LIGAND BASED PHARMACOPHORE MODELING AND QSAR ANALYSIS...

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Exploratory Animal and Medical Research, Vol.3, Issue 2, December, 2013

LIGAND BASED PHARMACOPHORE MODELING AND QSARANALYSIS OF HETEROCYCLIC DIAMIDINE DERIVATIVES

AS ANTI-PARASITIC DNA MINOR GROOVE BINDERS

Subhadip Banerjee1, Anil Kumar Saikia*2

ABSTRACT: Ligand based pharmacophore modelling (LBPM) of a group of 26 heterocyclic diamidinederivatives having clinical bioactivity against Trypanosoma brucei gambiense (TBG). A four pointpharmacophore model of anti-parasitic diamidines has been developed. Positive ionic and aromaticfeatures were identified as crucial features for showing bioactivity as DNA binders. A statisticallyfeasible 3D QSAR model was developed for further evaluation of the developed pharmacophore model.The good predictive ability of the model has been examined by various statistical parameters likeQ2=0.67, r2

pred=0.96 value. This model can be used for in silico screening and designing of potent anti-

parasitic molecules.

Key word: Anti-parasitic, Diamidine, Pharmacophore, 3D QSAR.

_____________________________________________________________________________1Bengal institute of Pharmaceutical Sciences, Kalyani, Nadia, Pin-741235, West Bengal, India.2Department of Pharmaceutical Sciences, Dibrugarh University, Pin-786004, Assam, India.* Corresponding author .

INTRODUCTIONDiseases like trypanosomiasis, leishmaniasis,

and malaria are now in a epidemic stage inhumans and animals, caused about millions offatality due to their transmission acrossgeographical barriers and due to absence ofpotent drug for treatment (Fairlamb 2003,Bouteille et al., 2003). Moreover emergence ofdrug resistant species have made the scientificcommunity attached with drug development tosearch for better options to have more advancedtherapeutic edge against the drug resistantspecies. The phenomenon of drug resistance isalmost inevitable and becoming clinicallyunmanageable. Hence developing anti-parasitic

drug becomes an important aspect for bettertherapeutic point of view.

Protozoan parasites exhibit a broad range ofpeculiarity, including polycistronictranscription, trans-splicing of precursormRNAs which is likely due to the earlydivergence of the eukaryotic lineage (Yeates2003). Mitochondrial DNA organization and theRNA controlling process are remarkablefeatures of kinetoplastids, which consists of asingle mitochondrion encircling a exclusivetype of DNA organization called kinetoplastDNA (kDNA), consisting of thousands ofinterlocked circular DNA molecules, referredto as minicircles and maxicircles (Este´vez and

Explor. Anim. Med. Res.,Vol.3, Issue - 2, 2013, p. 102-116

ISSN 2277- 470X (Print), ISSN 2319-247X (Online)Website: www.animalmedicalresearch.org

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Simpson 1999, Liu et al. 2005). This kinetoplastDNA has been an important target intrypanosomiasis caused by Trypanosoma cruzi.DNA minor groove binders show a variety ofbioactivity and is a effective strategy in rationaldrug design (Tidwell and Boykin 2003, Wilsonet al. 2005).

In the present study, a correlation betweenanti-parasitic activity with that of structuralproperties of heterocyclic diamidine has beenanalyzed employing ligand basedpharmacophore model (LBPM) approach. Thecompounds used in this study possess structuralsimilarity with bioactive, less toxic, orallyavailable pro-drug showing clinicalpracticability up to phase II (Athri et al. 2006,Bailly and Chaires 1998). The 3Dpharmacophore alignment based study isespecially very important for furtherquantification of structure activity relationshipin the 3D space. Thus it is a very important steptowards answering the structural basis of theactivity of these molecules.

However, pharmacophore analysis withrespect to chemical, structural, topological andother properties provide an outline for designingnew compounds. Moreover, the receptorinteraction pattern and important features of thesame can also be elucidated by this analysis. Itis reported that DNA binding is involved in thebioactivity of diamidine derivatives that targetinfectious disease organisms (Henderson andHurley 1995, Baraldi et al. 2004). The presentscenario turns out to be case where the ligandsbind to the minor groove of the AT-richsequence of DNA selectively can change,modify or stop the transcription of a specificenzyme (Cory et al. 1992, Wilson et al. 1998).In leishmania and trypanosomes, themitochondrial kineto-plast DNA (kDNA) is the

primary attack zone of diamidine (Shapiro andEnglund 1990 and 1995.) The trypanosomekinetoplast, have recurring AT zone sequencesform a specific and effective target forheterocyclic diamidines (Athri et al. 2006).Thepharmacophore based 3D QSAR model willhelp us to gain insight about the structuralrequirements for better activity.

