Every year, millions of women around the world are affected by breast cancer. As related to bio-molecular dynamics, breast carcinogenesis is not well understood due to a number of risk features. According to WHO statistical reports, the prevalence of female breast cancer, which makes up around 12% of all cancers globally and breast cancer risk factors have increased over the past few years, representing 23% of cancer-related mortalities in Asia [1]. Most breast cancers exhibit overexpression of progesterone receptor and estrogen receptors. It is necessary to ensure accurate testing of estrogen receptor (ER) and progesterone receptor (PR) expression for patient management [2]. Estrogen-induced activation promotes anti-apoptotic signaling, cell division, and tumor growth in ER-positive breast cancer. Endocrine therapy frequently works for ER-positive tumors since they are hormone dependent. Additionally, progesterone-induced activation controls apoptosis, differentiation, and cell proliferation. PR-positive tumors are typically less aggressive and more differentiated. Rapid cell proliferation and metastasis are fueled by constitutive activation of cell signaling pathways caused by overexpression of human epidermal growth factor receptor2 (HER2). Aggressive HER2-positive breast tumors usually need specific treatments to stop HER2 signaling and enhance patient outcomes [3]. Selecting the right treatments and forecasting how breast tumors will behave require an understanding of these pathways. The molecular characterization of breast cancer tumors is essential for identifying the best therapeutic approaches, and targeted medicines targeting ER, PR, and HER2 receptors are some of the most successful treatments against breast cancer. Breast cancer survival rates have drastically increased due to improvements in detection and modulation in therapies [4]. Agents against new molecular targets have been created in therapy exclusively active against malignancies with the targeted molecular modification [5]. Multi-target medicines are used to get around the drawbacks of single-target therapies. Drug therapies with several targets are more efficient and less likely to develop resistance [6]. Several medicinal plant extracts, either in crude form or their isolated pure compounds, are found effective against various cancer cell lines or targeted receptors. The use of bioactive substances derived from plants in the chemoprevention of cancer is highlighted in research fields. Many plant-based dietary components are also widely used in chemoprevention [7]. It is also due to failure of conventional chemotherapeutic methods, which calls for creating novel, effective drugs to inhibit cancer with minimum risk of unpleasant effects, plants serve as significant sources of these promising natural components. Up to 28% of all current medications are directly or ramblingly derived from higher plants, bringing to light the immense potential of longer-used plants. Additionally, about 60% of sources for anticancer medications come from the plant kingdom [8]. Another issue with disseminating traditional medical knowledge of various phytochemicals is the lack of documentation on the ethnobotanical use of anticancer medicinal plants and their active ingredients [9].
Many encouraging activities of plant-based compounds against human cancer models have been reported. Flavonoids, coumarins, glycosides, terpenoids, and monoterpenes are the different types of phytochemicals, and they have a range of therapeutic benefits such as chemotherapy, cytotoxicity, anti-neoplastic activity, anti-bacterial, and anti-inflammatory activity [10]. Essential oils (EOs) of medicinal plants include a wide range of secondary metabolites having a potential to reduce the spread of cancer or suppressing malignant cell survival. The use of plant-based EO and the application of these substances in various cancer technologies may all indicate that these substances have a significant potential to treat metastasis [11]. According to population-based studies, isolated terpenes and isoprenoids from plant derived EOs have been linked to a lower incidence of malignancies. Phytochemicals have been shown to have both therapeutic and preventive benefits against carcinogenic induced mammary tumors and gastric cancer [12].
The genera Vinca, also known as the genus Catharanthus, is a member of the Apocynaceae family. Many plant parts of C. roseus are frequently utilized in several medicines and have a strong reputation in the pharmaceutical industry for creating anticancer medications. Malignancies, genitourinary systems, and cardiovascular diseases can be well treated with such plant-based formulations [13]. More than 400 useful flavonoids, terpenes, sterols, and alkaloids, including vincristine, vinblastine, catharanthine, vindesine, ajmalicine, vindolicine, and vindoline, are present in Catharanthus roseus. The phytochemicals vinblastine, vincristine, vindeline, and tabersonine are primarily found in plant aerial portions [10]. Terpenes, terpenoids, alkaloids, and saponins are abundant in this genus [14]. Active terpene metabolites isolated are
Molecular docking is an advanced approach applied to study the molecular behavior of targeted receptors, their binding affinity, and interacted amino acid residues. It is used approximately in drug identification, and to find out the pharmacophore potential of phytocomponents. Animal testing and in vitro analyses are two of the more time-consuming and costly drug development techniques. The existing anti-breast cancer treatment plans are ineffectual and have numerous long-term drawbacks. The first line therapy may or may not be effective, it may help for a short while before ceasing, or it may have serious side effects. Investigating computer prediction technologies can assist in quickly and cheaply screening a large number of compounds for their anti-cancer potential without harming animals. By providing the initial data for in vivo testing, this can raise study’s success rate [23,24]. The presented study aims to evaluate potential lead terpene phytocompounds identified by C. roseus plant as potential inhibitors of breast cancer targets such as ER, PR, and HER2 receptors by using in silico docking, MD simulation approach, and by in vitro anticancer evaluation of selected terpene compounds. The probable in silico interactions, pharmacokinetic parameters and bioavailability properties of selected terpenes against breast cancer targets were assessed. Software used for the best scores in docking are commonly AutoDock vina, Pymol, and PyRx [10]. The interaction trajectories are typically obtained using molecular dynamics simulation, so observed as a universal tool to examine the interactions and conformational constancy of docked biomolecules [25]. The response to endocrine therapy in breast cancer is significantly influenced by the presence or absence of the ER, PR, and HER2 receptors. Furthermore, it is unclear that the best course of treatment for a patient with breast cancer can be determined simply by looking at the expression of cancer receptors. So, an effort has been made to evaluate the computational study of the selected terpene phytocompounds by molecular docking study and dynamic simulation analysis.
Catharanthus roseus plants were collected in spring season and identified by Dr. Iftikhar Ahmad. A voucher specimen (181-1-23) was deposited at the Department of Botany, Agriculture University Faisalabad. The fresh plants were air-dried in shade for several days, then ground to make a fine powder and then used for EO extraction.
C. roseus oil was extracted using hydro distillation with a Clevenger-type hydro distillation apparatus. The obtained C. roseus oil was stored at 4–6°C for further analysis and compound isolation [26]. The GC-MS analysis of the C. roseus oil was accomplished using a multi-dimensional gas chromatography technique [27]. To identify phyto-components in EO, a GC-MS study employing a capillary column and helium as the carrier gas was carried out. Comparison of their mass spectra and retention indices (RI) served as the basis for identifying compounds [28]. Using the National Institute of Standard and Technology (NIST) library’s database, the mass spectrum of identified terpene compounds in EO was interpreted.
