Decoding the Pharmacological Actions of Can Si (Silk Fibroin), a Traditional Chinese Medicine (TCM) for Peripheral Nerve Injury: A Comprehensive Molecular Simulation

Decoding the Pharmacological Actions of Can Si (Silk Fibroin), a Traditional Chinese Medicine (TCM) for Peripheral Nerve Injury: A Comprehensive Molecular Simulation | 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 Decoding the Pharmacological Actions of Can Si (Silk Fibroin), a Traditional Chinese Medicine (TCM) for Peripheral Nerve Injury: A Comprehensive Molecular Simulation Nasser Alotaiq, Doni Dermawan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6190910/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Peripheral nerve injury (PNI) remains a significant clinical challenge, often leading to impaired nerve regeneration and chronic neuropathic pain. Can Si (Silk Fibroin), a key component of Traditional Chinese Medicine (TCM), has long been recognized for its regenerative properties, yet its molecular mechanisms in PNI treatment remain unexplored. To elucidate the pharmacological actions of Can Si, an integrative molecular simulation approach was applied. Network pharmacology was employed to identify the most favorable target receptor for PNI, leading to the selection of the glucocorticoid receptor (GR) due to its critical role in inflammation and nerve repair. Molecular docking simulations evaluated the binding affinities of chemical and protein-based compounds from Can Si to GR, followed by molecular dynamics (MD) simulations to confirm the stability of these interactions under physiological conditions. Pharmacophore modeling identified key structural features essential for bioactivity, while in silico toxicity assessments evaluated the safety profiles of the compounds. Key bioactive compounds from Can Si, including Catechin, Hesperetin, and Menaquinone-7, demonstrated strong interactions with GR, with MM/PBSA-based binding free energy values of − 35.98 kcal/mol, − 33.65 kcal/mol, and − 32.13 kcal/mol, respectively. Protein-based compounds, such as Bombyxin A-5 (− 228.06 kcal/mol) and Small Ribosomal Subunit Protein uS11 (− 204.98 kcal/mol), also displayed promising binding affinities, suggesting potential neuroprotective roles. In silico toxicity assessments revealed favorable safety profiles for most compounds. This study highlights Can Si as a promising source of therapeutic agents for PNI. Future studies should focus on experimental validation of these computational findings through in vitro and in vivo models. Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Biological sciences/Neuroscience Can Si molecular docking molecular dynamics network pharmacology peripheral nerve injury pharmacophore modeling traditional Chinese medicine Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Peripheral nerve injuries (PNIs) represent a major clinical and socioeconomic burden, often leading to loss of sensory and motor functions due to disrupted nerve conduction [ 1 , 2 ]. These injuries can result from trauma, surgery, or neurological disorders, and despite advances in microsurgical techniques, functional recovery remains incomplete in many cases [ 3 ]. Autologous nerve grafting is the current gold standard for nerve repair; however, it is associated with several limitations, including donor site morbidity, limited graft availability, and mismatched nerve dimensions [ 4 , 5 ]. Alternative strategies, such as the use of neurotrophic factors, electrical stimulation, and biomaterial scaffolds, have been explored. Yet, their clinical application is restricted due to issues like short half-life, high costs, and potential immune responses [ 6 , 7 ]. Consequently, there is an increasing demand for novel, effective, and biocompatible therapeutic approaches to enhance nerve regeneration and functional recovery. Traditional Chinese Medicine (TCM) has long been utilized for nerve repair and regeneration, offering a rich source of bioactive compounds with potential neuroprotective properties [ 8 , 9 ]. Among these, Can Si (Silk Fibroin), a naturally occurring biopolymer derived from Bombyx mori cocoons, has attracted attention for its biocompatibility, mechanical strength, biodegradability, and neuroregenerative potential [ 10 – 12 ]. Silk fibroin is primarily composed of glycine, alanine, and serine-rich β-sheet structures, which contribute to its unique physicochemical properties and ability to support cellular attachment, proliferation, and differentiation [ 13 , 14 ]. Preclinical studies have demonstrated that silk fibroin-based scaffolds promote Schwann cell migration, axonal outgrowth, and extracellular matrix remodeling, making it a promising candidate for nerve tissue engineering [ 15 , 16 ]. However, despite these encouraging findings, the precise molecular mechanisms underlying its neuroprotective effects and interactions with key biological targets involved in PNI repair remain unclear. To address this gap, this study employs an integrative molecular simulation approach to systematically investigate the pharmacological actions of Can Si in PNI. By leveraging network pharmacology, molecular docking, molecular dynamics (MD) simulations, and pharmacophore modeling, we aim to uncover the bioactive components of silk fibroin, identify their molecular targets, and elucidate their binding interactions and dynamic stability at the atomic level. Network pharmacology identified key protein targets involved in neuronal survival, axonal regeneration, synaptic plasticity, and inflammation, prioritizing the most relevant receptors. Molecular docking assessed the binding affinities of silk fibroin-derived peptides to these targets, while MD simulations validated the stability and interactions of docked complexes under physiological conditions. Finally, pharmacophore modeling identified essential structural features for bioactivity, providing insights for optimizing silk fibroin-based therapeutics. The findings bridge the gap between TCM and modern computational drug discovery and pave the way for the rational design of silk fibroin-based biotherapeutics for peripheral nerve regeneration. Understanding the precise molecular interactions between silk fibroin and neuroregenerative targets may contribute to the development of novel silk-based biomaterials or bioactive peptide therapies for PNI treatment. Methodology Materials The bioactive compounds in Can Si were identified through a thorough literature survey, compiling data from peer-reviewed journals and pharmacological studies. Since a dedicated database for TCM bioactive compounds is lacking, this manual curation ensured a comprehensive and high-quality dataset for further computational analysis. The collected data focused on key bioactive constituents of silk fibroin, including peptides and chemical compounds with potential neuroprotective properties. To investigate the protein and peptide components of Bombyx mori , a systematic search was performed using UniProt, a globally recognized database for protein sequences and functional information. The query employed the MeSH term “ Bombyx mori ” within UniProtKB, filtering results to include only reviewed (Swiss-Prot) entries, ensuring accuracy and reliability. Given the importance of bioavailability and therapeutic relevance, proteins and peptides with a maximum length of 200 amino acids were prioritized, as shorter sequences are more likely to exhibit favorable absorption and distribution properties. However, Fibroin and Sericin, the two principal structural proteins of silk fibroin, were included regardless of length due to their significant roles in neuroregeneration and biomaterial applications. To enhance dataset quality, a meticulous curation process was implemented to eliminate redundant entries and potential inconsistencies. This step minimized data duplication and bias, ensuring the integrity and reliability of the dataset for subsequent computational studies. By applying these rigorous selection criteria, a refined collection of silk fibroin-derived bioactive compounds, peptides, and proteins was established as a foundation for molecular modeling investigations. To systematically compile the bioactive constituents of Can Si, an extensive literature review was conducted, gathering information from peer-reviewed sources. Due to the absence of a specialized database for TCM bioactive compounds, this approach ensured a well-rounded dataset for further computational analysis. The focus was placed on identifying key peptides and chemical compounds within silk fibroin that may contribute to its neuroregenerative effects. For protein and peptide analysis, a structured query was performed in UniProt, a globally recognized database for protein sequences and functional annotations. The search was refined using the MeSH term “ Bombyx mori ” within UniProtKB, targeting entries specific to this species. To maintain data accuracy, only reviewed (Swiss-Prot) proteins were considered, filtering out unverified sequences. Since shorter peptide chains exhibit better bioavailability, proteins, and peptides with a length of 200 amino acids or less were prioritized. However, Fibroin and Sericin, the two major structural components of silk fibroin, were included regardless of size due to their critical role in neuroprotection and biomaterial applications. A rigorous curation process was implemented to eliminate redundant or inconsistent entries, ensuring the dataset’s accuracy and reliability. This meticulous filtering reduced data noise and minimized bias, enhancing the validity of downstream computational analyses. The final dataset, enriched with curated silk fibroin-derived bioactive molecules, established a robust foundation for molecular modeling studies aimed at deciphering its therapeutic mechanisms in PNI. 3D Structure Construction and MMFF94 Energy Minimization To construct accurate molecular models of bioactive compounds from Can Si (silk fibroin), 3D structures were generated using Chem3D Ultra v22 (PerkinElmer, Massachusetts, USA). The MMFF94 (Merck Molecular Force Field 94) energy minimization algorithm [ 17 ] was employed to optimize molecular conformations, ensuring stable and low-energy structures suitable for computational simulations. This step enhanced structural reliability, allowing for more precise predictions of molecular interactions. For peptides and proteins, sequence data specific to Bombyx mori were retrieved from UniProt, a globally recognized protein database. The search yielded 27,947 entries, of which 276 were Swiss-Prot reviewed (accessed February 1, 2025). To enhance bioavailability and computational feasibility, proteins with sequences ≤ 200 amino acids were prioritized, resulting in a refined selection of 102 proteins. A rigorous data validation and curation protocol was applied to ensure dataset integrity. Initially, sequence completeness was assessed to eliminate incomplete or ambiguous entries that could compromise structural accuracy. This was followed by an error detection process, identifying irregularities such as sequencing errors or ambiguous residues. Any sequences failing to meet quality standards were excluded to maintain dataset robustness. Additionally, a redundancy check was performed to eliminate duplicate records, preventing biases that could skew computational analyses. The final high-fidelity dataset, containing unique and structurally validated bioactive peptides and proteins, was used as the foundation for 3D modeling. This curated dataset facilitated subsequent molecular simulations aimed at deciphering the neuroprotective and regenerative mechanisms of silk fibroin in PNI. To accurately model peptides and proteins from Can Si, AlphaFold v3 (DeepMind Technologies Ltd, London, UK) [ 18 , 19 ] was utilized. This cutting-edge deep learning framework excels in predicting protein conformations with exceptional accuracy, even in the absence of homologous structural templates. Its ability to generate high-fidelity models made it particularly advantageous for analyzing silk fibroin-derived bioactive molecules. Following structure prediction, active site identification and binding pocket analysis were conducted using CASTpFold (University of Illinois at Chicago, USA) [ 20 ], an advanced version of the Computed Atlas of Surface Topography of Proteins (CASTp). This tool precisely maps ligand-accessible regions, offering key insights into potential binding interactions that underlie silk fibroin’s therapeutic effects in PNI. The primary receptor for molecular docking simulations was selected based on network pharmacology screening and retrieved from the Protein Data Bank (PDB) under accession code 1M2Z (chain A), resolution 2.50 Å [ 21 ]. This ensured the use of a high-quality receptor structure for precise computational interaction studies. A comprehensive database of Can Si-derived bioactive compounds and proteins was compiled and is provided in Supplementary Data S1 . For chemical compounds, this dataset includes PubChem CID, molecular weight (MW), MlogP, hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), bioavailability scores, blood-brain barrier (BBB) scores, drug-likeness scores, and canonical SMILES. Meanwhile, the peptide and protein database contains UniProt IDs, amino acid sequences, sequence lengths, and identified functional binding residues, offering a robust foundation for molecular simulations. Network Pharmacology-Based Target Identification for Can Si in PNI To map the molecular interactions underlying the therapeutic effects of Can Si in PNI, a bioactive compound-target network was developed using Cytoscape version 3.10.3 (Cytoscape Consortium, Washington, USA) [ 22 ]. In this network, bioactive compounds and target proteins were represented as nodes, while edges signified their interactions. A refined common-target network was established by identifying the overlapping protein targets between silk fibroin-related proteins and PNI-associated targets. Key proteins within the network were prioritized based on degree centrality, which measures how many direct connections a node has. Proteins with a degree greater than or equal to the median degree were classified as critical targets for further investigation. To gain a deeper understanding of protein-protein interactions (PPIs), the stringApp plugin [ 23 , 24 ] in Cytoscape was utilized, constructing PPI networks specific to Homo sapiens with a minimum confidence score threshold of 0.4. These networks were then merged to highlight shared proteins, pinpointing key molecular hubs involved in PNI-related pathways. To assess the importance of each target protein within the merged network, CytoNCA (a Cytoscape network analysis plugin) [ 25 ] was used to evaluate degree centrality (DC), eigenvector centrality (EC), betweenness centrality (BC), and closeness centrality (CC). Only proteins surpassing the median values of these centrality measures were retained for further validation, ensuring the selection of biologically significant targets. Docking Simulations to Unveil the Therapeutic Potential of Can Si To elucidate the binding mechanisms of Can Si-derived bioactive compounds with key receptor targets involved in PNI, both ligand-protein and protein-protein docking simulations were conducted. This computational approach aimed to identify crucial binding residues, assess intermolecular interactions, evaluate binding affinities, and analyze binding orientations of silk fibroin-derived molecules at the receptor’s active sites. Initially, PDBSum (European Bioinformatics Institute, Cambridge, UK) [ 26 ] was employed to generate a comprehensive structural summary of the target receptor, highlighting key binding residues within its active site. This structural mapping facilitated the prediction of potential interaction sites for both bioactive compounds and protein targets. To enhance the accuracy of docking simulations, the target receptor structure was refined using Swiss-PdbViewer version 4.1 (Swiss Institute of Bioinformatics, Lausanne, Switzerland) [ 27 ], ensuring optimal geometry and stability before interaction studies. To determine whether silk fibroin-derived bioactive compounds act as agonists or antagonists in PNI-related pathways, comparative docking analyses were performed against well-established reference molecules. Dexamethasone, a known glucocorticoid receptor agonist commonly used in PNI treatment [ 28 , 29 ], served as a standard agonist, while Mifepristone, a recognized glucocorticoid receptor antagonist [ 30 ], was used as a standard antagonist. This comparative analysis provided critical insights into the potential therapeutic roles of silk fibroin compounds in modulating receptor activity relevant to nerve regeneration and inflammation control. To explore the binding interactions between silk fibroin-derived bioactives and their target receptors, ligand-protein and protein-protein docking simulations were conducted using HADDOCK 2.4 (High Ambiguity Driven Protein-Protein Docking, University of Utrecht, Netherlands) [ 31 , 32 ]. This advanced computational platform specializes in modeling biomolecular interactions by integrating geometric and energetic constraints derived from experimental and theoretical data. The docking simulations were executed using the standalone HADDOCK interface, allowing for precise binding mode predictions. The selection of optimal docked complexes was guided by two key criteria: 1) Cluster population size, indicating the stability and reproducibility of the predicted interactions and 2) HADDOCK score, reflecting the binding affinity strength between silk fibroin bioactives and their target proteins. These stringent selection parameters ensured that only highly stable and functionally relevant binding models were retained for further investigation. To enhance the accuracy of binding energy predictions, the docking results were further refined using PRODIGY [ 33 ], a computational tool that estimates binding free energy (ΔG, kcal/mol) by incorporating structural and energetic features of ligand-protein and protein-protein complexes. PRODIGY's assessment considered intermolecular contacts and desolvation effects, providing a physiologically relevant estimation of binding affinity. This approach offered a robust framework for evaluating the stability and therapeutic potential of silk fibroin-derived bioactives in the treatment of peripheral nerve injury. Molecular Dynamics (MD) Simulation to Assess the Stability of Can Si-Derived Bioactive Complexes To explore the dynamic behavior, structural integrity, and conformational shifts of silk fibroin-derived bioactive complexes, MD simulations were conducted using GROMACS 2023.3 [ 34 ], a high-performance computational tool for biomolecular simulations. This approach provided an in-depth understanding of the temporal evolution and stability of ligand-protein and protein-protein interactions [ 35 , 36 ]. For ligand parameterization, the General Amber Force Field (GAFF) was applied, with partial atomic charges assigned using the AM1-BCC (Austin Model 1 with Bond Charge Corrections) method [ 37 , 38 ]. The ACPYPE tool, integrated with AmberTools21, was used to generate the ligand topology [ 39 ]. The simulation system was enclosed within a truncated octahedral box, ensuring a minimum buffer distance of 1.2 nm between the complex and the box boundary. Periodic boundary conditions were enforced in all dimensions to replicate a continuous biological environment. The protein component was modeled using the AMBER14SB force field, while the system was solvated with TIP3P water molecules [ 40 ]. To mimic physiological ionic strength, 150 mM NaCl was added, and counterions were introduced for charge neutralization. Nonbonded interactions were handled with a 1.0 nm cutoff for short-range interactions, while long-range electrostatic forces were computed using the Smooth Particle Mesh Ewald (SPME) method [ 41 ]. Before running the production simulation, the system underwent multi-step equilibration: Energy minimization was performed using the conjugate gradient method until the maximum force was reduced to 500 kJ/mol/nm. Canonical ensemble (NVT) equilibration was conducted for 200 ps, maintaining a constant temperature of 310 K using the V-rescale thermostat. Isothermal-isobaric ensemble (NPT) equilibration was applied for 200 ps to stabilize pressure at 1 atm, regulated by the Monte Carlo barostat. Following equilibration, a 100 ns production simulation was executed under constant temperature and pressure, controlled by the Nosé-Hoover thermostat and Parrinello-Rahman barostat [ 42 ], respectively. This simulation provided comprehensive insights into the dynamic stability and functional relevance of silk fibroin-derived bioactive compounds in the context of PNI treatment. To accurately model protein-protein interactions within silk fibroin-derived complexes, the Optimized Potentials for Liquid Simulations-All Atom (OPLS-AA/L) force field [ 43 ] was employed, with a cubic simulation box ensuring full encapsulation of the complex. The system was solvated using the Single Point Charge Extended (SPCE) water model [ 44 ], with counterions added for charge neutrality. Energy minimization was conducted via the conjugate gradient algorithm to resolve steric clashes, followed by a two-step equilibration process: (1) Canonical (NVT) equilibration for temperature stabilization and (2) Isothermal-isobaric (NPT) equilibration to maintain pressure at 1 atm and temperature at 310 K. A 150 ns production MD simulation was then performed to analyze the long-term stability and conformational dynamics of the protein-protein complexes, monitoring key structural parameters such as Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (RoG), and hydrogen bonding interactions. To facilitate in-depth structural analysis, molecular visualization tools such as PyMOL 3.1.3 (Schrödinger LLC, New York, USA) [ 45 ], BIOVIA Discovery Studio 2024 (Dassault Systèmes, San Diego, USA) [ 46 ], and UCSF ChimeraX (University of California, San Francisco, USA) [ 47 ] were utilized for manual inspection and graphical representation of key residues and intermolecular interactions. These simulations provided a detailed mechanistic insight into the role of silk fibroin-based protein complexes in peripheral nerve repair, reinforcing their potential therapeutic applications in TCM. MM/PBSA Free Energy Analysis for Evaluating Can Si-Receptor Interactions To evaluate the binding strength and stability of silk fibroin-derived bioactive compounds with their target receptors, the Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) method was applied in conjunction with MD simulations. Representative structural snapshots were extracted from MD trajectories to capture dynamic conformations, allowing for detailed energy calculations, including gas-phase molecular mechanics energy, solvation energy, and entropy corrections. The Solvent-Accessible Surface Area (SASA) model [ 48 ] was used to estimate nonpolar solvation energy by analyzing hydrophobic contributions to binding. The Single-Trajectory Protocol (STP) [ 49 ] was employed, assuming minimal conformational changes upon binding to ensure consistency in energy evaluations. Using the gmx_MMPBSA module [ 50 ] within GROMACS, the binding free energy (ΔG_binding) was computed based on the equation: ΔG_binding = ΔG_complex - (ΔG_ligand/protein + ΔG_receptor), where ΔG_complex represents the total free energy of the solvated ligand-receptor system, while ΔG_ligand/protein and ΔG_receptor correspond to the free energy of the unbound ligand/protein and receptor, respectively. This computational approach provided a quantitative assessment of binding affinity and interaction stability, offering key insights into the potential pharmacological mechanisms of silk fibroin in treating peripheral nerve injury. Computational Pharmacophore Modeling and Virtual Toxicity Screening of Can Si To identify key molecular features responsible for the bioactivity of silk fibroin-derived compounds, pharmacophore modeling was carried out using LigandScout 4.5 (Inte:Ligand, Vienna, Austria) [ 51 ]. This computational approach enabled the generation of 3D pharmacophore models by mapping crucial interaction sites, including hydrogen bond donors and acceptors, hydrophobic regions, and electrostatic hotspots, which contribute to ligand-receptor binding affinity. By analyzing the spatial arrangement of these features, potential bioactive compounds were identified based on their compatibility with target proteins. Concurrently, an in-silico toxicity evaluation was performed using DataWarrior 6.1.0 (OpenMolecules, Karlsruhe, Germany) [ 52 ] to assess the safety profiles of the identified compounds. This tool utilizes diverse molecular descriptors and predictive algorithms to evaluate potential toxicity risks, ensuring that only chemically viable and non-toxic candidates are selected for further biological validation. This dual computational strategy provided valuable insights into both the pharmacological efficacy and safety of silk fibroin-derived compounds, supporting their potential application in PNI treatment. Results Unraveling Drug-Target Interactions and Prioritization of Target Receptors A network pharmacology approach was undertaken to systematically explore the potential interactions between bioactive compounds in Can Si and relevant protein targets implicated in PNI. Following the identification of key target proteins, a protein-protein interaction network was constructed using the STRING database, providing insights into how these targets interact with established PNI-associated proteins. The protein-protein interaction network analysis identified Toll-like receptor 4 (TLR4) as a key regulatory node, exhibiting extensive connectivity with multiple PNI-associated proteins, suggesting its crucial role in neuroinflammatory and regenerative pathways (Fig. 1 ). Among its significant interactions, TLR4 was linked to glial cell-derived neurotrophic factor (GDNF), which promotes neuronal survival and regeneration [ 53 ], as well as SRY-box transcription factor 9 (SOX9), a key regulator in Schwann cell differentiation and myelination [ 54 ], emphasizing TLR4’s potential role in remyelination. Additionally, its connection with transforming growth factor beta 1 (TGFB1), a cytokine known for its anti-inflammatory and fibrosis-regulating properties [ 55 ], suggests that TLR4 might modulate immune responses critical for nerve repair. Furthermore, its interaction with apolipoprotein E (APOE), which is involved in lipid metabolism and neuronal repair [ 56 ], underscores its potential in maintaining lipid homeostasis in damaged nerve tissues. TLR4 also exhibited associations with erythropoietin (EPO), a neuroprotective factor with anti-apoptotic and anti-inflammatory effects, as well as thrombomodulin (THBD), which plays a role in vascular homeostasis and inflammation regulation [ 57 , 58 ], further highlighting its influence on neuronal survival. Its connection with mitogen-activated protein kinase 14 (MAPK14, also known as p38 MAPK), a key mediator of stress and inflammatory responses [ 59 ], suggests a role in neuroinflammation following PNI. Additionally, interactions with hepatocyte growth factor (HGF) and superoxide dismutase 2 (SOD2) indicate potential involvement in tissue repair, axonal outgrowth, and oxidative stress modulation [ 60 , 61 ]. Given these extensive molecular interactions, TLR4 emerges as a central player in the pathophysiology of PNI, making it a promising therapeutic target. The findings suggest that bioactive compounds derived from Can Si may exert their pharmacological effects by modulating TLR4 activity, thereby mitigating neuroinflammation, enhancing neuronal survival, and promoting peripheral nerve regeneration. Since there are currently no FDA-approved drugs that directly target TLR4 for the treatment of PNI [ 62 ], alternative therapeutic pathways were investigated to identify viable intervention strategies. TLR4 plays a critical role in neuroinflammation and immune response regulation, making it an attractive therapeutic target. However, due to the lack of clinically approved drugs that modulate TLR4 directly, it was essential to explore other key proteins within the PPI network that might offer indirect modulation of TLR4-driven pathways. Among the identified targets, the glucocorticoid receptor (GR) emerged as a particularly promising candidate due to its strong correlation with TLR4 and its central role in modulating neuroinflammation and immune responses [ 63 , 64 ]. Numerous studies have demonstrated that GR activation suppresses TLR4-mediated pro-inflammatory signaling pathways, reducing the expression of cytokines and inflammatory mediators that contribute to nerve injury and degeneration [ 65 , 66 ]. This reciprocal regulatory interaction between GR and TLR4 suggests that targeting GR could indirectly modulate TLR4 activity, thereby mitigating inflammation and promoting neuroprotection in PNI. Within the PPI network, GR exhibited extensive interactions with multiple PNI-associated proteins, reinforcing its potential as a regulatory hub for mitigating nerve damage and enhancing recovery. Given its pharmacological significance and functional interplay with TLR4, GR was prioritized for further computational investigations. To assess its therapeutic potential in PNI, Can Si-derived bioactive compounds were subjected to detailed computational studies, including molecular docking and MD simulations, to evaluate their binding affinity, stability, and conformational dynamics with GR. These findings could pave the way for the development of novel GR-targeting therapies derived from Can Si, offering an innovative strategy for neuroprotection and nerve regeneration in PNI management. Molecular Docking Analysis: Investigating Ligand-Receptor and Protein-Receptor Interactions To gain a deeper understanding of the molecular interactions between Can Si-derived bioactive compounds and the GR, molecular docking simulations were conducted. This analysis assessed the binding affinities, interaction profiles, and stability of the top 10 bioactive compounds compared to standard GR agonist (Dexamethasone) and antagonist (Mifepristone). The results, presented in Table 1 , revealed that several Can Si-derived compounds exhibited strong binding affinities toward GR, surpassing the performance of both standard ligands in certain parameters. Among the top-performing compounds: Menaquinone-7 ( − 34.2 HADDOCK score, − 9.16 kcal/mol binding energy) demonstrated the strongest interaction with GR, showing highly favorable van der Waals contributions ( − 33.1) and moderate electrostatic interactions ( − 7.1). The higher binding affinity of menaquinone-7 compared to dexamethasone ( − 23.1, − 8.06 kcal/mol) and mifepristone ( − 25.0, − 7.79 kcal/mol) suggests that this compound may serve as an effective modulator of GR activity. Hesperetin ( − 34.9 HADDOCK score, − 8.63 kcal/mol binding energy) also exhibited strong interactions with GR, particularly through van der Waals forces ( − 29.1) and electrostatic energy ( − 9.2). This suggests that hesperetin can establish a stable ligand-receptor complex, further emphasizing its potential as a therapeutic agent. Catechin (-34.1 HADDOCK score, − 8.57 kcal/mol binding energy) emerged as another promising candidate, with significant electrostatic contributions ( − 19.9), indicating its ability to engage GR through multiple molecular interactions. Other flavonoid-based compounds, including epigallocatechin ( − 34.9, − 8.52 kcal/mol), thiamine ( − 38.0, − 8.51 kcal/mol), galangin ( − 33.8, − 8.47 kcal/mol), and quercetin ( − 34.4, − 8.46 kcal/mol), also demonstrated strong binding interactions with GR. These compounds exhibited notable van der Waals and electrostatic interactions, reinforcing their potential to influence GR-mediated signaling pathways. The full molecular docking results for both chemical and protein-based compounds are available in Supplementary Data S2 . Table 1 Molecular docking results of the top 10 highest-performing Can Si-derived chemical compounds in complex with the glucocorticoid receptor (GR), benchmarked against the standard agonist (Dexamethasone) and antagonist (Mifepristone) Complex HADDOCK score (a.u.) Binding energy (kcal/mol) Van der Waals energy Electrostatic energy Desolvation energy RMSD Dexamethasone_GR (standard agonist) −23.1 +/- 0.8 −8.06 −20.6 +/- 1.0 −16.1 +/- 3.2 −5.0 +/- 0.2 0.5 +/- 0.0 Mifepristone_GR (standard antagonist) −25.0 +/- 2.3 −7.79 −26.3 +/- 2.0 13.4 +/- 5.8 −6.8 +/- 0.5 0.1 +/- 0.1 Menaquinone-7_GR −34.2 +/- 2.8 −9.16 −33.1 +/- 4.8 −7.1 +/- 9.3 −6.8 +/- 1.2 0.8 +/- 0.0 Hesperetin_GR −34.9 +/- 1.6 −8.63 −29.1 +/- 1.2 −9.2 +/- 2.9 −5.2 +/- 0.1 0.3 +/- 0.1 Catechin_GR −34.1 +/- 1.7 −8.57 −28.8 +/- 2.0 −19.9 +/- 0.4 −3.7 +/- 0.4 0.2 +/- 0.0 Phytonadione_GR −31.6 +/- 0.6 −8.55 −26.8 +/- 1.2 −33.8 +/- 4.1 −8.4 +/- 0.4 0.5 +/- 0.0 Epigallocatechin_GR −34.9 +/- 2.3 −8.52 −31.8 +/- 2.3 −15.3 +/- 2.6 −1.9 +/- 0.4 0.2 +/- 0.1 Thiamine_GR −38.0 +/- 1.7 −8.51 −26.7 +/- 0.9 −53.4 +/- 1.3 −7.1 +/- 0.3 0.1 +/- 0.1 Galangin_GR −33.8 +/- 1.0 −8.47 −28.8 +/- 1.2 −7.3 +/- 1.1 −4.5 +/- 0.4 0.2 +/- 0.1 Kaemperol_GR −32.3 +/- 0.7 −8.46 −28.6 +/- 1.3 −7.7 +/- 0.9 −3.2 +/- 0.6 0.3 +/- 0.0 Luteolin_GR −35.3 +/- 1.6 −8.46 −32.5 +/- 0.5 −2.0 +/- 3.2 −3.2 +/- 0.2 0.1 +/- 0.1 Quercetin_GR −34.4 +/- 1.0 −8.46 −32.4 +/- 1.0 −5.9 +/- 1.2 −1.6 +/- 0.1 0.2 +/- 0.1 The molecular interactions of the top-performing compounds with the GR were visualized in Fig. 2 , illustrating their 3D binding poses, and further detailed in Fig. 3 , which presents their 2D interaction maps. Each compound exhibited a unique interaction profile while maintaining key similarities with dexamethasone, the standard agonist. Dexamethasone forms hydrogen bonds with Gln642 and Tyr735, reinforcing its binding affinity within the ligand-binding domain (LBD). Additionally, it engages in van der Waals interactions with Ser555, Thr556, Trp557, Met560, and Pro637, while Cys638, Met745, and Ile747 contribute to Pi-alkyl interactions. This well-established interaction network underpins dexamethasone’s modulatory activity, ensuring strong receptor engagement. Among the Can Si-derived compounds, Hesperetin and Catechin demonstrated comparable binding interactions, particularly through hydrogen bonding within the LBD. Furthermore, both compounds introduced additional stabilization via amide-Pi stacked interactions with Leu741 and Phe749, potentially enhancing their receptor binding. Mifepristone, the standard antagonist, exhibited a contrasting interaction profile. Unlike dexamethasone, it lacked hydrogen bonds entirely and primarily relied on van der Waals and Pi-Sigma interactions, which distinguish it as an antagonist. Notably, Menaquinone-7 and Phytonadione, while also lacking hydrogen bonds, did not display Pi-Sigma interactions akin to Mifepristone, indicating a different mode of receptor engagement that may influence their functional activity. The molecular docking simulations further examined the interactions between Can Si-derived proteins and the GR to assess their potential as GR modulators. By comparing their binding efficiency with the standard agonist and antagonist, the study aimed to identify promising protein-based inhibitors. The results, summarized in Table 2 , revealed a broad spectrum of interaction profiles, highlighting differences in binding energy (kcal/mol), van der Waals forces, electrostatic interactions, desolvation energy, and root-mean-square deviation (RMSD). These factors are critical in evaluating the strength and stability of each protein-receptor complex, as well as their potential efficacy as modulators of GR activity. Among the top-performing proteins, Bombyxin A-5 exhibited the highest binding affinity, with a HADDOCK score of -129.9 and a binding energy of -14.6 kcal/mol, indicating a strong and stable interaction with GR. This was further reinforced by significant van der Waals interactions (-67.0 kcal/mol) and a remarkably high electrostatic interaction energy (-269.6 kcal/mol), suggesting a favorable binding conformation within the receptor’s LBD. Similarly, the Mediator of RNA polymerase II transcription subunit 29 demonstrated a binding energy of -14.4 kcal/mol, with van der Waals forces of -89.6 kcal/mol and electrostatic energy of -146.2 kcal/mol, highlighting its strong affinity for GR despite slightly weaker electrostatic interactions compared to Bombyxin A-5. Additional promising candidates included Bombyxin A-3 (-13.2 kcal/mol), Small ribosomal subunit protein uS11 (-12.9 kcal/mol), and Fibroin (-12.5 kcal/mol), all of which showed considerable binding potential. Notably, Small ribosomal subunit protein uS11 exhibited the highest electrostatic energy (-443.9 kcal/mol) among the tested proteins, suggesting a strong charge-based interaction with GR, which could play a vital role in stabilizing the protein-ligand complex. In contrast, Fibroin displayed an electrostatic interaction of -322.7 kcal/mol, indicating a different mode of receptor engagement. Table 2 Molecular docking and interaction energies of Can Si-derived proteins with the glucocorticoid receptor. Complex HADDOCK score (a.u.) Binding energy (kcal/mol) Van der Waals energy Electrostatic energy Desolvation energy RMSD Bombyxin A-5_GR −129.9 +/- 10.6 −14.6 −67.0 +/- 5.8 −269.6 +/- 37.7 −32.7 +/- 3.8 0.5 +/- 0.3 Mediator of RNA polymerase II transcription subunit 29_GR −105.8 +/- 7.4 −14.4 −89.6 +/- 4.4 −146.2 +/- 28.2 −8.4 +/- 3.1 1.3 +/- 1.1 Bombyxin A-3_GR −104.0 +/- 16.1 −13.2 −49.4 +/- 10.9 −271.5 +/- 37.4 −14.1 +/- 8.9 0.8 +/- 0.5 Small ribosomal subunit protein uS11_GR −120.8 +/- 15.5 −12.9 −47.7 +/- 20.7 −443.9 +/- 43.2 −8.9 +/- 9.7 1.3 +/- 1.2 Fibroin_GR −111.1 +/- 0.7 −12.5 −62.7 +/- 6.4 −322.7 +/- 36.9 5.4 +/- 2.6 1.0 +/- 0.8 Chorion class CB protein M5H4_GR −70.6 +/- 31.4 −12.4 −62.6 +/- 18.7 −97.5 +/- 20.5 −15.5 +/- 4.3 0.9 +/- 0.6 Chorion class high-cysteine HCB protein 12_GR −77.6 +/- 14.7 −12.2 −51.4 +/- 10.1 −205.1 +/- 21.2 −2.2 +/- 2.3 1.0 +/- 0.6 Bombyxin B-9_GR −111.5 +/- 7.9 −12.1 −53.1 +/- 4.6 −358.4 +/- 23.5 −6.3 +/- 1.6 0.5 +/- 0.3 Bombyxin C-1_GR −92.5 +/- 3.4 −12.0 −61.5 +/- 2.3 −203.2 +/- 30.0 −9.0 +/- 1.9 1.5 +/- 0.5 Bombyxin E-1_GR −86.3 +/- 5.7 −11.8 −53.5 +/- 4.9 −204.5 +/- 42.8 −9.3 +/- 2.4 3.2 +/- 0.3 Among the chorion proteins, Chorion class CB protein M5H4 and Chorion class high-cysteine HCB protein 12 exhibited binding energies of -12.4 kcal/mol and − 12.2 kcal/mol, respectively, with moderate van der Waals interactions (-62.6 kcal/mol and − 51.4 kcal/mol, respectively). Their relatively lower electrostatic contributions (-97.5 kcal/mol for M5H4 and − 205.1 kcal/mol for HCB protein 12) suggest a weaker polar interaction component compared to the other proteins but still indicate a viable receptor affinity. The Bombyxin family proteins continued to demonstrate noteworthy GR interactions, with Bombyxin B-9 (-12.1 kcal/mol), Bombyxin C-1 (-12.0 kcal/mol), and Bombyxin E-1 (-11.8 kcal/mol) showing stable binding conformations. Bombyxin B-9, in particular, displayed significant electrostatic interactions (-358.4 kcal/mol), suggesting a highly charged binding mechanism within the LBD. Meanwhile, Bombyxin E-1, with an RMSD value of 3.2, indicated a greater degree of structural flexibility compared to other complexes, potentially reflecting a dynamic binding mode rather than a rigid fit. The 3D binding poses of the top-performing Can Si-derived proteins with GR are illustrated in Fig. 4 , showcasing their pose similarity within the LBD. Table 3 presents a comparative analysis of the intermolecular contacts (ICs) and non-interacting surface areas (NIS) for the top-ranked protein-GR complexes. The data reveal substantial variations in the interaction profiles, suggesting that different proteins engage with the GR through distinct binding mechanisms. These differences are particularly evident in the distribution of charged, polar, and apolar contacts, which influence the stability and specificity of the protein-receptor interactions. Among the investigated complexes, the Small ribosomal subunit protein uS11_GR exhibits the highest number of charged-charged interactions (11), indicating a significant contribution of electrostatic forces to its binding affinity. Electrostatic interactions often play a crucial role in stabilizing protein-ligand complexes, particularly when involving key residues within the receptor’s binding pocket. Similarly, Fibroin_GR demonstrates a relatively high count of charged-charged interactions (9), reinforcing the importance of electrostatic stabilization. In contrast, Chorion class high-cysteine HCB protein 12_GR and Mediator of RNA polymerase II transcription subunit 29_GR show the lowest number of such interactions (2 and 3, respectively), suggesting a weaker electrostatic contribution to their binding. Table 3 Intermolecular contacts and nonbonded interaction scores of Can Si-derived proteins with the glucocorticoid receptor (GR). Complex ICs charged-charged ICs charged-polar ICs charged-apolar ICs polar-polar ICs polar-apolar ICs apolar-apolar NIS charged NIS apolar Bombyxin A-5_GR 5 10 32 1 26 19 21.79 41.07 Mediator of RNA polymerase II transcription subunit 29_GR 3 8 20 1 31 27 19.43 42.75 Bombyxin A-3_GR 4 9 27 1 23 12 22.14 41.43 Small ribosomal subunit protein uS11_GR 11 13 30 1 20 12 27.04 41.37 Fibroin_GR 9 11 14 4 25 13 21.69 40.74 Chorion class CB protein M5H4_GR 3 12 33 1 21 32 15.75 51.10 Chorion class high-cysteine HCB protein 12_GR 2 8 29 2 18 19 18.24 42.77 Bombyxin B-9_GR 5 7 34 1 14 21 24.54 39.19 Bombyxin C-1_GR 4 15 13 1 23 16 21.43 41.07 Bombyxin E-1_GR 6 6 21 1 18 16 22.49 40.83 Note: • ICs: Number of intermolecular contacts • NIS: Non-interacting surface In terms of hydrophobic interactions, the apolar-apolar contact count varies significantly across the complexes. Chorion class CB protein M5H4_GR exhibits the highest number of apolar-apolar interactions (32), indicating a strong contribution from hydrophobic forces, which can enhance protein stability and specificity within the receptor binding site. On the other hand, Fibroin_GR and Small ribosomal subunit protein uS11_GR display the lowest numbers of apolar-apolar interactions (13 and 12, respectively), implying that their binding may rely more on polar and electrostatic contributions rather than hydrophobic stabilization. These differences suggest that while some proteins engage in extensive hydrophobic interactions with GR, others may utilize a combination of hydrogen bonding and electrostatic forces for stable binding. The NIS provides additional insights into the efficiency of protein-receptor binding. A larger NIS value indicates a greater portion of the protein's surface remains unengaged, which may suggest suboptimal binding or the presence of steric hindrance. Among the studied complexes, Chorion class CB protein M5H4_GR displays the highest apolar NIS (51.10 Ų), indicating a significant unutilized apolar surface that could potentially be optimized for stronger interactions. In contrast, Bombyxin B-9_GR has the smallest apolar NIS (39.19 Ų), suggesting that this complex achieves a more compact and efficient interaction with GR. Additionally, the Small ribosomal subunit protein uS11_GR exhibits the highest charged NIS (27.04 Ų), reflecting an unengaged charged surface that might influence the overall stability of the complex. The full details of the molecular interactions for both chemical and protein-based compound-receptor complexes are provided in Supplementary Data S3 . The molecular docking simulations offer valuable insights beyond just binding energy calculations by elucidating the contributions of different energy components to the overall binding interactions. Figure 5 A presents the correlation matrix for chemical compounds derived from Can Si, highlighting the relationship between binding affinity (ΔG in kcal/mol) and key energy components, such as Van der Waals energy, electrostatic energy, and desolvation energy. Notably, Van der Waals interactions show a moderate positive correlation with binding affinity (0.56), suggesting that favorable hydrophobic interactions play a significant role in stabilizing these compounds within the receptor’s binding pocket. In contrast, electrostatic energy exhibits a weak negative correlation (-0.049), implying that electrostatic interactions may not be a primary driving force for binding. Desolvation energy also demonstrates a moderate positive correlation (0.51), indicating that the removal of solvent molecules upon ligand binding contributes to affinity. Similarly, Fig. 5 B provides the correlation matrix for protein-based compounds, shedding light on the energetic determinants of their interactions with the receptor. In this case, Van der Waals energy maintains a moderate positive correlation with binding affinity (0.51), reinforcing the role of hydrophobic interactions in protein-ligand stability. However, electrostatic energy (0.037) and desolvation energy (0.011) show negligible correlations with binding affinity, suggesting that these energy components contribute minimally to the binding strength of protein-based compounds. This discrepancy between chemical and protein-based compounds suggests that different molecular interaction mechanisms govern their binding behavior, with hydrophobic forces playing a more dominant role in both cases. Molecular Dynamics Simulation Insights into Ligand-GR Complex Stability and Interaction To comprehensively evaluate the structural stability, dynamic behavior, and binding efficacy of Can Si-derived compounds in complex with the GR, MD simulations were conducted for 100 ns. The simulation results, summarized in Table 4 , provide valuable insights into the atomic-level interactions, flexibility, and compactness of these ligand-receptor complexes. The key parameters assessed include RMSD, root mean square fluctuation (RMSF), radius of gyration (RoG), and hydrogen bonding. RMSD is a critical parameter in MD simulations that quantifies the overall conformational stability of a ligand-receptor complex by measuring atomic displacement over time [ 67 ]. A lower RMSD value suggests that the complex remains structurally stable with minimal fluctuations, whereas higher RMSD values may indicate conformational flexibility or instability [ 68 ]. The standard GR agonist dexamethasone exhibited an RMSD of 1.776 Å, indicating a highly stable binding conformation throughout the simulation. Similarly, the standard antagonist mifepristone maintained an RMSD of 1.735 Å, suggesting consistent binding without major structural deviations. Among the chemical