For the successful generation of the 3DQSAR model we have used PHASE (Dixonet al. 2006) algorithm of Schrodinger molecularmodeling suite. A statistically significant 3DQSAR model which may be further used fordevelopment of new effective molecules orfinding a druggable hit out of a virtual screeningprocess of drug development on the basis ofthis 3D QSAR model.

MATERIALS AND METHODS1. Data sets and Biological activity:26 heterocyclic diamidine derivatives from

the literature by Athri et al. (2006) wereselected. The molecules were selectedconsidering their IC

50 values to be precise and

not in range. The insoluble compounds werediscarded. The biological activities taken forstudy were converted to pIC

50 values, using

Gaussian statistics, the distribution of the

activity were shown (Fig.1) to get a more

significant figure for visualizing bioactivity.Thus out of 26 heterocyclic diamidinederivatives, 70% compounds were taken astraining set and the rest of the compounds wereused as a test set. All the 2D molecularstructures were developed using Chem DrawUltra 8.0 and then were transformed into 3Dstructures. Energy minimization was done usingOPLS 2005 force field of Ligprep module. ThepH of ionization and other parameters of themolecules were adjusted and calculated from

Ligand based pharmacophore modeling and QSAR analysis of heterocyclic diamidine..

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default settings of aforesaid module. For thedevelopment of the pharmacophore model theseligands were imported in PHASE workinginterface. Conformation generation is animportant step in PHASE algorithm;conformations were generated using Configentaking GB/SA solvent treatment model. About10,000 conformers were generated per structureand ensuing 100 step preprocess and 50 steppost process minimization by varying theconformations of amide bonds. The minimizedconformers were filtered using a relative energyparameter limitation of 10 kcal mol-1 and aminimum atom deviation of 1.00 Å. If there isany occurrence in which the energy in aconformer is higher than this limit, and then itsincessant disposition was ensured. Thesuperfluous conformers were eliminated basedon RMSD filter window of 0.5Å for furtherrefinement. Thus we successfully incorporatedonly the lowest energy conformation of a ligandin the process of pharmacophore modeldevelopment. A couple of conformer wasdefined as identical if the distance between themis below 1.00 Å.

2. Creating Pharmacophore Sites andCommon pharmacophore hypothesisgeneration:

According to the pIC50

values the moleculeswere divided into active and inactive setting themaximum and minimum values in the activitythreshold window of PHASE. Pharmacophoresites of a ligand are represented in the 3D spaceby a set of points. These points coincide withvarious chemical characteristics with type,location and directionality, which facilitate noncovalent bonding with the receptor sites. Thepharmacophore features like hydrogen bondacceptor (A), hydrogen bond donor (D),

hydrophobic/Non-polar group (H), negativelyionizable (N), positively ionizable (P) andaromatic ring (R) present in the PHASE wereused to create the pharmacophore sites for theenergy calculated ligands. The followingfeatures were assigned using SMART queries.Tree based partition algorithm is used byPHASE for detection of commonpharmacophore from a set of variants takingmaximum tree depth 3. To find commonpharmacophore PHASE algorithm use anexhaustive analysis of k-point pharmacophorematch picked from the conformations of a setof active ligands on the basis of inter sitedistances, and then find all spatial arrangementsof pharmacophore features those are commonto at least 8 out of 10 active ligands. Thus thepharmacophores generated have matches acrossdifferent set of actives eliminating the chanceof its exclusiveness towards a small subset ofligands. The different pharmacophorehypothesis produced were further examined byusing a scoring function so that it produced thebest alignment of the ligands which are activeyet also incorporating the features from theinactives to make the model more versatile.

