issn: 0975-766x available online through research article ...significant 3d qsar model was produced...

Vasudeva Rao Avupati * et.al / International Journal Of Pharmacy & Technology

IJPT | Dec-2010 | Vol. 2 | Issue No.4 | 977-1010 Page 977

ISSN: 0975-766X

Available Online through Research Article www.ijptonline.com

COMPUTER ASSISTED MOLECULAR DOCKING AND 3D QSAR STUDIES ON 1,3,5-TRISUBSTITUTED ARYLS AS PPARδ AGONISTS

Vasudeva Rao Avupati*, Muralikrishna Kumar Muthyala and Rajendra Prasad Yejella. Pharmaceutical Chemistry Division, University College of Pharmaceutical Sciences, Andhra University,

Visakhapatnam-03, Andhra Pradesh, India. E-mail: [email protected]

Received on 30-09-2010 Accepted on 18-10-2010

ABSTRACT

Molecular docking studies was performed on a series of 1, 3, 5-Trisubstituted aryls as PPARδ agonists to understand

drug receptor interactions in modern drug design. The docking simulation was applied to dock a set of

representative compounds within the active site region of 2B50 using Molegro Virtual Docker v 4.0.0. For these

compounds, the correlation between binding free energy (kcal/mol) and log (1/EC50) values produces a good

correlation coefficient (r2 = 0.556). Based on the validations and interactions made by 1, 3, 5-Trisubstituted aryls,

results avail to understand the importance and type of interactions that occur between 1, 3, 5-Trisubstituted aryls

with PPARδ binding site region. In the next phase QSAR studies were carried out on the same series of compounds.

The studies include 3D molecular descriptors (eg. SALL , HAALL , NegALL , RALL etc.) derived from the similarity based

alignment of molecules with respect to group center overlap from each individual template point. Statistically

significant 3D QSAR model was produced by multiple linear regression(MLR) technique using 20 molecules in the

training set. The prediction ability of model was determined using a randomly chosen test set of 3 molecules which

gave predictive correlation coefficient (R2pred = 0.667) for 3D QSAR model; indicating good predictive power.

Key Words: Molegro Virtual Docker, PPARδ, QSAR, 3D descriptors.

1. Introduction: Peroxisome proliferator activated receptors (PPARs) are important members of the nuclear

hormone receptor superfamily. These receptors are ligand activated transcription factors known to play a key role in



the catabolism and storage of dietary fats [1]. Three PPAR isotypes: PPARα (NR1C1), PPARδ (also called β

{NR1C2}), PPARγ (NR1C3) have been identified so far. Once activated by their respective ligand, PPARs control

transcriptional rate of a large panel of genes implicated in various physiological functions, including lipid and

glucose homeostasis, inflammation, cell proliferation and differentiation. The various PPAR isotypes have different

physiological roles [2]. PPARδ is primarily expressed in adipose tissue, where it acts as a master regulator of

adipocyte formation [3]. Like other PPARs, PPARδ protein molecular structure consists of 5 regions : an N-terminal

region (A/B), a DNA binding domain (C), a flexible hinge region (D), ligand binding domain (E), and 2-C-terminal

region (F). X-ray crystallographic study revealed that PPARδ has an exceptionally large ligand binding pocket [4],

[5]. The ligand binding pocket of PPARδ receptor is considerably larger than other nuclear receptors displaying a

total volume of ~1300 Ao [6]. The pocket forms a “Y” shape comprised of three arms approximately 12 Ao in length

[7], [8]. The Y-shaped molecules are potent and selective PPARδ agonists and the chirality at the Y intersection is

pivotal to PPARδ agonist activity [9].

Recently, several synthetic ligands have been reported to selectively activate PPARδ include 3,4,5-trisubstituted

isoxazoles [10], 1,3,5-trisubstituted aryls [11], benzothiophenes, benzofuran and indole based compounds [12],

anthranilic acid GW9371 [13], phenylpropanoic acid derivatives bearing 6-substituted benzothiazoles [14], para-

alkyl thio phenoxy aceticacids [15]. The phenoxy acetic acid derivatives GW501516 and GW0742 are the highly

selective PPARδ ligands with nanomolar affinity and 1000-fold selectivity over other isotypes PPARα and PPARγ

[16]. Novel bisaryl substituted thiazoles and oxazoles are highly potent and selective PPARδ agonists [17]. The

other PPARδ agonists L796449, L165461 [18], KD3010 and MBX-8025 are currently in clinical development. A

selective antagonist for PPARδ, GSK0660 has recently reported to exhibit inverse agonist activity and competes

with agonist in cellular context [19].

[2] Materials and Methods [2.1] Data set A set of 1,3,5-Trisubstituted aryls as PPARδ agonists listed in Table 1, were taken from the biological data reported

by Epple et al [11].