Tools used in this study comprises DELL audio computer system (intel corei6, 64 GB RAM, Windows 10 operating system). PubChem database, protein data bank, ChemDraw 3D Pro 12.04 v, Open Babel GUI 3.1.1, Auto dock tools in Auto-dock 4.6 program, python, LigPlot+ version v.2.2.8, NAMD 2.13; 2020, and discovery studio 4.0 were the software’s used for presented in silico study.
The three-dimensional protein structure of the human estrogen receptor (protein data bank [PDB] id: 2iok), human progesterone receptor (PDB id: 1e3k), and HER2 receptor (PDB id: 3pp0) was downloaded in PDB format with the protein databank (http://www.rcsb.org/pdb/home/home.do) (Figure S1). The protein preparation was carried out to arrange and refined the structure for docking progress. The binding site was analyzed for all receptors, and protein files were prepared in the AutoDock4 program to save in PDBQT format. Docking studies used AutoDock Vina and Discovery Studio version 4.0.
Terpene phytocompounds as recognized from GC-MS analysis of Catharanthus roseus EO were further confirmed by PubChem (https://pubchem.ncbi.nlm.nih.gov) database. Phytochemicals with their 3D structures were attained from open-source PubChem (https://pubchem.ncbi.nlm.nih.gov/). The ligands were transformed into protein data bank (PDB) files using Open Babel software as designed for this purpose [29].
As ligands were bound to the constructed protein’s binding site by means of a docking receptor grid generating file, which was created by selecting molecules from a subset of PDB IDs. Then, pdbqt format was used to store all target proteins and chosen ligands. A 24 × 28 × 21 Å grid box was used for the molecular docking process. Specific dimensions and a spacing of 0.345 Å were required while designing grid boxes. According to x, y, and z coordinates, the grid’s center was at 17.42, 36.11, and 43.18. The processed protein data files in PDB format were used for docking to identify the active site amino acids. The ligand and receptor grid box were built, the exhaustiveness was set to 32, and the Autodock Vina docking process was executed. The grid box was positioned surrounding the active site. Lamarckian Genetic Algorithm technique was used for docking calculations, and the best conformation with the lowermost docked energy was selected [30]. Discovery Studio 4.0 version (2021 client) was used to visualize the interactions between docking complexes.
A virtual screening using Lipinski’s “rule of five” filters and toxicity analysis was performed. The Data Warrior program version 4.6.1, Swiss-ADME and pkCSM web server was used to determine the physicochemical features of terpenes, including drug-likeness and toxicity, Boiled-egg model. Parameters like blood-brain barrier (BBB), AMES mutagenesis, carcinogens toxicity, Caco-2 permeability, CYP enzymes and Log S values were also evaluated [31]. The best candidates for drugs are molecules that follow Lipinski’s rule. An ADME and pharmacodynamic study were conducted to verify parameters pertaining to absorption, distribution, metabolism, excretion, toxicity, solubility in mg/mol (LogS), CaCO-2 permeability, human intestinal absorption (HIA), cytochrome substrate/inhibitor and AMES toxicity [32].
The best-docked conformations with the lowest binding energies for these compounds were used for the MD simulation. MD simulations were performed to study the docked complexes of cancer receptors to examine the different conformations stability and receptor flexibility at nanosecond scales. NAMD 2020 was used for MD simulation analysis [33]. The 3D structure of the receptor–ligand complex was prepared. The structure can be obtained through docking studies. Amber tools and the force field were used to run the simulations, and trajectories were analyzed by CPPTRAJ [34]. Antechamber packages in Amber Tools 20 were used to create the ligands’ topology. The TIP3P water model was used to solvate and neutralize each system in a triclinic box. 100 ns MD simulations were run on the equilibrated structure. After equilibration, the production run was performed, which can range from hundreds of nanoseconds to microseconds, depending upon the system size and computational power of simulation. Periodic boundary condition modifications were applied to all trajectories prior to analysis. The backbone’s original conformation was determined using NAMD utilities to determine its root mean square deviation (RMSD). In addition, calculations were made for secondary protein structure, root mean square fluctuations (RMSF), and radius of gyration (Rg). A fresh trajectory file and the covariance matrix were produced using the principal component analysis (PCA). After calculating the PCA, a new trajectory file and the covariance matrix were diagonalized. The new trajectory was written, it was analyzed, and every structure was fitted to the eigenvector file [35]. The dynamic cross-correlation analysis was used to regulate the correlation coefficient to examine how residual displacements in docked proteins correlated throughout the MD simulation [25].
Human breast cancer (MDA-MB-231) cell lines were acquired from University of Lahore’s IMBB department cell culture lab. Cell lines are usually subjected to stringent characterization for standard validation procedures and mycoplasma testing. Using an inverted microscope, the cell separation was confirmed after a 1× PBS rinse and trypsin EDTA treatment. The culture was then used in additional in vitro investigations.
The percentage of cell viability were determined using the 3-(4,5-dimethylthiazol-2yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay with the chosen terpene compounds. Using an MTT assay, the impact of top docked terpenes (those having lowest binding energy and substantial ADMET properties) on breast cancer cells viability was examined. After being seeded in 96-well plates, the cells were cultured for 24 h. Terpene samples were then added to the cells. After that, the cells were cultivated once more in an incubator for 24 h. After the sample had been exposed for 24 h, 100 mL of MTT solution was added, incubated, and then quickly removed [36]. The selected terpenes were used to calculate the IC50 value and the percentage of cell viability using the 3-(4,5-dimethylthiazol-2yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay. The cells were seeded in 96-well plates and left to culture for 24 h. The cells were then treated with terpene samples. Then, the cells were cultured again for 24 h in an incubator. Following 24 h of exposure to the sample, 100 mL MTT solution was added, incubated and promptly removed. 100 mL of dimethyl sulfoxide (DMSO) were added to each well in order to dissolve the formazan crystal. Using a BIOBASE microplate reader (BioTek-ELx800) (Shan Dong, China), the optical density of the samples at 590 nm was measured, and the samples’ inhibitory concentration (IC50) was also determined [37].
