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Article

Glycine-Conjugated α-Mangostins as Potential Estrogen Receptor Alpha (ERα) Antagonists through Pharmacophore Modeling, Docking Analysis, and Molecular Dynamics Simulations

by
Hanggara Arifian
1,2,
Rani Maharani
3,4,
Sandra Megantara
1,4,
Nur Kusaira Khairul Ikram
5 and
Muchtaridi Muchtaridi
1,4,*
1
Department of Pharmaceutical Analysis and Medicinal Chemistry, Faculty of Pharmacy, Universitas Padjadjaran, Jln. Raya Bandung Sumedang KM. 21, Jatinangor 45363, West Java, Indonesia
2
Department of Pharmacochemistry, Faculty of Pharmacy, Universitas Mulawarman, Jl. Muara Muntai, Gn. Kelua, Samarinda 75242, East Kalimantan, Indonesia
3
Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Padjadjaran, Jln. Raya Bandung Sumedang KM. 21, Jatinangor 45363, West Java, Indonesia
4
Research Collaboration Centre for Radiopharmaceuticals Theranostic, BRIN, Jln. Raya Bandung Sumedang KM. 21, Jatinangor 45363, West Java, Indonesia
5
Institute of Biological Sciences, Faculty of Science, Universiti Malaya, Kuala Lumpur 50603, Malaysia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(13), 5549; https://doi.org/10.3390/app14135549
Submission received: 22 May 2024 / Revised: 19 June 2024 / Accepted: 21 June 2024 / Published: 26 June 2024
(This article belongs to the Special Issue Research on Organic and Medicinal Chemistry)
Browse Figures
Versions Notes

Abstract

:
Natural compounds have demonstrated good biological activity when combined with certain amino acids. For example, a glycine-conjugated glycyrrhetinic acid exhibits heightened efficiency against MCF7 cancer cells. Consequently, a molecular modeling analysis is conducted to construct glycine-conjugated α-mangostins and investigate their potential. According to pharmacophore modeling using the ligand-based drug design technique, only two glycine-conjugated α-mangostins conform to the pharmacophore features. The docking simulation results show that the Am1Gly conjugate can interact with the estrogen receptor-α (ERα) with a binding energy of −10.91 kcal/mol. This interaction is further supported by molecular dynamics simulations performed over a 200 ns timeframe. Based on molecular dynamics modeling using the MMPBSA method, the binding affinity of Am1Gly (ΔGTotal = −48.79 kcal/mol) is determined. The results of this analysis indicate that Am1Gly might function as an antagonist to estrogen receptors.

