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Journal of Proteomics & Bioinformatics
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Screening and Identification of Structural Analogs of GW9662 and T0070907 Potent Antagonists of Peroxisome Proliferator-Activated Receptor Gamma: In-Silico Drug-Designing Approach

Pramodkumar P Gupta1*, Shrinkhla Singh1, Pritam Kumar Panda1, Danish Ibrahim Jasnaik1, Santosh S Chhajed2 and Virupaksha A Bastikar3

1School of Biotechnology and Bioinformatics, D Y Patil University, Navi Mumbai, Maharashtra, India

2Department of Pharmaceutical Chemistry, MET Institute of Pharmacy, Nashik, Maharashtra, India

3Amity Institute of Biotechnology, Amity University, Mumbai, Maharashtra, India

*Corresponding Author:
Gupta PP
School of Biotechnology and Bioinformatics
D Y Patil University
Navi Mumbai, Maharashtra, India
Tel: 9920087817
E-mail: [email protected]; [email protected]

Received Date: January 24, 2017; Accepted Date: March 28, 2017; Published Date: April 03, 2017

Citation: Gupta PP, Singh S, Panda PK, Jasnaik DI, Chhajed SS, et al. (2017) Screening and Identification of Structural Analogs of GW9662 and T0070907 Potent Antagonists of Peroxisome Proliferator-Activated Receptor Gamma: In- Silico Drug-Designing Approach. J Proteomics Bioinform 10:85-93. doi: 10.4172/ jpb.1000428

Copyright: © 2017 Gupta PP, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Abstract

Peroxisome Proliferator-Activated Receptor Gamma encoded by PPARG gene is also known as type II nuclear receptor in humans plays a significant role in regulating the glucose metabolism, adipocyte differentiation and serves as a lipid sensor. This has been implicated in the pathology of various diseases like obesity, diabetes, atherosclerosis, and cancer. In search of drugs that uses PPAR gamma as a therapeutic target for its inhibition: Insilico CADD approaches has been widely used in this aspect to understand the intrinsic molecular aspects and their interaction with the chemicals. In-silico based virtual screening helps in identification of optimum molecule among the large dataset to elucidate the effects on a particular target through binding interaction and can be used for further experimentations. In the present study, two PPAR gamma/antagonists GW9662 and T0070907 were selected for this study as they serves as potent therapeutics to minimize the effects of PPAR gamma in chronic diseases. A set of structural analogs of GW9662 and T0070907 were screened from ZINC public database. Ligand based screening is followed by 80% similarity search, Lipinski filter, Pharmacophore based and toxicity based screening. Structure based virtual screening follows the output and final molecular docking using iGemdock and Autodock explained the binding affinity and pharmacological interactions. The results between the GW9662, T0070907 and screened structural analogs show better binding affinity with respect to the former one with similar pharmacological interactions