2.2 Ligand Preparation

The structures of 24 synthetic 1,3,5-trisubstituted aryls were modeled using Chemdraw ultra 10.0 (Cambridge

software), and then modeled structure is copied to Chem3D ultra 10.0 to create a 3D model and, finally subjected to

energy minimization using molecular mechanics (MM2). The minimization was executed until the root mean square

gradient value reached a value smaller than 0.001kcal/mol. Such energy minimized structures are considered for

molecular docking and 3D QSAR studies. However, corresponding MDL MOL file were prepared using Chem3D

ultra 10.0 integral options (save as /MDL MOL).

2.3 Receptor Preparation

All PPARδ X-ray crystal structures were obtained from the Brookhaven Protein Data Bank

(http://www.rcsb.org/pdb). The selection of protein for docking studies is based upon several factors i.e. structure

should be determined by X-ray diffraction, and resolution should be between 2.0-2.5Ao, it should contain a co-

crystallized ligand; the selected protein should not have any protein breaks in their 3D structure. However, we

considered ramachandran plot statistics as the important filter for protein selection that none of the residues present

in disallowed regions. Finally the resultant protein target was prepared for molecular docking simulation in such a

way that all heteroatoms (i.e., nonreceptor atoms such as water, ions, etc.) were removed. Kollmann charges were

assigned. All PPARδ X-ray crystal structures were obtained from the Brookhaven Protein Data Bank are shown in

the Table 2.



2.4 Validation of the Molecular Docking methodology

Software validation was performed in MVD using PPARδ X-ray crystal structures obtained from the Brookhaven

Protein Data Bank (http://www.rcsb.org/pdb). The X-ray crystal structure of PPARδ (3D5F) complex with L41501

was recovered from PDB. The bio active co-crystallized bound ligand {4-[3-(4-acetyl-3-hydroxy-2-propylphenoxy)

propoxy] phenoxy} acetic acid (L41501) was docked with in the active site region formed by Trp 264, Val 341, Arg

284, Leu 330, Lys 367, Ileu 364, Phe 282, Met 453, Thr 289, Leu 469, His 323, Cys 285, His 449, Thr 288 and Tyr

473 residues, respectively. The RMSD of all atoms between the two conformations is 0.97Ao indicating that the

parameters for docking simulation are good in reproducing X-ray crystal structure. Similar software validations

were performed for the rest of PDB proteins i.e. X-ray crystal structure of PPARδ (3GWX) complex with Epa1 was

recovered from PDB. The bio active co-crystallized bound ligand 5,8,11,14,17-eicosapentaenoic acid (Epa1) was

docked with in the active site region formed by Tyr 473, His 449, His 323, Leu 469, Cys 285, Thr 289, Thr 292, Thr

288, Ile 326, Ile 364, Leu 330, Met 329 residues, respectively. The RMSD of all atoms between the two

conformations is 1.25Ao indicating that the parameters for docking simulation are good in reproducing X-ray crystal

structure. X-ray crystal structure of PPARδ (2B50) complex with Vca1001 was recovered from PDB. The bio active

co-crystallized bound ligand vaccenic acid [(11e)-octadec-11-enoic acid] (Vca1001) was docked with in the active

site region formed by Tyr 473, His 449, Met 453, His 323, Leu 469, Thr 289, Cys 285, Val 341, Arg 284, Trp 264,

Leu 255 residues, respectively. The RMSD of all atoms between the two conformations is 0.97Ao indicating that the

parameters for docking simulation are good in reproducing X-ray crystal structure. X-ray crystal structure of PPARδ

(1Y0S) complex with 331482 was recovered from PDB. The bio active co-crystallized bound ligand (2s)-2-(4-[2-(3-

[2,4-difluorophenyl]-1-heptylureido) ethyl]phenoxy)-2-methylbutyric acid (Gw2331) (331482) was docked with in

the active site region formed by Leu 339, Ile 333, Val 348, Leu 330, Trp 264, Thr 288, Tyr 473, Val 281, Cys 285,

Gln 286, His 323, Ile 363, Thr 289, Ile 326, His 449, Phe 327, Leu 469, Met 453 residues, respectively. The RMSD

of all atoms between the two conformations is 0.93Ao indicating that the parameters for docking simulation are good

in reproducing X-ray crystal structure. X-ray crystal structure of PPARδ (2ZNP) complex with K55922 was



recovered from PDB. The bio active co-crystallized bound ligand [(S)-2-{4-butoxy-3-[(2-fluoro-4- trifluoromethyl

benzoyl amino) methyl] benzyl} butyric acid] (K55922) was docked with in the active site region formed by Tyr

473, His 449, His 323, Phe 282, Ile 363, Arg 284, Met 453, Thr 289, Leu 330, Leu 469, Cys 285, Thr 288, Val 341,

Val 348, Leu 339, Phe 368, Val 334 residues, respectively. The RMSD of all atoms between the two conformations

is 1.32Ao indicating that the parameters for docking simulation are good in reproducing X-ray crystal structure. X-

ray crystal structure of PPARδ (2ZNQ) complex with 401922 was recovered from PDB. The bio active co-

crystallized bound ligand [(S)-2-{3-[(2-fluoro-4-trifluoromethylbenzoylamino) methyl]-4-methoxybenzyl} butyric

acid] (401922) was docked with in the active site region formed by Tyr 473, Ile 363, His 449, Met 453, Leu 330,

Phe 282, Leu 339, His 323, Thr 289, Leu 469, Cys 285, Arg 284, Thr 288, Val 341, Val 348 residues, respectively.