ligands, catechin (1.145 Å) and epigallocatechin (1.217 Å) demonstrated the lowest RMSD values, implying exceptionally stable interactions with GR. These small molecules maintained their binding orientations without significant conformational shifts. On the other hand, protein-based ligands showed slightly higher RMSD values, ranging from 3.099 Å (Bombyxin E-1) to 3.869 Å (Fibroin). The increased RMSD in protein ligands is expected due to their larger molecular size and the inherent flexibility of peptide-protein interactions. Importantly, no ligand-receptor complex exhibited sudden spikes in RMSD throughout the 100 ns simulation, reinforcing the stability of these interactions over time. The chemical ligands maintained strong, steady interactions, indicating their suitability as small-molecule GR modulators. The protein-based ligands, despite exhibiting slightly higher RMSD and RoG values, remained within a stable dynamic range, supporting their potential as peptide-based GR modulators. None of the ligands caused disruptive conformational shifts in the receptor, further validating their binding reliability and biological relevance in targeting GR. Table 4 Molecular dynamics analysis of ligand-glucocorticoid receptor (GR) complexes: Structural stability and hydrogen bond interactions. Complex Average RMSD (Å) Average RMSF (Å) Average RoG (Å) Number of Hydrogen Bonds Between the Ligand-Receptor Dexamethasone_GR (standard agonist) 1.776 0.746 2.643 2 Mifepristone_GR (standard antagonist) 1.735 0.942 2.754 0 Menaquinone-7_GR 1.941 1.052 2.831 4 Hesperetin_GR 1.674 0.988 2.789 5 Catechin_GR 1.145 1.274 2.915 6 Phytonadione_GR 1.562 0.923 2.724 2 Epigallocatechin_GR 1.217 1.341 2.983 4 Thiamine_GR 1.487 0.894 2.712 3 Galangin_GR 1.234 1.217 2.862 5 Kaemperol_GR 1.931 1.159 2.847 4 Luteolin_GR 1.789 1.112 2.803 4 Quercetin_GR 1.846 1.135 2.818 5 Bombyxin A-5_GR 3.284 1.833 3.805 13 Mediator of RNA polymerase II transcription subunit 29_GR 3.521 1.742 3.916 10 Bombyxin A-3_GR 3.132 1.785 3.832 11 Small ribosomal subunit protein uS11_GR 3.754 1.654 3.799 9 Fibroin_GR 3.869 1.687 3.845 8 Chorion class CB protein M5H4_GR 3.615 1.573 3.756 7 Chorion class high-cysteine HCB protein 12_GR 3.788 1.629 3.782 8 Bombyxin B-9_GR 3.124 1.721 3.819 12 Bombyxin C-1_GR 3.212 1.693 3.801 10 Bombyxin E-1_GR 3.099 1.735 3.839 11 Radius of Gyration (RoG) assesses the compactness of a molecular system by measuring the spatial distribution of its atomic coordinates. A lower RoG value generally signifies a more tightly packed and stable complex, whereas higher values suggest structural expansion or flexibility. The standard agonist dexamethasone maintained an RoG of 2.643 Å, while the antagonist mifepristone had an RoG of 2.754 Å, reflecting the well-folded and compact nature of these ligand-receptor complexes. Chemical compounds exhibited RoG values ranging from 2.712 Å (Thiamine) to 2.983 Å (Epigallocatechin), suggesting a tightly bound configuration with minimal structural dispersion. Protein-based ligands displayed higher RoG values, ranging from 3.756 Å (Chorion Class CB Protein M5H4) to 3.916 Å (Mediator of RNA Polymerase II Transcription Subunit 29). The slightly expanded structures indicate the inherent flexibility of protein ligands, yet their values remained within an acceptable range, suggesting stable interactions with GR. Hydrogen bonding plays a crucial role in stabilizing ligand-receptor interactions by forming strong electrostatic attractions between hydrogen donors and acceptors. The number of hydrogen bonds formed between a ligand and GR directly correlates with binding affinity and stability. Dexamethasone (2 hydrogen bonds) and mifepristone (0 hydrogen bonds) served as reference points for binding interactions. Among the chemical ligands, catechin (6 hydrogen bonds) and quercetin (5 hydrogen bonds) exhibited the highest number of hydrogen bonds, indicating strong receptor-ligand interactions that contribute to enhanced stability. Protein-based ligands demonstrated a greater number of hydrogen bonds, with Bombyxin A-5 (13 bonds), Bombyxin B-9 (12 bonds), and Bombyxin A-3 (11 bonds) forming extensive hydrogen bond networks, reinforcing their potential as strong GR binders. The presence of multiple hydrogen bonds in protein-based ligands suggests that they can establish robust interactions with GR, potentially leading to prolonged binding retention. Figure 6 presents the Root Mean Square Fluctuation (RMSF) profiles of the top-performing chemical and protein-based complexes derived from Can Si, compared to the standard glucocorticoid receptor (GR) agonist Dexamethasone and antagonist Mifepristone. The RMSF analysis provides crucial insights into the local flexibility of residues in the receptor-ligand complexes, shedding light on the dynamic stability of each interaction over the simulation period. The RMSF profiles revealed a strong correlation in binding region stabilization between both chemical and protein-based compounds. To differentiate between agonist-like and antagonist-like interactions, we specifically focused on the key residues within the GR active site, including Trp557-Val575, Trp600-Pro625, and Ala700-Tyr735, which play a significant role in ligand binding and receptor activation. Among the tested chemical compounds, Menaquinone-7, Hesperetin, and Catechin exhibited stabilization patterns similar to the standard agonist (Dexamethasone) within the GR active site. Notably, these compounds maintained a steady fluctuation profile, suggesting that their interactions with GR were stable throughout the simulation. Catechin and Hesperetin showed greater stability than Dexamethasone, with lower fluctuations in the key active site regions. This suggests that these compounds may establish stronger and more consistent hydrogen bonding networks with GR, potentially enhancing their agonistic activity. Menaquinone-7, while maintaining relative stability, displayed slight fluctuations in the Trp600-Pro625 region, indicating potential flexibility in its binding conformation. These findings suggest that these natural compounds could act as potential GR modulators, given their ability to maintain stable interactions in regions critical for receptor activation. Conversely, Mifepristone, the standard GR antagonist, exhibited notable spikes and fluctuations across the key binding site regions. These fluctuations were particularly prominent in the Trp600-Pro625 and Ala700-Tyr735 regions, which are critical for receptor activation. The observed instability suggests that Mifepristone disrupts the local hydrogen bonding network, leading to increased flexibility and weakened ligand-receptor interactions. This behavior aligns with the expected antagonist mechanism, where increased fluctuations prevent GR activation by destabilizing key conformational states necessary for signal transduction. Similar to the chemical compounds, the protein-based ligands derived from Can Si followed a comparable trend in receptor stabilization. Among them: Bombyxin A-5 demonstrated the most stable fluctuations within the active GR residue regions, maintaining a low RMSF in the Trp557-Val575, Trp600-Pro625, and Ala700-Tyr735 regions, suggesting a strong binding conformation. Other protein-derived ligands, including Bombyxin C-1, Bombyxin E-1, and Small Ribosomal Subunit Protein uS11, also exhibited relatively stable RMSF values, indicating their potential to act as GR modulators. In contrast, Fibroin and Chorion class high-cysteine HCB protein 12 showed slightly higher fluctuations in critical regions, suggesting a more dynamic binding mode. These findings collectively highlight the potential of bioactive compounds from Can Si as GR modulators, with applications in developing novel therapeutic agents targeting glucocorticoid receptor pathways. The MM/PBSA binding affinity calculations provide crucial insights into the strength and stability of interactions between the bioactive compounds derived from Can Si and the GR (Table 5 ). The binding free energy values (ΔG_binding) indicate the thermodynamic favorability of complex formation, where more negative values correspond to stronger binding interactions. Comparing the derived compounds to standard agonists and antagonists helps to understand their potential functional roles in modulating GR activity. To establish a baseline, the standard GR agonist, Dexamethasone, exhibited a ΔG_binding of -27.56 ± 1.22 kcal/mol, while the standard antagonist, Mifepristone, showed a weaker binding affinity at -22.32 ± 1.01 kcal/mol. The lower binding energy of dexamethasone indicates a stable interaction with GR, which is expected for an agonist that activates the receptor. In contrast, the relatively weaker binding of mifepristone aligns with its role as an antagonist, as it disrupts receptor activation by preventing stable interactions. Table 5 MM/PBSA binding free energy calculations of Can Si-derived compounds and standard ligands with the glucocorticoid receptor (GR). Complex MM/PBSA Free Binding Energy ΔG_binding (kcal/mol) Dexamethasone_GR (standard agonist) −27.56 ± 1.22 Mifepristone_GR (standard antagonist) −22.32 ± 1.01 Menaquinone-7_GR −32.13 ± 1.12 Hesperetin_GR −33.65 ± 1.00 Catechin_GR −35.98 ± 1.54 Phytonadione_GR −28.88 ± 1.63 Epigallocatechin_GR −27.15 ± 1.38 Thiamine_GR −28.92 ± 1.34 Galangin_GR −27.14 ± 1.67 Kaemperol_GR −24.90 ± 1.33 Luteolin_GR −23.22 ± 1.29 Quercetin_GR −27.87 ± 1.38 Bombyxin A-5_GR −228.06 ± 6.29 Mediator of RNA polymerase II transcription subunit 29_GR −201.60 ± 7.14 Bombyxin A-3_GR −189.46 ± 8.12 Small ribosomal subunit protein uS11_GR −204.98 ± 6.94 Fibroin_GR −153.67 ± 6.65 Chorion class CB protein M5H4_GR −223.23 ± 6.52 Chorion class high-cysteine HCB protein 12_GR −164.06 ± 8.12 Bombyxin B-9_GR −163.90 ± 6.29 Bombyxin C-1_GR −157.59 ± 7.32 Bombyxin E-1_GR −156.23 ± 8.91 Among the chemical compounds derived from Can Si, several demonstrated binding affinities stronger than dexamethasone. Notably, Catechin (-35.98 ± 1.54 kcal/mol), Hesperetin (-33.65 ± 1.00 kcal/mol), and Menaquinone-7 (-32.13 ± 1.12 kcal/mol) exhibited the most favorable interactions, suggesting that these compounds may act as potent GR agonists. Their stronger binding suggests enhanced receptor stabilization, potentially leading to greater GR activation compared to dexamethasone. Other compounds, such as Phytonadione (-28.88 ± 1.63 kcal/mol), Epigallocatechin (-27.15 ± 1.38 kcal/mol), Thiamine (-28.92 ± 1.34 kcal/mol), and Quercetin (-27.87 ± 1.38 kcal/mol), displayed binding affinities comparable to dexamethasone, indicating moderate GR activation potential. In contrast, Kaempferol (-24.90 ± 1.33 kcal/mol) and Luteolin (-23.22 ± 1.29 kcal/mol) exhibited weaker binding energies, nearing the range of Mifepristone, suggesting reduced agonistic potential or potential partial antagonistic behavior. In comparison to the chemical compounds, the protein-based ligands derived from Can Si demonstrated significantly stronger binding interactions with GR. Bombyxin A-5 (-228.06 ± 6.29 kcal/mol) exhibited the highest binding affinity, followed by Chorion class CB protein M5H4 (-223.23 ± 6.52 kcal/mol), Mediator of RNA polymerase II transcription subunit 29 (-201.60 ± 7.14 kcal/mol), and Small Ribosomal Subunit Protein uS11 (-204.98 ± 6.94 kcal/mol). These exceptionally low ΔG_binding values suggest that these proteins form highly stable and energetically favorable interactions with GR, likely involving extensive hydrogen bonding, hydrophobic interactions, and electrostatic contacts. Additionally, other protein ligands, such as Fibroin (-153.67 ± 6.65 kcal/mol), Chorion class high-cysteine HCB protein 12 (-164.06 ± 8.12 kcal/mol), Bombyxin B-9 (-163.90 ± 6.29 kcal/mol), and Bombyxin C-1 (-157.59 ± 7.32 kcal/mol), also exhibited strong interactions, though with slightly higher ΔG_binding values compared to Bombyxin A-5. These results indicate that the protein ligands may function as strong GR modulators, potentially influencing receptor activity more significantly than chemical compounds. Pharmacophore Profiling, Drug-Likeness, and Toxicity Assessment of Can Si-derived Compounds Targeting GR The pharmacophore modeling analysis of chemical compounds derived from Can Si provided valuable insights into their potential interactions with the GR, particularly when compared to standard agonist and antagonist ligands. The top four performing compounds (Menaquinone-7, Hesperetin, Catechin, and Phytonadione) exhibited distinct yet overlapping pharmacophore features that contribute to their binding affinity and mode of interaction within the GR binding site. Dexamethasone, the standard agonist, was characterized by the presence of a hydrogen bond acceptor through its carbonyl functional group, which plays a crucial role in stabilizing its interaction with GR (Fig. 7 A). In contrast, the standard antagonist, Mifepristone, predominantly engaged in hydrophobic interactions via its benzene rings, suggesting a different binding mechanism that disrupts GR activation (Fig. 7 B). Among the Can Si-derived compounds, Hesperetin and Catechin demonstrated a strong resemblance to the standard agonist in their binding interactions. Both molecules formed hydrogen bond donor interactions through their hydroxyl (-OH) groups, effectively engaging key active site residues of GR (Fig. 7 D and Fig. 7 E). This similarity suggests that these compounds may exert agonist-like effects by stabilizing the receptor in an active conformation. On the other hand, Menaquinone-7 (Fig. 7 C) and Phytonadione (Fig. 7 F) primarily relied on hydrophobic interactions, predominantly facilitated by their isoprenoid side chains. These interactions, while contributing to binding stability, suggest a distinct mode of interaction compared to the hydroxyl-rich agonist-like compounds. These findings highlight the potential of Hesperetin and Catechin as GR agonists due to their hydrogen bonding characteristics, while Menaquinone-7 and Phytonadione may interact with GR via a more hydrophobic-driven mechanism. This differentiation in binding modes underscores the structural diversity of Can Si -derived compounds and their possible functional implications in modulating GR activity. The drug-likeness and toxicity profiles of the Can Si-derived chemical compounds targeting the glucocorticoid receptor (GR) were evaluated using multiple pharmacokinetic and safety parameters. These assessments included Lipinski’s Rule of Five violations, which predict oral bioavailability, as well as potential toxicological risks such as mutagenicity, tumorigenicity, reproductive toxicity, and irritant effects (Table 6 ). Among the evaluated compounds, Menaquinone-7 and Phytonadione exhibited Lipinski’s Rule violations, indicating potential challenges in their oral bioavailability. Menaquinone-7 exceeded the molecular weight threshold (MW > 500 g/mol) and had high lipophilicity (LogP > 5), which could impact its solubility and permeability. Phytonadione, on the other hand, violated only the lipophilicity criterion (LogP > 5), suggesting that while it remains relatively drug-like, its high lipophilicity might affect absorption and distribution. Despite these violations, both compounds exhibited favorable drug-likeness scores (0.62 and 0.93, respectively) and showed no indications of mutagenicity, tumorigenicity, reproductive toxicity, or irritant effects. Table 6 Drug-likeness and toxicity profiles of Can Si -derived compounds targeting the glucocorticoid receptor (GR). Molecule Lipinski violation Drug-likeness Mutagenic Tumorigenic Reproductive effective Irritant Menaquinone-7 2 violations: MW > 500 g/mol LogP > 5 0.62 None None None None Hesperetin 0 0.59 None None None None Catechin 0 0.64 None None None None Phytonadione 1 violation: LogP > 5 0.93 None None None None Epigallocatechin 1 violation: HBD > 5 −0.04 None None None None Thiamine 0 0.87 None None None None Galangin 0 −0.15 High None None None Kaemperol 0 0.50 High None None None Luteolin 0 0.38 None None None None Quercetin 0 0.52 High High None None Hesperetin, Catechin, Thiamine, Luteolin, and Kaempferol adhered to all of Lipinski’s criteria, indicating good oral bioavailability potential. Their drug-likeness scores ranged from 0.38 to 0.87, suggesting moderate to high suitability as drug candidates. Additionally, none of these compounds exhibited toxicological concerns, reinforcing their potential safety for further development. Epigallocatechin presented a single violation due to having more than five hydrogen bond donors (HBD > 5), which may affect its permeability and absorption. However, its toxicity profile was clean, and it did not display any mutagenic or carcinogenic properties. Despite this, its drug-likeness score (-0.04) was relatively low, which may indicate suboptimal pharmacokinetic properties. Conversely, Galangin and Quercetin raised toxicity concerns. While both complied with Lipinski’s Rule of Five, they exhibited high mutagenic potential, and Quercetin additionally showed tumorigenic properties. Their drug-likeness scores (-0.15 and 0.52, respectively) further suggest that their overall suitability as therapeutic candidates may be limited due to safety concerns. Thus, Hesperetin, Catechin, and Thiamine emerged as the most promising drug-like candidates, demonstrating compliance with Lipinski’s rules, favorable drug-likeness scores, and clean toxicity profiles. Menaquinone-7 and Phytonadione also exhibited potential, though their lipophilicity could pose formulation challenges. On the other hand, compounds like Galangin and Quercetin require further investigation due to their potential mutagenic and carcinogenic risks. Discussion This study provides a comprehensive evaluation of the therapeutic potential of Can Si-derived bioactive compounds in the treatment of PNI by targeting the GR. Using a combination of molecular docking, MD simulations, and pharmacophore modeling, we identified Catechin, Hesperetin, and Menaquinone-7 as promising small molecules with strong GR interactions. Additionally, protein-based bioactive compounds, such as Bombyxin A-5 and Small Ribosomal Subunit Protein uS11, demonstrated significant binding potential, suggesting a possible role in nerve repair and neuroprotection. GR is a crucial modulator of inflammation, oxidative stress, and cellular repair mechanisms, all of which are central to PNI pathology. Following nerve injury, excessive inflammation, and oxidative damage can impair Schwann cell function, delay axonal regeneration, and contribute to chronic neuropathic pain [ 69 , 70 ]. While synthetic glucocorticoids, such as dexamethasone and prednisolone, are widely used to manage inflammation in PNI, their long-term use is associated with serious side effects, including immunosuppression, metabolic disturbances, and delayed wound healing [ 71 , 72 ]. Therefore, the identification of natural GR modulators presents a promising alternative to harness the anti-inflammatory benefits of GR activation while minimizing adverse effects. The pharmacophore modeling results revealed that Catechin, Hesperetin, and Menaquinone-7 possess key structural features that facilitate GR interaction. Unlike synthetic corticosteroids, which predominantly engage GR through hydrogen bonding, these natural compounds demonstrated a balance between hydrogen bonding and hydrophobic interactions, which could contribute to a partial agonist effect. This selective modulation may help achieve anti-inflammatory benefits without triggering excessive GR activation, thereby reducing systemic adverse effects [ 73 ]. Among the tested compounds, Catechin and Hesperetin exhibited strong hydrogen bonding interactions with critical GR residues, suggesting potent anti-inflammatory and antioxidant properties. These interactions may contribute to the protection of Schwann cells and regenerating axons from oxidative stress, thereby promoting nerve repair [ 74 ]. Meanwhile, Menaquinone-7, which features a hydrophobic isoprenoid side chain, formed stable hydrophobic and π-stacking interactions with GR. This unique interaction pattern suggests that Menaquinone-7 may exert its effects through an alternative regulatory mechanism, potentially improving neuronal survival and reducing neuroinflammation. In addition to small molecules, protein-based compounds from Can Si also exhibited strong and stable interactions with GR, indicating their potential neuroprotective role. Bombyxin A-5, a known neuropeptide, has previously been associated with anti-inflammatory and neurotrophic activities [ 75 , 76 ]. Its interaction with GR suggests that it may play a hormone-like regulatory role, enhancing Schwann cell survival and promoting axonal regeneration. Similarly, Small Ribosomal Subunit Protein uS11, a regulatory protein involved in cellular signaling and translation control, displayed a favorable GR binding profile. This interaction suggests that uS11 may contribute to enhancing protein synthesis during nerve regeneration, a critical process following PNI. Previous research has highlighted the potential of neuropeptides in nerve repair by modulating inflammatory responses and neurotrophic signaling pathways [ 77 , 78 ]. The findings from this study align with these insights, reinforcing the idea that bioactive proteins can complement small molecule therapies to enhance PNI recovery. A key consideration for the clinical applicability of Can Si-derived compounds is their toxicity and pharmacokinetic properties. The toxicity assessment revealed that Catechin and Hesperetin demonstrated favorable drug-likeness profiles, making them strong candidates for therapeutic development. Their natural origin and previously reported neuroprotective properties further support their potential use in PNI therapy [ 79 – 81 ]. However, Menaquinone-7, despite its strong GR binding, exhibited high molecular weight and lipophilicity, which may pose challenges for oral bioavailability. Nonetheless, previous studies on vitamin K analogs have suggested that lipophilic modifications or advanced drug delivery systems, such as nanocarriers, could enhance systemic absorption [ 82 , 83 ]. These strategies could be explored to improve Menaquinone-7’s pharmacokinetic profile. One major advantage of these natural GR modulators over synthetic corticosteroids is their potential to reduce systemic toxicity. Given their ability to modulate GR activity in a tissue-specific manner, these natural compounds may hold promise as novel neuroprotective agents that can be incorporated into future PNI treatment strategies. Limitations, Clinical Implications, and Future Works Limitations Despite the promising findings of this study, several limitations must be acknowledged. First, the computational approach, including molecular docking, MD simulations, and pharmacophore modeling, provides valuable insights into the interactions of Can Si-derived bioactive compounds with the GR; however, these remain theoretical predictions. The actual binding affinity, stability, and biological effects of these compounds in a physiological system require experimental validation through in vitro and in vivo studies. Another limitation lies in the bioavailability, metabolic stability, and pharmacokinetics of the identified bioactive compounds. Natural compounds such as Catechin, Hesperetin, and Menaquinone-7 often suffer from poor solubility, rapid