Keywords

GW9662; T0070907; PPAR gamma; Virtual Screening; Molecular docking

Introduction

Breast cancer is measured as the most widespread cancer in women, with an estimate of 1.38 million new cases per year across the globe [1]. Usually classified on the basis of clinical features and histopathological findings, but an escalation has been seen that in cellular and molecular characteristics which are of significant importance. Estrogen alpha receptor is considered as the standard biomarker in prediction of breast cancer in response to endocrine treatment and has been found to be expressed in 70-80% of patient suffering from breast cancer. There are significant proportions of ER-positive tumours which are resistant to endocrine therapy, either anew or acquired, and more specific biomarkers as well as new therapeutic targets for endocrine-resistant tumours are needed [2]. The mechanisms of endocrine resistance include loss of ER expression or expression of truncated ER isoforms, post translational alteration or modification of the ER, elimination of cofactors, or overstimulation of tyrosine kinase receptor growth signalling pathways. The peroxisome proliferator-activated receptor γ (PPARγ) ligands show anticancer activity against a wide range of neoplastic cells in vitro. Peroxisome proliferator-activated receptor gamma (PPAR-γ or PPARG), also known as the glitazone receptor, orNR1C3 (nuclear receptor subfamily 1, group C, member 3) is a type II nuclear receptor which in humans is encoded by the PPARG gene [3]. It is expressed primarily in adipose tissue with less expression in cardiac, skeletal, and smooth muscle cells, islet cells, macrophages, and vascular endothelial cells. Along with adipocyte differentiation, PPAR activity also promotes uptake of circulating fatty acids into fat cells and the shifting of lipid stores from extra-adipose to adipose tissue. The uptake of circulating fatty acids is the basis for the pharmacological application of PPAR gamma in breast cancer patients. It is regulated by ligand binding and post-translational modifications [4]. The previous demonstration shows that endogenous transactivation promotes an aggressive phenotype of malignant breast cells. According to recent findings NR1D1 and the peroxisome proliferator-activated receptor-γ (PPARγ)- Binding Protein (PBP) both act through a common pathway and are responsible for upregulating several genes in the de novo fatty acid synthesis network, which is said to be highly active in ERBB2-positive breast cancer cells [4]. Both NR1D1 and PBP are functionally related to PPARγ, which is a well-established positive regulator of adipogenesis and lipid storage. The PPARγ pathway is responsible for reduction of Aldehyde Dehydrogenase (ALDH)-positive population in ERBB2- positive breast cancer cells. The in vitro tumorsphere formation assay shows that the two antagonist of PPARγ namely GW9662 and T0070907 are responsible for deduction of tumorsphere formation in ERBB2- positive cells, but not other breast cell [4]. Now talking about the two antagonists GW9662 and T0070907, GW9662 is potent antagonist which shows the inhibition of growing breast tumour cells and also promotes the anticancer effects of the PPARγ agonist rosiglitazone, independent of PPARγ activation [5]. Whereas T0070907 helps in suppression of breast cancer cell proliferation and motility via PPAR gamma-dependent as well as independent mechanisms [6]. The present work is aim to identify the diverse Structural analogs of GW9662 and T0070907 from public database that can act as prominent Antagonists to PPAR gamma.

Material and Methods

Selection of target

As per the literature review the Crystal Structure of PPAR gamma complexed with Telmisartan is downloaded from protein data bank with pdb_id 3VN2 an x-ray diffraction data at resolution 2.18 Angstrom [7]. It is a selective angiotensin II type 1 receptor blocker. Recently it was reported that telmisartan acts as agonist for PPAR gamma [8].

Active site identification

The active site residues of the Crystal Structure of PPAR gamma complexed with Telmisartan was predicted using Cast-P sever (Computer Atlas of Surface Topology of Proteins) [9], with probe radius 1.4 Armstrong, and validated through Auto dock 4.1 [10] and Discovery studio visualizer 4.0 [11].

Selection of ligands

PPAR gamma Antagonists: In this present study we have considered molecules they are GW9662 and T0070907 [5,6]. GW9662 is an irreversible PPARγ antagonist and inhibits connective tissue growth factor and activation of CD36 by IL-4 (Figure 1) and shows PPAR alpha agonist activity [5]. Whereas T0070907 (Figure 2) is very similar in structure and activity to PPARgamma antagonist GW 96662. It is more potent and has higher selectivity for PPAR gamma over all other subtypes that are about 800 fold more [6]. Based on GW9662 and T0070907 their structural analogs were retrieved from ZINC database [12].

proteomics-bioinformatics-GW

Figure 1: GW 9662.

proteomics-bioinformatics-T0070907

Figure 2: T0070907.

Data mining of ligands

Ligand based screening: Mining of optimum ligand structural analogs from public domain database is a difficult task. ZINC is public database which consist of more than 35 million commercial data sets and non-redundant datasets at noncommercial charges and hence mining of dataset from this database helps in acquiring a less non – redundant datasets [12].

An 80% similarity search was performed using ZINC database against GW9662 and T0070907, resulting in more than 1000 molecules were screened from 35 million compounds. Whereas a second pass filter was carried with property based activity and drug likeness features were exhibited (Table 1): a) Molecular weight; b) Logarithm of the calculated n-octanol/water partition coefficient; c) Number of hydrogen bond acceptors; d) Number of hydrogen bond donors; and e) Number of rotatable single bonds.