1. Introduction

Natural compounds can have their structures altered via conjugation with amino acids. Since amino acids are the fundamental monomers of living systems, they are selected for these modifications [1]. Furthermore, structural modification with a wider shift is possible because amino acids have different side chains [2].
Several conjugations of natural compounds with certain amino acids, especially glycine, show the expected biological response. Poly-R-(L-glutamic acid)-glycine-camptothecin demonstrated good anticancer activity, reducing the development of B-16 cancer cells following 48 h of therapy at a lower dose [3]. Glycine-conjugated podophyllotoxin exhibited excellent anticancer efficacy against the MCF7 cell line, with an IC50 of 7.2 nM [4]. Diglycinoyl curcumin demonstrated potent antitumor efficacy against the HeLa cell line [5]. It has been reported that the synthesis of glycyrrhetinic acid-amino acid derivatives has been performed to boost their cytotoxic activity. Through esterification with benzyl ester, a modification was carried out at the C-30 position. It was discovered that glycine-conjugated glycyrrhetinic acid was more effective than glycyrrhetinic acid in inhibiting the MCF7 cancer cell line, with an IC50 of 3.8 µM compared to >40 µM for pure glycyrrhetinic acid [6].
Research on α-mangostin, a compound found in Garcinia mangostana, has been conducted in the area of potential natural product intervention [7]. Previous research demonstrated that a mangosteen rind extract exhibits cytotoxic action against MCF-7 breast cancer cells with an IC50 value of 45 µg/mL, in addition to inducing apoptosis and changing the cells’ morphology [8]. α-Mangostin, which constitutes about 8–15% of the active ingredient in mangosteen, is primarily found in the pericarps of the plant [9]. The pharmacological activity of this chemical includes antibacterial [10], antioxidant, anti-inflammatory [11], and anticancer properties. Because of its natural [12] origin, α-mangostin has a lower adverse effect profile than tamoxifen [13]. Tamoxifen is a selective estrogen receptor modulator commonly used to treat estrogen-receptor-positive breast cancer [14]. It works by inhibiting the growth and proliferation of breast cancer cells by blocking their estrogen receptors, but it has been linked to cardiovascular challenges [15]. α-Mangostin has a more expansive mechanism of action, including antioxidant, anti-inflammatory, and antimetastatic effects, and can be effective in a variety of breast cancer types other than those that are ER-positive, indicating a potential for overcoming resistance due to its multiple mechanisms of action and ability to target cancer cells [16]. However, inadequate bioavailability was found in an α-mangostin pharmacokinetic investigation conducted in mice [17]. Additionally, the cytotoxicity of tyrosine-conjugated chlorambucil is greater than that of chlorambucil alone due to its increased intracellular absorption. L-type amino acid transporter 1 (LAT1), which is overexpressed in a number of cancer types, enhances the antiproliferative activity of the chlorambucil compound and aids in its intracellular uptake [18]. It is overexpressed in a number of cancer types [19]. Interestingly, lysine, arginine, and histidine have been conjugated with the C3 and C6 atoms of α-mangostin in an attempt to improve its selectivity against bacterial membranes [20].
In this study, glycine-conjugated α-mangostin was designed to target the estrogen receptor. A considerable majority of breast cancers are hormone receptor-positive, meaning they express estrogen and/or progesterone receptors [21]. Estrogen stimulates the growth of these tumors by attaching to their receptors and increasing gene expression, leading to cell proliferation [22]. Estrogen receptors are nuclear receptors that, when activated by estrogen, dimerize and translocate to the nucleus, where they bind to estrogen response regions on DNA, controlling gene transcription in cell cycle progression and survival. By interfering with this route, the proliferation of ER-positive breast cancer cells can be slowed or prevented [23].
Therefore, utilizing a pharmacophore model, docking simulation, and molecular dynamics, the screening of glycine-conjugated α-mangostin was represented in this study. The results of these models should be useful resources, clarifying the dynamics of the conjugates’ interactions with the target and providing additional suggestions for future research.

