Exploring Piperine as a Potential Treatment for Polycystic Ovarian Syndrome: Insights from In-silico Docking Studies

Exploring Piperine as a Potential Treatment for Polycystic Ovarian Syndrome: Insights from In-silico Docking Studies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exploring Piperine as a Potential Treatment for Polycystic Ovarian Syndrome: Insights from In-silico Docking Studies Rahul Francis, Ramanathan Kalyanaraman, Vasuki Boominathan, Sudharsan Parthasarathy, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4362153/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Polycystic Ovarian Syndrome (PCOS) is a multifaceted metabolic and hormonal condition that impacts women in their procreative ages, identified by ovarian dysfunction, hyperandrogenaemiaoverweight and insulin insensitivity.The piperine, an important alkaloid compound of black pepper has shown promise in modulating various physiological processes. In this work, employed computational docking studies to explore the potential of piperine as a treatment for PCOS. Utilizing computational methods, we analyzed the binding interactions between piperine and key molecular targets implicated in PCOS pathogenesis, including hyperandrogenism, and "oligomenorrhea. The network pharmacology analysis report found 988 PCOS-related genes, 108 hyperandrogenism-related genes, and 377 oligomenorrhea-related genes, and we finally shortlisted 5 common genes in PCOS, hyperandrogenism, and "oligomenorrhea": NR3C1, PPARG, FOS, CYP17A1, and H6PD. Our results reveal favorable binding affinities with PPARG (-8.34 Kcal/mol) and H6PD (-8.70 Kcal/mol) and interaction patterns, suggesting the potential of piperine to modulate these targets. Moreover, the reliabilityof the piperine-target interactions was revealed by molecular simulations studies. These findings support further experimental investigations to validate the therapeutic efficacy of piperine in PCOS management. The integration of computational approaches withexperimental studies has the potential to lay the groundwork for the creation of new therapies specifically targeting PCOS and related endocrine disorders. Biological sciences/Computational biology and bioinformatics/Cellular signalling networks Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Data processing Biological sciences/Computational biology and bioinformatics/Virtual drug screening PCOS Piperine In-silico docking studies H6PD PPARG Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Polycystic ovarian syndrome (PCOS) is a widely recognized endocrinological issue among women of conceptive age around the world 1 . The beginning of the symptomatology ordinarily happens during youth, with the side effects coming from feminine abnormalities (irregular periods), unwanted hair, obesity, insulin resistance, and related ailments 2 . In addition to a comprehensive clinical history, examination and diagnostic testsare crucial for the diagnosis of hyperandrogenism and the exclusion of alternative endocrine diseases like hyper prolactinemia or thyroid hormone abnormalities 3 .Controlling menstrual irregularities, managing hyperandrogenaemia,treating multiplecomorbidities,andimprovingqualityoflifearethemaingoalsofthepharmacological treatment of PCOS-affected patients who are unable to conceive. Overall, the pathophysiology of PCOS might be viewed as a cruel cycle of several complicated illnesses, with insulin resistance causing this complicated syndrome and hyperandrogenism acting as a predisposing factor 2 . Nutraceutical supplements derived from plants, such as botanicals, are intricate treatments that contain a variety of phytoconstituents that can have antagonistic, agonistic, or synergistic effects within and across components 4 .Piperine is a naturally occurring compound found in Piperaceae family plants, offering potential advantages over synthetic pharmaceuticals in terms of safety, tolerability, and accessibility. Piperine is a pungent alkaloid compound present in Piperaceae family plants, which are Piper nigrum and Piper longum. In animal investigations, piperine is crucial for drug metabolism 5 . The potential therapeutic advantages of piperine might arise from its properties that include anti-inflammatory, antioxidant, and immunomodulatory effects 6 .Piperine has demonstrated potential in promoting overall metabolic health, which includes controlling inflammation, managing weight and It shows promise as a natural remedy for managing diabetes 7 . Natural compounds like piperine frequently carry a lesser risk of negative effects in comparison to synthetic pharmaceuticals 8 . By harnessing the therapeutic potential of piperine, there may be an opportunity to minimize the occurrence of side effects commonly associated with conventional PCOS treatments. Network pharmacological analysis investigates the interactions among drugs, specific targeted proteins, medical conditions, genetic factors, and various additional factors to understand their interconnectedness and effects 9 . Using network pharmacological analysis, the modes of action of numerous drugs were evaluated. It has the potential to unveil the complex network connections among "ingredient-gene-target-disease" that aids in multi-dimensionally understanding the molecular basis of the diseases and predicting potential medication pharmacological mechanisms 4 .Nowadays, this technique is often be used for exploration of active ingredients of phyto-compounds from plants that have a wide range of medicinal uses 10 . Different networking’s can be employed to discover novel genes or elucidate the fundamental genetic mechanisms underlying polycystic ovarian syndrome (PCOS) 10 . However, the piperine network pharmacological technique hasn't yet been used for PCOS. One important factor leading to the lack of a comprehensive and verified database for PCOS-relevant medical data, genetics and proteomics is the absence. Our study, toinvestigatethepharmacologicalactionofthePiperineactivityinPCOStreatment. Piperine exhibits the ability to modulate multiple molecular targets implicated in PCOS pathogenesis, including hyperandrogenism, and "oligomenorrhea. This multi-targeted approach could lead to more comprehensive therapeutic effects compared to single-target interventions.Computational docking studies enable the prediction of binding interactions between piperine and specific molecular targets associated with individual PCOS phenotypes. This could aid in the creation of customized treatment plans modified to the distinctive features of each patient's condition.The identification of piperine as a promising therapeutic agent for PCOS may stimulate further research and development in the field of nutraceuticals. Piperine-based supplements or formulations could offer convenient and non-invasive options for PCOS management. Overall, exploring piperine as a treatment for PCOS presents opportunities for the development of novel, effective, and well-tolerated pharmaceutical interventions with the potential to improve the standard of life for people who suffer from this complex endocrine disorder. METHODOLOGY LigandPreparation Piperine's 3D structure was supplied in the structural-data export version by the NCBI PubChem library ( https://pubchem.ncbi.nlm.nih.gov/).Th e 3D structure of Piperine was energyminimized based on the Steepest descent method in SPDB Viewer software. The SwissADME web ( http://www.swissadme.ch/index.php ) server was utilised to analyse the physiochemical characteristics of piperine 11 . ADMET CharacteristicsofPiperine was predicted through PKCSM internet application ( https://biosig.lab.uq.edu.au/pkcsm/ ) 12 . Identification of Key Drug Targets in PCOS by Network Pharmacology Approach The DisGeNET database https://www.disgenet.org/homewas queried for genes connected with polycystic ovarian syndrome (PCOS), using "Polycystic Ovarian disease," "Hyperandrogenomia," and “Oligoanovalation" as search keywords. The outcomes of the three data sets were combined and de-duplicated to get genes that particularly matched the three search criteria. Construction of Disease Target Genes Analysis The study employed the online Venn diagram application at https://bioinfogp.cnb.csic.es/tools/venny/ to illustrate the points where disease hits meet 13 . These potential therapeutic targets were subsequently utilized toconstruct the KEGG Path Examination, Gene Ontology Predicting, and Protein-Protein Interacting Networks. Constructing Interactions Network of Protein-Protein Using STRING ( https://cn.string-db.org/ ), the intersection gene was imported and establishes the interaction connection between target proteins in order to identify the network's primary targets 14 . Constructing a network of protein-protein interactions was built by cytoscape 15 . The degree value was determined and the core targets were selected via Cytoscape's own analysis. Gene ontology’s (GO) and KEEG Enrichment Study ShinyGO 0.77, an online resource ( http://bioinformatics.sdstate.edu/go/ ), was followed by Ge et al ., (2020) and Enrichr online tool ( https://maayanlab.cloud/Enrichr/ ) followed by Kuleshov et al ., (2016) were used to examine fold enrichment analysis and significantly enhanced cellular composition, molecular action, and biological process (P < 0.05) 