Evaluation of the Antifungal, Antioxidant, and Anti-Diabetic Potential of the Essential Oil of Curcuma longa Leaves from the North-Western Himalayas by In Vitro and In Silico Analysis

Essential oils (EOs) have gained immense popularity due to considerable interest in the health, food, and pharmaceutical industries. The present study aimed to evaluate the antimicrobial and antioxidant activity and the anti-diabetic potential of Curcuma longa leaf (CLO) essential oil. Further, major phytocompounds of CLO were analyzed for their in-silico interactions with antifungal, antioxidant, and anti-diabetic proteins. CLO was found to have a strong antifungal activity against the tested Candida species with zone of inhibition (ZOI)-11.5 ± 0.71 mm to 13 ± 1.41 mm and minimum inhibitory concentration (MIC) was 0.63%. CLO also showed antioxidant activity, with IC50 values of 5.85 ± 1.61 µg/mL using 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging assay and 32.92 ± 0.64 µM using ferric reducing antioxidant power (FRAP) assay. CLO also showed anti-diabetic activity with an IC50 of 43.06 ± 1.24 µg/mL as compared to metformin (half maximal inhibitory concentration, IC50-16.503 ± 0.66 µg/mL). Gas chromatography–mass spectrometry (GC–MS) analysis of CLO showed the presence of (-)-zingiberene (17.84%); 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene)-(15.31%); cyclohexene, 4-methyl-3-(1-methylethylidene) (12.47%); and (+)-4-Carene (11.89%) as major phytocompounds. Molecular docking of these compounds with antifungal proteins (cytochrome P450 14 alpha-sterol demethylase, PDB ID: 1EA1, and N-myristoyl transferase, PDB ID: 1IYL), antioxidant (human peroxiredoxin 5, PDB ID: 1HD2), and anti-diabetic proteins (human pancreatic alpha-amylase, PDB ID: 1HNY) showed strong binding of 3,7-cyclodecadien-1-one with all the selected protein targets. Furthermore, molecular dynamics (MD) simulations for a 100 ns time scale revealed that most of the key contacts of target proteins were retained throughout the simulation trajectories. Binding free energy calculations using molecular mechanics generalized born surface area (MM/GBSA), and drug-likeness and toxicity analysis also proved the potential for 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene) to replace toxic synthetic drugs and act as natural antioxidants.


Introduction
Natural products have become an alternative and supplementary treatment technique due to their diverse pharmacological and biological applications [1,2]. Medicinal plants include a variety of phytoconstituents and have a wide range of pharmacological characteristics. Plants have long been utilized as natural remedies for asthma, colds, fevers,

Percentage Yield of CLO
The essential oil from fresh leaves of C. longa of the Zingiberaceae family from the lower regions of Himachal Pradesh (≈650 m above the sea level) was extracted using the hydro distillation method. The percentage yield of essential oil obtained from leaves was 0.10% (v/w). In contrast to our report, 0.65% of oil was extracted from fresh leaves of C. longa obtained from Uttaranchal, India, in September 2000 [38]. A study from Leela et al. [39] reported a 1.3% yield of EO from leaves of C. longa from Calicut. Similar to this study, a high yield of 1.45% (v/w) was also reported by Parveen et al. [40]. Essential oil content of leaf samples collected from different regions of Orissa varied from 0.37 to 0.8% [41]. The variations in geographical locations, genotypes, and season of collection are the major factors responsible for variation in extraction yield [42][43][44].

