Open Access
Article

Gas chromatography-mass spectrometry metabolic profiling, molecular simulation and dynamics of diverse phytochemicals of Punica granatum L. leaves against estrogen receptor

Talambedu Usha1,Sushil Kumar Middha2,*,Dhivya Shanmugarajan2,Dinesh Babu3,Arvind Kumar Goyal4,Hasan Soliman Yusufoglu5,Kora Rudraiah Sidhalinghamurthy1
1
Department of Biochemistry, Bangalore University, Bengaluru, 560029 Karnataka, India
2
DBT-BIF Facility, Department of Biotechnology, Maharani Lakshmi Ammanni College for Women, 560012 Bangalore, India
3
Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2E1, Canada
4
Centre for Bamboo Studies, Department of Biotechnology, Bodoland University, Kokrajhar, 783370 Assam, India
5
College of Pharmacy, Prince Sattam Bin Abdulaziz University, 16278 Al-Kharj, Saudi Arabia
DOI: 10.52586/4957 Volume 26 Issue 9, pp.423-441
Submited: 14 January 2021 Accepted: 19 March 2021 Published: 30 September 2021
(This article belongs to the Special Issue Aging in animals)
*Corresponding Author(s):  
Sushil Kumar Middha
E-mail:  
drsushilmiddha@gmail.com
Copyright: © 2021 The author(s). Published by BRI. This is an open access article under the CC BY 4.0 license (https://creativecommons.org/licenses/by/4.0/).
Abstract

Introduction: Breast cancer is the most common type of cancer globally and its treatment with many FDA-approved synthetic drugs manifests various side effects. Alternatively, phytochemicals are natural reserves of novel drugs for cancer therapy. Punica granatum commonly known as pomegranate is a rich source of phytopharmaceuticals. Methods: The phytoconstituents of Punica granatum leaves were profiled using GC-MS/MS in the present work. Cytoscape-assisted network pharmacology of principal and prognostic biomarkers, which are immunohistochemically tested in breast cancer tissue, was carried out for the identification of protein target. Followed by, rigorous virtual screening of 145 phytoconstituents against the three ER isoforms (α, β and γ) was performed using Discovery Studio. The docked complexes were further evaluated for their flexibility and stability using GROMACS2016 through 50 ns long molecular dynamic simulations. Results: In the current study, we report the precise and systematic GC-MS/MS profiling of phytoconstituents (19 novel metabolites out of 145) of hydromethanolic extract of Punica granatum L. (pomegranate) leaves. These phytocompounds are various types of fatty acids, terpenes, heterocyclic compounds and flavonoids. 4-coumaric acid methyl ester was identified as the best inhibitor of ER isoforms with drug-likeness and no toxicity from ADMET screening. γ-ligand binding domain complex showed the best interactions with minimum RMSD, constant Rg, and the maximum number of hydrogen bonds. Conclusion: We conclude that 4-coumaric acid methyl ester exhibits favourable drug-like properties comparable to tamoxifen, an FDA-approved breast cancer drug and can be tested further in preclinical studies.

Key words

Breast cancer; Docking; MD simulations; GC-MS/MS profiling; Natural compounds; Pomegranate; Estrogen receptor; ADMET; 4-coumaric acid methyl ester

2. Introduction

Drug discovery is a multifaceted and interdisciplinary endeavor that pursues a sequential process, begins with the discovery of a target and lead, followed by lead optimization and preclinical studies to define and verify the suitability of such lead agents through a number of predetermined guidelines for kick-starting clinical development [1, 2]. The high cost and time-consuming nature of these processes against an ailment demand a novel kind of cost-effective and less time-consuming, intensive in-silico approach [2, 3]. In-silico techniques employ bioinformatics tools to identify drug targets, to explore target protein structure for prediction of possible active sites to dock molecules with targets, rank ligands based on their binding affinities (i.e., energies), and optimize lead molecules [4]. Proficiency in calculating precise, significant, and diverse conformations of ligands within the target site make the molecular docking the most desired drug designing tool [5]. Hence, this tool can be used to understand the various interactions between phytochemicals and breast cancer targets or biomarkers. Despite the availability of modern tools and increasing expertise to identify a new anticancer molecule or lead, the potential of natural products (medicinal plants) and their compounds remains immense and undiscovered.

Many FDA-approved drugs such as paclitaxel and vinblastine (anticancer drugs), and quinine and artemisinin (antimalarial drugs) are plant-based. Therefore, combining the potential of phytomedicine with modern technology can help to develop more effective and economical drugs [6]. Phytochemicals from medicinal plants not only serve as drugs but also provide a lead for the development of new drugs. Punica granatum L., commonly known as pomegranate, is one such medicinal plant with a known reservoir of active biomolecules such as a series of compounds known as ellagitannins, and specifically punicalagin, the unique largest molecular weight polyphenol known to date [7]. A plethora of research reports describe the vasculo protective [8], antidiabetic, antioxidant [7], antiproliferative [9], antitumor [9, 10], anti-inflammatory [11], neuroprotective [12], and antimicrobial [13] properties of pomegranate along with its potential for treating asthma [14]. Previously, Usha et al. (2014) [15] have identified anticancer targets from pomegranate using an in-silico approach.

Breast cancer is the most common type of cancer worldwide, which affects 2.1 million women annually. In the year 2018, the World Health Organization estimated that 627,000 deaths in women were caused due to breast cancer accounting for nearly 15% of all cancer-related mortality among women (https://www.who.int/cancer/detection/breastcancer/en/) [16]. The current study aims at identifying a lead molecule for breast cancer from a natural source like pomegranate. Aromatase is an enzyme that converts C19 androgen to C18 estrogen that plays a vital role in breast cancer, which was reported to be present in high concentrations in breast tumors [17]. Several ellagitannins-derived compounds isolated from pomegranate were previously reported to have potential anti-aromatase activity and inhibit testosterone-induced breast cancer cell proliferation [18]. However, there is a dearth of knowledge about the molecular basis and mechanism(s) of these compounds against breast cancer. The aim of the present study is to design a drug-like entity for breast cancer from Punica granatum L. phytochemicals. To be specific, our study involves a gas chromatography-mass spectrometry (GC-MS)/MS-based metabolite profiling of hydromethanolic extract of Punica granatum L. leaves (HMPGL), to identify a lead candidate, which can act as an antagonist and further verification against the ligand-binding domains (LBDs) of the three different isoforms of human estrogen receptor (ER), namely, ER alpha (ERα), ER beta (ERβ) and estrogen-related receptor (ERR) gamma (ERRγ) using molecular dynamics approach. Our finding reveals that 2-propenoic acid, 3-(4-hydroxyphenyl)-, methyl ester alias 4-coumaric acid methyl ester could act as an antagonist for LBDs of ERα, ERβ and ERRγ isoforms of ER, suggesting its future potential for the development of breast cancer therapeutics.

3. Materials and methods

3.1 In-vitro analyses

3.1.1 Collection of plants and preparation of crude extract

The leaves of Punica granatum L. plant were collected from Gandhi Krishi Vignan Kendra (GKVK), Bengaluru, Karnataka, India. After authentication by the plant taxonomist, a voucher specimen (BOT/mLAC/BMPG001) has been submitted to Botany Department, Maharani Lakshmi Ammanni College for Women, Bengaluru, Karnataka, India. The leaves were shade dried, grounded, and the powder was subjected to soxhlation using 70% methanol as a solvent system. The extract was lyophilized using freeze-drying technology under –55 C and at constant pressure. The extract thus obtained was used further for analysis.

3.1.2 Determination of hydromethanolic leaf extract yield

The yield of hydromethanolic leaf extract was calculated using the following equation: Yield (g/100 g of dry plant material) = (W1 × 100) / W2, where W1-weight of the extract after solvent evaporation and W2-weight of dry plant material (leaves).

3.1.3 Preliminary screening of phytochemicals in hydromethanolic extract of Punica granatum L. leaves (HMPGL)

Qualitative phytochemical analysis of HMPGL, for the detection of alkaloids, carbohydrates, flavonoids, glycosides, phenols, saponins, steroids, tannins [19], fatty acids, coumarins and resins [20] was carried out as per the standard procedures.

