All chemicals and reagents used in this study were of analytical grade and were supplied by Sigma-Aldrich unless otherwise specified. C. longa L. (turmeric) rhizomes were purchased in ground form. Methanol (CH₃OH) and chloroform (CHCl₃) were used as solvents for TLC and HPLC. Formic acid (HCOOH) was employed as a mobile phase additive during HPLC. Sulfuric acid (H₂SO₄), sodium phosphate (Na₃PO₄), and ammonium molybdate ((NH₄)₆Mo₇O₂₄) were purchased for the total antioxidant for the total antioxidant capacity (TAC) assay. Potassium ferricyanide (K₃[Fe(CN)₆]) and trichloroacetic acid (C₂HCl₃O₂) were used in th ferric reducing antioxidant power (FRAP) assay. All reagents were prepared fresh before use, and solvents used for extraction and chromatography were of HPLC grade.

To prepare the, 25 g of ground C. longa rhizomes was boiled in 250 mL of distilled water at 100°C in a reflux water bath for 15 min. The resulting mixture was filtered using Whatman No. 1 filter paper, and the filtrate was centrifuged at 4,000 rpm for 20 min. The supernatant was concentrated to dryness using a rotary evaporator (Heidolph Laborota 4,000), and the residue was stored in a refrigerator at 4°C until use (Serairi-Beji et al., 2018).

The extraction yield (Y) was calculated as follows: Y   % = mass of flask with extract  g − mass of empty flask  g / mass of the sample  g ) × 100

The CAE maximum absorbance wavelength (λmax) was determined by scanning 1 mg/mL aqueous solution over a range of 400–800 nm using a UV-Vis-NIR spectrometer (JASCO V-770). Water was used as the blank, following the method described by Singh and Avupati (2017). To enhance the = measurement accuracy, data were acquired using the following parameters: scan speed of 200 nm/min, data interval of 1 nm, and slit width of 1 nm. Each measurement was taken in triplicate to ensure reproducibility, and the average absorbance values were recorded. The λmax value was identified as the wavelength at which the maximum absorbance occurred, indicating the most efficient light absorption by the active compounds present in the extract.

TLC using Millipore Sigma TM silica gel plate analyzed the curcuminoids in the CAE. To enhance the separation efficiency, the silica gel plates were first activated by heating at 110°C for 30 min (Waksmundzka-Hajnos et al., 2022). The mobile phase comprised various ratios of methanol and chloroform (10:90, 25:75, 45:55, 50:50, 60:40, 75:25, and 90:10), prepared freshly for optimal separation. Uniform spots of the extract and of commercial controls (i.e., curcumin, demethoxycurcumin, bisdemethoxycurcumin), at equivalent concentrations, were applied approximately 1 cm from the plate bottom using a micropipette, plates were placed in a pre-saturated development chamber, allowing the solvent front to ascend for 8–10 cm. After the development phase, plates were removed, dried in a fume hood, and examined under UV light at 365 nm to visualize the spotsthatconfirmed the presence of curcuminoids by their characteristic fluorescence. The retention factor (Rf) of each component was calculated using the formula: Rf = distance traveled by the sample  h / distance traveled by the solvent front  H

Each sample was analyzed in triplicate for reproducibility.

To identify polyphenolic compounds, The CAE was filtered through a 0.45 µm membrane and injected into an Agilent Series 1100 HPLC system equipped with a multi-wavelength UV/Vis detector. A Kinetex column (100 × 4.6 mm, 2.6 µm) was used for separation. The mobile phase consisted of 0.1% formic acid in water and methanol, with a linear gradient elution from 5% to 95% methanol over 15 min. Detection was performed at 280 nm, and compounds were identified based on their retention times and spectral profiles compared with reference standards.

The CAE was determined using the method described by Prieto et al. (1999) that, involves the formation of a green phosphate/Mo (V) complex. A reagent solution containing 0.3 N sulfuric acid, 28 mM sodium phosphate, and 4 mM ammonium molybdate was prepared. A 100 µL aliquot of diluted extract was mixed with 1 mL of reagent solution. After incubation in a boiling water bath for 90 min, the mixture was cooled, and absorbance was measured at 695 nm. Results were expressed as mg of gallic acid equivalent per Gram dry weight of the extract (mg GAE/g d.w.).