3. Scoring Pharmacophores according toactives and inactives:

The pharmacophore hypotheses were scoredpertaining to the active ligands. To ensure thatno inappropriate pharmacophore is inside thesurvived pharmacophore models least squaressite-to-site alignment is considered. Now thescoring of the pharmacophore hypotheses wasdone in relation to the information from theactive ligands considering various geometricand heuristic factors .The alignment to areference pharmacophore is consideredaccording to RMSD of the site points and the

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average cosine of the vectors keeping theirtolerance 1.2 Å and 0.5 respectively was set.To preferentially get the reference ligand fromthe most active set the ones scoring the upper10 % was considered for score calculation. Forfurther refinement volume scoring is also donein order to measure quantitatively of how eachnon-reference ligand is superimposing with thereference ligand, in account of Vander Waalsmodels of the structures and taking into accountall heavy atoms of the active ligands. Here thecutoff for volume scoring was kept at 1.00 forthe nonreference pharmacophores. To ensurethe lowest energy ligands for better binding tobe incorporated in the best pharmacophore therelative conformational energy of the referenceligand was constrained to 0.001. Thus wegenerated the survival active scores for thepharmacophore hypotheses. A ligand can beinactive due to number of ways but forsuccessful implementation of only thosecharacters which are important for good bindingwe need to incorporate the knowledge why theinactive molecules are inactive. This wouldmake our pharmacophore models a better onehaving ability to distinguish between an activeand an inactive molecule. This score inactiveis calculated with the help of fitness score whichis assessed with the same constraints as that ofscore active. A good hypothesis has a low fitnessscore multiplied by a user adjustable factorwhich was set to default mode. For thedevelopment of pharmacophore model weconsidered the highest active molecule 11, 21,24. The pharmacophore fitness score 3 is thehighest score observed for these ligands, whichdenotes how good a molecule fit with thepharmacophore hypothesis. These fitness scoreand bioactivity were correlated, when thedistance and angle between each feature are

variable, results the generation of tenpharmacophore hypotheses. The ranking isdone for these models on the basis of all thissaid scores, considering the ability todistinguish between active and inactive ligands.The scores are calculated on the basis ofcontributions from the alignment of site pointsand vectors, volume overlap, selectivity,number of ligands matched, relativeconformational energy, and activity. A commonpharmacophore model (CPM) among the groupof ten pharmacophore hypotheses were selectedaccording to their maximum active and inactivecontribution of features; followed by its fitnessand stability based on the smart scoringfunction. For generation of CPM both activeand in-active molecules are taken inconsideration for further refinement, such thatthe generated pharmacophore model had theability to distinguish between the active andinactive features of molecules.

4. 3D-QSAR model generation:Atom based 3D QSAR model of

pharmacophore hypotheses was generated byus. The atom based 3D QSAR model waschosen as our data set ligands showed quite verygood alignment as it consisted of a large varietyof derivatives of a parent molecule. The atombased 3D QSAR model provides more chemicalsignificance than pharmacophore based 3DQSAR model which only depends uponpharmacophore sites for alignment to thehypothesis selected. This is because of the factthat atom based 3D QSAR takes the totalmolecule and facts like probable sterichindrance with the receptor site can be takeninto account while building a model with thisatom based approach.

The PHASE algorithm uses a very versatile


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ID Structure QSAR PIC50

Predicted FitnessSet pIC

50

1 training 2.29 2.09 2.32

2 training 1.16 1.43 2.33

3 training 1.63 1.92 2.35

4 test 1.45 1.58 2.29

5 test 1.35 1.61 2.33

Table 1: Training and Test set showing activity, predicted activity and fitness.

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50

6 training 1.45 1.4 2.28

7 training 1.17 1.12 2.15

8 training 1.85 1.96 2.42

9 training 1.63 1.79 2.32

10 training 1.97 1.87 2.31


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50

11 test 2.52 2.26 2.67

12 test 2.18 2.13 2.3

13 training 0.99 1.03 2.19

14 training 1.91 1.86 2.21

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50

15 training 2 1.94 2.38

16 training 1.16 1.16 2.74

17 test 1.72 1.35 2.68

18 training 2.15 2.21 2.39

19 test 1.86 1.78 2.44


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50

20 training 0.77 0.68 2.2

21 training 2.77 2.37 3

22 training 2.31 2.25 2.33

23 training 1.77 1.89 2.14

24 test 2.68 2.07 2.51

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50

25 training 2.39 2.52 2.34

26 training 2.28 2.28 2.38

PF1 PF2 Distance

P4 P3 13.995

P4 R6 6.898

P4 R7 2.864

P3 R6 7.759

P3 R7 11.290

R6 R7 4.036

*PF= Pharmacophoric features

Table 2: Distance betweenpharmacophoric features.