3.1.4 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity

DPPH radical scavenging assay was used to determine the antioxidant activity of HMPGL. Sample aliquots of different concentrations were incubated with 1.8 mL of DPPH for 30 min at room temperature and the absorbance was measured at 540 nm [19]. The ability of the HMPGL to scavenge DPPH radical was calculated using the standard formula: DPPH free radical scavenging activity (%) = (Absorbance of control (DPPH)–Absorbance of DPPH radical + HMPGL)/(Absorbance of control (DPPH)) × 100.

3.1.5 Total phenolic content

The total phenolic content of HMPGL was estimated using the Folin-Ciocalteu reagent as previously reported by Goyal et al. 2010 [19].

3.1.6 Phytoconstituent profiling of hydromethonolic extract by GC-MS/MS analysis

The gas chromatogram-mass spectrometer (Agilent Technologies 7890B GC and Triple Quadrupole mass spectrometer 7000D series) was used for the analysis, having a blend silica column, bundled with 5% biphenyl 95% dimethylpolysiloxane (Elite-5MS), merged with a capillary column (30 m × 0.25 mm × 1 μm). Helium quench gas and nitrogen collision gas were deployed as a carrier gas at a regular flow rate of 2.25 mL/min and 1.5 mL/min, respectively, and a pressure of 8.745 psi to separate different components found in the test sample. Each chromatographic run was adjusted at 260 C injector temperature. 2 μL volume of the 100× diluted sample was injected in the instrument (a split ratio of 10 : 1). The oven temperature was in the range of 20 C to 260 C. The mass detector conditions were as follows: transfer line temperature 300 C; ion source temperature 260 C; ionization mode electron impact at 70 eV, a scan time 0.2 sec and scan interval of 0.1 sec. The sample was analyzed within a mass range of 50 m/z to 1000 m/z. The solvent delay was 0 to 2 min, and the total GC-MS/MS running time was 44 min. The mass spectrum of each peak in the total ion chromatogram was compared with the databases of The National Institute of Standards and Technology (NIST) 14 MS Library containing ‘276248’ number of compound’s spectral databases. The IUPAC names, molecular formulae, molecular weights, and structure of the metabolites of HMPGL were thereby ascertained.

3.2 Computational analysis

3.2.1 Preparation of ligands and proteins

The GC-MS/MS results were obtained to find the most probable lead molecules present in HMPGL. The metabolites thus obtained, if were available in PubChem, then their structures were downloaded from the PubChem compound database (https://www.ncbi.nlm.nih.gov/pccompound) [21] in 3D conformation and .sdf format. The structures of few metabolites, which were not available in PubChem, were obtained from NIST Library and sketched using ChemSketch. The standard protocols of BIOVIA Discovery Studio v3.5 (DS) software (Accelrys Software Inc., USA, 2012) was used for ligand preparation. This standard program was widely employed to prepare all the lead-like compounds to fix and resolve all different chemical properties such as different protonation states, ionization states, isomers, tautomers adding hydrogen, removing duplicates, and fixing bad valencies. This step is crucial because the receptor-ligand interactions have different protonation states; isomers and tautomers typically have different 3D geometries and binding characteristics.

This study is mainly centered on drug target proteins related to breast cancer; therefore, a list of principal biomarkers and prognostic biomarkers that were immunohistochemically tested in breast cancer tissue, are considered for the study [22]. A network of these proteins was constructed using a string database version 11.0 (https://string-db.org/) [23] and analyzed using Cytoscape, and the protein with a medium number of edges was used as a protein target. Basic sequence and structure analysis of the ER was conducted. The protein structures of the different isoforms of ER structure such as ERα LBD (PDB code 3ERD), ERβ LBD (PDB code 3OLS) and ERRγ LBD (PDB code 2GPU) were retrieved in .pdb formats from structural protein database (https://www.rcsb.org/) [24]. Initially, hetatoms and unbound water molecules were removed, following which, a CHARMM force field was applied. It removes alternative conformers and balances valencies of the amino acids. Subsequently, the protein predation protocol builds a loop and refines the side chain of proteins.

3.2.2 Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) and drug-likeness screening

The 145 compounds identified through GC-MS/MS analysis were then subjected to a dual-step virtual screening process, in which, first, the compounds were screened for their pharmacokinetic activities, such as ADMET, which were predicted using ADMET Descriptors protocol in DS v3.5.

ADME studies are widely employed in drug discovery to optimize the properties to convert leads into drug molecules or medication. The lead compounds are mostly identified through virtual high-throughput screening approaches or by virtual screening. Drug discovery statistics reveal that around 50% of the drugs fail in the course of clinical trials due to nonstandard effectiveness (efficacy), which has low bioavailability due to poor intestinal absorption and undesirable metabolic stability [25]. The failure of drug-like candidates in the later stages of drug development proves very expensive. Therefore, to scale back the value and clinical failures of recent drug-like molecules, the lead compounds were screened within the initial stages for ADMET. Secondly, the ADMET screened compounds were checked for Lipinski’s rule of five (RO5) violations to ensure that the compounds have drug-likeness properties [5]. Finally, the remaining compounds were taken for further process.

3.2.3 Protein-ligand docking

The docking study was performed by identifying the binding site of drug-target protein using the LibDock algorithm available in Discovery Studio 3.5. It uses protein binding site features to direct docking. It finds polar and apolar probes by placing a grid around the ligand 20 Å by 20 Å by 20 Å and extra space of 5 Å in each direction [26]. The site volume and binding site sphere will vary for all the three drug targets and were as follows; for ERα LBD 5.524X-0.284Y-5.750Z, ERβ LBD 25.17X-27.96Y-11.25Z and ERRγ LBD 62.44X-47.17Y-25.727Z. Bad hotspots were removed manually. Finally, pose optimization was done using Broyden-Fletcher-Goldfarb-Shanno (BFGS) and top-scoring ligand poses were ranked and retained.

3.2.4 Molecular dynamics and simulation (MDS)

The molecular dynamic simulation (MDS) was ideally carried out to inspect the stability and rationality of the binding patterns between the probable ligands and the specific target protein. The finest interaction hits obtained from receptor-ligand docking were used for MDS. In this experiment, top pose and elite compound interaction with individual drug targets were simulated using the GROMACS version 2016 package [3, 7]. 3ERD-2-propenoic acid, 3-(4-hydroxyphenyl)-, methyl ester, 3OLS-2-propenoic acid, 3-(4-hydroxyphenyl)-, methyl ester and 2GPU-2-propenoic acid, 3-(4-hydroxyphenyl)-, methyl ester complex topologies were prepared separately for the protein and the ligand. For the ligand molecular topology, the coordinate files were generated by the PRODRG 2.5 server, followed by solvating the receptor-ligand complex in the dodecahedral box with a minimal distance of 1.0 nm. The whole protein-ligand complex and aqueous system are maintained at neutral conditions using Na+ and Cl-ions. Subsequently, the steepest descent algorithm was employed to implement a minimum of 1000 to a maximum of 50,000 steps of minimization to retain the proper distance between the atoms accompanied by no structural changes in the compound. The temperature of 300 K and pressure of 1 bar were attained by making use of canonical ensemble (NVT) and isothermal-isobaric ensemble (NPT) equilibration simulations of 500 ps. The time step of all stages was set to 2 fs. In the end, the protein-ligand complexes were subjected to 30 ns MDS. For further investigation of the stability and flexibility of the complexes, root mean square deviation (RMSD), root mean square fluctuation (RMSF), the radius of gyration (Rg), and hydrogen bonds were analyzed through graphical representation.

4. Results

4.1 In-vitro analyses

4.1.1 Extract yield, preliminary phytochemical analysis and antioxidant activity

The % yield of the HMPGL was found to be 1.44%. Qualitative phytochemical analysis revealed the absence of resins and the presence of alkaloids, carbohydrates, coumarins, flavonoids, glycosides, saponins, steroids, tannins, fatty acids, phenols and terpenoids. The IC50 value for the antioxidant activity of the extract based on the DPPH free radical scavenging assay was identified to be 3.12 mg/L and was comparable with the standard quercetin (3.91 mg/L). Phenol content of the HMPGL found to be 86.17 ± 3.67 mg gallic acid equivalent 100 g-1 dry weight.