The DPPH radical-scavenging activity was assessed using the method described by Sánchez-Moreno et al. (1998). Briefly, 50 µL of extract at different concentrations (0.078–5 mg/mL) was mixed with 1.95 mL of DPPH solution (0.025 g/L in methanol). After 30 min in the dark, absorbance was measured at 515 nm. Ascorbic acid (vitamin C) is used under the same conditions as a positive control. The IC₅₀, the concentration required to inhibit 50% of DPPH radicals, was calculated as follows: I % = A 1 − A 2 /   A 1 × 100 Where I% is the percentage of inhibition, A1 is the absorbance of the control, and A2 is the absorbance in the presence of the extract.

The FRAP test was performed according to Wu et al. (2014). CAE solutions (0.078–5 mg/mL) were mixed with 2.5 mL of 0.2 M phosphate buffer (pH 6.6) and 2.5 mL of 1% potassium ferricyanide. After incubation at 50°C for 20 min, 2.5 mL of 10% trichloroacetic acid was added, followed by centrifugation at 3,000 rpm for 10 min. The supernatant (2.5 mL) was mixed with 2.5 mL of distilled water and 0.5 mL of 0.1% FeCl₃ solution, and absorbance was measured at 700 nm. Ascorbic acid (vitamin C) is used under the same conditions as a positive control. The Antioxidant power was expressed as EC₅₀ (i.e., the CAE concentration that causes the loss of 50% of the oxidant concentration).

The anti-inflammatory activity was evaluated using the albumin denaturation inhibition method, based on Nagavekar and Singhal (2018). A reaction mixture containing 1 mL of 1% egg or bovine serum albumin (BSA), 1 mL of PBS (pH 6.8), and 0.2 mL of the extract or standard drug (aspirin) was prepared and heated at 70°C for 5 min. After cooling, absorbance was measured at 660 nm. The percentage of albumin denaturation inhibition (I %) was calculated as follows: I % = A 1 − A 2 /   A 1 × 100 where A1 is the absorbance of the control and A2 is the absorbance of the test sample.

Potential ligands were selected among the bioactive compounds identified in the CAE by HPLC. The three-dimensional (3D) structures of these compounds were obtained from the PubChem database (pubchem.ncbi.nlm.nih.gov). The PDB IDs of the target proteins for this study were 4QWO (profiling-like protein), 8CEQ (cap-specific mRNA (nucleoside-2′-O-) - methyl transferase, and 8ORV (poxin-schlafen), which are critical enzymes involved in Mpox virus replication and pathogenicity. Their crystal structures were retrieved from the RCSB Protein Data Bank (www.rcsb.org). Enzyme preparation was performed using Molegro Virtual Docker 2.5, and molecular docking simulations were carried out with PyRx (version 0.8). The docking grid box parameters were: i) for 4QWO, the center coordinates were X: 3.3969, Y: 4.2924, Z: 14.3419, with dimensions of X: 52.8775 Å, Y: 59.5515 Å, and Z: 37.9783 Å; ii) for 8CEQ, the center coordinates were X: 21.8083 Å, Y: 25.8624 Å, and Z:12.8936, with dimensions of X: 56.8751 Å, Y: 51.9624 Å, and Z:31.5580 Å; andiii) for8ORV the center coordinates were X: 21.8083, Y: 25.8624, Z: 12.8936, with dimensions of X: 56.8751 Å, Y: 51.9624 Å, and Z: 31.5580 Å. The docking results were visualized with BIOVIA Discovery Studio Visualizer to provide detailed insights into the protein-ligand interactions, highlighting key hydrogen bonds and hydrophobic interactions between the ligands and the amino acid residues of the target proteins.

