# Biological Activity of Selected Compounds fromAnnona muricata Seed as Antibreast Cancer Agents: Theoretical Study

Authors: Abel Kolawole Oyebamiji; Gideon Femi Tolufashe; Olubukola Monisola Oyawoye; Temitope A. Oyedepo; Banjo Semire
Journal: Journal of Chemistry
 (2020)

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## Abstract

Several natural products have been of help to humans, and its effect is noticeable in the medicinal world. Soursop with botanical nameAnnona muricata L. possesses antidiarrhea, anticold fever, antirheumatism, and antineuralgia properties. In this work, five selected molecular compounds were studied against type 3 of 3α-hydroxysteroid dehydrogenase (3α-HSD). Its anticancer activity was investigated using the quantum chemical method via Spartan 14 software, molecular docking via Discovery studio 2017, AutoDock Tool 1.5.6, AutoDock Vina 1.1.2, and PyMol 1.7.4.4 and the molecular dynamic simulation method via AMBER14 molecular dynamics package. Many descriptors (EHOMO, ELUMO, dipole moment, energy bandgap, area, volume, polarizability, polar surface area, Log P, hydrogen bond donor, and hydrogen bond acceptor) which describe the anticancer activity of the studied compounds were obtained. Also, the docking study revealed the inhibiting ability of the studied compound, and it was observed that compound C possesses a greater ability to inhibit than other studied compounds as well as the standard (5FU).


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## Body

## 1. Introduction

The use of natural product compounds in supporting human health remains one of the vital ways of treatment globally [1]. Phytochemical compounds present in vegetation have played a crucial role in drug discovery, and their cytotoxicity has drawn the attention of researchers over the years [2]. Also, several scientists have declared plants with efficient biological importance to be source for well-being conservation [3]. As reported by Ko and Moon, the use of natural product in treating breast cancer is beneficial, and the chemical compounds present in these products have little or no negative effect compared to synthetic drug molecules [4].Soursop with botanical nameAnnona muricata L. belongs to the family Annonaceae, and it is a tropical fruit-bearing tree usually found in some areas of Africa, America, Australia, and Asia [5]. It is a tree with thick leaves that are oblate. Annona muricata possesses touchy, dark green fruit [6, 7]. Several researchers have reported this plant to be very useful in treating many diseases such as cancer, diabetes, inflammation, and hypertension [8]. According to Vijayameena et al., it helps in curing diabetic due to hypoglycemic and antioxidant features it contains [9]. Most parts of Annona muricata are useful in herbal medicine. Some parts, such as roots and stems, help in lowering blood pressure and also act as an antispasmodic agent [5]. Also, Annona muricata fruit plays a serious role in the medicinal world such as antidiarrhoea, anticold fever, antirheumatism, and antineuralgia and its consumption helps in amassing mother’s milk after giving birth [10]. As reported by some researchers, the biological activities of extracts from Annona muricata were observed to be very toxic to cancer cells than normal human cells [11–15]. Also, as reported by Shashanka and Devananda, most of the molecular compounds present in this plant have biomedical significance, and further study on this work is still ongoing [16]. According to Rieser et al., it was observed that muricatacin, as well as other compounds present in Annona muricata, possesses antiproliferative characteristics against breast, colorectal, and lung cancer cell line [17]. Wu et al. reported antilung cancer, antibreast cancer, and anticolon cancer activities of annomuricin A and annomuricin B. It was shown that they are potent against the breast, lung, and colon cancer [18]. Moreover, breast cancer has caused the death of several women over the years [19]. The rate at which this malignant neoplasia increases is on the high side [20], and as reported by Keogh et al., almost 95% of women have moderately growing risk of breast cancer [21]. The prospective methods for curing cancer remain chemotherapy, surgery, and radiotherapy; these methods have been used for several years ago with victory in several cases. Nevertheless, these methods have been recorded futile in some cases such as unconventional metastatic breast cancer [22]. More so, the rate of survival among the patient with breast cancer keeps increasing, and this could be attributed to improvement in drug designing and development [23].3α-HSD as a vital enzyme plays a serious role in breaking down of androgen [24]. In sex, 3α-HSD has been recognised in human to be a serious enzyme due to the role it played [25]. According to Aka et al., some estrogens like 17β-oestradiol ignite proliferation of breast cancer cells in woman although 5α-dihydrotestosterone (5α-DHT) have the strength to shrink the growth of breast cancer in woman [26]; as reported by Penning et al., 3α-HSD can hinder 5α-dihydrotestosterone, which will thereby increase the growth of breast cancer cells in woman [27]. Also, Bo et al. revealed that overriding 3α-HSD will spontaneously reduce MCF-7 cells growth [28].Therefore, this work is aimed at identifying the molecular descriptors responsible for antibreast cancer activities of selected compounds inAnnona muricata seed and studies the nonbonding interactions as well as the actual binding energy between the selected compounds and 3α-HSD using docking and molecular dynamics simulation methods, respectively.