16,17 .The target's signaling networks was examined using the KEGG database, which can be found in ( https://www.genome.jp/kegg/pathway.html ). Statistically enriched pathways (P 0.05) were found. A visual examination of the Gene ontology’s and KEGG findings was done to identify the specific PCOS treatment component. ReceptorPreparation Group C Nuclear Receptor Subfamily 1 Member (NR3C1)(PDB ID: 5UC1), Peroxisome Proliferators Activated Receptor Gamma (PPARG) (PDB ID: 3FUR), Transcription Factor AP-1 Subunit C-Fos (PDB ID: 1FOS), Member 1 of Cytochrome P450 Family 17 Subfamily A (CYP17A1) (PDB ID: 6WW0), and glucose 1-dehydrogenase/hexose 6-phosphate dehydrogenase (H6PD)(PDB ID: 8EM2) of Homo sapiens were selected for this study according to Network pharmacology approach .The Bank of protein Data (PDB) provided the 3Dimentional crystal forms of the receptors at www.pdb.org/pdb . Version 1.13 of the UCSF-Chimera Dock preparation tool may be found at http://www.cgl.ucsf.edu/chimerautilized to clean protein receptors of heteroatoms and prepare them for docking. In the settings of the SPDB Viewer program, all receptors had their energy reduced using the steepest descent approach. MolecularDockingStudy Approach for carrying out molecular docking investigations with an adjusted flexible docking methodology by Rizvi et al (2013) 18 . It involves using the MGL graphic tool with AutoDock for exploring interactions of piperine with target proteins shown in Table 1. Proteins are prepared from PDB files, and receptor grids are created, adapting to binding pockets. Docking parameters include allowing ligand rotation and selecting optimal docking postures based on RMSD, Ki, and binding energies. Cygwin software is used for manual comparison, with ten configurations generated per protein-ligand combination. An exhaustiveness of 10 is used, and Discovery Studio 2017 is employed for post-docking analysis of ligand-protein interactions 18 . Table.1 DockingGrid BoxParametersusedinAutoDockSoftware ProteinName PDBID CenterGridBox GridPoints X Y Z X Y Z Member 1 of Nuclear Receptor Subfamily 3 Group C 5UC1 -24.193 9.731 31.652 60 60 60 PeroxisomeProliferatorActivated ReceptorGamma 3FUR 3.965 8.302 16.054 60 60 60 Transcription FactorAP-1SubunitC-Fos 1FOS 36.201 10.895 -14.495 60 60 60 Member 1 of Cytochrome P450 Family 17 Subfamily A 6WW0 -8.55 17.172 -42.514 60 60 60 Hexose-6-PhosphateDehydrogenase/Glucose1-Dehydrogenase 8EM2 159.74 157.93 160.612 60 60 60 Molecular Simulation Study The molecular simulation study applied to the docking study-identified ligand with the greatest binding ability, using AutoDock software 19 . Hits were analyzed using the Program for Desmond Molecular Dynamics 20 . Each protein-ligand combination was enclosed in an orthorhombic box and hydrated using TIP3P water molecules.On addition, 0.15 M Sodium ions (Na+) and chloride ions (Cl-) were introduced into the system to neutralize it using the OPLS3e force field. The standard Desmond equilibration protocol was followed, including steps such as Brownian Dynamics NVT simulation, NPT simulation with constraints, and a final NPT simulation without constraints, each at a temperature of 10 K. The final MD simulation ran for 100 nanoseconds at standard temperature (300 K) and pressure (1.013 bars) 20 . Pressure and temperature both controlled utilizing the Nose-Hoover Chain thermometer and Martyna-Tobias-Klein barometer 21 , 22 . Using a smooth Particle Mesh Ewald (PME) approach with a RESPA integrator and a time step of two femtoseconds, long-distance electrostatic forces were calculated. Every 10 ps, coordinates and energy were saved for trajectory analysis. RESULTS 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 TableS1 23,24 . Table S1 (Supplementary file) Table.2: Piperine's Physicochemical Characteristics Properties Values Chemical Formula C 17 H 19 NO 3 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 PPIenrichment 25 . 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 observed 25 . 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 structure 27 . 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 MDstudy 28 .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 simulation 29 . 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 protein 26 . 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. S3b 30 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. 5a 31 . Four main types of protein-ligand interactions were identified: water bridges, hydrophobic interactions, ionic interactions, and hydrogen bonds 32 . 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 hydrogen 32 . 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.S4b 33 . 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 Å 2 . The SASA was calculated during MD simulation study. The SASA plot displayed an average SASA of approximately at 240 Å 2 . Like-wise the PSA plot represents the value of 75 Å 2 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). DISCUSSION PCOS is a particularly typical endocrinological illness, identified by persistent anovulation and hyperandrogenism 34 . If PCOS is not efficiently and adequately treated, it can drastically decrease life expectancy 35 . Many biomarkers have been linked to PCOS and could serve as potential treatment targets. However, the exact mechanism of gene regulation leading to the progression of the syndrome remains unclear 36 .The docking process assesses the binding affinity between molecules. In this research endeavor, we sought to investigate the therapeutic potential of piperine in treating PCOS by employing bioinformatics analysis to gain insights into their biological roles. Piperine, a natural polyphenol derived from black pepper ( Piper nigrum ) and long peppers ( Piper longum ), It's been used as a food spice and a potential therapy for several illnesses, including inflammation, obesity, and various malignancies 37 . Piperine is an orange needle-shaped crystal with melting and lighting points of 131–132°C. Chloroform and methanol soluble; unable to dissolve in water 38 . Priyanka et al. (2020) found that the results of the ADMET study on piperine derivatives derived from natural sources indicated that most ADME (Absorption, Distribution, Metabolism, and Excretion) features exhibited favorable characteristics, suggesting that these compounds are promising candidates for further development 39 . In the study investigation, the NCBI PubChem database makes the 3D structure of piperine accessible in the format of a structure-data file (SDF). The 3D structure of piperine was seen in Fig. S1 a. Using the Swiss ADME web server, the physiochemical characteristics of piperine were examined, and ADMET characteristics were predicted using the online PKCSM program. Table 2 displayed the physiochemical characteristics of piperine, while Table S1 provided a summary of its ADMET characteristics. In this study, we confirmed as same results through in silico ADMET models that piperine exhibits no harmful effects at end points. Kamboj et al. utilized molecular docking to assess the binding affinity between compounds and CYP17. Their findings indicate that all phytocompounds exhibited blocking or hindrance of the CYP17 enzyme's activity, with docked scores ranging from − 3.7 to -9.5 40 . Using computational molecular docking studies, Amudha studied the effectiveness of phytochemicals derived from Cadabafruticosa (L.) Druce in inhibiting CYP17. Docking scores ranged from − 3.3 to -7.9, indicating that all 20 drugs demonstrated moderate to strong inhibition. In particular, docking scores of -7.9 and − 6.8 indicated strong inhibition of androstan-3-one and 17-hydroxy-2, 4-dimethyl respectively 41 . A more extensive negative value in the docking score denotes a better affinity among the ligand and the protien. The docking of ligands and proteins was ranked according to their binding affinity, with lower (more negative) scores indicating stronger interactions. Upon conducting a computational docking study, we found that piperine has the greatest affinity for binding to the targeted proteins. The docked complexes of all the protein targets are illustrated in Fig.S2 through 13.Over all, the docking study revealed that piperine exhibited the highest binding affinity with Peroxisome Proliferator Activated Receptor Gamma (-8.34 kcal/mol) 26 and Hexose-6-Phosphate Dehydrogenase/Glucose 1-Dehydrogenase (-8.70 kcal/mol). The expression of PPAR-γ in the ovaries has been linked to the formation of follicles and ovarian function. Changes in PPAR-γ signalling might interfere with ovarian steroidogenesis and folliculogenesis, which could lead to reproductive problems in PCOS, including irregular menstruation and infertility (Komar, 2005) 42 . In addition, Ahmadian et al. (2013) highlighted the significant function of the PPAR-γ protein in regulating adipocyte development, glucose levels, and lipid homeostasis 43 . An important nuclear receptor recognized as peroxisome proliferator-activated receptor-γ (PPAR-γ) is connected with hyperandrogenemia and plays a role to regulating energy balance, as noted by Stump et al. (2015) 44 . Chen, et al ., (2015) noted that Androgen metabolism and androgen receptor signalling have been associated with PPAR-γ. The hyperandrogenic phenotype associated with