Antifungal Activity of CLO against Fungal Strains
Antimicrobial activity was determined in terms of diameter of ZOI (mm) and MIC value (µg/mL). CLO was found to be effective against both tested fungal strains. The diameter of ZOI of CLO was found to be 13.0 ± 1.41 mm and 11.5 ± 0.71 mm against C. albicans (MTCC90028) and C. albicans (ATCC277), respectively. Fluconazole (25µg) showed a ZOI of 18 ± 0.7 mm and 13 ± 0.71 mm against C. albicans (MTCC90028) and C. albicans (ATCC277), respectively ( Figure S1, Table 2). The MIC of CLO was found to be 0.63% against C. albicans (ATCC277) and C. albicans (MTCC90028). The MIC of fluconazole was found to be 0.063% for C. albicans (MTCC90028) and C. albicans (ATCC277), as shown in Table 2. The strong antimicrobial activity of the leaf oil of C. longa was also reported against B. cereus (MIC-78 µg/mL), S. aureus (MIC-78 µg/mL), and A. niger (MIC-19.5 µg/mL) by Essien et al. [45]. In another study, Parveen et al. [40] reported the maximum inhibition of leaves oil of C. longa against F. miniformes MAY 3629 (22 mm), followed by B. subtilis ATCC 6633 (21 mm) and A. flavus ATCC204304 (20 mm) after 48 h of incubation. The antimicrobial activity of C. longa extract has been attributed to compounds belonging to flavonoids and terpenes, particularly to borneol, cymene, cuparene, and careen [46].

In Vitro Antioxidant and Anti-Diabetic Activity of CLO
The antioxidant potential of CLO from leaves of C. longa was determined using % DPPH radical scavenging and the ferric reduction capacity (FRAP) method. The antioxidant capacity of CLO was found to be dose dependent ( Figure 2). The IC 50 values of CLO were found to be 5.85 ± 1.61 µg/mL and 32.92 ± 0.64 µM for DPPH and FRAP, respectively, whereas ascorbic acid showed IC 50 values of 3.11 ± 0.47 µg/mL and 24.09 ± 2.16 µM for DPPH and FRAP, respectively (Table 3). The antioxidant activity of rhizome was reported by several studies [47,48]. However, only a few studies have reported the antioxidant activity in C. longa leaves [32,44]. Chan et al. [32] evaluated the antioxidant activity of leaves of C. longa in fresh and freeze-dried samples, finding that fresh samples had high ascorbic acid equivalent antioxidant capacity (AEAC) (243 ± 28 mg AA/100 g) and ferricreducing power (FRP) (2.1 ± 0.1 mg GAE/g), as compared to that of freeze-dried samples (AEAC-222 ± 12 mg AA/100 g; FRP-1.8 ± 0.1 mg GAE/g). A study by Mishra et al. [44] compared the genetic diversity of C. longa in different accessions and also compared their biological activities. They reported antioxidant activities in different accessions using DPPH (46.56-

Molecular Docking of Selected Phytocompounds of CLO with Target Antifungal, Anti-Oxidant, and Anti-Diabetic Proteins and MM-GBSA Analysis of Best Docked Ligand
The molecular docking study was conducted in order to study the molecular mechanism of action of major phytocompounds of CLO with fungal proteins (IEA1 and 1IYL), antioxidant protein (1HD2), and diabetic protein (1HNY). Synthetic drugs such as fluconazole, ascorbic acid, and metformin were used as standard control. Among all selected phytocompounds, 3,7-cyclodecadien-1-one was found to show strong binding energy of −21.331 kcal mol −1 , −24.223 kcal mol −1 , −19.399 kcal mol −1 , and −20.819 kcal mol −1 against 1EA1, 1IYL, IHD2, and 1HNY proteins, respectively (Table 4). Fluconazole showed binding energy of −37.349 kcal mol −1 and −38.248 kcal mol −1 with 1EA1 and 1IYL proteins, respectively. Ascorbic acid showed binding energy of −23.999 kcal mol −1 against the 1HD2 protein, and metformin showed binding energy of −17.117 kcal mol −1 against the 1HNY protein. The binding energy, hydrogen bonds, and interactive amino acids of selected phytocompounds with protein targets and standard drugs are summarized in Table 5. Further interactions of 3,7-cyclodecadien-1-one with 1EA1, 1IYL, IHD2, and 1HNY proteins analyzed using Chimera 1.14 and Discovery Studio Visualizer are shown in Figure  3A-L. Binding interactions of casuarinin were analyzed using Discovery Studio (DS) Vis-  Table 3. Half maximal inhibitory concentration (IC 50 ) of CLO, ascorbic acid, and metformin in terms of antioxidant assay and anti-diabetic assay. DPPH and α-amylase inhibition activity were calculated in terms of µg/mL, while FRAP activity was calculated in terms of µM Fe (II) equivalents. The lower the value of IC 50 , the higher the antioxidant/anti-diabetic potential.