4.1.2 GC-MS/MS profiling

Fig. 1 indicates the GC-MS/MS chromatogram of the HMPGL. After comparing mass spectra of the components with the NIST Library, 145 phytocompounds were identified and listed in Table 1. Out of the 145 compounds screened, 102 and 42 compounds (Table 2, Ref. [27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67]) were identified to have more than 80% match with NIST library, respectively. Among the screened compounds, 19 were identified as novel metabolites. The most prevailing compounds among the 145 metabolites are 9,12,15-octadecatrienoic acid, (Z,Z,Z)- (linolenic acid), 1,2,3-benzenetriol(pyrogallol), n-hexadecanoic acid (palmitic acid), maltol, 5-hydroxymethylfurfural, 4H-Pyran-4-one, 2,3-dihydro-3,5-dihydroxy-6-methyl-, undecanol-5 and 3-furaldehyde. These phytocompounds are different fatty acids, terpenes, heterocyclic compounds, flavonoids, pyrrolidines, sesquiterpenoids and phenols. The mass spectrum corresponding to the structure of 2-propenoic acid, 3-(4 hydroxyphenyl)-, methyl ester (also known as 4-coumaric acid methyl ester), which exhibits the highest binding affinity with the breast cancer receptors considered in the current investigation, was represented in Fig. 2.

Figure 1: GC-MS/MS chromatogram of the hydromethanolic extract of Punica granatum L. leaves.

. Extracted ion chromatogram (EIC) peaks is the same component spectrum at a scale from 0.6 to 2.0