GC-MS chromatograms of the chemical constituents of C. roseus EO produced by hydrodistillation methods are given in Figure 1. Several compounds were detected from GC-MS analysis of C. roseus, including terpenes, flavonoids, terpenoids, alkanes, and aldehydes. Each peak identified the bioactive compounds recorded by comparing their peak retention time, molecular weight, and molecular formula to known compounds suggested by NIST library (https://www.nist.gov/nist-research-library). A few compounds observed are, beta-Pinene 0.40%, gamma-terpinene 1.19%, alpha-pinene 36.03%, camphene 2.16%, terpinolene 1.53%, o-cymene 1.85%,
GC-MS chromatogram of Catharanthus roseus EO.
The isolated (2-carene and incensole acetate) and identified mono terpene bioactive substances were selected for the in silico study, and their energy was minimized using UCSF Chimera 1.14 prior to docking for their right conformation. These selections were also based on the Rule of Five, GC-MS analysis data, and PubChem database. The pharmacophore model was virtually used to screen the terpene compounds with the best pharmacophoric properties and hit scores. Utilizing AutoDock Vina and a Python interpreter, the molecular docking of these compounds was performed. Based on visualization, the molecule with the most success and interaction residual stability was chosen for further MD simulation analysis.
The computational analysis was performed to examine the interaction between terpene phytochemicals of C. roseus EO and breast cancer targets. Selected compounds were docked with 3D structure of ER, PR, and HER2, and the results are shown in Table 1. Receptor–ligand interactions and hydrogen bonding were detected clearly. Negative least binding values determine favorable bond between estrogen and HER2 receptor with gamma-terpinene, terpinen-4-ol, and limonene in most favorable conformations.
Molecular interaction profile of breast cancer receptors with terpene hit phytocompounds identified from C. roseus EO
| Receptor | Compound | Vander Waals interaction | No. of H bonds | H-bond length (Å) | Hydrogen bond residues | Pi–Pi interaction | Docking score (kcal/mol) |
|---|---|---|---|---|---|---|---|
| Estrogen | Gamma-terpinene | THR227, GLY 259 | 4 | 1.722 | LYS 197, GLY 259, TYR 193, LEU 173 | PHE 229, TRP 53 | −6.9 |
| 1.451 | |||||||
| 2.311 | |||||||
| 1.321 | |||||||
| Terpinen-4-ol | LYS 197, ARG 130, GLY 259, PHY 229 | 1 | 1.97 | TYR 193 | TRP 53 | −6.3 | |
| Terpinolene | LEU 428, GLY 321 | 2 | 2.711 | MET 38, LEU 391, ALA 35, GLY 390 | LEU 428, MET 388 | −6.1 | |
| 2.177 | |||||||
| Limonene | LUE 349, ARG394, ALA 350 | 1 | 2.19 | ALA350, LEU391, GLY390, ARG 394 | MET 388, LEU 387, LEU 391 | −6.5 | |
| 2-Carene | TRP 383, ARG 394, LEU 349 | 1 | 1.069 | ALA 351, TRP 383, GLU 353 | ALA 350, LEU 387 | −6.3 | |
| Incensole -acetate | LEU384, MET388, PHE 404, GLY 521 | 3 | 2.196 | ALA 354, MET 388, LEU 375 | — | −8.0 | |
| 2.160 | |||||||
| 2.178 | |||||||
| Progesterone | Gamma-terpinene | LEU 797, TYR890, CYS 891, PHE 905 | 2 | 1.875 | ASN719, PHE 895 | PHE 794, LEU715, MET 801 | −6.2 |
| 1.421 | |||||||
| Terpinen-4-ol | GLU723, LEU726, ASN719, GLU 721 | 4 | 1.919 | LEU726, GLY 722 | MET 759 | −6.3 | |
| Terpinolene | ASN719, PHE 794, LEU 715 | 1 | 1.8997 | CYS 891 | TYR890, LEU887, PHE 905, MET 801 | −6.3 | |
| Limonene | PHE 778, LEU 718, ASN719, MET 801 | 5 | 2.54 | ASN719, MET801, CYS891, THR894, PHE 905 | LEU715, PHE 905, TYR890, VAL 903 | −6.2 | |
| 2-Carene | LEU 718, THR 894 | — | — | LEU 715, ASN 719, TAR 894 | VAL 903 | −6.3 | |
| Incensole -acetate | MET 759, LEU 887, TYR 890 | 3 | 1.91 | GLY 722, GLY 724, CYS 891 | — | −4.6 | |
| 2.46 | |||||||
| 2.08 | |||||||
| HER2 | Gamma terpinene | LEU 866, ARG 868 | 4 | 1.849 | ALA730, LYS 853, GLY804, ASP 863 | LEU852, VAL734, LYS 753 | −6.8 |
| 1.613 | |||||||
| 2.088 | |||||||
| Terpinen-4-ol | — | 4 | 1.770 | SER 783, MET774, LYS 753, THR 798 | VAL734 LYS 753 | −7.1 | |
| 2.652 | |||||||
| Terpinolene | THR862, MET774, ASP 863, GLU770, SER 783 | 3 | 1.797 | ALA730, LYS 753, THR798, ASP 863 | VAL734, LEU796, PHE 864, ALA 751 | −6.6 | |
| 2.058 | |||||||
| Limonene | THR798, ASP 863, THR 862 | 3 | 1.794 | ALA730, LYS 753, ASP 863 | ALA 751, VAL 734 | −6.6 | |
| 1.849 | |||||||
| 2-Carene | SER 783, THR798, ASP 863, LYS 753 | 4 | 1.613 | LYS 753, SER 783, MET 774, ASP 613 | PHE 864, MET 774, LEU 785 | −6.6 | |
| 1.770 | |||||||
| 2.435 | |||||||
| 1.7842 | |||||||
| Incensole -acetate | — | 4 | 1.797 | ALA 730, LYS 753, LEU 806 | — | −3.4 | |
| 1.746 | |||||||
| 2.058 |
From the docking results, gamma-terpinene, limonene, and incensole acetate showed better interactions with estrogen receptor while gamma-terpinene showed the best binding affinity of −6.9 kcal/mol with amino acid residues of estrogen receptor (Figure 2). Gamma-terpinene showed hydrogen bond interaction with LYS 197, TYR 193, and Van der Waals interaction with GLY 259 and THR 227. Terpinen-4-ol gives conventional H-bond interaction with TYR 193, Van der Waals interactions with LYS 197, PHY 229, GLY 259, and ARG 130 having binding energy of −6.3 kcal/mol. Terpinolene showing hydrogen interaction with amino acid residues of MET 38, GLY 390, and LEU 391 along with Van der Waals interactions with ALA 350, ARG 394, and LEU 349 and showed binding score of −6.1 kcal/mol. The compound limonene showed hydrogen bond interaction with amino acid residues ALA 350, LEU 391, ARG 394, and GLY 390 and Van der Waals interaction with LEU 340, ARG 353, ALA 350, and GLU 353 with docking score of −6.5 kcal/mol. Isolated terpene compound 2-carene showed H-bonds with amino acids, ALA 351, TRP 383, and GLU 353, having binding score of −6.3 kcal/mol. Incensole acetate showed better interactions with estrogen protein receptor with binding energy of −8.0 kcal/mol with the interaction residues of LEU 384, PHE 404, and hydrogen bond interactions with ALA 354 and MET 388.