Properties Minimum Maximum
MWa 180 500
log P -0.4 +5.6
HBAc 0 10
HBDd 0 5
NRBe 0 10

Table 1: Summary of physicochemical properties (Often used to predict “Drug- Likeness”).

Being with two pass filter the screened compounds still showed some structural diversity at the basic scaffold level. Hence, Considering a basic scaffold one to one screening was carried out on the basis of basic scaffold of structure and out of 500 molecules 146 molecules were screened, 62 belonging to GW9662 and 84 molecules belonging to T0070907 (Table 2). Filtering the duplication of molecules using ZINC-id 05 molecules identified as common in both the list and total 141 molecules were subjected for the analysis.

S. No. ZINC-id S. No. ZINC-id S. No. ZINC-id S. No. ZINC-id
1 ZINC00003381 37 ZINC35279453 73 ZINC78711287 109 ZINC08166092
2 ZINC00039173 38 ZINC37032921 74 ZINC80177501 110 ZINC08424193
3 ZINC00103118 39 ZINC37032925 75 ZINC80177506 111 ZINC13624060
4 ZINC00103124 40 ZINC37247723 76 ZINC81688589 112 ZINC15538832
5 ZINC00225355 41 ZINC37250760 77 ZINC82115191 113 ZINC19230212
6 ZINC00240687 42 ZINC37286046 78 ZINC82262180 114 ZINC19260792
7 ZINC00242270 43 ZINC37673015 79 ZINC82264113 115 ZINC19264532
8 ZINC00266758 44 ZINC37778784 80 ZINC82698016 116 ZINC19392770
9 ZINC00290615 45 ZINC37778789 81 ZINC91495084 117 ZINC19399505
10 ZINC00377492 46 ZINC37995873 82 ZINC92348575 118 ZINC19427956
11 ZINC00434561 47 ZINC40292647 83 ZINC92349427 119 ZINC19477700
12 ZINC00438391 48 ZINC40292649 84 ZINC94665032 120 ZINC20194110
13 ZINC00458448 49 ZINC47916551 85 ZINC00091503 121 ZINC20194113
14 ZINC01056124 50 ZINC49157598 86 ZINC00101971 122 ZINC20194128
15 ZINC01994110 51 ZINC49317454 87 ZINC00103088 123 ZINC20194139
16 ZINC03153944 52 ZINC49376264 88 ZINC00103094 124 ZINC20194142
17 ZINC04045634 53 ZINC50700058 89 ZINC00103103 125 ZINC20194166
18 ZINC05800824 54 ZINC54414540 90 ZINC00103116 126 ZINC20194169
19 ZINC06715798 55 ZINC61680394 91 ZINC00126121 127 ZINC20194214
20 ZINC08780493 56 ZINC62725653 92 ZINC00168318 128 ZINC20194217
21 ZINC09497554 57 ZINC62725669 93 ZINC00231396 129 ZINC20194220
22 ZINC12223463 58 ZINC62725736 94 ZINC00259707 130 ZINC20194223
23 ZINC12620369 59 ZINC62725751 95 ZINC00268121 131 ZINC20194235
24 ZINC16604122 60 ZINC63063491 96 ZINC00293261 132 ZINC20194280
25 ZINC17584697 61 ZINC69771610 97 ZINC00375580 133 ZINC20475562
26 ZINC19470368 62 ZINC70073342 98 ZINC00433206 134 ZINC20478180
27 ZINC19478031 63 ZINC70160175 99 ZINC00438022 135 ZINC36282691
28 ZINC20194172 64 ZINC70230817 100 ZINC00438645 136 ZINC37767839
29 ZINC21959624 65 ZINC70231003 101 ZINC00442284 137 ZINC40311239
30 ZINC21968551 66 ZINC71412398 102 ZINC00456001 138 ZINC40311732
31 ZINC22141304 67 ZINC71414120 103 ZINC00493277 139 ZINC40311738
32 ZINC22148806 68 ZINC73846507 104 ZINC01216529 140 ZINC40311787
33 ZINC23634253 69 ZINC73846563 105 ZINC01227343 141 ZINC50225099
34 ZINC29017357 70 ZINC73846631 106 ZINC02573600
35 ZINC29020806 71 ZINC73847263 107 ZINC05538460
36 ZINC35121683 72 ZINC78645916 108 ZINC05672437

Table 2: Structural analogs of GW9662 and T0070907.