Figure 2: Mass spectrum of the component (2-propenoic acid, 3-(4-hydroxyphenyl)-, methyl ester) at retention time (RT) 15.2297 (represented in the insert) with a y-axis scale from 0 to 0.9 × 102 and its structure identified in hydromethanolic extract of Punica granatum L. leaves by GC-MS/MS. Extracted ion chromatogram (EIC) peaks is the same component spectrum at a scale from 0.6 to 2.0 ×106 (insert). Molecular formula: C10H10O3, Molecular weight: 178.18.
Table 1: List of 145 phytocompounds identified in the hydromethanolic extract of Punica granatum L. leaves by GC-MS/MS.
* is used for novel compounds identified (19 metabolites).
S. No Retention time Compound name Chemical formula Area MW (g/mol)
1 11.6496 (3-Nitrophenyl) methanol, n-propyl ether C10H13NO3 728600034 195.21
2 12.1305 (4H)4a,5,6,7,8,8a-Hexahydrobenzopyran-5-one-3-carboxamide,2-(2-hydroxypentyl)-8a-methoxy-4a-methyl C17H27NO5 28662212 325.40
3 24.6876 Alpha-Tocospiro A C29H50O4 1087514521 462.70
4 24.8403 Alpha-Tocospiro B C29H50O4 909893093 462.00
5 25.9167 Gamma-Tocopherol C28H48O2 77931713 416.70
6 6.9628 [1,2,3,4]Tetrazolo[1,5-b][1,2,4]triazine,5,6,7,8-tetrahydro- C3H6N6 1406037645 126.12
7 12.5427 1,1,4,5,6-Pentamethyl-2,3-dihydro-1H-indene C14H20 470619079 188.31
8 11.4893 1,2,3,6-Tetrahydropyridine, 1-methyl-5-phenyl- C12H15N 2272849467 173.25
9 10.9855 1,2,3-Benzenetriol C6H6O3 29449616456 126.11
10* 9.8863 1,8-Dioxaspiro[4.5]decan-2-one, 4-(2-aminothiazol-4-yl)-7,7-dimethyl- C13H18N2O3S 392180530 282.00
11* 3.3827 1H-Cyclopropa[3,4]benz[1,2-e]azulene- 4a,5,7b,9,9a(1aH)-pentol, 3-[(acetyloxy)methyl]-1b,4,5,7a,8a9-hexahydro-1,1,6,8-tetramethyl-,5,9,9a-triacetate, [1aR-(1a.alpha., 1b.beta.,4a.beta.,5.beta.,7a.alpha.,7b.alpha.,8.alpha.,9.beta.,9a.alpha) C28H38O10 50122079 534.59
12 8.3139 1H-Imidazole, 1-methyl- C4H6N2 58733639 82.10
13 12.9549 1H-Inden-1-one, 2,3-dihydro-3,3,4,6-tetramethyl- C13H16O 920997462 188.26
14 11.7031 1H-Inden-1-one, 2,3-dihydro-3,3,5,6-tetramethyl C13H16O 548026939 188.26
15 16.1457 2-(3-Isopropyl-4-methyl-pent-3-en-1-ynyl)-2-methyl-cyclobutanone C14H20O 383367099 204.31
16* 12.8862 2-(3-Isopropyl-4-methyl-pent-3-en-1-ynyl)-2-methyl-pent-3-en-1-ynyl-2-methylcyclobutanone C14H20O 264669072 204.00
17 12.8328 2(4H)-Benzofuranone, 5,6,7,7a-tetrahydro-4,4,7a-trimethyl-,(R)- C11H16O2 828417676 180.24
18 3.5354 2(5H)-Furanone C4H4O2 532754993 84.07
4.4285 287429887
19 3.3140 2,2’-Bioxirane C4H6O2 455025294 86.09
20 6.4666 2,4(1H,3H)-Pyrimidinedione, 5-hydroxy- C4H4N2O3 90374515 128.09
21* 2.7949 2,4,6,8,10-Tetradecapentaenoic acid, 9a- decahydro-4a,7b-dihydroxy-3-(hydroxymethyl)- cyclopropa[3,4]benz[1,2-e]azulen-9-yl ester, (1a.alpha.,1b.beta.,4a.beta.,7a.alpha.,7b.alpha, 8.alpha, 9.beta,9a.alpha.) C36H46O8 1729589562 606.00
22 5.1842 2,4-Dihydroxy-2,5-dimethyl-3(2H)-furan-3-one C6H8O4 1800447816 144.12
23 12.474 2,4-Di-tert-butylphenol C14H22O 162473760 206.32
24 13.1534 2,5-Dimethoxy-4-ethylamphetamine C13H21NO2 258752481 223.31
25 6.6040 2,5-Furandicarboxaldehyde C6H4O3 194264167 124.09
26* 3.0926 2-[4-Chloro-2-nitrophenyl]-1-(2-diethyaminoethyl)-3-formyl-1H-indole C21H22ClNO3 55662163 399
27 4.5201 2-Amino-4-methyl-oxazole C4H6N2O 518087636 98.10
28 14.1915 2-Cyclohexen-1-one, 4-(3-hydroxy-1-butenyl)-3,5,5-trimethyl- C13H20O2 560078241 208.30
29 15.6113 2-Dodecen-1-yl(-)succinic anhydride C16H26O3 133093417 266.38
30 4.9781 2-Furancarboxaldehyde, 5-methyl- C6H6O2 898805469 110.11
31 6.9017 2-Furancarboxylic acid C5H4O3 661021535 112.08
32 3.9018 2-Furanmethanol C5H6O2 2300435655 98.10
33 6.1460 2-Heptanol, 5-ethyl- C9H20O 358696294 144.25
34 4.7873 2-Hexene, 4-methyl-, (E)- C7H14 92798753 98.19
35 9.3062 2H-Pyran, 2-(bromomethyl)tetrahydro- C6H11BrO 39730800 179.05
36 5.4056 2H-Pyran-2,6(3H)-dione C5H4O3 511684838 112.08
37 6.3674 2-Methylthio-2,3-dimethylbutane C7H16S 191303503 132.27
38 15.0541 2-Propanone, 1-hydroxy-3-(4-hydroxy-3-methoxyphenyl)- C10H12O4 76991990 196.20
39 15.2297 2-Propenoic acid, 3-(4-hydroxyphenyl)-, methyl ester or 4- Coumaric acid methyl ester C10H10O3 79623370 178.18
40 11.7489 3,3-Dimethyl-4-phenyl-4-penten-2-one C13H16O 815120462 188.26
41 13.2984 3a,7-Methano-3aH-cyclopentacyclooctene, decahyydro-1,1,7-trimethyl-,[3aS-(3a.alpha.,7.alpha.,9a.beta.)]- C15H26 99198977 191.00
42 8.1918 3-Amino-N-(pyridin-4-yl)propanamide C8H11N3O 46243181 165.19
43* 4.8789 3-Aminopyrazine 1-oxide C4H5 N3O 69030002 111.00
44 14.5503 3-Chloropropionic acid, heptadecyl ester C20H39ClO2 971108451 347.00
45 3.7338 3-Furaldehyde C5H4O2 2958433873 96.08
6.6804 1614869390
46 3.6422 3-Furanmethanol C5H6O2 505585085 98.10
47 14.8327 3-Furoic acid, benzyldimethylsilyl ester C14H16O3Si 203867247 260.36
48 15.9319 3H-Cyclodeca[b]furan-2-one, 4,9-dihydroxy-6-methyl-3,10,dimethylene-3a,4,7,8,9,10,11,11a-octahydro- C15H20O4 257866769 264.32
49 16.1075 3-Hexadecyne C16H30 586051279 222.41
50 16.3670 3-Octadecyne C18H34 162514947 250.50
51 12.1840 4-(2,6,6-Trimethylcyclohexa-1,3-dienyl)but-3-en-2-one C13H18O 19815134 190.28
52 14.2984 4,4,5,8-Tetramethylchroman-2-ol C13H18O2 1110933501 206.28
53 7.7261 4H-Pyran-4-one, 2,3-dihydro-3,5-dihydroxy-6-methyl- C6H8O4 7618503933 144.12
54 8.2757 4H-Pyran-4-one, 3,5-dihydroxy-2-methyl- C6H6O4 434272719 142.11
55 7.4284 4H-Pyran-4-one, 5-hydroxy-2-methyl- C6H6O3 802533562 126.11
56 9.9627 4-Hydroxy-2-methylacetophenone C9H10O2 1075532343 150.17
57 17.8479 5-(2-Morpholino-1-thiophen-2-yl-vinyl)-1,2,4-thiadiazole C12H13N3OS2 39520603 279.40
58 15.7335 5,5,8a-Trimethyl-3,5,6,7,8,8a-hexahydro-2H-chromene C12H20O 458070336 180.29
59* 25.39 5H-Cyclopropa[3,4]benz[1,2-e]azulen-5-one, chloro-1,1a,1b,2,3,4,4a,7a,7b,8,9,9a- tetramethyl-, [1aR-(1a.aplha.,7b.alpha.,8.alpha.9.beta.,9a.alpha)] C28H37ClO11 53025185 489
60* 18.4891 5H-Cyclopropa[3,4]benz[1,2-e]azulen-5-one,9,9a-bis(acetyloxy)-3-[(acetoxy)methyl]-1,1a,1b,2,3,4,4a,7a,7b,8,9,9a-dodecahydro-2,3,4a,7b-tetrahydroxy-1,,6,8-tetramethyl-,[1ar-(1a.alpha.,1b.beya,2.alpha.,3.alpha.,4a.beta.,7a.alpha.,7b.alpha.,8.alpha.,9.beta.,9a.alpha) C26H36O11 62085503 446.00
61 9.0390 5-Hydroxymethylfurfural C6H6O3 12338767206 126.11
62 13.6572 5-Isopropenyl-2-methylcyclopent-1-enecarboxaldehyde C10H14O 344352899 150.22
63 15.5197 6-Hydroxy-4,4,7a-trimethyl-5,6,7,7a-tetrahydrobenzofuran-2(4H)-one C11H16O3 607605365 196.24
64 18.4128 7,8,9,10-Tetrahydro-6(5H)-phenanthridinone C13H13NO 83259442 199.25
65 25.8709 7,8-Epoxylanostan-11-ol, 3-acetoxy- C32H54O4 53150204 502.80
66 16.5502 7-Heptadecyne, 17-chloro- C17H31Cl 206699451 270.90
67 20.5654 8,11,14-Eicosatrienoic acid, methyl ester,(Z,Z,Z)- C21H36O2 114885329 320.50
68 19.1532 9,12,15-Octadecatrienoic acid, (Z,Z,Z)- C18H30O2 30390789406 278.40
69 16.4739 9,12,15-Octadecatrienoic acid, 2,3-dihydroxypropyl, ester (Z,Z,Z)- C21H36O4 80048369 352.50
23.5395 2227344617
70 22.5120 9,12,15-Octadecatrienoic acid, ethyl ester, (Z,Z,Z)- C20H34O2 364409437 306.50
71 18.6799 9,12,15-Octadecatrienoic acid, methyl ester,(Z,Z,Z) C19H32O2 5071324434 292
72 18.6112 9,12-Octadecadienoic acid (Z,Z)-, methyl ester C19H34O2 2018420993 294.50
73 18.0922 9-Hexadecenoic acid C16H30O2 510568704 254.41
74* 23.2907 Acetic acid, 17-acetoxy-3-hydroxyimino-4,4,13-hexadecahydrocyclopenta[a]phenanthren-10-ylmetyl ester C25H39ONO5 90416430 433.00
75 11.3290 Azetidine, 1,1’-methylenebis[2-methyl- C9H18N2 4498160447 154.25
76 11.9550 Benzaldehyde, 4-ethyl- C9H10O 327861429 134.17
77 8.6345 Benzofuran, 2,3-dihydro- C8H8O 2678521082 120.15
78 16.7869 Benzoic acid, 3,4,5-trihydroxy-, methyl ester C8H8O5 168553503 184.15
79 12.6649 Benzoic acid, 4-ethoxy-, ethyl ester C11H14O3 183455032 194.23
80 9.3749 Benzoic acid, 4-methyl- C8H8O2 28589154 136.15
81 5.9705 Benzyl alcohol C7H8O 821031061 108.14
82 7.9857 beta.-1,5-Dibenzoyl-2-deoxy-ribofuranose C19H18O6 372597103 342.3
83 13.8251 Bicyclo[2.2.1]hept-2-ene, 1,7,7-trimethyl- C10H16 275857190 136.23
84 4.1384 But-1-ene-3-yne, 1-ethoxy- C6H8O 568495293 96.13
85 13.4358 Butyrovanillone C11H14O3 22430637 194.22
86* 24.2296/25.5884 Carbonic acid, eicosyl vinyl ester C23H44O3 702086983/675042558 368.60
87 8.436 Catechol C6H6O2 104187122 110.11
88 23.3517 cis-5,8,11,14-Eicosatetraenoic acid, picolinyl ester C26H37NO2 75303340 395.60
89* 8.52 Cyclobutane, 1,2:3,4-di-O-ethylboranediyl- C8H14B2O4 117519035 195.82