2D interaction of terpenes with surrounding amino acids of estrogen receptor: (a) ER-gamma-terpinene, (b) ER-terpinen-4-ol, (c) ER-terpinolene, (d) ER-limonene, (e) ER-2-carene, and (f) ER-incensole acetate.
Progesterone protein receptor shown that gamma-terpinene shown hydrogen bonding interactions with amino acid residues, ASN 719 and PHE 895, with binding energy of −6.2 kcal/mol as well as Van der Waals interactions with amino acid residues, CYS 891, TYR 890, LEU 797, and PHE 905. Terpinen-4-ol gives H-bond interaction with LEU 726 and GLY 722 and having binding score of −6.3 kcal/mol. Similarly, terpinolene showed hydrogen interaction with amino acid residue CYS 891 and showed binding score of −6.3 kcal/mol. The compound limonene showed good hydrogen bond interaction with amino acid residues ASN 719, MET 801, THR 894, and PHE 905 and Van der Waals interactions with LEU 7I8, ASN 719, PHE 778, and MET 801 with binding energy of −6.2 kcal/mol. 2-carene compound showed Van der Waals interactions with amino acid residues of LEU 718 and THR 894. The second isolated compound obtained from essential oil of C. roseus, incensole acetate showed interaction with estrogen protein receptor at amino acid residues of MET 759, LEU 887, and TYR 890 with binding energy of –4.6 kcal/mol. 2D structure of docked images are shown in Figure 3.
2D interaction of terpenes with surrounding amino acids of progesterone receptor: (a) PR-gamma-terpinene, (b) PR-terpinen-4-ol, (c) PR-terpinolene, (d) PR-limonene, (e) PR-2-carene, and (f) PR-incensole acetate.
Docking results observed from HER2 protein and selected terpenes interaction showed that gamma-terpinene gives hydrogen bonding interaction with amino acid residues LYS 853, GLY 804, ASP 863, and ALA 730, with binding score of −6.8 kcal/mol as well as Van der Waals interactions with amino acid residues MET 801, ASP 863, and LEU 800. Terpinen-4-ol gives H-bond interaction with LYS 753, MET 774, SER 783, and THR 798 having binding energy value of −7.1 kcal/mol. Terpinolene shown hydrogen interaction with amino acid residues, ALA 730, LYS 753, THR 798, and ASP 863. The compound limonene showed good hydrogen bond interaction with amino acid residues ALA 730, ASP 863, LYS 753, and Van der Waals interactions with THR 862, ASP 863, and THR 798 with binding energy of −6.6 kcal/mol. 2-carene showed binding score of −6.6 kcal/mol and incensole acetate showed binding energy of –3.4 kcal/mol (Figure 4).
2D interaction of terpenes with surrounding amino acids of HER2 receptor: (a) HER2-gamma-terpinene, (b) HER2-terpinen-4-ol, (c) HER2-terpinolene, (d) HER2-limonene, (e) HER2-2-carene, and (f) HER2 - incensole acetate.
From docking data, it was observed that gamma-terpinene docked fine with ER with docking value −6.9 kcal/mol with four hydrogen bonds and six hydrophobic interactions. This terpene phytocompound interrelated with 11 amino acid residues of ER receptor protein making strong H-bonds with residues, while limonene with estrogen receptor showed highest binding affinity of −6.5 kcal/mol. The strong binding affinity was due to the presence of hydrogen bonds with amino acid residues, ALA 350, LEU 391, GLY 390, ARG 394, and hydrophobic interactions formed with MET 388. Incensole acetate showed −8.0 kcal/mol binding energy but had no favorable interaction residues for hydrophobic and pi–pi interaction. Likewise, terpinen-4-ol docked fine with HER2 with a binding free energy value −7.1 kcal/mol and gamma terpinene with HER2 receptor with highest binding affinity and the lowest binding energy of −6.8 kcal/mol was reported. Constancy in the binding site of ER, PR, and HER2 were predictable by amino acid residue forming interfaces like Pi–Pi interaction, H-bonding and Van der Waals interactions, and hydrophobic interaction [42]. Figure 5 presents the 3D visualization of all docked complexes. Out of all selected terpene ligands, top scored best hit docked complexes had significant docking interactions as they act as inhibitors against selected cancer targets and their hydrophobic interaction was further observed by LigPlot+ representation (Figure 6).
3D images of selected terpenes with interacting amino acid residues: (a) ER-gamma terpinene, (b) ER-terpinen-4-ol, (c) ER-terpinolene, (d) ER-limonene, (e) ER-2-carene, (f) ER-incensole acetate, (g) PR-gamma-terpinene, (h) PR-terpinen-4-ol, (i) PR-terpinolene, (j) PR-limonene, (k) ER-2-carene, (l) ER-incensole acetate, (m) HER2-gamma-terpinene, (n) HER2-terpinen-4-ol, (o) HER2-terpinolene, (p) HER2-limonene, (q) ER-2-carene, and (r) ER-incensole acetate.
Two-dimensional LigPlot demonstration of top docked ligands interacting with amino acid residues of receptor protein: Interaction of gamma terpinene with ER, interaction of limonene with ER, interaction of gamma terpinene with incensole acetate, interaction of gamma terpinen with HER2, and interaction of terpinen-4-ol with HER2. Red dotted lines specify the hydrophobic interactions while green residues denote the hydrogen bonds involved in the interaction.