metabolism, and potential challenges in crossing the blood-brain barrier (BBB). These factors could limit their therapeutic potential for PNI. Moreover, while toxicity predictions suggest a favorable safety profile, a more detailed analysis of long-term toxicity, immunogenicity, and possible off-target effects is necessary before clinical application. Furthermore, this study primarily focuses on GR-mediated pathways, yet PNI is a multifactorial condition involving oxidative stress, neurotrophic signaling, immune modulation, and mitochondrial dysfunction. A more comprehensive systems biology approach integrating multiple targets and pathways would provide a deeper understanding of how Can Si-derived compounds promote nerve regeneration. Clinical Implications The identification of natural GR modulators from Can Si offers a potential alternative to synthetic glucocorticoids for treating PNI-related inflammation and nerve degeneration. Conventional glucocorticoids, such as dexamethasone and prednisolone, are widely used to suppress inflammatory responses and prevent fibrosis after nerve injury. However, their long-term use is associated with significant side effects, including muscle atrophy, osteoporosis, hyperglycemia, and immunosuppression. The findings from this study suggest that natural compounds, particularly Catechin and Hesperetin, could act as selective GR modulators, potentially minimizing these side effects while maintaining neuroprotective and anti-inflammatory benefits. Additionally, protein-based bioactive molecules from Can Si, such as Bombyxin A-5 and Small Ribosomal Subunit Protein uS11, may provide novel therapeutic avenues by influencing neuroinflammatory pathways and Schwann cell function. These compounds could be explored as potential adjuncts to existing neuroprotective treatments. Their ability to enhance nerve regeneration, reduce oxidative stress, and improve functional recovery suggests that they might be beneficial when combined with neurotrophic factors or stem cell therapies. From a translational perspective, these findings could inspire the development of novel therapeutic formulations, such as oral nutraceuticals containing GR-modulating flavonoids and neuropeptides to support nerve recovery in PNI patients. Combination therapies that integrate natural bioactive compounds with standard neuroprotective agents. Targeted drug delivery systems, such as hydrogels or nanoparticles, to enhance bioavailability and localized effects on injured peripheral nerves. Future Works To bridge the gap between computational findings and clinical application, further research should focus on experimental validation and drug development strategies. First, in vitro studies should be conducted to confirm the binding affinity, selectivity, and biological effects of these compounds. Schwann cell and neuronal cultures could be used to assess their neuroprotective, anti-inflammatory, and antioxidative properties. Techniques such as Western blotting, RT-PCR, and immunofluorescence could help determine whether these compounds influence GR expression and downstream signaling pathways involved in nerve repair. Second, in vivo studies are crucial for evaluating the therapeutic efficacy of Catechin, Hesperetin, and Menaquinone-7 in animal models of PNI. Rat or mouse models of sciatic nerve crush or transection injury could be used to assess functional recovery through electrophysiology, histological analysis, and behavioral tests. Additionally, pharmacokinetic studies should determine the absorption, metabolism, and tissue distribution of these compounds to optimize dosage and delivery strategies. Beyond single-target approaches, multi-target network pharmacology should be explored to investigate how Can Si-derived compounds influence other critical pathways in nerve repair, such as neurotrophic signaling (NGF, BDNF), mitochondrial function, and oxidative stress response (Nrf2 pathway). Integrating transcriptomics, proteomics, and metabolomics would provide a holistic understanding of their therapeutic potential. Finally, efforts should be made to improve the drug delivery and formulation of these bioactive compounds. Nano-formulations, such as liposomes, polymeric nanoparticles, and hydrogels, could enhance their stability, bioavailability, and targeted delivery to injured peripheral nerves. Localized delivery systems, including microneedle patches or nerve conduit-based drug release, could minimize systemic exposure and maximize therapeutic efficacy. Conclusions In conclusion, this study provides compelling computational evidence supporting the potential therapeutic role of Can Si-derived bioactive compounds in PNI. Through an integrative approach combining network pharmacology, molecular docking, MD simulations, and pharmacophore modeling, we identified several promising bioactive molecules, including Catechin, Hesperetin, and Menaquinone-7, that exhibit strong interactions with the glucocorticoid receptor. Additionally, protein-based bioactive molecules such as Bombyxin A-5 and Small Ribosomal Subunit Protein uS11 showed promising binding affinity and stability, suggesting their potential role in modulating neuroinflammatory pathways and nerve regeneration processes. The findings highlight the multi-faceted therapeutic mechanisms of these bioactive compounds, particularly in reducing neuroinflammation, promoting Schwann cell function, and enhancing neuronal survival, all of which are critical for functional recovery following PNI. Furthermore, the potential neuroprotective effects of these compounds, coupled with their relatively favorable safety profiles, suggest that Can Si-derived natural products could serve as alternative or adjunctive therapies to conventional synthetic glucocorticoids, which are often associated with significant side effects. Despite these promising findings, several limitations remain, including the lack of in vitro and in vivo validation, as well as challenges related to bioavailability, metabolic stability, and drug delivery. Future research should focus on experimental confirmation of the predicted molecular interactions, in vivo efficacy testing in animal models of PNI, and formulation optimization using advanced drug delivery systems to enhance targeted nerve repair. Declarations Data availability The original contributions presented in this study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author(s). Author contributions Conceptualization: N.A and D.D; Methodology: N.A and D.D; Software: D.D; Validation: N.A and D.D; Formal analysis: D.D; Investigation: N.A and D.D; Resources: N.A and D.D; Data Curation: D.D; Writing - Original Draft: N.A and D.D; Writing - Review & Editing: N.A and D.D; Visualization: D.D; Supervision: N.A; Project administration: N.A; Funding acquisition: N.A. All authors have read and approved the published version of the manuscript. Funding This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-DDRSP2501). Competing interests The authors declare no competing interests. Ethics approval and consent to participate Informed consent was obtained from all subjects involved in the study. Correspondence and requests for materials should be addressed to N.A. 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Additional Declarations No competing interests reported. Supplementary Files SupplementaryDataS1ChemicalCompoundandProteinDataset.xlsx SupplementaryDataS2MolecularDockingResults.xlsx SupplementaryDataS3MolecularInteractions.xlsx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6190910","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":442945245,"identity":"f339b664-df8e-4851-b103-5d3219a9bb99","order_by":0,"name":"Nasser Alotaiq","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYHACxgNAgoeBvbEBKpBAWM8BoCIeBp6DJGphYJCAqySgxZz98IMDH3/YyfDdfNy64WfOYQZ+9hwDxh81uLVY9qQZHJyRkMwjeTux7WbvtsMMkj1vDJh5juHWYnAgweAwTwIzjwFQyw1eoBaDGzkGzAxseLScf/4BqKWex+Dmwbabf4Fa7G+AHPYPjxagAqCWwzwGNxjbboNtkcgxYOBtw6flTcHBGWnHeSTPJLbdlt2WziNx5lnBYd4+fA5L3/jgg021Pd/x489uvt1mLcffnrzx4Y9vuLUgwAEIxYPEJlLLKBgFo2AUjAIMAADi4VsdGhSWrQAAAABJRU5ErkJggg==","orcid":"","institution":"Imam Mohammad Ibn Saud Islamic University (IMSIU)","correspondingAuthor":true,"prefix":"","firstName":"Nasser","middleName":"","lastName":"Alotaiq","suffix":""},{"id":442945246,"identity":"e4862f4e-a02e-4f23-8615-56a6dd737f78","order_by":1,"name":"Doni Dermawan","email":"","orcid":"","institution":"Warsaw University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Doni","middleName":"","lastName":"Dermawan","suffix":""}],"badges":[],"createdAt":"2025-03-10 01:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6190910/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6190910/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81283880,"identity":"473b45d0-73c2-402a-93ad-d3b4bcd82df2","added_by":"auto","created_at":"2025-04-24 10:35:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":6787877,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork pharmacology analysis identifies Toll-like receptor 4 (TLR4) as a central regulatory hub, exhibiting extensive interactions with multiple proteins influenced by Can Si-derived bioactive compounds. Due to its strong correlation with the glucocorticoid receptor (GR) and GR’s critical role in neuroinflammation modulation, GR was chosen as the primary target for further computational investigations.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6190910/v1/c975c811a732d4a9a6877a58.png"},{"id":81285120,"identity":"5d238e87-40e3-4c82-99ee-8da444798aef","added_by":"auto","created_at":"2025-04-24 10:43:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6746226,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking simulation results, depicting 3D representations of binding poses and interactions between the glucocorticoid receptor (GR) and the top four highest-performing Can Si-derived compounds, alongside standard reference ligands—Dexamethasone (agonist) and Mifepristone (antagonist). \u003cstrong\u003e(A)\u003c/strong\u003e Dexamethasone_GR complex, illustrating the agonist’s binding interaction within the ligand-binding domain (LBD), characterized by a single hydrogen bond. \u003cstrong\u003e(B)\u003c/strong\u003e Mifepristone_GR complex, representing the antagonist’s binding mode, notably lacking hydrogen bonds. \u003cstrong\u003e(C)\u003c/strong\u003e Menaquinone-7_GR complex. \u003cstrong\u003e(D)\u003c/strong\u003e Hesperetin_GR complex, highlighting three hydrogen bonds within the LBD. \u003cstrong\u003e(E)\u003c/strong\u003e Catechin_GR complex, demonstrating one hydrogen bond. \u003cstrong\u003e(F)\u003c/strong\u003e Phytonadione_GR complex.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6190910/v1/271a9648fb6d6522612eb33f.png"},{"id":81283888,"identity":"ea805c52-eea0-4eed-b3c5-243ee0bbc060","added_by":"auto","created_at":"2025-04-24 10:35:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3786663,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking interaction maps of ligand-receptor complexes. 2D interaction maps illustrating the binding interactions between the GR receptor and the top three performing compounds from Can Si, alongside the standard agonist and antagonist. \u003cstrong\u003e(A)\u003c/strong\u003eDexamethasone_GR complex (standard agonist). \u003cstrong\u003e(B)\u003c/strong\u003e Mifepristone_GR complex (standard antagonist). \u003cstrong\u003e(C)\u003c/strong\u003e Menaquinone-7_GR complex. \u003cstrong\u003e(D)\u003c/strong\u003eHesperetin_GR complex. \u003cstrong\u003e(E)\u003c/strong\u003e Catechin_GR complex. \u003cstrong\u003e(F)\u003c/strong\u003ePhytonadione_GR complex.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6190910/v1/fc41a30935f8e427ec0a2157.png"},{"id":81283887,"identity":"22be5f45-7278-4636-8e73-1c71ddec7e50","added_by":"auto","created_at":"2025-04-24 10:35:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4992857,"visible":true,"origin":"","legend":"\u003cp\u003e3D binding poses of Can Si-derived protein-based compounds within the GR binding pocket. \u003cstrong\u003e(A)\u003c/strong\u003eBombyxin A-5_GR complex\u003cstrong\u003e. (B)\u003c/strong\u003e Small ribosomal subunit protein uS11_GR complex. \u003cstrong\u003e(C)\u003c/strong\u003e Fibroin_GR complex. \u003cstrong\u003e(D)\u003c/strong\u003e Chorion class CB protein M5H4_GR complex.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6190910/v1/ed6a4c6615c6f88bf89936d0.png"},{"id":81285126,"identity":"b6513669-884c-4017-a6a6-4e59125cbdd3","added_by":"auto","created_at":"2025-04-24 10:43:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1840656,"visible":true,"origin":"","legend":"\u003cp\u003eEnergy component correlation analysis. \u003cstrong\u003e(A)\u003c/strong\u003e A correlation matrix representing the association between binding energy (kcal/mol) and various energy components for chemical compounds derived from Can Si. \u003cstrong\u003e(B)\u003c/strong\u003e A similar correlation matrix displaying the relationship between binding energy (kcal/mol) and individual energy components for protein-based compounds derived from Can Si. The correlation coefficients range from -1 to 1, where 1 indicates a strong positive correlation, -1 denotes a strong negative correlation, and 0 suggests no significant correlation between variables.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6190910/v1/55f5a126e793089bb23b82f3.png"},{"id":81285493,"identity":"8396fdd7-9b12-43f1-b792-77b91160ed2e","added_by":"auto","created_at":"2025-04-24 10:51:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3013787,"visible":true,"origin":"","legend":"\u003cp\u003eRMSF profiles of ligand-receptor complexes. \u003cstrong\u003e(A) \u003c/strong\u003eRMSF profiles of chemical compounds derived from Can Si (Menaquinone-7, Hesperetin, and Catechin) in comparison with the standard agonist (Dexamethasone) and antagonist (Mifepristone), illustrating fluctuation stabilization in key GR binding regions. \u003cstrong\u003e(B)\u003c/strong\u003e RMSF profiles of protein-based compounds from Can Si (Bombyxin A-5, Mediator of RNA Polymerase II Transcription Subunit 29, and Chorion Class CB Protein M5H4), demonstrating similar stabilization patterns and hydrogen bond retention in critical GR binding regions.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6190910/v1/717ae5bddb69e124de6f4cba.png"},{"id":81285124,"identity":"a29de917-d180-4750-9df4-edf960635405","added_by":"auto","created_at":"2025-04-24 10:43:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":9061218,"visible":true,"origin":"","legend":"\u003cp\u003e3D structure-based pharmacophore modeling of ligand-receptor complexes. Visualization of key pharmacophore features in the GR receptor complexes with the standard agonist, antagonist, and top-performing compounds from Can Si. \u003cstrong\u003e(A) \u003c/strong\u003eDexamethasone_GR complex (standard agonist). \u003cstrong\u003e(B)\u003c/strong\u003e Mifepristone_GR complex (standard antagonist). \u003cstrong\u003e(C)\u003c/strong\u003e Menaquinone-7_GR complex. \u003cstrong\u003e(D)\u003c/strong\u003e Hesperetin_GR complex. \u003cstrong\u003e(E)\u003c/strong\u003e Catechin_GR complex. \u003cstrong\u003e(F)\u003c/strong\u003e Phytonadione_GR complex.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6190910/v1/59a578a921faa66d4dee9201.png"},{"id":82128465,"identity":"76df16da-70fa-4c2a-8f8f-e8c1721ce011","added_by":"auto","created_at":"2025-05-07 04:46:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":34265970,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6190910/v1/9d013976-2e08-452d-a243-129891192333.pdf"},{"id":81283879,"identity":"5c14599c-17c4-4c5b-85cb-81159dec3201","added_by":"auto","created_at":"2025-04-24 10:35:17","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":29889,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDataS1ChemicalCompoundandProteinDataset.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6190910/v1/7b7c4d72388fa46719a8cd9a.xlsx"},{"id":81283881,"identity":"3660917e-b000-40a2-919a-1c8739552dd9","added_by":"auto","created_at":"2025-04-24 10:35:17","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32793,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDataS2MolecularDockingResults.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6190910/v1/2da2c4c2d9c4d8b1cf5b0f15.xlsx"},{"id":81283882,"identity":"9c146119-9ec1-4084-b4b2-c09ed8dde0f6","added_by":"auto","created_at":"2025-04-24 10:35:17","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22755,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDataS3MolecularInteractions.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6190910/v1/62774fe9abd2ff84300a2759.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Decoding the Pharmacological Actions of Can Si (Silk Fibroin), a Traditional Chinese Medicine (TCM) for Peripheral Nerve Injury: A Comprehensive Molecular Simulation","fulltext":[{"header":"Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePeripheral nerve injuries (PNIs) represent a major clinical and socioeconomic burden, often leading to loss of sensory and motor functions due to disrupted nerve conduction [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These injuries can result from trauma, surgery, or neurological disorders, and despite advances in microsurgical techniques, functional recovery remains incomplete in many cases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Autologous nerve grafting is the current gold standard for nerve repair; however, it is associated with several limitations, including donor site morbidity, limited graft availability, and mismatched nerve dimensions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Alternative strategies, such as the use of neurotrophic factors, electrical stimulation, and biomaterial scaffolds, have been explored. Yet, their clinical application is restricted due to issues like short half-life, high costs, and potential immune responses [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Consequently, there is an increasing demand for novel, effective, and biocompatible therapeutic approaches to enhance nerve regeneration and functional recovery.\u003c/p\u003e \u003cp\u003eTraditional Chinese Medicine (TCM) has long been utilized for nerve repair and regeneration, offering a rich source of bioactive compounds with potential neuroprotective properties [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Among these, Can Si (Silk Fibroin), a naturally occurring biopolymer derived from \u003cem\u003eBombyx mori\u003c/em\u003e cocoons, has attracted attention for its biocompatibility, mechanical strength, biodegradability, and neuroregenerative potential [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Silk fibroin is primarily composed of glycine, alanine, and serine-rich β-sheet structures, which contribute to its unique physicochemical properties and ability to support cellular attachment, proliferation, and differentiation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Preclinical studies have demonstrated that silk fibroin-based scaffolds promote Schwann cell migration, axonal outgrowth, and extracellular matrix remodeling, making it a promising candidate for nerve tissue engineering [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, despite these encouraging findings, the precise molecular mechanisms underlying its neuroprotective effects and interactions with key biological targets involved in PNI repair remain unclear.\u003c/p\u003e \u003cp\u003eTo address this gap, this study employs an integrative molecular simulation approach to systematically investigate the pharmacological actions of Can Si in PNI. By leveraging network pharmacology, molecular docking, molecular dynamics (MD) simulations, and pharmacophore modeling, we aim to uncover the bioactive components of silk fibroin, identify their molecular targets, and elucidate their binding interactions and dynamic stability at the atomic level. Network pharmacology identified key protein targets involved in neuronal survival, axonal regeneration, synaptic plasticity, and inflammation, prioritizing the most relevant receptors. Molecular docking assessed the binding affinities of silk fibroin-derived peptides to these targets, while MD simulations validated the stability and interactions of docked complexes under physiological conditions. Finally, pharmacophore modeling identified essential structural features for bioactivity, providing insights for optimizing silk fibroin-based therapeutics. The findings bridge the gap between TCM and modern computational drug discovery and pave the way for the rational design of silk fibroin-based biotherapeutics for peripheral nerve regeneration. Understanding the precise molecular interactions between silk fibroin and neuroregenerative targets may contribute to the development of novel silk-based biomaterials or bioactive peptide therapies for PNI treatment.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMaterials\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe bioactive compounds in Can Si were identified through a thorough literature survey, compiling data from peer-reviewed journals and pharmacological studies. Since a dedicated database for TCM bioactive compounds is lacking, this manual curation ensured a comprehensive and high-quality dataset for further computational analysis. The collected data focused on key bioactive constituents of silk fibroin, including peptides and chemical compounds with potential neuroprotective properties. To investigate the protein and peptide components of \u003cem\u003eBombyx mori\u003c/em\u003e, a systematic search was performed using UniProt, a globally recognized database for protein sequences and functional information. The query employed the MeSH term \u0026ldquo;\u003cem\u003eBombyx mori\u003c/em\u003e\u0026rdquo; within UniProtKB, filtering results to include only reviewed (Swiss-Prot) entries, ensuring accuracy and reliability. Given the importance of bioavailability and therapeutic relevance, proteins and peptides with a maximum length of 200 amino acids were prioritized, as shorter sequences are more likely to exhibit favorable absorption and distribution properties. However, Fibroin and Sericin, the two principal structural proteins of silk fibroin, were included regardless of length due to their significant roles in neuroregeneration and biomaterial applications. To enhance dataset quality, a meticulous curation process was implemented to eliminate redundant entries and potential inconsistencies. This step minimized data duplication and bias, ensuring the integrity and reliability of the dataset for subsequent computational studies. By applying these rigorous selection criteria, a refined collection of silk fibroin-derived bioactive compounds, peptides, and proteins was established as a foundation for molecular modeling investigations.