ID PLS Factor SD R2 RMSE Q2 Pearson-R R2pred

PPRR.3 3 0.22 0.85 0.3 0.6 0.84 0.955

Table 3: Statistical result of 3D QSAR model.

approach for the development of 3D QSARmodel. It considers a rectangular grid of 1 Ågrid distance in a 3D space. Thus it createscubes of said dimension in the 3D space. Theatoms of the molecules which are consideredas overlapping Vander Waal spheres fall insidethese cubes depending on the volume of theatomic spheres. These occupied cube spaces aretermed as volume bits. A volume bit is allocatedfor each different class of atom that occupies acube. There are six atom classes two hydrogenbond acceptor (A), one positively ionizable (P)and two aromatic ring (R) used for classifyingthe atom characteristics. The total number ofvolume bits consigned to a specified cube is


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Fig. 1: Histogram showing activity distribu-tion of the dataset.

based on how many training set moleculesoccupy that cube. A single cube may representthe occupation by one or various atoms or sites,and even those from the same molecule or maybe from unlike molecules of the training set.Thus A molecule may be represented by a binarystring concurrent to the occupied cubes, and alsothe various types of atomic sites that exist inthose cubes. To create an Atom based QSAR

model, these volume bits which encodes thegeometries and chemical characteristics of themolecule are regarded as independent variablesin PLS (Partial Least square) regressionanalysis. For generating a predictive QSARmodel we have to select 3 number of PLS factor.The maximum PLS factor that can be taken isN/5 where N is the number of ligands presentin the training set.

RESULT AND DISCUSSIONIn our successful effort to develop a

predictive and statistically significantpharmacophore based 3D QSAR model, weselected a pharmacophore hypothesis accordingto their highest survival active and inactivescore that also shows good structural alignment(Fig.2) to the highest active molecules as wellas had the ability to align to non model inactiveligands having less value than active thresholdset for the model as well. It also illustrateddiversified variants ensuring its uniqueness andselectivity. Thus we tried to avoidpharmacophore hypotheses with less variantcontribution or less specificity or selectivity inits fingerprint. Out of 10 pharmacophorehypotheses the highest ranking was PPRR.3(Fig.3) calculated according to the effective

Fig. 2: Structural alignment of the data setand pharmacophore.

Fig. 3: The pharmacophore hypothesis.

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scoring function already described in section2.3. Thus in the process of scoring andvalidation based evolution among thepharmacophore alternatives, one got finallyselected; it is a four point pharmacophorecontaining two variants; two positivelyionizable (P) groups and two aromatic rings (R).The distance and angle between eachpharmacophoric features are summarized inTable 2. This pharmacophore model was furtherexploited for aligning the ligands used in 3DQSAR model generation.

1. Pharmacophore modelThree compounds with highest activity from

the total data set were selected for commonpharmacophore hypotheses (CPH) generation.Using a tree-based partition algorithm requiringthat all active compounds must match, 16 fourfeatured probable common pharmacophorehypotheses were generated from the list of

variants. No common pharmacophorehypotheses were obtained for five and sixcommon features. On applying the scoringfunction for four-featured commonpharmacophore hypotheses using defaultvalues, 10 common pharmacophore hypothesessurvived, belonging to the types PPRR. Trainingset compounds were aligned on these commonpharmacophore hypotheses and analyzed byPLS analysis described in PHASE with ten PLSfactors.

A good alignment is the primary requirementfor a finer QSAR analysis. However validationis an imperative aspect for QSAR analysis, as amatter of fact diffused distribution of charactersand activities amount the training set is aprerogative,70% of the total ligands werechosen in the training set and the rest was takenin test set (Table 1) based on the activity guidedHierarchical Clustering method for internalvalidation purpose. The statistical outcomes of

Fig.4: H-Bond donor contribution. Fig.5: Structure skeleton.

Fig. 6: Electron withdrawing contribution.Fig.7: Positive ionic contribution.


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the model thus produced indicated its statisticalsignificance and predictive ability. The PLSanalysis results of the model thus producedamong various hypotheses was the best furtherjustified our selected pharmacophore was thefittest. The various statistical parametersR2(Correlation coefficient), Q2 (q2 for thepredicted activity), SD (StandardDeviation), RMSE (Root mean squareerror), P(Significance level of varianceratio), F-Statistics, Pearson-R (correlationbetween the predicted and observed activity oftest set), and r2

pred which was calculated from

the formular2

pred = (SD-PRESS)/SD

where SD is the sum of the squareddifferences between the experimental biologicalactivities of the test set ligands and mean of theexperimental activities of the training setmolecules and PRESS is the sum of squareddifferences between predicted and actualexperimental activity values for every ligandin test set . To avoid over-fitting of the resultsfor PLS factor 3 were used and sum up of thesestatistical parameters are shown in Table 3.

