Identifying Natural Sonic Hedgehog Signalling Pathway Modulators with Potential Antifibrotic Activity in Chronic Kidney Disease: An in silico approach

Identifying Natural Sonic Hedgehog Signalling Pathway Modulators with Potential Antifibrotic Activity in Chronic Kidney Disease: An in silico approach | 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 Identifying Natural Sonic Hedgehog Signalling Pathway Modulators with Potential Antifibrotic Activity in Chronic Kidney Disease: An in silico approach Indraneel Rakshit, Pritha Bhattacharjee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7411385/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 Chronic kidney disease (CKD), often following unresolved acute kidney injury (AKI), is characterised by persistent fibrosis induced by several pathways, such as Sonic Hedgehog (SHH). Reactivation of the SHH pathway facilitates the profibrotic communication between epithelial cells and fibroblasts, contributing to CKD progression. In this study, we explored the inhibitory potential of three natural bioactive compounds against SHH pathway receptors: SHH protein, GLi, and SMO. Molecular docking, followed by 100 ns molecular dynamics simulations, was performed to assess binding stability and conformational behaviour. Key parameters, including RMSD, RMSF, hydrogen bonding, principal component analysis (PCA), and free energy landscape, were evaluated to understand dynamic interactions. The binding free energy was also calculated based on the MM/GBSA method. The three selected ligands (Kaempferol, Quercetin, and Naringenin) showed the highest binding affinity compared to other ligands. All these ligands showed favourable energy profiles, with significant interactions influencing structural flexibility and energetics of the SHH pathway receptors. These findings suggest that the selected compounds may effectively target SHH-mediated profibrotic signalling, offering an effective strategy to mitigate CKD progression through natural compound-based inhibition. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Drug discovery Health sciences/Nephrology Sonic Hedgehog Pathway Chronic Kidney Disease Bioactive Compounds Molecular Docking Molecular Dynamics Simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Chronic kidney disease (CKD) represents a major global health burden, affecting over 10% of the adult population, and contributing significantly to morbidity, mortality, and healthcare costs (Carney, 2020 ). Current treatments are largely limited to blood pressure control, glycemic regulation, and renin-angiotensin pathway inhibition, which only provide partial protection against disease progression (Puglisi et al., 2021 ; Leoncini et al., 2020 ). As a result, there is an urgent need for novel molecular targets and therapeutic strategies to effectively counteract and reverse the fibrotic and inflammatory process central to CKD pathogenesis. Recent evidence implicates the Sonic Hedgehog (SHH) pathway as a key driver of maladaptive repair after renal injury (Li and Gan, 2022 ). The SHH pathway was originally characterised for its role in embryonic development; however, in adults, the signalling pathway is aberrantly reactivated in tissues during injury and disease, including the kidney (Zhou et al., 2016 ). This reactivation promotes profibrotic crosstalk between epithelial cells and fibroblasts, contributing to the progression of CKD (Chen and Xue, 2025 ). Notably, components of the SHH pathway, such as SHH protein, Smoothened (SMO), and transcription factor Gli1, have emerged as potential pharmacological targets capable of modulating the fibrogenic cascade (Hu et al., 2024 ; Cao et al., 2020 ) In parallel, natural bioactive compounds garnered considerable interest as therapeutic agents due to their inherent structural diversity, multitarget capabilities, as well as favourable safety profiles (Ravikumar et al., 2025 ; Zhao et al., 2023 ). Several phytochemicals have demonstrated renoprotective, anti-inflammatory, and anti-fibrotic effects in preclinical models, yet their potential to modulate SHH signalling in the context of CKD remains underexplored. In this study, we employed a comprehensive in silico investigation aimed at identifying and characterising an interaction between bioactive compounds with the key SHH pathway receptors (SHH protein, GLI1, and SMO) involved in CKD progression. By employing an integrative computational pipeline encompassing molecular docking and molecular dynamics simulations, we provide a detailed assessment of binding stability, conformational dynamics, and energetic favourability. Our findings offer mechanistic insights into SHH pathway modulation and identify promising receptor-ligand interactions that warrant further experimental validation in the context of fibrosis and CKD-related therapies. Materials and Methods Receptor Preparation: Bioactive compounds were shortlisted for the study through literature surveys, and their structures were downloaded from PubChem in SDF format. The crystal structures of the human Sonic Hedgehog protein (PDB ID: 3M1N, Resolution: 1.85Å), Gli (PDB ID: 2GLI, Resolution: 2.60Å), and Smoothened (4O9R, Resolution: 3.2Å) were obtained from RCSB-PDB ( https://rcsb.org ) (Berman, 2000 ). The DNA molecule was deleted from the crystal structure of 2GLI, and Cyclopamine was removed from the crystal structure of 4O9R. The catalytic sites of the receptors were identified using the DogSite Scorer ( https://proteins.plus/ ). Ligand preparation: Based on the available literature, 4 bioactive compounds (Kaempferol, Quercetin, Curcumin, and Naringenin) that have a therapeutic effect against CKD were chosen. 3-D structures of ligands were obtained from the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ). The structures of the ligands were then optimised using Avogadro, and hydrogen atoms were added. The energy minimisation of the structures was done in OpenBabel using MMFF94 (Merck Molecular Force Field) to reduce steric clashes. Docking Study: All bioactive compounds were docked against 3M1N, 2GLI, and 4O9R using the Autodock Vina application. The receptors were cleaned of any heteroatoms, water molecules, or attached complexes. Polar hydrogens and Kollman Charges were added to receptor proteins, and Gasteiger Charges were added to ligands. The grid box was set based on the position of the catalytic sites obtained from the DogSite Scorer. The information about the grid boxes is provided in the Supplementary Material. The ligands were then docked onto the chosen binding sites of the receptor proteins to generate protein-ligand complexes. A total of 8 poses were generated with 8 exhaustiveness. The visualisation of the docked complexes was done in Discovery Studio. The top three ligands with the most negative binding affinity(kcal/mol) were considered for further molecular dynamics simulations. Molecular Dynamics Simulations: Based on the most negative binding energy from docking analysis, molecular dynamics simulations (MDS) were performed for 100 ns using the GROMACS 2023.3 version. Protein structures were extracted from the docked complex and saved in PDB format, while the ligand structure was extracted and saved in mol2 format. In GROMACS, protein topology was generated using pdb2gmx by selecting the CHARMM36 forcefield. Additionally, the ligand topology was generated using CGenFF software ( https://cgenff.com/ ). The protein-ligand complex was hydrated using the TIP3P water model in a solvation box with a separation of 2.1 nm from the periodic boundary. The system was neutralised with 150 mM NaCl. After system assembly, energy minimisation was performed to ensure that there were no steric clashes or inappropriate geometry between atoms. The energy-minimised system was then subjected to NVT (number of particles, volume of the system, and temperature) and NPT (number of particles, pressure of the system, and temperature) equilibration for 1 ns each at 300K temperature and 1 bar pressure, respectively. During NVT equilibration, V-rescale temperature coupling was used, while in NPT equilibration, Berendsen pressure coupling was used (Berendsen et al., 1984 ). After energy minimisation, a 100 ns production run was performed using V-rescale temperature coupling and Parrinello-Rahman pressure coupling (Parrinello & Rahman, 1981 ). Post-production analysis was performed using several built-in GROMACS tools. Principal Component Analysis (PCA) : In this study, a small subset of principal components (PCs) was extracted to simplify the trajectory dynamics data and distinguish the collective motions from local dynamics. The principal component analysis (PCA) was performed using the gmx covar and gmx anaeig scripts of GROMACS. The gmx covar calculates the covariance matrix of atomic fluctuations to identify dominant collective