\u003c/p\u003e \u003cp\u003eTo systematically compile the bioactive constituents of Can Si, an extensive literature review was conducted, gathering information from peer-reviewed sources. Due to the absence of a specialized database for TCM bioactive compounds, this approach ensured a well-rounded dataset for further computational analysis. The focus was placed on identifying key peptides and chemical compounds within silk fibroin that may contribute to its neuroregenerative effects. For protein and peptide analysis, a structured query was performed in UniProt, a globally recognized database for protein sequences and functional annotations. The search was refined using the MeSH term \u0026ldquo;\u003cem\u003eBombyx mori\u003c/em\u003e\u0026rdquo; within UniProtKB, targeting entries specific to this species. To maintain data accuracy, only reviewed (Swiss-Prot) proteins were considered, filtering out unverified sequences. Since shorter peptide chains exhibit better bioavailability, proteins, and peptides with a length of 200 amino acids or less were prioritized. However, Fibroin and Sericin, the two major structural components of silk fibroin, were included regardless of size due to their critical role in neuroprotection and biomaterial applications. A rigorous curation process was implemented to eliminate redundant or inconsistent entries, ensuring the dataset\u0026rsquo;s accuracy and reliability. This meticulous filtering reduced data noise and minimized bias, enhancing the validity of downstream computational analyses. The final dataset, enriched with curated silk fibroin-derived bioactive molecules, established a robust foundation for molecular modeling studies aimed at deciphering its therapeutic mechanisms in PNI.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3D Structure Construction and MMFF94 Energy Minimization\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo construct accurate molecular models of bioactive compounds from Can Si (silk fibroin), 3D structures were generated using Chem3D Ultra v22 (PerkinElmer, Massachusetts, USA). The MMFF94 (Merck Molecular Force Field 94) energy minimization algorithm [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] was employed to optimize molecular conformations, ensuring stable and low-energy structures suitable for computational simulations. This step enhanced structural reliability, allowing for more precise predictions of molecular interactions. For peptides and proteins, sequence data specific to \u003cem\u003eBombyx mori\u003c/em\u003e were retrieved from UniProt, a globally recognized protein database. The search yielded 27,947 entries, of which 276 were Swiss-Prot reviewed (accessed February 1, 2025). To enhance bioavailability and computational feasibility, proteins with sequences\u0026thinsp;\u0026le;\u0026thinsp;200 amino acids were prioritized, resulting in a refined selection of 102 proteins. A rigorous data validation and curation protocol was applied to ensure dataset integrity. Initially, sequence completeness was assessed to eliminate incomplete or ambiguous entries that could compromise structural accuracy. This was followed by an error detection process, identifying irregularities such as sequencing errors or ambiguous residues. Any sequences failing to meet quality standards were excluded to maintain dataset robustness. Additionally, a redundancy check was performed to eliminate duplicate records, preventing biases that could skew computational analyses. The final high-fidelity dataset, containing unique and structurally validated bioactive peptides and proteins, was used as the foundation for 3D modeling. This curated dataset facilitated subsequent molecular simulations aimed at deciphering the neuroprotective and regenerative mechanisms of silk fibroin in PNI.\u003c/p\u003e \u003cp\u003eTo accurately model peptides and proteins from Can Si, AlphaFold v3 (DeepMind Technologies Ltd, London, UK) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] was utilized. This cutting-edge deep learning framework excels in predicting protein conformations with exceptional accuracy, even in the absence of homologous structural templates. Its ability to generate high-fidelity models made it particularly advantageous for analyzing silk fibroin-derived bioactive molecules. Following structure prediction, active site identification and binding pocket analysis were conducted using CASTpFold (University of Illinois at Chicago, USA) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], an advanced version of the Computed Atlas of Surface Topography of Proteins (CASTp). This tool precisely maps ligand-accessible regions, offering key insights into potential binding interactions that underlie silk fibroin\u0026rsquo;s therapeutic effects in PNI. The primary receptor for molecular docking simulations was selected based on network pharmacology screening and retrieved from the Protein Data Bank (PDB) under accession code 1M2Z (chain A), resolution 2.50 \u0026Aring; [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This ensured the use of a high-quality receptor structure for precise computational interaction studies. A comprehensive database of Can Si-derived bioactive compounds and proteins was compiled and is provided in \u003cb\u003eSupplementary Data S1\u003c/b\u003e. For chemical compounds, this dataset includes PubChem CID, molecular weight (MW), MlogP, hydrogen bond donors (HBD), hydrogen bond acceptors (HBA), bioavailability scores, blood-brain barrier (BBB) scores, drug-likeness scores, and canonical SMILES. Meanwhile, the peptide and protein database contains UniProt IDs, amino acid sequences, sequence lengths, and identified functional binding residues, offering a robust foundation for molecular simulations.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eNetwork Pharmacology-Based Target Identification for Can Si in PNI\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo map the molecular interactions underlying the therapeutic effects of Can Si in PNI, a bioactive compound-target network was developed using Cytoscape version 3.10.3 (Cytoscape Consortium, Washington, USA) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In this network, bioactive compounds and target proteins were represented as nodes, while edges signified their interactions. A refined common-target network was established by identifying the overlapping protein targets between silk fibroin-related proteins and PNI-associated targets. Key proteins within the network were prioritized based on degree centrality, which measures how many direct connections a node has. Proteins with a degree greater than or equal to the median degree were classified as critical targets for further investigation. To gain a deeper understanding of protein-protein interactions (PPIs), the stringApp plugin [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] in Cytoscape was utilized, constructing PPI networks specific to Homo sapiens with a minimum confidence score threshold of 0.4. These networks were then merged to highlight shared proteins, pinpointing key molecular hubs involved in PNI-related pathways. To assess the importance of each target protein within the merged network, CytoNCA (a Cytoscape network analysis plugin) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] was used to evaluate degree centrality (DC), eigenvector centrality (EC), betweenness centrality (BC), and closeness centrality (CC). Only proteins surpassing the median values of these centrality measures were retained for further validation, ensuring the selection of biologically significant targets.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eDocking Simulations to Unveil the Therapeutic Potential of Can Si\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo elucidate the binding mechanisms of Can Si-derived bioactive compounds with key receptor targets involved in PNI, both ligand-protein and protein-protein docking simulations were conducted. This computational approach aimed to identify crucial binding residues, assess intermolecular interactions, evaluate binding affinities, and analyze binding orientations of silk fibroin-derived molecules at the receptor\u0026rsquo;s active sites. Initially, PDBSum (European Bioinformatics Institute, Cambridge, UK) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] was employed to generate a comprehensive structural summary of the target receptor, highlighting key binding residues within its active site. This structural mapping facilitated the prediction of potential interaction sites for both bioactive compounds and protein targets. To enhance the accuracy of docking simulations, the target receptor structure was refined using Swiss-PdbViewer version 4.1 (Swiss Institute of Bioinformatics, Lausanne, Switzerland) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], ensuring optimal geometry and stability before interaction studies. To determine whether silk fibroin-derived bioactive compounds act as agonists or antagonists in PNI-related pathways, comparative docking analyses were performed against well-established reference molecules. Dexamethasone, a known glucocorticoid receptor agonist commonly used in PNI treatment [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], served as a standard agonist, while Mifepristone, a recognized glucocorticoid receptor antagonist [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], was used as a standard antagonist. This comparative analysis provided critical insights into the potential therapeutic roles of silk fibroin compounds in modulating receptor activity relevant to nerve regeneration and inflammation control.\u003c/p\u003e \u003cp\u003eTo explore the binding interactions between silk fibroin-derived bioactives and their target receptors, ligand-protein and protein-protein docking simulations were conducted using HADDOCK 2.4 (High Ambiguity Driven Protein-Protein Docking, University of Utrecht, Netherlands) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This advanced computational platform specializes in modeling biomolecular interactions by integrating geometric and energetic constraints derived from experimental and theoretical data. The docking simulations were executed using the standalone HADDOCK interface, allowing for precise binding mode predictions. The selection of optimal docked complexes was guided by two key criteria: 1) Cluster population size, indicating the stability and reproducibility of the predicted interactions and 2) HADDOCK score, reflecting the binding affinity strength between silk fibroin bioactives and their target proteins. These stringent selection parameters ensured that only highly stable and functionally relevant binding models were retained for further investigation. To enhance the accuracy of binding energy predictions, the docking results were further refined using PRODIGY [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], a computational tool that estimates binding free energy (ΔG, kcal/mol) by incorporating structural and energetic features of ligand-protein and protein-protein complexes. PRODIGY's assessment considered intermolecular contacts and desolvation effects, providing a physiologically relevant estimation of binding affinity. This approach offered a robust framework for evaluating the stability and therapeutic potential of silk fibroin-derived bioactives in the treatment of peripheral nerve injury.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMolecular Dynamics (MD) Simulation to Assess the Stability of Can Si-Derived Bioactive Complexes\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo explore the dynamic behavior, structural integrity, and conformational shifts of silk fibroin-derived bioactive complexes, MD simulations were conducted using GROMACS 2023.3 [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], a high-performance computational tool for biomolecular simulations. This approach provided an in-depth understanding of the temporal evolution and stability of ligand-protein and protein-protein interactions [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. For ligand parameterization, the General Amber Force Field (GAFF) was applied, with partial atomic charges assigned using the AM1-BCC (Austin Model 1 with Bond Charge Corrections) method [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The ACPYPE tool, integrated with AmberTools21, was used to generate the ligand topology [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The simulation system was enclosed within a truncated octahedral box, ensuring a minimum buffer distance of 1.2 nm between the complex and the box boundary. Periodic boundary conditions were enforced in all dimensions to replicate a continuous biological environment. The protein component was modeled using the AMBER14SB force field, while the system was solvated with TIP3P water molecules [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. To mimic physiological ionic strength, 150 mM NaCl was added, and counterions were introduced for charge neutralization. Nonbonded interactions were handled with a 1.0 nm cutoff for short-range interactions, while long-range electrostatic forces were computed using the Smooth Particle Mesh Ewald (SPME) method [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Before running the production simulation, the system underwent multi-step equilibration: Energy minimization was performed using the conjugate gradient method until the maximum force was reduced to 500 kJ/mol/nm. Canonical ensemble (NVT) equilibration was conducted for 200 ps, maintaining a constant temperature of 310 K using the V-rescale thermostat. Isothermal-isobaric ensemble (NPT) equilibration was applied for 200 ps to stabilize pressure at 1 atm, regulated by the Monte Carlo barostat. Following equilibration, a 100 ns production simulation was executed under constant temperature and pressure, controlled by the Nos\u0026eacute;-Hoover thermostat and Parrinello-Rahman barostat [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], respectively. This simulation provided comprehensive insights into the dynamic stability and functional relevance of silk fibroin-derived bioactive compounds in the context of PNI treatment.\u003c/p\u003e \u003cp\u003eTo accurately model protein-protein interactions within silk fibroin-derived complexes, the Optimized Potentials for Liquid Simulations-All Atom (OPLS-AA/L) force field [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] was employed, with a cubic simulation box ensuring full encapsulation of the complex. The system was solvated using the Single Point Charge Extended (SPCE) water model [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], with counterions added for charge neutrality. Energy minimization was conducted via the conjugate gradient algorithm to resolve steric clashes, followed by a two-step equilibration process: (1) Canonical (NVT) equilibration for temperature stabilization and (2) Isothermal-isobaric (NPT) equilibration to maintain pressure at 1 atm and temperature at 310 K. A 150 ns production MD simulation was then performed to analyze the long-term stability and conformational dynamics of the protein-protein complexes, monitoring key structural parameters such as Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), Radius of Gyration (RoG), and hydrogen bonding interactions. To facilitate in-depth structural analysis, molecular visualization tools such as PyMOL 3.1.3 (Schr\u0026ouml;dinger LLC, New York, USA) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], BIOVIA Discovery Studio 2024 (Dassault Syst\u0026egrave;mes, San Diego, USA) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and UCSF ChimeraX (University of California, San Francisco, USA) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] were utilized for manual inspection and graphical representation of key residues and intermolecular interactions. These simulations provided a detailed mechanistic insight into the role of silk fibroin-based protein complexes in peripheral nerve repair, reinforcing their potential therapeutic applications in TCM.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMM/PBSA Free Energy Analysis for Evaluating Can Si-Receptor Interactions\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo evaluate the binding strength and stability of silk fibroin-derived bioactive compounds with their target receptors, the Molecular Mechanics/Poisson\u0026ndash;Boltzmann Surface Area (MM/PBSA) method was applied in conjunction with MD simulations. Representative structural snapshots were extracted from MD trajectories to capture dynamic conformations, allowing for detailed energy calculations, including gas-phase molecular mechanics energy, solvation energy, and entropy corrections. The Solvent-Accessible Surface Area (SASA) model [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] was used to estimate nonpolar solvation energy by analyzing hydrophobic contributions to binding. The Single-Trajectory Protocol (STP) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] was employed, assuming minimal conformational changes upon binding to ensure consistency in energy evaluations. Using the gmx_MMPBSA module [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] within GROMACS, the binding free energy (ΔG_binding) was computed based on the equation: ΔG_binding\u0026thinsp;=\u0026thinsp;ΔG_complex - (ΔG_ligand/protein\u0026thinsp;+\u0026thinsp;ΔG_receptor), where ΔG_complex represents the total free energy of the solvated ligand-receptor system, while ΔG_ligand/protein and ΔG_receptor correspond to the free energy of the unbound ligand/protein and receptor, respectively. This computational approach provided a quantitative assessment of binding affinity and interaction stability, offering key insights into the potential pharmacological mechanisms of silk fibroin in treating peripheral nerve injury.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eComputational Pharmacophore Modeling and Virtual Toxicity Screening of Can Si\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo identify key molecular features responsible for the bioactivity of silk fibroin-derived compounds, pharmacophore modeling was carried out using LigandScout 4.5 (Inte:Ligand, Vienna, Austria) [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This computational approach enabled the generation of 3D pharmacophore models by mapping crucial interaction sites, including hydrogen bond donors and acceptors, hydrophobic regions, and electrostatic hotspots, which contribute to ligand-receptor binding affinity. By analyzing the spatial arrangement of these features, potential bioactive compounds were identified based on their compatibility with target proteins. Concurrently, an in-silico toxicity evaluation was performed using DataWarrior 6.1.0 (OpenMolecules, Karlsruhe, Germany) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] to assess the safety profiles of the identified compounds. This tool utilizes diverse molecular descriptors and predictive algorithms to evaluate potential toxicity risks, ensuring that only chemically viable and non-toxic candidates are selected for further biological validation. This dual computational strategy provided valuable insights into both the pharmacological efficacy and safety of silk fibroin-derived compounds, supporting their potential application in PNI treatment.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eUnraveling Drug-Target Interactions and Prioritization of Target Receptors\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA network pharmacology approach was undertaken to systematically explore the potential interactions between bioactive compounds in Can Si and relevant protein targets implicated in PNI. Following the identification of key target proteins, a protein-protein interaction network was constructed using the STRING database, providing insights into how these targets interact with established PNI-associated proteins. The protein-protein interaction network analysis identified Toll-like receptor 4 (TLR4) as a key regulatory node, exhibiting extensive connectivity with multiple PNI-associated proteins, suggesting its crucial role in neuroinflammatory and regenerative pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among its significant interactions, TLR4 was linked to glial cell-derived neurotrophic factor (GDNF), which promotes neuronal survival and regeneration [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], as well as SRY-box transcription factor 9 (SOX9), a key regulator in Schwann cell differentiation and myelination [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], emphasizing TLR4\u0026rsquo;s potential role in remyelination. Additionally, its connection with transforming growth factor beta 1 (TGFB1), a cytokine known for its anti-inflammatory and fibrosis-regulating properties [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], suggests that TLR4 might modulate immune responses critical for nerve repair. Furthermore, its interaction with apolipoprotein E (APOE), which is involved in lipid metabolism and neuronal repair [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], underscores its potential in maintaining lipid homeostasis in damaged nerve tissues. TLR4 also exhibited associations with erythropoietin (EPO), a neuroprotective factor with anti-apoptotic and anti-inflammatory effects, as well as thrombomodulin (THBD), which plays a role in vascular homeostasis and inflammation regulation [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], further highlighting its influence on neuronal survival. Its connection with mitogen-activated protein kinase 14 (MAPK14, also known as p38 MAPK), a key mediator of stress and inflammatory responses [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], suggests a role in neuroinflammation following PNI. Additionally, interactions with hepatocyte growth factor (HGF) and superoxide dismutase 2 (SOD2) indicate potential involvement in tissue repair, axonal outgrowth, and oxidative stress modulation [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Given these extensive molecular interactions, TLR4 emerges as a central player in the pathophysiology of PNI, making it a promising therapeutic target. The findings suggest that bioactive compounds derived from Can Si may exert their pharmacological effects by modulating TLR4 activity, thereby mitigating neuroinflammation, enhancing neuronal survival, and promoting peripheral nerve regeneration.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSince there are currently no FDA-approved drugs that directly target TLR4 for the treatment of PNI [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], alternative therapeutic pathways were investigated to identify viable intervention strategies. TLR4 plays a critical role in neuroinflammation and immune response regulation, making it an attractive therapeutic target. However, due to the lack of clinically approved drugs that modulate TLR4 directly, it was essential to explore other key proteins within the PPI network that might offer indirect modulation of TLR4-driven pathways. Among the identified targets, the glucocorticoid receptor (GR) emerged as a particularly promising candidate due to its strong correlation with TLR4 and its central role in modulating neuroinflammation and immune responses [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Numerous studies have demonstrated that GR activation suppresses TLR4-mediated pro-inflammatory signaling pathways, reducing the expression of cytokines and inflammatory mediators that contribute to nerve injury and degeneration [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. This reciprocal regulatory interaction between GR and TLR4 suggests that targeting GR could indirectly modulate TLR4 activity, thereby mitigating inflammation and promoting neuroprotection in PNI. Within the PPI network, GR exhibited extensive interactions with multiple PNI-associated proteins, reinforcing its potential as a regulatory hub for mitigating nerve damage and enhancing recovery. Given its pharmacological significance and functional interplay with TLR4, GR was prioritized for further computational investigations. To assess its therapeutic potential in PNI, Can Si-derived bioactive compounds were subjected to detailed computational studies, including molecular docking and MD simulations, to evaluate their binding affinity, stability, and conformational dynamics with GR. These findings could pave the way for the development of novel GR-targeting therapies derived from Can Si, offering an innovative strategy for neuroprotection and nerve regeneration in PNI management.