PCOS may be attributed to dysregulated PPAR-γ activation 45 . Unluturk et al. (2007) found that PPAR-γ genes are associated with PCOS occurrences across different ethnic populations. Their study provided evidence linking PPAR-γ with the pathogenesis of PCOS 46 . According to our docking study, piperine exhibits the highest binding affinity with the Gamma-Activated Peroxisome Proliferator Receptor, with a calculated energy of -8.34 kcal/mol. Overall, PPAR-γ docking studies in PCOS research give important insights into the underlying molecular processes of the illness and this could potentially lead to the development of novel treatment approaches. Docking studies involving Hexose-6-phosphate dehydrogenase (H6PD) genes and their relevance to PCOS are not commonly reported in the literature. While H6PD is involved in various metabolic pathways and play the important role in PCOS pathophysiology, specific docking studies focusing on H6PD genes in the context of PCOS are limited 47 . However, computational docking studies involving other genes or proteins implicated in PCOS, such as PPAR-γ or insulin receptors, have been conducted to explore potential therapeutic interventions. Continuing research exploring the interaction between H6PD genes and pertinent molecular targets in PCOS could yield valuable insights into the fundamental mechanisms of the disorder. Hexose-6-phosphate dehydrogenase (H6PD) genes play the important role in polycystic ovarian syndrome (PCOS) by influencing various metabolic pathways and hormonal regulation. These genes contribute to glucose metabolism, oxidative stress management, steroid hormone production, insulin sensitivity, and lipid metabolism, all of which are dysregulated in PCOS (Li Yan et al. , 2019) 48 . Understanding the involvement of H6PD in PCOS can provide insights into the disorder's mechanisms and potential therapeutic strategies. Cortisone reductase deficiency, caused by inactivating mutations in the enzyme Glucose 1-dehydrogenase/hexose-6-phosphate dehydrogenase (H6PD), mimics of PCOS and manifests as hyperandrogenism that cannot be explained by standard tests (Qin and Rosenfield 2011) 49 . Based on docking study, it was found that piperine demonstrates the greatest binding affinity with the Glucose 1-dehydrogenase/hexose-6-phosphate dehydrogenase (H6PD), showing a calculated energy of -8.70 kcal/mol. Molecule Dynamic Simulation is regularly utilized to refine proteins or any molecular structures and find previously elusive information about the time evolution of conformations (Vanommeslaeghe et al. , 2014) 50 . The protein-ligand RMSD showed that hexose-6-phosphate dehydrogenase docking complexes with 3.6 were present. The RMSD value of piperine when it binds to hexose-6-phosphate dehydrogenase (3.6) is larger than in the natural crystal structure of Glucose 1-dehydrogenase/hexose-6-phosphate dehydrogenase (H6PD). To confirm the adaptability and flexibility of amino acids during simulation for 100 nano seconds, the root of mean squares change (RMSF) was determined. Every residue of the amino acid RMSF value is checked as it cooperates with the ligand along a direction (De Vita et al. , 2021) 51 . A well-known method for calculating the binding affinities of docked complexes is MM/PBSA. According to our study's MM/PBSA calculations, the docked complex of hexose-6-phosphate dehydrogenase and piperine exhibits a binding affinity of -19.71 kcal/mol, as seen in Fig.S3b. CONCLUSION In conclusion, the combination of network pharmacology and molecular docking analyses offers empirical evidence supporting the potential pharmaceutical application of exploring the potential role of piperine in the treatment or management of polycystic ovarian syndrome (PCOS). Through computational analyses, piperine exhibited promising drug-like characteristics and demonstrated favorable interactions with key proteins associated with PCOS pathogenesis, particularly Hexose-6-phosphate dehydrogenase. Molecular dynamics simulations further confirmed the stability and efficacy of the piperine-Hexose-6-phosphate dehydrogenase complex, suggesting its potential as a therapeutic agent for managing PCOS symptoms. These findings lay a solid scientific foundation for further exploration and development of piperine-based treatments for PCOS. Declarations Acknowledgements We are thankful to the Researchers Supporting Project number (RSPD2024R930), King Saud University, Riyadh, Saudi Arabia. The authors also thank Srimad Andavan Arts and Science College (Autonomous), Trichy, and Dhanalakshmi Srinivasan University, Samayapuram, Trichy, for providing facilities to carry out the research work. Contributions RF: investigation; methodology; visualization; RK: writing-review and editing, VB: conceptualization; data curation; SP: investigation; methodology; visualization; writing-review; AC: conceptualization; methodology; data curation; software; IAA: software; visualization; writing-review and editing; SAA: Funding & data curation; HMA: data curation; software; JC: methodology; data curation; software; SVT: investigation; methodology; visualization; Supervision. Ethics declarations Data Availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Funding This research project was supported by the Researchers Supporting Project Number (RSPD2024R930), King Saud University, Riyadh, Saudi Arabia. References Bozdag G, et al . The prevalence and phenotypic features of polycystic ovary syndrome: a systematic review and meta-analysis. Hum. Reprod. 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Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 18 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 15 Jul, 2024 Reviews received at journal 11 Jul, 2024 Reviewers agreed at journal 21 Jun, 2024 Reviews received at journal 19 Jun, 2024 Reviewers agreed at journal 24 May, 2024 Reviewers agreed at journal 24 May, 2024 Reviewers invited by journal 24 May, 2024 Editor assigned by journal 24 May, 2024 Editor invited by journal 15 May, 2024 Submission checks completed at journal 15 May, 2024 First submitted to journal 03 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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06:00:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4362153/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4362153/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-72800-6","type":"published","date":"2024-09-18T15:57:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57071770,"identity":"e525e30e-1e0f-4bff-a89f-cb5ecd05deeb","added_by":"auto","created_at":"2024-05-24 08:24:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":583475,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eBubblechartofFoldenrichmentchartofcommonproteintargetsinPCOS\u003cstrong\u003e(b)\u003c/strong\u003e BiologicalProcess ofcommonproteintargets\u003cstrong\u003e(c)\u003c/strong\u003eMolecularFunctionProcess ofcommonproteintargets\u003cstrong\u003e(d)\u003c/strong\u003eCellularComponentofcommonproteintargets\u003cstrong\u003e(e)\u003c/strong\u003eResults of the enrichment analysis in a bar chart.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4362153/v1/a71ba5287d0b96916d0d3bdf.png"},{"id":57071775,"identity":"5b8bf652-fa17-413e-b0f0-4b6735d16af9","added_by":"auto","created_at":"2024-05-24 08:24:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":812568,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eGroup C Nuclear Receptor Subfamily 1 Member in Complex with Piperine. \u003cstrong\u003e(b)\u003c/strong\u003ePiperine complex with Peroxisome Proliferator Activated Receptor Gamma (\u003cstrong\u003ec)\u003c/strong\u003eTranscription FactorAP-1SubunitC-FosincomplexwithPiperine\u003cstrong\u003e(d)\u003c/strong\u003eCytochrome P450Family17SubfamilyA Member incomplex with Piperine\u003cstrong\u003e(e)\u003c/strong\u003e Combined with piperine, hexose-6-phosphate dehydrogenase / glucose 1-dehydrogenase.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4362153/v1/9d75fa42169d3685e3882de6.png"},{"id":57071769,"identity":"ba0ec8ad-1ab3-4de1-9325-b9eb459dac24","added_by":"auto","created_at":"2024-05-24 08:24:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":530324,"visible":true,"origin":"","legend":"\u003cp\u003e2DInteractionPlotofDockedComplexes\u003cstrong\u003e(a)\u003c/strong\u003eGroup C Nuclear Receptor Subfamily 1 Member\u003cstrong\u003e(b)\u003c/strong\u003eGamma-Activated Peroxisome Proliferator Receptor\u003cstrong\u003e(c)\u003c/strong\u003eTranscription Factor AP-1 Subunit C-Fos\u003cstrong\u003e(d)\u003c/strong\u003eMember 1 of Family 17 Subfamily A of Cytochrome P450 \u003cstrong\u003e(e)\u003c/strong\u003eGlucose 1-dehydrogenase/hexose-6-phosphate dehydrogenase (H6PD)\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4362153/v1/00401c58c6972bee073681f8.png"},{"id":57071772,"identity":"ccd4a6a4-7339-46f2-8ae1-4b01831f370c","added_by":"auto","created_at":"2024-05-24 08:24:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":921776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eHexose-6-phosphate dehydrogenase (H6PD) secondary structure.The alpha helix and beta strands seen in the protein structure during the 100-ns simulation study are depicted. The plot shows there 24.16% are alpha helix, 17.94 % are beta strands, and totally 42.10% are for secondary structures.