Molecular dynamics (MD) simulations were conducted using the GROMACS 2023-GPU package with the CHARMM-36–2019 force field to validate docking results and evaluate the dynamic stability of protein-ligand complexes (Boudou et al., 2024). The study focused on key monkeypox virus proteins, including 4QWO (profiling-like protein), 8CEQ (cap-specific mRNA (nucleoside-2′-O-) methyltransferase), and 8ORV (poxin-schlafen), which are crucial for viral replication and pathogenicity. Only the top-scoring protein-ligand complexes were selected for simulations. The systems were solvated in a cubic box with TIP3P water models, neutralized using Na⁺ and Cl⁻ ions, and subjected to energy minimization to eliminate steric clashes and optimize geometry. Subsequently, equilibration was carried out in two phases: constant volume (NVT ensemble) for 2 nanoseconds and constant pressure (NPT ensemble) for another 2 nanoseconds. Final production runs were conducted for 100 nanoseconds under constant temperature (300 K) and pressure (1 bar), enabling the assessment of stability and flexibility throughout the simulation.

All data are presented as mean ± SEM (Standard Error of the Mean). Statistical analyses were performed using Sigma Plot 11.0 for Windows. Comparisons between two means were evaluated using Student’s t-test, while comparisons among multiple means were conducted using Analysis of Variance (ANOVA) followed by post hoc tests recommended by the software, such as Student-Newman-Keuls, Bonferroni, or Tukey’s test. Significant differences between experimental groups are indicated by distinct letters (a-e), where groups not sharing the same letter are considered significantly different at p < 0.05.

The CAE yield using the decoction method was 7.80% ± 0.81%, indicating the effective recovery of bioactive components from the plant material. The λmax of the CAE 470 nm (Figure 2), in agreement with the established λmaxrange (425–493 nm) for curcuminoids (Khumsupan et al., 2011; Sathishkumar et al., 2015). This finding supports the presence of curcuminoids, particularly curcumin, consistent with previous research (Pothitirat and Gritsanapan, 2006; de Oliveira Filho et al., 2021). The λmax value is essential for characterizing specific compounds and provides the foundation for the subsequent analytical and functional studies (Lozada-Ramírez et al., 2021).

The TLC analysis successfully identified curcumin, demethoxycurcumin, and bisdemethoxycurcumin as the major curcuminoids in the extract (Figure 3). The observed Rf values (Table 1) were in line with previous reports, reinforcing TLC reliability as a preliminary phytochemical screening method (Sarwat et al., 2017). The HPLC analysis (Figure 4; Table 2) confirmed that curcumin was the predominant compound (36.33%), and that demethoxycurcumin, bisdemethoxycurcumin, and tetrahydrocurcuminwere present in smaller quantities (0.93%; 0.87%, 5.12%, respectively) in line with previous studies on C. longa extract phytochemical composition (Jyotirmayee and Mahalik, 2022). The identification of additional compounds, including curcuminol (2.39%) and Ar-curcumenone (4.92%), improves the understanding of C. longa chemical diversity, suggesting that these lesser-known constituents may contribute to its overall therapeutic efficacy. Moreover, the two unidentified compounds (0.32% and 0.33%) may represent additional bioactive substances, warranting additional investigations.

The HPLC results confirmed that curcumin, recognized for its anti-inflammatory, antioxidant, and anticancer activities (Sengupta and Tripathi, 2023), was the major component. The presence of demethoxycurcumin (0.93%) and bisdemethoxycurcumin (0.87%), although in smaller quantities, enhances the extract potential because these compounds exhibit similar biological effects (Anand et al., 2008). Moreover, tetrahydrocurcumin, known for its potential neuroprotective effects, and curcuminol associated with improved bioavailability, enhance the extract therapeutic profile (Nile and Park, 2015; Fuloria et al., 2022).

The in vitro antioxidant activity of the CAE was assessed using three assays: total antioxidant capacity, DPPH radical scavenging, and FRAP radical scavenging (Figure 5). The results, summarized in Table 3, demonstrated a substantial antioxidant potential of the CAE. Specifically, its TAC was 36.55 ± 0.01 µg GAE/g d.w., in line with previous reports highlighting the potent antioxidant properties of C. longa. extract, primarily attributed to the high content of bioactive compounds, particularly curcuminoids (Sabale et al., 2013). The TAC value reflects the CAE overall ability to neutralize free radicals, a crucial function for limiting oxidative stress and preventing cellular damage.

