Exploring the Anti-Breast Cancer Potential of Chalcomoracin, Guangsangon E, and Morushalunin: A Computational Analysis of Compounds from Morus sp.

    Morus sp is a plant containing polyphenol compounds such as Chalcomoracin, Morushalunin, and Guangsangon E. These compounds play a crucial role in modifying proteins and signaling pathways that influence the progression of cancer cells, including breast cancer. Therefore, this study aimed to analyze the interaction between Chalcomoracin, Morushalunin, and Guangsangon E on PD-1 and PPAR proteins as well as determine the physicochemical and pharmacological properties of these compounds. To achieve this, molecular docking was conducted on PD-1 (PDB ID: 57w9) and PPAR (PDB ID: 5two) human proteins. The results showed that Chalcomoracin and Guangsangon E had binding capabilities to both PD-1 and PPAR, while Morushalunin interacted exclusively with PD-1 protein. The  interaction between Guangsangon E and PPAR was -12.29 (Kcal/mol), and for Chalcomoracin with PPAR, it was -5.69 (Kcal/mol). Docking scores for Chalcomoracin, Morushalunin, and Guangsangon E on PD-1 were -6.21 kcal/mol, -8.91 kcal/mol, and -9.28/kcal/mol, respectively. Based on PASS analysis, Morushalunin had potential as an HIF1a-inhibitor, while Chalcomoracin demonstrated activity as an MMP-9 expression inhibitor. Guangsangon E showed activity on both proteins. Additionally, drug-likeness score (DLS) for Chalcomoracin, Morushalunin, and Guangsangon E were 1.14, 1.09, and 0.79, respectively. These concluded that the compounds could effectively interact with PD-1 and PPAR two important proteins in breast cancer.

Breast cancer; Morus sp; PD-1/PDL-1 ; PPAR- ; Triple negative breast cancer

2.1.   The Biological Activity Prediction Using PASS

        The 3 isolated compounds from Morus sp, namely Chalcomoracin, Morushalunin, and Guangsangon E, obtained through tissue culture, were analyzed for their biological activity using the Prediction of Activity Spectra for Substances (PASS). The online platform, accessible at http://www.pharmaexpert.ru/passonline, was used for this purpose. Canonical SMILES of each compound were inputted on the website and a list of the biological activity was generated based on the existing database on PASS. The results included Pa (Probably active) and Pi (Probably active) values. Finally, when the Pa and Pi values are closer to 1 and 0, respectively, it signified better and good performance.

2.2.   Prediction of Pharmacological Activity (ADME) of Compounds and Drug Likeness Score

   ADME characteristics of the compounds were analyzed using SwissADME (http://www.swissadme.ch/). Canonical SMILES of each compound were incorporated into Swiss ADME. Swiss ADME, providing a predicted pharmacological profile. Additionally, https://www.molinspiration.com/ and https://molsoft.com/mprop/ were constituted to verify compliance with Lipinski's rules.

2.3.   Molecular Docking

    The proteins used were Human PD-1 (PDB ID: 57w9) and PPAR(PDB ID: 5two), sourced from the RCSB Protein Data Bank (https://www.rcsb.org/). Removal of unnecessary water molecules, ligands, and chains was conducted. Autodock Vina 1.5.7 served as the docking software. Validation of grid box dimensions was performed, with the dimensions for PD-1 being x= 50, y=50, and z = 50, centered at x= 14.974 Å, y= 30.713 Å, z= 187.813 Å, and for PPAR x= 40, y=40, and z = 40, centered at x= -23.939 Å, y= -20.434 Å, z= 9.727 Å. The grid box dimensions were selected based on the RMSD value (Sahlan et al., 2020). After docking, visualization was performed using Discovery Studio.