\u003cstrong\u003e(b)\u003c/strong\u003eProtein secondary structure component (SSE) like as beta strands and alpha helices are constantly seen over the 100 ns simulation.Based on residue index, this graph illustrates how SSEs are distributed across structure of the protein.The SSE formation in every path structure during the MD study is summarised and at the bottom tracks the cumulative SSE assignment for every residue.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4362153/v1/9b9e05fe4222f6be3acb05f5.png"},{"id":57072362,"identity":"03f95d96-6e33-4c13-b788-c4a9c8f705ef","added_by":"auto","created_at":"2024-05-24 08:32:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":336794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Protein-ligand contact plots\u003cstrong\u003e(b) \u003c/strong\u003eThe timeline graph displayed the interactions between the Hexose-6-phosphate dehydrogenase with Piperine in all directions in the trajectory frames (a deeper orange color indicates more than one explicit interaction with the ligand).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4362153/v1/2f8b49d5453744111d3555fd.png"},{"id":65104662,"identity":"ee0d5207-0e14-4872-bab4-b2571fd8948d","added_by":"auto","created_at":"2024-09-23 16:14:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4500655,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4362153/v1/e5af3d78-8ad1-4354-afb2-40e0305402be.pdf"},{"id":57072361,"identity":"3a65fbad-f6c6-42bc-b8cb-77f1fcd7fdc9","added_by":"auto","created_at":"2024-05-24 08:32:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1156004,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4362153/v1/b8bab266bf236249ff55b01e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Piperine as a Potential Treatment for Polycystic Ovarian Syndrome: Insights from In-silico Docking Studies","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePolycystic ovarian syndrome (PCOS) is a widely recognized endocrinological issue among women of conceptive age around the world\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The beginning of the symptomatology ordinarily happens during youth, with the side effects coming from feminine abnormalities (irregular periods), unwanted hair, obesity, insulin resistance, and related ailments\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In addition to a comprehensive clinical history, examination and diagnostic testsare crucial for the diagnosis of hyperandrogenism and the exclusion of alternative endocrine diseases like hyper prolactinemia or thyroid hormone abnormalities\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.Controlling menstrual irregularities, managing hyperandrogenaemia,treating multiplecomorbidities,andimprovingqualityoflifearethemaingoalsofthepharmacological treatment of PCOS-affected patients who are unable to conceive. Overall, the pathophysiology of PCOS might be viewed as a cruel cycle of several complicated illnesses, with insulin resistance causing this complicated syndrome and hyperandrogenism acting as a predisposing factor\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNutraceutical supplements derived from plants, such as botanicals, are intricate treatments that contain a variety of phytoconstituents that can have antagonistic, agonistic, or synergistic effects within and across components\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.Piperine is a naturally occurring compound found in Piperaceae family plants, offering potential advantages over synthetic pharmaceuticals in terms of safety, tolerability, and accessibility. Piperine is a pungent alkaloid compound present in Piperaceae family plants, which are Piper nigrum and Piper longum. In animal investigations, piperine is crucial for drug metabolism\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. The potential therapeutic advantages of piperine might arise from its properties that include anti-inflammatory, antioxidant, and immunomodulatory effects\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.Piperine has demonstrated potential in promoting overall metabolic health, which includes controlling inflammation, managing weight and It shows promise as a natural remedy for managing diabetes\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Natural compounds like piperine frequently carry a lesser risk of negative effects in comparison to synthetic pharmaceuticals\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. By harnessing the therapeutic potential of piperine, there may be an opportunity to minimize the occurrence of side effects commonly associated with conventional PCOS treatments.\u003c/p\u003e \u003cp\u003eNetwork pharmacological analysis investigates the interactions among drugs, specific targeted proteins, medical conditions, genetic factors, and various additional factors to understand their interconnectedness and effects\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Using network pharmacological analysis, the modes of action of numerous drugs were evaluated. It has the potential to unveil the complex network connections among \"ingredient-gene-target-disease\" that aids in multi-dimensionally understanding the molecular basis of the diseases and predicting potential medication pharmacological mechanisms\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.Nowadays, this technique is often be used for exploration of active ingredients of phyto-compounds from plants that have a wide range of medicinal uses\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Different networking\u0026rsquo;s can be employed to discover novel genes or elucidate the fundamental genetic mechanisms underlying polycystic ovarian syndrome (PCOS)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. However, the piperine network pharmacological technique hasn't yet been used for PCOS. One important factor leading to the lack of a comprehensive and verified database for PCOS-relevant medical data, genetics and proteomics is the absence.\u003c/p\u003e \u003cp\u003eOur study, toinvestigatethepharmacologicalactionofthePiperineactivityinPCOStreatment. Piperine exhibits the ability to modulate multiple molecular targets implicated in PCOS pathogenesis, including hyperandrogenism, and \"oligomenorrhea. This multi-targeted approach could lead to more comprehensive therapeutic effects compared to single-target interventions.Computational docking studies enable the prediction of binding interactions between piperine and specific molecular targets associated with individual PCOS phenotypes. This could aid in the creation of customized treatment plans modified to the distinctive features of each patient's condition.The identification of piperine as a promising therapeutic agent for PCOS may stimulate further research and development in the field of nutraceuticals. Piperine-based supplements or formulations could offer convenient and non-invasive options for PCOS management. Overall, exploring piperine as a treatment for PCOS presents opportunities for the development of novel, effective, and well-tolerated pharmaceutical interventions with the potential to improve the standard of life for people who suffer from this complex endocrine disorder.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLigandPreparation\u003c/h2\u003e \u003cp\u003ePiperine's 3D structure was supplied in the structural-data export version by the NCBI PubChem library (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/).Th\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/).Th\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ee 3D structure of Piperine was energyminimized based on the Steepest descent method in SPDB Viewer software. The SwissADME web (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch/index.php\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) server was utilised to analyse the physiochemical characteristics of piperine\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. ADMET CharacteristicsofPiperine was predicted through PKCSM internet application (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biosig.lab.uq.edu.au/pkcsm/\u003c/span\u003e\u003cspan address=\"https://biosig.lab.uq.edu.au/pkcsm/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e12\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Key Drug Targets in PCOS by Network Pharmacology Approach\u003c/h2\u003e \u003cp\u003eThe DisGeNET database \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.disgenet.org/homewas\u003c/span\u003e\u003cspan address=\"https://www.disgenet.org/homewas\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e queried for genes connected with polycystic ovarian syndrome (PCOS), using \"Polycystic Ovarian disease,\" \"Hyperandrogenomia,\" and \u0026ldquo;Oligoanovalation\" as search keywords. The outcomes of the three data sets were combined and de-duplicated to get genes that particularly matched the three search criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of Disease Target Genes Analysis\u003c/h2\u003e \u003cp\u003eThe study employed the online Venn diagram application at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/\u003c/span\u003e\u003cspan address=\"https://bioinfogp.cnb.csic.es/tools/venny/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e