In the DPPH radical scavenging assay, the CAE IC₅₀ value was 0.77 ± 0.04 mg/mL, indicating its effective capacity to scavenge free radicals. The IC₅₀ value was comparable to that ofother plant extracts rich in phenolic and flavonoid compounds, which are known for their free radical scavenging activity (Pereira et al., 2018). The presence of curcumin and other curcuminoids, such asdemethoxycurcumin and bisdemethoxycurcumin, likely contributes to this activity. It is well known that curcumin can donate hydrogen atoms and neutralize free radicals, reinforcing the observed scavenging efficacy (Aggarwal et al., 2007). Studies have shown that curcumin exhibits the highest antioxidant activity, followed by demethoxycurcumin and bisdemethoxycurcumin, confirming the significant role of these curcuminoids in free radical scavenging (Jayaprakasha et al., 2006). Additionally, demethoxycurcumin and bisdemethoxycurcumin have been found to enhance the antioxidant activity of curcumin when tested in refined sunflower oil, further demonstrating their synergistic effect in antioxidant mechanisms (Rege et al., 2014). Moreover, hydrogenated derivatives of curcumin, such as tetrahydrocurcumin, have been shown to exhibit even stronger antioxidant activities (Somparn et al., 2007).

The FRAP assay result (EC₅₀ value of 3.46 ± 0.11 mg/mL) further supports the CAE antioxidant profile. This test evaluates the electron-donating capacity of an extract that is essential for breaking free radical chains and preventing oxidative damage. The CAE reducing power indicates its ability to act as an electron donor, converting ferric ions (Fe³⁺) into ferrous ions (Fe²⁺), a key mechanism in the antioxidant defense. This activity is consistent with the properties of curcuminoids, and other polyphenolic compounds found in C. longa that display strong electron-donating ability (Wang et al., 2017). Although the extract shows lower activity than ascorbic acid, its significant antioxidant potential indicates its promise as a natural alternative for further development and synergistic applications.

The CAE in vitro anti-inflammatory activity was evaluated by measuring the inhibition of heat-induced protein denaturation, using egg albumin and BSA (Chandra et al., 2012). The maximum protein denaturation inhibition values 78.88% ± 0.53% for egg albumin and 90.51% ± 0.29% for BSA were obtained with 5,000 μg/mL CAE (Table 4) indicating significant anti-inflammatory potential. The in vitro anti-inflammatory activity of the C. longa aqueous extract was evaluated by measuring its inhibition of heat-induced protein denaturation, using egg albumin and BSA as models. The maximum inhibition values were 78.88% ± 0.53% for egg albumin and 90.51% ± 0.29% for BSA at a concentration of 5,000 μg/mL CAE (Table 4), demonstrating significant anti-inflammatory potential. This result aligns with findings from Bagad et al. (2013), who also reported notable anti-inflammatory effects of an aqueous extract of C. longa in both acute and chronic inflammation models. Additionally, curcumin, the primary bioactive compound in C. longa, is well-known for its ability to inhibit key pro-inflammatory pathways, as detailed by Peng et al. (2021), which underscores its effectiveness in treating various inflammation-related diseases.

Aspirin (acetylsalicylic acid) used as the reference anti-inflammatory compound, demonstrated higher protein denaturation inhibition rates at 1,000 μg/mL, with values of 88.80% ± 0.28% for egg albumin and 95.37% ± 0.23% for BSA. In comparison, the inhibition effects of the C. longa aqueous extract decreased at lower concentrations, showing a clear dose-dependent response. For instance, at 625 μg/mL, the extract inhibited egg albumin and BSA denaturation by 59.36% ± 0.27% and 79.50% ± 0.29%, respectively. This indicates that the anti-inflammatory potential of CAE is concentration-dependent, with higher doses providing stronger inhibition. Although the exact mechanism by which CAE inhibits protein denaturation remains to be fully understood, research by Chen et al. (2022) offers insight into how certain phenolic compounds, such as tannic acid, epigallocatechin gallate, quercetin, and quercitin, interact with proteins like casein. These phenolic compounds promote protein aggregation, enhance emulsifying and foaming stability, and alter protein structure. Their interactions are influenced by factors such as molecular weight, hydroxyl group positioning, acylation, and glycosylation, which affect protein behavior. It is possible that similar interactions involving tannins in CAE protect proteins from heat-induced damage by forming chemical bonds that stabilize the protein structure. This stabilization may reduce the entropy of the polypeptide chain, slowing down the denaturation process and providing a protective effect.