Antioxidant Activity
Anti-Diabetic Activity   The anti-diabetic potential of CLO was evaluated using the α-amylase inhibition method and was found to have an IC 50 value of 43.06 ± 2.51 µg/mL as compared to that of the standard drug, metformin (16.51 ± 2.11 µg/mL) (Table 3). However, both CLO and metformin were found to show dose-dependent α-amylase inhibition activity ( Figure 2). The anti-diabetic activity of C. longa rhizome extract or oil was reported in several reports [49][50][51][52]. Fresh (IC 50 -64.7 ± 5.9 µg/mL) and dry rhizomes (IC 50 -34.3 ± 6.2 µg/mL) of C. longa were found to have strong α-amylase inhibition as compared to that of acarbose (296.3 ± 12.7 µg/mL) [50]. Kalaycıoglu et al. [52] evaluated the α-amylase inhibitory activity in three curcuminoids, namely, bisdemethoxycurcumin, demethoxycurcumin, and curcumin, isolated from C. longa rhizome with IC 50 values of 12.5 ± 0.2 µM, 21.1 ± 0.3 µM, and 12.5 ± 0.2 µM, respectively, as compared to that of the standard genistein (2.50 ± 0.02 µM). However, our study is the first report in which the antidiabetic potential of essential oil from leaves of C. longa has been shown. The molecular docking study was conducted in order to study the molecular mechanism of action of major phytocompounds of CLO with fungal proteins (IEA1 and 1IYL), antioxidant protein (1HD2), and diabetic protein (1HNY). Synthetic drugs such as fluconazole, ascorbic acid, and metformin were used as standard control. Among all selected phytocompounds, 3,7-cyclodecadien-1-one was found to show strong binding energy of −21.331 kcal mol −1 , −24.223 kcal mol −1 , −19.399 kcal mol −1 , and −20.819 kcal mol −1 against 1EA1, 1IYL, IHD2, and 1HNY proteins, respectively (Table 4). Fluconazole showed binding energy of −37.349 kcal mol −1 and −38.248 kcal mol −1 with 1EA1 and 1IYL proteins, respectively. Ascorbic acid showed binding energy of −23.999 kcal mol −1 against the 1HD2 protein, and metformin showed binding energy of −17.117 kcal mol −1 against the 1HNY protein. The binding energy, hydrogen bonds, and interactive amino acids of selected phytocompounds with protein targets and standard drugs are summarized in Table 5. Further interactions of 3,7-cyclodecadien-1-one with 1EA1, 1IYL, IHD2, and 1HNY proteins analyzed using Chimera 1.14 and Discovery Studio Visualizer are shown in Figure

MD Simulations
On the basis of molecular docking results, the best ligand-protein complexes were selected for MD simulations. Since, 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene) reported in CLO was found to have the best binding energy with all selected target proteins, complexes of 3,7-cyclodecadien-1-one with 1EA1, 1IYL, IHD2, and IHNY proteins were selected for MD simulations for 100 ns. Different colors indicate different types of interactions, namely, Van der Waals interactions in light green, conventional hydrogen bonds in green color, π-sigma in purple color, π-π-T shaped in dark pink color, and alkyl and π-alkyl bonds in light pink color. Table 4. Docking scores of phytocompounds and standard drugs with target protein receptors. Different colors indicate different types of interactions, namely, Van der Waals interactions in light green, conventional hydrogen bonds in green color, π-sigma in purple color, π-π-T shaped in dark pink color, and alkyl and π-alkyl bonds in light pink color.