to illustrate the points where disease hits meet\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. These potential therapeutic targets were subsequently utilized toconstruct the KEGG Path Examination, Gene Ontology Predicting, and Protein-Protein Interacting Networks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eConstructing Interactions Network of Protein-Protein\u003c/h2\u003e \u003cp\u003eUsing STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), the intersection gene was imported and establishes the interaction connection between target proteins in order to identify the network's primary targets\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Constructing a network of protein-protein interactions was built by cytoscape\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The degree value was determined and the core targets were selected via Cytoscape's own analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGene ontology\u0026rsquo;s (GO) and KEEG Enrichment Study\u003c/h2\u003e \u003cp\u003eShinyGO 0.77, an online resource (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.sdstate.edu/go/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.sdstate.edu/go/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), was followed by Ge \u003cem\u003eet al\u003c/em\u003e., (2020) and Enrichr online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) followed by Kuleshov \u003cem\u003eet al\u003c/em\u003e., (2016) were used to examine fold enrichment analysis and significantly enhanced cellular composition, molecular action, and biological process (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003csup\u003e16,17\u003c/sup\u003e.The target's signaling networks was examined using the KEGG database, which can be found in (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genome.jp/kegg/pathway.html\u003c/span\u003e\u003cspan address=\"https://www.genome.jp/kegg/pathway.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Statistically enriched pathways (P 0.05) were found. A visual examination of the Gene ontology\u0026rsquo;s and KEGG findings was done to identify the specific PCOS treatment component.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eReceptorPreparation\u003c/h2\u003e \u003cp\u003eGroup C Nuclear Receptor Subfamily 1 Member (NR3C1)(PDB ID: 5UC1), Peroxisome Proliferators Activated Receptor Gamma (PPARG) (PDB ID: 3FUR), Transcription Factor AP-1 Subunit C-Fos (PDB ID: 1FOS), Member 1 of Cytochrome P450 Family 17 Subfamily A (CYP17A1) (PDB ID: 6WW0), and glucose 1-dehydrogenase/hexose 6-phosphate dehydrogenase (H6PD)(PDB ID: 8EM2) of Homo sapiens were selected for this study according to Network pharmacology approach .The Bank of protein Data (PDB) provided the 3Dimentional crystal forms of the receptors at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://pubchem.ncbi.nlm.nih.gov/).Th\" target=\"_blank\"\u003ewww.pdb.org/pdb\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.pdb.org/pdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Version 1.13 of the UCSF-Chimera Dock preparation tool may be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cgl.ucsf.edu/chimerautilized\u003c/span\u003e\u003cspan address=\"http://www.cgl.ucsf.edu/chimerautilized\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e to clean protein receptors of heteroatoms and prepare them for docking. In the settings of the SPDB Viewer program, all receptors had their energy reduced using the steepest descent approach.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMolecularDockingStudy\u003c/h2\u003e \u003cp\u003eApproach for carrying out molecular docking investigations with an adjusted flexible docking methodology by Rizvi et al (2013)\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. It involves using the MGL graphic tool with AutoDock for exploring interactions of piperine with target proteins shown in Table\u0026nbsp;1. Proteins are prepared from PDB files, and receptor grids are created, adapting to binding pockets. Docking parameters include allowing ligand rotation and selecting optimal docking postures based on RMSD, Ki, and binding energies. Cygwin software is used for manual comparison, with ten configurations generated per protein-ligand combination. An exhaustiveness of 10 is used, and Discovery Studio 2017 is employed for post-docking analysis of ligand-protein interactions\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTable.1\u003c/strong\u003e \u003cp\u003eDockingGrid BoxParametersusedinAutoDockSoftware\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProteinName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePDBID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCenterGridBox\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eGridPoints\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMember 1 of Nuclear Receptor Subfamily 3 Group C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5UC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-24.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeroxisomeProliferatorActivated ReceptorGamma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3FUR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTranscription FactorAP-1SubunitC-Fos\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1FOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-14.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMember 1 of Cytochrome P450 Family 17 Subfamily A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6WW0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-8.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-42.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHexose-6-PhosphateDehydrogenase/Glucose1-Dehydrogenase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8EM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e159.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e157.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e160.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Simulation Study\u003c/h2\u003e \u003cp\u003eThe molecular simulation study applied to the docking study-identified ligand with the greatest binding ability, using AutoDock software\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Hits were analyzed using the Program for Desmond Molecular Dynamics\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Each protein-ligand combination was enclosed in an orthorhombic box and hydrated using TIP3P water molecules.On addition, 0.15 M Sodium ions (Na+) and chloride ions (Cl-) were introduced into the system to neutralize it using the OPLS3e force field. The standard Desmond equilibration protocol was followed, including steps such as Brownian Dynamics NVT simulation, NPT simulation with constraints, and a final NPT simulation without constraints, each at a temperature of 10 K. The final MD simulation ran for 100 nanoseconds at standard temperature (300 K) and pressure (1.013 bars)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Pressure and temperature both controlled utilizing the Nose-Hoover Chain thermometer and Martyna-Tobias-Klein barometer\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Using a smooth Particle Mesh Ewald (PME) approach with a RESPA integrator and a time step of two femtoseconds, long-distance electrostatic forces were calculated. Every 10 ps, coordinates and energy were saved for trajectory analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe 3D structure of piperine was depicted in Fig. S1a.\u0026nbsp;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.\u0026nbsp;The pkcsm tools provided predictions for a range of properties concerning piperine, including adsorption, distribution, release, waste elimination, and toxic effects represents in TableS1\u003csup\u003e23,24\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable S1 (Supplementary file)\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable.2: Piperine\u0026apos;s Physicochemical Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"71.71717171717172%\"\u003e\n \u003cp\u003e\u003cstrong\u003eProperties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e\u003cstrong\u003eValues\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"71.71717171717172%\"\u003e\n \u003cp\u003eChemical Formula\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003eC\u003csub\u003e17\u003c/sub\u003eH\u003csub\u003e19\u003c/sub\u003eNO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"71.71717171717172%\"\u003e\n \u003cp\u003eMolecular mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e285.34 g/mol\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"71.71717171717172%\"\u003e\n \u003cp\u003eThe quantity hefty ions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"71.71717171717172%\"\u003e\n \u003cp\u003eThe quantity of heavy aromatic ions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"71.71717171717172%\"\u003e\n \u003cp\u003eCsp3 portion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"71.71717171717172%\"\u003e\n \u003cp\u003eQuantity of movable bonds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"71.71717171717172%\"\u003e\n \u003cp\u003eHydrogen\u0026apos;s bond\u0026nbsp;acceptor number\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"71.71717171717172%\"\u003e\n \u003cp\u003eDonors of\u0026nbsp;Hydrogen\u0026apos;s bond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"71.71717171717172%\"\u003e\n \u003cp\u003eAtomic Refractivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e85.