Figure 2 Research Frameworks of This Study

3.1.   Prediction of the Biological Activity of Compounds

A computer program called PASS, accessible at (http://www.pharmaexpert.ru/passonline/) was used to predict bioactivity spectra based on chemical structures. This computational method facilitated potential in vivo bioactivity for chalcomoracin, guangsangon E, and morushalunin. Furthermore, this method produced a comprehensive list of biological activities along with their Pa and Pi. From the PASS analysis, activities related to anticancer mechanisms were selected with a cut-off value of >0.6. The selected activities include free radical scavenger, HIF-1a inhibitor, apoptotic agonist, MMP9 Expression inhibitor, and chemopreventive. HIF-1a had a relationship with increased PD-L1 during hypoxia. Additionally, it can increase PD-1 protein expression (Guo et al., 2022). Table 1 shows the result of the PASS analysis. Guangsangon E shows HIF-1a inhibitor effects, but its Pa value falls below Morushalunin. MMP-9, a crucial element in cancer metastasis, was influenced by chalcomoracin and guangsangon compounds, indicating their activity on this protein.

Table 1 Biological activity related to cancer prediction results analyzed using PASS.

3.2.   Prediction of ADME of Compounds and Drug Likeness Score

Bioavailability Radar of SwissADME showed 6 physicochemical properties, including lipophilicity, size, polarity, solubility, flexibility, and saturation. The pink area represented the optimal range for each property, which comprises lipophilicity: XLOGP3 between -0.7 and +5.0, size: MW between 150 and 500 g/mol, polarity: TPSA between 20 and 130 Å2, solubility: log S not higher than 6, saturation: fraction of carbons in the sp3 hybridization not less than 0.25, and flexibility: no more than 9 rotatable bonds (Daina, Michielin and Zoete, 2017). Based on the SwissADME bioavailability radar, it was observed that the 3 compounds have poor bioavailability due to their physico-chemical properties. According to several studies, polyphenol indicated biological activity at low plasma concentrations. To enhance the bioavailability of phenolic compounds, various methods were adopted, such as modifying the formulation or engaging in chemical derivatization. Curcumin is an example of a beneficial polyphenol with poor bioavailability (Abourashed, 2013).

Drug-likeness scores (DLS) from the 3 compounds were assessed using the Molinspiration web server, as presented in Table 2. These scores compared the physicochemical properties of the compounds with those of existing drugs based on Lipinski’s rules. DLS usually ranged from 0 to 1, where a score of 1 indicated a good candidate for drug development. Conversely, a score of 0 implies that the compound is less likely to be a drug (Sampat et al., 2022). In the context of this study, a DLS score above 0 was observed. This information is valuable in predicting whether the compound can be synthesized or evaluated.

Figure 3 Bioavailability Radar of SwissADME analysis (a) Chalcomoracin, (b) Guangsangon E, (c) Morushalunin

Table 2 ADME of Compounds and Drug Likeness Score

171.81

149.82

117.99

149.82

Notes: MW: Molecular Weight; TPSA: The Polar Surface Area; HBD: Hydrogen Bond Donor; HBA: Hydrogen Bond

Acceptor; DLS: Drug Likeness Score

3.3.   Molecular Docking

Molecular docking was conducted to explore the potential interactions between chemical compounds derived from Morus sp and the PD-1 and PPAR proteins. Furthermore, it is a computational method aimed at identifying ligands that are geometrically and energetically suitable for a given receptor (Suhartanto et al., 2017). Autodock Vina software was chosen for this analysis, as previous studies have showed its superior accuracy compared to other options such as PatchDoc (Sahlan et al. 2023).The critical residues included in the PD-1/PD-L1 interaction were VAL64, ILE126, LEU128, ALA132, ILE134, ILE54, TYR56, MET115, ALA121, and TYR123. The result shows chalcomoracin was bound to protein through 4 hydrogen bonds at amino acid residues such as ALA 121, ASP 122, SER 17, and TYR 123. Guangsangon showed hydrogen binding on ALA121, ILE54, and TYR123, while morushalunin interacted with ARG 125, ASP 122, and TYR 123. The 3 compounds had protein interaction through hydrogen bonds on several critical residues in the PD-1/PDL-1 interaction, as detailed in Table 3. They can bond with the TYR 123 amino acid residue. Morushalunin had the lowest  compared to chalcomoracin and guangsangon E. Additionally, it had the highest inhibition constant (Ki) value.