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Docking Analysis: Investigating Ligand-Receptor and Protein-Receptor Interactions\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo gain a deeper understanding of the molecular interactions between Can Si-derived bioactive compounds and the GR, molecular docking simulations were conducted. This analysis assessed the binding affinities, interaction profiles, and stability of the top 10 bioactive compounds compared to standard GR agonist (Dexamethasone) and antagonist (Mifepristone). The results, presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, revealed that several Can Si-derived compounds exhibited strong binding affinities toward GR, surpassing the performance of both standard ligands in certain parameters. Among the top-performing compounds: Menaquinone-7 (\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;34.2 HADDOCK score, \u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;9.16 kcal/mol binding energy) demonstrated the strongest interaction with GR, showing highly favorable van der Waals contributions (\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;33.1) and moderate electrostatic interactions (\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;7.1). The higher binding affinity of menaquinone-7 compared to dexamethasone (\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;23.1, \u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;8.06 kcal/mol) and mifepristone (\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;25.0, \u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;7.79 kcal/mol) suggests that this compound may serve as an effective modulator of GR activity. Hesperetin (\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;34.9 HADDOCK score, \u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;8.63 kcal/mol binding energy) also exhibited strong interactions with GR, particularly through van der Waals forces (\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;29.1) and electrostatic energy (\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;9.2). This suggests that hesperetin can establish a stable ligand-receptor complex, further emphasizing its potential as a therapeutic agent. Catechin (-34.1 HADDOCK score, \u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;8.57 kcal/mol binding energy) emerged as another promising candidate, with significant electrostatic contributions (\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;19.9), indicating its ability to engage GR through multiple molecular interactions. Other flavonoid-based compounds, including epigallocatechin (\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;34.9, \u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;8.52 kcal/mol), thiamine (\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;38.0, \u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;8.51 kcal/mol), galangin (\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;33.8, \u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;8.47 kcal/mol), and quercetin (\u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;34.4, \u003cem\u003e\u0026minus;\u003c/em\u003e\u0026thinsp;8.46 kcal/mol), also demonstrated strong binding interactions with GR. These compounds exhibited notable van der Waals and electrostatic interactions, reinforcing their potential to influence GR-mediated signaling pathways. The full molecular docking results for both chemical and protein-based compounds are available in \u003cb\u003eSupplementary Data S2\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular docking results of the top 10 highest-performing Can Si-derived chemical compounds in complex with the glucocorticoid receptor (GR), benchmarked against the standard agonist (Dexamethasone) and antagonist (Mifepristone)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" 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=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHADDOCK score (a.u.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinding energy (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVan der Waals energy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElectrostatic energy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesolvation energy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDexamethasone_GR (standard agonist)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;23.1 +/- 0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;8.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;20.6 +/- 1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;16.1 +/- 3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;5.0 +/- 0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.5 +/- 0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMifepristone_GR (standard antagonist)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;25.0 +/- 2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;7.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;26.3 +/- 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e13.4 +/- 5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;6.8 +/- 0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.1 +/- 0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenaquinone-7_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;34.2 +/- 2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;9.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;33.1 +/- 4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;7.1 +/- 9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;6.8 +/- 1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.8 +/- 0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHesperetin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;34.9 +/- 1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;8.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;29.1 +/- 1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;9.2 +/- 2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;5.2 +/- 0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.3 +/- 0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatechin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;34.1 +/- 1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;8.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;28.8 +/- 2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;19.9 +/- 0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;3.7 +/- 0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.2 +/- 0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhytonadione_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;31.6 +/- 0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;8.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;26.8 +/- 1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;33.8 +/- 4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;8.4 +/- 0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.5 +/- 0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpigallocatechin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;34.9 +/- 2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;8.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;31.8 +/- 2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;15.3 +/- 2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;1.9 +/- 0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.2 +/- 0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiamine_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;38.0 +/- 1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;8.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;26.7 +/- 0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;53.4 +/- 1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;7.1 +/- 0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.1 +/- 0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGalangin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;33.8 +/- 1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;8.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;28.8 +/- 1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;7.3 +/- 1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;4.5 +/- 0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.2 +/- 0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaemperol_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;32.3 +/- 0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;8.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;28.6 +/- 1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;7.7 +/- 0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;3.2 +/- 0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.3 +/- 0.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuteolin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;35.3 +/- 1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;8.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;32.5 +/- 0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;2.0 +/- 3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;3.2 +/- 0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.1 +/- 0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuercetin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;34.4 +/- 1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;8.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;32.4 +/- 1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;5.9 +/- 1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;1.6 +/- 0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.2 +/- 0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe molecular interactions of the top-performing compounds with the GR were visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, illustrating their 3D binding poses, and further detailed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which presents their 2D interaction maps. Each compound exhibited a unique interaction profile while maintaining key similarities with dexamethasone, the standard agonist. Dexamethasone forms hydrogen bonds with Gln642 and Tyr735, reinforcing its binding affinity within the ligand-binding domain (LBD). Additionally, it engages in van der Waals interactions with Ser555, Thr556, Trp557, Met560, and Pro637, while Cys638, Met745, and Ile747 contribute to Pi-alkyl interactions. This well-established interaction network underpins dexamethasone\u0026rsquo;s modulatory activity, ensuring strong receptor engagement. Among the Can Si-derived compounds, Hesperetin and Catechin demonstrated comparable binding interactions, particularly through hydrogen bonding within the LBD. Furthermore, both compounds introduced additional stabilization via amide-Pi stacked interactions with Leu741 and Phe749, potentially enhancing their receptor binding. Mifepristone, the standard antagonist, exhibited a contrasting interaction profile. Unlike dexamethasone, it lacked hydrogen bonds entirely and primarily relied on van der Waals and Pi-Sigma interactions, which distinguish it as an antagonist. Notably, Menaquinone-7 and Phytonadione, while also lacking hydrogen bonds, did not display Pi-Sigma interactions akin to Mifepristone, indicating a different mode of receptor engagement that may influence their functional activity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe molecular docking simulations further examined the interactions between Can Si-derived proteins and the GR to assess their potential as GR modulators. By comparing their binding efficiency with the standard agonist and antagonist, the study aimed to identify promising protein-based inhibitors. The results, summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, revealed a broad spectrum of interaction profiles, highlighting differences in binding energy (kcal/mol), van der Waals forces, electrostatic interactions, desolvation energy, and root-mean-square deviation (RMSD). These factors are critical in evaluating the strength and stability of each protein-receptor complex, as well as their potential efficacy as modulators of GR activity. Among the top-performing proteins, Bombyxin A-5 exhibited the highest binding affinity, with a HADDOCK score of -129.9 and a binding energy of -14.6 kcal/mol, indicating a strong and stable interaction with GR. This was further reinforced by significant van der Waals interactions (-67.0 kcal/mol) and a remarkably high electrostatic interaction energy (-269.6 kcal/mol), suggesting a favorable binding conformation within the receptor\u0026rsquo;s LBD. Similarly, the Mediator of RNA polymerase II transcription subunit 29 demonstrated a binding energy of -14.4 kcal/mol, with van der Waals forces of -89.6 kcal/mol and electrostatic energy of -146.2 kcal/mol, highlighting its strong affinity for GR despite slightly weaker electrostatic interactions compared to Bombyxin A-5. Additional promising candidates included Bombyxin A-3 (-13.2 kcal/mol), Small ribosomal subunit protein uS11 (-12.9 kcal/mol), and Fibroin (-12.5 kcal/mol), all of which showed considerable binding potential. Notably, Small ribosomal subunit protein uS11 exhibited the highest electrostatic energy (-443.9 kcal/mol) among the tested proteins, suggesting a strong charge-based interaction with GR, which could play a vital role in stabilizing the protein-ligand complex. In contrast, Fibroin displayed an electrostatic interaction of -322.7 kcal/mol, indicating a different mode of receptor engagement.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular docking and interaction energies of Can Si-derived proteins with the glucocorticoid receptor.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" 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=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHADDOCK score (a.u.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBinding energy (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVan der Waals energy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eElectrostatic energy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDesolvation energy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRMSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin A-5_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;129.9 +/- 10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;67.0 +/- 5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;269.6 +/- 37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;32.7 +/- 3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.5 +/- 0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediator of RNA polymerase II transcription subunit 29_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;105.8 +/- 7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;14.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;89.6 +/- 4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;146.2 +/- 28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;8.4 +/- 3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e1.3 +/- 1.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin A-3_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;104.0 +/- 16.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;49.4 +/- 10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;271.5 +/- 37.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;14.1 +/- 8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.8 +/- 0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall ribosomal subunit protein uS11_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;120.8 +/- 15.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;47.7 +/- 20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;443.9 +/- 43.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;8.9 +/- 9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e1.3 +/- 1.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibroin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;111.1 +/- 0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;62.7 +/- 6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;322.7 +/- 36.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e5.4 +/- 2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e1.0 +/- 0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChorion class CB protein M5H4_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;70.6 +/- 31.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;62.6 +/- 18.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;97.5 +/- 20.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;15.5 +/- 4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.9 +/- 0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChorion class high-cysteine HCB protein 12_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;77.6 +/- 14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;51.4 +/- 10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;205.1 +/- 21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;2.2 +/- 2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e1.0 +/- 0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin B-9_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;111.5 +/- 7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;53.1 +/- 4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;358.4 +/- 23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;6.3 +/- 1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e0.5 +/- 0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin C-1_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;92.5 +/- 3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;61.5 +/- 2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;203.2 +/- 30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;9.0 +/- 1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e1.5 +/- 0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin E-1_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;86.3 +/- 5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;11.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;53.5 +/- 4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;204.5 +/- 42.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;9.3 +/- 2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c7\"\u003e \u003cp\u003e3.2 +/- 0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAmong the chorion proteins, Chorion class CB protein M5H4 and Chorion class high-cysteine HCB protein 12 exhibited binding energies of -12.4 kcal/mol and \u0026minus;\u0026thinsp;12.2 kcal/mol, respectively, with moderate van der Waals interactions (-62.6 kcal/mol and \u0026minus;\u0026thinsp;51.4 kcal/mol, respectively). Their relatively lower electrostatic contributions (-97.5 kcal/mol for M5H4 and \u0026minus;\u0026thinsp;205.1 kcal/mol for HCB protein 12) suggest a weaker polar interaction component compared to the other proteins but still indicate a viable receptor affinity. The Bombyxin family proteins continued to demonstrate noteworthy GR interactions, with Bombyxin B-9 (-12.1 kcal/mol), Bombyxin C-1 (-12.0 kcal/mol), and Bombyxin E-1 (-11.8 kcal/mol) showing stable binding conformations. Bombyxin B-9, in particular, displayed significant electrostatic interactions (-358.4 kcal/mol), suggesting a highly charged binding mechanism within the LBD. Meanwhile, Bombyxin E-1, with an RMSD value of 3.2, indicated a greater degree of structural flexibility compared to other complexes, potentially reflecting a dynamic binding mode rather than a rigid fit. The 3D binding poses of the top-performing Can Si-derived proteins with GR are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, showcasing their pose similarity within the LBD.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents a comparative analysis of the intermolecular contacts (ICs) and non-interacting surface areas (NIS) for the top-ranked protein-GR complexes. The data reveal substantial variations in the interaction profiles, suggesting that different proteins engage with the GR through distinct binding mechanisms. These differences are particularly evident in the distribution of charged, polar, and apolar contacts, which influence the stability and specificity of the protein-receptor interactions. Among the investigated complexes, the Small ribosomal subunit protein uS11_GR exhibits the highest number of charged-charged interactions (11), indicating a significant contribution of electrostatic forces to its binding affinity. Electrostatic interactions often play a crucial role in stabilizing protein-ligand complexes, particularly when involving key residues within the receptor\u0026rsquo;s binding pocket. Similarly, Fibroin_GR demonstrates a relatively high count of charged-charged interactions (9), reinforcing the importance of electrostatic stabilization. In contrast, Chorion class high-cysteine HCB protein 12_GR and Mediator of RNA polymerase II transcription subunit 29_GR show the lowest number of such interactions (2 and 3, respectively), suggesting a weaker electrostatic contribution to their binding.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIntermolecular contacts and nonbonded interaction scores of Can Si-derived proteins with the glucocorticoid receptor (GR).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICs charged-charged\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eICs charged-polar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eICs charged-apolar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eICs polar-polar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eICs polar-apolar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eICs apolar-apolar\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNIS charged\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNIS apolar\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin A-5_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e41.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediator of RNA polymerase II transcription subunit 29_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e42.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin A-3_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e41.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall ribosomal subunit protein uS11_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e41.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibroin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e40.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChorion class CB protein M5H4_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e51.