motions in a biomolecular system. It diagonalises the covariance matrix to obtain eigenvectors and eigenvalues. The gmx anaeig works in conjunction with gmx covar and allows visualisation of dominant motions by projecting the trajectory onto selected eigenvectors. In our work, 2-D projections of the trajectory were generated by overlapping the first two principal components. Free Energy Landscape (FEL): The FEL was constructed to explore the conformational dynamics of the complex by using the first two principal components (PC1 and PC2) obtained from principal component analysis of the molecular dynamics trajectory. These components capture the dominant collective motions of the system. The free energy was calculated using the Boltzmann relation: G(x,y) = − k B ​T ln P (x,y) Where kb is the Boltzmann constant, T = 300K is the absolute temperature, and P (x,y) is the normalised probability distribution of the system projected along PC1 and PC2. A 3-D FEL plot was generated using a Python script, revealing energy minima corresponding to the most stable conformational state. Additionally, a 2D contour plot was also generated to visualise the lowest energy basins. This PCA-based approach allows a better understanding of the energetically favourable regions of the conformational space. MM/GBSA Calculation: A GROMACS plugin known as gmxMMPBSA was used to calculate the binding free energy of the complex for the final 20 ns of simulation by employing the MM/GBSA (Molecular Mechanics Generalised Born Surface Area) technique (Miller et al., 2012 ; Valdés-Tresanco et al., 2021 ). The following equations are involved to calculate the MM/GBSA calculation: ΔG = G complex – (G receptor + G ligand ) (1) ΔG binding = ΔH -TΔS (2) ΔH = ΔG + ΔG (3) ΔG GAS = ΔE EL + ΔE VDWAALS (4) ΔG SOLV = ΔE GB + ΔE SURF (5) ΔE SURF = γ. SASA (6) In Eq. (1), G complex, G receptor, and G ligand represent the total free energy of the protein-ligand complex, receptor protein, and ligand in the solvent, respectively. Eq. (2–6) further describes key energetic contributions, including the change in solvation energy (ΔG SOLV ), the change in conformational entropy (-TΔS), the change in enthalpy (ΔH), and the change in gas-phase energy (ΔG GAS ). The solvent-accessible surface area (SASA) and the solvent surface tension (γ) were also considered for this study. Additionally, the change in van der Waals and electrostatic energy was denoted as ΔE VDWAALS and ΔE EL , respectively. The polar and nonpolar solvation energy contributions were represented by ΔE GB and ΔE SURF , respectively. Results and Discussion Docking Study: Molecular docking was performed using the prepared receptors and ligands. The binding affinity of the ligands was obtained from the Autodock Vina for 3M1N, 2GLI, and 4O9R, and is represented in Table 1. In this study, it was observed that Kaempferol, Quercetin, Naringenin, and Curcumin showed the highest negative binding affinity with the receptors. Interactions of these bioactive compounds with the receptors were visualised using Discovery Studio (https://www.3ds.com/products/biovia/discovery-studio) and are represented in Figures 1, 2, and 3. The top three ligands with the highest negative binding affinity were taken into consideration for further analysis. Molecular Dynamics Simulation Molecular dynamics simulations were performed on the top-ranked docked complexes of Kaempferol, Quercetin, and Naringenin with their respective receptors. Additionally, simulations of the unbound receptors (apo) were conducted as controls to assess the specific effects of ligand binding on receptor dynamics. MD simulation trajectories were used to investigate several criteria that are further described. Table 1 Binding scores of the bioactive compounds with binding affinity to 3M1N, 2GLI, and 4O9R Bioactive Compounds Binding affinity (3M1N) (kcal/mol) Binding affinity (2GLI) (kcal/mol) Binding affinity (4O9R) (kcal/mol) Kaempferol -7.5 -6.6 -8.8 Quercetin -7.5 -6.7 -9.1 Naringenin -7.3 -6.3 -8.8 Curcumin -6.8 -5.4 -8.5 Root mean square deviation (RMSD): The stability of the complex structures during MD simulation was assessed using protein backbone RMSD analysis. The structural stability of a complex is inversely proportional to the RMSD values (Liu et al., 2017). For 3M1N, RMSD of Kaempferol and Quercetin-bound forms showed small fluctuations (~1.2-1.4 nm) compared to ligand-unbound (Apo) forms. In contrast, the Naringenin-bound complex exhibited significantly elevated RMSD values (peaking at ~2.5 nm within the first 20 ns), suggesting substantial conformational change or reduced stability upon ligand binding (Figure 4a). In case of 2GLI, the Kaempferol- and Quercetin-bound forms, along with Apo, reached higher RMSD values (~ 1.5-2 nm), indicating a dynamic and flexible protein core shown in Figure 4.b). Interestingly, Naringenin showed a markedly stabilising effect, with RMSD plateauing around 0.4-0.6 nm, suggesting constrained backbone motion and potential conformational stabilisation of 2GLI. For 4O9R, all systems exhibited relatively low and stable RMSD values (<1.0 nm), implying intrinsic structural rigidity (Figure 4.c). While minor variations of RMSD were observed in ligand-bound forms, these did not substantially deviate from the Apo, suggesting ligand binding exerts a minimal destabilising effect in the case of 4O9R at the backbone level. Similarly, the RMSD of three selected phytochemicals was also calculated from the 100 ns MD simulation with the aligned protein structures, as shown in Figure 5. In 3M1N, Kaempferol (green), Kaempferol exhibited consistently low RMSD, indicating a stable binding pose throughout the simulation. Quercetin showed initial fluctuation, but it stabilised after 20 ns. In contrast, Naringenin showed very high fluctuations, suggesting either conformational rearrangement or partial unbinding from the binding site. In the case of 2GLI, an inverse pattern was observed, as Naringenin and Quercetin maintained a low and stable RMSD value. In contrast, Kaempferol showed high fluctuations (~ 10 nm) but eventually became stable after 60 ns. For 4O9R, Kaempferol maintained an exceptionally stable RMSD (~ 0.1 nm after 40 ns). Quercetin and Naringenin showed slightly higher fluctuations, but remained within the acceptable range, indicative of stable binding. Radius of Gyration (R g ) To evaluate the structural compactness and stability of a protein upon ligand binding, the radius of gyration (R g ) was calculated over the 100 ns simulation for the apo form and the ligand-bound form (Figure 6). For 3M1N, the apo form showed moderate fluctuations in R g values ranging between ~2.4 nm and 2.9 nm, indicating a relatively stable structure Throughout the simulation (Figure 6.a). Notably, the Kaempferol-bound complex maintained the most consistent Rg, signifying enhanced compactness upon binding. The quercetin-bound complex shows slightly higher Rg values with moderate fluctuations, indicating a less compact but stable structure. On the other hand, naringenin binding caused significant instability, with R g values picking above 4.5 nm during the first 20 ns, before gradually stabilising around ~3.0 nm in the latter half of the trajectory. For 2GLI, the Kaempferol-bound complex showed a notable decline in R g value over time, suggesting enhanced structural compactness. In contrast, the quercetin-bound complex maintained relatively higher R g values with modest fluctuations, indicative of expanded conformations. The Apo form and naringenin-bound systems showed moderate fluctuations in R g , tending towards compaction at the end of the simulation. This supports the observation that Kaempferol binding induces structural compaction, while Quercetin binding sustains a relatively relaxed state. In the case of 4O9R, all systems showed relatively stable R g values throughout the simulation. Kaempferol and Naringenin complexes exhibited lower R g values, implying a more compact structure than the Quercetin-bound system, which maintained slightly elevated values throughout. The Apo form maintained an intermediate compactness, lying between Kaempferol and Naringenin. This minor yet consistent trend reinforces the stabilising effect of Kaempferol compared to the other two ligands. Solvent Accessible Surface Area (SASA): To examine the extent of surface exposure to solvent molecules, we calculate the solvent-accessible surface area (SASA) for Apo and its complexes with Kaempferol, Quercetin, and Naringenin over a 100 ns simulation (Figure 7). For 3M1N, the Kaempferol-bound complex showed the lowest SASA values (~175-195 nm 2 ) throughout the simulation, indicating reduced solvent exposure and higher structural compactness. In contrast, the Naringenin-bound system showed higher SASA (~210-215 nm 2 ), suggesting a less compact structure. The Quercetin-bound system exhibited a consistent SASA value (~190 nm 2 ) throughout the simulation. The observed trend implies that Kaempferol binding enhances the structural compaction compared to Naringenin and Quercetin (Figure 7a). For 2GLI, the Kaempferol-bound complex showed higher SASA fluctuations from 40 ns to 70 ns but maintained lower SASA than other systems. The Apo system exhibited greater variability during the early and later stages of the simulation. While the Quercetin-bound complex showed a higher but consistent SASA value, the Naringenin-bound system presented a trend of moderate fluctuation and a slightly lower value compared to the Apo. This result again suggests that Kaempferol contributes to more compact and solvent-shielded conformations. For 4O9R, Kaempferol and Quercetin complexes maintained a lower SASA value (~230-235 nm 2 ) in the later stage, while the Apo and Naringenin complex exhibited higher SASA values (~235-240 nm 2 ), suggesting Kaempferol and Naringenin binding leads to a buried protein structure, in contrast to the APO and Naringenin-bound system. H-Bond Several hydrogen bonds are formed between the ligands and the receptor molecules during the course of the simulation. 