Root Mean Square Deviation (RMSD) of Protein-Ligand Complexes
On performing MD simulations, the root mean square deviation (RMSD) is used to measure the average change in displacement of a selection of atoms for a particular frame with respect to a reference frame. It is calculated for all frames in the trajectory. The plots in Figure 4 showed the RMSD evolution of a protein (left Y-axis). The docked pose of ligand and protein as a whole complex is considered as the reference starting frame, and then the movement from this reference position during the MD simulation is measured by aligning all the protein frames obtained during the MD trajectories. Checking the RMSD of the protein can provide knowledge in terms of its auxiliary 3-D structural movement on a graph during the simulation. RMSD examination can demonstrate if the simulation has equilibrated-its changes towards the finish of the recreation are around some thermal energetically stable conformation. Changes in the range of 1-3 Å are completely satisfactory for small globular proteins. However, this range of value widens as the size of the protein increases. The RMSD graph of 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene) in a complex with the 1EA1 protein was found to be stabilized between 1.6 and 3.2 Å from 0 to 100 ns ( Figure 4A), while the RMSD of 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1methylethylidene) in complex with the 1IYL protein was found to be stable between 2.5 and 4.0 Å from 0 to 100 ns ( Figure 4B). The RMSD of the 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene)-1HD2 protein complex was found to be unstable at 0-65 ns, but became stable between 65 and 85 ns between 1.5 and 2.5 Å ( Figure 4C). The RMSD of the 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene)-1HNY complex was found to be stabilized between 2 and 2.5 Å from 0 to 100 ns ( Figure 4D). RMSD data revealed the stability of 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene) in the binding pocket of all the selected target proteins.   The ligand RMSD (right Y-axis, plots of Figure 4) suggests the stability of ligand posture concerning the docked position of the ligand in the binding cleft of the protein.
For this, the values slightly larger than the protein's RMSD are considered satisfactory, but if the values observed are significantly larger than the RMSD of the protein, then it is likely that the ligand acquires a different stable position than the original posture. For the 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene)-1EA1 complex ( Figure 4A), the Lig fit Prot stayed significantly lower than the protein's RMSD from 0 to 10 ns and then from 70 to 90 ns during simulation, suggesting slight changes in pose between 10 and 70 ns; thereafter, the orientation of the ligand remained stable. For the 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene)-1IYL complex ( Figure 4B), the Lig fit Prot stayed significantly lower than the protein's RMSD throughout the simulation, suggesting that the orientation of the ligand remained the same during the simulation process. For the 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene)-1HD2 complex ( Figure 4C), the Lig fit Prot value stabilized up to 40 ns, suggesting the casuarinin changing posed after 40 ns and then stabilized to a constant pose, and for the 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene)-1HNY complex ( Figure 4D), the Lig fit Prot value stayed significantly lower than the protein's RMSD throughout the simulation, suggesting that the orientation of the ligand remained the same during the simulation process.

RMSF of Protein-Ligand Complexes
The root mean square fluctuation (RMSF) is useful for portraying confined changes along the protein chain ( Figure 5). In the graph, the peaks demonstrate regions of the protein that vary the most throughout the simulation. Ordinarily, the tails (N-and C-termini) show maximum change as compared to other internal regions of the protein. Secondary regions of proteins such as alpha helices and beta strands are generally more inflexible and rigid than the unstructured regions and hence vacillate, not exactly like loop-forming portions of protein. Alpha-helical and beta-strand areas are featured in red and blue foundations separately. These districts are characterized by helices or strands that endure over 70% of the whole re-enactment. Protein deposits that contact ligands are set apart by green-hued vertical bars. The RMSF of the protein can likewise be related to the exploratory x-beam B-factor (right Y-hub). Because of the distinction between the RMSF and B-factor definitions, balanced correspondence ought not to be normal. Notwithstanding, the reproduction results should resemble crystallographic information. It was found that the RMSF plot for 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene) fit over 1EA1 and 1IYL proteins and showed less residual fluctuation within the range of 0.8-1.6 Å in α-helical and β-strands ( Figure 5A, B). The RMSF plot for 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene)-1HD2 was found to show less residual fluctuation in α-helical and β-strands between 0.6 and 1.6 Å ( Figure 5C), while the 3,7-cyclodecadien-1-one, 3,7dimethyl-10-(1-methylethylidene)-1HNY complex was found to be a fit over proteins with less fluctuation ( Figure 5D). RMSF plot for 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene) fit over 1EA1 and 1IYL proteins and showed less residual fluctuation within the range of 0.8-1.6 Å in α-helical and β-strands ( Figure 5A, B). The RMSF plot for 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene)-1HD2 was found to show less residual fluctuation in α-helical and β-strands between 0.6 and 1.6 Å ( Figure 5C), while the 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene)-1HNY complex was found to be a fit over proteins with less fluctuation ( Figure 5D). Protein interactions with the ligand can be monitored throughout the simulation. These interactions can be categorized by type and summarized, as shown in Figure 6  Protein interactions with the ligand can be monitored throughout the simulation. These interactions can be categorized by type and summarized, as shown in Figure 6 Figure 6D). The total number of specific contacts of the ligand with selected proteins was also studied throughout the simulation (0-100 ns). Some residues of proteins were found to show more than one specific contact with the ligands, which is represented by a darker shade of orange color, as shown in Figure 7A Figure 6D). The total number of specific contacts of the ligand with selected proteins was also studied throughout the simulation (0-100 ns). Some residues of proteins were found to show more than one specific contact with the ligands, which is represented by a darker shade of orange color, as shown in Figure 7A-G.