## 2. Methodology

### 2.1. Optimization and Molecular Docking

Five molecular compounds selected fromAnnona muricata seed were screened against 3α-HSD via an in silico method. The crystal structure was retrieved from protein data bank [29] with the accession code 4XO6 [28]. The selected compounds were optimized using Spartan 14 [30]. Also, the docking study was executed using a series of software (Discovery studio 2017, AutoDock Tool 1.5.6, AutoDock Vina 1.1.2, and EduPyMol version 1.7.4.4), and the calculated binding affinities were reported.This docking study involves the preparation of 3α-HSD and the studied ligands using PyMol. The binding site in the studied receptor was located using AutoDock Tool, and the docking calculation was performed via AutoDock Vina. More so, the nonbonding interaction was viewed through Discovery studio 2017. The observed grid box was as follows: center (X = 12.444, Y = −0.171, and Z = 2.943) and size (X = 62, Y = 60, and Z = 74), and the spacing was set to be 1.00 Å.The selected compounds fromAnnona muricata seed are (2S)-2-methyl-4-[(2R, 10R, 13R)-2, 10, 13-trihydroxy-13-[(2R, 5R)-5-[(1R)-1-hydroxytridecyl]oxolan-2-yl]tridecyl]-2H-furan-5-one (A), (2S)-2-methyl-4-[(2R, 8R, 13R)-2,8,13-trihydroxy-13-[(2R, 5S)-5-[(1S)-1-hydroxytridecyl]oxolan-2-yl]tridecyl]-2H-furan-5-one (B), 3-{2, 13-dihydroxy-13-[5-(1-hydroxytridecyl)oxolan-2-yl]-8-oxotridecyl}-4-methyl-2,5-dihydrofuran-2-one (C), (2S)-2-methyl-4-[(2R, 8R, 11R)-2, 8, 11-trihydroxy-11-[(2R, 5S)-5-[(1S)-1-hydroxypentadecyl]oxolan-2-yl]undecyl]-2H-furan-5-one (D), and 5-(1-hydroxytridecyl)oxolan-2-one (E) (Figure 1).Figure 1
Schematic structure of the studied compounds.
(a)(b)(c)(d)(e)


### 2.2. ADMET Properties

Physicochemical and absorption, distribution, metabolism, excretion, and the toxicity properties of the studied molecular compounds were examined using admetSAR (http://lmmd.ecust.edu.cn/admetsar1) [31]. In this study, several factors were considered such as Ames test, human intestinal absorption, blood-brain barrier, and Caco-2 cell permeability.


### 2.3. Molecular Dynamic Simulation Studies

The compound with better binding affinity was selected from the compounds docked, and chloroquine was used as a standard; both were subjected to molecular dynamics simulation on AMBER14 molecular dynamics package [32]. Hydrogen atom was added to the studied complexes using a leap segment of AMBER14. The protein was described with AMBER force field 99SB while the ligand with general AMBER force field [33, 34]. To neutralize the studied complexes before solvation, the required number of counterions was added. The solvation was implemented in a condensed octahedral cell of TIP3P [35] water molecules, and 12 Å was stretched outside the protein on each side.The complexes were minimized using 5000 frames of steepest descent minimization and 10000 frames of conjugated gradient minimization to eliminate bad atom interactions. The entire systems were heated at 300 K and 50000 ps. Molecular dynamics simulation was accomplished for the studied complex at 300 K and 1 atm via the particle mesh Ewald method [36]. CPPTRAJ module [33] implemented in AMBER14 software was used for molecular dynamics trajectories analysis.


## 2.1. Optimization and Molecular Docking

Five molecular compounds selected fromAnnona muricata seed were screened against 3α-HSD via an in silico method. The crystal structure was retrieved from protein data bank [29] with the accession code 4XO6 [28]. The selected compounds were optimized using Spartan 14 [30]. Also, the docking study was executed using a series of software (Discovery studio 2017, AutoDock Tool 1.5.6, AutoDock Vina 1.1.2, and EduPyMol version 1.7.4.4), and the calculated binding affinities were reported.This docking study involves the preparation of 3α-HSD and the studied ligands using PyMol. The binding site in the studied receptor was located using AutoDock Tool, and the docking calculation was performed via AutoDock Vina. More so, the nonbonding interaction was viewed through Discovery studio 2017. The observed grid box was as follows: center (X = 12.444, Y = −0.171, and Z = 2.943) and size (X = 62, Y = 60, and Z = 74), and the spacing was set to be 1.00 Å.The selected compounds fromAnnona muricata seed are (2S)-2-methyl-4-[(2R, 10R, 13R)-2, 10, 13-trihydroxy-13-[(2R, 5R)-5-[(1R)-1-hydroxytridecyl]oxolan-2-yl]tridecyl]-2H-furan-5-one (A), (2S)-2-methyl-4-[(2R, 8R, 13R)-2,8,13-trihydroxy-13-[(2R, 5S)-5-[(1S)-1-hydroxytridecyl]oxolan-2-yl]tridecyl]-2H-furan-5-one (B), 3-{2, 13-dihydroxy-13-[5-(1-hydroxytridecyl)oxolan-2-yl]-8-oxotridecyl}-4-methyl-2,5-dihydrofuran-2-one (C), (2S)-2-methyl-4-[(2R, 8R, 11R)-2, 8, 11-trihydroxy-11-[(2R, 5S)-5-[(1S)-1-hydroxypentadecyl]oxolan-2-yl]undecyl]-2H-furan-5-one (D), and 5-(1-hydroxytridecyl)oxolan-2-one (E) (Figure 1).Figure 1
Schematic structure of the studied compounds.
(a)(b)(c)(d)(e)


## 2.2. ADMET Properties

Physicochemical and absorption, distribution, metabolism, excretion, and the toxicity properties of the studied molecular compounds were examined using admetSAR (http://lmmd.ecust.edu.cn/admetsar1) [31]. In this study, several factors were considered such as Ames test, human intestinal absorption, blood-brain barrier, and Caco-2 cell permeability.