The RMSD of all atoms between the two conformations is 1.11Ao indicating that the parameters for docking

simulation are good in reproducing X-ray crystal structure. X-ray crystal structure of PPARδ (2J14) complex with

Gni1440 was recovered from PDB. The bio active co-crystallized bound ligand (3-{4-[2-(2,4-dichloro-phenoxy)-

ethylcarbamoyl]-5-phenyl-isoxazol-3-yl}-phenyl)-acetic acid (Gni1440) was docked with in the active site region

formed by Val 312, Arg 248, Thr 252, Val 305, Leu 303, Val 298, Phe 332, Val 245, Cys 249, Leu 294, Ile 328, Phe

246, His 413, Lys 331, Met 417, Thr 253, Leu 433, Tyr 437, His 287 residues, respectively. The RMSD of all

atoms between the two conformations is 1.75Ao indicating that the parameters for docking simulation are good in

reproducing X-ray crystal structure. X-ray crystal structure of PPARδ (3ET2) complex with ET11 was recovered

from PDB. The bio active co-crystallized bound ligand {5-methoxy-1-[(4-methoxyphenyl)sulfonyl]-1h-indol-3-

yl}propanoic acid (ET11) was docked with in the active site region formed by His 287, His 413, Tyr 437, Phe 291,

Thr 253, Gln 250, Phe 291, Thr 253, Leu 433, Lys 331, Leu 294, Phe 246, Met 417, Ile 327, Cys 249, Thr 252, Phe

324, Val 245 residues, respectively. The RMSD of all atoms between the two conformations is 0.99Ao indicating

that the parameters for docking simulation are good in reproducing X-ray crystal structure. X-ray crystal structure of

PPARδ (3GZ9) complex with D321 was recovered from PDB. The bio active co-crystallized bound ligand(2,3-

dimethyl-4-{[2-(prop-2-yn-1-yloxy)-4-{[4-(trifluoromethyl) phenoxy] methyl} phenyl] sulfanyl} phenoxy) acetic


IJPT | Dec-2010 | Vol. 2 | Issue No.4 | 977-1010 85

acid (D321) was docked with in the active site region formed by Tyr 473, His 323, Phe 327, Thr 289, His 449, Leu

469, Gln 286, Lys 367, Phe 368, Ile 364, Val 334, Met 453, Phe 282, Leu 330, Leu 339, Ile 363, Cys 285, Val 341,

Arg 284 residues, respectively. The RMSD of all atoms between the two conformations is 1.67Ao indicating that the

parameters for docking simulation are good in reproducing X-ray crystal structure. X-ray crystal structure of PPARδ

(3DY6) complex with DY6478 was recovered from PDB. The bio active co-crystallized bound ligand 2-({[3-(3,4-

dihydroisoquinolin-2(1h)-yl sulfonyl) phenyl] carbonyl} amino) benzoic acid (DY6478) was docked with in the

active site region formed by Phe 327, Lys 367, Leu 330, His 449, Tyr 473, Thr 289, Ile 364, Met 453, Cys 285, Thr

288, Leu 339, Arg 284, Trp 264, Val 281, Val 348, Val 341 residues, respectively. The RMSD of all atoms between

the two conformations is 0.96Ao indicating that the parameters for docking simulation are good in reproducing X-

ray crystal structure. X-ray crystal structure of PPARδ (2BAW) complex with B7G921 was recovered from PDB.

The bio active co-crystallized bound ligand Heptyl-beta-d-glucopyranoside (B7G921) was docked with in the active

site region formed by Lys 319, Thr 316, Asn 312, Val 315, Leu 311 residues, respectively. The RMSD of all atoms

between the two conformations is 0.92Ao indicating that the parameters for docking simulation are good in

reproducing X-ray crystal structure. X-ray crystal structure of PPARδ (2AWH) complex with B7G920 was

recovered from PDB. The bio active co-crystallized bound ligand Heptyl-beta-d-glucopyranoside (B7G920) was

docked with in the active site region formed by Leu 311, Phe 310, Thr 316, Val 315, Asn 312 residues, respectively.

The RMSD of all atoms between the two conformations is 1.06Ao indicating that the parameters for docking

simulation are good in reproducing X-ray crystal structure. The RMSD values were ranging from 0.91-1.75A0 as

shown in Table 3.



Another validation was carried out based on the important interactions made by the bound ligand with active site

residues of all PPARδ proteins from PDB. In order to perform the task, the H-bond interactions formed by various

bound co-crystallized ligands of PPARδ protein structures such as 3D5F, 3GWX, 2B50, 1Y0S, 2ZNP, 2ZNQ, 2J14,

3ET2, 3GZ9, 3DY6, 2BAW and 2AWH were collected from ligplot interactions, deposited in PDB summary (PDB

sum, http//www.ebi.ac.uk) database and listed in Table 3.