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"71.71717171717172%\"\u003e\n \u003cp\u003eTPSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.282828282828284%\"\u003e\n \u003cp\u003e38.77 \u0026Aring;\u0026sup2;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFigure S1 (Supplementary file)\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure. S1: (a)\u003c/strong\u003ePiperineChemical Structure\u003cstrong\u003e(b)\u003c/strong\u003eTargets in Poly Cystic Ovary Common Protein Syndrome\u003cstrong\u003e(c)\u0026nbsp;\u003c/strong\u003eProtein-Protein (PPI) interaction network of common targets in PCOS\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNetwork \u0026nbsp; Pharmacology Protein Target Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePolycystic 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\u0026quot;PolyCysticOvarySyndrome(PCOS)\u0026quot;,\u0026quot;Hyperandrogenism\u0026quot;and\u0026quot;Oligomenorrhea\u0026quot;: 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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Ontology\u0026rsquo;s Prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn fold enhancement analysis, the False Detection Ratio (FDR) is calculated using the nominal P-value obtained from the hyper geometric test.\u0026nbsp;In order to calculate fold enrichment, divide the proportion of your list\u0026apos;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\u0026apos;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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein-Protein Interaction Network Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe utilizedPPI networks to investigate the connections that exist between various gene targets and to locateimportantnetworkgenes.Withaconfidencelevelof\u0026gt;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\u0026lt; 1.0e-16 for PPIenrichment\u003csup\u003e25\u003c/sup\u003e. 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 observed\u003csup\u003e25\u003c/sup\u003e. So, wepredicted 5commongenesin\u0026quot;PolyCysticOvarySyndrome(PCOS)\u0026quot;,\u0026quot;Hyperandrogenism\u0026quot;and\u0026quot;Oligomenorrhea\u0026quot;. \u0026nbsp;The five genes that are cross-targets to the piperine for the treatment of PCOS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKEGG Enrichment Pathway Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral 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\u0026thinsp;\u0026lt;\u0026thinsp;0.05) as identified by the KEGG enrichment analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable S2 (Supplementary file)\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular Docking Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing extensive validation utilizing the Lipinski rule of five targets and ADMET properties, we performed docking of piperine against the target proteins, including\u0026nbsp;glucose 1-dehydrogenase/hexose-6-phosphate dehydrogenase (H6PD),Subunit C-Fos of Transcription Factor AP-1,Peroxisome Proliferators\u0026apos; Activated Receptor Gamma (PPARG),\u0026nbsp;Group C Nuclear Receptor Subfamily 1 Member and Member 1 of Family 17 Subfamily A of Cytochrome P450 (CYP17A1)\u003csup\u003e26\u003c/sup\u003e.\u0026nbsp;The target and piperine\u0026apos;s molecular docking investigation showed that piperine had the greatest binding affinity.The piperine had -7.96 Kcal/mol (Ki = 1.43 \u0026mu;M ), -8.34 Kcal/mol (Ki = 771.66 nM), -6.42 Kcal/mol (Ki = 19.77 \u0026mu;M), -6.43 Kcal/mol (Ki = 19.34\u0026mu;M)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)\u003csup\u003e26\u003c/sup\u003e. 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\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable. 3:\u0026nbsp;\u003c/strong\u003eMinimum binding energy scores of Piperine\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.36082474226804%\"\u003e\n \u003cp\u003e\u003cstrong\u003eName of the protein\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDB ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum Binding value (Kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eConstant Inhibition (Ki)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.36082474226804%\"\u003e\n \u003cp\u003eGroup C Nuclear Receptor Subfamily 1 Member\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e5UC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-7.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e1.43 \u0026mu;M\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.36082474226804%\"\u003e\n \u003cp\u003ePeroxisome Proliferator-Activated ReceptorGamma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e3FUR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e771.66 nM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.36082474226804%\"\u003e\n \u003cp\u003eTranscription Factor AP-1SubunitC-Fos\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e1FOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-6.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e19.77\u0026mu;M\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.36082474226804%\"\u003e\n \u003cp\u003eMember 1 of Family 17 Subfamily A of Cytochrome P450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e6WW0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-6.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e19.34\u0026mu;M\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.36082474226804%\"\u003e\n \u003cp\u003eGlucose 1-dehydrogenase/hexose-6-phosphate dehydrogenase (H6PD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.309278350515465%\"\u003e\n \u003cp\u003e8EM2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.742268041237114%\"\u003e\n \u003cp\u003e-8.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e420.17 nM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eMolecules Dynamic Simulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe database of proteins (PDB) typically contains the desired biomolecule\u0026apos;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 structure\u003csup\u003e27\u003c/sup\u003e. During this step, the molecules\u0026apos; surrounding environment (solvation, ionic strength) is frequently also positioned. \u0026nbsp;Hexose-6-phosphate dehydrogenase (H6PD) secondary structure show in (Fig. 4a). The plot illustrates the presence of the protein\u0026apos;s beta strands and alpha helicesstructure was observedin this MDstudy\u003csup\u003e28\u003c/sup\u003e.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\u0026nbsp;during the duration of the 100 ns simulation\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe 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\u0026nbsp;Protein on its own and in conjunction with ligandsto assess protein fluctuation, measured by C-alpha atoms\u0026apos; RMSD values. A stable deviation between the\u0026nbsp;proteins\u0026apos; RMSD values alone and the complexes was considered acceptable, typically falling between one to two.\u003c/p\u003e\n\u003cp\u003eThe RMSD values of the hexose-6-phosphate dehydrogenase\u0026ndash;ligand complex (specifically with piperine) was analysed, with the docking complex showing an RMSD of 3.6 \u0026Aring; (Fig.S2). Notably, the ligand piperine demonstrated significantly stable behaviourwith respect to the protein\u0026apos;s natural structure\u0026nbsp;with an RMSD significant of 2 \u0026Aring;. 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFigure S2 (Supplimetary file)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure. S2:\u003c/strong\u003eThe stability of the receptor-ligand complex is assessed by employing the root mean square deviation (RMSD) value.\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003eDuring 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFigure S3 (Supplimetary file)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure. S3: (a)\u003c/strong\u003eFor ligand (Piperine), the average RMSF values of Hexose-6-phosphate dehydrogenase amino acid residues have been calculated using Desmond software\u003cstrong\u003e(b)\u003c/strong\u003eFluctuation of the Ligand Root Means Square (L-RMSF)\u003c/p\u003e\n\u003cp\u003eThe ligand\u0026nbsp;roots of mean square fluctuations\u0026nbsp;(RMSF) can offer insights into the entropic contributions of ligand fragments in the binding process and their interactions with the protein\u003csup\u003e26\u003c/sup\u003e. 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. S3b\u003csup\u003e30\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe 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. 5a\u003csup\u003e31\u003c/sup\u003e. Four main types of protein-ligand interactions were identified: water bridges, hydrophobic interactions, ionic interactions, and hydrogen bonds\u003csup\u003e32\u003c/sup\u003e. Each of these interaction types exhibited specific subtypes that could be further investigated using\u0026nbsp;\u0026quot;Simulation Interactions Diagram.\u003c/p\u003e\n\u003cp\u003eIn 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.