The 3D structure of piperine was depicted in Fig. S1a. Table 2 presents the physiochemical properties of piperine, while Table S1 summarizes its ADMET properties. The ADMET properties shown that piperine has no toxicity at endpoints in silico models. The pkcsm tools provided predictions for a range of properties concerning piperine, including adsorption, distribution, release, waste elimination, and toxic effects represents in TableS123,24.
Table S1 (Supplementary file)
Table.2: Piperine's Physicochemical Characteristics
Properties
|
Values
|
Chemical Formula
|
C17H19NO3
|
Molecular mass
|
285.34 g/mol
|
The quantity hefty ions
|
21
|
The quantity of heavy aromatic ions
|
6
|
Csp3 portion
|
0.35
|
Quantity of movable bonds
|
4
|
Hydrogen's bond acceptor number
|
3
|
Donors of Hydrogen's bond
|
0
|
Atomic Refractivity
|
85.47
|
TPSA
|
38.77 Ų
|
Figure S1 (Supplementary file)
Figure. S1: (a)PiperineChemical Structure(b)Targets in Poly Cystic Ovary Common Protein Syndrome(c) Protein-Protein (PPI) interaction network of common targets in PCOS
Network Pharmacology Protein Target Analysis
Polycystic ovarysyndrome - 988 Genes(https://www.disgenet.org/browser/0/1/0/C0032460/)Hyperandrogenism-108Genes(https://www.disgenet.org/browser/0/1/0/C0206081/),Oligomenorrhea-37Genes(https://www.disgenet.org/browser/0/1/0/C0028949/).Wepredicted 5commongenesin"PolyCysticOvarySyndrome(PCOS)","Hyperandrogenism"and"Oligomenorrhea": NR3C1, PPARG, FOS, CYP17A1, H6PD. Moreover, through intersection analysis of significant pharmacological targets and genes related to PCOS, A collective of five genes has been recognized as to be viable cross-targets for PCOS treatment (Fig.S1b).
Gene Ontology’s Prediction
In fold enhancement analysis, the False Detection Ratio (FDR) is calculated using the nominal P-value obtained from the hyper geometric test. In order to calculate fold enrichment, divide the proportion of your list's genes that belong to a certain pathway by the proportion of background genes in that route. FDR reveals the probability of the enrichment occurring by chance. Large paths often have reduced FDRs because of improved statistical power. Fold Enrichment chart (Fig. 1a) shows how significantly overrepresented a certain pathway's genes are as a metric of impact magnitude. The charts for biological activities (Fig. 1b), molecular mechanisms (Fig. 1c), and cell component (Fig. 1d) showed the projected GO keywords.
Protein-Protein Interaction Network Analysis
We utilizedPPI networks to investigate the connections that exist between various gene targets and to locateimportantnetworkgenes.Withaconfidencelevelof>0.900, Homo sapiens was the chosen species, and five common targets of proteins were inserted into STRING, (Fig. S1c) shows that therewere35nodes,288edges,a16.5typicalnodedegree,a0.781the typical local clustered ratio,87 expectededges,andap-valueof< 1.0e-16 for PPIenrichment25. Protein interactions involving Nuclear Receptor Subfamily 3 Group C Member 1 (NR3C1), Peroxisome Proliferator Activated Receptor Gamma (PPARG), Transcription Factor AP-1 Subunit C-Fos (FOS), Cytochrome P450 Family 17 Subfamily A Member 1 (CYP17A1), and Hexose-6-Phosphate Dehydrogenase/Glucose 1-Dehydrogenase (H6PD) were directly observed25. So, wepredicted 5commongenesin"PolyCysticOvarySyndrome(PCOS)","Hyperandrogenism"and"Oligomenorrhea". The five genes that are cross-targets to the piperine for the treatment of PCOS.
KEGG Enrichment Pathway Analysis
Several KEGG pathways were found to be closely linked with PCOS, including Prolactin signaling, Pentose phosphate pathway, Osteoclast differentiation, Thyroid cancer, endocrinal hormones biosynthesis,Non-alcohol fatty liver disease, Ovarian steroidogenesis, Lipid metabolisms and atherosclerosis, Cortisol synthesis and secretion, and Amphetamine addiction. Table S2 illustrates the 30 pathways deemed most crucial for PCOS. Furthermore, Fig. 1e displays the top pathways with statistically significant differences (P < 0.05) as identified by the KEGG enrichment analysis.