Pharmacophore based screening: A 2D structure of 141 structural analogs of GW9662 and T0070907 were sketched using Chemsketch [13] and 3D optimization is carried out using Merck molecular force field (MMFF) in Ligand Scout [14]. A pharmacophore model is created with following features: hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), ring aromatic (RA), and hydrophobic (HY), by considering GW9662 one of the dataset in training and T0070907 as one of the test set condition with 70% and 30% to identify the most closest aligned Pharmacophore model.

Toxicity based screening: The theoretical toxicity based screening was calculated for 92 structural analogs using OSIRIS property explorer (http://www.organic-chemistry.org/prog/peo/) [15], and represented by toxicity risks (mutagenic, irritant, tumorigenic and reproductive effects), states high, medium and low risks profile.

OSIRIS compares input dataset with predefined four subsets of the chemical datasets from RTECS database; they are 7504 mutagenic compounds, 2841 tumorogenic compounds, 2372 irritant compounds and 3570 reproductive effective compounds [15]. The prediction process relies on a precomputed set of 5300 structural fragments from RTECS database datasets that are known to be active in a certain toxicity class and give rise to toxicity alerts in case if they meet in the input structural data [15].

Structure based screening and molecular docking study: Molecular interactions plays an important role in all biological reactions. Drugs are either mimicking or copying the effect of native ligands binding to the receptor by applying the pharmacological and biological reactions. Computational approaches are used to recognize and understand this mode of binding, interacting and multiple conformations of ligands into the active site to their receptors which is called as Molecular Docking [16,17].

As pharmacological interactions are useful for understanding ligand binding mechanisms to a therapeutic target. These interactions are often inferred from a set of active compounds that were acquired experimentally. Moreover, most docking programs loosely coupled the stages (binding-site and ligand preparations, virtual screening, and post-screening analysis) of structure-based virtual screening (VS). iGEMDOCK is an integrated virtual screening environment from preparations through post-screening analysis with types of bonding and pharmacological interactions [18]. To initially screen on the basis of binding energy and types of bonding we selected the therapeutic protein target 3VN2.pdb [7] and 52 low toxicity risk structural analogs of GW9662 and T0070907. After the generations of the profiles, the compounds were finally subjected to second pass molecular docking, which is carried out using Auto dock 4.1. [10].

Results and Discussion

Active site

The predicted size of active site with an area of 3045.3 and a volume of 4343.7 units Armstrong followed by the input co-ordinates in Auto dock: x=45.444; y=21.713 and z=26.876 respectively and size value of 40 to all the coordinate space (Figure 3), with the following residual information in Table 3.

proteomics-bioinformatics-site-region

Figure 3: Active site region of PPAR-gamma (Pdb-id: 3VN2).

Amino acid Amino acid Amino acid Amino acid
1 TYR (222) 28 HIS (323)
2 PHE (226) 29 ILE (326)
3 PRO (227) 30 TYR (327)
4 LEU (228) 31 MET (329)
5 THR (229) 32 LEU (340)
6 LYS (230) 33 ILE (341)
7 ILE (249) 34 SER (342)
8 LEU (255) 35 GLU (343)
9 GLY (258) 36 MET (348)
10 GLU (259) 37 ARG (350)
11 ILE (262) 38 LEU (353)
12 ALA (278) 39 LYS (354)
13 ARG (280) 40 LEU(356)
14 ILE (281) 41 PHE (360)
15 PHE (282) 42 GLY (361)
16 GLU (284) 43 PHE (363)
17 CYS (285) 44 MET (364)
18 GLN (286) 45 GLU (365)
19 PHE (287) 46 LYS (367)
20 ARG (288) 47 PHE (368)
21 SER (289) 48 LEU (381)
22 GLU (291) 49 HIS (449)
23 ALA (292) 50 LEU (453)
24 VAL (293) 51 LEU (465)
25 GLU (295) 52 LEU (469)
26 ILE (296) 53 ILE (472)
27 VAL (322) 54 TYR (473).