2.5 Molecular Docking Studies

MolDock is an execution of EA, focused on molecular docking simulations [20] [21]. Computational

approximations of an evolution process, called genetic operators, are applied to reproduce the permanence of the

most affirmative features. In a sample space, where there is a trouble or a search regular and many dissimilar



possible solutions (ligand poses), each preference is ranked based on a set of parameters (scoring function, or fitness

function), and only the best ranked solutions are kept for the next iteration. This process is repeated until a best

possible solution can be found.

The program MolDock makes use of a slender distinction of the EA, which is called guided differential evolution

algorithm. This methodology is based on an EA modification called differential evolution (DE), which brings a

different method to choose and alter candidate solutions (individuals). The major original idea in DE is to generate

issue from a weighted difference of parent solutions. The DE works as follows. In the first step, all individuals are

initialized and evaluated according to the fitness function. Subsequently, the following process will be carried out if

the termination condition is not satisfied. For each individual in the population, an issue is created by adding a

weighted difference of the parent solutions, which are randomly chosen from the population. After that the issue

replaces the parent, if and only if it is fitter. Otherwise, the parent survives and is passed on to the next generation

(iteration of the algorithm). The termination condition is reached when the current number of fitness (energy)

evaluations performed exceeded the maximum number of evaluations allowed (max evaluations parameter setting).

Furthermore, early termination was permitted if the variance of the population was below a certain threshold (0.01).

Moreover guided differential evolution employs a cavity prediction algorithm to limit predicted conformations

(poses) during the search procedure. More specifically, if a candidate solution is placed outside the cavity, it is

translated so that a randomly chosen ligand atom will be located within the region spanned by the cavity. Obviously

this strategy is only employed if a cavity has been found. If no cavities are reported, the search process does not

limit the candidate solutions.

In MolDock, only the ligand properties are represented in the individuals since the protein remains rigid during the

docking simulation. Consequently a candidate solution is determined by a vector of real valued numbers

representing ligand position, orientation, and conformation as Cartesian coordinates for the ligand translation, four

variables specifying the ligand orientation (encoded as a rotation vector and a rotation angle), and one angle for each

flexible torsion angle in the ligand (if present). For each individual in the initial population, each of the three



translational parameters (encoded as a position relative to the crystallographic native ligand) for x, y, and z is

assigned an evenly distributed random number between -15.0 and 15.0 Å, which is added to the center of the

crystallographic reference ligand. Initializing the orientation is carried out the Shoemake [22] for generating

identical chance quaternions and converted these quaternions to their rotation axis/rotation angle representation. The

flexible torsion angles (if present) are given a random angle ranging from -180° to +180°.

The scoring function used by MolDock is derived from the piecewise linear potential PLP scoring functions [23].

The scoring function used by MolDock further improves these scoring functions with a new hydrogen bonding term

and new charge schemes [20]. Based on above described EA classification MolDock algorithm may be classified as

an ES, since it employs direct ranking of the solutions and the crossover operators. MolDock showed better overall

performance in docking simulations when compared with Surflex [24], Flexx [25, 26] and GOLD [27, 28].

MolDock presents a very gracious interface, which facilitates its exercise. In addition, MolDock is available for

linux, windows and MAC OSX.

Molecular docking technique was employed to dock the selected PPARδ agonists listed in (Table 1) against PPARδ

receptor 2B50 using MVD to locate the interaction between various 1,3,5-Trisubstituted aryls [11] and PPARδ.

MVD requires the receptor and ligand coordinates in either Mol2 or PDB format. Non polar hydrogen atoms were

removed from the receptor file and their partial charges were added to the corresponding carbon atoms. Molecular

docking was performed using MolDock docking engine of Molegro software. The binding site was defined as a

spherical region which encompasses all protein atoms within 15.0 Ao of bound crystallographic ligand atom

(dimensions X (10.49 A°), Y (-0.57 A°), Z (37.24 A°) axes, respectively). Default settings were used for all the

calculations. Docking was performed using a grid resolution of 0.3 Ao and for each of the 10 independent runs; a

maximum number of 1500 iterations were executed on a single population of 50 individuals. Side chain flexibility

of the amino acids present in the binding site region of 2B50 is incorporated during docking run was performed.



3. Results and Discussion

Docking simulation with 2B50 bound ligand Vca1001 resulted in a Moldock score of -119.217 kcal/mol and a

RMSD value of 0.97 Ao showed major hydrogen bond interactions with Thr 289, His 323, His 449, and Tyr 473

residues respectively. The predicted binding conformation of Vca1001 superimposed with the X-ray

crystallographic orientation is shown in Figure 1. The superimposed binding orientation of docked conformer of

2B50 co-crystallized ligand Vca1001 with best fit (least energy conformer) in the active binding site region of 2B50

along with hydrogen bond interacting residues were shown in Figure 1.