## 2.3. Molecular Dynamic Simulation Studies

The compound with better binding affinity was selected from the compounds docked, and chloroquine was used as a standard; both were subjected to molecular dynamics simulation on AMBER14 molecular dynamics package [32]. Hydrogen atom was added to the studied complexes using a leap segment of AMBER14. The protein was described with AMBER force field 99SB while the ligand with general AMBER force field [33, 34]. To neutralize the studied complexes before solvation, the required number of counterions was added. The solvation was implemented in a condensed octahedral cell of TIP3P [35] water molecules, and 12 Å was stretched outside the protein on each side.The complexes were minimized using 5000 frames of steepest descent minimization and 10000 frames of conjugated gradient minimization to eliminate bad atom interactions. The entire systems were heated at 300 K and 50000 ps. Molecular dynamics simulation was accomplished for the studied complex at 300 K and 1 atm via the particle mesh Ewald method [36]. CPPTRAJ module [33] implemented in AMBER14 software was used for molecular dynamics trajectories analysis.


## 3. Result and Discussion

### 3.1. Density Functional Theory Calculations

Series of molecular descriptors obtained from carefully selected five molecular compounds inAnnona muricata seed via density functional theory (DFT) are highest occupied molecular orbital energy (EHOMO), lowest unoccupied molecular orbital energy (ELUMO), dipole moment, energy bandgap, area, volume, polarizability, polar surface area, log P, hydrogen bond donor, and hydrogen bond acceptor (Table 1). In this work, the contribution of dipole moment to the biological activity of studied compounds was observed. According to David et al., it was reported that the presence of dipole moment as a nonbonded interaction in the drug-like compound-protein complex is vital [37]. The accepted range for dipole moment is 3–5 kJ/mol; thus, compound E was the only compound that falls within the set standard, so it is expected to have strong nonbonded connections 3α-HSD.Table 1
Calculated molecular descriptors of the studied compounds.
EHOMO (eV)ELUMO (eV)BGDMMWLogPOVAPSAPOLHBDHBAA−6.74−1.005.7410.40596.896.801.9895.5394.9646B−6.69−1.145.559.82596.896.801.9897.3595.0346C−6.66−1.155.518.38594.876.951.9891.7194.6536D−6.75−1.005.759.90568.835.961.9495.5391.9946E−7.17−0.037.144.86284.444.541.6141.2966.7412Notes. BG: band gap (ELUMO-EHOMO); DM: dipole moment; MW: molecular weight; OVA: ovality; POL: polarizability; HBD: hydrogen bond donor; HBA: hydrogen bond acceptor.Furthermore, the calculated logP reveals the dissolving capacity of drug-like molecules in lipophilic media [38]. As reported by Meanwell 2011, there may be complications in oral absorption if the calculated log P is greater than the standard value for log PLogP≤5 [36]. Therefore, compound E is expected to dissolve well in nonaqueous substance and possesses the ability to be administered through the human mouth. More so, the calculated EHOMO which describes the tendency of drug-like molecules to donate electron to the neighbouring compounds was calculated to be −6.74 (A), −6.69 (B), −6.66 (C), −6.75 (D), and −7.17 (E). As shown in Table 1, compound C is expected to inhibit than other studied compounds [39]. The calculated ELUMO shows the capability of compounds to accept electrons from nearby molecules [40]. In this regard, compound C with −1.15 eV is expected to possess a greater ability to accept electron from the nearby compounds than other studied compounds. Other studied calculated descriptors are displayed in Table 1.More so, the calculated band gap shows the level of reactivity of compounds with drug potentials. Also, many reports from several researchers have proved that level of reactivity of molecular compounds is a function of lower bandgap; thus, compoundC is expected to react better than other studied compounds [41]. Figure 2 shows the EHOMO-ELUMO overlay.Figure 2
StudiedEHOMO-ELUMO overlay.Furthermore, chiral information of some stereogenic centers of the studied compounds as displayed in Figure1 was investigated. In compound A, seven (7) chiral centers were observed (4R and 3S), and seven chiral centers were also observed in compound B with 7R and 6R as the chiral center was observed in compound C. More so, seven (7) and two (2) chiral centers were detected in compounds D and E, respectively.