Table 3: Active site residues.

Ligand based screening

Mining a data set from over a 35 million compounds is a difficult task, but with a ligand structure based similarity search of 80% has revealed the ease ness of compound selection with an additional filters of physiochemical properties based too. A set of more than 1000 compounds/structural analogs of GW9662 and T0070907 compounds been reduced to set of 500 compounds. With the one to one selection criteria considering the basic scaffold as a prime target the compounds were more refined and 146 total compounds, 62 belonging to GW9662 and 84 molecules belonging to T0070907. Filtering with ZINC-id, 5 duplicates were removed and total 141 compounds were further considered for analysis.

Based on pharmacophore features HBD, HBA, RA and HY alignment in Ligand scout exhibited 92 structural analogs with closest structural feature analogs of GW9662 and T0070907 (Figure 4).

proteomics-bioinformatics-pharmacophore-model

Figure 4: Aligned pharmacophore model.

Toxicity prediction: These entire 92 molecules is dividing in two set of toxicity level tested via OSIRIS online tool grouping 40 compounds with high risk to toxicity level for mutagenic, irritant, tumorogenic and reproductive effects, and 52 compounds with low risk to toxicity level (Table 4).

S. No. Molecule with high toxicity risk S. No. Molecule with low toxicity risk S. No. Molecule with low toxicity risk
1 ZINC00101971 1 ZINC00091503 41 ZINC62725751
2 ZINC00103094 2 ZINC00103088 42 ZINC69771610
3 ZINC00168318 3 ZINC00103103 43 ZINC70231003
4 ZINC00231396 4 ZINC00103124 44 ZINC71414120
5 ZINC00259707 5 ZINC00126121 45 ZINC73846507
6 ZINC00438022 6 ZINC00268121 46 ZINC73846563
7 ZINC00438645 7 ZINC00293261 47 ZINC73846631
8 ZINC00442284 8 ZINC00375580 48 ZINC73847263
9 ZINC00456001 9 ZINC01216529 49 ZINC78645916
10 ZINC08166092 10 ZINC02573600 50 ZINC78711287
11 ZINC19230212 11 ZINC05538460 51 ZINC82115191
12 ZINC19392770 12 ZINC05672437 52 ZINC94665032
13 ZINC19427956 13 ZINC08424193  
14 ZINC20194113 14 ZINC13624060
15 ZINC20194128 15 ZINC00039173
16 ZINC20194142 16 ZINC00103118
17 ZINC20194166 17 ZINC00225355
18 ZINC20194169 18 ZINC00266758
19 ZINC20194217 19 ZINC00377492  
20 ZINC20194220 20 ZINC00438391
21 ZINC20194223 21 ZINC00458448
22 ZINC20194235 22 ZINC03153944
23 ZINC20478180 23 ZINC04045634
24 ZINC40311239 24 ZINC12223463
25 ZINC40311732 25 ZINC21959624
26 ZINC40311787 26 ZINC23634253
27 ZINC63063491 27 ZINC29017357
28 ZINC00434561 28 ZINC29020806
29 ZINC09497554 29 ZINC35121683
30 ZINC17584697 30 ZINC37032921
31 ZINC21968551 31 ZINC37032925
32 ZINC22148806 32 ZINC37247723
33 ZINC37673015 33 ZINC37250760
34 ZINC40292647 34 ZINC37286046
35 ZINC54414540 35 ZINC37778784
36 ZINC62725736 36 ZINC37778789
37 ZINC70160175 37 ZINC47916551
38 ZINC70230817 38 ZINC50700058
39 ZINC91495084 39 ZINC61680394
40 ZINC92349427 40 ZINC62725669

Table 4: Results from OSIRIS.

Structure based screening

Determining the structure based virtual screening, here we submitted 52 low risk compounds for screening, the output from IGemdock resulted in a good prediction of binding energy and exhibited the hydrogen bond, Vanderwaal and electro-static interactions with the receptor and ligand (Table 5).