Isolated terpenes 2-carene and incensole acetate obtained from EO along with some other identified terpenes such as terpinen-4-ol, gamma terpinene, terpinolene, and limonene were patterned for the ADMET profile using Swiss-ADME software and findings are shown in Table 2. Using ADMET and pkCSM web server, the toxicity of all compounds was predicted. The drug-induced (Mutagenic potential of chemical compound) AMES toxicity, hepatotoxicity, carcinogenic property, and acute toxicity of selected terpene compounds are listed in Table 3. According to the Lipinski role, a compound needs to have a molar weight of less than 500 Da, a molar refractivity of 40–130 m3 mol1, high lipophilicity (log P < 5), less than five hydrogen bond donors, and fewer than ten acceptors in order to be considered a possible ligand. Any compound that failed to comply with two or more of the previously stated criteria was excluded from additional research. All selected terpenes have very poor BBB permeability and are incapable of penetrating central nervous system (CNS). All terpene compounds were not CYP2C9, CYP2D6, and CYP3A4 inhibitor. At 1.351 and −0.369, limonene has the lowest total clearance value and gamma terpinene has the highest. The renal organic transporter (OCT2) does not use any of the phytochemicals that were chosen as substrates. It was observed from ADME analysis that all selected terpene compounds qualify rule of 5 so they may be considered for cancer drug development and further computational analysis was successfully performed against breast cancer targets.
ADMET properties of terpenes phytocompounds reported in C. roseus EO
| ADMET parameters | Gamma terpinene | Terpinen-4-ol | Terpinolene | Limonene | 2-Carene | Incensole acetate |
|---|---|---|---|---|---|---|
| Aq. solubility (mg/mol) | 4.33 | 2.54 | 3.41 | 3.31 | 4.51 | 4.65 |
| BBB penetration | Yes | Yes | Yes | Yes | Yes | Yes |
| Bioavailability | 0.55 | 0.55 | 0.55 | 0.55 | 0.55 | 0.55 |
| VDss | Moderate | Low | Moderate | High | High | Low |
| Lipinski violation | 0 | 0 | 0 | 0 | 1 | 1 |
| Gastrointestinal (GI) absorption | Low | High | Low | Low | Low | Low |
| CYP3A4 inhibitor | No | No | No | No | No | No |
| Log S (ESOL)mg/mol | 4.33 | −2.78 | −3.5 | −3.5 | −2.48 | −3.26 |
| Bioavailability | 0.55 | 0.52 | 0.55 | 0.55 | 0.55 | 0.56 |
| CNS permeability (log PS) | −2.762 | −2.9883 | −3.542 | −2.994 | −3.775 | −3.551 |
| CYP1A2 inhibitor | No | No | No | No | No | No |
| CYP2C19 inhibitor | No | No | No | No | No | No |
| Total clearance (log mL/min/kg) | 1.343 | 1.247 | 1.225 | 0.433 | 0.552 | 0.732 |
| Renal OCT2 substrate | No | No | No | No | No | No |
Assessment of the toxicity properties of terpenes phytocompounds reported in C. roseus EO
| Toxicity parameters | Gamma terpinene | Terpinen-4-ol | Terpinolene | Limonene | 2-carene | Incensole acetate |
|---|---|---|---|---|---|---|
| AMES toxicity | Nontoxic | Nontoxic | Nontoxic | Nontoxic | Nontoxic | Nontoxic |
| Hepatotoxicity | No | No | No | No | No | Yes |
| Carcinogen | Noncarcinogen | Noncarcinogen | Noncarcinogen | Noncarcinogen | Noncarcinogen | Noncarcinogen |
| Biodegradation | Not readily biodegradable | Not readily biodegradable | Not readily biodegradable | Not readily biodegradable | Not readily biodegradable | Not readily biodegradable |
| hERG-1 inhibitor | Weak inhibitor | Weak inhibitor | Weak inhibitor | Weak inhibitor | Weak inhibitor | Weak inhibitor |
| HIA | HIA+ | HIA+ | HIA+ | HIA+ | HIA+ | HIA+ |
All selected terpenes might be proficiently permeable through BBB and able to absorb by intestine. Gamma-terpinene, terpinen-4-ol, limonene, and 2-carene were non-inhibitors of CYP enzyme, an important biomarker assessing their potential effects on the liver and renal functions. AMES toxicity processes the mutagenic latent of compounds. Out of all tested compounds, only incensole acetate exhibited some hepatotoxicity while all compounds were predicted to be non-carcinogenic and showed no AMES toxicity. Limonene, terpinen-4-ol, terpinolene, 2-carene, and gamma terpinene were shown to be non-AMES poisonous and to have weak inhibitory effects. With a likelihood value of 0.572, acute oral toxicity category 3 determined that all terpenes are harmless and orally non-toxic.
An analysis was conducted to determine the drug-likeliness of terpene compounds with lower binding energy. Using the Swiss-ADME online tool, a bioavailability radar was created by taking into account six physiochemical properties: size, solubility, flexibility, polarity, lipophilicity, and saturation. Large departures from these values in compounds imply that the chemical is not orally bioactive.
Because the ligand radar completely fit the pink shaded area, the study concluded that all of the identified terpene compounds were orally accessible as shown in Figure 7. The radar chart’s shaded area depicts the ideal values for the previously listed physiochemical-characteristics.
The radar-like representation of the drug-likeness of the tested terpene compounds: (a) gamma terpinene, (b) terpinen-4-ol, (c) terpinolene, (d) limonene, (e) 2-carene, and (F) incensole acetate. LIPO (Lipophility): −0.5 < XLOGP3 < +5.0. SIZE: 150 g/mol < MV < 500 g/mol. POLAR (Polarity): 20 Å2 < TPSA < 130 Å2.
The boiled-egg forecast also included representations of the drug-like characteristics and GI absorption of particular terpene components. The boiled-egg graph’s yellow zone contains substances that can cross the BBB. In the Boiled-egg model (Figure 8), the white part characterizes the GI tract’s passive absorption whereas the yellow region (yolk) specifies BBB penetration. The findings demonstrated that limonene, gamma terpinene, terpinolene, and terpinen-4-ol have high expected distribution volumes (VDss).
Boiled-egg model of selected terpenes for brain penetration and absorption in the GI tract. The BBB is thought to be passively crossed by molecules in the yolk of boiled eggs.
Terpenes which have the best pharmacophore hit score and the best stability docking interactions were further evaluated for molecular flexibility and dynamics, which is very beneficial for understanding the association between protein structure and its dynamics with the ligand [43]. Following the simulated screening of phytocompounds, MD simulations at nanosecond time scales were used to evaluate the lead compound’s flexibility using NAMD 2.13. Complexes with best binding affinity (ER with gamma-terpinene and limonene, HER2 with gamma-terpinene, and terpinen-4-ol) were chosen and a 100 ns MD simulation was performed to determine structural flexibility. The conformational changes in docked complexes were studied by measuring protein backbone RMSD and residue fluctuation by RMSF during the course of the full simulation run in the environmental system. Rg and PCA were also used to evaluate the MD trajectory’s stability and flexibility. The results of the molecular dynamics studies are depicted in Figures 9–12.