3.1 Hydrogen bond interactions

The active site region of PPAR δ (2B50) comprises of amino acids residues such as [Hydrophilic : His 44, His 323,

Thr 289, Cys 285, Arg 284], [Hydrophobic : Trp 264, Tyr 473, Met 453, Val 341, Leu 255, Leu 469]. As ratio of



amino acid residues forming hydrophobic and hydrophilic in the active site region are equal. So they are involved in

the both polar and non-polar interactions with the 1,3,5-Trisubstituted aryls.

Molecular docking (flexibility in sidechains) studies on 1,3,5-Trisubstituted aryls against PPAR δ (2B50) showed

that most of the compounds are involved in hydrogen bonding with residues Thr 289, Tyr 473, Thr 292 shown in

Table 4. But only two compounds 19 and 22 are involved in forming a set of new amino acid residues such as Met

228, Asn 343, Thr 288, Lys 367, His 449, Thr 222 (Table 4) in the active binding site region of 2B50. Therefore,

although other H-bond interactions exist, these hydrogen bonds are relevant for the binding activities of 1,3,5-

Trisubstituted aryls to be highly selective and potent PPARδ agonists. Moreover, from the data given in (Table 3), it

appears that the residues Thr 288, Thr 289, His 323, Tyr 473, Lys 319 and Glu 471 represent important residues for

binding diverse range of agonists. Therefore, the important residues that participate in H-bond interactions as listed

above were recognized by our studies on experimental compounds (Table 1). Moreover, compound 18 scored least

binding energy with 1 hydrogen bond interaction and the corresponding interacting residue is Thr 289 this hydrogen

bond not only relevant for the binding 1,3,5-Trisubstituted aryls to 2B50 to exhibit highly selective and potent

binding affinity. Whereas, from the data given in Table 3, it appears that the different co-crystallized ligands showed

hydrogen bond interactions with the residues Gln 72, Met 40, Gln 164, Gly 158, His 47, Asp 61, Val 187, Met 195

are represent important residues for binding. The residue that participates in H-bond interaction with compound 18

is an established interaction with co-crystallized ligand previously. Moldock score of compound 18 down to

minimum of -197.406 kcal/mol (Table 1), highly lower than the co-crystallized compound vaccenic acid (Vca1001)

-119.217 kcal/mol. Since, the binding orientation of both the vaccenic acid (Vca1001) and compound 18 in the

binding cavity of 2B50 was observed, 1,3,5-Trsiubstituted aryl compound 18 is showed high resemblance from the

crystallographic binding orientation of the active co-crystallized ligand as shown in the Figure 1. Although a new H-

bond interacting residues such as Met 228, Asn 343, Thr 288, Lys 367, His 449, Thr 222 formed by compounds 19

and 22 are novel H-bond interactions which may contribute for further improvement of the binding efficiency of

1,3,5-Trsiubstituted aryls as highly selective PPAR δ agonists. In this study we analyzed binding pocket of 2B50



using multiple receptor conformation (MRC) docking method [30]. We incorporated flexibility in binding pocket

residues Tyr 473, His 449, Met 453, His 323, Leu 469, Thr 289, Cys 285, Val 341, Arg 284, Trp 264, and Leu 255

of PPAR δ (2B50). Hence we resolve that the hydrogen bond interaction shown between conformation of 2B50 and

docked conformers of 1,3,5-Trsiubstituted aryls are flexible.

The results obtained from the docking simulations, 24 experimental compounds ligand protein interactions and there

binding orientation in the active site region of 2B50 are shown in the Figure 2 (Part-A), Figure 2 (Part-B), Figure 2

(Part-C), Figure 2 (Part-D).



To further strengthen the molecular docking approach employed we examined statistical correlation between

Moldock scores (kcal/mol) and log(1/EC50) values them computationally. The EC50 values (EC50 in µM were

converted to negative logarithmic values in order to assure the linear distribution of data) and the Moldock scores

(compounds 1 to 24) showed in Table 1 and established by linear regression technique, according to the regression

equation (Eq)-1, given below. The correlation between these two parameters was found to be reliable and produces a

correlation coefficient (r2 = 0.556) showed in (Figure 3).

Predicted log (1/EC50) = -0.035285 x (Moldock Score) - 5.33697----- (Equation-1)

4. QSAR STUDIES 4.1 Data set In the next phase of QSAR study, biological and chemical data from the same 24 compounds belongs to 1,3,5-

trisubsttituted aryls are used, which has been reported in the work of Epple et al. [11] (Table 1). In order to model



and predict the biological effect of the specific compounds as functional agonists of PPAR delta, four 3D descriptors

and molecular weight, ClogP, wiener index, were considered as possible input candidates to the model.

4.2 Separation into training and validation sets.

The separation of the data into training and validation (test) sets was performed using random selection process. The

ratio of the training and validation sets is 80%:20% (Table 5).

