In Silico Discovery of Natural and Synthetic Inhibitors Targeting AKT1 in Prostate Cancer

In Silico Discovery of Natural and Synthetic Inhibitors Targeting AKT1 in Prostate Cancer | 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 Research Article In Silico Discovery of Natural and Synthetic Inhibitors Targeting AKT1 in Prostate Cancer Hemantha Mani Kumar Chakravarthi Chanda, Sudheer Kumar Katari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7627265/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Apr, 2026 Read the published version in Molecular Diversity → Version 1 posted 9 You are reading this latest preprint version Abstract The serine/threonine kinase AKT1 plays a pivotal role in cancer progression and therapy resistance, particularly in castration-resistant prostate cancer (CRPC). This study employed an integrated in silico approach to identify potential AKT1 catalytic domain inhibitors from a library of 13,000 compounds sourced from Drugbank and the IMPPAT database. Structure-based virtual screening using AutoDock Vina and AutoDock 4.2 identified five promising candidates, among which 4-Carboxy imidazole and Balanol Analog 2 showed the most favourable binding interactions. Molecular dynamics (MD) simulations revealed that both compounds exhibited low RMSD and RMSF values, indicating stable binding throughout the simulation period. Notably, 4-Carboxy imidazole maintained persistent hydrogen bonding and low solvent exposure, suggesting a compact binding mode. Principal component analysis (PCA) and free energy landscape analyses further supported the conformational stability of these complexes. ADME profiling showed that 4-Carboxy imidazole had superior drug-like properties, while Balanol Analog 2 raised potential concerns related to metabolism. Density Functional Theory (DFT) calculations highlighted favourable electronic properties for both top ligands, with 4-Carboxy imidazole exhibiting a low dipole moment and moderate reactivity, suggesting specificity and stability. While the results are promising, further experimental validation is required to confirm inhibitory activity and therapeutic potential. Overall, this study identifies 4-Carboxy imidazole and Balanol Analog 2 as promising lead compounds for the development of AKT1-targeted therapies in CRPC. AKT1 inhibitors Castration-resistant prostate cancer (CRPC) MD simulation In silico drug design ADME profiling Density Functional Theory (DFT) Binding affinity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Introduction Prostate cancer is the second most frequently diagnosed malignancy and a leading cause of cancer-related death among men globally. Despite advances in early detection and therapeutic interventions, including androgen deprivation therapy (ADT), anti-androgen agents, and chemotherapy, many patients eventually develop CRPC [ 1 – 4 ]. This stage of the disease is associated with therapeutic resistance, recurrence, and poor prognosis, underscoring the need for new, targeted treatment strategies[ 5 ]. One of the most consistently altered signalling pathways in prostate cancer is the PI3K/AKT pathway, which regulates cell survival, proliferation, and metabolism. Among the three AKT isoforms (AKT1, AKT2, and AKT3), AKT1 is the predominant isoform expressed in prostate cancer cells and plays a central role in tumour growth, survival, and invasion[ 6 , 7 ]. AKT1 is a serine-threonine kinase that acts downstream of phosphatidylinositol 3-kinase (PI3K), serving as a key transducer of growth factor signals and an integrator of oncogenic signalling cascades[ 8 , 9 ] AKT1 dysregulation in prostate cancer often results from the loss of PTEN, a tumour suppressor that negatively regulates the PI3K/AKT axis. PTEN loss occurs in over 50% of advanced prostate cancers, leading to hyperactivation of AKT1 and downstream targets involved in proliferation and anti-apoptotic signalling[ 10 , 11 ]. Mouse models show that even partial AKT1 deficiency significantly delays tumour development in PTEN-null backgrounds[ 12 , 13 ]. Additionally, AKT1 expression is significantly elevated in clinical prostate cancer specimens and is associated with higher Gleason scores, elevated PSA levels, and poor prognosis[ 14 – 16 ]. While activating mutations in AKT1 (e.g., E17K) are relatively rare in prostate cancer (≤ 1.4%), gene amplification of AKT1 occurs in up to 4.5% of cases, especially in advanced disease[ 17 , 18 ]. AKT1’s functional role has been further validated through overexpression studies in prostate cancer cell lines and xenograft models, where it significantly enhances cell proliferation, tumour growth, and survival through regulation of key cell cycle and apoptotic proteins such as p27 kip1 , cyclin D1, and BAD[ 19 – 21 ]. AKT1 also plays a dual role in metastasis, functioning as both a promoter and, under certain conditions, a suppressor. AKT1 enhances prostate cancer cell migration and bone metastasis via upregulation of integrins and CXCL12/CXCR4 signalling, particularly in PTEN-deficient contexts[ 22 , 23 ]. However, in other contexts, AKT1 ablation paradoxically promotes metastasis through epithelial-to-mesenchymal transition (EMT) and TGF-β pathway activation[ 24 , 25 ]. Importantly, AKT1 is strongly implicated in androgen independence and resistance to anti-androgen therapies. Its activation enables prostate cancer cells to survive in androgen-deprived conditions and to evade AR-targeted treatments such as bicalutamide and abiraterone[ 15 , 26 ]. AKT1 also interacts synergistically with other oncogenic pathways (e.g., ERK/MAPK), promoting therapy resistance and suggesting a role for dual-pathway inhibition as a viable therapeutic strategy[ 27 , 28 ]. Given its central role in disease progression, metastasis, and therapeutic resistance, AKT1 has emerged as a valuable therapeutic and prognostic target in prostate cancer. However, challenges such as isoform redundancy, compensatory pathway activation, and kinase domain conservation have limited the clinical success of current AKT inhibitors. To address this, computational drug discovery approaches offer a powerful and cost-effective means to identify novel small-molecule AKT1 inhibitors. Techniques such as virtual screening, molecular docking, and MD simulations facilitate the identification and optimization of lead compounds with favourable binding properties and pharmacodynamic potential. Databases like Drugbank and IMPPAT (Indian Medicinal Plants, Phytochemistry and Therapeutics) provide access to thousands of biologically active compounds that can be screened in silico for AKT1 inhibition. Among the top five lead compounds identified, three were sourced from the Drugbank database, they are Tucidinostat, Balanol Analog 2, and 4-Carboxy imidazole, and two from the IMPPAT database, they are Mugineic acid (from Oryza sativa) and S-Methyl glutathione (from Vigna mungo), each demonstrating strong binding to the AKT1 catalytic domain and unique therapeutic potential. The following sections summarize the known biological activities and potential anticancer relevance of these compounds in the context of AKT1 inhibition. 4-Carboxy imidazole is a heterocyclic molecule characterized by its imidazole ring and carboxyl substituent, known for its ability to act as a chelating ligand and participate in acyl transfer reactions. Although primarily used in synthetic chemistry, its zwitterionic forms and capacity for metal coordination suggest potential utility in modulating metalloprotein function or kinase activity[ 29 , 30 ]. Balanol Analog 2 is a derivative of balanol, a natural product known for its potent inhibition of protein kinases, particularly serine/threonine kinases such as PKC and PKA. Structurally mimicking ATP, Balanol Analog 2 acts as a competitive kinase inhibitor and has shown promise in targeting kinase-driven oncogenic pathways[ 31 , 32 ]. Tucidinostat is a benzamide class histone deacetylase inhibitor that has received regulatory approval in China and Japan for relapsed/refractory peripheral T-cell lymphoma. It exhibits robust antitumor activity through epigenetic modulation and is currently under clinical evaluation for other malignancies including diffuse large B-cell lymphoma, multiple myeloma, and melanoma[ 33 , 34 ]. The diverse mechanisms of action of these Drugbank-derived compounds highlight their potential as structurally and functionally distinct inhibitors of AKT1 signalling in prostate cancer. Interestingly, beyond its well-established role as a natural iron chelator in plant systems, Mugineic acid, a phytosiderophore derived from Oryza sativa (rice), has demonstrated promising potential as an AKT1 inhibitor in this study. In murine models, Mugineic acid has been shown to be highly effective and non-toxic in alleviating iron overload conditions, such as thalassemia and myelodysplastic syndromes, with efficacy comparable to the commercial iron chelator ICL670 (Deferasirox). Its strong binding affinity and stable interaction profile with the AKT1 catalytic domain suggest a novel anticancer mechanism beyond iron metabolism. Considering the interplay between iron homeostasis and cancer progression, where excess intracellular iron promotes oxidative stress and activates survival pathways like PI3K/AKT Mugineic acid’s dual functionality as an iron chelator and AKT1 inhibitor highlights its therapeutic versatility. Its natural origin, oral bioavailability, and low toxicity further underscore its potential as a cost-effective, plant-derived candidate for integrative prostate cancer therapy, particularly in tumours characterized by dysregulated iron metabolism and AKT1 hyperactivation[ 35 , 36 ]. In this study, we employed a comprehensive in silico pipeline to identify and characterize potential AKT1 inhibitors for the treatment of prostate cancer. A library of approximately 13,000 compounds from the Drugbank and IMPPAT databases, including both approved and investigational drugs, was virtually screened against the AKT1 protein. Top-ranking hits were revalidated using AutoDock4 to estimate binding affinities and inhibition constants (Ki). Subsequently, 300 ns (nanoseconds) MD simulations were performed on the five most promising AKT1-ligand complexes to evaluate their structural stability, dynamic behaviour, and interaction patterns. PCA was applied to the MD trajectories to capture large-scale motions and conformational transitions within the complexes. Our study aims to highlight novel small-molecule inhibitors that exhibit strong binding affinity, structural stability, and potential therapeutic relevance against AKT1 in prostate cancer. These leads may serve as promising candidates for future experimental validation and preclinical development. Methodology 3.1. Ligand Library Preparation and Structure-Based Virtual Screening To identify potential inhibitors of AKT1, a structure-based virtual screening protocol was implemented using a curated library of approximately 13,000 small molecules comprising both approved and investigational drugs sourced from the Drugbank and IMPPAT databases. All ligands were prepared using AutoDock Tools embedded within the Python Molecular Viewer, where hydrogens were added, Gasteiger partial charges were computed, and torsional bonds were defined[ 37 , 38 ]. The AKT1 protein structure was obtained from the AlphaFold Protein Structure Database (Model ID: AF-P31749-F1-model_v4) and processed by removing water molecules, adding polar hydrogens, and assigning Kollman charges using AutoDock Tools to ensure compatibility with docking protocols[ 38 ]. Molecular docking was performed using AutoDock Vina (v1.1.2) [ 39 ] with an exhaustiveness parameter set to 32 to ensure sufficient sampling of the conformational space. The docking grid was centred on the catalytic site, specifically around the Asp274 residue, with grid centre coordinates defined as x = 0.95, y = 0.60, z = 3.28, and a grid box size of 20 Å × 20 Å × 20 Å. Compounds were ranked based on predicted binding affinities (ΔG in kcal/mol), and the top five ligands demonstrating the most favourable binding scores and critical interactions with the active site were selected for downstream validation and analysis[ 40 ]. 3.2. Re-docking and Binding Affinity Validation Using AutoDock 4.2 To enhance the reliability and accuracy of the initial virtual screening results, the top five lead compounds identified from the AutoDock Vina screening were re-evaluated using AutoDock 4.2.6[ 41 ]. Receptor and ligand preparation was carried out in accordance with the established AutoDock protocol, including the assignment of Kollman charges, Gasteiger partial charges, and the definition of rotatable bonds for ligand flexibility. Molecular docking was performed using the Lamarckian Genetic Algorithm (LGA), with 50 independent genetic algorithm runs, a population size of 300, and a maximum of 2.5 × 10⁶ energy evaluations per run to ensure extensive conformational sampling[ 38 ]. The grid box dimensions and coordinates were kept consistent with those used in the Vina docking protocol to maintain comparability. For each ligand, the binding free energy (ΔG) and the corresponding Ki were calculated using the AutoDock scoring function, thereby enabling quantitative assessment of binding affinity and facilitating robust lead prioritization. 3.3. Molecular Dynamics Simulations and Trajectory Analysis To evaluate the structural stability and dynamic behaviour of the top five protein–ligand complexes identified through re-docking, all-atom molecular dynamics simulations were performed using the Desmond v6.5 simulation package (Schrödinger, LLC). Each complex was embedded in an explicit solvent environment using the TIP3P water model, confined within an orthorhombic periodic boundary box with a 10 Å buffer distance from the protein surface to the box edge[ 42 , 43 ]. The system was neutralized by adding counterions (Na⁺ or Cl⁻), and an ionic concentration of 0.15 M NaCl was maintained to replicate physiological saline conditions. Energy minimization was performed using the Steepest Descent algorithm to relieve any steric clashes or unfavourable contacts, followed by equilibration under the default relaxation protocol. The OPLS-2005 force field was applied for all protein and ligand atoms, ensuring accurate representation of bonded and non-bonded interactions[ 44 , 45 ]. Production simulations were carried out for 300 ns under an isothermal-isobaric (NPT) ensemble, with the temperature maintained at 300 K using the Nose-Hoover chain thermostat, and pressure regulated at 1.01325 bar (1 atm) using the Martyna-Tobias-Klein barostat. Trajectory frames were recorded every 300 picoseconds (ps), generating a total of 1,000 frames per complex, all of which were used for post-simulation analyses[ 46 ]. Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF) of AKT1 (protein) and drugs individually, Radius of Gyration (Rg), and hydrogen bond occupancy were computed to assess structural stability, flexibility, and compactness of the complexes over the simulation period[ 47 ]. PCA was also performed on the Cα atom coordinates to investigate essential motions and conformational transitions in the protein-ligand systems. For this, the MDAnalysis Python package was used to convert trajectory files into the DCD format, which is compatible with the Bio3D library in R for further PCA and dynamic analyses[ 47 ]. The reference structure used for this conversion was taken in PDB format, corresponding to the docked complex that served as the input for system building in the MD simulations[ 48 ]. 