Figure 4 Compounds Three Dimension Structures (a) Chalcomoracin, (b) Guangsangon E, (c) Morushalunin

Tables 3 Molecular Docking Results of Chalcomoracin, Guangsangon E, and Morushalunin on PD-1/PDL1 Protein

Phenolic compounds such as chalcomoracin, guangsangon E, and chalcomoracin can form complexes with protein through covalent or non-covalent interaction, hydrogen, van der Waals, electrostatic, and hydrophobic bonding. The primary modes of interaction are predominantly hydrophobic interaction and hydrogen binding (Shahidi and Dissanayaka, 2023).

Hydrophobic interaction between chalcomoracin and protein PD-1/PDL-1 occurs through pi-alkyl binding with amino acid residue MET115, ILE54, ALA121, and TYR56. Additionally, there was evidence of pi-cation interaction comprising LYS124 and ASP 122, which showed a high strength compared to hydrogen bonds. A P-alkyl binding pattern was identified between morushalunin and PD-1/PDL-1, interacting with residues MET115, ILE54, ALA121, and TYR56 similars to chalcomoracin. In the case of guangsangon, pi-alkyl interaction was specifically observed with residue ALA18.

Figure 5 Two-Dimensional (2D) Visualizations Interaction with Protein PD-1 (a) Chalcomoracin, (b) Guangsangon E, (c) Morushalunin

Table 4 Molecular Docking Results of Chalcomoracin, and Guangsangon E on PPAR

       In the field of cancer study, PPAR has been identified in various tumor types, including breast cancer. Its role after binding to a ligand has been associated with the initiation, progression, and spread of tumors. Furthermore, antagonists of PPAR were considered effective drugs for breast cancer metastasis, particularly in the case of TNBC (Wang et al., 2018). In the context of ligand binding affinity, Guangsangon E and Chalcomoracin had  values of -12.29 and -5.69 (Kcal/mol). However, Morushalunin data was excluded due to a positive  result. Guangsangon E demonstrated interaction with amino acid residue ARG 280, CYS 285, SER299, and TYR327, while Chalcomoracin bound to GLU343, GLY284, and HIS449. Therefore, it was concluded that Guangsangon E, with the lowest bonding  and Ki values, was the best ligand for PPAR. This was the most spontaneous interaction and the most stable protein-ligand complexes compared to chalcomoracin (See Table 4).

This study showed that Guangsangon E, with its lower  and Ki values, were the superior ligand for PPAR. This is supported by its more spontaneous interactions and the formation of more stable protein-ligand complexes, as evidenced in Table 4.

Figure 6 Two-Dimensional (2D) Visualizations Interaction with Protein PPAR (a) Chalcomoracin, (b) Guangsangon E

In conclusion, the PASS analysis indicated that Morushalunin and Chalcomoracin had activity as HIF1a and MMP-9 expression inhibitors, respectively. Meanwhile, Guangsangon E showed activity on both proteins. DLS for Chalcomoracin, Morushalunin, and Guangsangon E were 1.14, 1.09, and 0.79 respectively. According to the SwissADME bioavailability radar, all three compounds demonstrated poor bioavailability due to their physicochemical properties. It is important to note that several polyphenols manifested biological activity at low plasma concentrations. To address the issue of poor bioavailability, various strategies were adopted, such as modifying the formulation or chemical derivatization. In this study, the  interaction between Guangsangon E and PPAR-y was -12.29 kcal/mol, while of Chalcomoracin with PPAR-y was -5.69 kcal/mol. The docking scores for Chalcomoracin, Morushalunin, and Guangsangon E on PD-1 proteins were -6.21 kcal/mol, -8.91 kcal/mol, and -9.28 kcal/mol, respectively. Both Chalcomoracin and Guangsangon E are bound to PD-1 and PPAR- suggesting potential significance in breast cancer pathogenesis. On the other hand, Morushalunin exclusively interacted with the PD-1 protein. These indicated the potential of the compound to relate with PD-1 and PPAR key proteins in breast cancer pathogenesis. Therefore, further study is needed to validate these predictions.

The authors are grateful to the Directorate of Research and Development, Universitas Indonesia under HIBAH PUTI 2022 Grant No. NKB-1248/UN2.RST/HKP.05.00/2022, for funding this study.