RMSD plot vs time (ns): (a) RMSD of estrogen with gamma terpinene (7,461) and estrogen with limonene (22,311) and (b) RMSD of HER2 with gamma terpinene (7,461) and HER2 with terpinen-4-ol (5,325,830).
(a) RMSF of ER with gamma terpinene (7,461) and ER with limonene (22,311) and (b) RMSF values of HER2 with gamma terpinene (7,461) and HER2 with terpinen-4-ol (5,325,830).
Rg vs time (ns) plot: (a) ER with gamma terpinen (7,461), ER with limonene (22,311). (b) HER2 with gamma terpinen (7,461) and HER2 with terpinen-4-ol (5,325,830).
PCA analysis of ER docked trajectories with gamma terpinene (a) and limonene (b). The corresponding eigen value’s total % of mean square displacements is used to calculate the logged deviations in the residue location for each direction. The steady color transition from blue to white to red indicates periodic jumps between the structural conformations derived from simulated trajectories.
When a complex system reaches a stable state, RMSD is used to evaluate it by computing the average deviation between the current conformation at a given time [44]. Figure 9 displays the RMSD profile of ER and HER2 receptors with terpene ligands, and it was shown that there was a gradual increase in RMSD of ER and HER2 receptor complexes with ligand terpenes. Figure 9(a) shows that ER-gamma-terpinene complex configuration’s RMSD value steadily increased during the simulation run until it gradually settled at about 2.5 Å after 40 ns but again shows fluctuation and rises up to 100 ns. Similar to this, after 50 ns, the RMSD value of the ER-limonene and complexes finally attained an RMSD level of about 2.5 Å, but it also displayed a small instability between 60 and 85 ns. RMSD value of the gamma terpinene/HER2 complex shows an average of 2.0 Å while the complex experienced a small fluctuation during the periods between 70 and 90 ns Figure 9(b). This indicated that docked complexes when analyzed at 100 ns MD simulation attain a steady rise at the end of the simulation run. Providentially, the complex of HER2 and gamma-terpinene was stable within first 50 ns and its equilibrium speed was also closer to HER2-tepinen-4-ol and the RMSD value was observed to be 2.4 Å. So, the four complexes were stable and it was observed that the average RMSDs of selected terpenes were generally distributed around 2.0–3.8 Å for estrogen receptor–ligand complexes and 1.2–2.6 Å with HER2 receptor complexes.
The average RMSF value of estrogen-terpene complex and HER2-terpene complexes were observed when residues index was plotted against residue numbers along the simulated trajectory, as shown in Figure 10, to evaluate the mobility of proteins and ligands in complex state. Lower RMSF values of docked terpene ligand complexes were showing that the binding of ligands reduced the variations in receptor molecules. RMSF analysis indicated that residues located at far distance from binding site were responsible for fluctuations, which was more than 2.5 Å. Findings indicate that ER-gamma terpinene atoms changed only little as compared to pure state and to a limited extent (<4.1 Å), ER-limonene occasionally displayed substantial fluctuation with an average value of 4.4 Å as observed in Figure 10(a). Interactions between HER2 receptor with hit compounds shown in Figure 10(b) were almost stable during most of the simulation’s duration with average RMSF value of 1.8 Å, which is considered as a standard value. Therefore, it can be determined that the interactions between breast cancer (BC) targets and top hit terpene compounds were almost stable during most of the RMSF simulation duration.
The time evolution of Rg is an excellent assessment of the protein collapse dynamics [45]. As shown in Figure 11(a) and (b), a plot of Rg vs simulation duration was visualized to assess protein compactness with terpenes. Through the Rg data, the expected protein receptor’s compactness across the full simulation period may be determined. The related Rg data for the template receptor proteins (ER and HER2) and predicted structure during the MD simulation displays comparatively constant values. By the time the Rg of each system had stabilized at 20–50 ns, the MD simulation had then reached equilibrium. During the simulation, the Rg values of the ER-gamma terpinene complex barely changed but most of the time stabilized and ER–limonene complex values become stabilized up to 50 ns but kept fluctuating between 50 and 70 ns, respectively with average Rg value of 20.2 Å, representing that binding region shows small effect on their structures. The Rg value of HER2 complexes (Figure 11b) shows a stable steady trend during the whole 100 ns of simulation, while the maximum average Rg value was observed up to 20.3 Å, indicating that their structure became more compact. The results show that Rg of HER2−terpinen-4-ol complex is smaller than those of other simulated complexes, which indicates that the structural tightness of this complex was better than other observed terpene molecules.
Simulated trajectories were further subjected to essential dynamics, sometimes referred to as PCA, in order to gather the important eigenvalues and gain a deeper understanding of protein dynamics, receptor domains, and residual displacements. [46]. Particularly, PCA components for receptors ER and HER2 coupled with top docked and stabilized terpene complexes were observed in this section. Figure 12 displays the mean square positional fluctuations of ER receptor with gamma terpinene and limonene in the covariance matrix and the eigen percentage as a function of eigen modes. A large amount of conformational motion caused by the docked terpene ligands within the binding pocket of the ER breast cancer receptor was indicated by a dramatic fall in eigen fraction that synchronized with early three eigen modes in the docked systems for the top docked compounds. However, after the fourth eigen value, no change in the fraction’s fluctuations were discovered. These results indicated that ER receptor significantly exhibits the flexibility when docked with gamma terpinene and well observed during early proceeding of MD simulation. The first three ER eigen vectors that docked with ligand components demonstrated compact and cluster motions for ER–gamma terpinene and ER–limonene complex in the relevant trajectories (Figure 12(a) and (b)). The screen plot involves comparing the percentage variation to the eigenvalue rank in numeric values. For PC1, which has the most components relative to the total number of components, the RMSF has been displayed against the residue position. Depending on the complex dominant motion, the first three PCA of PC1 vs PC2, PC3 vs PC2, and PC1 vs PC3 are shown in Figure 12a, respectively. These three components account for 46.8% of the variance. PCA of PC1 vs PC2, PC3 vs PC2, and PC1 vs PC3 are also shown for ER–limonene complex (Figure 12b). The first PC accounts for more than a third of the variation (41.91%), giving it a large advantage in variance dominance. PC2 has 12.9% variability while PC3 has 8.42% variability.