These findings also align with previous research on the anti-inflammatory effects of C. longa extracts, supporting its traditional use in managing inflammatory conditions (Koosirirat et al., 2010; Bagad et al., 2013). The inhibition of albumin denaturation suggests that C. longa aqueous extract (CAE) could serve as a natural alternative to conventional anti-inflammatory drugs like aspirin, providing therapeutic benefits with potentially fewer side effects. The effectiveness of CAE in inhibiting protein denaturation is largely attributed to its active compounds, primarily curcumin and its derivatives. Curcumin’s anti-inflammatory properties are well-documented, and its mechanism of action includes the inhibition of key enzymes, such as cyclooxygenases (COX) and lipoxygenases (LOX), which are involved in the production of pro-inflammatory mediators like prostaglandins and leukotrienes. Additionally, curcumin suppresses the activity of pro-inflammatory cytokines, including TNF-α, IL-1β, and IL-6, which are central to the inflammatory process. Its ability to inhibit the NF-κB signaling pathway, a critical regulator of inflammation, further supports curcumin’s role in reducing the transcription of genes linked to inflammation (Kunnumakkara et al., 2017; Gupta et al., 2013). These combined mechanisms highlight C. longa’s potential as an effective natural anti-inflammatory agent.

The molecular docking results (Table 5; Figure 6) offer insights into the interaction between curcumin derivatives and three target proteins (PDB IDs: 4QWO, 8CEQ, and 8ORV) implicated in Mpox virus replication and pathogenicity. These interactions revealed the binding affinities, and the residues involved in ligand-target protein interactions, suggesting how these compounds could inhibit specific biological pathways involved in viral infection.

4QWO:demethoxycurcuminshowed the strongest binding affinity (−7.7 kcal/mol), and interacted with Arg B: 114, Arg B: 115, and Glu B: 83. This high affinity indicates strong molecular interactions with the target virus protein, suggesting that demethoxycurcuminmay have a significant inhibitory effect on the targeted pathway. Curcumin had a slightly lower binding affinity (−6.7 kcal/mol) and alsointeracted with key residues (Arg B: 114, Arg B: 115, Tyr B: 118), indicating that it retains substantial efficacy.

8CEQ: Curcumin exhibited the highest binding affinity (−8.1 kcal/mol) and interacted with Val B: 116 and Tyr A: 189. This suggests that curcumin can strongly bind to the active site of this protein, potentially inhibiting its function. The strong affinity of bisdemethoxycurcumin (−7.9 kcal/mol) and demethoxycurcumin (−7.8 kcal/mol) and interaction with key residues in 8CEQ further support their role as effective anti-inflammatory/antiviral agents.

8ORV:Demethoxycurcumin again showed the strongest binding affinity (−7.4 kcal/mol) and, interacted with Ala A: 8 and Gly A: 21, highlighting its broad potential across different target proteins. Curcumin and curcuminol (-7.1 kcal/mol and −7.07 kcal/mol respectively), also showed significant interactions with key residues.

In a related study, Mulu et al. (2021) demonstrated that curcumin-derived polyphenols, specifically demethoxycurcumin and bisdemethoxycurcumin, exhibited significant binding affinities to the COVID-19 main protease, indicating their potential as effective antiviral agents. The therapeutic effects of these curcumin derivatives have been extensively researched, particularly concerning inflammation and oxidative stress-related diseases. Recent literature indicates that demethoxycurcumin and bisdemethoxycurcumin modulate various molecular targets, including inflammatory enzymes like COX-2, transcription factors such as NF-κB, and cytokines like TNF-α and IL-6 (Bahuguna et al., 2021). The molecular docking results from this study further substantiate the ability of curcumin derivatives to interact with specific amino acid residues within the active sites of viral proteins, which may play a role in inflammatory responses. Notably, demethoxycurcumin exhibits superior binding affinity, suggesting that the absence of one methoxy group enhances its interactions with target proteins, thereby increasing its efficacy compared to curcumin.

This finding also aligns with previous reports indicating that demethoxycurcumin demonstrates higher biological activity than curcumin in anti-inflammatory assays. Across all protein structures tested, demethoxycurcumin consistently showed the strongest binding affinities, suggesting it may be the most potent ligand among the curcumin derivatives. Its interactions with critical residues, such as Arg, Tyr, and Glu, in the active sites of these proteins may account for its enhanced therapeutic effects. These molecular docking results are consistent with experimental studies demonstrating that demethoxycurcumin effectively modulates inflammatory responses by interacting with relevant enzymes and signaling pathways.