3.4. In Silico ADME and Drug-Likeness Profiling To assess the pharmacokinetic behaviour and drug-likeness of the shortlisted AKT1-binding candidates, comprehensive ADME (absorption, distribution, metabolism, and excretion) profiling was performed using the SwissADME online platform[ 49 ]. Each compound was evaluated for gastrointestinal (GI) absorption potential, blood–brain barrier (BBB) permeability, and likelihood of acting as a P-glycoprotein (P-gp) substrate, providing insight into passive and active transport mechanisms.[ 50 ] Drug-likeness was assessed based on compliance with Lipinski’s Rule of Five, Veber’s criteria, and additional medicinal chemistry filters including bioavailability scores and synthetic accessibility indices. Moreover, predictive models for cytochrome P450 (CYP450) enzyme inhibition were utilized to evaluate potential metabolic liabilities, focusing on major isoforms such as CYP3A4, CYP2D6, and CYP2C9, which are critically involved in drug metabolism. These parameters were collectively used to prioritize compounds with optimal oral bioavailability, metabolic stability, and favourable physicochemical properties for downstream optimization[ 49 ]. 3.5. DFT calculations analysis To gain insights into the electronic properties and reactivity of the shortlisted lead compounds, DFT calculations were carried out using Gaussian 16W software. All molecular geometries were optimized in the gas phase employing the B3LYP hybrid exchange-correlation functional in combination with the 6-311 + + G(d,p) basis set, which provides a balanced description of valence and diffuse electron behaviour suitable for drug-like molecules[ 51 , 52 ]. Frontier molecular orbital (FMO) analysis was conducted to determine the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels. The HOMO-LUMO energy gap (ΔE gap ) was calculated for each compound, serving as a descriptor of molecular reactivity, electronic stability, and charge transfer characteristics. These quantum chemical parameters were used to support the evaluation of lead compound reactivity and their potential for further optimization in drug design pipelines[ 53 ]. Results 4.1. Molecular Docking and Binding Affinity and Interaction Profiling Analysis A total of 13,000 compounds from the Drugbank and IMPPAT databases were virtually screened against the AKT1 catalytic domain using molecular docking approaches. Based on binding affinities and docking scores, five top-ranking compounds were identified: three from Drugbank and two from IMPPAT: 4-Carboxy imidazole (DB16194), Balanol Analog 2 (DB01940), Tucidinostat (DB06334), Mugineic acid (IMPHY000275), and S-Methyl glutathione (IMPHY000456). The AutoDock Vina docking scores for these ligands ranged from − 9.553 to -6.070 kcal/mol, with 4-Carboxy imidazole and Balanol Analog 2 showing the most favourable scores − 9.553 and − 9.301 kcal/mol, respectively, indicating strong predicted binding affinities. These were followed by Mugineic acid − 7.874 kcal/mol and Tucidinostat − 7.723 kcal/mol, while S-Methyl glutathione exhibited the least favourable binding score − 6.070 kcal/mol among the top five selected compounds (Table 1 ). Table 1 Molecular docking parameters and interaction profiles of selected ligands with AKT1. Parameter 4-Carboxy imidazole (DB16194) Balanol Analog 2 (DB01940) Mugineic acid (IMPHY000275) S-Methyl glutathione (IMPHY000456) Tucidinostat (DB06334) Molecular Formula C 4 H 4 N 2 O 2 C 27 H 26 N 2 O 6 C 12 H 20 N 2 O 8 C 12 H 21 N 3 O 6 S C 22 H 19 FN 4 O 2 Docking Score -9.553 -9.301 -7.874 -6.070 -7.723 Estimated Binding Energy (kcal/mol) -3.54 -8.84 -4.28 -5.48 -6.42 Inhibition Constant (molar) 2.53 milli molar 328.83 nano molar 731.06 micro molar 95.98 micro molar 19.72 micro molar Intermolecular Energy (kcal/mol) -4.14 -11.53 -7.86 -9.06 -8.51 vdW + Hbond + Desolv Energy -4.43 -9.43 -5.51 -6.55 -8.30 Electrostatic Energy (kcal/mol) + 0.29 -2.10 -2.35 -2.51 -0.20 Torsional Free Energy (kcal/mol) + 0.60 + 2.68 + 3.58 + 3.58 + 2.09 Interactions Hydrogen bonds (4) Arg 273, Leu 275, Tyr 315, Ala 317 Hydrogen bonds (5) Thr 160, Lys 179, Glu 228, Ala 230, Asn 279 Hydrogen bonds (6) Thr 160, Phe 161, Lys 276, Asn 279 (2), Asp 292 Salt bridges (3) Lys 276, Asp 292 (2) Hydrogen bonds (6) Thr 160, Lys 179, Thr 291, Asp 292 (3), Salt bridges (2) Lys 179 (2) Hydrogen bonds (4) Thr 160, Asp 274, Asp 292 (2) Subsequent redocking using AutoDock 4.2 provided refined estimates of binding free energy and Ki. Balanol Analog 2 demonstrated the strongest binding affinity ΔG = -8.84 kcal/mol; Ki = 328.83 nano molar, followed by Tucidinostat ΔG = -6.42 kcal/mol; Ki = 19.72 micro molar, Mugineic acid ΔG = -4.28 kcal/mol; Ki = 731.06 micro molar and S-Methyl glutathione ΔG = -5.48 kcal/mol; Ki = 95.98 micro molar. 4-Carboxy imidazole, despite its strong Vina score, exhibited a relatively higher binding free energy and Ki -3.54 kcal/mol and 2.53 milli molar respectively, suggesting lower binding stability in the AutoDock 4.2 scoring framework. Detailed interaction analysis revealed that all top ligands formed multiple hydrogen bonds and salt bridges with key residues in the AKT1 active site. The key interacting residues included Thr160, Asp274, Asp292, Asn279, and Lys179, which are critical for ligand binding activity. Balanol Analog 2 formed five hydrogen bonds with residues Thr160, Lys179, Glu228, Ala230, and Asn279, and demonstrated a highly favourable intermolecular energy of -11.53 kcal/mol shown in Table 1 . Mugineic acid and S-Methyl glutathione both formed six hydrogen bonds and engaged in three and two salt bridge interactions respectively further contributing to complex stability. Tucidinostat and 4-Carboxy imidazole both formed four hydrogen bonds and their intermolecular energies are − 4.14 and − 8.51 respectively from Table 1 (Table 1 , Fig. 1 ). 4.2. MD simulation results analysis MD simulations were performed to evaluate the structural stability of the AKT1-ligand complexes and to validate the molecular docking results. The RMSD of the protein backbone and ligand atoms was monitored throughout the 300 ns simulation to assess the conformational stability of each system. Following initial fluctuations during the equilibration phase, commonly attributed to kinetic relaxation, all complexes reached a relatively stable plateau, indicating the system’s convergence and stable binding. Among the studied compounds, Balanol Analog 2 demonstrated the lowest average RMSD values across protein regions and ligand orientations, suggesting high structural stability and minimal deviation from the initial docked conformation. And 4-Carboxy imidazole showing some lowest RMSD from 400th to 600th time frame of MD simulation. Conversely, S-Methyl glutathione exhibited the highest RMSD values, particularly in the ligand with respect to the protein, reflecting greater conformational drift and a more dynamic binding mode. The remaining complexes, including Tucidinostat and Mugineic acid, showed moderate but stable RMSD values, indicating that their interactions with AKT1 were well-maintained throughout the simulation. RMSD of ligand with respective to the AKT1 of 4-Carboxy imidazole was very low when compare with Balanol Analog 2 also. These results suggest that the selected ligands form stable complexes with AKT1 and mainly 4-Carboxy imidazole and Balanol Analog 2 complexes are maintained high stability during the simulation period (Table 2 , Figs. 2 , 3 ). Table 2 RMSD and RMSF analysis of protein-ligand complexes from MD simulations Inhibitors Average protein-Ligand RMSD (Å): Cα, backbone, sidechain, protein hetero atoms, ligand w.r.t protein, ligand w.r.t ligand (Range Min - Max) Average protein RMSF (Å): Cα, backbone, sidechain, protein hetero atoms (Range Min - Max) Average ligand RMSF (Å): ligand w.r.t protein, ligand w.r.t ligand (Range Min - Max) 4-Carboxy imidazole 6.247 (2.13–8.26), 6.237 (2.12–8.28), 6.673 (2.89–8.39), 6.445 (2.50–8.33), 1.783 (0.64–3.76), 0.148 (0.045–1.01) 2.929 (0.17–9.71), 2.980 (0.90–10.26), 3.377 (0.95–11.09), 3.188 (0.90–10.68) 1.517 (1.28–1.78), 0.110 (0.052–0.172) Balanol Analog 2 4.789 (2.14–6.76), 4.775 (2.15–6.74), 5.354 (2.90–7.08), 5.084 (2.48–6.94), 2.934 (1.84–5.53), 1.299 (0.62–2.14) 1.979 (0.60–7.18), 1.987 (0.60–7.17), 2.431 (0.74–7.85), 2.220 (0.67–7.26) 1.326 (0.96–2.04), 0.409 (0.23–0.81) Mugineic acid 5.934 (2.44–6.84), 5.933 (2.43–6.86), 6.241 (3.00–7.20), 6.123 (2.72–7.05), 3.260 (1.39–5.59), 1.334 (0.85–1.88) 1.913 (0.61–8.49), 1.924 (0.63–8.36), 2.385 (0.77–9.28), 2.166 (0.66–8.92) 2.041 (1.45–2.87), 0.897 (0.36–1.52) S-Methyl glutathione 6.836 (1.66–8.20), 6.829 (1.68–8.17), 7.114 (2.45–8.44), 6.990 (2.02–8.33), 8.009 (1.63–10.61), 2.047 (0.60–2.81) 2.130 (0.71–8.97), 2.139 (0.75–8.89), 2.607 (0.79–9.90), 2.383 (0.75–9.47) 3.612 (2.76–4.52), 1.094 (0.57–1.90) Tucidinostat 6.122 (2.59–7.61), 6.120 (2.57–7.61), 6.514 (3.42–7.82), 6.317 (2.99–7.72), 3.957 (2.11–5.52), 1.062 (0.55–1.58) 1.868 (0.60–8.62), 1.879 (0.62–8.42), 2.319 (0.71–9.04), 2.110 (0.68–8.42) 1.536 (0.85–2.64), 0.632 (0.26–1.42) The RMSF analysis was performed to evaluate the flexibility of individual amino acid residues within the AKT1-ligand complexes. RMSF calculates the average deviation of each residue from its mean position over the simulation trajectory, providing insights into local structural fluctuations. As expected, the N-terminal (first 120 amino acids), C-terminal (after 400 amino acids), and loop regions exhibited higher fluctuations compared to the more rigid secondary structural elements within the catalytic domain. Importantly, residues within the active site, particularly those involved in ligand binding such as Thr160, Lys179, Asp274, and Asn279 displayed relatively low RMSF values across all complexes, indicating stable interactions and minimal perturbation during the simulation. Among the five ligands, the Balanol Analog 2 complex showed the least fluctuation across the binding site residues, consistent with its strong and stable binding profile observed in RMSD analysis. 4-Carboxy imidazole showing lowest fluctuations starting from 120 to 400 amino acids (nearly 70% of the structure), remaining 30% still within an acceptable range. In contrast, slightly elevated RMSF values were observed in the S-Methyl glutathione complex, though still within an acceptable range, suggesting moderate flexibility without compromising structural integrity. These findings support the conclusion that ligand binding does not induce significant conformational changes in the AKT1 active site and further confirm the dynamic stability of the protein-ligand complexes throughout the 300 ns simulation (Fig. 5 ). Hydrogen bond analysis was performed to evaluate the stability and strength of interactions between the protein and the ligands during the MD simulations. The average number of hydrogen bonds formed throughout the simulation time is presented in Table 3 . Mugineic acid exhibited the highest average number of hydrogen bonds 4.297, indicating stronger and potentially more stable interactions with the protein. Notably, 4-Carboxy imidazole showed 4 hydrogen bond contacts with AKT1 for 600 frames, corresponding to 60% of the simulation time, highlighting persistent interactions. Both 4-Carboxy imidazole and Balanol Analog 2 showed moderate hydrogen bonding, with average counts of 3.597 and 3.317, respectively, suggesting balanced interaction stability. In contrast, Tucidinostat showed the lowest average hydrogen bonding (1.348), implying relatively weaker interactions. These results correlate well with the RMSD and RMSF analyses, supporting the overall stability of the protein–ligand complexes (Table 3 , Figs. 6 , 7 ). Table 3 Interaction types and total contacts of AKT1-ligand complexes during MD simulations. S. No. Compound name 4-Carboxy imidazole Balanol Analog 2 Mugineic acid S-Methyl glutathione Tucidinostat 1. Hydrogen bonds 3,597 3,317 4,297 3,914 1,348 2. Hydrophobic interactions 383 1,664 122 533 2,432 3. Ionic interactions - - 429 459 - 4. Metallic interactions - 223 1,268 551 4 5. Pi-cation interactions - 949 - 63 292 6. Pi-pi stacking interactions 402 51 - - 316 7. Water bridge interactions 378 1,392 5,332 3,687 3,116 8. Total number of Interactions 4,760 7,596 11,448 9,207 7,508 Table 4 Average total energy, potential energy, and simulation properties of ligand-bound AKT1 systems. S. No Compound Name Average total energy (kcal/mol) Average potential energy (kcal/mol) Degrees of freedom Number of particles 1 4-Carboxy imidazole -164,474.666 -200,103.125 120,044 58,009 2 Balanol Analog 2 -164,146.035 -199,763.711 120,007 57,977 3 Mugineic acid -164,560.537 -200,146.814 119,903 57,931 4 S-Methyl glutathione -164,537.780 200,126.812 119,911 57,935 5 Tucidinostat -164,195.608 -199,798.147 119,957 57,955 In addition to hydrogen bonding, other non-covalent interactions such as hydrophobic, ionic, metallic, pi-cation, pi-pi stacking, and water bridge interactions were analysed to gain a comprehensive understanding of the ligand–protein binding dynamics. Mugineic acid also showed the highest total number of interactions 11,448, with a significant contribution from water bridges 5,332 and ionic interactions 429, highlighting its multifaceted binding mechanism. Balanol Analog 2 displayed considerable 1,664 hydrophobic, 949 pi-cation, and 1,392 water bridge interactions, supporting its moderate yet stable binding profile. Conversely, 4-Carboxy imidazole had fewer: 383 hydrophobic, and 378 water bridge interactions but compensated through 402 pi-pi stacking interactions, reflecting its moderate overall binding. S-Methyl glutathione presented substantial 459 ionic, 551 metallic, and 3,687 water bridge interactions, reinforcing its stable interaction network despite fewer hydrophobic and pi interactions. Tucidinostat, despite lower hydrogen bonds, showed notable 2,432 hydrophobic, and 3,116 water bridge interactions but minimal ionic or metallic contributions (Table 3 ). MD simulation analysis of ligand properties revealed that 4-Carboxy imidazole maintained the most compact structure, with the lowest Rg 1.92 Å and minimal solvent exposure (SASA 0.81 Ų). Balanol Analog 2 and Tucidinostat showed higher Rg values 5.44 Å and 5.75 Å, reflecting their larger and more flexible conformations. Mugineic acid and S-Methyl glutathione exhibited intermediate compactness but higher solvent accessible surface areas 174.30 Ų and 177.16 Ų, consistent with their polar nature. Intramolecular hydrogen bonds were negligible for most ligands except for slight presence in Mugineic acid and S-Methyl glutathione. Polar surface area values aligned with these trends, with 4-Carboxy imidazole showing the lowest 147.76 Ų and Mugineic acid the highest 311.87 Ų. These results support the stability and varying flexibility of the ligands during binding and are consistent with their predicted drug-like profiles (Table 5 , Fig. 4 ). Table 5 Ligand-specific properties including Rg, intramolecular hydrogen bonds, MolSA, SASA, and PSA during MD simulations. Parameter 4-Carboxy imidazole Balanol Analog 2 Mugineic acid S-Methyl glutathione Tucidinostat Rg (Å) (Range: Min - Max) 1.920 (1.857–1.974) 5.435 (5.035–5.769) 3.802 (3.307–4.360) 3.767 (3.372–4.251) 5.745 (5.295–6.322) intraHB (Range: Min - Max) 0 (0–0) 0 (0–0) 0.080 (0–2) 0.335 (0–3) 0.001 (0–1) Average MolSA (Ų) (Range: Min - Max) 112.56 (110.47–114.66) 433.90 (425.69–441.54) 281.42 (264.82–290.53) 305.06 (273.49–325.86) 378.31 (372.51–383.94) Average SASA (Ų) (Range: Min - Max) 0.81 (0.00–12.80) 79.24 (37.26–136.41) 174.30 (15.13–281.26) 177.16 (71.93–328.73) 113.92 (30.72–249.20) Average PSA (Ų) (Range: Min - Max) 147.76 (141.50–153.92) 208.47 (196.75–221.73) 311.87 (259.74–339.20) 265.64 (204.74–305.83) 164.27 (151.12–173.05) Molecular mechanics energy calculations were performed to assess the stability of the AKT1-ligand complexes during MD simulations. Among the ligands, Mugineic acid and 4-Carboxy imidazole exhibited the lowest average total energies, recorded at -164,560.54 kcal/mol and − 164,474.67 kcal/mol, respectively, indicating highly stable systems. Their average potential energies were similarly favourable, with Mugineic acid showing − 200,146.81 kcal/mol and 4-Carboxy imidazole closely following at -200,103.13 kcal/mol. The degrees of freedom and number of particles were consistent across all complexes, confirming comparable system sizes and simulation parameters. These energy profiles corroborate the binding stability observed in docking and MD analyses, highlighting Mugineic acid and 4-Carboxy imidazole as ligands with marginally more favourable energetic states (Table 6 , Fig. 8 ). Table 6 ADME and drug-likeness properties of selected ligands predicted using SwissADME. Parameter 4-Carboxy imidazole Balanol Analog 2 Mugineic acid S-Methyl glutathione Tucidinostat Molecular Weight (Da) 112.09 474.51 320.3 335.38 390.41 TPSA (Ų) 65.98 124.96 167.63 184.12 97.11 H-bond Acceptors 3 7 10 7 4 H-bond Donors 2 4 6 5 3 Rotatable Bonds 1 8 10 13 8 Consensus LogP -0.17 2.96 -3.31 -1.87 2.74 Solubility Class Very soluble Moderately soluble Highly soluble Highly soluble Soluble GI Absorption High High Low Low High P-gp Substrate No Yes Yes No Yes CYP Inhibitor (1A2, 2C19, 2C9, 2D6, 3A4) No Some No No All Lipinski Rule Violations 0 0 1 0 0 Bioavailability Score 0.55 0.55 0.11 0.11 0.55 Synthetic Accessibility 1.22 3.98 3.59 3.49 2.84 Although the selected ligands exhibit some variation in RMSD values during the simulations, these conformations correspond to relatively low free energy states, indicating favourable binding stability. The 4-Carboxy imidazole-AKT1 complex predominantly shows RMSD values in the 0.4–0.6 nm range, with 400 trajectories deposited below 2 kcal/mol free energy, reflecting a highly stable binding conformation. Additionally, 537 trajectories fall within the 0.6–0.8 nm RMSD range, corresponding to free energies below 8 kcal/mol, suggesting accessible yet still favourable states. Overall, this accounts for approximately 93% of the simulation time spent in stable conformations despite observed RMSD fluctuations. Similarly, the Balanol Analog 2 complex presents 41 trajectories at RMSD values below 0.4 nm and above 0.6 nm with free energies under 2 kcal/mol, alongside 960 trajectories in the 0.4–0.6 nm range exhibiting free energies between 3 and 10 kcal/mol. In contrast, the Mugineic acid complex shows 439 trajectories in the 0.4–0.6 nm RMSD range with free energies from 5 to 9 kcal/mol, and 551 trajectories within the 0.6–0.8 nm RMSD range with higher free energies between 10 and 11 kcal/mol. These observations highlight that despite some RMSD fluctuations, the selected ligands, particularly 4-Carboxy imidazole and Balanol Analog 2 remain in energetically favourable states throughout the simulation, supporting their potential as stable AKT1 inhibitors (Fig. 9 ). 4.3. PCA results analysis of MD simulations PCA was performed to explore the large-scale conformational motions of the AKT1-ligand complexes during MD simulations. The first three principal components (PC1, PC2, and PC3) collectively captured the majority of the essential dynamics. Among the studied complexes, 4-Carboxy imidazole exhibited the highest contribution from PC1 44.02%, followed closely by Mugineic acid 41.85%, indicating that these systems experienced more defined and directional motions along the primary eigenvector. Balanol Analog 2 displayed a more balanced distribution between PC1 38.10% and PC3 38.10%, suggesting a more complex, multi-directional dynamic behaviour. In contrast, Tucidinostat and S-Methyl glutathione showed lower PC1 contributions 28.87% and 28.43%, respectively, reflecting comparatively less pronounced motion along the principal mode. These findings suggest that 4-Carboxy imidazole and Mugineic acid complexes undergo more coordinated and directional conformational shifts, which may contribute to their stable binding behaviour observed in RMSD and hydrogen bonding analyses (Fig. 10 ). Further interpretation of the PCA displacement plots (residue index vs. displacement) revealed that the N-terminal ~ 150 residues, representing approximately 30% of the AKT1 structure exhibited higher atomic displacements along both PC1 and PC2, indicating localized flexibility in this region. In contrast, the remaining ~ 60% of the protein showed relatively minimal displacement, suggesting a more rigid structural core. Among the ligand-bound complexes, Mugineic acid, S-Methyl glutathione, and Tucidinostat induced only minimal displacements along PC1 and PC2, reflecting a limited influence on global protein motion. Conversely, 4-Carboxy imidazole and Balanol Analog 2 triggered more pronounced conformational shifts, particularly in the flexible N-terminal region, which may correspond to functionally relevant dynamics associated with ligand binding (Fig. 11 ). Additionally, the free energy funnel plot for the 4-Carboxy imidazole-AKT1 complex demonstrated a deep and well-defined minimum, with the majority of trajectories clustered within a low-energy region below 5 kJ/mol, represented by the dark blue basin. Only a small fraction of conformations deviated from this stable minimum, reinforcing the thermodynamic stability and favourable binding characteristics of the complex (Figs. 11 , 12 ). 4.4. Pharmacokinetic profile of selected ligands The drug-likeness and pharmacokinetic properties of the selected ligands against AKT1 were evaluated using SwissADME (Table 6 ). All compounds, except Mugineic acid, complied with Lipinski’s Rule of Five, indicating favourable oral bioavailability for potential AKT1 inhibition. 4-Carboxy imidazole showed the most drug-like profile with low molecular weight 112.09 Da, high GI absorption, very good solubility, and no CYP inhibition, supporting its potential as an effective AKT1 inhibitor. Balanol Analog 2 also demonstrated high GI absorption, acceptable LogP 2.96, and no Lipinski violations, though it is a P-gp substrate and a CYP inhibitor, which may impact metabolism and efficacy in AKT1 targeting. Tucidinostat exhibited favourable ADME properties with high GI absorption and balanced lipophilicity, but predicted inhibition of multiple CYP450 enzymes suggests possible drug–drug interactions in AKT1-related therapies. In contrast, Mugineic acid and S-Methyl glutathione showed low GI absorption, high TPSA values (> 140 Ų), and low bioavailability scores 0.11, indicating limited membrane permeability despite high solubility, which may restrict their utility as AKT1 inhibitors without further optimization. Overall, 4-Carboxy imidazole and Balanol Analog 2 present the most promising drug-likeness and ADME profiles for AKT1 inhibition (Table 6 ). 4.5. DFT analysis of selected ligands To further elucidate the electronic properties and chemical reactivity of the selected ligands, DFT calculations were performed. Balanol Analog 2 and Tucidinostat exhibited the smallest ΔE gap among the ligands, suggesting high chemical reactivity and a greater tendency for electron exchange within the AKT1 active site. These electronic features correlate well with their strong binding affinities and stable interactions observed in molecular docking and MD simulations. Conversely, 4-Carboxy imidazole showed a moderate ΔE gap , reflecting a favourable balance between stability and reactivity, supporting selective binding with minimal off-target interactions. Mugineic acid and S-Methyl glutathione displayed wider energy gaps, suggesting lower reactivity despite strong solvation tendencies, which may reduce their effectiveness in initiating efficient electronic interactions within the active site (Table 7 , Fig. 13 ). Table 7 DFT-based quantum chemical descriptors and electronic properties of selected ligands. Parameter 4-Carboxy imidazole Balanol Analog 2 Mugineic acid S-Methyl glutathione Tucidinostat Dipole Moment (D) 1.984 8.266 4.978 3.963 8.960 HOMO (eV) -0.33822 (α) -0.33797 (α) -0.37000 (α) / -0.27618 (β) -0.37209 (α) / -0.35273 (β) -0.32395 (α) LUMO (eV) -0.18082 (α) -0.22547 (α) -0.14044 (α) / -0.14687 (β) -0.14687 (α) / -0.27618 (β) -0.22652 (α) HOMO-LUMO Gap (eV) 0.15740 (α) 0.11250 (α) 0.22956 (α) / 0.12931 (β) 0.22522 (α) / 0.07655 (β) 0.09743 (α) Solvation Energy (kcal/mol) -17.65 -23.23 -96.91 -68.97 -25.93 Zero Point Energy (kcal/mol) 54.097 307.777 210.111 212.654 235.674 Enthalpy (kcal/mol) 4.533 18.400 13.457 14.196 15.203 Free Energy (kcal/mol) -19.321 -34.798 -29.600 -30.792 -32.148 Entropy (cal/mol/K) 80.004 178.427 144.412 150.888 158.816 Polarizability (QPpolrz) 10.086 50.165 25.700 28.232 42.439 PSA (Ų) 80.036 146.582 202.001 192.035 110.149 Volume (ų) 396.95 1437.438 988.491 1051.752 1239.264 Dipole moment and polarizability values provided insights into the compounds’ behaviour in polar biological environments. Balanol Analog 2 and Tucidinostat demonstrated higher dipole moments and polarizabilities, suggesting flexible electrostatic interactions and adaptability within the AKT1 pocket. In contrast, 4-Carboxy imidazole exhibited lower values, supporting its compact, rigid conformation and potentially tighter fit into the binding site. PSA and molecular volume values further aligned with predicted permeability and bioavailability, highlighting 4-Carboxy imidazole as a promising drug-like molecule due to its small size and lower polarity. Thermodynamic parameters such as free energy and enthalpy reinforced the stability of Balanol Analog 2 and Tucidinostat, aligning with their dynamic performance. Collectively, these DFT results position 4-Carboxy imidazole as a compact, selective inhibitor and Balanol Analog 2 as a chemically reactive and adaptable AKT1-binding candidate. Discussion This study applied a structure-based virtual screening approach, followed by MD simulation, ADME, and DFT analyses, to identify potential inhibitors of the AKT1 catalytic domain: a key target in CRPC. From a screening of 13,000 compounds, five ligands were shortlisted, with 4-Carboxy imidazole and Balanol Analog 2 emerging as the most promising candidates. Initial docking using AutoDock Vina highlighted these two compounds with favourable binding scores; however, further redocking using AutoDock 4.2 revealed differing trends: while Balanol Analog 2 maintained strong binding affinity, 4-Carboxy imidazole, despite its high Vina score, exhibited comparatively weaker predicted affinity. This discrepancy underscores a well-recognized limitation of molecular docking, where differences in scoring algorithms across tools can significantly impact compound ranking. As such, docking predictions should be interpreted cautiously and supported by additional dynamic analyses to better understand binding stability and ligand performance. MD simulation analyses provided valuable insights into the dynamic behaviour and stability of the AKT1-ligand complexes. Both Balanol Analog 2 and 4-Carboxy imidazole exhibited consistently low RMSD and RMSF values throughout most of the simulation, indicating stable binding within the active site. Importantly, the RMSD trajectories compared to the initial docking poses predominantly occupied lower-energy conformational states, further supporting the stability of these complexes over time. Notably, 4-Carboxy imidazole maintained four hydrogen bonds for over 60% of the simulation and showed low solvent-accessible surface area, reflecting a compact and persistent interaction mode. In contrast, although Mugineic acid formed the highest number of hydrogen bonds and total interactions, its elevated RMSD and large polar surface area suggested a more flexible and less deeply buried binding conformation, which may adversely affect its pharmacokinetic suitability. PCA and energy landscape analyses further confirmed the stability of the 4-Carboxy imidazole and Balanol Analog 2 complexes, as both ligands maintained low-energy conformational states throughout the simulation. These findings were supported by ADME predictions, where 4-Carboxy imidazole demonstrated favourable oral bioavailability, high gastrointestinal absorption, good solubility, and no predicted CYP enzyme inhibition. Balanol Analog 2 also showed acceptable pharmacokinetic properties but raised potential metabolic concerns due to CYP inhibition and P-glycoprotein substrate characteristics. In contrast, compounds like Mugineic acid and S-Methyl glutathione, despite strong docking interactions, exhibited poor membrane permeability and low bioavailability scores, indicating a need for structural optimization. Complementing these results, DFT analyses provided insight into the electronic properties of the ligands; Balanol Analog 2 and Tucidinostat displayed low HOMO-LUMO gaps, suggesting higher reactivity and stronger potential interactions within the AKT1 binding pocket, whereas 4-Carboxy imidazole showed moderate reactivity and a low dipole moment, indicative of selective and stable binding with potentially fewer off-target effects. Despite the encouraging in silico performance of the selected ligands, this study has several limitations. The reliance on a static AlphaFold-predicted AKT1 structure may not fully capture the protein’s conformational flexibility or allosteric regulatory features, potentially influencing binding predictions. Additionally, the absence of experimental validation restricts the immediate translational applicability of the results. Discrepancies between scoring functions used in different docking tools, along with the lack of toxicity and selectivity assessments, further constrain the robustness of the conclusions. To address these gaps, future research should include in vitro validation of the top candidates to confirm AKT1 inhibition and evaluate cytotoxicity in CRPC models. Structural optimization may be necessary to improve the pharmacokinetic properties of polar compounds like Mugineic acid, while selectivity profiling will be essential to ensure isoform-specific inhibition. Conclusion This study aimed to identify novel high-affinity inhibitors of the AKT1 catalytic domain using an integrated in silico approach. A total of 13,000 compounds from Drugbank and IMPPAT databases were virtually screened, leading to the identification of five promising candidates. Among them, 4-Carboxy imidazole and Balanol Analog 2 emerged as the most stable and potent binders, based on molecular docking, molecular dynamics simulations, and binding free energy profiles. The MD simulations confirmed stable interactions of these ligands with key active site residues, supported by sustained hydrogen bonding, low RMSD and RMSF values, and energetically favourable conformations. Additionally, PCA and energy landscape analyses highlighted consistent low-energy states throughout the trajectory. ADME profiling suggested that 4-Carboxy imidazole had the most favourable drug-like properties, while Balanol Analog 2 also showed good absorption but potential metabolic liabilities. DFT calculations further supported their suitability by revealing favourable electronic properties, with 4-Carboxy imidazole demonstrating a stable and selective binding profile. In conclusion, 4-Carboxy imidazole and Balanol Analog 2 represent promising AKT1 inhibitor candidates, warranting further experimental validation and development for potential therapeutic application in castration-resistant prostate cancer. Declarations Author Contribution Declaration Hemantha Mani Kumar Chakravarthi Chanda a : Executed in silico analyses including molecular docking and molecular dynamics simulations, and contributed to the writing and editing of the manuscript. Sudheer Kumar Katari a : Contributed to the study's conceptualization and design, and conducted in silico analysis, and including target prediction. Affiliations a Department of Bioinformatics, Vignan's Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur-522213, India. Corresponding Author Sudheer Kumar Katari Vignan's Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur-522213, India. [email protected] Subject Area: Bioinformatics, Computational Biology, Drug Discovery, Structural Biology Funding Declaration The authors declare that no financial support was received for the research, authorship, and / or publication of this article. Conflict of interest The authors declare that there is no conflict of interest. Acknowledgement Authors are highly thankful to VFSTR (Deemed to be University) for providing faculty seed grant (F.No. VFSTR/REG/A6/30/2023-24/01 dated 16-05-2023) facility. The authors acknowledge the use of AI tools, including ChatGPT (OpenAI) and Gemini (Google), for language editing and sentence refinement. These tools were employed solely to enhance the clarity and grammar of the manuscript, and were not used for generating scientific content, analysis, technical writing, or interpretation. Data availability The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request. 