3M1 displayed the highest number of H-bonds with Quercetin, followed by Kaempferol. In several frames, the number of hydrogen bonds is 2 and 3, rarely 4 and 5, only for Quercetin. For 2GLI, the same trends have been noticed. Where up to 7 hydrogen bonds are noticed. For 4O9R, Quercetin and Naringenin formed the highest number of H-bonds. The H-bond analysis revealed crucial information about the binding stability of the ligand-protein complex. Quercetin and kaempferol showed sustained hydrogen binding throughout the simulation, indicating high binding affinity towards the receptor. In the case of Naringenin, it exhibits low and inconsistent numbers of H-bonds with the receptors, suggesting a weak binding affinity. These findings also align with the previous ligand RMSD analysis. In conclusion, hydrogen bonding gives valuable insights into the candidacy of suitable drugs of these compounds. PCA Principal Component Analysis (PCA) is a multivariate statistical method used to simplify complex trajectory data by highlighting the key variables that contribute to structural stability (Bharadwaj et al., 2021). Eigenvectors define the principal directions of collective atomic motion, while eigenvalues represent the magnitude of these motions, reflecting the contribution of each mode in the ligand-bound system trajectories (Sahoo et al., 2020). PCA was performed on Apo-proteins and ligand-bound forms, and the light-yellow-coloured bubbles represent the early stage of simulation, while the black-coloured bubbles represent the later stage (Figure 8). In the case of 3M1N, the Apo form exhibited extensive conformational variation across PC1 and PC2 axes, as indicated in Figure 8a. The wide distributions suggest the presence of metastable states and highlight the structural flexibility of the apo protein. The system explored a large conformational space, consistent with the dynamic nature typically observed in the absence of ligand stabilisation. In contrast, the ligand-bound systems (Figure 8b to 8d) displayed progressively restricted motions. The Kaempferol-bound system (Figure 8b) showed moderate conformational sampling, with the trajectory localised within fewer clusters and reduced variance along both principal components. The Quercetin-bound system (Figure 8c) demonstrated even greater restriction, with the trajectory confined to a narrow region in PCA space. Notably, transitions between conformational states became less frequent, indicating that ligand interaction had begun to stabilise specific structural states. The Naringenin-bound system exhibited the most pronounced conformational constraint; this result implies that the bound ligand imposes substantial rigidity, effectively locking the protein into a single dominant state. In the case of GLI-1, the apo form (Figure 8e) exhibits higher flexibility, whereas the Kaempferol- and Quercetin-bound forms (Figure 8f-8g) show lower flexibility, indicating increased restricted conformational restrictions, which suggests ligand-induced stabilisation. The Naringenin-bound form (Figure 8h) showed tighter clusters, suggesting higher conformational restrictions compared to the other two. The apo system for 4O9R (Figure 8i) displays two distinct conformational clusters separated along PC1, suggesting the presence of multiple metastable states. In the Kaempferol-bound state (Figure 8j), the system still explores multiple substates, but the transitions are less frequent. For the Quercetin-bound system, shown in Figure 8k, the PCA spread is minimal, implying a conformationally restricted ensemble. Finally, the Naringenin-bound form shows a similarly compact structure, but site directionally oriented distribution. The PCA reveals that the ligand binding reduces the conformational entropy, restricts the accessible phase, and directs the receptor toward specific conformational subsets. The progressive narrowing of PCA across systems highlights a transition from entropically dominated apo form to enthalpically stabilised ligand-bound states, an observation that is further substantiated by the subsequent free-energy landscape analysis. FEL To probe the conformational dynamics of the receptor in the apo and ligand-bound form, we constructed a free energy landscape (FEL) based on projection along the first two principal components (PC1 and PC2). The resulting FELs shown in Figure 9 reveal distinct energy topographies across the system. For 3M1N, the apo-form (figure 9a) exhibits three energy minima which are separated by the energy barrier of 25-30 kJ/mol. This distribution suggests a high degree of conformational plasticity, likely reflective of multiple metastable states in the absence of ligands. In contrast, the ligand-bound state (Figure 9b-9d) showed progressive funnelling of the landscape in fewer and deeper wells. Especially in the case of Quercetin-bound form (Figure 9c), it induced a dominant energy basin at PC1 -10 and PC2= -5, suggesting a conformationally restrained state. This funnel-like structure is indicative of ligand-induced stabilisation toward a specific conformation. In the case of 2GLI, the apo-form (Figure 9e) reveals a single dominant energy minimum centered near PC1=5 and PC2=0, surrounded by shallow energy regions. This suggests a relatively stable conformational ensemble, probably due to intra-domain interaction in the protein itself. Ligand-bound conformations, however, showed diverse effects on receptor dynamics. The Kaempferol-bound form (Figure 9f) exhibits a broadened well with extended sampling with both PCs, indicating a ligand-induced flexibility and a less-rigid conformation. Quercetin-bound form (Figure 9g) showed ring-like energy basins, which suggest the presence of several metastable states. Naringenin-bound form (Figure 9h) reverts to a deep, sharply defined minimum, and defines a distinct ligand-stabilised conformation with reduced flexibility. The apo-form in 4O9R (Figure 9i) presents relatively broad and shallow basins, suggesting a modest conformational heterogeneity. In contrast, all ligand-bound forms (Figure 9j-9k) showed narrow and defined wells, pointing to progressive restriction in conformational space. These results suggest that ligand binding showed conformational ensembles towards thermodynamically stable and structurally rigidified states. The presence of a limited number of alternative low-energy states further indicates potential reduction in receptor plasticity upon ligand binding. MM/GBSA To evaluate thermodynamic contributions underlying protein-ligand binding, MM/GBSA calculation was performed (Figure 10). Across the three proteins (3M1N, 2GLI, 4O9R), Kaempferol (Figure 10a, 10d, and 10f) demonstrated the most favourable total binding free energies, which is supported by strong van der Waals interaction and moderate electrostatic interaction, and non-polar solvation contributions. In the case of 4O9R, Kaempferol showed the highest energetic stabilisation, indicating a highly complementary binding environment. Quercetin showed moderate binding affinities, with greater variability across the three receptors (Figure 10b, 10e, and 10h). Naringenin showed the lowest favourable binding across all the systems (shown in Figure 10c, 10f, and 10i), reflecting both weaker and substantial desolvation penalties. The energy profile of Naringenin suggests a poor complementarity with all the binding sites evaluated. Together, these results suggest ligand specificity and protein-context dependence in determining the binding specificity. The MM/GBSA trends are consistent with conformational restriction patterns observed in PCA and FEL, where Kaempferol