The resulting graphs also demonstrated that cluster distribution in each conformation varied over the course of the MD simulation. The blue to red color gradient depicts the docked complexes and frequent leaps between its various structural locations. PCA of docked complexes under study exhibit a fluctuating motion of ER receptor during MD simulation and it illustrates stability of the accompanying docked complexes. Simulation clusters of the HER2–gamma terpinene complex are seen on the PCA score plot in Figure 13(a) and (b). The loading plot of the PCA reveals that, limonene having a greater energy range than the other two complexes, the energies of the docked groups strongly correlate with those of the dihedral angles. The first three PCA for HER2–gamma terpinene complex are shown in Figure 13(a) and (b), respectively. These three components account for 49.3 and 48.22% of the variance, respectively. It is possible that the simulation’s dynamic character is what causes the HER2-gamma terpinene protein complex to spread more widely in the PCA score plot. The terpinen-4-ol-HER2 complex, which differs considerably from other terpene phytocompound complexes by producing cluster patterns, shows the greatest divergence during complex formation compared to the other candidates in Figure 13(b) (blue-magenta).
PCA analysis of HER2 receptor docked simulated MD trajectories with gamma terpinene (a) and terpinen-4-ol (b). The corresponding value of mean square displacements is used to calculate the logged deviations in the residue location for each direction.
Based on the locations of C-alpha atoms, DCCM investigation was employed to calculate frequency of related motions in order to determine the structural dynamics alterations in breast cancer receptors as a result of the docked ligands’ inhibitory effect. Figure 14(a) and (b) shows ER receptor–ligand complexes motions from light blue to cyan color (+1), which have a high correlation, and HER2 receptor motions from light blue to cyan pink are shown in Figure 14(c) and (d), which have a low correlation with residues present in docking complex. To observe functional conformational changes, the coordinated movements of receptor protein residues were investigated using DCCM. Strongly linked motions between residues are highlighted in pastel color, whereas anti-correlated motions are shown in pink as observed in Figure 14. Examination of the residue cross correlation, which raised the possibility of significant correlated motions, showed no such motions to be present in any of the systems, with the exclusion of complexes docked with HER2-gamma terpinene and HER2-terpinen-4-ol, Figure 14(c) and (d). In two other complexes, the residues involved in the relevant ligand’s molecular interactions are somewhat different. The findings conclusively demonstrate that top docked complexes such as ER-gamma terpinene and ER-limonene exhibit significant correlation during the MD simulation that might undergo conformational changes. It was hypothesized that the compounds in the screening set have a moderate to severe impending to inhibit the activity of ER and HER2 as well as disrupting the conformation of receptor’s active pockets based on the structural scrutiny of the MD simulation outcomes.
Dynamic cross correlation for ER complexed with (a) gamma terpinene and (b) limonene. HER2 receptor complex with (c) gamma terpinene and (d) terpinen-4-ol. (Residues are numbered from 1 to 200 as in crystal structure). During 100 ns simulation interval, the crusade of residues displays dynamic positive correlation in cyan blue color and a negative correlation in cyan pink color.
The MTT test was used as a reliable and easy technique for measuring cell cytotoxicity. Figure 15 depicts a plot of cytotoxicity of cells vs selected top docked terpene compounds. Gamma terpinene, limonene, 2-carene, and incensole acetate reduced the percentage viability of cancer cells, albeit to erratic degrees, according to the results of MTT experiments. When compared to other terpenes, it was discovered that incensole acetate induced more cytotoxicity toward cancer cell lines (p < 0.0001) (Figure 15(a)). Limonene and 2-carene also exhibited cytotoxic effect (p < 0.001) toward cancerous cell lines but extensive results were obtained with 2-carene and incensole acetate. As indicated, treatment of cancer cells with terpene compounds resulted in growth inhibition of MDA-MB-231.
(a) MTT test for cytotoxicity and (b) IC50 concentration of selected terpene compounds. P values indicated as *(0.05), **(0.001), and ***(0.0001).
Less IC50 values of terpene were perceived after 48 h (p < 0.001) and it was shown that less IC50 value reveals more cytotoxicity. IC50 strangely decreased after 48 h compared to 24 h in cancer cells as presented in Figure 15(b). The IC50 concentration obtained for limonene was more significant as compared to terpinen-4-ol, while 2-carene and incensole acetate showed less IC50 value where p < 0.05 and p < 0.001, respectively. A significant difference in obtained IC50 concentration of control and terpene compounds (limonene, gamma terpinene, and incensole acetate) was observed where p < 0.05 and p < 0.001, respectively. A significant difference was found in the toxicity level of these selected terpenes when observed with MDA MB-231 breast cancer cell lines, as associated with control (p < 0.05).
Natural compounds, and phytomolecules can significantly help in the treatment of human illnesses, including cancer, which is a major cause of mortality [47]. In silico drug design employs theoretical and computational techniques to discover fresh leads against certain physiologically active macromolecules and make it possible to swiftly determine how well bioactive agents bind to a target macromolecule [25]. Because it required extensive in vitro and in vivo testing and calculating a compound’s binding capacity was time-consuming and expensive in traditional drug development, so in silico drug designing and molecular docking strategy can be used for this purpose. The aim of above-described study is to analyze in silico interactions of breast cancer receptors with different terpene compounds and to evaluate molecular dynamic characterization of top docked complexes to predict some anti-cancer inhibitors for future evaluation of pharmacokinetic parameters and for further clinical studies. To determine their molecular interaction and activity against breast cancer, some terpenes identified by GC-MS analysis of Catharanthus roseus essential oil and having best pharmacophore properties were selected and their binding energy values, interactions with active site residues, and docking affinities with targeted receptors are described here. It was found from in silico analysis that the binding affinities ranged from −3.4 to −8.0 kcal/mol. The best docked compounds were chosen based on their superior capacity to inhibit the activation of cancer proteins, as indicated by their high negative binding affinity values. In comparison to other chosen ligands with some unfavorable interactions and a weak binding affinity, gamma terpinene was found to have the greatest negative docking score of − 7.1 kcal/mol with numerous hydrogen bonds, hydrophobic interactions, and favorable interactions. When compared to the apo form of ER and HER2 receptors, it was shown that the protein–ligand complex, i.e., gamma terpinene and terpinen-4-ol complex system, was structurally more compact. Therefore, it may be concluded that the protein’s binding to the ligand has increased the protein structure’s compactness, which in turn has increased the overall stability. Protein–ligand interactions depend heavily on hydrogen bonding, which can also affect drug absorption, specificity, and affinity. The data unequivocally demonstrate these compounds’ high affinity for breast cancer receptors, opening the door for further investigation of their potential therapeutic benefits in both in vitro and in vivo settings for future studies of therapeutically effective terpene compounds. In this study, we also predicted the anticancer potential of terpenes using in silico techniques like pharmacoinformatics against ER, PR, and HER2 receptors by using molecular docking, ADMET, toxicity evaluation, and MD simulations for estimation of the conformational changes, and intermolecular interactions between cancer protein receptors and the terpene phyto-compounds.