S. No. Ligands Total Energy VDW HBond Elec
1 ZINC08424193 -119.544 -108.079 -12.3227 0.858135
2 ZINC00103088 -118.973 -107.122 -12.8735 1.02287
3 ZINC00293261 -118.433 -107.879 -11.6563 1.10243
4 ZINC05672437 -118.087 -102.728 -16.5583 1.19925
5 ZINC13624060 -113.671 -106.244 -7.42713 0
6 ZINC37250760 -110.782 -98.1172 -13.3657 0.701022
7 ZINC00266758 -110.704 -98.0516 -13.353 0.701062
8 ZINC37032921 -109.816 -103.521 -6.2946 0
9 ZINC37032925 -109.706 -103.266 -6.44082 0
10 ZINC94665032 -108.577 -90.9768 -19.0222 1.42239
11 ZINC73846631 -107.334 -99.9581 -7.37555 0
12 ZINC05538460 -107.057 -96.3787 -11.6946 1.01642
13 ZINC73846507 -106.945 -102.002 -4.94304 0
14 ZINC00103118 -106.758 -87.3968 -20.8344 1.47341
15 ZINC00103124 -106.444 -87.9431 -19.948 1.4476
16 ZINC73847263 -106.153 -99.4458 -6.70742 0
17 ZINC78711287 -106.085 -103.713 -2.37159 0
18 ZINC61680394 -105.741 -85.5873 -21.6387 1.48481
19 ZINC37778784 -105.611 -99.184 -6.42655 0
20 ZINC69771610 -105.591 -94.7184 -10.8727 0
21 ZINC50700058 -105.552 -82.2008 -25.1425 1.79108
22 ZINC37778789 -105.528 -99.1107 -6.41745 0
23 ZINC00103103 -105.476 -85.1052 -22.128 1.7567
24 ZINC02573600 -105.338 -105.613 0 0.274777
25 ZINC00225355 -105.3 -91.7738 -14.6161 1.09016
26 ZINC47916551 -103.789 -99.53 -4.25899 0
27 ZINC00268121 -102.408 -92.5018 -10.2201 0.314099
28 ZINC82115191 -102.134 -93.9295 -8.20443 0
29 ZINC00039173 -101.158 -80.8592 -22.052 1.75293
30 ZINC37247723 -101.155 -91.6675 -10.208 0.720593
31 ZINC71414120 -101.029 -101.354 0 0.324669
32 ZINC29017357 -100.804 -96.2192 -4.58458 0
33 ZINC73846563 -100.691 -95.0983 -5.59268 0
34 ZINC01216529 -100.655 -92.831 -8.5 0.675868
35 ZINC37286046 -100.469 -80.1132 -22.1099 1.75373
36 ZINC00438391 -100.326 -88.1606 -13.2737 1.10821
37 ZINC23634253 -100.294 -96.857 -3.4374 0
38 ZINC70231003 -99.1922 -78.0809 -22.4145 1.30312
39 ZINC00458448 -99.1543 -94.9479 -4.20635 0
40 ZINC00375580 -98.7094 -80.191 -19.7748 1.25644
41 ZINC78645916 -98.6416 -97.6431 -0.99846 0
42 ZINC35121683 -98.5428 -95.5498 -2.993 0
43 ZINC29020806 -98.4981 -95.4803 -3.01782 0
44 ZINC00091503 -95.4933 -94.1309 -1.36242 0
45 ZINC62725669 -94.5289 -89.2391 -5.28982 0
46 ZINC00126121 -93.8833 -93.8833 0 0
47 ZINC03153944 -93.6787 12.1913 -2.14548 -0.67793
48 ZINC00377492 -93.4082 -93.4082 0 0
49 ZINC62725751 -93.3056 -87.9294 -5.37627 0
50 ZINC04045634 -91.4898 -88.0334 -3.45643 0
51 ZINC12223463 -83.0465 -79.5647 -3.4818 0
52 ZINC21959624 -82.8496 -82.8496 0 0

Table 5: Result from IGem dock.