90 5.5735 Cyclohexane, 1,3,5-trimethyl-2-octadecyl- C27H54 43383586 126.10
91* 16.1762 Cyclopropane, 1 heptaonyl-3-methylene-2-pentyl- C16H28O 301623110 378.7
92* 17.138 Cyclopropanepctanoic acid, 2-[(-pentylcyclopropyl)methyl]-,methyl ester, trans, trans- C12H13N3OS2 39520603 322.00
93* 5.7644 Cyclopropylamine, N-isobutylidene- C7H13N 133377747 111.18
94* 13.0465 Dihydroxanthin C17H24O5 492844334 280.00
95 14.0846 Doconexent C22H32O2 146857459 328.5
96* 21.2677 Doconexent, TBDMS derivative C28H46O2Si 31973570 442.70
97 11.9931 E-11,13-Tetradecadien-1-ol C14H26O 430872421 210.36
98 20.9319 Eicosanoic acid C20H40O2 795269485 312.50
99 4.3827 Ethanone, 1-(2-furanyl)- C6H6O2 165889275 110.11
100 20.3364 Ethyl 5,8,11,14,17-icosapentaenoate C22H34O2 252266022 330.50
101* 24.5731/24.7945 Furan, 2,5-bis(3,4-dimethoxyphenyl)tetrahydro-3,4-dimethyl-,[2R-(2.alpha.,3.beta.,4.beta.,5.alpha.)] C22H28O5 101227511/485260974 372.00
102 4.3064 Furan-2-ylmethyl palmitate C21H36O3 36491320 336.50
103 21.4662 Heneicosanoic acid, methyl ester C22H44O2 34798280 340.60
104 22.0158/22.7792 Heptacosane C27H56 291521130/296780030 380.70
105 22.199 Hexadecanoic acid, 1-(hydroxymethyl)-1,2-ethanediyl ester C35H68O5 317213707 568.90
106 17.9624 Hexadecanoic acid, 15-methyl-, methyl ester C18H36O2 82387516 284.50
107* 22.1151 Hexadecanoic acid, 2-hydroxy-1-(hydroxymethyl)ethyl ester C19H38O4 2065095285 330.00
108 16.9853 Hexadecanoic acid, methyl ester C17H34O2 3020358912 270.50
109 19.9395 Indane-4-carbonitrile, 2,2,5,7-tetramethyl-1-oxo C14H15NO 71510002 213.27
110 9.7108 Indole C8H7N 20622699 117.15
111 22.9395 Licarin A C20H22O4 224689115 326.40
112 7.2223 Maltol C6H6O3 14939408876 126.11
113 15.3671 Mannofuranoside, 1-allyl-2,3-5,6-tetra-O-acetyl- C17H24O9 25787993 372.40
114 13.7717 Megastigmatrienone C13H18O 240529958 190.00
115* 16.0617 Methanone, (2,6-dimethyl-4-morpholyl)(9H-xanthen-9-yl)- C20H21NO3 238398621 323.40
116 5.3293 Methanone, [4-(2-furfurylthio)-3-nitrophenyl](morpholino) C16H16N2O5S 76240602 348.40
117 23.7487 Methyl 18-methylicosanoate C22H44O2 148572467 340.60
118 20.6418 Methyl 18-methylnonadecanoate C21H42O2 237808761 326.60
119 18.8937/22.2525 Methyl stearate C19H38O2 831716164/166816879 298.50
120 5.459 N-Butyl-tert-butylamine C8H19N 514116590 129.24
121 17.451 n-Hexadecanoic acid C16H32O2 16562951600 256.42
122 16.6266 Nootkaton-11,12-epoxide C15H22O2 174277673 234.33
123 17.6418/21.0769 Octadecanal, 2-bromo- C18H35BrO 305383125/46483241 347.4
124 21.2143 Octadecane, 3-ethyl-5-(2-ethylbutyl)- C26H54 94740807 364.00
125 5.8789/21.909 Octadecanedioic acid C18H34O4 34874865/234355861 314.5
126 19.283 Octadecanoic acid C18H36O2 3813012247 284.5
127 23.6647 Octadecanoic acid, 2,3-dihydroxypropyl ester C21H42O4 433327098 358.6
128 20.4357 Oxiranedodecanoic acid, 3-octyl-, cis- C22H42O3 15785811 354.6
129 22.4357 Phthalic acid, octyl tridec-2-yn-1-yl ester C29H44O4 486477919 456.7
130 18.7792 Phytol C20H40O 776137812 296.50
131 20.7563 Pregnane-7,8,9,11,20-pentaol-18-oic acid,7,11-diacetate-18,20-lactone C25H34O9 49740067 478.50
132 3.2224 Propane, 2-fluoro- C3H7F 193906460 62.09
133 6.5506 Pyridazine C4H4N2 251310771 80.09
134 24.4739 Squalene C30H50 923530021 410.70
135 24.9167 Sulfurous acid, hexyl pentadecyl ester C21H44O3S 1241617690 376.60
136 13.9091/13.3595 syn-Tricyclo[5.1.0.0(2,4)]oct-5-ene, 3,3,5,6,8,8-hexamethyl- C14H22 1025801501/321999626 190.32
137 15.2831/18.2906 Tetradecanoic acid C14H28O2 324806663/394717404 228.37
138 20.0234 Tetradecanoic acid, 2-hydroxy- C14H28O3 269881507 244.37
139 6.833 Thymine C5H6N2O2 1117152651 126.11
140 14.6419 Tibolone C21H28O2 783232577 312.40
141 3.0621 Trifluoromethyltrimethylsilane C4H9F3Si 112646880 142.19
142 16.3136 Undecanoic acid, 2-nonyl-, methyl ester C21H42O2 109936717 326.60
143 10.4588 Undecanol-5 C11H24O 5922534581 172.31
144 20.2067/21.6494 Ursodeoxycholic acid C24H40O4 34678472/97533782 396.60
145 20.0845 Z-(13,14-Epoxy)tetradec-11-en-1-ol acetate C16H28O3 320849197 268.39
Table 2: Bioactivity of 42 phytocompounds identified from the hydromethanolic extract of Punica granatum L. leaves by GC-MS/MS with more than 80% match score from the NIST Library.
S. No Compound Bioactivity and references
1 2-Furanmethanol or Furfuryl alcohol Flavoring agent [27] and antioxidant [28]
2 2-Furancarboxaldehyde, 5-methyl- Food additive and used for fragrance [29]
3 2,4-Dihydroxy-2,5-dimethyl-3(2H)-furan-3-one Flavoring agent [30]
4 2H-Pyran-2,6(3H)-dione Antiallergic [31], analgesic, mild sedative, soporific, fungicide, fungistatic, antihypoxic, spasmolytic, and muscle relaxant activities [32]
5 Benzyl alcohol Antimicrobial activity [33]
6 2-Furancarboxylic acid NR*
7 Maltol Antitumor [34] and antinephrotoxicity activities [35]; alleviates hepatic fibrosis [36]
8 4H-Pyran-4-one, 5-hydroxy-2-methyl- NR*
9 4H-Pyran-4-one, 2,3-dihydro-3,5-dihydroxy-6-methyl- Alleviates male reproductive toxicity [37]; stimulates the autonomic nerve activity [38]; antiproliferative and pro-apoptotic [39]; antibiotic activities [40]
10 4H-Pyran-4-one, 3,5-dihydroxy-2-methyl- or 5-Hydroxymaltol Nutrient [41]
11 Benzofuran, 2,3-dihydro- Antiarrhythmic, spasmolytic, and antiviral activities [42]
12 5-Hydroxymethylfurfural Antiinflammatory, antioxidant and antiproliferative activities [43]
13 1,2,3-Benzenetriol or Pyrogallol Antitumor, cytotoxic and antiproliferative activities [44]
14 Syn-tricyclo(5.1.0.0(2,4))oct-5-ene, 3,3,5,6,8,8-hexamethyl- NR*
15 Megastigmatrienone Flavoring agent [45]
16 2-(3-Isopropyl-4-methyl-pent-3-en-1-ynyl)-2-methyl-cyclobutanone NR*
17 n-Hexadecanoic acid or Palmitic acid Antifungal [46] and antioxidant activities [47]
18 9,12-Octadecadienoic acid (Z,Z)- or Linoleic acid Antimicrobial activity [47]
19 9,12,15-Octadecatrienoic acid, (Z,Z,Z)- or Alpha-linolenic acid Regulates butyrylcholinesterase [48], antioxidant and antimicrobial [47]; antiinflammatory activities, antiacne, antiandrogenic, antiarthritic, antibacterial and anticandidal, anticancer, anticoronary, antieczemic, antihistaminic, hepatoprotective, hypocholesterolemic, insectifuge, nematicide, 5-alpha reductase inhibitor and cancer preventive activities [40]
20 Octadecanoic acid Antimicrobial [40]
21 3-Furaldehyde Inhibits polyphenol oxidase 2, phenolase, cresolase and tyrosinase [49]
22 1H-Inden-1-one, 2,3-dihydro-3,3,5,6-tetramethyl- Antifungal activity [50]
23 1,1,4,5,6-Pentamethyl-2,3-dihydro-1H-indene NR*
24 Benzoic acid, 4-ethoxy-, ethyl ester Antimicrobial activity [40]
25 4,4,5,8-Tetramethylchroman-2-ol Possible treatment against oligospermy and oliguria [45]
26 3-Hexadecyne Antiandrogenic agent [51]
27 Hexadecanoic acid, methyl ester or Methyl palmitate Prevents Kupffer cell activation [52]; antiinflammatory [53, 54] and antifibrotic activities [53]
28 9-Hexadecenoic acid Regulates lipogenesis, desaturation, and β-oxidation in bovine adipocytes [55]
29 Phytol Antioxidant [47], anticancer, antiinflammatory, antidiuretic, antimicrobial activities [40]; Fragrance [56] Antinociceptive activities [57]
30 Methyl stearate Nutrient, membrane stabilizer and energy source [58]
31 Eicosanoic acid/Arachidic acid Nutrient, membrane stabilizer and energy source [59]
32 Phthalic acid, octyl tridec-2-yn-1-yl ester Antiplatelet activity [60]
33 Heptacosane NR*
34 Squalene Antitumor and anticancer effects [breast, colon, lung and ovarian cancer], hypocholesterolemic activity, reduces skin damage caused by UV radiation, cardioprotective effect and , detoxifying agent [61]
35 Alpha-Tocospiro A NR*
36 Alpha-Tocospiro B Cytotoxic [62] and α-Glucosidase inhibiting activities [63]
37 Sulfurous acid, hexyl pentadecyl ester NR*
38 Carbonic acid, eicosyl vinyl ester NR*
39 4-Hydroxy-2-methylacetophenone Acaricidal activity [64]
40 2,5-Furandicarboxaldehyde Insulin receptor partial antagonist [65]
41 4H-Pyran-4-one, 5-hydroxy-2-(hydroxymethyl)-methyl- or Kojic acid Skin-lightening agent [inhibits tyrosinase] antioxidant, antidiabetic, anticancer, antiinflammatory, antimicrobial, antiproliferative, antiparasitic, antiviral, antitumor, antispeck, pesticidal,insecticidal, radio protective properties [66]
42 Hexadecanoic acid, 2-hydroxy-1- (hydroxymethyl)ethyl ester Food additive [67]
Or 2-Palmitoylglycerol
NR*, no activity reported.