4.3 Generation of 3D Descriptors

The 3D descriptors were generated using Molegro Virtual Docker (www.molegro.com). A template (Figure 4)

constituting specific atomic groups in a molecule can be useful for aligning ligands, if the information about its 3D

conformation is available. A template is a collection of specific chemical features of an atom shown in Figure 4. By



defining a reference template using one or more ligands, the other molecules can then be docked and aligned with

respect to it shown in Figure 5. Thus, detailed information can be extracted regarding the similarity based overlap

from each individual template group (Figure 5). The following Gaussian formula is used for each center, wherein an

atom is rewarded with respect to its distance from the group centers.

Where d is the distance from the position of an atom to the center in the group; x signifies the weight factor for the

template group; r0 is a distance parameter. The property based descriptors, molecular weight, ClogP, Wiener index

were generated using Chem 3D Ultra 10.0 shown in Table 5.



4.4 Statistical analysis

The complete regression analysis was carried out using multiple linear regression (MLR) the values of descriptors

selected for developing MLR model are presented in the Table 5. QSAR model was generated using MLR based on

back propagation techniques using in-built data analyzer and were correlated to biological activity. Leave-one-out



(LOO) method (Figure 9), N-cross validated (N-CV) method (Figure 10), feature selection method (forward

selection (Figure 7), backward elimination (Figure 8)), Manual selection (Figure 6) were used to validate the results

are shown in Table 7. PPARδ agonistic activity (log1/EC50 µM) was taken as the dependent variable. The auto

generated random seed used in the model training was 3252885320. In this Equation 2, n is the number of

compounds, r is the correlation coefficient, q2 is the cross-validated r2 from the (LOO) or (NCV) procedure, q (rho)

is the Spearman rank correlation, MSE is the mean squared error, and PRESS is the predictive sum of squares.



5. Results and Discussion

The intention of our study is to generate 3D descriptors related to similarity based group center overlap [29] with

reference to the template obtained from the selected ligand. In the next phase, to perform QSAR and generate model

using these 3D descriptors. However, to cross-validate the results we used the help of a test-set shown in Figure 11.

By defining a template from a ligand as a reference template (Figure 4), the other molecules can be docked and

aligned to the template (Figure 5). In the present study, we have selected compound 1 with highest activity (EC50

0.01µM) of the series as a reference ligand (Figure 4). Here the flexibility of the ligand was taken into account,

where the docking engine tries to find the optimal conformation of the ligand while fitting to the template (Figure

5). The template score was normalized and the resulting score found using the procedure above was divided by the

score of a perfectly fitting ligand (i.e. if the template was constructed from one ligand, only that ligand would have a

normalized template score of 1.0). An overall normalization of the similarity score term was balanced with other

scoring terms by setting the default overall normalization to 2500.0 while docking. Each atom from the remaining

ligands of the dataset was compared with the existing centers from the template being constructed. If an atom was

closer to an existing center than the threshold specified in the docking wizard (default: 2.0 A ˚) the atom was

considered equal to that center. A center can be a part of several template groups i.e. if any of the existing groups

that the center was part of, do not match the atom, the center was removed from them to match both the current

atom and the atom, which defined the original group. The following groups were chosen as templates (Figure 4): (a)

Steric (The steric group matches all atoms and was used for shape matching without taking any chemical groups

into account): (40 groups), (b) Hydrogen Acceptor (The hydrogen acceptor group matches any hydrogen acceptor

atom): (4 groups), (c) negative charge (the negative charge matches any negative charges ): (2 groups), (d) Ring

(The ring group matches all atoms which are part of both aromatic and aliphatic rings: (12 groups). All values were

normalized so that a value of 1.0 corresponded to an optimal match shown in Figure 5.



A ‘‘3D QSAR’’ approach based on the values of the group center overlap was performed by analyzing the training

set of ligands (20) mentioned in Table 1, aligned using template docking. Next to four 3D descriptors we have

considered another 3 relevant 2D descriptors, viz., octanol/water partition coefficient (ClogP), Wiener index,

molecular weight.

A regression model was generated for correlation between descriptors and PPARδ agonistic activity (log1/EC50

µM), an experimentally observed quantity. Table 5 presents PPARδ agonistic activity (log1/EC50 µM), four 3D

descriptors based on group center overlap templates of steric (SALL ), hydrogen acceptor (HAALL ), Negative (NegALL )

and ring (RALL ) and 3 relevant 2D descriptors, octanol/water partition coefficient (ClogP), Wiener index, molecular

weight for the training (20) and test (3) compounds.

In case of MLR, feature selection method using backward elimination (Figure 8) gave the best results (MLR:

r2=0.814), corresponding residual values are shown in the Table 6 and the best model relating biological activity

with the descriptors derived using MLR (backward selection) method is presented below:



Predicted (log1/EC50) = 1.89646 x (SALL ) - 5.18021 x (HAALL ) + 6.7067 x (NegALL ) + 13.0226 x (RALL ) + 0.018055

x (Mol.Wt) - 0.000610044 x (Wiener Index) + 0.014204 x (ClogP) - 21.5025----(Equation-2)

(Where, n = 61, r = 0.902, r2 = 0.814, ρ = 0.886, mean square deviation = 0.0438)



One compound (14) is observed to be outlier and is eliminated from the studies. The statistical measures related to

different MLR techniques employed for building QSAR models are shown in Table 7.