Table S2 (Supplementary file)
Molecular Docking Analysis
Following extensive validation utilizing the Lipinski rule of five targets and ADMET properties, we performed docking of piperine against the target proteins, including glucose 1-dehydrogenase/hexose-6-phosphate dehydrogenase (H6PD),Subunit C-Fos of Transcription Factor AP-1,Peroxisome Proliferators' Activated Receptor Gamma (PPARG), Group C Nuclear Receptor Subfamily 1 Member and Member 1 of Family 17 Subfamily A of Cytochrome P450 (CYP17A1)26. The target and piperine's molecular docking investigation showed that piperine had the greatest binding affinity.The piperine had -7.96 Kcal/mol (Ki = 1.43 μM ), -8.34 Kcal/mol (Ki = 771.66 nM), -6.42 Kcal/mol (Ki = 19.77 μM), -6.43 Kcal/mol (Ki = 19.34μM)and -8.70 Kcal/mol (Ki = 420.17 nM) docking scores respectively show in (Table. 3).The dockedcomplexes of all the protein targets wereshown in (Fig. 2a, 2b, 2c, 2d, 2e)26. The 2D interaction analysis of targeted proteins with the piperine we analyzed the protein-ligand docked complexes show in(Fig.3a, 3b, 3c, 3d, 3e). This suggests that target binding necessitates the amino acidresidues
Table. 3: Minimum binding energy scores of Piperine
Name of the protein
|
PDB ID
|
Minimum Binding value (Kcal/mol)
|
Constant Inhibition (Ki)
|
Group C Nuclear Receptor Subfamily 1 Member
|
5UC1
|
-7.96
|
1.43 μM
|
Peroxisome Proliferator-Activated ReceptorGamma
|
3FUR
|
-8.34
|
771.66 nM
|
Transcription Factor AP-1SubunitC-Fos
|
1FOS
|
-6.42
|
19.77μM
|
Member 1 of Family 17 Subfamily A of Cytochrome P450
|
6WW0
|
-6.43
|
19.34μM
|
Glucose 1-dehydrogenase/hexose-6-phosphate dehydrogenase (H6PD)
|
8EM2
|
-8.70
|
420.17 nM
|
Molecules Dynamic Simulation
The database of proteins (PDB) typically contains the desired biomolecule's structural data (X-ray or NMR). In any case, model structure procedures like homology strategies can be utilized to get coordinate and geometry data of protein structure27. During this step, the molecules' surrounding environment (solvation, ionic strength) is frequently also positioned. Hexose-6-phosphate dehydrogenase (H6PD) secondary structure show in (Fig. 4a). The plot illustrates the presence of the protein's beta strands and alpha helicesstructure was observedin this MDstudy28.The plot shows there 24.16% are alpha helix, 17.94 % are beta strands, and totally 42.10% are for secondary structures.In Fig. 4b, alpha helices and beta strands, which are fundamental protein secondary structure components (SSE), are consistently observed during the duration of the 100 ns simulation29.
The study conducted molecular dynamics simulations over a 100 ns period to analyze the reliability of proteins, ligands, and their interactions. Specifically, comparisons were made between the trajectories of the Protein on its own and in conjunction with ligandsto assess protein fluctuation, measured by C-alpha atoms' RMSD values. A stable deviation between the proteins' RMSD values alone and the complexes was considered acceptable, typically falling between one to two.
The RMSD values of the hexose-6-phosphate dehydrogenase–ligand complex (specifically with piperine) was analysed, with the docking complex showing an RMSD of 3.6 Å (Fig.S2). Notably, the ligand piperine demonstrated significantly stable behaviourwith respect to the protein's natural structure with an RMSD significant of 2 Å. This indicates that when piperine binds to hexose-6-phosphate dehydrogenase, the stability of the resulting complex remains consistent During the entirety of the 100 nanosecond molecular dynamics (MD) simulation trajectory.
Figure S2 (Supplimetary file)
Figure. S2:The stability of the receptor-ligand complex is assessed by employing the root mean square deviation (RMSD) value.
The study also investigated the roots of mean square fluctuations (RMSF) measurements of the docked complex between hexose-6-phosphate dehydrogenase and piperine, which were depicted in Fig. S3a. Analysis revealed that when receptor amino acid residues bind to bioactive substances like piperine, their average RMSF values are lower compared to those in their natural protein structure. This suggests the establishment of a firm and enduring complex between the docked ligand (piperine) and hexose-6-phosphate dehydrogenase.
During the 100 ns MD simulation, amino acids with higher RMSF values exhibited fluctuation, while those with lower RMSF values remained inflexible and rigid. The RMSF plot highlighted specific ranges of amino acid residues (60 to 75, 250 to 300, 350 to 380, and 410 to 430) as being particularly affected and fluctuating throughout the simulation study.