### 3.2. Prediction of ADMET Properties

Series of obtained ADMET properties using admetSAR server [31] were reported. As shown in Table 2, it was observed that compounds A, B, D, and E had better human intestinal absorption than the standard used (5FU) except compound C since drug-like compound with the higher value of human intestinal absorption can be absorbed well in the intestine [42]. The values obtained for the blood-brain barrier for compounds A–E showed that compound E has a very closer value to that of 5FU. Also, the blood-brain barrier value for compounds A–D falls within the same ranges shown in Table 2. The obtained P-glycoprotein report (nonsubstrate and noninhibitor) for all the studied compounds was similar to those of 5FU. Also, to know the level of mutagenicity of the studied compounds, AMES was considered. Thus, in terms of AMES study, all the studied compounds were negative, and this also correlated with the standard (5FU). As shown in Table 2, all the studied compounds were not carcinogenic and these were similar to the similar used. The calculated cytochrome P450 showed that it could be inhibited by some of the studied compounds.Table 2
Prediction of ADMET.
ModeCompoundACompoundBCompoundCCompoundDCompoundE5FUResultProbabilityResultProbabilityResultProbabilityResultProbabilityResultProbabilityResultProbabilityBlood-brain barrierBBB+0.7186BBB+0.7186BBB+0.6825BBB+0.7186BBB+0.9398BBB+0.9791Human intestinal absorptionHIA+0.9637HIA+0.9637HIA+0.9408HIA+0.9637HIA+0.9921HIA+0.9605Caco-2 permeabilityCaco-2−0.5236Caco-2−0.5236Caco-2−0.5635Caco-2−0.5236Caco-2−0.6145Caco-2−0.7583P−Glycoprotein substrateSubstrate0.6777Substrate0.6777Substrate0.6876Substrate0.6777Substrate0.5000Nonsubstrate0.7752P−Glycoprotein inhibitorNoninhibitor0.8357Noninhibitor0.8357Noninhibitor0.7735Noninhibitor0.8357Noninhibitor0.8489Noninhibitor0.8991Noninhibitor0.8228Noninhibitor0.8228Noninhibitor0.8460Noninhibitor0.8228Noninhibitor0.7578Noninhibitor1.0000Renal organic cation transporterNoninhibitor0.8945Noninhibitor0.8945Noninhibitor0.8667Noninhibitor0.8945Noninhibitor0.8726Noninhibitor0.9053Subcellular localizationMitochondria0.7433Mitochondria0.7433Mitochondria0.8200Mitochondria0.7433Mitochondria0.6912Mitochondria0.6099CYP450 2C9 substrateNonsubstrate0.8690Nonsubstrate0.8690Nonsubstrate0.8836Nonsubstrate0.8690Nonsubstrate0.7757Nonsubstrate0.7898CYP450 2D6 substrateNonsubstrate0.8739Nonsubstrate0.8739Nonsubstrate0.8807Nonsubstrate0.8739Nonsubstrate0.8521Nonsubstrate0.9116CYP450 3α4 substrateSubstrate0.5768Substrate0.5768Substrate0.5873Substrate0.5768Nonsubstrate0.5851Nonsubstrate0.7558CYP450 1A2 inhibitorNoninhibitor0.7870Noninhibitor0.7870Noninhibitor0.8304Noninhibitor0.7870Noninhibitor0.7422Noninhibitor0.9045CYP450 2C9 inhibitorNoninhibitor0.8834Noninhibitor0.8834Noninhibitor0.8791Noninhibitor0.8834Noninhibitor0.8551Noninhibitor0.9658CYP450 2D6 inhibitorNoninhibitor0.8954Noninhibitor0.8954Noninhibitor0.8902Noninhibitor0.8954Noninhibitor0.9517Noninhibitor0.9361CYP450 2C19 inhibitorNoninhibitor0.6454Noninhibitor0.6454Noninhibitor0.8277Noninhibitor0.6454Noninhibitor0.7020Noninhibitor0.9688CYP450 3α4 inhibitorNoninhibitor0.6168Noninhibitor0.6168Inhibitor0.5244Noninhibitor0.6168Noninhibitor0.8858Noninhibitor0.9661CYP inhibitory promiscuityLow CYP inhibitory promiscuity0.8548Low CYP inhibitory promiscuity0.8548Low CYP inhibitory promiscuity0.9361Low CYP inhibitory promiscuity0.8548Low CYP inhibitory promiscuity0.9447Low CYP inhibitory promiscuity0.9839Human Ether-a-go-go-Related Gene InhibitionWeak inhibitor0.9404Weak inhibitor0.9404Weak inhibitor0.9435Weak inhibitor0.9404Weak inhibitor0.9575Weak inhibitor0.9685Noninhibitor0.8093Noninhibitor0.8093Noninhibitor0.7379Noninhibitor0.8093Noninhibitor0.8589Noninhibitor0.9325AMES toxicityNon-AMES toxic0.8637Non-AMES toxic0.8637Non-AMES toxic0.8877Non-AMES toxic0.8637Non-AMES toxic0.9507Non-AMES toxic0.8941CarcinogensNoncarcinogens0.9532Noncarcinogens0.9532Noncarcinogens0.9554Noncarcinogens0.9532Noncarcinogens0.9268Noncarcinogens0.9288Fish toxicityHigh FHMT0.9146High FHMT0.9146High FHMT0.8980High FHMT0.9146High FHMT0.7714Low FHMT0.6050Tetrahymena pyriformis toxicityHigh TPT0.9968High TPT0.9968High TPT0.9960High TPT0.9968High TPT0.9952High TPT0.9526Honey bee toxicityHigh HBT0.7560High HBT0.7560High HBT0.7209High HBT0.7560High HBT0.6880Low HBT0.8479BiodegradationReadily biodegradable0.6696Readily biodegradable0.6696Readily biodegradable0.8621Readily biodegradable0.6696Readily biodegradable0.7498Not readily biodegradable0.9220