5.1 Cross validation with a Test set

A test set consisting of three 1,3,5-trisubstituted aryls was considered to review the reliability of the

abovementioned results obtained. PPARδ agonistic activity, molecular weight, octanol/water partition coefficient

(ClogP), Wiener index, and the four 3D descriptors based on the group center overlap templates (steric (SALL ),

hydrogen acceptor (HAALL ), Negative (NegALL ) and ring (RALL )) for the 3 compounds are of the test set are shown in

the Table 5 .

The experimental and predicted activity using MLR model of the 1,3,5-Trisubstituted aryls of the test set and their

corresponding residual values indicating a good quality of connectedness. A moderately linear correlation was

observed between the experimental and predicted activity (r2 = 0.667) as shown in Figure 11.



6. Conclusion

In this work 1,3,5-Trisubstituted aryls were investigated using Molecular docking and 3D QSAR methodologies to

outline the structural requirements on ligands to discover and design the most effective compound as potent PPARδ

agonist. Molecular docking studies of experimentally identified compounds showed an moderate correlation (r2 =

0.556) between binding free energy (kcal/mol) and experimental log (1/EC50) values against PPARδ. We studied the

binding modes exhibited by various 1,3,5-Trisubstituted aryls illustrate the importance of specific residues forming

H-bonds with in the active site region of 2B50. From our study it is clear that the efficiency of binding of agonists

towards PPARδ would certainly get enhanced when H-bonds are favoured with Tyr 473, Thr 289, Thr 292, Met 228,

Asn 343, Thr 288, Lys 367, His 449 and Thr 222 residues respectively. In the next phase of our study, we have

considered a quantitative concept based on structure-property similarity wherein we have considered “alignment

dependent” 3D descriptors based on group center overlap and other relevant “alignment averaged” ligand properties

(octanol/water partition coefficient (ClogP), wiener index and molecular weight). As the relationships between

structural attributes and bioactive properties. In order to authenticate the relationship between structural features and

PPARδ agonistic activity we have used tremendous statistical methods and cross validation using a test set gave

satisfactory results. Hence, this simple QSAR model can serve as efficient filters to virtual screening large

commercial compound databases to yield potential hit candidates and lead optimization. The outcome of this

research is a sign of competent In Silico methods via computational intelligence are capable of differentiate potential

ligands from non drug like molecules. The utilization of computational tools in the discovery of novel agonists to

PPARδ can be used to save time and reduce the workbench exertion of a medicinal chemist.

7. Acknowledgments

One of the authors, Mr.VasudevaRao Avupati is grateful and thankful to Dr. Rene Thomsen, Molegro ApS, C.F.

Moellers Alle8, Building 1110, DK-8000 Aarhus C, Denmark for providing Molegro Virtual Docker software

(www.molegro.com) accessibility for completion of this research work.



8. References

1. J. Berger, D. E. Moller, Annu. Rev. Med., 2002, Vol 3, pp409.

2. L Serge, G Celine, H Dorte, L. S. Joaquin, J. P. Chantal, F. Alexander, A. G. Paul, Biochimica et

Biophysica Acta., 2005, Vol 1740, pp313– 317.

3. E. D. Rosen, C. J. Walkey, P. Puigserver, B. M. Spiegelman, B. M. Gene Dev., 2000, Vol 14, pp1293.

4. S. A. Fyffe, M. S. Alphey, L. Buetow, Journal of Molecular Biology, 2006, Vol 356, pp1005–1013.

5. H. E. Xu, M. H. Lambert, V. G. Montana, Molecular Cell, 1999, Vol 3, pp397–403.

6. R. T. Nolte, G. B. Wisely, S. Westin, Nature, 1998, Vol 395, pp137–143.

7. H. E. Xu, M. H. Lambert, V. G. Montana, Molecular Cell, 1999, Vol 3, pp397–403.

8. S. A. Fyffe, M. S. Alphey, L. Buetow, Journal of Molecular Biology, 2006, Vol 356, pp1005–1013.

9. R. Zhang, A. DeAngelis, A. Wang, M. Sieber, R. Russell, P. Pelton, J. Xu, P. Zhu, L. Zhou, K. Demarest,

W. V. Murray, G. H. Kuo, Bioorg. Med. Chem. Lett , 2007, Vol 17, pp3855.

10. E. Robert, R. Ross, A. Mihai, C. Christopher, X. Yongping, W . Xing, W. John, K. Don, G. Andrea, I.

Maya, S. Enrique, H. S. Martin, S. T. Shin, Bioorg. Med. Chem. Lett, 2006, Vol 16, pp4376–4380.