complexes consistently attended low energy basins with minimal structural fluctuations. Conclusion This study demonstrates that the selected bioactive compounds (Kaempferol, Quercetin, and Naringenin) exhibit strong and stable binding interactions with key SHH pathway receptors. Molecular docking and molecular dynamics simulations revealed favourable binding, stable conformations, and a significant effect on receptor flexibility. Indicating the potential inhibitory effect of SHH-mediated profibrotic signalling. These findings highlight the therapeutic promise of natural bioactive compounds as modulators of fibrosis. Further in vitro and in vivo validation is warranted to advance these candidates toward clinical application. Declarations Author Contribution IR designed the study, performed experiments, and wrote the manuscript. PB suggested edits, manuscript correction and supervised the study. Acknowledgement The authors are thankful to Biosymphony Pvt. Ltd. (Kapsid Simulations) for their technical support (HPC server). IR is thankful to the Council of Scientific and Industrial Research (CSIR), India, for providing his fellowship. The authors want to thank the University of Calcutta for providing structural support. Funding Declaration The authors did not receive any funding for this research work. Data Availability The datasets analysed during the current study are available from the corresponding author on request. References Berendsen, H. J., Postma, J. van, Van Gunsteren, W. F., DiNola, A., & Haak, J. R. (1984). Molecular dynamics with coupling to an external bath. 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Sonic hedgehog signaling in kidney fibrosis: a master communicator. Science China Life Sciences , 59 (9), pp.920-929. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial1.docx 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-7411385","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":517261308,"identity":"8c00f981-9d22-4c5c-b4ed-d90a585471bb","order_by":0,"name":"Indraneel Rakshit","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACNjiLvYGB4QEDAw+EZ0CMFp4DDAwJxGhBAIkEsBbCgE/68LHHPH8Y8uVnPn74IbHtjozBAeaHHxgK7uB2GF9aujFvG4PlhttpxhKJbc94DA6wGUswGDzDrYWHx0xyZgODgYF0DgNQy2EeyQYGM6BfDuPRwv9NcsYfBgP5mWeYf0C0sH8joIWHTeIDGzCEbgAZIC38DDyEbGEzk/jYBnTYmTQzi4RzQC3MPMUSCXi0yPcwP5NIADms/fDjGx/KDtuzsbdv/PDhD24tUPAfic3MQGQEjYJRMApGwSjACQCmT0WQta3QNgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Calcutta","correspondingAuthor":true,"prefix":"","firstName":"Indraneel","middleName":"","lastName":"Rakshit","suffix":""},{"id":517261309,"identity":"412c364b-f5c8-4075-87fd-4e3f27f46e8e","order_by":1,"name":"Pritha Bhattacharjee","email":"","orcid":"","institution":"University of Calcutta","correspondingAuthor":false,"prefix":"","firstName":"Pritha","middleName":"","lastName":"Bhattacharjee","suffix":""}],"badges":[],"createdAt":"2025-08-19 19:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7411385/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7411385/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91702530,"identity":"72d58baa-7a16-4d17-b113-25560308074d","added_by":"auto","created_at":"2025-09-19 10:50:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1187354,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2D interactions of 3M1N with (a)Kaempferol, (b) Quercetin, (c) Naringenin (d) Curcumin.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7411385/v1/eafb572f0dd510e3fd1f1c91.png"},{"id":91704810,"identity":"a399d85d-3f7d-418a-9e97-de4dd3b04064","added_by":"auto","created_at":"2025-09-19 11:22:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1135943,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2D interactions of 2GLI with (a)Kaempferol, (b) Quercetin, (c) Naringenin, and (d) Curcumin.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7411385/v1/b44432638a95dfa078038042.png"},{"id":91702528,"identity":"51abd92f-f07c-4761-aaf2-84b7b797914a","added_by":"auto","created_at":"2025-09-19 10:50:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1042374,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2D interactions of 4O9R with (a)Kaempferol, (b) Quercetin, (c) Naringenin (d) Curcumin.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7411385/v1/3ffed771a6c72c72d61ad6da.png"},{"id":91704621,"identity":"5bdf9708-44b8-476c-8bf3-027f1b590e88","added_by":"auto","created_at":"2025-09-19 11:14:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1689516,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of RMSD of protein backbone for unbound (Apo) and ligand-bound states (a) 3M1N, (b) 2GLI, and (c) 4O9R.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7411385/v1/c8f54ecd8c049a15a97fc9e9.png"},{"id":91703615,"identity":"de8c0c74-dc99-488a-b6cb-f68617640007","added_by":"auto","created_at":"2025-09-19 10:58:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":927715,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative RMSD profiles of three ligands across three protein systems (a) over 100 ns MD simulations. RMSD values (nm) of (a) Kaempferol (green), (b) Quercetin (blue), and (c) Naringenin (yellow) were calculated to assess the conformational stability of each ligand within its respective binding pocket.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7411385/v1/1c13c929c04730243fecae20.png"},{"id":91703610,"identity":"db44ff6f-5974-47e1-b11b-a392871db6ba","added_by":"auto","created_at":"2025-09-19 10:58:53","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1960464,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of the radius of gyration (Rg) profiles over 100 ns MD simulation for the apo protein and its complexes with (a) Kaempferol, (b) Quercetin, and (c) Naringenin.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7411385/v1/c06e2801483391eb2883b180.png"},{"id":91703619,"identity":"3ca71367-3ce6-4822-965c-6aebe8367bc9","added_by":"auto","created_at":"2025-09-19 10:58:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3365598,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of the solvent accessible surface area (SASA) profiles over 100 ns MD simulation for the apo protein and its complexes with (a) Kaempferol, (b Quercetin, and (c) Naringenin.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7411385/v1/82b7c13cbdd9e5f638924a08.png"},{"id":91703613,"identity":"46b3e49c-996a-4edc-aa30-4e281a9ebbc2","added_by":"auto","created_at":"2025-09-19 10:58:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":71642,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNumber of H-bonds between three ligands, Kaempferol, Quercetin, and Naringenin, and receptors (a) 3M1N (b) 2GLI (c) 4O9R.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7411385/v1/65f4693e72673425f5d6addf.png"},{"id":91702544,"identity":"61fa063c-57cf-4783-9469-1bcaf6228f2f","added_by":"auto","created_at":"2025-09-19 10:50:53","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4110058,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCartesian coordinate PCA plots for the first two principal components, derived from 100 ns MD simulations. Each point represents a snapshot from the MD trajectory, with color gradients indicating the progression of simulation time (black to yellow). PCA projections for 3M1N for (a) Apo and (b) Kaempferol-, (c) Quercetin-, (d) Naringenin-bound states, (e-h) for 2GLI and (i-l) for 4O9R in the same order.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-7411385/v1/656e683a8dc7774a0c068a72.png"},{"id":91702585,"identity":"68d355ad-49e3-4905-a166-a59fe9b8f49d","added_by":"auto","created_at":"2025-09-19 10:50:54","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":5106767,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e3D FEL surfaces constructed from the first two principal components (PC1 and PC2) of the MD trajectories for SHH pathway receptor-ligand complexes over 100 ns. The Gibbs free energy (G) is plotted on the Z-axis, with colour gradients indicating relative energy levels (blue: low-energy minima; red: high-energy regions). FEL is constructed for 3M1N (a) Apo and (b) Kaempferol-, (c) Quercetin- (d) Naringenin-bound states, (e-h) for 2GLI and (i-l) for 4O9R in the same order.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-7411385/v1/79fd0dd0fa4dbee3ba7678fe.png"},{"id":91702550,"identity":"b6ef1611-b0e0-4c52-9806-a1a6f0c9e75e","added_by":"auto","created_at":"2025-09-19 10:50:53","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":804703,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMM/GBSA binding free energy decomposition of protein–ligand complexes. MM/GBSA was calculated for 3M1N (a) Kaempferol-, (b) Quercetin-, (c) Naringenin-bound states, (d-f) for 2GLI and (g-i) for 4O9R in the same order.