### 3.3. Molecular Docking and Scoring Studies

Series of selected molecular compounds fromAnnona muricata seed were docked into the active site of 3α-HSD to observe the nonbonding interaction that was involved. To establish the accuracy of the engaged docking method, the docked ligand retrieved from the crystal structure of 3α-HSD was docked again into the active site of the 3α-HSD to determine the similarity of the best conformation of the redocked ligand (i.e., compound with the lowest energy) to the position of the native ligand (Figure 3). Hence, the similarity index of the native drug-like compound retrieved from the downloaded receptor to the redocked drug compound was similar to 1. In this work, the docking method used is observed to be dependable. In this study, it was observed that compound C has a promising ability to inhibit than other studied compounds. Also, the correlation between the molecular descriptors was observed and it was discovered that EHOMO, ELUMO, and bandgap as displayed in Table 1 greatly contributed to the promising inhibiting activity of compound C than other studied compounds.Figure 3
Overlay of native drug-like compounds over redocked drug compound.The docking score, as well as the binding affinity obtained for the studied complexes, was calculated to be negative, and this is a sign that the studied compounds could serve as the starting point for developing effective drugs targeting 3α-HSD, thereby reducing breast cancer in woman globally. Also, as shown in Table 3, the entire studied compound proved to be more effective than the standard used. The residues involve in each of the conformation are PRO-226, TRP-227, ALA-27, GLU-224, LEU-306, TYR-24, HIS-222, HIS-117, and LYS-270 for A; LYS-270, LEU-268, GLY-22, THR-23, ASP-50, TYR-55, GLN-190, TYR-24, VAL-54, and VAL-128 for B; LYS-270, HIS-222, SER-217, ASP-50, TYR-55, HIS-117, TRP-227, TYR-24, and ALA-27 for C; PRO-226, TRP-227, LEU-308, LEU-306, TYR-55, TYR-24, HIS-222, and LYS-270 for D; and THR-23, TYR-65, TYR-24, and TRP-227 for E (Figure 4).Table 3
Scoring of selected compounds inAnnona muricata seed with 3α-HSD.
Binding affinity (kcal/mol)Amino acid residueA−7.0PRO-226, TRP-227, ALA-27, GLU-224, LEU-306, TYR-24, HIS-222, HIS-117, LYS-270B−7.3LYS-270, LEU-268, GLY-22, THR-23, ASP-50, TYR-55, GLN-190, TYR-24, VAL-54, VAL 128C−7.5LYS-270, HIS-222, SER-217, ASP-50, TYR-55, HIS-117, TRP-227, TYR-24, ALA-27D−7.1PRO-226, TRP-227, LEU-308, LEU-306, TYR-55, TYR-24, HIS-222, LYS-270E−7.2THR-23, TYR-65, TYR-24, TRP-2275FU−5.6TYR-55, ASP-50, THR-23, TYR-24, GLY-22Figure 4
Binding interaction of compoundsA, B, C, D, and E with 3α-HSD, respectively.
(a)(b)(c)(d)(e)


### 3.4. Molecular Dynamic Simulation Result

#### 3.4.1. Root of Mean Square Deviation (RMSD)

RMSD of 3α-HSD backbone atoms in connection to the initial molecular structure of the studied enzyme in forming complexes with compounds C and 5FU during the 100 ns MD simulation is displayed in Figure 5. This was accomplished to investigate the extent of deviation from the initial structure upon binding and the steadiness of the simulated complexes. Thus, the structures shown in Figure 5 proved that the conformation of the studied complexes was stable and the average RMSD value for compounds C-3α-HSD and 5FU-3α-HSD was 1.17 Å and 1.47 Å, respectively. The systems were well equilibrated and stable after 60 ns of the simulation time.Figure 5
RMSD of compoundsC-3α-HSD (black) and 5FU-3α-HSD (red) complexes during the 100 ns MD simulations.


#### 3.4.2. Root of Mean Square Fluctuation

The calculated root mean square fluctuation (RMSF) reveals the flexibility of residues upon binding of the two compounds tested during simulation time. The studied RMSF helped to know the flexibility of the residue present in the studied enzymes during 50000 ps simulation time (Figure6). As displayed in Figure 5, there is a similarity in the trend at which the observed residue vacillates; however, little variation was observed between compounds C-3α-HSD and 5FU-3α-HSD. The pattern shown in Figure 5 proved that compound C-3α-HSD exhibited a minor degree of deviation and this shows a higher level of affinity for compound C to inhibit 3α-HSD.Figure 6
RMSF plots of compoundsC-3α-HSD (black) and 5FU-3α-HSD (red) complexes during the 100 ns MD simulations.


#### 3.4.3. Binding Free Energy

The binding energy for compoundsC-3α-HSD and 5FU-3α-HSD was calculated using the MM/GBSA method (Table 4).Table 4
Calculated binding free energies and its components for the complexes using the MM-GBSA method extracted from the last 1000 frames of 100 ns.
CompoundΔEvdwΔEeleΔGgasΔGpolarΔGnonpolarΔGsolvationΔGbindC−87.98−10.94−98.9134.36−11.1623.20−75.715FU−17.39−2.24−19.636.96−2.324.63−15.00The energy components are in kcal/mol.As shown in Table4, it was observed that compound C possesses a better ability to inhibit 3α-HSD than the standard used. The calculated free energy includes van der Waal energy (−87.98 kcal/mol), electrostatic energy (−10.94 kcal/mol), polar solvation energy (34.36 kcal/mol), and nonpolar solvation energy (−11.16 kcal/mol). This specifies that van der Waal energy, electrostatic energy, and nonpolar solvation energy are favourable; however, polar solvation energy was not favourable for the molecular binding of compound C to 3α-HSD.