Terpenes have been the subject of therapeutic potential molecules and large number of scientific research works looking at their bioactivity against diseases, particularly antibacterial activity, cytotoxicity, and anticancer potential [48,49]. The creation of a strong medication that targets both the ER and PR will lead to a revolution in dealing of BC [50]. ER inhibition has emerged as a successful technique for the prevention as well as treatment of breast cancer. PR is a byproduct of estrogen release in objective tissues, showing an ER pathway to function, it is typically overexpressed in breast cancer, which influences the unnecessary synthesis of ER. Epithelial growth factor receptor has been discovered to have a significant role in breast cancer, which is phenotypically demarcated as negative for ER, PR, and HER-2 [51]. On the basis of association of these receptor response with progression of cancer, a better insight into the usefulness of phytocompounds over clinically used medication is provided by this comparative computational analysis of a few selected terpene phytocompounds against ER, PR, and HER2 breast cancer targets. Supporting our findings, previous studies have highlighted the potential of phytocompounds as effective agents against breast cancer and are beneficial and this was discussed in a study conducted for molecular docking analysis of terpenes and phytochemicals produced from basil, including phytocompounds eucalyptol, eugenol, linalool, and geraniol was carried out to demonstrate the promising candidate against BC by using SIRT2 protein. Some phytochemicals displayed exceptional binding power as compared to others and it was suggested that some of such phytocompounds could be used to treat BC. These outcomes highlight the significance of phytochemicals for improving chemotherapeutic agents for use as anti-BC candidates [15]. Cannabinoid delta-9-tetrahydrocannabinol, in particular, has been shown to boost the expression of ER-β and inhibit BC cell growth [52]. According to molecular docking and simulation results, findings in our study concluded that top docked terpene-receptor complexes such as ER-gamma terpinene and HER2-terpineol have the lowest RMSD values compared to the other complexes, indicating greater stability. Moreover, even without creating a compound with a ligand, the lowest RMSF value demonstrates its compactness. The brief description provided by MD simulation results of gamma terpinene, terpinen-4-ol, and limonene when docked with breast cancer receptors is significantly revealed by PCA, which is also confirmed by RMSD, RMSF, and Rg analysis. Principal component analysis of docked complexes indicates that the primary components regulating the MD simulation dynamics of limonene, gamma-terpinene, and terpinen-4-ol. The architecture of these complexes are consistent and stable over the simulations, according to RMSD. According to RMSF, the chosen terpenes have comparable flexibility in their protein contexts. This is corroborated by Rg analysis, which demonstrates that these systems are similarly compact and structurally sound. Together, these analyses confirm that the terpene–protein complexes exhibit high structural similarity in terms of flexibility and stability. Another study explained that in BC cells, HER2 overexpression can boost ER, VEGF, and HER2 gene expression, which may trigger an angiogenic response. Clinical trials also reported that BC patients with different gene amplifications responded better to therapy with phytochemicals compared to BC patients whose breast genes were expressed at a normal level [50]. It is important to remember that terpenes may be able to fight BC in ways other than through their interactions with certain cancer cell proteins [53,54]. They can also serve as co-drugs to boost the therapeutic efficiency of medications that are not easily accessible [31]. Further in vitro and in vivo studies of the known natural terpenes against breast cancer must be conducted even though these findings can benefit future research on dependable, clean, and natural product-based anti-cancer medicines [55,56].
A trajectory’s prominent patterns of motion can be explored using PCAPCA [57]. It was also observed that PCA analysis of the corresponding trajectories was carried out to get insight into the motion variations when ER and HER2 alone were subjected to simulation and when it is bound to the top docked terpene ligands. In general, the examination of the MD simulation trajectory showed that complex formation in the presence of a lead phytochemical terpene such as gamma terpinene and terpinen-4-l were stable and favorable for targeting cancer receptors and might be useful for cancer inhibition and proliferation. The broadest possible context is presented in relation to these results and their consequences. Despite the in silico drug study and pharmacokinetic potential of these phytocompounds, further research is required to determine how well they work in other in vitro and in vivo pharmacokinetic conditions.
In short, the interactions of selected terpene molecules with breast cancer receptors were considered by molecular docking, molecular dynamics simulation, drug likeness analysis, and ADMET profile. This study was designed to explore the potential lead terpene phytocompound as identified from C. roseus essential oil to combat breast cancer targets. Docking results predicted that terpenes, limonene, gamma terpinene, and terpinen-4-ol having best binding affinities were bound in the obligatory cavities of ER, PR, and HER2 receptors. The in silico docking analysis of all complexes concluded that ER has best docking interaction with gamma terpinene, limonene, and incensole acetate, PR has good docking affinity for terpinen-4-ol whereas HER2 has higher affinity for both gamma terpinene and terpinen-4-ol. Due to highest docking score and the most molecular interactions, gamma terpinene, limonene, and terpinen-4-ol were determined to be the best drugs that could be used to target the ER and HER2 cancer protein. The docked complex (ER-gamma terpinene and HER2-terpinen-4-ol) showed overall greater stability according to MD simulation experiments. The stability of receptor–ligand complexes was significantly influenced by hydrophobicity, hydrogen bond interactions, and Pi–Pi interactions. Small compounds can interact with cancer targets; therefore, it follows that stronger inhibitors may be possible to synthesize synthetically for anticancer activity. Rg and RMSD values for gamma–terpinene–ER and HER2–terpinen-4-ol complexes were smaller and stabilized than those of the other docked complex systems, suggesting that the terpinen-4-ol–HER2 and gamma–terpinene–ER complexes have anticancer potential and serve as inhibitors against breast cancer receptors. Also, the simulation’s fluctuation curve of the atoms and residual index of chosen complexes demonstrate that the interactions between the ER and HER2 with ligands remain consistent during the course of the simulation. Results gained in this work determine that these moieties curb breast cancer cell growth, opening a new approach toward exploiting these compounds in developing novel and more effective anticancer agents. The correctness of docking results is further demonstrated by the reliability of molecular dynamics simulation results as well as further in vitro and in vivo pharmacokinetic settings need to be investigated accordingly.