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frequency of H-bonds for different ligands during simulation\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7627265/v1/0c81e614cd3520bcb221ec41.png"},{"id":92275660,"identity":"396a17be-230a-4add-b323-a02c4af08b54","added_by":"auto","created_at":"2025-09-26 15:28:46","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":619208,"visible":true,"origin":"","legend":"\u003cp\u003eTotal energy over time for different ligands during simulation\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7627265/v1/00a2421e397f398d8574625b.jpeg"},{"id":92275696,"identity":"fb553db1-bbe6-4f26-b102-077b848490e9","added_by":"auto","created_at":"2025-09-26 15:28:55","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":659539,"visible":true,"origin":"","legend":"\u003cp\u003eFree energy profiles against RMSD of complexes of different ligands within AKTI inhibitors during MD simulation.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7627265/v1/034da2161ead362aaf557bd9.jpeg"},{"id":92275576,"identity":"02aa1097-d9c4-4692-83cf-e6f912ffd7fa","added_by":"auto","created_at":"2025-09-26 15:28:39","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1795476,"visible":true,"origin":"","legend":"\u003cp\u003ePCA plots of complexes of AKT1 with different ligands during MD simulation, A) 4-Carboxy imidazole B) Balanol Analog 2 C) Mugineic acid D) S-Methyl glutathione and E) Tucidinostat\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7627265/v1/253c688a3d4b09e6b127dddb.jpeg"},{"id":92275713,"identity":"02f28817-dbcf-4381-8e9a-974aeced3c80","added_by":"auto","created_at":"2025-09-26 15:28:56","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":491452,"visible":true,"origin":"","legend":"\u003cp\u003eDisplacement of PC1, PC2 for complexes of AKT1 with different ligands during MD simulation, A) 4-Carboxy imidazole B) Balanol Analog 2 C) Mugineic acid D) S-Methyl glutathione and E) Tucidinostat\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7627265/v1/2d995f8203c019ac31c26650.jpeg"},{"id":92275520,"identity":"69874091-ccaa-4cd3-aac9-d0877006547b","added_by":"auto","created_at":"2025-09-26 15:28:35","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":5495908,"visible":true,"origin":"","legend":"\u003cp\u003eFree energy landscape for complexes of AKT1 with different ligands during MD simulation\u003c/p\u003e","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7627265/v1/4a24ff10a9e6fd376f7de831.jpeg"},{"id":92275683,"identity":"fec1b866-8458-4aac-9184-22864a66b464","added_by":"auto","created_at":"2025-09-26 15:28:52","extension":"jpeg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":704981,"visible":true,"origin":"","legend":"\u003cp\u003eDFT calculated LUMO, HOMO, and their energies of selected ligands\u003c/p\u003e","description":"","filename":"floatimage13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7627265/v1/e6a424943b630818d5386a38.jpeg"},{"id":107927750,"identity":"fe557309-d519-4167-b7b0-ea5b1c704b04","added_by":"auto","created_at":"2026-04-27 16:03:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15862040,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7627265/v1/00ba8fd0-63db-465a-8120-18fd0a3b11a6.pdf"},{"id":92275567,"identity":"a182a795-91ac-44be-b5cf-558c796ced21","added_by":"auto","created_at":"2025-09-26 15:28:39","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5468152,"visible":true,"origin":"","legend":"","description":"","filename":"SupportedFigs.docx","url":"https://assets-eu.researchsquare.com/files/rs-7627265/v1/5773f28cba59dc2f662b6129.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"In Silico Discovery of Natural and Synthetic Inhibitors Targeting AKT1 in Prostate Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer is the second most frequently diagnosed malignancy and a leading cause of cancer-related death among men globally. Despite advances in early detection and therapeutic interventions, including androgen deprivation therapy (ADT), anti-androgen agents, and chemotherapy, many patients eventually develop CRPC [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This stage of the disease is associated with therapeutic resistance, recurrence, and poor prognosis, underscoring the need for new, targeted treatment strategies[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOne of the most consistently altered signalling pathways in prostate cancer is the PI3K/AKT pathway, which regulates cell survival, proliferation, and metabolism. Among the three AKT isoforms (AKT1, AKT2, and AKT3), AKT1 is the predominant isoform expressed in prostate cancer cells and plays a central role in tumour growth, survival, and invasion[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. AKT1 is a serine-threonine kinase that acts downstream of phosphatidylinositol 3-kinase (PI3K), serving as a key transducer of growth factor signals and an integrator of oncogenic signalling cascades[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eAKT1 dysregulation in prostate cancer often results from the loss of PTEN, a tumour suppressor that negatively regulates the PI3K/AKT axis. PTEN loss occurs in over 50% of advanced prostate cancers, leading to hyperactivation of AKT1 and downstream targets involved in proliferation and anti-apoptotic signalling[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Mouse models show that even partial AKT1 deficiency significantly delays tumour development in PTEN-null backgrounds[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Additionally, AKT1 expression is significantly elevated in clinical prostate cancer specimens and is associated with higher Gleason scores, elevated PSA levels, and poor prognosis[\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile activating mutations in AKT1 (e.g., E17K) are relatively rare in prostate cancer (\u0026le;\u0026thinsp;1.4%), gene amplification of AKT1 occurs in up to 4.5% of cases, especially in advanced disease[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. AKT1\u0026rsquo;s functional role has been further validated through overexpression studies in prostate cancer cell lines and xenograft models, where it significantly enhances cell proliferation, tumour growth, and survival through regulation of key cell cycle and apoptotic proteins such as p27\u003csup\u003ekip1\u003c/sup\u003e, cyclin D1, and BAD[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAKT1 also plays a dual role in metastasis, functioning as both a promoter and, under certain conditions, a suppressor. AKT1 enhances prostate cancer cell migration and bone metastasis via upregulation of integrins and CXCL12/CXCR4 signalling, particularly in PTEN-deficient contexts[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, in other contexts, AKT1 ablation paradoxically promotes metastasis through epithelial-to-mesenchymal transition (EMT) and TGF-β pathway activation[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eImportantly, AKT1 is strongly implicated in androgen independence and resistance to anti-androgen therapies. Its activation enables prostate cancer cells to survive in androgen-deprived conditions and to evade AR-targeted treatments such as bicalutamide and abiraterone[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. AKT1 also interacts synergistically with other oncogenic pathways (e.g., ERK/MAPK), promoting therapy resistance and suggesting a role for dual-pathway inhibition as a viable therapeutic strategy[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven its central role in disease progression, metastasis, and therapeutic resistance, AKT1 has emerged as a valuable therapeutic and prognostic target in prostate cancer. However, challenges such as isoform redundancy, compensatory pathway activation, and kinase domain conservation have limited the clinical success of current AKT inhibitors.\u003c/p\u003e\u003cp\u003eTo address this, computational drug discovery approaches offer a powerful and cost-effective means to identify novel small-molecule AKT1 inhibitors. Techniques such as virtual screening, molecular docking, and MD simulations facilitate the identification and optimization of lead compounds with favourable binding properties and pharmacodynamic potential. Databases like Drugbank and IMPPAT (Indian Medicinal Plants, Phytochemistry and Therapeutics) provide access to thousands of biologically active compounds that can be screened in silico for AKT1 inhibition.\u003c/p\u003e\u003cp\u003eAmong the top five lead compounds identified, three were sourced from the Drugbank database, they are Tucidinostat, Balanol Analog 2, and 4-Carboxy imidazole, and two from the IMPPAT database, they are Mugineic acid (from Oryza sativa) and S-Methyl glutathione (from Vigna mungo), each demonstrating strong binding to the AKT1 catalytic domain and unique therapeutic potential. The following sections summarize the known biological activities and potential anticancer relevance of these compounds in the context of AKT1 inhibition.\u003c/p\u003e\u003cp\u003e4-Carboxy imidazole is a heterocyclic molecule characterized by its imidazole ring and carboxyl substituent, known for its ability to act as a chelating ligand and participate in acyl transfer reactions. Although primarily used in synthetic chemistry, its zwitterionic forms and capacity for metal coordination suggest potential utility in modulating metalloprotein function or kinase activity[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Balanol Analog 2 is a derivative of balanol, a natural product known for its potent inhibition of protein kinases, particularly serine/threonine kinases such as PKC and PKA. Structurally mimicking ATP, Balanol Analog 2 acts as a competitive kinase inhibitor and has shown promise in targeting kinase-driven oncogenic pathways[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Tucidinostat is a benzamide class histone deacetylase inhibitor that has received regulatory approval in China and Japan for relapsed/refractory peripheral T-cell lymphoma. It exhibits robust antitumor activity through epigenetic modulation and is currently under clinical evaluation for other malignancies including diffuse large B-cell lymphoma, multiple myeloma, and melanoma[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The diverse mechanisms of action of these Drugbank-derived compounds highlight their potential as structurally and functionally distinct inhibitors of AKT1 signalling in prostate cancer.\u003c/p\u003e\u003cp\u003eInterestingly, beyond its well-established role as a natural iron chelator in plant systems, Mugineic acid, a phytosiderophore derived from Oryza sativa (rice), has demonstrated promising potential as an AKT1 inhibitor in this study. In murine models, Mugineic acid has been shown to be highly effective and non-toxic in alleviating iron overload conditions, such as thalassemia and myelodysplastic syndromes, with efficacy comparable to the commercial iron chelator ICL670 (Deferasirox). Its strong binding affinity and stable interaction profile with the AKT1 catalytic domain suggest a novel anticancer mechanism beyond iron metabolism. Considering the interplay between iron homeostasis and cancer progression, where excess intracellular iron promotes oxidative stress and activates survival pathways like PI3K/AKT Mugineic acid\u0026rsquo;s dual functionality as an iron chelator and AKT1 inhibitor highlights its therapeutic versatility. Its natural origin, oral bioavailability, and low toxicity further underscore its potential as a cost-effective, plant-derived candidate for integrative prostate cancer therapy, particularly in tumours characterized by dysregulated iron metabolism and AKT1 hyperactivation[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we employed a comprehensive \u003cem\u003ein silico\u003c/em\u003e pipeline to identify and characterize potential AKT1 inhibitors for the treatment of prostate cancer. A library of approximately 13,000 compounds from the Drugbank and IMPPAT databases, including both approved and investigational drugs, was virtually screened against the AKT1 protein. Top-ranking hits were revalidated using AutoDock4 to estimate binding affinities and inhibition constants (Ki). Subsequently, 300 ns (nanoseconds) MD simulations were performed on the five most promising AKT1-ligand complexes to evaluate their structural stability, dynamic behaviour, and interaction patterns. PCA was applied to the MD trajectories to capture large-scale motions and conformational transitions within the complexes.\u003c/p\u003e\u003cp\u003eOur study aims to highlight novel small-molecule inhibitors that exhibit strong binding affinity, structural stability, and potential therapeutic relevance against AKT1 in prostate cancer. These leads may serve as promising candidates for future experimental validation and preclinical development.