## 3.1. Density Functional Theory Calculations

Series of molecular descriptors obtained from carefully selected five molecular compounds inAnnona muricata seed via density functional theory (DFT) are highest occupied molecular orbital energy (EHOMO), lowest unoccupied molecular orbital energy (ELUMO), dipole moment, energy bandgap, area, volume, polarizability, polar surface area, log P, hydrogen bond donor, and hydrogen bond acceptor (Table 1). In this work, the contribution of dipole moment to the biological activity of studied compounds was observed. According to David et al., it was reported that the presence of dipole moment as a nonbonded interaction in the drug-like compound-protein complex is vital [37]. The accepted range for dipole moment is 3–5 kJ/mol; thus, compound E was the only compound that falls within the set standard, so it is expected to have strong nonbonded connections 3α-HSD.Table 1
Calculated molecular descriptors of the studied compounds.
EHOMO (eV)ELUMO (eV)BGDMMWLogPOVAPSAPOLHBDHBAA−6.74−1.005.7410.40596.896.801.9895.5394.9646B−6.69−1.145.559.82596.896.801.9897.3595.0346C−6.66−1.155.518.38594.876.951.9891.7194.6536D−6.75−1.005.759.90568.835.961.9495.5391.9946E−7.17−0.037.144.86284.444.541.6141.2966.7412Notes. BG: band gap (ELUMO-EHOMO); DM: dipole moment; MW: molecular weight; OVA: ovality; POL: polarizability; HBD: hydrogen bond donor; HBA: hydrogen bond acceptor.Furthermore, the calculated logP reveals the dissolving capacity of drug-like molecules in lipophilic media [38]. As reported by Meanwell 2011, there may be complications in oral absorption if the calculated log P is greater than the standard value for log PLogP≤5 [36]. Therefore, compound E is expected to dissolve well in nonaqueous substance and possesses the ability to be administered through the human mouth. More so, the calculated EHOMO which describes the tendency of drug-like molecules to donate electron to the neighbouring compounds was calculated to be −6.74 (A), −6.69 (B), −6.66 (C), −6.75 (D), and −7.17 (E). As shown in Table 1, compound C is expected to inhibit than other studied compounds [39]. The calculated ELUMO shows the capability of compounds to accept electrons from nearby molecules [40]. In this regard, compound C with −1.15 eV is expected to possess a greater ability to accept electron from the nearby compounds than other studied compounds. Other studied calculated descriptors are displayed in Table 1.More so, the calculated band gap shows the level of reactivity of compounds with drug potentials. Also, many reports from several researchers have proved that level of reactivity of molecular compounds is a function of lower bandgap; thus, compoundC is expected to react better than other studied compounds [41]. Figure 2 shows the EHOMO-ELUMO overlay.Figure 2
StudiedEHOMO-ELUMO overlay.Furthermore, chiral information of some stereogenic centers of the studied compounds as displayed in Figure1 was investigated. In compound A, seven (7) chiral centers were observed (4R and 3S), and seven chiral centers were also observed in compound B with 7R and 6R as the chiral center was observed in compound C. More so, seven (7) and two (2) chiral centers were detected in compounds D and E, respectively.


## 3.2. Prediction of ADMET Properties

Series of obtained ADMET properties using admetSAR server [31] were reported. As shown in Table 2, it was observed that compounds A, B, D, and E had better human intestinal absorption than the standard used (5FU) except compound C since drug-like compound with the higher value of human intestinal absorption can be absorbed well in the intestine [42]. The values obtained for the blood-brain barrier for compounds A–E showed that compound E has a very closer value to that of 5FU. Also, the blood-brain barrier value for compounds A–D falls within the same ranges shown in Table 2. The obtained P-glycoprotein report (nonsubstrate and noninhibitor) for all the studied compounds was similar to those of 5FU. Also, to know the level of mutagenicity of the studied compounds, AMES was considered. Thus, in terms of AMES study, all the studied compounds were negative, and this also correlated with the standard (5FU). As shown in Table 2, all the studied compounds were not carcinogenic and these were similar to the similar used. The calculated cytochrome P450 showed that it could be inhibited by some of the studied compounds.Table 2
Prediction of ADMET.
ModeCompoundACompoundBCompoundCCompoundDCompoundE5FUResultProbabilityResultProbabilityResultProbabilityResultProbabilityResultProbabilityResultProbabilityBlood-brain barrierBBB+0.7186BBB+0.7186BBB+0.6825BBB+0.7186BBB+0.9398BBB+0.9791Human intestinal absorptionHIA+0.9637HIA+0.9637HIA+0.9408HIA+0.9637HIA+0.9921HIA+0.9605Caco-2 permeabilityCaco-2−0.5236Caco-2−0.5236Caco-2−0.5635Caco-2−0.5236Caco-2−0.6145Caco-2−0.7583P−Glycoprotein substrateSubstrate0.6777Substrate0.6777Substrate0.6876Substrate0.6777Substrate0.5000Nonsubstrate0.7752P−Glycoprotein inhibitorNoninhibitor0.8357Noninhibitor0.8357Noninhibitor0.7735Noninhibitor0.8357Noninhibitor0.8489Noninhibitor0.8991Noninhibitor0.8228Noninhibitor0.8228Noninhibitor0.8460Noninhibitor0.8228Noninhibitor0.7578Noninhibitor1.0000Renal organic cation transporterNoninhibitor0.8945Noninhibitor0.8945Noninhibitor0.8667Noninhibitor0.8945Noninhibitor0.8726Noninhibitor0.9053Subcellular localizationMitochondria0.7433Mitochondria0.7433Mitochondria0.8200Mitochondria0.7433Mitochondria0.6912Mitochondria0.6099CYP450 2C9 substrateNonsubstrate0.8690Nonsubstrate0.8690Nonsubstrate0.8836Nonsubstrate0.8690Nonsubstrate0.7757Nonsubstrate0.7898CYP450 2D6 substrateNonsubstrate0.8739Nonsubstrate0.8739Nonsubstrate0.8807Nonsubstrate0.8739Nonsubstrate0.8521Nonsubstrate0.9116CYP450 3α4 substrateSubstrate0.5768Substrate0.5768Substrate0.5873Substrate0.5768Nonsubstrate0.5851Nonsubstrate0.7558CYP450 1A2 inhibitorNoninhibitor0.7870Noninhibitor0.7870Noninhibitor0.8304Noninhibitor0.7870Noninhibitor0.7422Noninhibitor0.9045CYP450 2C9 inhibitorNoninhibitor0.8834Noninhibitor0.8834Noninhibitor0.8791Noninhibitor0.8834Noninhibitor0.8551Noninhibitor0.9658CYP450 2D6 inhibitorNoninhibitor0.8954Noninhibitor0.8954Noninhibitor0.8902Noninhibitor0.8954Noninhibitor0.9517Noninhibitor0.9361CYP450 2C19 inhibitorNoninhibitor0.6454Noninhibitor0.6454Noninhibitor0.8277Noninhibitor0.6454Noninhibitor0.7020Noninhibitor0.9688CYP450 3α4 inhibitorNoninhibitor0.6168Noninhibitor0.6168Inhibitor0.5244Noninhibitor0.6168Noninhibitor0.8858Noninhibitor0.9661CYP inhibitory promiscuityLow CYP inhibitory promiscuity0.8548Low CYP inhibitory promiscuity0.8548Low CYP inhibitory promiscuity0.9361Low CYP inhibitory promiscuity0.8548Low CYP inhibitory promiscuity0.9447Low CYP inhibitory promiscuity0.9839Human Ether-a-go-go-Related Gene InhibitionWeak inhibitor0.9404Weak inhibitor0.9404Weak inhibitor0.9435Weak inhibitor0.9404Weak inhibitor0.9575Weak inhibitor0.9685Noninhibitor0.8093Noninhibitor0.8093Noninhibitor0.7379Noninhibitor0.8093Noninhibitor0.8589Noninhibitor0.9325AMES toxicityNon-AMES toxic0.8637Non-AMES toxic0.8637Non-AMES toxic0.8877Non-AMES toxic0.8637Non-AMES toxic0.9507Non-AMES toxic0.8941CarcinogensNoncarcinogens0.9532Noncarcinogens0.9532Noncarcinogens0.9554Noncarcinogens0.9532Noncarcinogens0.9268Noncarcinogens0.9288Fish toxicityHigh FHMT0.9146High FHMT0.9146High FHMT0.8980High FHMT0.9146High FHMT0.7714Low FHMT0.6050Tetrahymena pyriformis toxicityHigh TPT0.9968High TPT0.9968High TPT0.9960High TPT0.9968High TPT0.9952High TPT0.9526Honey bee toxicityHigh HBT0.7560High HBT0.7560High HBT0.7209High HBT0.7560High HBT0.6880Low HBT0.8479BiodegradationReadily biodegradable0.6696Readily biodegradable0.6696Readily biodegradable0.8621Readily biodegradable0.6696Readily biodegradable0.7498Not readily biodegradable0.9220