2. Materials and Methods

Ligand Design: The hydroxy group at positions 3 and/or 6 of the α-mangostin structure was conjugated to glycine to create the ligands, as shown in Figure 1. The chemical structures of conjugates of α-mangostin were graphically drawn using Marvinsketch (Chemaxon®, Budapest, Hungary) [24]. All sketched conjugates were then accurately refined by geometric optimization, utilizing the MMFF94 force field in Avogadro [25].
The Prediction of Pharmacokinetic and Toxicological Profiles: The toxicity, distribution, and absorption profiles of α-mangostin and its conjugates were predicted using the PreADMET webserver. Pharmacokinetic properties such as Caco-2 permeability, human intestine absorption, blood–brain barrier penetration, and protein plasma binding were evaluated. Caco-2 permeability pertains to a compound’s capacity to pass through a monolayer of Caco-2 cells, which act as an intestinal epithelial model representing the degree to which a compound can be absorbed into the human intestine [26]. Human intestine absorption refers to the proportion of a medicine taken orally that is absorbed into the bloodstream through the intestinal wall [27]. Blood–brain barrier penetration is the ability of a compound to pass across the blood–brain barrier and reach the central nervous system [28]. Plasma binding protein indicates the degree to which a drug binds to plasma proteins such as albumin [29]. To forecast toxicity parameters, three tests were employed: the Ames test, a frequently used assay for determining the mutagenic potential of chemical substances. The Ames test is based on multiple strains of the bacterium Salmonella typhimurium that have mutations in genes involved in histidine production. These strains cannot grow in a histidine-free media until they undergo a mutation that restores their capacity to synthesize histidine [30]. The Ames test determines the ability of a chemical substance to cause such alterations. The carcinoma test in rodents (rat and mice) was also employed. Rats and mice metabolize chemicals differently, resulting in differences in toxicity profiles and responses to toxicants. Additionally, rats have a longer lifespan and bigger body size than mice, which might influence the duration and scale of toxicity studies [31,32].
Pharmacophore Modeling: The 3D ligand-based pharmacophore model was constructed using LigandScout 4.4.3 (Inte:Ligand GmbH®, Vienna, Austria) [33]. The model databases were collected from the Database of Useful Decoys. To verify the robustness of the model in capturing the 3D ligand-based interaction features, a set of 20,818 decoys and a set of 627 actives were screened during the validation process. Upon validation, the established 3D ligand-based pharmacophore model was used to match features with conjugates of α-mangostin. Pharmacophore fit scores were computed to determine the extent of fit with the pharmacophore models [34].
Molecular Docking: The molecular structure of ERα complexed with 4-hydroxytamoxifen was obtained from the Protein Data Bank using PDB ID 3ERT [35]. Models for docking were prepared using AutoDockTools 1.5.6 (Scripps Research, La Jolla, CA, USA) [36]. Before molecular docking, all the ligands were created by adding hydrogen atoms and merging them with nonpolar hydrogen atoms, adding Gasteiger charges, defining rotatable bonds, and then saving in PDBQT format [37]. The Gasteiger charge, which was utilized on the ligands for a potent postprediction, performed well in all ligand sizes and was particularly successful on ligands with great flexibility [38]. Hydrogens were added to the macromolecule, Kollman charges were applied, and the charged macromolecule was saved in PDBQT format [39]. Kollman charges are calculated using quantum mechanics and then adjusted empirically. The charge represents the electrostatic potential of macromolecules and accurately models the interactions between the ligand and the receptor during docking [40]. A grid box measuring 40 × 40 × 40 Å, centered on the active site of ERα (x = 30.010, y = −1.913, and z = 24.207), was set up for the docking simulation conducted with AutoDock 4.2. The Lamarckian Genetic Algorithm was run iteratively 100 times. The ligand conformation with the lowest free binding energy (ΔG) was selected from the generated clusters for further investigation of ideal ligand interactions. Discovery studios Visualizer 2024 (Dassault Systemes BIOVIA®, San Diego, CA, USA) was used to clarify receptor–ligand interaction [41,42].
Molecular Dynamics Simulation: The molecular dynamics simulations were performed using the AMBER ff14SB protein force field [43]. In the beginning, we generated partial charges of ligands using Austin Model 1—Bond Charge Corrections (AM1-BCC), which were implemented in the antechamber program AmberTools22. The ERα structure was set up with pdb4amber and set ff19SB force field [44]. TIP3P water was placed into a 10 × 10 × 10 Å box for the simulation setting, and the ligands were subjected to the general AMBER force field [45]. Topology files were created after the ligand’s charges were balanced using a limited electrostatic potential. The minimization, heating, and equilibration procedures were completed using the Sander module [46]. For both ligands and receptors, a heating function was applied prior to starting the production phase of the molecular dynamics simulations. This required a three-step heating procedure that mimicked physiological settings by gradually increasing the system temperature from 0 to 310 °K at regular intervals [47]. The system was then allowed to stabilize via equilibration, arriving at a steady state before molecular dynamics simulations were generated. To further evaluate the structural dynamics and fluctuations in the system, investigations using Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF) were carried out.