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Ligand Library Preparation and Structure-Based Virtual Screening\u003c/h2\u003e\u003cp\u003eTo identify potential inhibitors of AKT1, a structure-based virtual screening protocol was implemented using a curated library of approximately 13,000 small molecules comprising both approved and investigational drugs sourced from the Drugbank and IMPPAT databases. All ligands were prepared using AutoDock Tools embedded within the Python Molecular Viewer, where hydrogens were added, Gasteiger partial charges were computed, and torsional bonds were defined[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The AKT1 protein structure was obtained from the AlphaFold Protein Structure Database (Model ID: AF-P31749-F1-model_v4) and processed by removing water molecules, adding polar hydrogens, and assigning Kollman charges using AutoDock Tools to ensure compatibility with docking protocols[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMolecular docking was performed using AutoDock Vina (v1.1.2) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] with an exhaustiveness parameter set to 32 to ensure sufficient sampling of the conformational space. The docking grid was centred on the catalytic site, specifically around the Asp274 residue, with grid centre coordinates defined as x\u0026thinsp;=\u0026thinsp;0.95, y\u0026thinsp;=\u0026thinsp;0.60, z\u0026thinsp;=\u0026thinsp;3.28, and a grid box size of 20 \u0026Aring; \u0026times; 20 \u0026Aring; \u0026times; 20 \u0026Aring;. Compounds were ranked based on predicted binding affinities (ΔG in kcal/mol), and the top five ligands demonstrating the most favourable binding scores and critical interactions with the active site were selected for downstream validation and analysis[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Re-docking and Binding Affinity Validation Using AutoDock 4.2\u003c/h2\u003e\u003cp\u003eTo enhance the reliability and accuracy of the initial virtual screening results, the top five lead compounds identified from the AutoDock Vina screening were re-evaluated using AutoDock 4.2.6[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Receptor and ligand preparation was carried out in accordance with the established AutoDock protocol, including the assignment of Kollman charges, Gasteiger partial charges, and the definition of rotatable bonds for ligand flexibility. Molecular docking was performed using the Lamarckian Genetic Algorithm (LGA), with 50 independent genetic algorithm runs, a population size of 300, and a maximum of 2.5 \u0026times; 10⁶ energy evaluations per run to ensure extensive conformational sampling[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The grid box dimensions and coordinates were kept consistent with those used in the Vina docking protocol to maintain comparability. For each ligand, the binding free energy (ΔG) and the corresponding Ki were calculated using the AutoDock scoring function, thereby enabling quantitative assessment of binding affinity and facilitating robust lead prioritization.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Molecular Dynamics Simulations and Trajectory Analysis\u003c/h2\u003e\u003cp\u003eTo evaluate the structural stability and dynamic behaviour of the top five protein\u0026ndash;ligand complexes identified through re-docking, all-atom molecular dynamics simulations were performed using the Desmond v6.5 simulation package (Schr\u0026ouml;dinger, LLC). Each complex was embedded in an explicit solvent environment using the TIP3P water model, confined within an orthorhombic periodic boundary box with a 10 \u0026Aring; buffer distance from the protein surface to the box edge[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe system was neutralized by adding counterions (Na⁺ or Cl⁻), and an ionic concentration of 0.15 M NaCl was maintained to replicate physiological saline conditions. Energy minimization was performed using the Steepest Descent algorithm to relieve any steric clashes or unfavourable contacts, followed by equilibration under the default relaxation protocol. The OPLS-2005 force field was applied for all protein and ligand atoms, ensuring accurate representation of bonded and non-bonded interactions[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eProduction simulations were carried out for 300 ns under an isothermal-isobaric (NPT) ensemble, with the temperature maintained at 300 K using the Nose-Hoover chain thermostat, and pressure regulated at 1.01325 bar (1 atm) using the Martyna-Tobias-Klein barostat. Trajectory frames were recorded every 300 picoseconds (ps), generating a total of 1,000 frames per complex, all of which were used for post-simulation analyses[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRoot Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF) of AKT1 (protein) and drugs individually, Radius of Gyration (Rg), and hydrogen bond occupancy were computed to assess structural stability, flexibility, and compactness of the complexes over the simulation period[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. PCA was also performed on the Cα atom coordinates to investigate essential motions and conformational transitions in the protein-ligand systems. For this, the MDAnalysis Python package was used to convert trajectory files into the DCD format, which is compatible with the Bio3D library in R for further PCA and dynamic analyses[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The reference structure used for this conversion was taken in PDB format, corresponding to the docked complex that served as the input for system building in the MD simulations[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.4. In Silico ADME and Drug-Likeness Profiling\u003c/h2\u003e\u003cp\u003eTo assess the pharmacokinetic behaviour and drug-likeness of the shortlisted AKT1-binding candidates, comprehensive ADME (absorption, distribution, metabolism, and excretion) profiling was performed using the SwissADME online platform[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Each compound was evaluated for gastrointestinal (GI) absorption potential, blood\u0026ndash;brain barrier (BBB) permeability, and likelihood of acting as a P-glycoprotein (P-gp) substrate, providing insight into passive and active transport mechanisms.[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eDrug-likeness was assessed based on compliance with Lipinski\u0026rsquo;s Rule of Five, Veber\u0026rsquo;s criteria, and additional medicinal chemistry filters including bioavailability scores and synthetic accessibility indices. Moreover, predictive models for cytochrome P450 (CYP450) enzyme inhibition were utilized to evaluate potential metabolic liabilities, focusing on major isoforms such as CYP3A4, CYP2D6, and CYP2C9, which are critically involved in drug metabolism. These parameters were collectively used to prioritize compounds with optimal oral bioavailability, metabolic stability, and favourable physicochemical properties for downstream optimization[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.5. DFT calculations analysis\u003c/h2\u003e\u003cp\u003eTo gain insights into the electronic properties and reactivity of the shortlisted lead compounds, DFT calculations were carried out using Gaussian 16W software. All molecular geometries were optimized in the gas phase employing the B3LYP hybrid exchange-correlation functional in combination with the 6-311\u0026thinsp;+\u0026thinsp;+\u0026thinsp;G(d,p) basis set, which provides a balanced description of valence and diffuse electron behaviour suitable for drug-like molecules[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFrontier molecular orbital (FMO) analysis was conducted to determine the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels. The HOMO-LUMO energy gap (ΔE\u003csub\u003egap\u003c/sub\u003e) was calculated for each compound, serving as a descriptor of molecular reactivity, electronic stability, and charge transfer characteristics. These quantum chemical parameters were used to support the evaluation of lead compound reactivity and their potential for further optimization in drug design pipelines[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Molecular Docking and Binding Affinity and Interaction Profiling Analysis\u003c/h2\u003e\u003cp\u003eA total of 13,000 compounds from the Drugbank and IMPPAT databases were virtually screened against the AKT1 catalytic domain using molecular docking approaches. Based on binding affinities and docking scores, five top-ranking compounds were identified: three from Drugbank and two from IMPPAT: 4-Carboxy imidazole (DB16194), Balanol Analog 2 (DB01940), Tucidinostat (DB06334), Mugineic acid (IMPHY000275), and S-Methyl glutathione (IMPHY000456). The AutoDock Vina docking scores for these ligands ranged from \u0026minus;\u0026thinsp;9.553 to -6.070 kcal/mol, with 4-Carboxy imidazole and Balanol Analog 2 showing the most favourable scores \u0026minus;\u0026thinsp;9.553 and \u0026minus;\u0026thinsp;9.301 kcal/mol, respectively, indicating strong predicted binding affinities. These were followed by Mugineic acid \u0026minus;\u0026thinsp;7.874 kcal/mol and Tucidinostat \u0026minus;\u0026thinsp;7.723 kcal/mol, while S-Methyl glutathione exhibited the least favourable binding score \u0026minus;\u0026thinsp;6.070 kcal/mol among the top five selected compounds (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\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 parameters and interaction profiles of selected ligands with AKT1.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4-Carboxy imidazole (DB16194)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBalanol Analog 2 (DB01940)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMugineic acid (IMPHY000275)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS-Methyl glutathione (IMPHY000456)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTucidinostat (DB06334)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMolecular Formula\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eC\u003csub\u003e4\u003c/sub\u003eH\u003csub\u003e4\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eC\u003csub\u003e27\u003c/sub\u003eH\u003csub\u003e26\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eC\u003csub\u003e12\u003c/sub\u003eH\u003csub\u003e20\u003c/sub\u003eN\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e8\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC\u003csub\u003e12\u003c/sub\u003eH\u003csub\u003e21\u003c/sub\u003eN\u003csub\u003e3\u003c/sub\u003eO\u003csub\u003e6\u003c/sub\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eC\u003csub\u003e22\u003c/sub\u003eH\u003csub\u003e19\u003c/sub\u003eFN\u003csub\u003e4\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDocking Score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-9.553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-9.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-7.874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-7.723\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEstimated Binding Energy (kcal/mol)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-8.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-6.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInhibition Constant (molar)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.53 milli molar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e328.83 nano molar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e731.06 micro molar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95.98 micro molar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.72 micro molar\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntermolecular Energy (kcal/mol)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-11.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-7.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-9.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.51\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003evdW\u0026thinsp;+\u0026thinsp;Hbond\u0026thinsp;+\u0026thinsp;Desolv Energy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-4.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-9.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-5.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-6.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-8.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eElectrostatic Energy (kcal/mol)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTorsional Free Energy (kcal/mol)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u0026thinsp;0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u0026thinsp;2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u0026thinsp;3.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u0026thinsp;3.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u0026thinsp;2.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eInteractions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHydrogen bonds (4)\u003c/p\u003e\u003cp\u003eArg 273, Leu 275, Tyr 315, Ala 317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHydrogen bonds (5)\u003c/p\u003e\u003cp\u003eThr 160, Lys 179, Glu 228, Ala 230, Asn 279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHydrogen bonds (6)\u003c/p\u003e\u003cp\u003eThr 160, Phe 161, Lys 276, Asn 279 (2), Asp 292\u003c/p\u003e\u003cp\u003eSalt bridges (3)\u003c/p\u003e\u003cp\u003eLys 276, Asp 292 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHydrogen bonds (6)\u003c/p\u003e\u003cp\u003eThr 160, Lys 179, Thr 291, Asp 292 (3),\u003c/p\u003e\u003cp\u003eSalt bridges (2)\u003c/p\u003e\u003cp\u003eLys 179 (2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHydrogen bonds (4)\u003c/p\u003e\u003cp\u003eThr 160, Asp 274, Asp 292 (2)\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\u003eSubsequent redocking using AutoDock 4.2 provided refined estimates of binding free energy and Ki. Balanol Analog 2 demonstrated the strongest binding affinity ΔG = -8.84 kcal/mol; Ki\u0026thinsp;=\u0026thinsp;328.83 nano molar, followed by Tucidinostat ΔG = -6.42 kcal/mol; Ki\u0026thinsp;=\u0026thinsp;19.72 micro molar, Mugineic acid ΔG = -4.28 kcal/mol; Ki\u0026thinsp;=\u0026thinsp;731.06 micro molar and S-Methyl glutathione ΔG = -5.48 kcal/mol; Ki\u0026thinsp;=\u0026thinsp;95.98 micro molar. 4-Carboxy imidazole, despite its strong Vina score, exhibited a relatively higher binding free energy and Ki -3.54 kcal/mol and 2.53 milli molar respectively, suggesting lower binding stability in the AutoDock 4.2 scoring framework.