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-7411385/v1/aacc1b69f5559f56995592eb.png"},{"id":92401934,"identity":"7cc1faf3-0e54-465f-a0e7-bf7820a474b2","added_by":"auto","created_at":"2025-09-29 10:24:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27096319,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7411385/v1/1044406c-c84e-4711-92e9-46c92fcb9efa.pdf"},{"id":91702538,"identity":"b70b6495-2893-481c-8023-dfc84d68470c","added_by":"auto","created_at":"2025-09-19 10:50:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":307657,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7411385/v1/008d745fbe7a32d514eb3755.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identifying Natural Sonic Hedgehog Signalling Pathway Modulators with Potential Antifibrotic Activity in Chronic Kidney Disease: An in silico approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic kidney disease (CKD) represents a major global health burden, affecting over 10% of the adult population, and contributing significantly to morbidity, mortality, and healthcare costs (Carney, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Current treatments are largely limited to blood pressure control, glycemic regulation, and renin-angiotensin pathway inhibition, which only provide partial protection against disease progression (Puglisi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Leoncini et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a result, there is an urgent need for novel molecular targets and therapeutic strategies to effectively counteract and reverse the fibrotic and inflammatory process central to CKD pathogenesis.\u003c/p\u003e\u003cp\u003eRecent evidence implicates the Sonic Hedgehog (SHH) pathway as a key driver of maladaptive repair after renal injury (Li and Gan, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The SHH pathway was originally characterised for its role in embryonic development; however, in adults, the signalling pathway is aberrantly reactivated in tissues during injury and disease, including the kidney (Zhou et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This reactivation promotes profibrotic crosstalk between epithelial cells and fibroblasts, contributing to the progression of CKD (Chen and Xue, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Notably, components of the SHH pathway, such as SHH protein, Smoothened (SMO), and transcription factor Gli1, have emerged as potential pharmacological targets capable of modulating the fibrogenic cascade (Hu et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Cao et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eIn parallel, natural bioactive compounds garnered considerable interest as therapeutic agents due to their inherent structural diversity, multitarget capabilities, as well as favourable safety profiles (Ravikumar et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Several phytochemicals have demonstrated renoprotective, anti-inflammatory, and anti-fibrotic effects in preclinical models, yet their potential to modulate SHH signalling in the context of CKD remains underexplored.\u003c/p\u003e\u003cp\u003eIn this study, we employed a comprehensive in silico investigation aimed at identifying and characterising an interaction between bioactive compounds with the key SHH pathway receptors (SHH protein, GLI1, and SMO) involved in CKD progression. By employing an integrative computational pipeline encompassing molecular docking and molecular dynamics simulations, we provide a detailed assessment of binding stability, conformational dynamics, and energetic favourability. Our findings offer mechanistic insights into SHH pathway modulation and identify promising receptor-ligand interactions that warrant further experimental validation in the context of fibrosis and CKD-related therapies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eReceptor Preparation:\u003c/h2\u003e\u003cp\u003eBioactive compounds were shortlisted for the study through literature surveys, and their structures were downloaded from PubChem in SDF format. The crystal structures of the human Sonic Hedgehog protein (PDB ID: 3M1N, Resolution: 1.85\u0026Aring;), Gli (PDB ID: 2GLI, Resolution: 2.60\u0026Aring;), and Smoothened (4O9R, Resolution: 3.2\u0026Aring;) were obtained from RCSB-PDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rcsb.org\u003c/span\u003e\u003cspan address=\"https://rcsb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (Berman, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The DNA molecule was deleted from the crystal structure of 2GLI, and Cyclopamine was removed from the crystal structure of 4O9R. The catalytic sites of the receptors were identified using the DogSite Scorer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://proteins.plus/\u003c/span\u003e\u003cspan address=\"https://proteins.plus/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLigand preparation:\u003c/h3\u003e\n\u003cp\u003eBased on the available literature, 4 bioactive compounds (Kaempferol, Quercetin, Curcumin, and Naringenin) that have a therapeutic effect against CKD were chosen. 3-D structures of ligands were obtained from the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The structures of the ligands were then optimised using Avogadro, and hydrogen atoms were added. The energy minimisation of the structures was done in OpenBabel using MMFF94 (Merck Molecular Force Field) to reduce steric clashes.\u003c/p\u003e\n\u003ch3\u003eDocking Study:\u003c/h3\u003e\n\u003cp\u003eAll bioactive compounds were docked against 3M1N, 2GLI, and 4O9R using the Autodock Vina application. The receptors were cleaned of any heteroatoms, water molecules, or attached complexes. Polar hydrogens and Kollman Charges were added to receptor proteins, and Gasteiger Charges were added to ligands. The grid box was set based on the position of the catalytic sites obtained from the DogSite Scorer. The information about the grid boxes is provided in the Supplementary Material. The ligands were then docked onto the chosen binding sites of the receptor proteins to generate protein-ligand complexes. A total of 8 poses were generated with 8 exhaustiveness. The visualisation of the docked complexes was done in Discovery Studio. The top three ligands with the most negative binding affinity(kcal/mol) were considered for further molecular dynamics simulations.\u003c/p\u003e\n\u003ch3\u003eMolecular Dynamics Simulations:\u003c/h3\u003e\n\u003cp\u003eBased on the most negative binding energy from docking analysis, molecular dynamics simulations (MDS) were performed for 100 ns using the GROMACS 2023.3 version. Protein structures were extracted from the docked complex and saved in PDB format, while the ligand structure was extracted and saved in mol2 format. In GROMACS, protein topology was generated using pdb2gmx by selecting the CHARMM36 forcefield. Additionally, the ligand topology was generated using CGenFF software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cgenff.com/\u003c/span\u003e\u003cspan address=\"https://cgenff.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The protein-ligand complex was hydrated using the TIP3P water model in a solvation box with a separation of 2.1 nm from the periodic boundary. The system was neutralised with 150 mM NaCl. After system assembly, energy minimisation was performed to ensure that there were no steric clashes or inappropriate geometry between atoms. The energy-minimised system was then subjected to NVT (number of particles, volume of the system, and temperature) and NPT (number of particles, pressure of the system, and temperature) equilibration for 1 ns each at 300K temperature and 1 bar pressure, respectively. During NVT equilibration, V-rescale temperature coupling was used, while in NPT equilibration, Berendsen pressure coupling was used (Berendsen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). After energy minimisation, a 100 ns production run was performed using V-rescale temperature coupling and Parrinello-Rahman pressure coupling (Parrinello \u0026amp; Rahman, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). Post-production analysis was performed using several built-in GROMACS tools.\u003c/p\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003ePrincipal Component Analysis (PCA)\u003c/b\u003e:\u003c/div\u003e\u003cp\u003eIn this study, a small subset of principal components (PCs) was extracted to simplify the trajectory dynamics data and distinguish the collective motions from local dynamics. The principal component analysis (PCA) was performed using the \u003cem\u003egmx covar\u003c/em\u003e and \u003cem\u003egmx anaeig\u003c/em\u003e scripts of GROMACS. The \u003cem\u003egmx covar\u003c/em\u003e calculates the covariance matrix of atomic fluctuations to identify dominant collective motions in a biomolecular system. It diagonalises the covariance matrix to obtain eigenvectors and eigenvalues. The \u003cem\u003egmx anaeig\u003c/em\u003e works in conjunction with gmx covar and allows visualisation of dominant motions by projecting the trajectory onto selected eigenvectors. In our work, 2-D projections of the trajectory were generated by overlapping the first two principal components.