3. Results and Discussion

3.1. Pharmacokinetic and Toxicology Prediction

The distribution, metabolism, and absorption characteristics of α-mangostin and its glycine conjugates were assessed using the PreADMET server. The predicted pharmacokinetic and toxicology are summarized in Table 1.
The predictive pharmacokinetic is represented in Figure 2. It was found that the anticipated absorption values for the Caco-2 cell were between 15 and 21 nm/s. Consequently, the permeability of each conjugate gathered for analysis was moderate [48]. The conjugates might also be well absorbed into the intestinal cells, as evidenced by the expected HIA values for each component being greater than 90%. Although α-mangostin has a predicted PPB value of 96%, indicating it is a well-bound compound, all of the glycine-α-mangostin conjugates had expected PPB values of less than 90%, suggesting that these compounds are weakly bound molecules. When α-mangostin has a Cbrain/Cblood value greater than 2.0, it signifies that the compound is absorbed more deeply into the central nervous system, indicating its ability to penetrate the blood–brain barrier. Because the results ranged from 2.0 to 0.1 and were less than 0.1, the conjugates’ Cbrain/Cblood ratio was determined to have moderate to low absorption. On the rodent toxicity test (mice and rats), the “negative” sign designates compounds having clear indications of carcinogenic activity, while the “positive” sign indicates the absence of such evidence. However, the only conjugate that did not show carcinogenic activity in mice was Am1Gly. Otherwise, on the Ames test, all of the models showed negative signs, which means none of the models induced mutagenicity toward the Salmonella typhimurium population in Ames’s model.

3.2. Pharmacophore Modeling

The nature and three-dimensional conformation of compound functionalities in ligands essential for molecular interactions with macromolecular targets can be ascertained using 3D pharmacophores. Compounds’ activities must be categorized into more general pharmacophore features, such as hydrogen donors, hydrophobic areas, aromatic ring systems, acceptors of hydrogen bonds, and negatively and positively ionizable groups [49].
Pharmacophore modeling requires a thorough comparison of all conjugate structures with a well-selected database of substances that have been scientifically proven to have an inhibitory effect against ERα. For filtering and reference, this database—taken from the Database of Useful Decoys: Enhanced with ESR1 code—serves as the standard [50].
Validation is required for the pharmacophore model created from the active compound database. This validation highlights the validity of the pharmacophore-based screening approach in identifying putative bioactive compounds among the glycine-conjugated α-mangostins that target ERα receptors [39]. An AUC100 value of 0.83 was found in the validation results (Figure 3), demonstrating the suitability of the used methodology. The AUC100 value’s closeness to one indicates a good screening strategy and a thorough screening process.
The results from the pharmacophore model showed that a model drug needed to have two hydrophobic groups (yellow spheres), one hydrogen bond accepting group (red sphere), and one hydrogen bond donor group (green sphere) (Figure 4).
After establishing the pharmacophore model, all glycine-conjugated α-mangostins were thoroughly screened to find putative ERα antagonist ligands. All the conjugates that complied with the model pharmacophore are shown in Figure 5.
Following that, the pharmacophore model (Figure 4) obtained from the active databases and the proposed conjugate model were aligned. Aligned conjugates showed that only Am3Gly was not able to properly align with the pharmacophore model by interacting with hydrogen bond donors and acceptors, as well as with two hydrophobic moieties (Figure 5). This result highlights the capacity of Am1Gly and Am2Gly to act as ERα antagonist ligands in accordance with the defined pharmacophore parameters and support continued screening to molecular docking.