\u003c/p\u003e\u003cp\u003eDetailed interaction analysis revealed that all top ligands formed multiple hydrogen bonds and salt bridges with key residues in the AKT1 active site. The key interacting residues included Thr160, Asp274, Asp292, Asn279, and Lys179, which are critical for ligand binding activity. Balanol Analog 2 formed five hydrogen bonds with residues Thr160, Lys179, Glu228, Ala230, and Asn279, and demonstrated a highly favourable intermolecular energy of -11.53 kcal/mol shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Mugineic acid and S-Methyl glutathione both formed six hydrogen bonds and engaged in three and two salt bridge interactions respectively further contributing to complex stability. Tucidinostat and 4-Carboxy imidazole both formed four hydrogen bonds and their intermolecular energies are \u0026minus;\u0026thinsp;4.14 and \u0026minus;\u0026thinsp;8.51 respectively from Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e4.2. MD simulation results analysis\u003c/h2\u003e\u003cp\u003eMD simulations were performed to evaluate the structural stability of the AKT1-ligand complexes and to validate the molecular docking results. The RMSD of the protein backbone and ligand atoms was monitored throughout the 300 ns simulation to assess the conformational stability of each system. Following initial fluctuations during the equilibration phase, commonly attributed to kinetic relaxation, all complexes reached a relatively stable plateau, indicating the system\u0026rsquo;s convergence and stable binding. Among the studied compounds, Balanol Analog 2 demonstrated the lowest average RMSD values across protein regions and ligand orientations, suggesting high structural stability and minimal deviation from the initial docked conformation. And 4-Carboxy imidazole showing some lowest RMSD from 400th to 600th time frame of MD simulation. Conversely, S-Methyl glutathione exhibited the highest RMSD values, particularly in the ligand with respect to the protein, reflecting greater conformational drift and a more dynamic binding mode. The remaining complexes, including Tucidinostat and Mugineic acid, showed moderate but stable RMSD values, indicating that their interactions with AKT1 were well-maintained throughout the simulation. RMSD of ligand with respective to the AKT1 of 4-Carboxy imidazole was very low when compare with Balanol Analog 2 also. These results suggest that the selected ligands form stable complexes with AKT1 and mainly 4-Carboxy imidazole and Balanol Analog 2 complexes are maintained high stability during the simulation period (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eRMSD and RMSF analysis of protein-ligand complexes from MD simulations\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInhibitors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage protein-Ligand RMSD (\u0026Aring;): Cα, backbone, sidechain, protein hetero atoms, ligand w.r.t protein, ligand w.r.t ligand (Range Min - Max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAverage protein RMSF (\u0026Aring;): Cα, backbone, sidechain, protein hetero atoms (Range Min - Max)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAverage ligand RMSF (\u0026Aring;): ligand w.r.t protein, ligand w.r.t ligand (Range Min - Max)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4-Carboxy imidazole\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.247 (2.13\u0026ndash;8.26), 6.237 (2.12\u0026ndash;8.28), 6.673 (2.89\u0026ndash;8.39), 6.445 (2.50\u0026ndash;8.33), 1.783 (0.64\u0026ndash;3.76), 0.148 (0.045\u0026ndash;1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.929 (0.17\u0026ndash;9.71), 2.980 (0.90\u0026ndash;10.26), 3.377 (0.95\u0026ndash;11.09), 3.188 (0.90\u0026ndash;10.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.517 (1.28\u0026ndash;1.78), 0.110 (0.052\u0026ndash;0.172)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBalanol Analog 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.789 (2.14\u0026ndash;6.76), 4.775 (2.15\u0026ndash;6.74), 5.354 (2.90\u0026ndash;7.08), 5.084 (2.48\u0026ndash;6.94), 2.934 (1.84\u0026ndash;5.53), 1.299 (0.62\u0026ndash;2.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.979 (0.60\u0026ndash;7.18), 1.987 (0.60\u0026ndash;7.17), 2.431 (0.74\u0026ndash;7.85), 2.220 (0.67\u0026ndash;7.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.326 (0.96\u0026ndash;2.04), 0.409 (0.23\u0026ndash;0.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMugineic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.934 (2.44\u0026ndash;6.84), 5.933 (2.43\u0026ndash;6.86), 6.241 (3.00\u0026ndash;7.20), 6.123 (2.72\u0026ndash;7.05), 3.260 (1.39\u0026ndash;5.59), 1.334 (0.85\u0026ndash;1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.913 (0.61\u0026ndash;8.49), 1.924 (0.63\u0026ndash;8.36), 2.385 (0.77\u0026ndash;9.28), 2.166 (0.66\u0026ndash;8.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.041 (1.45\u0026ndash;2.87), 0.897 (0.36\u0026ndash;1.52)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS-Methyl glutathione\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.836 (1.66\u0026ndash;8.20), 6.829 (1.68\u0026ndash;8.17), 7.114 (2.45\u0026ndash;8.44), 6.990 (2.02\u0026ndash;8.33), 8.009 (1.63\u0026ndash;10.61), 2.047 (0.60\u0026ndash;2.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.130 (0.71\u0026ndash;8.97), 2.139 (0.75\u0026ndash;8.89), 2.607 (0.79\u0026ndash;9.90), 2.383 (0.75\u0026ndash;9.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.612 (2.76\u0026ndash;4.52), 1.094 (0.57\u0026ndash;1.90)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTucidinostat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.122 (2.59\u0026ndash;7.61), 6.120 (2.57\u0026ndash;7.61), 6.514 (3.42\u0026ndash;7.82), 6.317 (2.99\u0026ndash;7.72), 3.957 (2.11\u0026ndash;5.52), 1.062 (0.55\u0026ndash;1.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.868 (0.60\u0026ndash;8.62), 1.879 (0.62\u0026ndash;8.42), 2.319 (0.71\u0026ndash;9.04), 2.110 (0.68\u0026ndash;8.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.536 (0.85\u0026ndash;2.64), 0.632 (0.26\u0026ndash;1.42)\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\u003e\u003c/p\u003e\u003cp\u003eThe RMSF analysis was performed to evaluate the flexibility of individual amino acid residues within the AKT1-ligand complexes. RMSF calculates the average deviation of each residue from its mean position over the simulation trajectory, providing insights into local structural fluctuations. As expected, the N-terminal (first 120 amino acids), C-terminal (after 400 amino acids), and loop regions exhibited higher fluctuations compared to the more rigid secondary structural elements within the catalytic domain. Importantly, residues within the active site, particularly those involved in ligand binding such as Thr160, Lys179, Asp274, and Asn279 displayed relatively low RMSF values across all complexes, indicating stable interactions and minimal perturbation during the simulation. Among the five ligands, the Balanol Analog 2 complex showed the least fluctuation across the binding site residues, consistent with its strong and stable binding profile observed in RMSD analysis. 4-Carboxy imidazole showing lowest fluctuations starting from 120 to 400 amino acids (nearly 70% of the structure), remaining 30% still within an acceptable range. In contrast, slightly elevated RMSF values were observed in the S-Methyl glutathione complex, though still within an acceptable range, suggesting moderate flexibility without compromising structural integrity. These findings support the conclusion that ligand binding does not induce significant conformational changes in the AKT1 active site and further confirm the dynamic stability of the protein-ligand complexes throughout the 300 ns simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHydrogen bond analysis was performed to evaluate the stability and strength of interactions between the protein and the ligands during the MD simulations. The average number of hydrogen bonds formed throughout the simulation time is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Mugineic acid exhibited the highest average number of hydrogen bonds 4.297, indicating stronger and potentially more stable interactions with the protein. Notably, 4-Carboxy imidazole showed 4 hydrogen bond contacts with AKT1 for 600 frames, corresponding to 60% of the simulation time, highlighting persistent interactions. Both 4-Carboxy imidazole and Balanol Analog 2 showed moderate hydrogen bonding, with average counts of 3.597 and 3.317, respectively, suggesting balanced interaction stability. In contrast, Tucidinostat showed the lowest average hydrogen bonding (1.348), implying relatively weaker interactions. These results correlate well with the RMSD and RMSF analyses, supporting the overall stability of the protein\u0026ndash;ligand complexes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\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\u003eInteraction types and total contacts of AKT1-ligand complexes during MD simulations.\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=\"left\" 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\u003eS. No.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCompound name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4-Carboxy imidazole\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBalanol Analog 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMugineic acid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eS-Methyl glutathione\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTucidinostat\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHydrogen bonds\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3,317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4,297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3,914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1,348\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHydrophobic interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,664\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2,432\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIonic interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMetallic interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e551\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePi-cation interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e949\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e292\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePi-pi stacking interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e316\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater bridge interactions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5,332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3,687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3,116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e8.\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTotal number of Interactions\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4,760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7,596\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11,448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9,207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7,508\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\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\u003eAverage total energy, potential energy, and simulation properties of ligand-bound AKT1 systems.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS. No\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCompound Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAverage total energy (kcal/mol)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAverage potential energy (kcal/mol)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDegrees of freedom\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNumber of particles\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4-Carboxy imidazole\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-164,474.666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-200,103.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e120,044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e58,009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBalanol Analog 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-164,146.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-199,763.711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e120,007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e57,977\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMugineic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-164,560.537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-200,146.814\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e119,903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e57,931\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS-Methyl glutathione\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-164,537.780\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e200,126.812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e119,911\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e57,935\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTucidinostat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-164,195.608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-199,798.147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e119,957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e57,955\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\u003eIn addition to hydrogen bonding, other non-covalent interactions such as hydrophobic, ionic, metallic, pi-cation, pi-pi stacking, and water bridge interactions were analysed to gain a comprehensive understanding of the ligand\u0026ndash;protein binding dynamics. Mugineic acid also showed the highest total number of interactions 11,448, with a significant contribution from water bridges 5,332 and ionic interactions 429, highlighting its multifaceted binding mechanism. Balanol Analog 2 displayed considerable 1,664 hydrophobic, 949 pi-cation, and 1,392 water bridge interactions, supporting its moderate yet stable binding profile. Conversely, 4-Carboxy imidazole had fewer: 383 hydrophobic, and 378 water bridge interactions but compensated through 402 pi-pi stacking interactions, reflecting its moderate overall binding. S-Methyl glutathione presented substantial 459 ionic, 551 metallic, and 3,687 water bridge interactions, reinforcing its stable interaction network despite fewer hydrophobic and pi interactions. Tucidinostat, despite lower hydrogen bonds, showed notable 2,432 hydrophobic, and 3,116 water bridge interactions but minimal ionic or metallic contributions (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMD simulation analysis of ligand properties revealed that 4-Carboxy imidazole maintained the most compact structure, with the lowest Rg 1.92 \u0026Aring; and minimal solvent exposure (SASA 0.81 \u0026Aring;\u0026sup2;). Balanol Analog 2 and Tucidinostat showed higher Rg values 5.44 \u0026Aring; and 5.75 \u0026Aring;, reflecting their larger and more flexible conformations. Mugineic acid and S-Methyl glutathione exhibited intermediate compactness but higher solvent accessible surface areas 174.30 \u0026Aring;\u0026sup2; and 177.16 \u0026Aring;\u0026sup2;, consistent with their polar nature. Intramolecular hydrogen bonds were negligible for most ligands except for slight presence in Mugineic acid and S-Methyl glutathione. Polar surface area values aligned with these trends, with 4-Carboxy imidazole showing the lowest 147.76 \u0026Aring;\u0026sup2; and Mugineic acid the highest 311.87 \u0026Aring;\u0026sup2;. These results support the stability and varying flexibility of the ligands during binding and are consistent with their predicted drug-like profiles (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\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\u003eLigand-specific properties including Rg, intramolecular hydrogen bonds, MolSA, SASA, and PSA during MD simulations.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4-Carboxy imidazole\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBalanol Analog 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMugineic acid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS-Methyl glutathione\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTucidinostat\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRg (\u0026Aring;) (Range: Min - Max)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.920 (1.857\u0026ndash;1.974)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.435 (5.035\u0026ndash;5.769)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.802 (3.307\u0026ndash;4.360)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.767 (3.372\u0026ndash;4.251)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.745 (5.295\u0026ndash;6.322)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eintraHB (Range: Min - Max)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0\u0026ndash;0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.080 (0\u0026ndash;2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.335 (0\u0026ndash;3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001 (0\u0026ndash;1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAverage MolSA (\u0026Aring;\u0026sup2;) (Range: Min - Max)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112.56 (110.47\u0026ndash;114.