4.2 Computational analysis

4.2.1 Sequence and structure assessment of estrogen receptors and their related ligand-binding domains

The protein network of text mined and experimental interactions; and interaction score with 0.9 confidence, consisted of 81 number of nodes and 234 edges with a P-value of <1.0e-16, 0.072, network density and 0.919 network heterogenicity. The network revealed Tumor protein 53 (TP53), RAC-alpha serine/threonine-protein kinase (AKT1), cyclin D1(CCND1), epidermal growth factor receptor (EGFR), phosphatase and tensin homolog deleted on chromosome 10 (PTEN), human epidermal growth factor receptor 2 (ErbB2/HER2), DNA mismatch repair protein (MSH2) and MYC as the hub proteins and ER as a suitable drug target with a medium number of edges, a middle degree node, 0.533 closeness centrality and 0.13 clustering coefficient (Fig. 3).

With the advent of new technology and computational tools, there is a massive increase in the deposition of protein structures in PDB. This consequently creates an increased level of difficulty in selecting an optimal PDB entry for docking. X-ray crystallographic structures of ERα LBD (PDB ID:3ERD), ERβ LBD (PDB ID:3OLS) and ERRγ LBD (PDB ID: 2GPU) were selected after a thorough screening of 329 entries of ER’ structures with a minimum resolution, no missing atoms and completeness of the binding domain. The ERα LBD, ERβ LBD and ERRγ LBD have orthogonal bundle architecture. They mainly consist of α-helix, belong to the family of nuclear receptor and exhibited retinoid-X-receptor topology. Largely, there are numerous conserved regions observed in-between the three sequences. However, ERα LBD and ERβ LBD showed 83.5% sequence and structure similarities, whereas ERRγ-ERα and ERRγ-ERβ showed a similarity of 62.2% and 61.5%, respectively. Overall, multiple sequence alignment showed 55.7% similarity between the three LBDs of ERα, ERβ and ERRγ. The raw protein structures were refined using a protein preparation tool prior to molecular docking. A loop of ERβ and ERα was built based on SEQRES data, the atoms were arranged, and its side chains were refined using PDB data. Notably, the structural analysis also revealed that the binding pocket of ERα is larger and broader than that of ERβ (Fig. 4).

Figure 3: Protein interaction network of principal and prognostic biomarkers of breast cancer.

Figure 4: Active site representation of isomers of human estrogen receptor.

4.2.2 In-silico pharmacokinetics properties and Lipinski’s rule (RO5) validation

Efficacy and toxicity are the pivotal determinants of successful drug development. Therefore, all the GC-MS/MS characterized metabolites were subjected to the virtual screening process thoroughly. Initial screening with Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) drastically narrowed down the count of molecules (Figs. 5,6) followed by RO5 violation AlogP 5, with molecular weight 500, hydrogen bond donor 5, and hydrogen bond acceptor 10 were evaluated to justify their drug-likeness behaviour. Fig. 5 represents the pharmacokinetic profile of all the metabolites identified in HMPGL. Out of 145 compounds analyzed, 77 revealed good and optimal solubility with a score of 3 and 4. Total, 103 molecules demonstrated high, medium and low blood-brain barrier (BBB) penetration. The human intestinal absorption of 109 compounds was 0 and 1, indicating good and moderate absorption of these hits (Fig. 5). 125 compounds were non-inhibitors of CYPD26, 96 were nontoxic to liver cells and 50 exhibited low binding affinity to plasma proteins. The 16 and 47 compounds showed no carcinogenicity in female and male rats, respectively. 122 compounds were non-mutagens in TOPKAT screening (Fig. 6). Therefore, systematic analysis of all these pharmacokinetic properties revealed only nine compounds that are nontoxic and safe. Among them, 9,12,15-octadecatrienoic acid, 2,3-dihydroxypropyl, ester (Z,Z,Z)- and hexadecanoic acid, 2-hydroxy-1-(hydroxymethyl)ethyl ester were not drug-like molecules as their AlogP were ‘greater than five’ (violation of Lipinski’s RO5). However, 19 novel metabolites were not docked against the ER due to their limitation of exhibited carcinogenicity based on in-silico prediction.

. (a,b) represent molecules before and after ADMET screening. (c) The ellipses define regions where well-absorbed Human Intestinal Absorption (HIA) compounds are expected to be found after oral administration. (d) The graph represents compounds with low and medium blood-brain barrier (BBB) penetration.

Figure 5: The graph of ADMET 2D polar surface area (PSA_2D) vs. ALogP of 145 phytochemicals of Punica granatum L. representing the confidence limit ellipses of 95% and 99% corresponding to the intestinal absorption models and blood-brain barrier (BBB). (a,b) represent molecules before and after ADMET screening. (c) The ellipses define regions where well-absorbed Human Intestinal Absorption (HIA) compounds are expected to be found after oral administration. (d) The graph represents compounds with low and medium blood-brain barrier (BBB) penetration.

Figure 6: Number of phytocompounds of hydromethanolic extract of Punica granatum L. leaves predicted to be CYPD26 inhibitors, non hepatotoxic, no plasma protein binding (PPB) affinity, non-carcinogens in Female (F) and Male (M) non-mutagens and obey RO5.

Detailed tabulations of the screened compounds, which completely satisfied the ADMET properties and drug-likeness, are given in Table 3. It is evident from the table that the standard drug, tamoxifen, an anti-estrogen that is widely used in the clinic to treat ER-positive breast tumors) violated both RO5 and exhibits hepatotoxicity and high affinity to plasma binding proteins. 2-Propenoic acid, 3-(4-hydroxyphenyl)-, methyl ester, a natural compound, followed RO5 and did not exhibit any toxicity in ADMET prediction suggesting its efficacy and nontoxic nature. Some of the natural compounds of HMPGL also showed hepatotoxicity and affinity to plasma proteins.