\u003c/p\u003e\n\u003cp\u003eFig.5b presents a timeline representation of contacts and interactions, including hydrophobic bonds, Water bridges, ionic bonds, and bonds of hydrogen\u003csup\u003e32\u003c/sup\u003e. The panel of theSimulation Interactions Diagram\u0026nbsp;displays normalized, stacked bar charts along the trajectory, indicating that various protein residues formed interactions with the ligand (piperine)\u0026nbsp;during the MD simulation\u0026apos;s 100 ns duration.\u003c/p\u003e\n\u003cp\u003eSpecifically, 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,\u0026nbsp;enhancing the protein-ligand complex\u0026apos;s general stability and specificity throughout the simulation period.\u003c/p\u003e\n\u003cp\u003eFig.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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFigure S4 (Supplimentary file)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure.S4: (a)\u003c/strong\u003eA 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.\u003cstrong\u003e(b)\u0026nbsp;\u003c/strong\u003eThe conformational development of potentially rotatable bonds is depicted by a ligand (Piperine) torsion map through the simulation study (100 ns).\u003c/p\u003e\n\u003cp\u003eIn 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.S4b\u003csup\u003e33\u003c/sup\u003e.\u0026nbsp;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.\u0026nbsp;In the topmost panel of Fig.S4b, the rotatable bonds are visually distinguished by colour on a 2D diagram representing the ligand.\u003c/p\u003e\n\u003cp\u003eThe ligand RMSD plot indicates that the piperine has an RMSD value of 1.0 \u0026Aring;, 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 \u0026Aring;, between 4.6 to 4.8 \u0026Aring;, the protein underwent some unfolding, but it remained compact for the remaining time.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, 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 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e. \u0026nbsp;The SASA was calculated during MD simulation study. The SASA plot displayed an average SASA of approximately at 240 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e. Like-wise the PSA plot represents the value of 75 \u0026Aring;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFigure S5 (Supplimentary file)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure. S5: (a)\u003c/strong\u003eAccording to the RMSD ligand plot, the piperine has an RMSD value of 1.0. \u0026Aring;.\u003cstrong\u003e(b)\u0026nbsp;\u003c/strong\u003eMM/PBSA calculation Plot of Hexose-6-phosphate dehydrogenase/Piperine docked complex.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMM/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).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003ePCOS is a particularly typical endocrinological illness, identified by persistent anovulation and hyperandrogenism\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. If PCOS is not efficiently and adequately treated, it can drastically decrease life expectancy\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Many biomarkers have been linked to PCOS and could serve as potential treatment targets. However, the exact mechanism of gene regulation leading to the progression of the syndrome remains unclear\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.The docking process assesses the binding affinity between molecules. In this research endeavor, we sought to investigate the therapeutic potential of piperine in treating PCOS by employing bioinformatics analysis to gain insights into their biological roles. Piperine, a natural polyphenol derived from black pepper (\u003cem\u003ePiper nigrum\u003c/em\u003e) and long peppers (\u003cem\u003ePiper longum\u003c/em\u003e), It's been used as a food spice and a potential therapy for several illnesses, including inflammation, obesity, and various malignancies\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Piperine is an orange needle-shaped crystal with melting and lighting points of 131\u0026ndash;132\u0026deg;C. Chloroform and methanol soluble; unable to dissolve in water\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePriyanka \u003cem\u003eet al.\u003c/em\u003e (2020) found that the results of the ADMET study on piperine derivatives derived from natural sources indicated that most ADME (Absorption, Distribution, Metabolism, and Excretion) features exhibited favorable characteristics, suggesting that these compounds are promising candidates for further development\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In the study investigation, the NCBI PubChem database makes the 3D structure of piperine accessible in the format of a structure-data file (SDF). The 3D structure of piperine was seen in Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea. Using the Swiss ADME web server, the physiochemical characteristics of piperine were examined, and ADMET characteristics were predicted using the online PKCSM program. Table\u0026nbsp;2 displayed the physiochemical characteristics of piperine, while Table\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e provided a summary of its ADMET characteristics. In this study, we confirmed as same results through in silico ADMET models that piperine exhibits no harmful effects at end points.\u003c/p\u003e \u003cp\u003eKamboj et al. utilized molecular docking to assess the binding affinity between compounds and CYP17. Their findings indicate that all phytocompounds exhibited blocking or hindrance of the CYP17 enzyme's activity, with docked scores ranging from \u0026minus;\u0026thinsp;3.7 to -9.5 \u003csup\u003e40\u003c/sup\u003e. Using computational molecular docking studies, Amudha studied the effectiveness of phytochemicals derived from \u003cem\u003eCadabafruticosa\u003c/em\u003e (L.) Druce in inhibiting CYP17. Docking scores ranged from \u0026minus;\u0026thinsp;3.3 to -7.9, indicating that all 20 drugs demonstrated moderate to strong inhibition. In particular, docking scores of -7.9 and \u0026minus;\u0026thinsp;6.8 indicated strong inhibition of androstan-3-one and 17-hydroxy-2, 4-dimethyl respectively\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. A more extensive negative value in the docking score denotes a better affinity among the ligand and the protien. The docking of ligands and proteins was ranked according to their binding affinity, with lower (more negative) scores indicating stronger interactions. Upon conducting a computational docking study, we found that piperine has the greatest affinity for binding to the targeted proteins. The docked complexes of all the protein targets are illustrated in Fig.S2 through 13.Over all, the docking study revealed that piperine exhibited the highest binding affinity with Peroxisome Proliferator Activated Receptor Gamma (-8.34 kcal/mol)\u003csup\u003e26\u003c/sup\u003e and Hexose-6-Phosphate Dehydrogenase/Glucose 1-Dehydrogenase (-8.70 kcal/mol).\u003c/p\u003e \u003cp\u003eThe expression of PPAR-γ in the ovaries has been linked to the formation of follicles and ovarian function. Changes in PPAR-γ signalling might interfere with ovarian steroidogenesis and folliculogenesis, which could lead to reproductive problems in PCOS, including irregular menstruation and infertility (Komar, 2005)\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. In addition, Ahmadian \u003cem\u003eet al.\u003c/em\u003e (2013) highlighted the significant function of the PPAR-γ protein in regulating adipocyte development, glucose levels, and lipid homeostasis\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. An important nuclear receptor recognized as peroxisome proliferator-activated receptor-γ (PPAR-γ) is connected with hyperandrogenemia and plays a role to regulating energy balance, as noted by \u003cem\u003eStump et al.\u003c/em\u003e (2015)\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Chen, \u003cem\u003eet al\u003c/em\u003e., (2015) noted that Androgen metabolism and androgen receptor signalling have been associated with PPAR-γ. The hyperandrogenic phenotype associated with PCOS may be attributed to dysregulated PPAR-γ activation\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Unluturk\u003cem\u003eet al.\u003c/em\u003e (2007) found that PPAR-γ genes are associated with PCOS occurrences across different ethnic populations. Their study provided evidence linking PPAR-γ with the pathogenesis of PCOS\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. According to our docking study, piperine exhibits the highest binding affinity with the Gamma-Activated Peroxisome Proliferator Receptor, with a calculated energy of -8.34 kcal/mol. Overall, PPAR-γ docking studies in PCOS research give important insights into the underlying molecular processes of the illness and this could potentially lead to the development of novel treatment approaches.\u003c/p\u003e \u003cp\u003eDocking studies involving Hexose-6-phosphate dehydrogenase (H6PD) genes and their relevance to PCOS are not commonly reported in the literature. While H6PD is involved in various metabolic pathways and play the important role in PCOS pathophysiology, specific docking studies focusing on H6PD genes in the context of PCOS are limited\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. However, computational docking studies involving other genes or proteins implicated in PCOS, such as PPAR-γ or insulin receptors, have been conducted to explore potential therapeutic interventions. Continuing research exploring the interaction between H6PD genes and pertinent molecular targets in PCOS could yield valuable insights into the fundamental mechanisms of the disorder.