3.3. Molecular Docking

The redocked ligand’s position and bond alignment with those seen during the crystallization process, supported by an RMSD value of two, determined the legitimacy of the docking process. Redocking 4-hydroxytamoxifen produced hydrogen bond interactions with residues Glu353 and Arg394, validating the use of the AutoDock4 application [51]. This interaction profile further confirms the precision and dependability of the AutoDock4 application in replicating the binding configuration of 4-hydroxytamoxifen with the active site of the ERα receptor [52].
A molecular docking simulation was carried out with Am1Gly and Am2Gly as ligand models. The docking procedure was aided by the use of the previously specified parameters. After a thorough evaluation, it was concluded that 2,500,000 iterations using 100 receptor samples was the ideal number of genetic algorithm runs [53]. This strategy leverages the established effectiveness of the genetic algorithm method to provide a robust creation of different samples for the investigation of ligand conformations [54].
Redocking of the ligand 4-hydroxytamoxifen, which complexed and crystallized at 3ERT, was conducted in order to confirm the docking process. In Figure 6, the location obtained from the crystallographic result is represented in orange, and the conclusion of the redocking process is graphically presented in purple.
Table 2 illustrates that the conjugates’ bond energy estimates using Autodock varied, ranging from −11.23 to −9.52 kcal/mol.
Am1Gly and Am2Gly molecular docking results were analyzed based on binding energies and essential molecular interactions against the ERα receptor. As shown in Table 2, Am1Gly (−10.91 kcal/mol) had slightly lower binding energy than Am2Gly (−10.41 kcal/mol). These data demonstrated that the Am1Gly conjugate has the potential for stability and favorable binding affinity within the target receptor’s active region. On the other hand, a compound is deemed antagonistic to the ERα receptor when it does not interact with certain amino acid residues, especially His524 [51]. Antagonists are defined as ligands that exhibit hydrogen bonding with the His524 residue and helix 12 [55]. Based on the 4-hydroxytamoxifen (4-OHT) evidence, two other essential amino acid residues for ERα interaction are Arg394 and Glu353 [56]. Antagonism against ERα may be strengthened by ligands through possible hydrophobic interactions with amino acid Glu419, His524, or Lys531 [57]. After the analysis of both conjugates’ interactions with ERα, it was found that Am1Gly’s interactions are more consistent with those anticipated according to previous theories (Figure 7). Molecular dynamics simulations were then utilized to examine Am1Gly conjugates.

3.4. Molecular Dynamics Simulation

The properties and interactions of the Am1Gly complex with ERα were observed using molecular dynamics simulations. Under specific pressure and temperature settings, the ligand–receptor complex was observed over a duration of 200 ns [58]. In addition, molecular dynamic simulations of the 4-hydroxytamoxifen-ER complex were performed to aid in the comparison of antagonist complex properties with those in previous studies [36,59].
The molecular dynamics simulation of Am1Gly was carried out for 200 ns using AMBER22, and the results were analyzed using RMSD and RMSF charts [60]. The ligand–ERα interaction was parameterized using the ff14SB, GAFF, and AM1-BCC force fields [59]. The average distance that atoms traveled away from the reference structure is shown by the RMSD value. The Erα–Am1Gly combination reached stability around frame 2000, with an RMSD value similar to that of the ERα-4-hydroxytamoxifen complex. The ERα–Am1Gly complex demonstrated a slight stability in comparison to the ERα-4-hydroxytamoxifen complex (Figure 8). The RMSD values for Am1Gly and 4OHT are similar, measuring around 0–2 Å. The ERα receptor’s flexibility is stable when interacting with Am1Gly and 4OHT, as the protein remains folded and does not alter significantly during dynamic simulations [61].