Table 3: Predicted drug-likeness and ADMET properties of phytoconstituents from hydromethanolic extract of Punica granatum L. leaves.
Compounds ID RO5 Solubility level BBB level Absorption CYPD26 Hepatotoxic PPB NTP Ames mutagen
Female & male rat
7428 Y 4 3 0 F F F NC NM
14334 Y 3 3 0 F F F NC NM
85447 Y 4 1 0 F F F NC NM
92203 Y 4 2 0 F F F NC NM
586459 Y 4 3 0 F F F NC NM
587806 Y 3 3 0 F F F NC NM
6432173 Y 3 1 0 F F F NC NM
2733526 N 1 0 1 T T T NC NM
Tamoxifen
(Standard drug)
RO5 (Lipinski’s rule of five): Y, Yes (Follow RO5); N, No (Don’t follow RO5). Solubility level: (1) No; (2) Very low but possible; (3) Good; (4) Optimal. BBB (Blood Brain barrier): (0) Very high penetrant; (1) High Medium; (2) Medium; (3) Low. Human Intestinal Absorption: (0) Good; (1) Moderate. CYP2D6: F, False (Non-binding); T, True (Binding). Hepatoxicity: T, True (Toxic); F, False (Nontoxic). PPB (Plasma protein binding): F, False (Non-binding); NC, Non-carcinogen; NM, Non-mutagen.

4.2.3 Binding and interaction analysis

The screened compounds and the standard drug tamoxifen were docked with three different ER structures. The docked poses were examined based on the LibDock score (Kcal/mol) and various types of interactions in hydrogen/hydrophobic interaction analyses. The structural conformations of 4-coumaric acid methyl ester (92203) were the most favorable for the binding cavity of all the three receptors, especially hydroxyl group (-OH) exhibits a major structure-activity relationship, where the removal of (-OH) in the para position of the structure is directly proportional to dock score values. Thus, this position is considered crucial for the binding of this phytochemical to these receptors. Other compounds like benzoic acid, 3,4,5-trihydroxy-, methyl ester (7428), 2-propanone,1-hydroxy-3-(4-hydroxy-3-methoxyphenyl) (586459), (4H)4a,5,6,7,8,8a-hexahydrobenzopyran-5-one-3-carboxamide,2-(2-hydroxypentyl)-8a-methoxy-4a-methyl (587806) and tamoxifen (2733526) also showed favorable binding, but the standard drug tamoxifen violated the RO5 and demonstrated toxicity. The structure of 4-coumaric acid methyl ester showed high complementarity to αLBD with a docking score of 75.16 kcal/mol, whereas γLBD is favorable for tamoxifen. Nevertheless, βLBD was structurally compatible with both 4-coumaric acid methyl ester and tamoxifen, as represented in Fig. 7.

Figure 7: The binding energy of 4-coumaric acid methyl ester present in hydromethanolic extract of Punica granatum L. leaves and tamoxifen with three isoforms of estrogen receptor (3ERD, 3OLS and 2GPU corresponds to the PDB ID of the LBDs of ERα, ERβ and ERRγ, respectively).

The binding pattern and chemical interactions of nontoxic natural 4-coumaric acid methyl ester present in HMPGL with good docking score, with the three isoforms of ER, were demonstrated in Fig. 8. The α (W393, E353, R394, L449 and E323) and β (F356, L343, L339 and A302) LBD residues are considered as crucial active site amino acids for ligand binding and responsible for antagonist activity. R394 of α binding domain makes crucial Pi interactions with the ligand. In contrast, R316 and L309 amino acids play a vital role in ERRγ LBD. In αLBD, 4-coumaric acid methyl ester interacts with W393, R353, R394, L449 and E323 to form strong hydrogen interaction and Pi-cation, respectively, whereas, with β and related γ, it forms hydrogen bonding with F356 and R316, and L309, respectively. In all these interactions, -OH forms hydrogen bond interaction; so, it can be considered as an active pharmacophore for 4-coumaric acid methyl ester.

4.2.4 Time-dependent parameter analysis

A time-dependent MD simulation at 50 ns was conducted using GROMACS 2016 to investigate the flexibility and overall stability of docked complexes. MD simulation were carried for the best candidate molecule, 4-coumaric acid methyl ester, with three drug target receptors and time-dependent parameters, were analyzed as described by Dhivya et al., 2018 [3]. The root mean square deviation (RMSD), the radius of gyration (Rg) and hydrogen bond (H-bond) interaction graphs were generated by using Xmgrace software in a Linux environment (Fig. 9).

. 4-Coumaric acid methyl ester docked with 3ERD (the ligand-binding domain of ER

Figure 8: Both 3D and 2D docked representation 4-Coumaric acid methyl ester with three different estrogen receptors’ (drug targets) active sites. 4-Coumaric acid methyl ester docked with 3ERD (the ligand-binding domain of ERα; A&B), 3OLS (the ligand-binding domain of ERβ; C&D), and 2GPU (the ligand-binding domain of ERRγ; E&F).

. (a) Root mean square deviation (RMSD) plot. (b) Radius of gyration (Rg) plot. (c) Hydrogen bond (H-bond) plot.

Figure 9: Time-dependent MD and simulation of 4-coumaric acid methyl ester and tamoxifen with three different ERs’ (drug targets) active sites (LBD of ERα, ERβ and ERRγ). (a) Root mean square deviation (RMSD) plot. (b) Radius of gyration (Rg) plot. (c) Hydrogen bond (H-bond) plot.

As is evident in Fig. 9a.1, the increasing trend of RMSD was observed for all the six complexes, 3ERD/ERα LBD–4-coumaricacid methyl ester, 3OLS/ERβ LBD–4-coumaricacid methyl ester, and 2GPU/ERRγ LBD–4-coumaricacid methyl ester, having diverse RMSD values 0–0.30. Initially, the ERα LBD complex showed little jiggling, whereas ERβ LBD showed high fluctuations, but ERRγ LBD exhibited a gradual increase in RMSD until 10 ns. The fluctuations of the ERα LBD complex were between 0.10 to 0.30 nm, but after 30 ns, this complex attained stability with slight deviations in RMSD value. However, the fluctuations of the ERβ LBD complex elevated up to 0.25 nm at 5 ns, dropped to 0.2 nm at 12.5 and 20 ns, with a sudden drop of RMSD to 0.2 nm at around 12.5 and 20 ns, fluctuated till 35 ns and steadily increased at the end of dynamics. The RMSD of the ERRγ LBD complex gradually increased until 35 ns and attained plateau, which was in the range of 0–0.2. The Rg analysis usually measures the compactness of a protein or system. The predicted results in Fig. 9b.1 demonstrated that all the three complexes ERα LBD, ERβ LBD and ERRγ LBD- were static and constant with Rg values 1.8 to 1.85 nm, 1.775 to 1.825 and 1.750 to 1.850 nm, respectively, throughout the simulation time 0–50 ns, the complexes slightly fluctuated to attain its stability. Comparative MD simulations revealed that the residual backbone and appropriate conformation of the ERα LBD complex was stable compared to other complexes. The H-bond plot of the simulation was presented in Fig. 9c.1. It is practically impossible to inspect H-bond stability by examining the crystal, nuclear magnetic resonance (NMR), and electron microscopy-solved structures. Consequently, it is significant to probe the stability of the H-bonds utilizing MD simulations. The presence of H-bond interactions in the docked complexes was identified by the gmx hbond tool in the accepted geometry (distance <3.5 Å and 180 ± 30, between the donor and acceptor). H-bonds between the ligand and the receptor jiggled all through the simulation. The maximum number of H-bonds bound confirmation for ERα LBD, ERβ LBD and ERRγ LBD complexes are 3, 3 and 4, respectively. It is evident from the 3ERD/ERα LBD–Tamoxifen, 3OLS/ERβ LBD–Tamoxifen, and 2GPU/ERRγ LBD–Tamoxifen, having diverse RMSD values 0–0.30. (Fig. 9a.2). All the three complexes exhibited higher stability compared to complexes represented in Fig. 9a.1. From Fig. 9b.2 it can be noted that 3OLS/ERβ LBD–Tamoxifen was more stable all through the run. Only 3OLS/ERβ LBD–Tamoxifen showed H interactions.