Molecular docking study

The final docking study was performed on Auto dock 4.1 running on Windows 7. The Auto dock 4.1 uses an evolutionary genetic algorithm approximates a systematic search of positions, orientations and conformations of the ligand in the receptor-binding pocket via a series of hierarchical filters. The shape and properties of the binding site from receptor protein are represented on a grid by a rectangular box confining the translations of the mass center of the ligand. A set of initial ligand conformations or poses were created, of which the most accurately binded ligand pose are selected on the basis of minimum binding energy and desired pharmacological interactions were studied. The binding energy for GW9662 and T0070907 is 9.0 and 8.5 KJ/mol, whereas the ZINC00293261 (Figure 5), ZINC05672437 (Figure 6), ZINC00103124, ZINC29020806, ZINC00438391, ZINC03153944, ZINC35121683 and ZINC37250760 exhibits a more stable energy with binding energy value of -10.7, -10.1, -9.8, -9.6, -9.5, -9.3, -9.1 and -9.0 KJ/mol respectively (Table 6).

proteomics-bioinformatics-interaction

Figure 5: Molecule ZINC00293261 interaction with PPAR-gamma, Pdb-id: 3VN2.

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Figure 6: Molecule ZINC05672437 interaction with PPAR-gamma, Pdb-id: 3VN2.

S. No. Compound Hydrogen bonding Pi-interaction Energy
1 GW9662 TYR 327 CYS 285, PHE 363, MET 364, HIS 449, LEU 453 -9.0
2 T00070907 SER 289, TYR 327, TYR 473 PHE 282, CYS 285, PHE 363, MET 364, HIS 449 -8.5
3 ZINC00293261 CYS 285, SER 289, TYR 327, TYR 473 ILE 281, PHE 282, LEU 356, PHE 360, PHE 363, MET 364, LEU 453, HIS 449, LEU 469 -10.7
4 ZINC05672437 CYS 285, SER 289, TYR 327, TYR 473 PHE 282, PHE 363, MET 364, HIS 449, LEU 453, LEU 469 -10.1
5 ZINC00103124 CYS 285, SER 289, TYR 327, TYR 473 ILE 281, PHE 282, LEU 356, PHE 363, MET 364, HIS 449, LEU 453 -9.8
6 ZINC29020806 CYS 285, TYR 473 PHE 282, CYS 285, PHE 363, MET 364, LEU 453, TYR 473 -9.6
7 ZINC00438391 CYS 285, SER 289, MET 364, TYR 473 PHE 282, CYS 285, PHE 363, MET 364 -9.5
8 ZINC03153944 SER 289, TYR 327, MET 364, TYR 473 CYS 285, PHE 363, MET 364, HIS 449, LEU 469 -9.3
9 ZINC35121683 CYS 285, TYR 327, TYR 473 PHE 282, CYS 285, PHE 363, MET 364, HIS 449, LEU 453, LEU 469 -9.1
10 ZINC37250760 SER 289, TYR 327, TYR 473 ILE 281, PHE 282, CYS 285, PHE 363, MET 364, HIS 449 -9.0
11 ZINC37032925 TYR 327, TYR 473 PHE 282, CYS 285, PHE 363, MET 364, LEU 469, TYR 473 -8.9
12 ZINC23634253 CYS 285 PHE 282, CYS 285, PHE 363, MET 364, HIS 449, LEU 453 -8.7
13 ZINC29017357 CYS 285, TYR 327, PHE 360 CYS 285, LEU 353, PHE 363, MET 364, HIS 449 -8.6
14 ZINC00458448 CYS 285, SER 289, TYR 327, HIS 449, TYR 473 PHE 282, CYS 285, ILE 326, PHE 363, MET 364 -8.6
15 ZINC02573600 ARG 288 CYS 285, ARG 288, ALA 292, ILE 326, MET 329, LEU 330, LEU 333 -8.5
16 ZINC00103088 ARG 288, SER 289, PHE 363 CYS 285, ARG 288, ILE 326, TYR 327, LEU 330 -8.4
17 ZINC12223463 CYS 285 PHE 282, CYS 285, PHE 363, MET 364, HIS 449, LEU 453 -8.3
18 ZINC21959624 CYS 285 PHE 282, CYS 285, PHE 363, MET 364, HIS 449, LEU 453 -8.3
19 ZINC05538460 LEU 228, ARG 288 ALA 292, GLU 295, ILE 326, MET 329, LEU 330, LEU 333 -8.1
20 ZINC37286046 CYS 285, SER 289, TYR 327, TYR 473, HIS 449 ILE 326, PHE 363, MET 364 -7.9
21 ZINC37247723 CYS 285, ARG 288, SER 289 CYS 285, ARG 288, ALA 292, ILE 326, MET 329, LEU 330, VAL 339, MET 364 -7.6
22 ZINC00091503 CYS 285, SER 289, TYR 473 CYS 285, VAL 322, HIS 323, ILE 326, PHE 363, MET 364, HIS 449, LEU 469 -7.6
23 ZINC00126121 ARG 288 ARG 288, ALA 292, GLU 295, ILE 326, MET 329, LEU 330, LEU 333 -7.5
24 ZINC01216529 SER 288 CYS 285, ARG 288, LEU 330, ILE 341 -7.5
25 ZINC00039173 ARG 288, SER 289 CYS 285, ARG 288, TYR 327, LEU 330 -7.5
26 ZINC00375580 ARG 288 CYS 285, ARG 288, ALA 292, ILE 326, LEU 330, ILE 341 -7.4
27 ZINC04045634 CYS 285, SER 289, TYR 473 PHE 282, CYS 285, ILE 326, PHE 363, MET 364, HIS 449, LEU 469 -7.3
28 ZINC00103103 SER 289 CYS 285, ARG 288, LEU 330 -7.1
29 ZINC00434561 ARG 288 LEU 228, ARG 288, ALA 292, GLU 295, ILE 326, MET 329, LEU 330, LEU 333 -7.1
30 ZINC00377492 SER 289 ARG 288, ALA 292, ILE 326, MET 329, LEU 330 -7.0
31 ZINC00225355 SER 342 ILE 281, GLY 284, CYS 285, ARG 288, ILE 341 -6.7