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e433.90 (425.69\u0026ndash;441.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e281.42 (264.82\u0026ndash;290.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e305.06 (273.49\u0026ndash;325.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e378.31 (372.51\u0026ndash;383.94)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAverage SASA (\u0026Aring;\u0026sup2;) (Range: Min - Max)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.81 (0.00\u0026ndash;12.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.24 (37.26\u0026ndash;136.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e174.30 (15.13\u0026ndash;281.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e177.16 (71.93\u0026ndash;328.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e113.92 (30.72\u0026ndash;249.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAverage PSA (\u0026Aring;\u0026sup2;) (Range: Min - Max)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e147.76 (141.50\u0026ndash;153.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e208.47 (196.75\u0026ndash;221.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e311.87 (259.74\u0026ndash;339.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e265.64 (204.74\u0026ndash;305.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e164.27 (151.12\u0026ndash;173.05)\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\u003eMolecular mechanics energy calculations were performed to assess the stability of the AKT1-ligand complexes during MD simulations. Among the ligands, Mugineic acid and 4-Carboxy imidazole exhibited the lowest average total energies, recorded at -164,560.54 kcal/mol and \u0026minus;\u0026thinsp;164,474.67 kcal/mol, respectively, indicating highly stable systems. Their average potential energies were similarly favourable, with Mugineic acid showing \u0026minus;\u0026thinsp;200,146.81 kcal/mol and 4-Carboxy imidazole closely following at -200,103.13 kcal/mol. The degrees of freedom and number of particles were consistent across all complexes, confirming comparable system sizes and simulation parameters. These energy profiles corroborate the binding stability observed in docking and MD analyses, highlighting Mugineic acid and 4-Carboxy imidazole as ligands with marginally more favourable energetic states (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\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\u003eADME and drug-likeness properties of selected ligands predicted using SwissADME.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4-Carboxy imidazole\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBalanol Analog 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMugineic acid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS-Methyl glutathione\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTucidinostat\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMolecular Weight (Da)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e112.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e474.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e320.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e335.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e390.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTPSA (\u0026Aring;\u0026sup2;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e65.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e167.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e184.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e97.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eH-bond Acceptors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eH-bond Donors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRotatable Bonds\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConsensus LogP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSolubility Class\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery soluble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerately soluble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHighly soluble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHighly soluble\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSoluble\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGI Absorption\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP-gp Substrate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCYP Inhibitor (1A2, 2C19, 2C9, 2D6, 3A4)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAll\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLipinski Rule Violations\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBioavailability Score\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSynthetic Accessibility\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.84\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\u003eAlthough the selected ligands exhibit some variation in RMSD values during the simulations, these conformations correspond to relatively low free energy states, indicating favourable binding stability. The 4-Carboxy imidazole-AKT1 complex predominantly shows RMSD values in the 0.4\u0026ndash;0.6 nm range, with 400 trajectories deposited below 2 kcal/mol free energy, reflecting a highly stable binding conformation. Additionally, 537 trajectories fall within the 0.6\u0026ndash;0.8 nm RMSD range, corresponding to free energies below 8 kcal/mol, suggesting accessible yet still favourable states. Overall, this accounts for approximately 93% of the simulation time spent in stable conformations despite observed RMSD fluctuations. Similarly, the Balanol Analog 2 complex presents 41 trajectories at RMSD values below 0.4 nm and above 0.6 nm with free energies under 2 kcal/mol, alongside 960 trajectories in the 0.4\u0026ndash;0.6 nm range exhibiting free energies between 3 and 10 kcal/mol. In contrast, the Mugineic acid complex shows 439 trajectories in the 0.4\u0026ndash;0.6 nm RMSD range with free energies from 5 to 9 kcal/mol, and 551 trajectories within the 0.6\u0026ndash;0.8 nm RMSD range with higher free energies between 10 and 11 kcal/mol. These observations highlight that despite some RMSD fluctuations, the selected ligands, particularly 4-Carboxy imidazole and Balanol Analog 2 remain in energetically favourable states throughout the simulation, supporting their potential as stable AKT1 inhibitors (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e4.3. PCA results analysis of MD simulations\u003c/h2\u003e\u003cp\u003ePCA was performed to explore the large-scale conformational motions of the AKT1-ligand complexes during MD simulations. The first three principal components (PC1, PC2, and PC3) collectively captured the majority of the essential dynamics. Among the studied complexes, 4-Carboxy imidazole exhibited the highest contribution from PC1 44.02%, followed closely by Mugineic acid 41.85%, indicating that these systems experienced more defined and directional motions along the primary eigenvector. Balanol Analog 2 displayed a more balanced distribution between PC1 38.10% and PC3 38.10%, suggesting a more complex, multi-directional dynamic behaviour. In contrast, Tucidinostat and S-Methyl glutathione showed lower PC1 contributions 28.87% and 28.43%, respectively, reflecting comparatively less pronounced motion along the principal mode. These findings suggest that 4-Carboxy imidazole and Mugineic acid complexes undergo more coordinated and directional conformational shifts, which may contribute to their stable binding behaviour observed in RMSD and hydrogen bonding analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurther interpretation of the PCA displacement plots (residue index vs. displacement) revealed that the N-terminal\u0026thinsp;~\u0026thinsp;150 residues, representing approximately 30% of the AKT1 structure exhibited higher atomic displacements along both PC1 and PC2, indicating localized flexibility in this region. In contrast, the remaining\u0026thinsp;~\u0026thinsp;60% of the protein showed relatively minimal displacement, suggesting a more rigid structural core. Among the ligand-bound complexes, Mugineic acid, S-Methyl glutathione, and Tucidinostat induced only minimal displacements along PC1 and PC2, reflecting a limited influence on global protein motion. Conversely, 4-Carboxy imidazole and Balanol Analog 2 triggered more pronounced conformational shifts, particularly in the flexible N-terminal region, which may correspond to functionally relevant dynamics associated with ligand binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Additionally, the free energy funnel plot for the 4-Carboxy imidazole-AKT1 complex demonstrated a deep and well-defined minimum, with the majority of trajectories clustered within a low-energy region below 5 kJ/mol, represented by the dark blue basin. Only a small fraction of conformations deviated from this stable minimum, reinforcing the thermodynamic stability and favourable binding characteristics of the complex (Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Pharmacokinetic profile of selected ligands\u003c/h2\u003e\u003cp\u003eThe drug-likeness and pharmacokinetic properties of the selected ligands against AKT1 were evaluated using SwissADME (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). All compounds, except Mugineic acid, complied with Lipinski\u0026rsquo;s Rule of Five, indicating favourable oral bioavailability for potential AKT1 inhibition. 4-Carboxy imidazole showed the most drug-like profile with low molecular weight 112.09 Da, high GI absorption, very good solubility, and no CYP inhibition, supporting its potential as an effective AKT1 inhibitor. Balanol Analog 2 also demonstrated high GI absorption, acceptable LogP 2.96, and no Lipinski violations, though it is a P-gp substrate and a CYP inhibitor, which may impact metabolism and efficacy in AKT1 targeting. Tucidinostat exhibited favourable ADME properties with high GI absorption and balanced lipophilicity, but predicted inhibition of multiple CYP450 enzymes suggests possible drug\u0026ndash;drug interactions in AKT1-related therapies. In contrast, Mugineic acid and S-Methyl glutathione showed low GI absorption, high TPSA values (\u0026gt;\u0026thinsp;140 \u0026Aring;\u0026sup2;), and low bioavailability scores 0.11, indicating limited membrane permeability despite high solubility, which may restrict their utility as AKT1 inhibitors without further optimization. Overall, 4-Carboxy imidazole and Balanol Analog 2 present the most promising drug-likeness and ADME profiles for AKT1 inhibition (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.5. DFT analysis of selected ligands\u003c/h2\u003e\u003cp\u003eTo further elucidate the electronic properties and chemical reactivity of the selected ligands, DFT calculations were performed. Balanol Analog 2 and Tucidinostat exhibited the smallest ΔE\u003csub\u003egap\u003c/sub\u003e among the ligands, suggesting high chemical reactivity and a greater tendency for electron exchange within the AKT1 active site. These electronic features correlate well with their strong binding affinities and stable interactions observed in molecular docking and MD simulations. Conversely, 4-Carboxy imidazole showed a moderate ΔE\u003csub\u003egap\u003c/sub\u003e, reflecting a favourable balance between stability and reactivity, supporting selective binding with minimal off-target interactions. Mugineic acid and S-Methyl glutathione displayed wider energy gaps, suggesting lower reactivity despite strong solvation tendencies, which may reduce their effectiveness in initiating efficient electronic interactions within the active site (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDFT-based quantum chemical descriptors and electronic properties of selected ligands.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4-Carboxy imidazole\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBalanol Analog 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMugineic acid\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eS-Methyl glutathione\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTucidinostat\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDipole Moment (D)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.984\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.960\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHOMO (eV)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.33822 (α)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.33797 (α)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.37000 (α) /\u003c/p\u003e\u003cp\u003e-0.27618 (β)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.37209 (α) /\u003c/p\u003e\u003cp\u003e-0.35273 (β)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.32395 (α)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLUMO (eV)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.18082 (α)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.22547 (α)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.14044 (α) /\u003c/p\u003e\u003cp\u003e-0.14687 (β)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.14687 (α) /\u003c/p\u003e\u003cp\u003e-0.27618 (β)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.22652 (α)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHOMO-LUMO Gap (eV)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.15740 (α)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.11250 (α)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.22956 (α) /\u003c/p\u003e\u003cp\u003e0.12931 (β)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.22522 (α) /\u003c/p\u003e\u003cp\u003e0.07655 (β)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.09743 (α)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSolvation Energy (kcal/mol)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-17.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-23.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-96.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-68.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-25.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eZero Point Energy (kcal/mol)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e307.777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e210.111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e212.654\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e235.674\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEnthalpy (kcal/mol)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13.457\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14.196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15.203\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFree Energy (kcal/mol)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-19.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-34.798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-29.