## 3.3. Molecular Docking and Scoring Studies

Series of selected molecular compounds fromAnnona muricata seed were docked into the active site of 3α-HSD to observe the nonbonding interaction that was involved. To establish the accuracy of the engaged docking method, the docked ligand retrieved from the crystal structure of 3α-HSD was docked again into the active site of the 3α-HSD to determine the similarity of the best conformation of the redocked ligand (i.e., compound with the lowest energy) to the position of the native ligand (Figure 3). Hence, the similarity index of the native drug-like compound retrieved from the downloaded receptor to the redocked drug compound was similar to 1. In this work, the docking method used is observed to be dependable. In this study, it was observed that compound C has a promising ability to inhibit than other studied compounds. Also, the correlation between the molecular descriptors was observed and it was discovered that EHOMO, ELUMO, and bandgap as displayed in Table 1 greatly contributed to the promising inhibiting activity of compound C than other studied compounds.Figure 3
Overlay of native drug-like compounds over redocked drug compound.The docking score, as well as the binding affinity obtained for the studied complexes, was calculated to be negative, and this is a sign that the studied compounds could serve as the starting point for developing effective drugs targeting 3α-HSD, thereby reducing breast cancer in woman globally. Also, as shown in Table 3, the entire studied compound proved to be more effective than the standard used. The residues involve in each of the conformation are PRO-226, TRP-227, ALA-27, GLU-224, LEU-306, TYR-24, HIS-222, HIS-117, and LYS-270 for A; LYS-270, LEU-268, GLY-22, THR-23, ASP-50, TYR-55, GLN-190, TYR-24, VAL-54, and VAL-128 for B; LYS-270, HIS-222, SER-217, ASP-50, TYR-55, HIS-117, TRP-227, TYR-24, and ALA-27 for C; PRO-226, TRP-227, LEU-308, LEU-306, TYR-55, TYR-24, HIS-222, and LYS-270 for D; and THR-23, TYR-65, TYR-24, and TRP-227 for E (Figure 4).Table 3
Scoring of selected compounds inAnnona muricata seed with 3α-HSD.
Binding affinity (kcal/mol)Amino acid residueA−7.0PRO-226, TRP-227, ALA-27, GLU-224, LEU-306, TYR-24, HIS-222, HIS-117, LYS-270B−7.3LYS-270, LEU-268, GLY-22, THR-23, ASP-50, TYR-55, GLN-190, TYR-24, VAL-54, VAL 128C−7.5LYS-270, HIS-222, SER-217, ASP-50, TYR-55, HIS-117, TRP-227, TYR-24, ALA-27D−7.1PRO-226, TRP-227, LEU-308, LEU-306, TYR-55, TYR-24, HIS-222, LYS-270E−7.2THR-23, TYR-65, TYR-24, TRP-2275FU−5.6TYR-55, ASP-50, THR-23, TYR-24, GLY-22Figure 4
Binding interaction of compoundsA, B, C, D, and E with 3α-HSD, respectively.
(a)(b)(c)(d)(e)


## 3.4. Molecular Dynamic Simulation Result

### 3.4.1. Root of Mean Square Deviation (RMSD)

RMSD of 3α-HSD backbone atoms in connection to the initial molecular structure of the studied enzyme in forming complexes with compounds C and 5FU during the 100 ns MD simulation is displayed in Figure 5. This was accomplished to investigate the extent of deviation from the initial structure upon binding and the steadiness of the simulated complexes. Thus, the structures shown in Figure 5 proved that the conformation of the studied complexes was stable and the average RMSD value for compounds C-3α-HSD and 5FU-3α-HSD was 1.17 Å and 1.47 Å, respectively. The systems were well equilibrated and stable after 60 ns of the simulation time.Figure 5
RMSD of compoundsC-3α-HSD (black) and 5FU-3α-HSD (red) complexes during the 100 ns MD simulations.