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eFree Energy Landscape (FEL):\u003c/h2\u003e\u003cp\u003eThe FEL was constructed to explore the conformational dynamics of the complex by using the first two principal components (PC1 and PC2) obtained from principal component analysis of the molecular dynamics trajectory. These components capture the dominant collective motions of the system. The free energy was calculated using the Boltzmann relation:\u003c/p\u003e\u003cp\u003eG(x,y)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;k\u003csub\u003eB\u003c/sub\u003e\u003cem\u003e​T\u003c/em\u003eln\u003cem\u003eP\u003c/em\u003e(x,y)\u003c/p\u003e\u003cp\u003eWhere \u003cem\u003ekb\u003c/em\u003e is the Boltzmann constant, T\u0026thinsp;=\u0026thinsp;300K is the absolute temperature, and P (x,y) is the normalised probability distribution of the system projected along PC1 and PC2.\u003c/p\u003e\u003cp\u003eA 3-D FEL plot was generated using a Python script, revealing energy minima corresponding to the most stable conformational state. Additionally, a 2D contour plot was also generated to visualise the lowest energy basins. This PCA-based approach allows a better understanding of the energetically favourable regions of the conformational space.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMM/GBSA Calculation:\u003c/h3\u003e\n\u003cp\u003eA GROMACS plugin known as gmxMMPBSA was used to calculate the binding free energy of the complex for the final 20 ns of simulation by employing the MM/GBSA (Molecular Mechanics Generalised Born Surface Area) technique (Miller et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Vald\u0026eacute;s-Tresanco et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The following equations are involved to calculate the MM/GBSA calculation:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eΔG\u0026thinsp;=\u0026thinsp;G\u003csub\u003ecomplex\u003c/sub\u003e \u0026ndash; (G\u003csub\u003ereceptor\u003c/sub\u003e + G\u003csub\u003eligand\u003c/sub\u003e) (1)\u003c/p\u003e\u003cp\u003eΔG\u003csub\u003ebinding\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;ΔH -TΔS (2)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\n\u003cp\u003eΔH = ΔG + ΔG (3)\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003cp\u003eΔG\u003csub\u003eGAS\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;ΔE\u003csub\u003eEL\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ΔE\u003csub\u003eVDWAALS\u003c/sub\u003e (4)\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003cp\u003eΔG\u003csub\u003eSOLV\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;ΔE\u003csub\u003eGB\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ΔE\u003csub\u003eSURF\u003c/sub\u003e (5)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eΔE\u003csub\u003eSURF\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;γ. SASA (6)\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn Eq.\u0026nbsp;(1), G\u003csub\u003ecomplex,\u003c/sub\u003e G\u003csub\u003ereceptor,\u003c/sub\u003e and G\u003csub\u003eligand\u003c/sub\u003e represent the total free energy of the protein-ligand complex, receptor protein, and ligand in the solvent, respectively. Eq.\u0026nbsp;(2\u0026ndash;6) further describes key energetic contributions, including the change in solvation energy (ΔG\u003csub\u003eSOLV\u003c/sub\u003e), the change in conformational entropy (-TΔS), the change in enthalpy (ΔH), and the change in gas-phase energy (ΔG\u003csub\u003eGAS\u003c/sub\u003e). The solvent-accessible surface area (SASA) and the solvent surface tension (γ) were also considered for this study. Additionally, the change in van der Waals and electrostatic energy was denoted as ΔE\u003csub\u003eVDWAALS\u003c/sub\u003e and ΔE\u003csub\u003eEL\u003c/sub\u003e, respectively. The polar and nonpolar solvation energy contributions were represented by ΔE\u003csub\u003eGB\u003c/sub\u003e and ΔE\u003csub\u003eSURF\u003c/sub\u003e, respectively.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003eDocking Study:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular docking was performed using the prepared receptors and ligands. The binding affinity of the ligands was obtained from the Autodock Vina for 3M1N, 2GLI, and 4O9R, and is represented in Table 1. In this study, it was observed that Kaempferol, Quercetin, Naringenin, and Curcumin showed the highest negative binding affinity with the receptors. Interactions of these bioactive compounds with the receptors were visualised using Discovery Studio (https://www.3ds.com/products/biovia/discovery-studio) and are represented in Figures 1, 2, and 3. The top three ligands with the highest negative binding affinity were taken into consideration for further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular Dynamics Simulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMolecular dynamics simulations were performed on the top-ranked docked complexes of Kaempferol, Quercetin, and Naringenin with their respective receptors. Additionally, simulations of the unbound receptors (apo) were conducted as controls to assess the specific effects of ligand binding on receptor dynamics. MD simulation trajectories were used to investigate several criteria that are further described.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Binding scores of the bioactive compounds with binding affinity to 3M1N, 2GLI, and 4O9R\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eBioactive Compounds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eBinding affinity (3M1N) (kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eBinding affinity (2GLI) (kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eBinding affinity (4O9R) (kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eKaempferol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eQuercetin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eNaringenin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-6.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003eCurcumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 154px;\"\u003e\n \u003cp\u003e-8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eRoot mean square deviation (RMSD):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe stability of the complex structures during MD simulation was assessed using protein backbone RMSD analysis. The structural stability of a complex is inversely proportional to the RMSD values (Liu et al., 2017). For 3M1N, RMSD of Kaempferol and Quercetin-bound forms showed small fluctuations (~1.2-1.4 nm) compared to ligand-unbound (Apo) forms. In contrast, the Naringenin-bound complex exhibited significantly elevated RMSD values (peaking at ~2.5 nm within the first 20 ns), suggesting substantial conformational change or reduced stability upon ligand binding (Figure 4a).\u003c/p\u003e\n\u003cp\u003eIn case of 2GLI, the Kaempferol- and Quercetin-bound forms, along with Apo, reached higher RMSD values (~ 1.5-2 nm), indicating a dynamic and flexible protein core shown in Figure 4.b). Interestingly, Naringenin showed a markedly stabilising effect, with RMSD plateauing around 0.4-0.6 nm, suggesting constrained backbone motion and potential conformational stabilisation of 2GLI.\u003c/p\u003e\n\u003cp\u003eFor 4O9R, all systems exhibited relatively low and stable RMSD values (\u0026lt;1.0 nm), implying intrinsic structural rigidity (Figure 4.c). While minor variations of RMSD were observed in ligand-bound forms, these did not substantially deviate from the Apo, suggesting ligand binding exerts a minimal destabilising effect in the case of 4O9R at the backbone level.