The RMSF analysis establishes the stability pattern in order to study changes in protein and amino acid composition that occur during the interaction of the Am1Gly and 4OHT complexes [62]. RMSF focuses on the protein’s complex region and the fluctuating amino acid residues throughout its structure. During the MD simulations, higher RMSF values reflect weaker stability and greater flexibility, while a lesser RMSF amplitude shows strong consistency for ligand complexes [63]. The RMSF analysis (Figure 9) shows quite similar residual fluctuations between Am1Gly and 4-hydroxytamoxifen. Pro333, Thr334, Arg335, Cys417, Ser464, Lys531, and Pro552 were among the residues that exhibited changes as a result of Am1Gly and 4OHT. The ERα protein’s structure includes a hydrogen bond network between Glu419, His524, and Lys531. This network ensures Helix-3 and Helix-11 of ERα maintain tight touch [64]. If a ligand disrupts the hydrogen bond through fluctuations, it can act as an antagonist to the ERα receptor [65]. Am1Gly and 4OHT damage the hydrogen bond by causing variations between Ser432 and Ser521. This suggests that Am1Gly may have anti-ERα properties.
The hydrogen bond analysis results indicate that Am1Gly has the majority of its hydrogen bond interactions with residue Asp351 (49.80% occupancy), Phe404 (12.85% occupancy), and Glu353 (4.87% occupancy) (Table 3). These interactions differ significantly from those of 4OHT. Despite the difference in the occupancy of amino acid residues between 4OHT and Am1Gly, especially the Asp351 residue, it can still be inferred that the Am1Gly compound may act as an antagonist to ERα. This inference is supported by the results of molecular dynamics by Mardianingrum et al., which show that Am1Gly exhibits important hydrogen interactions with residues Asp351, Arg394, and Glu353, which are stabilizing interactions characteristic of an ERα antagonist [59,66,67]. These findings, drawn from both molecular docking and dynamics data, suggest that Am1Gly is necessary for ERα antagonistic activity. This also suggests that Am1Gly may have a similar antagonistic ERα action to 4OHT.
The binding energy was then computed using the MMPBSA method to predict the binding affinity of Am1Gly against ERα. The MMPBSA approach is regarded as more advanced for calculating binding free energy [68]. To validate the molecular docking findings of the examined drugs and complexes such Am1Gly–ERα and 4OHT–ERα, the binding free energy (ΔGTotal) was estimated using the MMPBSA approach. Table 4 shows the computed total energies of anticancer Am1Gly–ERα and 4OHT–ERα complexes. ΔGEL electronic energy has a moderate effect on the stability profile of ligand complexes. The van der Waals energy (ΔGVDW) has a significant impact on the stability of complex states, with high values for Am1Gly (−56.34 kcal/mol) and 4OHT (−53.14 kcal/mol). The results show that Am1Gly (−48.79 kcal/mol) and 4-OHT (−53.25 kcal/mol) have comparable binding energies. This underscores Am1Gly’s dependability as an ERα antagonist. The binding energy of Am1Gly is largely influenced by van der Waals interactions, which is consistent with the hydrophobic characteristic of the active sites in the ERα pocket [69].