\u003c/p\u003e \u003cp\u003eHexose-6-phosphate dehydrogenase (H6PD) genes play the important role in polycystic ovarian syndrome (PCOS) by influencing various metabolic pathways and hormonal regulation. These genes contribute to glucose metabolism, oxidative stress management, steroid hormone production, insulin sensitivity, and lipid metabolism, all of which are dysregulated in PCOS (Li Yan \u003cem\u003eet al.\u003c/em\u003e, 2019)\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Understanding the involvement of H6PD in PCOS can provide insights into the disorder's mechanisms and potential therapeutic strategies. Cortisone reductase deficiency, caused by inactivating mutations in the enzyme Glucose 1-dehydrogenase/hexose-6-phosphate dehydrogenase (H6PD), mimics of PCOS and manifests as hyperandrogenism that cannot be explained by standard tests (Qin and Rosenfield 2011)\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Based on docking study, it was found that piperine demonstrates the greatest binding affinity with the Glucose 1-dehydrogenase/hexose-6-phosphate dehydrogenase (H6PD), showing a calculated energy of -8.70 kcal/mol.\u003c/p\u003e \u003cp\u003eMolecule Dynamic Simulation is regularly utilized to refine proteins or any molecular structures and find previously elusive information about the time evolution of conformations (Vanommeslaeghe et \u003cem\u003eal.\u003c/em\u003e, 2014)\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. The protein-ligand RMSD showed that hexose-6-phosphate dehydrogenase docking complexes with 3.6 were present. The RMSD value of piperine when it binds to hexose-6-phosphate dehydrogenase (3.6) is larger than in the natural crystal structure of Glucose 1-dehydrogenase/hexose-6-phosphate dehydrogenase (H6PD). To confirm the adaptability and flexibility of amino acids during simulation for 100 nano seconds, the root of mean squares change (RMSF) was determined. Every residue of the amino acid RMSF value is checked as it cooperates with the ligand along a direction (De Vita \u003cem\u003eet al.\u003c/em\u003e, 2021)\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. A well-known method for calculating the binding affinities of docked complexes is MM/PBSA. According to our study's MM/PBSA calculations, the docked complex of hexose-6-phosphate dehydrogenase and piperine exhibits a binding affinity of -19.71 kcal/mol, as seen in Fig.S3b.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, the combination of network pharmacology and molecular docking analyses offers empirical evidence supporting the potential pharmaceutical application of exploring the potential role of piperine in the treatment or management of polycystic ovarian syndrome (PCOS). Through computational analyses, piperine exhibited promising drug-like characteristics and demonstrated favorable interactions with key proteins associated with PCOS pathogenesis, particularly Hexose-6-phosphate dehydrogenase. Molecular dynamics simulations further confirmed the stability and efficacy of the piperine-Hexose-6-phosphate dehydrogenase complex, suggesting its potential as a therapeutic agent for managing PCOS symptoms. These findings lay a solid scientific foundation for further exploration and development of piperine-based treatments for PCOS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are thankful to the Researchers Supporting Project number (RSPD2024R930), King Saud University, Riyadh, Saudi Arabia. The authors also thank Srimad Andavan Arts and Science College (Autonomous), Trichy, and Dhanalakshmi Srinivasan University, Samayapuram, Trichy, for providing facilities to carry out the research work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRF: investigation; methodology; visualization; RK: writing-review and editing, VB: conceptualization; data curation; SP: investigation; methodology; visualization; writing-review; AC: conceptualization; methodology; data curation; software; IAA: software; visualization; writing-review and editing; SAA: Funding \u0026amp; data curation; HMA: data curation; software; JC: methodology; data curation; software; SVT: investigation; methodology; visualization; Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research project was supported by the Researchers Supporting Project Number (RSPD2024R930), King Saud University, Riyadh, Saudi Arabia.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBozdag G,\u003cem\u003eet al\u003c/em\u003e. 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Insights into the ligand binding to bromodomain-containing protein 9 (BRD9): a guide to the selection of potential binders by computational methods. \u003cem\u003eMolecules\u003c/em\u003e. 27;26(\u003cstrong\u003e23\u003c/strong\u003e):7192 (2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PCOS, Piperine, In-silico, docking studies, H6PD, PPARG","lastPublishedDoi":"10.21203/rs.3.rs-4362153/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4362153/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePolycystic Ovarian Syndrome (PCOS) is a multifaceted metabolic and hormonal condition that impacts women in their procreative ages, identified by ovarian dysfunction, hyperandrogenaemiaoverweight and insulin insensitivity.The piperine, an important alkaloid compound of black pepper has shown promise in modulating various physiological processes. In this work, employed computational docking studies to explore the potential of piperine as a treatment for PCOS. Utilizing computational methods, we analyzed the binding interactions between piperine and key molecular targets implicated in PCOS pathogenesis, including hyperandrogenism, and \"oligomenorrhea. The network pharmacology analysis report found 988 PCOS-related genes, 108 hyperandrogenism-related genes, and 377 oligomenorrhea-related genes, and we finally shortlisted 5 common genes in PCOS, hyperandrogenism, and \"oligomenorrhea\": NR3C1, PPARG, FOS, CYP17A1, and H6PD. Our results reveal favorable binding affinities with PPARG (-8.34 Kcal/mol) and H6PD (-8.70 Kcal/mol) and interaction patterns, suggesting the potential of piperine to modulate these targets. Moreover, the reliabilityof the piperine-target interactions was revealed by molecular simulations studies. These findings support further experimental investigations to validate the therapeutic efficacy of piperine in PCOS management. The integration of computational approaches withexperimental studies has the potential to lay the groundwork for the creation of new therapies specifically targeting PCOS and related endocrine disorders.\u003c/p\u003e","manuscriptTitle":"Exploring Piperine as a Potential Treatment for Polycystic Ovarian Syndrome: Insights from In-silico Docking Studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-24 08:24:53","doi":"10.21203/rs.3.rs-4362153/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-15T09:35:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-11T13:54:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15894038750080135092892991134993212397","date":"2024-06-21T04:46:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-20T00:44:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154047252261414971381927129987240320101","date":"2024-05-24T11:42:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"139199744955250137947235099441680750793","date":"2024-05-24T08:23:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-24T08:19:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-24T08:13:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-15T12:38:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-15T12:32:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-05-03T05:59:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"903267fd-6c82-47ea-8399-18c03bc51666","owner":[],"postedDate":"May 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":32303575,"name":"Biological sciences/Computational biology and bioinformatics/Cellular signalling networks"},{"id":32303576,"name":"Biological sciences/Computational biology and bioinformatics/Computational models"},{"id":32303577,"name":"Biological sciences/Computational biology and bioinformatics/Data processing"},{"id":32303578,"name":"Biological sciences/Computational biology and bioinformatics/Virtual drug screening"}],"tags":[],"updatedAt":"2024-09-23T16:11:31+00:00","versionOfRecord":{"articleIdentity":"rs-4362153","link":"https://doi.org/10.1038/s41598-024-72800-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-09-18 15:57:53","publishedOnDateReadable":"September 18th, 2024"},"versionCreatedAt":"2024-05-24 08:24:53","video":"","vorDoi":"10.1038/s41598-024-72800-6","vorDoiUrl":"https://doi.org/10.1038/s41598-024-72800-6","workflowStages":[]},"version":"v1","identity":"rs-4362153","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4362153","identity":"rs-4362153","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}