11. E. Robert, A. Mihai, R. Ross, B. Badry, S. T. Shinc, G. Andrea, I. Maya, Bioorg. Med. Chem. Lett., 2006,

Vol 16, pp2969–297.

12. F. F. Gary, B. Larry, M. C. Xue, E. Noe, G. Andrew, L. Chitase, L. Gina, P. Jim, J. S. Roderick, P. C.

Unangst, B. K. Trivedi, Bioorg. Med. Chem. Lett., 2007, Vol 17, pp3630–363.

13. G. S. Barry, S. P. Hari, N. B. Andrew, M. W. James, A. W. Deborah, H. L. Millard, X. X. Robert, M. L.

Lisa, V. M. Raymond, H. Stephane, M. W. Timothy, Bioorg. Med. Chem. Lett., 2008, Vol 18, pp5018–

5022.

14. F. Hiroki, U. Shinya, U. Takayoshi, N. Hidehiko, O. Michitaka, M. Makoto, M. Naoki, Bioorg. Med.

Chem. Lett., 2007, Vol 17, pp4351–4357.



15. Z. Rui, W. Aihua, D. Alan, P. Patricia, X. Jun, Z. Peifang, Z. Lubing, D. Keith, V. William, H. K. Gee,

Bioorg. Med. Chem. Lett., 2007, Vol 17, pp3855–3859.

16. M. L. Sznaidman, C. D. Haffner, P. R. Maloney, Bioorg. Med. Chem. Lett., 2003, Vol 13, pp1517–1521.

17. E. Robert, C. Christopher, X. Yongping, A. Mihai, R. Ross, W. Xing, W. John, S. K. Donald, T. Tove, T.

B. N. T. Van, C. N. Cara, H. David, S. Enrique, S. Tracy, G. Andrea, I. Maya, S. Martin, S. T. Shin, J.

Med. Chem , 2010, Vol 1, pp77–105.

18. J. Berger, M. D. Leibowitz, T. W. Doebber, J. Biol. Chem, 1999, Vol 274, pp6718–6729.

19. B. G. Shearer, D. J. Steger, J. M. Way, Molecular Endocrinology, 2008, Vol 22, pp523–529.

20. R. Thomsen, MH. Christensen MolDock: a new technique for high accuracy molecular docking. J Med

Chem, 2006; Vol 49, pp 3315-21.

21. DE. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, New York: Addison-

Wiley 1989.

22. K. Shoemake, In Graphics Gems III; Kirk D, Ed. Boston: AP Professional (Academic Press) 1992, pp 124-

32.

23. JM. Yang, CC. Chen, GEMDOCK: a generic evolutionary method for molecular docking. Proteins, 2004,

Vol 55, pp288-304.

24. AN. Jain Surflex: fully automatic flexible molecular docking using a molecular similarity-based search

engine. J Med Chem 2003, Vol46, pp499-511.

25. M. Rarey, B. Kramer, T. Lengauer, G. Klebe : A fast flexible docking method using an incremental

construction algorithm. J Mol Biol, 1996, Vol 261, 470-89.

26. B. Kramer, M. Rarey, T. Lengauer Evaluation of the FLEXX incremental construction algorithm for

protein-ligand docking. Proteins, 1999, Vol37, pp228-41.

27. ML. Verdonk, JC. Cole, MJ. Hartshorn, CW. Murray, Taylor RD. Improved protein-ligand docking using

GOLD. Proteins, 2003, Vol52, pp609-23.



28. S. Joy, PS. Nair, R. Hariharan, MR. Pillai, Detailed comparison of the protein-ligand docking efficiencies

of GOLD, a commercial package and Argus Lab, a licensable freeware. In Silico Biol 2006, Vol6, pp601-5.

29. Sapre. Nitin s, Swagata Gupta, Nilanjana Pancholi, Neelima Sapre, “A Group Center Overlap Based

Approach For ‘‘3d Qsar’’ studies on TIBO derivatives” Journal of Computational Chemistry, 2008, Vol.

30, pp 6.

30. T. Maxim, A. Ruben, Curr. Opin. Struct. Biol., 2008, Vol18, pp178–184.

Addresses of authors

A. Vasudeva Rao* Dr. M. Muralikrishna Kumar

II/II M-Pharmacy Assistant Professor

Pharmaceutical Chemistry Division Pharmaceutical Chemistry Division

University College of Pharmaceutical Sciences University College of Pharmaceutical Sciences

Andhra University Andhra University

Visakhapatnam-530003 Visakhapatnam-530003

Prof. Y. Rajendra Prasad

Professor (Head of the department)

Pharmaceutical Chemistry Division

University College of Pharmaceutical Sciences

Andhra University

Visakhapatnam-530003.

Address of Research Lab

University College of Pharmaceutical Sciences,

Pharmaceutical Chemistry Division,

Andhra University (NAAC “A+” and ISO 9001:2008 Quality Certified Institution),

Visakhapatnam-530003,

Andhra Pradesh, India.

E-mail: [email protected]

issn: 0975-766x available online through research article ...significant 3d qsar model was produced...

Documents