4. Conclusions

The glycine-conjugated α-mangostins, particularly Am1Gly and Am2Gly, demonstrated appropriate pharmacophore model features when compared to databases of active drugs targeting the ERα receptor. Molecular docking data revealed that Am1Gly aligns well with key residues essential for functioning as 4-hydroxytamoxifen-like ERα antagonists. Am1Gly exhibited similar dynamic interactions with ERα as those observed with 4-hydroxytamoxifen, further supported by consistent amino acid variation patterns over 200 ns. Binding affinity analyses, dynamic simulations, molecular docking, and pharmacophore modeling collectively support the potential of Am1Gly to act as an ERα antagonist. It is recommended to synthesize Am1Gly conjugates and evaluate their anticancer activity in order to further confirm the antagonistic activity of these compounds.

Author Contributions

Conceptualization, H.A., R.M., S.M., N.K.K.I. and M.M.; methodology, H.A., S.M. and M.M.; software, H.A., S.M. and M.M.; validation, H.A. and S.M.; formal analysis, H.A., S.M. and M.M.; investigation, H.A., S.M. and M.M.; resources, R.M., S.M. and M.M.; data curation, H.A.; writing—original draft preparation, H.A.; writing—review and editing, H.A., N.K.K.I. and M.M.; visualization, H.A.; supervision, R.M., S.M. and M.M.; project administration, M.M.; funding acquisition, R.M. and M.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Doctoral Research Grant of The Ministry of Education, Culture, Research, and Technology Indonesia no. 1503/UN6.3.1/PT.00/2024.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Applsci 14 05549 g001
Figure 1. Modified α-mangostin. (a) α-Mangostin, R1 and R2: OH; (b) Am1Gly, R1: L-glycine; (c) Am2Gly, R2: L-glycine; (d) Am3Gly, R1 and R2: L-glycine.
Figure 1. Modified α-mangostin. (a) α-Mangostin, R1 and R2: OH; (b) Am1Gly, R1: L-glycine; (c) Am2Gly, R2: L-glycine; (d) Am3Gly, R1 and R2: L-glycine.
Applsci 14 05549 g001
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Figure 2. Predictive pharmacokinetics of α-mangostin and its glycine conjugates.
Figure 2. Predictive pharmacokinetics of α-mangostin and its glycine conjugates.
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Figure 3. Pharmacophore database validation.
Figure 3. Pharmacophore database validation.
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Figure 4. Pharmacophore model against ERα derived from active databases. Hydrogen bond donor (green sphere), hydrogen bond acceptor (red sphere), and hydrophobic area (yellow spheres) are the properties of the model.
Figure 4. Pharmacophore model against ERα derived from active databases. Hydrogen bond donor (green sphere), hydrogen bond acceptor (red sphere), and hydrophobic area (yellow spheres) are the properties of the model.
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Figure 5. Matching characteristics of every glycine-conjugated α-mangostin in the pharmacophore model based on active databases. From top to bottom: Am1Gly, Am2Gly, and Am3Gly.
Figure 5. Matching characteristics of every glycine-conjugated α-mangostin in the pharmacophore model based on active databases. From top to bottom: Am1Gly, Am2Gly, and Am3Gly.
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Figure 6. Overlay shows the 4-hydroxytamoxifen ligand position from the crystallographic result (orange) against the redocking result (purple). The molecular interactions were hydrophobic bonds (purple dashed line) and hydrogen bonds (green dashed line).
Figure 6. Overlay shows the 4-hydroxytamoxifen ligand position from the crystallographic result (orange) against the redocking result (purple). The molecular interactions were hydrophobic bonds (purple dashed line) and hydrogen bonds (green dashed line).
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Figure 7. The molecular interaction of Am1Gly with the ERα receptor showed essential hydrogen bonds (green lines) and hydrophobic interactions (pink lines).
Figure 7. The molecular interaction of Am1Gly with the ERα receptor showed essential hydrogen bonds (green lines) and hydrophobic interactions (pink lines).
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Figure 8. Am1Gly and 4-hydroxytamoxifen’s RMSD plot vs. ERα.