Figure S3 (Supplimetary file)
Figure. S3: (a)For ligand (Piperine), the average RMSF values of Hexose-6-phosphate dehydrogenase amino acid residues have been calculated using Desmond software(b)Fluctuation of the Ligand Root Means Square (L-RMSF)
The ligand roots of mean square fluctuations (RMSF) can offer insights into the entropic contributions of ligand fragments in the binding process and their interactions with the protein26. After an initial adjustment of the proteins-ligand interactions along the protein spine, the agonist RMSF is calculated for the heaviest molecules or atoms of the ligand, as depicted in Fig. S3b30
The molecular dynamics simulation allowed for the observation and analysis of interactions occurring between the protein and the ligand, which was categorized into distinct types as illustrated in Fig. 5a31. Four main types of protein-ligand interactions were identified: water bridges, hydrophobic interactions, ionic interactions, and hydrogen bonds32. Each of these interaction types exhibited specific subtypes that could be further investigated using "Simulation Interactions Diagram.
In the interaction plots between the protein and ligand, hydrogen bonding interactions were observed in residues TYR 205, LYS 208, ARG 250, ASP 262, LYS 360, ARG 365, and HIS 204. Hydrophobic interactions were detected in residues LEU 37, TYR 41, TYR 205, MET 241, PHE 253, ARG 365, ILE 402, and HIS 404. Additionally, formations of water bridges were observed in residues ASP 36, HIS 204, LYS 208, GLU 243, ARG 250, ASP 262, LYS 360, and ARG 365. Throughout the simulation, these interactions play a crucial role in maintaining the stability and specificity of the protein-ligand complex.
Fig.5b presents a timeline representation of contacts and interactions, including hydrophobic bonds, Water bridges, ionic bonds, and bonds of hydrogen32. The panel of theSimulation Interactions Diagram displays normalized, stacked bar charts along the trajectory, indicating that various protein residues formed interactions with the ligand (piperine) during the MD simulation's 100 ns duration.
Specifically, TYR 205, MET 241, ARG 250, ARG 365, ILE 402, and HIS 404 were identified as amino acid residues that interacted with piperine through more than one specific contact during the simulation. This indicates the pivotal role played by these residues to forming stable and diverse interactions with the ligand, enhancing the protein-ligand complex's general stability and specificity throughout the simulation period.
Fig.S4a shows interactions that take place over almost 30.0% of the simulation period within the chosen pathway (0.00 to 100.00 nanoseconds). In particular, these graphic shows interactions that were maintained for 76% of the simulation period in the 100 ns pathways.
Figure S4 (Supplimentary file)
Figure.S4: (a)A thorough representation of the molecular reactions taking place inside the protein-ligand complex would be provided by a diagram showing the precise connections between the protein and ligand atoms.(b) The conformational development of potentially rotatable bonds is depicted by a ligand (Piperine) torsion map through the simulation study (100 ns).
In our MD simulation study for 100 ns, ligand (Piperine) as a synthetic compound moves without breaking the compound bonds. The plot depicting the torsion profile of the ligandis represented in Fig.S4b33. The ligand torsions provide a summary of the conformational changes observed in each rotatable bond (RB) of the ligand throughout the simulation trajectory, spanning from 0.00 to 100.00 nanoseconds. In the topmost panel of Fig.S4b, the rotatable bonds are visually distinguished by colour on a 2D diagram representing the ligand.
The ligand RMSD plot indicates that the piperine has an RMSD value of 1.0 Å, as illustrated in Fig. S5a. The high value of RoG shows the unfolding events during simulation. The RoG plot shows that the system remained compact between 4.6 to 4.8 Å, between 4.6 to 4.8 Å, the protein underwent some unfolding, but it remained compact for the remaining time.
Furthermore, the plot reveals that the protein retained its compacted state for a portion of the 100 ns simulation.The analysis found that the piperine structure (Ligand) lacks an intramolecular hydrogen bond. The Molecular surface calculation plot shows piperine has 291 to 296 Å2. The SASA was calculated during MD simulation study. The SASA plot displayed an average SASA of approximately at 240 Å2. Like-wise the PSA plot represents the value of 75 Å2
Figure S5 (Supplimentary file)
Figure. S5: (a)According to the RMSD ligand plot, the piperine has an RMSD value of 1.0. Å.(b) MM/PBSA calculation Plot of Hexose-6-phosphate dehydrogenase/Piperine docked complex.
MM/PBSA is a famous strategy to work out calculates the binding affinities in docked complex. In our study, the MM/PBSA calculation shows that the docked complex of Hexose-6-phosphate dehydrogenase/Piperine has the binding affinity of -19.71 kcal/mol (Fig.S5b).