2. Interpretation of Atom based3D QSAR modelThe ligand based 3D QSAR model out of

the selected pharmacophore hypothesis thusproduced showed the contributions of variouschemical variants or atoms or groups to enhanceactivity or decrease the activity of ligands. Formapping and visualization of our atom based3D QSAR model result we interpreted thevarious physicochemical contributionsresponsible on the basis of 21 the highest activeligand as the template. Using the map we alsocorrelated our predicted result with that ofin vitro bioactivity and find the structural and

chemical basis for the activity of other activeand inactive ligands under an activity threshold.Here we discussed the various chemicalcontributions of the ligands according to themodel.

i). H-Bond donor contribution:The color mapping of the ligand according

to the model shows the H-bond donor (HBD)contributions (Fig.4). The blue cube regions ofthe ligands will show favorable contribution andthe red cube regions are unfavorable for thecontribution of H-bond donor property. Thatindicates that presence of HBD group like –NH

2 will be favorable for showing

antitrypanosome bio-activity. Appearance of redcolor blocks around the =NH group ofbenzimidamido fragment indicates reduction ofthis group to –NH

2 group may be favorable for

bio activity. This is clear from the featuresattaining from lowest active molecule20, contain N-(4-methoxycyclohexyl)benzimidamido residue. But in moderatelyactive molecule 14, 26 blue color region comeinto sight around the –NH

2 group of terminal

phenylmethanamine and picolinimidamidoresidue indicates the importance of –NH

2 group.

Moreover it can be distinctly observed thataround position 1 and 2 of skeleton structure(Fig.5) a red cube is also there which refers thatif there is a if H-bond donor atom it will havenegative impact on bioactivity . It may be dueto donor-donor field interaction at two handsattached to the position 1 and 2 explaining thefact we see that in compounds1,11,18,21,22,24,25,26 show high activity dueto the presence of a H atom attached to N andno other HBD is present. While in ligands 13,14 and 20 due the presence of additionalH-bond donor like N and O attached to a ring

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around 1 and 2 positions show quite decreasedactivity around 1or less due the effect of twoH-bond donors nullifying each other. Based onthis line of evidence it can be concluded thatfree –NH

2 HBD type of groups are essential to

produce superior bioactivity.

ii). Electron withdrawing contribution:The color map of (Fig.6) shows the

contribution of electron withdrawing effect(EWE). The green cubes shows positive orfavorable contribution and the red cubes showsnegative contribution. The highest activity ofthe ligand is due to this positive contributiononly. It’s clearly shown that the ligand is havingtight fitting to the green cubes at thepharmacophore points having electronwithdrawing groups at those positionscontributing for its highest activity. Thus if aligand can be designed in such a way that thefollowing green regions contain some electronwithdrawing group will show high activity.Thus the N atom at positions 1, 2, 22, 23 of(Fig.5) where the green cubes are placedquantify for the bioactivity of all thecompounds. The different atoms as electronwithdrawing groups at position 11 of theskeleton structure (Fig.5) cause change in theactivity. In 1, 2, 21, 22,23,24,25 have O atomat the position show quite an increased activityothers like 11 sulphur atom show less activityand 8, 18, 19 having N atom show slightly loweractivity as the following groups have weakelectron withdrawing contributioncomparatively. The red cubes at position 1 ofthe skeleton explain the decreased activity ofthe compounds 2,3,4,5,6,7,9,16,19 due to thepresence of various electron withdrawinggroups at the unfavorable position.

iii). Positive ionic contribution:The color map (Fig.7) shows the positive

ionic contribution of the ligand by the model.The red cubes shows negative contribution andthe violet cubes show the positive contributionto decrease and increase the activityrespectively. It’s seen that the positions 1, 2,22, 23 of the structural skeleton (Fig.5) ifpositive ionic groups are present it will givenegative activity.

CONCLUSIONThus to summarize our result of the 3D

QSAR model indicates it to be very predictiveand statistically significant model. It may behelpful to design potent ligands in future fordevelopment of anti-trypanosomiasis drug. Thestructural insight will also be helpful to knowabout its receptor binding and bioactivity.

ACKNOWLEDGEMENTWe acknowledge AICTE for a project grant

sanction under RPS for molecular modelingsoftware.

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*Cite the article as: Banerjee S and Saikia AK.(2013). Ligand based pharmacophore modelingand QSAR analysis of heterocyclic diamidine derivatives as anti-parasitic DNA minor groovebinders. Explor.Anim.Med.Res. 3(2): 102-116.

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