Table 6: Binding energy and interactions between GW9662, T0070907, structural analogs and PPAR-gamma.

Pharmacophore based mapping and ligand features mapping has made an enormous knowledge generation in the field of drug screening and development. Where each atom from ligand and receptor protein interaction is considered on the type of bonding and nature of their interactions such as hydrogen bonding, electrostatic interaction, hydrophobic interactions etc. Comparing the 52 structural analogs of GW9662 and T0070907 with low toxicity risk profile 29 molecules appropriately placed them in the cavity of the receptor protein and forms a hydrogen bonding and pi-interaction and forms similar pharmacophoric interactions as compared to GW9662 and T0070907 with PPAR-gamma receptor protein.

Conclusion

The work presented here was to identify the optimum structural analogs from public database with respect to structural, binding affinity and pharmacological interaction. A number of in-silico techniques have been implemented to screen the diverse molecule from the set of molecules. The screening was not dependent on the structural similarity but also on the physio chemical parameters. The pharmacophore model based screening was one where Hydrogen Bond Donors (HBD), Hydrogen Bond Acceptors (HBA), Ring Aromatic (RA), and Hydrophobic (HY) has generated a immense knowledge to screened the most optimum on the basis of these activity. Whereas the criteria for toxicity prediction; Mutagenicity, Tumorigenicity, Irritating effects and Reproductive Effect helped in reducing those dataset which has close structural and physiochemical similarity to the parent one. Finally the structure based screening has generated the most promising results where few structural analogs showed better binding affinity and very close pharmacological interaction patterns with respect to GW9662 and T0070907. Further these molecules could be studied in in vitro conditions further to evaluate its detail function over PPAR gamma.

Competing Interests

The author(s) of manuscript “Screening and Identification of Structural Analogs of GW9662 and T0070907 Potent Antagonists of Peroxisome Proliferator-Activated Receptor Gamma: In silico Drug-Designing Approach” declare that they have no competing interests.

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