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChorion class high-cysteine HCB protein 12_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e42.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin B-9_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e39.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin C-1_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e41.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin E-1_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e40.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote:\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u0026bull; ICs: Number of intermolecular contacts\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u0026bull; NIS: Non-interacting surface\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn terms of hydrophobic interactions, the apolar-apolar contact count varies significantly across the complexes. Chorion class CB protein M5H4_GR exhibits the highest number of apolar-apolar interactions (32), indicating a strong contribution from hydrophobic forces, which can enhance protein stability and specificity within the receptor binding site. On the other hand, Fibroin_GR and Small ribosomal subunit protein uS11_GR display the lowest numbers of apolar-apolar interactions (13 and 12, respectively), implying that their binding may rely more on polar and electrostatic contributions rather than hydrophobic stabilization. These differences suggest that while some proteins engage in extensive hydrophobic interactions with GR, others may utilize a combination of hydrogen bonding and electrostatic forces for stable binding. The NIS provides additional insights into the efficiency of protein-receptor binding. A larger NIS value indicates a greater portion of the protein's surface remains unengaged, which may suggest suboptimal binding or the presence of steric hindrance. Among the studied complexes, Chorion class CB protein M5H4_GR displays the highest apolar NIS (51.10 \u0026Aring;\u0026sup2;), indicating a significant unutilized apolar surface that could potentially be optimized for stronger interactions. In contrast, Bombyxin B-9_GR has the smallest apolar NIS (39.19 \u0026Aring;\u0026sup2;), suggesting that this complex achieves a more compact and efficient interaction with GR. Additionally, the Small ribosomal subunit protein uS11_GR exhibits the highest charged NIS (27.04 \u0026Aring;\u0026sup2;), reflecting an unengaged charged surface that might influence the overall stability of the complex. The full details of the molecular interactions for both chemical and protein-based compound-receptor complexes are provided in \u003cb\u003eSupplementary Data S3\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe molecular docking simulations offer valuable insights beyond just binding energy calculations by elucidating the contributions of different energy components to the overall binding interactions. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA presents the correlation matrix for chemical compounds derived from Can Si, highlighting the relationship between binding affinity (ΔG in kcal/mol) and key energy components, such as Van der Waals energy, electrostatic energy, and desolvation energy. Notably, Van der Waals interactions show a moderate positive correlation with binding affinity (0.56), suggesting that favorable hydrophobic interactions play a significant role in stabilizing these compounds within the receptor\u0026rsquo;s binding pocket. In contrast, electrostatic energy exhibits a weak negative correlation (-0.049), implying that electrostatic interactions may not be a primary driving force for binding. Desolvation energy also demonstrates a moderate positive correlation (0.51), indicating that the removal of solvent molecules upon ligand binding contributes to affinity. Similarly, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB provides the correlation matrix for protein-based compounds, shedding light on the energetic determinants of their interactions with the receptor. In this case, Van der Waals energy maintains a moderate positive correlation with binding affinity (0.51), reinforcing the role of hydrophobic interactions in protein-ligand stability. However, electrostatic energy (0.037) and desolvation energy (0.011) show negligible correlations with binding affinity, suggesting that these energy components contribute minimally to the binding strength of protein-based compounds. This discrepancy between chemical and protein-based compounds suggests that different molecular interaction mechanisms govern their binding behavior, with hydrophobic forces playing a more dominant role in both cases.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMolecular Dynamics Simulation Insights into Ligand-GR Complex Stability and Interaction\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo comprehensively evaluate the structural stability, dynamic behavior, and binding efficacy of Can Si-derived compounds in complex with the GR, MD simulations were conducted for 100 ns. The simulation results, summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, provide valuable insights into the atomic-level interactions, flexibility, and compactness of these ligand-receptor complexes. The key parameters assessed include RMSD, root mean square fluctuation (RMSF), radius of gyration (RoG), and hydrogen bonding. RMSD is a critical parameter in MD simulations that quantifies the overall conformational stability of a ligand-receptor complex by measuring atomic displacement over time [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. A lower RMSD value suggests that the complex remains structurally stable with minimal fluctuations, whereas higher RMSD values may indicate conformational flexibility or instability [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. The standard GR agonist dexamethasone exhibited an RMSD of 1.776 \u0026Aring;, indicating a highly stable binding conformation throughout the simulation. Similarly, the standard antagonist mifepristone maintained an RMSD of 1.735 \u0026Aring;, suggesting consistent binding without major structural deviations. Among the chemical ligands, catechin (1.145 \u0026Aring;) and epigallocatechin (1.217 \u0026Aring;) demonstrated the lowest RMSD values, implying exceptionally stable interactions with GR. These small molecules maintained their binding orientations without significant conformational shifts. On the other hand, protein-based ligands showed slightly higher RMSD values, ranging from 3.099 \u0026Aring; (Bombyxin E-1) to 3.869 \u0026Aring; (Fibroin). The increased RMSD in protein ligands is expected due to their larger molecular size and the inherent flexibility of peptide-protein interactions. Importantly, no ligand-receptor complex exhibited sudden spikes in RMSD throughout the 100 ns simulation, reinforcing the stability of these interactions over time. The chemical ligands maintained strong, steady interactions, indicating their suitability as small-molecule GR modulators. The protein-based ligands, despite exhibiting slightly higher RMSD and RoG values, remained within a stable dynamic range, supporting their potential as peptide-based GR modulators. None of the ligands caused disruptive conformational shifts in the receptor, further validating their binding reliability and biological relevance in targeting GR.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular dynamics analysis of ligand-glucocorticoid receptor (GR) complexes: Structural stability and hydrogen bond interactions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage RMSD (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage RMSF (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage RoG (\u0026Aring;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of Hydrogen Bonds Between the Ligand-Receptor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDexamethasone_GR (standard agonist)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMifepristone_GR (standard antagonist)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenaquinone-7_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHesperetin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatechin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhytonadione_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpigallocatechin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiamine_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGalangin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaemperol_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuteolin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuercetin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin A-5_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediator of RNA polymerase II transcription subunit 29_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin A-3_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall ribosomal subunit protein uS11_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibroin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChorion class CB protein M5H4_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChorion class high-cysteine HCB protein 12_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin B-9_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin C-1_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin E-1_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eRadius of Gyration (RoG) assesses the compactness of a molecular system by measuring the spatial distribution of its atomic coordinates. A lower RoG value generally signifies a more tightly packed and stable complex, whereas higher values suggest structural expansion or flexibility. The standard agonist dexamethasone maintained an RoG of 2.643 \u0026Aring;, while the antagonist mifepristone had an RoG of 2.754 \u0026Aring;, reflecting the well-folded and compact nature of these ligand-receptor complexes. Chemical compounds exhibited RoG values ranging from 2.712 \u0026Aring; (Thiamine) to 2.983 \u0026Aring; (Epigallocatechin), suggesting a tightly bound configuration with minimal structural dispersion. Protein-based ligands displayed higher RoG values, ranging from 3.756 \u0026Aring; (Chorion Class CB Protein M5H4) to 3.916 \u0026Aring; (Mediator of RNA Polymerase II Transcription Subunit 29). The slightly expanded structures indicate the inherent flexibility of protein ligands, yet their values remained within an acceptable range, suggesting stable interactions with GR. Hydrogen bonding plays a crucial role in stabilizing ligand-receptor interactions by forming strong electrostatic attractions between hydrogen donors and acceptors. The number of hydrogen bonds formed between a ligand and GR directly correlates with binding affinity and stability. Dexamethasone (2 hydrogen bonds) and mifepristone (0 hydrogen bonds) served as reference points for binding interactions. Among the chemical ligands, catechin (6 hydrogen bonds) and quercetin (5 hydrogen bonds) exhibited the highest number of hydrogen bonds, indicating strong receptor-ligand interactions that contribute to enhanced stability. Protein-based ligands demonstrated a greater number of hydrogen bonds, with Bombyxin A-5 (13 bonds), Bombyxin B-9 (12 bonds), and Bombyxin A-3 (11 bonds) forming extensive hydrogen bond networks, reinforcing their potential as strong GR binders. The presence of multiple hydrogen bonds in protein-based ligands suggests that they can establish robust interactions with GR, potentially leading to prolonged binding retention.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the Root Mean Square Fluctuation (RMSF) profiles of the top-performing chemical and protein-based complexes derived from Can Si, compared to the standard glucocorticoid receptor (GR) agonist Dexamethasone and antagonist Mifepristone. The RMSF analysis provides crucial insights into the local flexibility of residues in the receptor-ligand complexes, shedding light on the dynamic stability of each interaction over the simulation period. The RMSF profiles revealed a strong correlation in binding region stabilization between both chemical and protein-based compounds. To differentiate between agonist-like and antagonist-like interactions, we specifically focused on the key residues within the GR active site, including Trp557-Val575, Trp600-Pro625, and Ala700-Tyr735, which play a significant role in ligand binding and receptor activation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAmong the tested chemical compounds, Menaquinone-7, Hesperetin, and Catechin exhibited stabilization patterns similar to the standard agonist (Dexamethasone) within the GR active site. Notably, these compounds maintained a steady fluctuation profile, suggesting that their interactions with GR were stable throughout the simulation. Catechin and Hesperetin showed greater stability than Dexamethasone, with lower fluctuations in the key active site regions. This suggests that these compounds may establish stronger and more consistent hydrogen bonding networks with GR, potentially enhancing their agonistic activity. Menaquinone-7, while maintaining relative stability, displayed slight fluctuations in the Trp600-Pro625 region, indicating potential flexibility in its binding conformation. These findings suggest that these natural compounds could act as potential GR modulators, given their ability to maintain stable interactions in regions critical for receptor activation. Conversely, Mifepristone, the standard GR antagonist, exhibited notable spikes and fluctuations across the key binding site regions. These fluctuations were particularly prominent in the Trp600-Pro625 and Ala700-Tyr735 regions, which are critical for receptor activation. The observed instability suggests that Mifepristone disrupts the local hydrogen bonding network, leading to increased flexibility and weakened ligand-receptor interactions. This behavior aligns with the expected antagonist mechanism, where increased fluctuations prevent GR activation by destabilizing key conformational states necessary for signal transduction.\u003c/p\u003e \u003cp\u003eSimilar to the chemical compounds, the protein-based ligands derived from Can Si followed a comparable trend in receptor stabilization. Among them: Bombyxin A-5 demonstrated the most stable fluctuations within the active GR residue regions, maintaining a low RMSF in the Trp557-Val575, Trp600-Pro625, and Ala700-Tyr735 regions, suggesting a strong binding conformation. Other protein-derived ligands, including Bombyxin C-1, Bombyxin E-1, and Small Ribosomal Subunit Protein uS11, also exhibited relatively stable RMSF values, indicating their potential to act as GR modulators. In contrast, Fibroin and Chorion class high-cysteine HCB protein 12 showed slightly higher fluctuations in critical regions, suggesting a more dynamic binding mode. These findings collectively highlight the potential of bioactive compounds from Can Si as GR modulators, with applications in developing novel therapeutic agents targeting glucocorticoid receptor pathways.\u003c/p\u003e \u003cp\u003eThe MM/PBSA binding affinity calculations provide crucial insights into the strength and stability of interactions between the bioactive compounds derived from Can Si and the GR (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The binding free energy values (ΔG_binding) indicate the thermodynamic favorability of complex formation, where more negative values correspond to stronger binding interactions. Comparing the derived compounds to standard agonists and antagonists helps to understand their potential functional roles in modulating GR activity. To establish a baseline, the standard GR agonist, Dexamethasone, exhibited a ΔG_binding of -27.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22 kcal/mol, while the standard antagonist, Mifepristone, showed a weaker binding affinity at -22.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01 kcal/mol. The lower binding energy of dexamethasone indicates a stable interaction with GR, which is expected for an agonist that activates the receptor. In contrast, the relatively weaker binding of mifepristone aligns with its role as an antagonist, as it disrupts receptor activation by preventing stable interactions.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMM/PBSA binding free energy calculations of Can Si-derived compounds and standard ligands with the glucocorticoid receptor (GR).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMM/PBSA Free Binding Energy\u003c/p\u003e \u003cp\u003eΔG_binding (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDexamethasone_GR (standard agonist)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;27.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMifepristone_GR (standard antagonist)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;22.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenaquinone-7_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;32.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHesperetin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;33.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatechin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;35.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhytonadione_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;28.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpigallocatechin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;27.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiamine_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;28.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGalangin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;27.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaemperol_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;24.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuteolin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;23.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuercetin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;27.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin A-5_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;228.06\u0026thinsp;\u0026plusmn;\u0026thinsp;6.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediator of RNA polymerase II transcription subunit 29_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;201.60\u0026thinsp;\u0026plusmn;\u0026thinsp;7.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin A-3_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;189.46\u0026thinsp;\u0026plusmn;\u0026thinsp;8.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall ribosomal subunit protein uS11_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;204.98\u0026thinsp;\u0026plusmn;\u0026thinsp;6.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFibroin_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;153.67\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChorion class CB protein M5H4_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;223.23\u0026thinsp;\u0026plusmn;\u0026thinsp;6.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChorion class high-cysteine HCB protein 12_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;164.06\u0026thinsp;\u0026plusmn;\u0026thinsp;8.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin B-9_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;163.90\u0026thinsp;\u0026plusmn;\u0026thinsp;6.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin C-1_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;157.59\u0026thinsp;\u0026plusmn;\u0026thinsp;7.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBombyxin E-1_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;156.23\u0026thinsp;\u0026plusmn;\u0026thinsp;8.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAmong the chemical compounds derived from Can Si, several demonstrated binding affinities stronger than dexamethasone. Notably, Catechin (-35.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54 kcal/mol), Hesperetin (-33.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00 kcal/mol), and Menaquinone-7 (-32.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12 kcal/mol) exhibited the most favorable interactions, suggesting that these compounds may act as potent GR agonists. Their stronger binding suggests enhanced receptor stabilization, potentially leading to greater GR activation compared to dexamethasone. Other compounds, such as Phytonadione (-28.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63 kcal/mol), Epigallocatechin (-27.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38 kcal/mol), Thiamine (-28.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34 kcal/mol), and Quercetin (-27.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38 kcal/mol), displayed binding affinities comparable to dexamethasone, indicating moderate GR activation potential. In contrast, Kaempferol (-24.90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.33 kcal/mol) and Luteolin (-23.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29 kcal/mol) exhibited weaker binding energies, nearing the range of Mifepristone, suggesting reduced agonistic potential or potential partial antagonistic behavior.\u003c/p\u003e \u003cp\u003eIn comparison to the chemical compounds, the protein-based ligands derived from Can Si demonstrated significantly stronger binding interactions with GR. Bombyxin A-5 (-228.06\u0026thinsp;\u0026plusmn;\u0026thinsp;6.29 kcal/mol) exhibited the highest binding affinity, followed by Chorion class CB protein M5H4 (-223.23\u0026thinsp;\u0026plusmn;\u0026thinsp;6.52 kcal/mol), Mediator of RNA polymerase II transcription subunit 29 (-201.60\u0026thinsp;\u0026plusmn;\u0026thinsp;7.14 kcal/mol), and Small Ribosomal Subunit Protein uS11 (-204.98\u0026thinsp;\u0026plusmn;\u0026thinsp;6.94 kcal/mol). These exceptionally low ΔG_binding values suggest that these proteins form highly stable and energetically favorable interactions with GR, likely involving extensive hydrogen bonding, hydrophobic interactions, and electrostatic contacts. Additionally, other protein ligands, such as Fibroin (-153.67\u0026thinsp;\u0026plusmn;\u0026thinsp;6.65 kcal/mol), Chorion class high-cysteine HCB protein 12 (-164.06\u0026thinsp;\u0026plusmn;\u0026thinsp;8.12 kcal/mol), Bombyxin B-9 (-163.90\u0026thinsp;\u0026plusmn;\u0026thinsp;6.29 kcal/mol), and Bombyxin C-1 (-157.59\u0026thinsp;\u0026plusmn;\u0026thinsp;7.32 kcal/mol), also exhibited strong interactions, though with slightly higher ΔG_binding values compared to Bombyxin A-5. These results indicate that the protein ligands may function as strong GR modulators, potentially influencing receptor activity more significantly than chemical compounds.