### 3.4.2. Root of Mean Square Fluctuation

The calculated root mean square fluctuation (RMSF) reveals the flexibility of residues upon binding of the two compounds tested during simulation time. The studied RMSF helped to know the flexibility of the residue present in the studied enzymes during 50000 ps simulation time (Figure6). As displayed in Figure 5, there is a similarity in the trend at which the observed residue vacillates; however, little variation was observed between compounds C-3α-HSD and 5FU-3α-HSD. The pattern shown in Figure 5 proved that compound C-3α-HSD exhibited a minor degree of deviation and this shows a higher level of affinity for compound C to inhibit 3α-HSD.Figure 6
RMSF plots of compoundsC-3α-HSD (black) and 5FU-3α-HSD (red) complexes during the 100 ns MD simulations.


### 3.4.3. Binding Free Energy

The binding energy for compoundsC-3α-HSD and 5FU-3α-HSD was calculated using the MM/GBSA method (Table 4).Table 4
Calculated binding free energies and its components for the complexes using the MM-GBSA method extracted from the last 1000 frames of 100 ns.
CompoundΔEvdwΔEeleΔGgasΔGpolarΔGnonpolarΔGsolvationΔGbindC−87.98−10.94−98.9134.36−11.1623.20−75.715FU−17.39−2.24−19.636.96−2.324.63−15.00The energy components are in kcal/mol.As shown in Table4, it was observed that compound C possesses a better ability to inhibit 3α-HSD than the standard used. The calculated free energy includes van der Waal energy (−87.98 kcal/mol), electrostatic energy (−10.94 kcal/mol), polar solvation energy (34.36 kcal/mol), and nonpolar solvation energy (−11.16 kcal/mol). This specifies that van der Waal energy, electrostatic energy, and nonpolar solvation energy are favourable; however, polar solvation energy was not favourable for the molecular binding of compound C to 3α-HSD.


## 3.4.1. Root of Mean Square Deviation (RMSD)

RMSD of 3α-HSD backbone atoms in connection to the initial molecular structure of the studied enzyme in forming complexes with compounds C and 5FU during the 100 ns MD simulation is displayed in Figure 5. This was accomplished to investigate the extent of deviation from the initial structure upon binding and the steadiness of the simulated complexes. Thus, the structures shown in Figure 5 proved that the conformation of the studied complexes was stable and the average RMSD value for compounds C-3α-HSD and 5FU-3α-HSD was 1.17 Å and 1.47 Å, respectively. The systems were well equilibrated and stable after 60 ns of the simulation time.Figure 5
RMSD of compoundsC-3α-HSD (black) and 5FU-3α-HSD (red) complexes during the 100 ns MD simulations.


## 3.4.2. Root of Mean Square Fluctuation

The calculated root mean square fluctuation (RMSF) reveals the flexibility of residues upon binding of the two compounds tested during simulation time. The studied RMSF helped to know the flexibility of the residue present in the studied enzymes during 50000 ps simulation time (Figure6). As displayed in Figure 5, there is a similarity in the trend at which the observed residue vacillates; however, little variation was observed between compounds C-3α-HSD and 5FU-3α-HSD. The pattern shown in Figure 5 proved that compound C-3α-HSD exhibited a minor degree of deviation and this shows a higher level of affinity for compound C to inhibit 3α-HSD.Figure 6
RMSF plots of compoundsC-3α-HSD (black) and 5FU-3α-HSD (red) complexes during the 100 ns MD simulations.


## 3.4.3. Binding Free Energy

The binding energy for compoundsC-3α-HSD and 5FU-3α-HSD was calculated using the MM/GBSA method (Table 4).Table 4
Calculated binding free energies and its components for the complexes using the MM-GBSA method extracted from the last 1000 frames of 100 ns.
CompoundΔEvdwΔEeleΔGgasΔGpolarΔGnonpolarΔGsolvationΔGbindC−87.98−10.94−98.9134.36−11.1623.20−75.715FU−17.39−2.24−19.636.96−2.324.63−15.00The energy components are in kcal/mol.As shown in Table4, it was observed that compound C possesses a better ability to inhibit 3α-HSD than the standard used. The calculated free energy includes van der Waal energy (−87.98 kcal/mol), electrostatic energy (−10.94 kcal/mol), polar solvation energy (34.36 kcal/mol), and nonpolar solvation energy (−11.16 kcal/mol). This specifies that van der Waal energy, electrostatic energy, and nonpolar solvation energy are favourable; however, polar solvation energy was not favourable for the molecular binding of compound C to 3α-HSD.


## 4. Conclusion

Breast cancer is an incessantly growing menace worldwide. Despite several ways to combat this deadly disease by many scientists/researchers, its rate of operation among women keeps increasing. Also, 3α-HSD contributes to the existence of breast cancer in woman, and inhibiting 3α-HSD will impulsively decrease MCF-7 cells growth. In this work, an investigation was carried out on Annona muricata L. seed as an anticancer agent via density functional theory, docking study, and molecular dynamics simulation studies. All the studied compounds proved to be active as an antibreast cancer agent. Also, the molecular descriptors that described the antihuman breast cancer activities in the studied compounds and their contribution to antihuman breast cancer of the studied compounds were identified. More so, compound C possesses a better ability to inhibit 3α-HSD than other studied compounds and the standard (5FU). Therefore, this study shows the potential ability of Annona muricata L. seed as an effective antibreast cancer agent.



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*Source: 6735232-2020-11-09.xml*