The molecular dynamics (MD) simulations of the three selected complexes, 8CEQ-Curcumin, 4QVO-DMC, and 8QRV-DMC, were performed to evaluate their stability, compactness, and residue flexibility. These complexes were chosen because they represented the best docking results, exhibiting the highest binding affinities and optimal interactions with the target protein during the molecular docking analysis.

The RMSD analysis (Figure 7A) showed that all three complexes reached equilibrium during the simulation period, with deviations ranging between 0.2 and 0.6 nm. Among them, 4QVO-DMC exhibited the lowest fluctuations, indicating greater stability compared to the other two complexes. The 8CEQ-Curcumin complex showed slightly higher fluctuations during the middle phase of the simulation but stabilized toward the end, whereas the 8QRV-DMC complex maintained relatively moderate fluctuations throughout the simulation.

The Rg analysis (Figure 7C) provided insights into the compactness of the complexes over time. All three complexes displayed consistent Rg values between 1.8 and 2.0 nm, suggesting stable and compact structures throughout the simulation. Notably, 4QVO-DMC exhibited the most consistent compactness, further emphasizing its stability. In contrast, 8CEQ-Curcumin and 8QRV-DMC showed slightly more variability, with occasional increases in Rg, indicating minor structural rearrangements during the simulation.

The RMSF analysis (Figure 7B) evaluated the flexibility of individual residues in the protein-ligand complexes. Most residues displayed minimal fluctuations, generally below 1 nm, indicating structural rigidity in all complexes. However, higher fluctuations were observed at terminal and loop regions, which are typically more flexible. The 8CEQ-Curcumin complex exhibited higher residue fluctuations at specific regions compared to the other two complexes. The 4QVO-DMC complex showed the least residue fluctuations, while the 8QRV-DMC complex exhibited intermediate flexibility, suggesting a balance between stability and dynamic behavior.

The observations of stability and dynamic behavior align with previous studies, such as the work by Dutt et al. (2023), which investigated the stability of small molecule inhibitors targeting DNA-dependent RNA polymerase (DdRp) in Mpox virus through MD simulations. In their study, the top-ranked complexes, including lumacaftor and conivaptan, were subjected to 200 ns of MD simulations and principal component analysis (PCA), which revealed highly stable interactions of these inhibitors with DdRp. The high stability observed in these drug-target complexes underscores the value of MD simulations in validating docking results and understanding the dynamic interplay between ligands and their target proteins.

These findings align with studies such as Dutt et al. (2023), which explored the stability of small molecule inhibitors targeting DNA-dependent RNA polymerase (DdRp) in monkeypox virus. In their MD simulations, stable interactions were observed between top-ranked inhibitors and DdRp, highlighting the importance of MD simulations in validating docking results and understanding protein-ligand dynamics. Similarly, Mulu et al. (2021) found that curcumin-derived polyphenols exhibit variations in flexibility and binding affinity when interacting with target proteins, emphasizing the role of dynamic regions in protein-ligand interactions. Despite fluctuations, key residues such as Gln817, His613, Asp764, and His617 remained stable in all three complexes, indicating that flexibility does not disrupt specific interactions, aligning with Tuffery and Derreumaux (2012).

The 8CEQ-Curcumin complex, although stable overall, displayed more flexibility, suggesting less robust binding compared to the DMC complexes. In contrast, both 4QVO-DMC and 8QRV-DMC exhibited greater stability and compactness, consistent with Rampogu et al. (2021), which demonstrated that curcumin analogues, including demethoxycurcumin (DMC), exhibit stable interactions and balanced flexibility in MD simulations. The 4QVO-DMC complex, in particular, demonstrated superior stability, making it the most promising candidate for further investigation. The 8QRV-DMC complex also displayed stable interactions but with slightly higher flexibility compared to 4QVO-DMC. These results highlight DMC’s potential as a more stable curcumin derivative, consistent with Maiti et al. (2021), which showed that DMC retains strong binding stability and therapeutic potential in silico analyses.