5. Discussion

Breast cancer is regarded as one of the major burdens observed in women. Consequently, there is an upsurge in efforts for the discovery of new drugs to combat this disease. Plants are widely regarded as a reservoir of various types of bioactive metabolites with various therapeutic and pharmacological potentials [68]. Due to their diverse nature, several plant-based molecules are used as drugs and are in the process of discovery routes [69]. Most of the research studies reported to date involved the characterization of peel, seed and bark by GC-MS and very few studies on leaves of Punica granatum L. using the NMR technique. However, no studies have been reported on leaves by GCMS analysis. Notably, in the present study, we attempted to characterize the possible phytochemicals exclusively by GCMS in Punica granatum L. leaves [70, 71]. The present study identified 145 phytoconstituents from HMPGL by GC-MS profiling. Similar to our report, 5-hydroxymethylfurfural was reported in high concentrations in ethyl acetate extract of the fruit peel of Punica granatum by Barathikannan et al. (2016) [71]. These phytochemicals are known to contribute to the diverse medicinal properties of the plant, as previously reported [69]. A majority of these phytocompounds were reported to have antioxidant, antiinflammatory, anticancer, and antimicrobial activities, and used as food additives (Table 1). Due to the presence of alkaloids, coumarins, flavonoids, glycosides, phenols, saponins, tannins and terpenoids, HMPGL has better DPPH radical scavenging activity. Bekir et al. (2013) [72] had previously reported the antioxidant potential of Punica granatum L. leaves extracted with different solvents based on their polarity.

Using the in-silico approach, the pharmacokinetics and toxicity studies of compounds are investigated before evaluating their biological activity [73, 74]. Poor pharmacokinetic profile and toxicity are the main reasons for last stage failures in drug discovery. Therefore, in the present study, all the identified 145 phytochemicals from the HMPGL were initially screened for ADMET and Lipinski’s RO5, and only eight molecules were identified to be drug-like nontoxic molecules. Among the 138 phytoconstituents, a majority of them were identified to be fatty acids and terpenes. Due to the presence of the long hydrophobic -acyl chains and terpenes, the fatty acids exhibited low solubility and high affinity to plasma binding proteins indicating low efficacy. They will be highly toxic as they can penetrate BBB, bind to CYPD26 and exhibit hepatotoxicity.

Docking studies were carried out with 51 metabolites (eight drug-like nontoxic molecules + 42 mentioned in Table 1). Those 42 metabolites were also used for docking studies as most of them exhibited hepatotoxicity, and it is one very common toxic property noted in many FDA-approved drugs like tamoxifen. One of the most important steps in developing a new lead molecule is target selection. Systematic analysis of protein interaction networks (disease networks) and identification of hub protein enhances the understanding of the molecular basis of the disease. They also help in determining the key node as a potential target protein in the drug discovery process. Several hub proteins have been identified to be not suitable as drug targets as their inhibition may affect crucial activities of the cell; However, ER, a middle degree node and with high cluster coefficient, is an optimal drug target for breast cancer treatment [75, 76]. Our binding pocket analysis revealed that ERα is larger and broader than ERβ, which has been previously speculated to be due to the possible sequence diversity of ER Paterni et al. (2014) [77]. Targeting more than one receptor helps in an in-depth understanding of the efficacy and toxicity of the drugs, including complex interactions [78]. So, in this study, we exclusively focused on three significant receptors involved in breast cancer mechanisms (ERα, ERβ and ERRγ). In our docking results, there was notable variation seen in the binding energies with the three receptors for all the docked molecules and these differences can be attributed to selective binding of the phytoconstituents to the diverse LBDs of these receptors. In all the interactions, the amino acids Arg, Leu and Glu of the LBD were making crucial interactions with the ligand (4-coumaric acid methyl ester). Interestingly Arg394 alpha binding domain has been reported to make crucial pi interactions with the ligand [79]. The deletion of the -OH of the 4-coumaric acid methyl ester reduces the binding affinity. Hence, it can be considered a pharmacophoric feature due to its hydrogen bonding with the receptors.

MD simulations of the docked complexes from docking studies help refine docking and enhance the accuracy of the binding affinity predictions [3, 68]. Post docking MD simulations at 50ns reflected the time-dependent behaviour of the docked complexes. The ligand in the ERRγ LBD complex was having the least RMSD values and attained stability at 30 ns and remained stable till the end compared to ERβ LBD and ERα LBD complexes. The Rg values are almost in the same range (1.75 to 1.85) for all the complexes, whereas the ERRγ LBD complex with minimal vibrations, fluctuations, maximum H-bonds depicts its greater stability compactness and binding affinity with 4-coumaric acid methylester. This compound is a natural phenylpropenoic acid compound, belonging to a class of phenolic acids and is a phenolic antioxidant. The antioxidant potential of this compound might contribute to the anticancer effect against breast cancer plausibly due to its high binding affinity to ERα, ERβ and ERRγ LBDs.

6. Conclusions

To conclude, the current research work was an attempt to draw insight into the structural, functional, and dynamical aspects of phytocompounds of Punica granatum L. In the process, the GC-MS/MS metabolite profiling revealed the presence of 145 compounds. These can be deployed to discover novel drugs against various cancers, as Punica granatum L. is reported to have anticancer properties. Estrogen receptor plays a crucial role in cellular proliferation and differentiation of breast cancer cells, which was identified as a novel target for breast cancer by network pharmacology. 96% of the phytoconstituents exhibited toxicity in the virtual ADMET screen, and 35% did not exhibit drug-likeness. In-silico, molecular docking was performed against three isoforms of ER and compared with the standard drug tamoxifen. 4-Coumaric acid methyl ester, a nontoxic natural, demonstrated the highest affinity with core residues of 3ERD (ERα LBD), with 3OLS (ERβ LBD), with 2GPU (ERRγ LBD). Thus, the docking poses of the identified hit, 4-coumaric acid methyl ester, were further evaluated by dynamic simulation at 50 ns. Among the various ER receptors, the 2GPU complex, which is ERRγ LBD, is “the best” with minimum RMSD, constant Rg and maximum number of H-bonds indicating a stable and compact system. The presence of 4-coumaric acid methyl ester and various other bioactive metabolites reported justifies the use of Punica granatum L. for treating breast cancer by traditional practitioners. Taken together, our results represent a promising starting point for the preclinical evaluation of 4-coumaric acid methyl ester as a possible treatment for breast cancer.

7. Author contributions

SKM and KRS conceived the idea. TU performed in-vitro and computational studies and drafted the manuscript. DS assisted in in-silico experimentations. AKG helped in the experimental procedure and partially drafted the manuscript. HSY helped in the computational facility and wrote a part of the manuscript. DB helped in GC-MS/MS analysis and thoroughly revised the manuscript. SKM arranged the funds and supervised the whole study, edited, and upgraded the final version of the manuscript. All the authors analyzed, discussed the results, and approved the version to be submitted.

8. Ethics approval and consent to participate

Not applicable.

9. Acknowledgment

The authors acknowledge Neoscience Labs Private Limited, Chennai, India for allowing to utilize their GC-MS facility and their technical support. DBT-BIF computational facility and BiSEP facility at MLACW was used to carry out the research work. Dhivya also expresses her sincere thanks for the fellowship provided by the DBT-BIF facility at MLACW by Govt of India. The authors are grateful to Dr. V.R. Devraj, Professor, Bangalore Central University and Dr. C.S. Karigar, Professor, Bangalore University, India, for their valuable suggestions. This publication was supported by the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University, Al-kharj, Saudi Arabia.

10. Funding

This research was partially supported by a minor research grant (MLACW-MRP-057) from MLACW, Bengaluru, India.

11. Conflict of interest

The authors declare no conflict of interest.

12. Sample availability

The pomegranate extract samples are available with the authors.

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Talambedu Usha, Sushil Kumar Middha, Dhivya Shanmugarajan, Dinesh Babu, Arvind Kumar Goyal, Hasan Soliman Yusufoglu, Kora Rudraiah Sidhalinghamurthy. Gas chromatography-mass spectrometry metabolic profiling, molecular simulation and dynamics of diverse phytochemicals of Punica granatum L. leaves against estrogen receptor. Frontiers in Bioscience-Landmark. 2021. 26(9); 423-441.