\u003c/p\u003e\n\u003cp\u003eSimilarly, the RMSD of three selected phytochemicals was also calculated from the 100 ns MD simulation with the aligned protein structures, as shown in Figure 5. In 3M1N, Kaempferol (green), Kaempferol exhibited consistently low RMSD, indicating a stable binding pose throughout the simulation. Quercetin showed initial fluctuation, but it stabilised after 20 ns. In contrast, Naringenin showed very high fluctuations, suggesting either conformational rearrangement or partial unbinding from the binding site. In the case of 2GLI, an inverse pattern was observed, as Naringenin and Quercetin maintained a low and stable RMSD value. In contrast, Kaempferol showed high fluctuations (~ 10 nm) but eventually became stable after 60 ns. For 4O9R, Kaempferol maintained an exceptionally stable RMSD (~ 0.1 nm after 40 ns). Quercetin and Naringenin showed slightly higher fluctuations, but remained within the acceptable range, indicative of stable binding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRadius of Gyration (R\u003csub\u003eg\u003c/sub\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the structural compactness and stability of a protein upon ligand binding, the radius of gyration (R\u003csub\u003eg\u003c/sub\u003e) was calculated over the 100 ns simulation for the apo form and the ligand-bound form (Figure 6). For 3M1N, the apo form showed moderate fluctuations in R\u003csub\u003eg\u003c/sub\u003e values ranging between ~2.4 nm and 2.9 nm, indicating a relatively stable structure\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThroughout the simulation (Figure 6.a). Notably, the Kaempferol-bound complex maintained the most consistent Rg, signifying enhanced compactness upon binding. The quercetin-bound complex shows slightly higher Rg values with moderate fluctuations, indicating a less compact but stable structure. On the other hand, naringenin binding caused significant instability, with R\u003csub\u003eg\u0026nbsp;\u003c/sub\u003evalues picking above 4.5 nm during the first 20 ns, before gradually stabilising around ~3.0 nm in the latter half of the trajectory.\u003c/p\u003e\n\u003cp\u003eFor 2GLI, the Kaempferol-bound complex showed a notable decline in R\u003csub\u003eg\u003c/sub\u003e value over time, suggesting enhanced structural compactness. In contrast, the quercetin-bound complex maintained relatively higher R\u003csub\u003eg\u003c/sub\u003e values with modest fluctuations, indicative of expanded conformations. The Apo form and naringenin-bound systems showed moderate fluctuations in R\u003csub\u003eg\u003c/sub\u003e, tending towards compaction at the end of the simulation. This supports the observation that Kaempferol binding induces structural compaction, while Quercetin binding sustains a relatively relaxed state.\u003c/p\u003e\n\u003cp\u003eIn the case of 4O9R, all systems showed relatively stable R\u003csub\u003eg\u003c/sub\u003e values throughout the simulation. Kaempferol and Naringenin complexes exhibited lower R\u003csub\u003eg\u003c/sub\u003e values, implying a more compact structure than the Quercetin-bound system, which maintained slightly elevated values throughout. The Apo form maintained an intermediate compactness, lying between Kaempferol and Naringenin. This minor yet consistent trend reinforces the stabilising effect of Kaempferol compared to the other two ligands.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSolvent Accessible Surface Area (SASA):\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo examine the extent of surface exposure to solvent molecules, we calculate the solvent-accessible surface area (SASA) for Apo and its complexes with Kaempferol, Quercetin, and Naringenin over a 100 ns simulation (Figure 7). For 3M1N, the Kaempferol-bound complex showed the lowest SASA values (~175-195 nm\u003csup\u003e2\u003c/sup\u003e) throughout the simulation, indicating reduced solvent exposure and higher structural compactness. In contrast, the Naringenin-bound system showed higher SASA (~210-215 nm\u003csup\u003e2\u003c/sup\u003e), suggesting a less compact structure. The Quercetin-bound system exhibited a consistent SASA value (~190 nm\u003csup\u003e2\u003c/sup\u003e) throughout the simulation. The observed trend implies that Kaempferol binding enhances the structural compaction compared to Naringenin and Quercetin (Figure 7a).\u003c/p\u003e\n\u003cp\u003eFor 2GLI, the Kaempferol-bound complex showed higher SASA fluctuations from 40 ns to 70 ns but maintained lower SASA than other systems. The Apo system exhibited greater variability during the early and later stages of the simulation. While the Quercetin-bound complex showed a higher but consistent SASA value, the Naringenin-bound system presented a trend of moderate fluctuation and a slightly lower value compared to the Apo. This result again suggests that Kaempferol contributes to more compact and solvent-shielded conformations.\u003c/p\u003e\n\u003cp\u003eFor 4O9R, Kaempferol and Quercetin complexes maintained a lower SASA value (~230-235 nm\u003csup\u003e2\u003c/sup\u003e) in the later stage, while the Apo and Naringenin complex exhibited higher SASA values (~235-240 nm\u003csup\u003e2\u003c/sup\u003e), suggesting Kaempferol and Naringenin binding leads to a buried protein structure, in contrast to the APO and Naringenin-bound system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH-Bond\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral hydrogen bonds are formed between the ligands and the receptor molecules during the course of the simulation. 3M1 displayed the highest number of H-bonds with Quercetin, followed by Kaempferol. In several frames, the number of hydrogen bonds is 2 and 3, rarely 4 and 5, only for Quercetin. For 2GLI, the same trends have been noticed. Where up to 7 hydrogen bonds are noticed. For 4O9R, Quercetin and Naringenin formed the highest number of H-bonds.\u003c/p\u003e\n\u003cp\u003eThe H-bond analysis revealed crucial information about the binding stability of the ligand-protein complex. Quercetin and kaempferol showed sustained hydrogen binding throughout the simulation, indicating high binding affinity towards the receptor. In the case of Naringenin, it exhibits low and inconsistent numbers of H-bonds with the receptors, suggesting a weak binding affinity. These findings also align with the previous ligand RMSD analysis. In conclusion, hydrogen bonding gives valuable insights into the candidacy of suitable drugs of these compounds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCA \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrincipal Component Analysis (PCA) is a multivariate statistical method used to simplify complex trajectory data by highlighting the key variables that contribute to structural stability (Bharadwaj et al., 2021). Eigenvectors define the principal directions of collective atomic motion, while eigenvalues represent the magnitude of these motions, reflecting the contribution of each mode in the ligand-bound system trajectories (Sahoo et al., 2020). PCA was performed on Apo-proteins and ligand-bound forms, and the light-yellow-coloured bubbles represent the early stage of simulation, while the black-coloured bubbles represent the later stage (Figure 8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the case of 3M1N, the Apo form exhibited extensive conformational variation across PC1 and PC2 axes, as indicated in Figure 8a. The wide distributions suggest the presence of metastable states and highlight the structural flexibility of the apo protein. The system explored a large conformational space, consistent with the dynamic nature typically observed in the absence of ligand stabilisation. In contrast, the ligand-bound systems (Figure 8b to 8d) displayed progressively restricted motions. The Kaempferol-bound system (Figure 8b) showed moderate conformational sampling, with the trajectory localised within fewer clusters and reduced variance along both principal components. The Quercetin-bound system (Figure 8c) demonstrated even greater restriction, with the trajectory confined to a narrow region in PCA space. Notably, transitions between conformational states became less frequent, indicating that ligand interaction had begun to stabilise specific structural states. The Naringenin-bound system exhibited the most pronounced conformational constraint; this result implies that the bound ligand imposes substantial rigidity, effectively locking the protein into a single dominant state.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the case of GLI-1, the apo form (Figure 8e) exhibits higher flexibility, whereas the Kaempferol- and Quercetin-bound forms (Figure 8f-8g) show lower flexibility, indicating increased restricted conformational restrictions, which suggests ligand-induced stabilisation. The Naringenin-bound form (Figure 8h) showed tighter clusters, suggesting higher conformational restrictions compared to the other two.\u003c/p\u003e\n\u003cp\u003eThe apo system for 4O9R (Figure 8i) displays two distinct conformational clusters separated along PC1, suggesting the presence of multiple metastable states. In the Kaempferol-bound state (Figure 8j), the system still explores multiple substates, but the transitions are less frequent. For the Quercetin-bound system, shown in Figure 8k, the PCA spread is minimal, implying a conformationally restricted ensemble. Finally, the Naringenin-bound form shows a similarly compact structure, but site directionally oriented distribution.