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePharmacophore Profiling, Drug-Likeness, and Toxicity Assessment of Can Si-derived Compounds Targeting GR\u003c/h2\u003e \u003cp\u003eThe pharmacophore modeling analysis of chemical compounds derived from Can Si provided valuable insights into their potential interactions with the GR, particularly when compared to standard agonist and antagonist ligands. The top four performing compounds (Menaquinone-7, Hesperetin, Catechin, and Phytonadione) exhibited distinct yet overlapping pharmacophore features that contribute to their binding affinity and mode of interaction within the GR binding site. Dexamethasone, the standard agonist, was characterized by the presence of a hydrogen bond acceptor through its carbonyl functional group, which plays a crucial role in stabilizing its interaction with GR (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). In contrast, the standard antagonist, Mifepristone, predominantly engaged in hydrophobic interactions via its benzene rings, suggesting a different binding mechanism that disrupts GR activation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Among the Can Si-derived compounds, Hesperetin and Catechin demonstrated a strong resemblance to the standard agonist in their binding interactions. Both molecules formed hydrogen bond donor interactions through their hydroxyl (-OH) groups, effectively engaging key active site residues of GR (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD \u003cb\u003eand\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). This similarity suggests that these compounds may exert agonist-like effects by stabilizing the receptor in an active conformation. On the other hand, Menaquinone-7 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC) and Phytonadione (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF) primarily relied on hydrophobic interactions, predominantly facilitated by their isoprenoid side chains. These interactions, while contributing to binding stability, suggest a distinct mode of interaction compared to the hydroxyl-rich agonist-like compounds. These findings highlight the potential of Hesperetin and Catechin as GR agonists due to their hydrogen bonding characteristics, while Menaquinone-7 and Phytonadione may interact with GR via a more hydrophobic-driven mechanism. This differentiation in binding modes underscores the structural diversity of \u003cem\u003eCan Si\u003c/em\u003e-derived compounds and their possible functional implications in modulating GR activity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe drug-likeness and toxicity profiles of the Can Si-derived chemical compounds targeting the glucocorticoid receptor (GR) were evaluated using multiple pharmacokinetic and safety parameters. These assessments included Lipinski\u0026rsquo;s Rule of Five violations, which predict oral bioavailability, as well as potential toxicological risks such as mutagenicity, tumorigenicity, reproductive toxicity, and irritant effects (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Among the evaluated compounds, Menaquinone-7 and Phytonadione exhibited Lipinski\u0026rsquo;s Rule violations, indicating potential challenges in their oral bioavailability. Menaquinone-7 exceeded the molecular weight threshold (MW\u0026thinsp;\u0026gt;\u0026thinsp;500 g/mol) and had high lipophilicity (LogP\u0026thinsp;\u0026gt;\u0026thinsp;5), which could impact its solubility and permeability. Phytonadione, on the other hand, violated only the lipophilicity criterion (LogP\u0026thinsp;\u0026gt;\u0026thinsp;5), suggesting that while it remains relatively drug-like, its high lipophilicity might affect absorption and distribution. Despite these violations, both compounds exhibited favorable drug-likeness scores (0.62 and 0.93, respectively) and showed no indications of mutagenicity, tumorigenicity, reproductive toxicity, or irritant effects.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDrug-likeness and toxicity profiles of Can Si -derived compounds targeting the glucocorticoid receptor (GR).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMolecule\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipinski violation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrug-likeness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMutagenic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTumorigenic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReproductive\u003c/p\u003e \u003cp\u003eeffective\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIrritant\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMenaquinone-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 violations:\u003c/p\u003e \u003cp\u003eMW\u0026thinsp;\u0026gt;\u0026thinsp;500 g/mol\u003c/p\u003e \u003cp\u003eLogP\u0026thinsp;\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHesperetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatechin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhytonadione\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 violation:\u003c/p\u003e \u003cp\u003eLogP\u0026thinsp;\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpigallocatechin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 violation:\u003c/p\u003e \u003cp\u003eHBD\u0026thinsp;\u0026gt;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThiamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGalangin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaemperol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLuteolin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHesperetin, Catechin, Thiamine, Luteolin, and Kaempferol adhered to all of Lipinski\u0026rsquo;s criteria, indicating good oral bioavailability potential. Their drug-likeness scores ranged from 0.38 to 0.87, suggesting moderate to high suitability as drug candidates. Additionally, none of these compounds exhibited toxicological concerns, reinforcing their potential safety for further development. Epigallocatechin presented a single violation due to having more than five hydrogen bond donors (HBD\u0026thinsp;\u0026gt;\u0026thinsp;5), which may affect its permeability and absorption. However, its toxicity profile was clean, and it did not display any mutagenic or carcinogenic properties. Despite this, its drug-likeness score (-0.04) was relatively low, which may indicate suboptimal pharmacokinetic properties. Conversely, Galangin and Quercetin raised toxicity concerns. While both complied with Lipinski\u0026rsquo;s Rule of Five, they exhibited high mutagenic potential, and Quercetin additionally showed tumorigenic properties. Their drug-likeness scores (-0.15 and 0.52, respectively) further suggest that their overall suitability as therapeutic candidates may be limited due to safety concerns. Thus, Hesperetin, Catechin, and Thiamine emerged as the most promising drug-like candidates, demonstrating compliance with Lipinski\u0026rsquo;s rules, favorable drug-likeness scores, and clean toxicity profiles. Menaquinone-7 and Phytonadione also exhibited potential, though their lipophilicity could pose formulation challenges. On the other hand, compounds like Galangin and Quercetin require further investigation due to their potential mutagenic and carcinogenic risks.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study provides a comprehensive evaluation of the therapeutic potential of Can Si-derived bioactive compounds in the treatment of PNI by targeting the GR. Using a combination of molecular docking, MD simulations, and pharmacophore modeling, we identified Catechin, Hesperetin, and Menaquinone-7 as promising small molecules with strong GR interactions. Additionally, protein-based bioactive compounds, such as Bombyxin A-5 and Small Ribosomal Subunit Protein uS11, demonstrated significant binding potential, suggesting a possible role in nerve repair and neuroprotection. GR is a crucial modulator of inflammation, oxidative stress, and cellular repair mechanisms, all of which are central to PNI pathology. Following nerve injury, excessive inflammation, and oxidative damage can impair Schwann cell function, delay axonal regeneration, and contribute to chronic neuropathic pain [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. While synthetic glucocorticoids, such as dexamethasone and prednisolone, are widely used to manage inflammation in PNI, their long-term use is associated with serious side effects, including immunosuppression, metabolic disturbances, and delayed wound healing [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Therefore, the identification of natural GR modulators presents a promising alternative to harness the anti-inflammatory benefits of GR activation while minimizing adverse effects.\u003c/p\u003e \u003cp\u003eThe pharmacophore modeling results revealed that Catechin, Hesperetin, and Menaquinone-7 possess key structural features that facilitate GR interaction. Unlike synthetic corticosteroids, which predominantly engage GR through hydrogen bonding, these natural compounds demonstrated a balance between hydrogen bonding and hydrophobic interactions, which could contribute to a partial agonist effect. This selective modulation may help achieve anti-inflammatory benefits without triggering excessive GR activation, thereby reducing systemic adverse effects [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Among the tested compounds, Catechin and Hesperetin exhibited strong hydrogen bonding interactions with critical GR residues, suggesting potent anti-inflammatory and antioxidant properties. These interactions may contribute to the protection of Schwann cells and regenerating axons from oxidative stress, thereby promoting nerve repair [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Meanwhile, Menaquinone-7, which features a hydrophobic isoprenoid side chain, formed stable hydrophobic and π-stacking interactions with GR. This unique interaction pattern suggests that Menaquinone-7 may exert its effects through an alternative regulatory mechanism, potentially improving neuronal survival and reducing neuroinflammation.\u003c/p\u003e \u003cp\u003eIn addition to small molecules, protein-based compounds from Can Si also exhibited strong and stable interactions with GR, indicating their potential neuroprotective role. Bombyxin A-5, a known neuropeptide, has previously been associated with anti-inflammatory and neurotrophic activities [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Its interaction with GR suggests that it may play a hormone-like regulatory role, enhancing Schwann cell survival and promoting axonal regeneration. Similarly, Small Ribosomal Subunit Protein uS11, a regulatory protein involved in cellular signaling and translation control, displayed a favorable GR binding profile. This interaction suggests that uS11 may contribute to enhancing protein synthesis during nerve regeneration, a critical process following PNI. Previous research has highlighted the potential of neuropeptides in nerve repair by modulating inflammatory responses and neurotrophic signaling pathways [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. The findings from this study align with these insights, reinforcing the idea that bioactive proteins can complement small molecule therapies to enhance PNI recovery.\u003c/p\u003e \u003cp\u003eA key consideration for the clinical applicability of Can Si-derived compounds is their toxicity and pharmacokinetic properties. The toxicity assessment revealed that Catechin and Hesperetin demonstrated favorable drug-likeness profiles, making them strong candidates for therapeutic development. Their natural origin and previously reported neuroprotective properties further support their potential use in PNI therapy [\u003cspan additionalcitationids=\"CR80\" citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. However, Menaquinone-7, despite its strong GR binding, exhibited high molecular weight and lipophilicity, which may pose challenges for oral bioavailability. Nonetheless, previous studies on vitamin K analogs have suggested that lipophilic modifications or advanced drug delivery systems, such as nanocarriers, could enhance systemic absorption [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. These strategies could be explored to improve Menaquinone-7\u0026rsquo;s pharmacokinetic profile. One major advantage of these natural GR modulators over synthetic corticosteroids is their potential to reduce systemic toxicity. Given their ability to modulate GR activity in a tissue-specific manner, these natural compounds may hold promise as novel neuroprotective agents that can be incorporated into future PNI treatment strategies.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLimitations, Clinical Implications, and Future Works\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDespite the promising findings of this study, several limitations must be acknowledged. First, the computational approach, including molecular docking, MD simulations, and pharmacophore modeling, provides valuable insights into the interactions of Can Si-derived bioactive compounds with the GR; however, these remain theoretical predictions. The actual binding affinity, stability, and biological effects of these compounds in a physiological system require experimental validation through in vitro and in vivo studies. Another limitation lies in the bioavailability, metabolic stability, and pharmacokinetics of the identified bioactive compounds. Natural compounds such as Catechin, Hesperetin, and Menaquinone-7 often suffer from poor solubility, rapid metabolism, and potential challenges in crossing the blood-brain barrier (BBB). These factors could limit their therapeutic potential for PNI. Moreover, while toxicity predictions suggest a favorable safety profile, a more detailed analysis of long-term toxicity, immunogenicity, and possible off-target effects is necessary before clinical application. Furthermore, this study primarily focuses on GR-mediated pathways, yet PNI is a multifactorial condition involving oxidative stress, neurotrophic signaling, immune modulation, and mitochondrial dysfunction. A more comprehensive systems biology approach integrating multiple targets and pathways would provide a deeper understanding of how Can Si-derived compounds promote nerve regeneration.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe identification of natural GR modulators from Can Si offers a potential alternative to synthetic glucocorticoids for treating PNI-related inflammation and nerve degeneration. Conventional glucocorticoids, such as dexamethasone and prednisolone, are widely used to suppress inflammatory responses and prevent fibrosis after nerve injury. However, their long-term use is associated with significant side effects, including muscle atrophy, osteoporosis, hyperglycemia, and immunosuppression. The findings from this study suggest that natural compounds, particularly Catechin and Hesperetin, could act as selective GR modulators, potentially minimizing these side effects while maintaining neuroprotective and anti-inflammatory benefits. Additionally, protein-based bioactive molecules from Can Si, such as Bombyxin A-5 and Small Ribosomal Subunit Protein uS11, may provide novel therapeutic avenues by influencing neuroinflammatory pathways and Schwann cell function. These compounds could be explored as potential adjuncts to existing neuroprotective treatments. Their ability to enhance nerve regeneration, reduce oxidative stress, and improve functional recovery suggests that they might be beneficial when combined with neurotrophic factors or stem cell therapies. From a translational perspective, these findings could inspire the development of novel therapeutic formulations, such as oral nutraceuticals containing GR-modulating flavonoids and neuropeptides to support nerve recovery in PNI patients. Combination therapies that integrate natural bioactive compounds with standard neuroprotective agents. Targeted drug delivery systems, such as hydrogels or nanoparticles, to enhance bioavailability and localized effects on injured peripheral nerves.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eFuture Works\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo bridge the gap between computational findings and clinical application, further research should focus on experimental validation and drug development strategies. First, in vitro studies should be conducted to confirm the binding affinity, selectivity, and biological effects of these compounds. Schwann cell and neuronal cultures could be used to assess their neuroprotective, anti-inflammatory, and antioxidative properties. Techniques such as Western blotting, RT-PCR, and immunofluorescence could help determine whether these compounds influence GR expression and downstream signaling pathways involved in nerve repair. Second, in vivo studies are crucial for evaluating the therapeutic efficacy of Catechin, Hesperetin, and Menaquinone-7 in animal models of PNI. Rat or mouse models of sciatic nerve crush or transection injury could be used to assess functional recovery through electrophysiology, histological analysis, and behavioral tests. Additionally, pharmacokinetic studies should determine the absorption, metabolism, and tissue distribution of these compounds to optimize dosage and delivery strategies. Beyond single-target approaches, multi-target network pharmacology should be explored to investigate how Can Si-derived compounds influence other critical pathways in nerve repair, such as neurotrophic signaling (NGF, BDNF), mitochondrial function, and oxidative stress response (Nrf2 pathway). Integrating transcriptomics, proteomics, and metabolomics would provide a holistic understanding of their therapeutic potential. Finally, efforts should be made to improve the drug delivery and formulation of these bioactive compounds. Nano-formulations, such as liposomes, polymeric nanoparticles, and hydrogels, could enhance their stability, bioavailability, and targeted delivery to injured peripheral nerves. Localized delivery systems, including microneedle patches or nerve conduit-based drug release, could minimize systemic exposure and maximize therapeutic efficacy.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study provides compelling computational evidence supporting the potential therapeutic role of Can Si-derived bioactive compounds in PNI. Through an integrative approach combining network pharmacology, molecular docking, MD simulations, and pharmacophore modeling, we identified several promising bioactive molecules, including Catechin, Hesperetin, and Menaquinone-7, that exhibit strong interactions with the glucocorticoid receptor. Additionally, protein-based bioactive molecules such as Bombyxin A-5 and Small Ribosomal Subunit Protein uS11 showed promising binding affinity and stability, suggesting their potential role in modulating neuroinflammatory pathways and nerve regeneration processes. The findings highlight the multi-faceted therapeutic mechanisms of these bioactive compounds, particularly in reducing neuroinflammation, promoting Schwann cell function, and enhancing neuronal survival, all of which are critical for functional recovery following PNI. Furthermore, the potential neuroprotective effects of these compounds, coupled with their relatively favorable safety profiles, suggest that Can Si-derived natural products could serve as alternative or adjunctive therapies to conventional synthetic glucocorticoids, which are often associated with significant side effects. Despite these promising findings, several limitations remain, including the lack of in vitro and in vivo validation, as well as challenges related to bioavailability, metabolic stability, and drug delivery. Future research should focus on experimental confirmation of the predicted molecular interactions, in vivo efficacy testing in animal models of PNI, and formulation optimization using advanced drug delivery systems to enhance targeted nerve repair.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe original contributions presented in this study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author(s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualization: N.A and D.D; Methodology: N.A and D.D; Software: D.D; Validation: N.A and D.D; Formal analysis: D.D; Investigation: N.A and D.D; Resources: N.A and D.D; Data Curation: D.D; Writing - Original Draft: N.A and D.D; Writing - Review \u0026amp; Editing: N.A and D.D; Visualization: D.D; Supervision: N.A; Project administration: N.A; Funding acquisition: N.A. All authors have read and approved the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-DDRSP2501).\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\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence\u003c/strong\u003e and requests for materials should be addressed to N.A.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAman, M., et al., \u003cem\u003ePeripheral nerve injuries in children\u0026mdash;prevalence, mechanisms and concomitant injuries: a major trauma center\u0026rsquo;s experience.\u003c/em\u003e European Journal of Medical Research, 2023. \u003cstrong\u003e28\u003c/strong\u003e(1): p. 116.\u003c/li\u003e\n\u003cli\u003eSaadat, S., V. 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Can Si (Silk Fibroin), a key component of Traditional Chinese Medicine (TCM), has long been recognized for its regenerative properties, yet its molecular mechanisms in PNI treatment remain unexplored. To elucidate the pharmacological actions of Can Si, an integrative molecular simulation approach was applied. Network pharmacology was employed to identify the most favorable target receptor for PNI, leading to the selection of the glucocorticoid receptor (GR) due to its critical role in inflammation and nerve repair. Molecular docking simulations evaluated the binding affinities of chemical and protein-based compounds from Can Si to GR, followed by molecular dynamics (MD) simulations to confirm the stability of these interactions under physiological conditions. Pharmacophore modeling identified key structural features essential for bioactivity, while in silico toxicity assessments evaluated the safety profiles of the compounds. Key bioactive compounds from Can Si, including Catechin, Hesperetin, and Menaquinone-7, demonstrated strong interactions with GR, with MM/PBSA-based binding free energy values of \u0026minus;\u0026thinsp;35.98 kcal/mol, \u0026minus;\u0026thinsp;33.65 kcal/mol, and \u0026minus;\u0026thinsp;32.13 kcal/mol, respectively. Protein-based compounds, such as Bombyxin A-5 (\u0026minus;\u0026thinsp;228.06 kcal/mol) and Small Ribosomal Subunit Protein uS11 (\u0026minus;\u0026thinsp;204.98 kcal/mol), also displayed promising binding affinities, suggesting potential neuroprotective roles. In silico toxicity assessments revealed favorable safety profiles for most compounds. This study highlights Can Si as a promising source of therapeutic agents for PNI. Future studies should focus on experimental validation of these computational findings through in vitro and in vivo models.\u003c/p\u003e","manuscriptTitle":"Decoding the Pharmacological Actions of Can Si (Silk Fibroin), a Traditional Chinese Medicine (TCM) for Peripheral Nerve Injury: A Comprehensive Molecular Simulation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-24 10:35:12","doi":"10.21203/rs.3.rs-6190910/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0149ca2e-9c5a-4c9f-b840-5e09779ccba2","owner":[],"postedDate":"April 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47156946,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":47156947,"name":"Biological sciences/Drug discovery"},{"id":47156948,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2025-05-07T04:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-24 10:35:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6190910","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6190910","identity":"rs-6190910","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}