Figure 8. Am1Gly and 4-hydroxytamoxifen’s RMSD plot vs. ERα.
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Figure 9. RMSF plot of 4-hydroxytamoxifen and Am1Gly against ERα.
Figure 9. RMSF plot of 4-hydroxytamoxifen and Am1Gly against ERα.
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Table 1. Predictive pharmacokinetics of α-mangostin and its glycine conjugates.
Table 1. Predictive pharmacokinetics of α-mangostin and its glycine conjugates.
MoleculesPharmacokinetics PredictionToxicity Prediction
PCaco-2 (nm/s)BBB (Cbrain/Cblood)HIA (%)PBB (%)RatMiceAmes
am20.693.9491.8196.62+--
Am1Gly18.050.1192.4981.68-+-
Am2Gly17.350.1192.578.35---
Am3Gly15.940.0286.731.44---
Table 2. Docking results between glycine-conjugated α-mangostin derivatives and 4-hydroxytamoxifen are compared.
Table 2. Docking results between glycine-conjugated α-mangostin derivatives and 4-hydroxytamoxifen are compared.
CompoundsBinding Affinity (kcal/mol)Molecular Interactions
Hydrogen BondAlkyl
4OHT−11.66Arg394, Glu353Leu346, Leu391, Met421, Leu387, Ala350, Leu525
Am1Gly−10.91Glu353, Asp351, Thr347Leu346, Leu525, Met421, Ile424, Ala350, Leu525
Am2Gly−10.41Asp351, Thr347Leu346, Leu525, Met421, Ile424, Ala350, Leu384
Table 3. Hydrogen bonds were analyzed in 200 ns using MD trajectories from all systems.
Table 3. Hydrogen bonds were analyzed in 200 ns using MD trajectories from all systems.
SystemH-Bond Acceptor (atom@res)H-Bond Donor (atom@res)Occupancy (%)Avg. Distance (Å)Avg. Angle (°)
4OHTO@Glu353H@4OHT10.382.65160.57
O@Glu353H@4OHT9.862.66158.39
N@4OHTH@Ser5278.532.83164.10
O@Leu387H@4OHT7.192.78164.08
O@4OHTH@Arg3942.022.91147.06
O@4OHTH@Ser5271.512.84155.07
O@Phe404H@4OHT0.592.79152.73
N@4OHTH@Lys5290.202.88156.65
N@4OHTH@Lys5290.192.89158.56
N@4OHTH@Lys5290.172.87153.03
O@4OHTH@Arg3940.132.91143.01
O@4OHTH@Lys5290.112.89149.94
O@4OHTH@Thr3470.112.84161.15
O@4OHTH@Lys5290.092.89151.44
O@Leu349H@4OHT0.072.77164.23
Am1GlyO@Asp351H@Am1Gly49.802.60164.41
O@Phe404H@Am1Gly12.852.87155.19
O@Glu353H@Am1Gly4.872.85160.29
H@Am1GlyH@Arg3943.572.86147.49
H@Am1GlyH@Asn5320.612.92159.48
N@Am1GlyH@Arg3940.122.90142.21
O@Asn532H@Am1Gly0.122.80144.27
N@Am1GlyH@Arg3940.122.90149.09
O@Am1GlyH@Leu5250.112.88158.21
O@Am1GlyH@Lys5310.062.91151.57
O@Am1GlyH@Arg3940.052.88141.58
O@Am1GlyH@Lys5310.052.90150.17
O@Am1GlyH@Lys5310.052.91150.97
O@Am1GlyH@Lys5310.032.87149.78
O@Am1GlyH@Asn5320.032.91147.49
O@Am1GlyH@Lys5310.032.91155.83
O@Thr347H@Am1Gly0.032.80157.33
O@Am1GlyH@Lys5310.022.92151.21
O@Leu349H@Am1Gly0.022.90151.34
O@Leu349H@Am1Gly0.012.90146.00
O@Am1GlyH@Lys5310.012.91153.19
N@Arg394H@Am1Gly0.012.91150.45
N@Arg394H@Am1Gly0.012.95141.14
O@Leu346H@Am1Gly0.012.84152.03
O@Am1GlyH@Arg3940.012.88151.99
O@Thr347H@Am1Gly0.012.76149.76
O@Am1GlyH@Lys5310.012.87150.60
Table 4. Relative binding energies.
Table 4. Relative binding energies.
Energy ComponentBond Energy (kcal/mol)
4OHTAm1Gly
ΔGVDW−53.14−56.34
ΔGEL−10.45−26.49
ΔEGB17.8742.11
ΔESURF−7.53−8.07
ΔGGAS−63.59−82.23
ΔGSOLV10.3432.04
ΔGTOTAL−53.25−48.79
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Arifian, H.; Maharani, R.; Megantara, S.; Ikram, N.K.K.; Muchtaridi, M. Glycine-Conjugated α-Mangostins as Potential Estrogen Receptor Alpha (ERα) Antagonists through Pharmacophore Modeling, Docking Analysis, and Molecular Dynamics Simulations. Appl. Sci. 2024, 14, 5549. https://doi.org/10.3390/app14135549

AMA Style

Arifian H, Maharani R, Megantara S, Ikram NKK, Muchtaridi M. Glycine-Conjugated α-Mangostins as Potential Estrogen Receptor Alpha (ERα) Antagonists through Pharmacophore Modeling, Docking Analysis, and Molecular Dynamics Simulations. Applied Sciences. 2024; 14(13):5549. https://doi.org/10.3390/app14135549

Chicago/Turabian Style

Arifian, Hanggara, Rani Maharani, Sandra Megantara, Nur Kusaira Khairul Ikram, and Muchtaridi Muchtaridi. 2024. "Glycine-Conjugated α-Mangostins as Potential Estrogen Receptor Alpha (ERα) Antagonists through Pharmacophore Modeling, Docking Analysis, and Molecular Dynamics Simulations" Applied Sciences 14, no. 13: 5549. https://doi.org/10.3390/app14135549

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