\u003c/p\u003e\n\u003cp\u003eThe PCA reveals that the ligand binding reduces the conformational entropy, restricts the accessible phase, and directs the receptor toward specific conformational subsets. The progressive narrowing of PCA across systems highlights a transition from entropically dominated apo form to enthalpically stabilised ligand-bound states, an observation that is further substantiated by the subsequent free-energy landscape analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFEL\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo probe the conformational dynamics of the receptor in the apo and ligand-bound form, we constructed a free energy landscape (FEL) based on projection along the first two principal components (PC1 and PC2). The resulting FELs shown in Figure 9 reveal distinct energy topographies across the system.\u003c/p\u003e\n\u003cp\u003eFor 3M1N, the apo-form (figure 9a) exhibits three energy minima which are separated by the energy barrier of 25-30 kJ/mol. This distribution suggests a high degree of conformational plasticity, likely reflective of multiple metastable states in the absence of ligands. In contrast, the ligand-bound state (Figure 9b-9d) showed progressive funnelling of the landscape in fewer and deeper wells. Especially in the case of Quercetin-bound form (Figure 9c), it induced a dominant energy basin at PC1 -10 and PC2= -5, suggesting a conformationally restrained state. This funnel-like structure is indicative of ligand-induced stabilisation toward a specific conformation.\u003c/p\u003e\n\u003cp\u003eIn the case of 2GLI, the apo-form (Figure 9e) reveals a single dominant energy minimum centered near PC1=5 and PC2=0, surrounded by shallow energy regions. This suggests a relatively stable conformational ensemble, probably due to intra-domain interaction in the protein itself. Ligand-bound conformations, however, showed diverse effects on receptor dynamics. The Kaempferol-bound form (Figure 9f) exhibits a broadened well with extended sampling with both PCs, indicating a ligand-induced flexibility and a less-rigid conformation. Quercetin-bound form (Figure 9g) showed ring-like energy basins, which suggest the presence of several metastable states. Naringenin-bound form (Figure 9h) reverts to a deep, sharply defined minimum, and defines a distinct ligand-stabilised conformation with reduced flexibility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe apo-form in 4O9R (Figure 9i) presents relatively broad and shallow basins, suggesting a modest conformational heterogeneity. In contrast, all ligand-bound forms (Figure 9j-9k) showed narrow and defined wells, pointing to progressive restriction in conformational space. These results suggest that ligand binding showed conformational ensembles towards thermodynamically stable and structurally rigidified states. The presence of a limited number of alternative low-energy states further indicates potential reduction in receptor plasticity upon ligand binding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMM/GBSA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate thermodynamic contributions underlying protein-ligand binding, MM/GBSA calculation was performed (Figure 10). Across the three proteins (3M1N, 2GLI, 4O9R), Kaempferol (Figure 10a, 10d, and 10f) demonstrated the most favourable total binding free energies, which is supported by strong van der Waals interaction and moderate electrostatic interaction, and non-polar solvation contributions. In the case of 4O9R, Kaempferol showed the highest energetic stabilisation, indicating a highly complementary binding environment.\u003c/p\u003e\n\u003cp\u003eQuercetin showed moderate binding affinities, with greater variability across the three receptors (Figure 10b, 10e, and 10h). Naringenin showed the lowest favourable binding across all the systems (shown in Figure 10c, 10f, and 10i), reflecting both weaker and substantial desolvation penalties. The energy profile of Naringenin suggests a poor complementarity with all the binding sites evaluated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTogether, these results suggest ligand specificity and protein-context dependence in determining the binding specificity. The MM/GBSA trends are consistent with conformational restriction patterns observed in PCA and FEL, where Kaempferol complexes consistently attended low energy basins with minimal structural fluctuations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that the selected bioactive compounds (Kaempferol, Quercetin, and Naringenin) exhibit strong and stable binding interactions with key SHH pathway receptors. Molecular docking and molecular dynamics simulations revealed favourable binding, stable conformations, and a significant effect on receptor flexibility. Indicating the potential inhibitory effect of SHH-mediated profibrotic signalling. These findings highlight the therapeutic promise of natural bioactive compounds as modulators of fibrosis. Further \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e validation is warranted to advance these candidates toward clinical application.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eIR designed the study, performed experiments, and wrote the manuscript. PB suggested edits, manuscript correction and supervised the study.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors are thankful to Biosymphony Pvt. Ltd. (Kapsid Simulations) for their technical support (HPC server). IR is thankful to the Council of Scientific and Industrial Research (CSIR), India, for providing his fellowship. The authors want to thank the University of Calcutta for providing structural support.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFunding Declaration\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe authors did not receive any funding for this research work.\u003c/p\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analysed during the current study are available from the corresponding author on request.\u003c/p\u003e\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBerendsen, H. J., Postma, J. van, Van Gunsteren, W. F., DiNola, A., \u0026amp; Haak, J. R. (1984). 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Sonic hedgehog signaling in kidney fibrosis: a master communicator. \u003cem\u003eScience China Life Sciences\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(9), pp.920-929.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Sonic Hedgehog Pathway, Chronic Kidney Disease, Bioactive Compounds, Molecular Docking, Molecular Dynamics Simulation","lastPublishedDoi":"10.21203/rs.3.rs-7411385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7411385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChronic kidney disease (CKD), often following unresolved acute kidney injury (AKI), is characterised by persistent fibrosis induced by several pathways, such as Sonic Hedgehog (SHH). Reactivation of the SHH pathway facilitates the profibrotic communication between epithelial cells and fibroblasts, contributing to CKD progression. In this study, we explored the inhibitory potential of three natural bioactive compounds against SHH pathway receptors: SHH protein, GLi, and SMO. Molecular docking, followed by 100 ns molecular dynamics simulations, was performed to assess binding stability and conformational behaviour. Key parameters, including RMSD, RMSF, hydrogen bonding, principal component analysis (PCA), and free energy landscape, were evaluated to understand dynamic interactions. The binding free energy was also calculated based on the MM/GBSA method. The three selected ligands (Kaempferol, Quercetin, and Naringenin) showed the highest binding affinity compared to other ligands. All these ligands showed favourable energy profiles, with significant interactions influencing structural flexibility and energetics of the SHH pathway receptors. These findings suggest that the selected compounds may effectively target SHH-mediated profibrotic signalling, offering an effective strategy to mitigate CKD progression through natural compound-based inhibition.\u003c/p\u003e","manuscriptTitle":"Identifying Natural Sonic Hedgehog Signalling Pathway Modulators with Potential Antifibrotic Activity in Chronic Kidney Disease: An in silico approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-19 10:50:48","doi":"10.21203/rs.3.rs-7411385/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":"95cf84cf-5740-4c6a-b36d-ac63190c3b49","owner":[],"postedDate":"September 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54967766,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":54967767,"name":"Health sciences/Diseases"},{"id":54967768,"name":"Biological sciences/Drug discovery"},{"id":54967769,"name":"Health sciences/Nephrology"}],"tags":[],"updatedAt":"2025-09-29T10:08:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-19 10:50:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7411385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7411385","identity":"rs-7411385","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}