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-30.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-32.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEntropy (cal/mol/K)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e178.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e144.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e150.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e158.816\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePolarizability (QPpolrz)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50.165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28.232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e42.439\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePSA (\u0026Aring;\u0026sup2;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e146.582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e202.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e192.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e110.149\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVolume (\u0026Aring;\u0026sup3;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e396.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1437.438\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e988.491\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1051.752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1239.264\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\u003eDipole moment and polarizability values provided insights into the compounds\u0026rsquo; behaviour in polar biological environments. Balanol Analog 2 and Tucidinostat demonstrated higher dipole moments and polarizabilities, suggesting flexible electrostatic interactions and adaptability within the AKT1 pocket. In contrast, 4-Carboxy imidazole exhibited lower values, supporting its compact, rigid conformation and potentially tighter fit into the binding site. PSA and molecular volume values further aligned with predicted permeability and bioavailability, highlighting 4-Carboxy imidazole as a promising drug-like molecule due to its small size and lower polarity. Thermodynamic parameters such as free energy and enthalpy reinforced the stability of Balanol Analog 2 and Tucidinostat, aligning with their dynamic performance. Collectively, these DFT results position 4-Carboxy imidazole as a compact, selective inhibitor and Balanol Analog 2 as a chemically reactive and adaptable AKT1-binding candidate.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study applied a structure-based virtual screening approach, followed by MD simulation, ADME, and DFT analyses, to identify potential inhibitors of the AKT1 catalytic domain: a key target in CRPC. From a screening of 13,000 compounds, five ligands were shortlisted, with 4-Carboxy imidazole and Balanol Analog 2 emerging as the most promising candidates. Initial docking using AutoDock Vina highlighted these two compounds with favourable binding scores; however, further redocking using AutoDock 4.2 revealed differing trends: while Balanol Analog 2 maintained strong binding affinity, 4-Carboxy imidazole, despite its high Vina score, exhibited comparatively weaker predicted affinity. This discrepancy underscores a well-recognized limitation of molecular docking, where differences in scoring algorithms across tools can significantly impact compound ranking. As such, docking predictions should be interpreted cautiously and supported by additional dynamic analyses to better understand binding stability and ligand performance.\u003c/p\u003e\u003cp\u003eMD simulation analyses provided valuable insights into the dynamic behaviour and stability of the AKT1-ligand complexes. Both Balanol Analog 2 and 4-Carboxy imidazole exhibited consistently low RMSD and RMSF values throughout most of the simulation, indicating stable binding within the active site. Importantly, the RMSD trajectories compared to the initial docking poses predominantly occupied lower-energy conformational states, further supporting the stability of these complexes over time. Notably, 4-Carboxy imidazole maintained four hydrogen bonds for over 60% of the simulation and showed low solvent-accessible surface area, reflecting a compact and persistent interaction mode. In contrast, although Mugineic acid formed the highest number of hydrogen bonds and total interactions, its elevated RMSD and large polar surface area suggested a more flexible and less deeply buried binding conformation, which may adversely affect its pharmacokinetic suitability.\u003c/p\u003e\u003cp\u003ePCA and energy landscape analyses further confirmed the stability of the 4-Carboxy imidazole and Balanol Analog 2 complexes, as both ligands maintained low-energy conformational states throughout the simulation. These findings were supported by ADME predictions, where 4-Carboxy imidazole demonstrated favourable oral bioavailability, high gastrointestinal absorption, good solubility, and no predicted CYP enzyme inhibition. Balanol Analog 2 also showed acceptable pharmacokinetic properties but raised potential metabolic concerns due to CYP inhibition and P-glycoprotein substrate characteristics. In contrast, compounds like Mugineic acid and S-Methyl glutathione, despite strong docking interactions, exhibited poor membrane permeability and low bioavailability scores, indicating a need for structural optimization. Complementing these results, DFT analyses provided insight into the electronic properties of the ligands; Balanol Analog 2 and Tucidinostat displayed low HOMO-LUMO gaps, suggesting higher reactivity and stronger potential interactions within the AKT1 binding pocket, whereas 4-Carboxy imidazole showed moderate reactivity and a low dipole moment, indicative of selective and stable binding with potentially fewer off-target effects.\u003c/p\u003e\u003cp\u003eDespite the encouraging in silico performance of the selected ligands, this study has several limitations. The reliance on a static AlphaFold-predicted AKT1 structure may not fully capture the protein\u0026rsquo;s conformational flexibility or allosteric regulatory features, potentially influencing binding predictions. Additionally, the absence of experimental validation restricts the immediate translational applicability of the results. Discrepancies between scoring functions used in different docking tools, along with the lack of toxicity and selectivity assessments, further constrain the robustness of the conclusions. To address these gaps, future research should include in vitro validation of the top candidates to confirm AKT1 inhibition and evaluate cytotoxicity in CRPC models. Structural optimization may be necessary to improve the pharmacokinetic properties of polar compounds like Mugineic acid, while selectivity profiling will be essential to ensure isoform-specific inhibition.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study aimed to identify novel high-affinity inhibitors of the AKT1 catalytic domain using an integrated in silico approach. A total of 13,000 compounds from Drugbank and IMPPAT databases were virtually screened, leading to the identification of five promising candidates. Among them, 4-Carboxy imidazole and Balanol Analog 2 emerged as the most stable and potent binders, based on molecular docking, molecular dynamics simulations, and binding free energy profiles. The MD simulations confirmed stable interactions of these ligands with key active site residues, supported by sustained hydrogen bonding, low RMSD and RMSF values, and energetically favourable conformations. Additionally, PCA and energy landscape analyses highlighted consistent low-energy states throughout the trajectory. ADME profiling suggested that 4-Carboxy imidazole had the most favourable drug-like properties, while Balanol Analog 2 also showed good absorption but potential metabolic liabilities. DFT calculations further supported their suitability by revealing favourable electronic properties, with 4-Carboxy imidazole demonstrating a stable and selective binding profile. In conclusion, 4-Carboxy imidazole and Balanol Analog 2 represent promising AKT1 inhibitor candidates, warranting further experimental validation and development for potential therapeutic application in castration-resistant prostate cancer.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution Declaration\u003c/h2\u003e\n\u003cp\u003eHemantha Mani Kumar Chakravarthi Chanda\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e: Executed \u003cem\u003ein silico\u0026nbsp;\u003c/em\u003eanalyses including molecular docking and molecular dynamics simulations, and contributed to the writing and editing of the manuscript.\u003c/p\u003e\n\u003cp\u003eSudheer Kumar Katari\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e: Contributed to the study\u0026apos;s conceptualization and design, and conducted \u003cem\u003ein silico\u0026nbsp;\u003c/em\u003eanalysis, and including target prediction.\u003c/p\u003e\n\u003cp\u003eAffiliations\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Department of Bioinformatics, Vignan\u0026apos;s Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur-522213, India.\u003c/p\u003e\n\u003cp\u003eCorresponding Author\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSudheer Kumar Katari Vignan\u0026apos;s Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur-522213, India.\u003c/p\u003e\n\u003cp\[email protected]\u003c/p\u003e\n\u003cp\u003eSubject Area: Bioinformatics, Computational Biology, Drug Discovery, Structural Biology\u003c/p\u003e\n\u003ch2\u003eFunding Declaration\u003c/h2\u003e\n\u003cp\u003eThe authors declare that no financial support was received for the research, authorship, and / or publication of this article.\u003c/p\u003e\n\u003ch2\u003eConflict of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAuthors are highly thankful to VFSTR (Deemed to be University) for providing faculty seed grant (F.No. VFSTR/REG/A6/30/2023-24/01 dated 16-05-2023) facility.\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the use of AI tools, including ChatGPT (OpenAI) and Gemini (Google), for language editing and sentence refinement. These tools were employed solely to enhance the clarity and grammar of the manuscript, and were not used for generating scientific content, analysis, technical writing, or interpretation.\u003c/p\u003e\n\u003ch2\u003eData availability\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003cbr\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRawla P (2019) Epidemiology of prostate cancer. World J Oncol 10:63\u003c/li\u003e\n\u003cli\u003eRebello RJ, Oing C, Knudsen KE, et al (2021) Prostate cancer. Nat Rev Dis Primers 7:9. https://doi.org/10.1038/s41572-020-00243-0\u003c/li\u003e\n\u003cli\u003eBergengren O, Pekala KR, Matsoukas K, et al (2023) 2022 Update on Prostate Cancer Epidemiology and Risk Factors\u0026mdash;A Systematic Review. 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NPJ Comput Mater 10:213. https://doi.org/10.1038/s41524-024-01403-6\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-diversity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"modi","sideBox":"Learn more about [Molecular Diversity](http://link.springer.com/journal/11030)","snPcode":"11030","submissionUrl":"https://submission.nature.com/new-submission/11030/3","title":"Molecular Diversity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"AKT1 inhibitors, Castration-resistant prostate cancer (CRPC), MD simulation, In silico drug design, ADME profiling, Density Functional Theory (DFT), Binding affinity","lastPublishedDoi":"10.21203/rs.3.rs-7627265/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7627265/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe serine/threonine kinase AKT1 plays a pivotal role in cancer progression and therapy resistance, particularly in castration-resistant prostate cancer (CRPC). This study employed an integrated in silico approach to identify potential AKT1 catalytic domain inhibitors from a library of 13,000 compounds sourced from Drugbank and the IMPPAT database. Structure-based virtual screening using AutoDock Vina and AutoDock 4.2 identified five promising candidates, among which 4-Carboxy imidazole and Balanol Analog 2 showed the most favourable binding interactions. Molecular dynamics (MD) simulations revealed that both compounds exhibited low RMSD and RMSF values, indicating stable binding throughout the simulation period. Notably, 4-Carboxy imidazole maintained persistent hydrogen bonding and low solvent exposure, suggesting a compact binding mode. Principal component analysis (PCA) and free energy landscape analyses further supported the conformational stability of these complexes. ADME profiling showed that 4-Carboxy imidazole had superior drug-like properties, while Balanol Analog 2 raised potential concerns related to metabolism. Density Functional Theory (DFT) calculations highlighted favourable electronic properties for both top ligands, with 4-Carboxy imidazole exhibiting a low dipole moment and moderate reactivity, suggesting specificity and stability. While the results are promising, further experimental validation is required to confirm inhibitory activity and therapeutic potential. Overall, this study identifies 4-Carboxy imidazole and Balanol Analog 2 as promising lead compounds for the development of AKT1-targeted therapies in CRPC.\u003c/p\u003e","manuscriptTitle":"In Silico Discovery of Natural and Synthetic Inhibitors Targeting AKT1 in Prostate Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-26 15:15:36","doi":"10.21203/rs.3.rs-7627265/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-06T12:03:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-28T05:01:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231432447170648870564860813826837211777","date":"2025-09-27T06:04:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208491144113058937445720470578702800588","date":"2025-09-18T08:05:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"240732460336702335083204048111780270610","date":"2025-09-17T11:27:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-17T11:19:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-16T10:42:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-16T09:19:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Diversity","date":"2025-09-16T07:28:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-diversity","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"modi","sideBox":"Learn more about [Molecular Diversity](http://link.springer.com/journal/11030)","snPcode":"11030","submissionUrl":"https://submission.nature.com/new-submission/11030/3","title":"Molecular Diversity","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e2e06639-d93b-4730-bd62-d7ee7108c186","owner":[],"postedDate":"September 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:01:38+00:00","versionOfRecord":{"articleIdentity":"rs-7627265","link":"https://doi.org/10.1007/s11030-026-11563-w","journal":{"identity":"molecular-diversity","isVorOnly":false,"title":"Molecular Diversity"},"publishedOn":"2026-04-26 15:57:30","publishedOnDateReadable":"April 26th, 2026"},"versionCreatedAt":"2025-09-26 15:15:36","video":"","vorDoi":"10.1007/s11030-026-11563-w","vorDoiUrl":"https://doi.org/10.1007/s11030-026-11563-w","workflowStages":[]},"version":"v1","identity":"rs-7627265","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7627265","identity":"rs-7627265","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}