated by control dockingexperiments. Amongst all the inhibitors identified, the com-pound ZINC33025157 showed the highest predicted Ki

value of 0.03 nM for TbPTR1. The predicted Ki value is 233folds higher than the strongest inhibitor of the TbPTR1DDD00071204 (PDB ID: 2WD8, Ki = 0.007 mM). These novelinhibitors could prove to be the promising lead com-pounds that can be further taken up for the developmentof novel therapeutic entities against the trypanosomatidparasite.

Although the basic overall fold including the conforma-tion of the cofactor NADPH binding site and the catalyticregion in the two species L. major and T. brucei PTR1 areconserved, there exist subtle differences at the active sitebetween the two species. Therefore, a non-selective mole-cule which can bind to PTR1 from both these species maynot prove to be the ideal drug candidate due to the differ-ential behavior of their binding sites. Thus, a compound de-veloped against one infection, albeit the same protein, maynot be the best candidate for inhibition in the other spe-cies. It will result in the reduction of the severity of the in-fection but not eliminate it. In such a scenario, the possibili-ties of the recurrence of infection may be enhanced whichmight subsequently contribute to the development of drugresistance. Therefore, it is imperative to identify species-specific compounds for the treatment of different infec-tions even if the same protein is being targeted. Theunique structural and sequence specific features of the Try-panosome PTR1, identified in this study, can be exploitedfor the development of species-specific lead molecules asinhibitors. Overall, the current study elucidates the struc-ture-activity relationship for PTR1 inhibitors. It imparts anunderstanding of the protein�ligand interactions involvedbetween the enzyme and its inhibitors and could probablyaid in the development of highly selective and species spe-cific inhibitors against PTR1 as anti-parasitic agents.

Figure 8. The surface electrostatic potential of PTR1 is colored coded so that regions with negative electrostatic potential (acidic) are red,whereas those with positive values (basic) are blue. The active site contacting residues which are different in T. brucei and L. major speciesare mapped on the surface. The inhibtor and the coenzyme are shown in sphere representation. (A) The LmPTR1 docked with inhibitorZINC07127727 is taken from the recently identified inhibitors for the same.[20] (B) The TbPTR1 docked with inhibitor ZINC20149815.



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Acknowledgements

Authors acknowledge the Indian Council of Medical Re-search, India for a grant of Bio-Medical Informatics Centre.DD thanks the Department of Science & Technology, Indiafor financial support.

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Received: February 13, 2013Accepted: November 5, 2013

Published online: February 2, 2014



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Pharmacophore Mapping, In Silico Screening and Molecular Docking to Identify Selective ...

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