Binding Free Energy Evaluation
Binding energy calculation provides an insight into the ligand potential to strongly interact with the amino acids of a target protein. After simulation analysis of the best docked phytocompounds, 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene) with all the target proteins was performed using MM-GBSA by taking snapshots of the trajectory profiles developed on performing the 100 ns MD simulation. Table 6 predicts the MM/GBSA profile of 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene) with all selected proteins and shows effective binding of this ligand with target proteins. Binding energy calculation provides an insight into the ligand potential to strongly interact with the amino acids of the protein. The energy released (∆G bind ) due to bond formation, or rather interaction of the ligand with protein, is in the form of binding energy and it determines the stability of any given protein-ligand complex. The free energy of a favorable reaction is negative. It was observed that 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene) showed negative ∆G bind with all target proteins. Van der Waals interactions (∆G vdW ) of 3,7-cyclodecadien-1-one with the selected target proteins were found to be between −13.85 and −25.67 kcal/mol, suggesting that 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1methylethylidene) tends to stay in the vicinity of the interacting amino amides of target proteins. Coulomb energy was found to be negative for all complexes, indicating poor potential energy of 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene) with all target proteins and suggesting better stability of protein-ligand complexes. In addition to the total energy, the contributions to the total energy from different components such as hydrogen bonding correction, lipophilic energy, and Van der Waals energy is provided in Table 6.

Collection and Identification of Plant Samples
The fresh leaves of C. longa were collected from Kangra, Himachal Pradesh, India (32.0998 • N, 76.2691 • E), in the month of October 2019. The plant specimen was identified in the Department of Forest Products at the Y.S. Parmar University of Horticulture and Forestry, Nauni, Solan, H.P., India. A sample voucher was submitted in the herbarium with voucher number UHF-965. The plant name was checked with the plant list (http: //www.theplantlist.org, accessed on 7 October 2022).

Extraction of Essential Oil
Extraction of essential oil from C. longa leaves (CLO) was carried out by the hydrodistillation method using Clevenger assembly for oil lighter than water [53]. The leaves of C. longa were collected and thoroughly washed with distilled water to remove the dust particles, and then excess moisture was absorbed using a paper towel. About 200 g leaves of C. longa were cut into small pieces, mixed with distilled water, and boiled at 50 • C for 4 h in a round-bottom flask. Percentage extraction yield of CLO was determined on the basis of the weight of leaves and oil obtained. The collected CLO was stored at 4 • C in the dark for further analysis.

Fungal Strains and Growth Conditions
The two fungal strains Candida albicans (MTCC277) and C. albicans (MTCC90028) used in this study were procured from Microbial Type Culture Collection, Institute of Microbial Technology (IMTECH), Chandigarh, India. Both of these strains were maintained on potato dextrose agar (PDA) medium and grown in potato dextrose broth (PDB) at 28 ± 2 • C.

Agar Well Diffusion Method for Antifungal Activity
Antimicrobial activity of CLO was determined using the agar well diffusion method [54] and was expressed as diameter of zone of inhibition (ZOI) against the tested strains. Fluconazole (Himedia Biosciences, Mumbai, India) was used as a positive control, whereas DMSO (solvent) was used as a negative control. The experiment was repeated twice, and results are expressed as mean ± S.D.

Minimum Inhibitory Concentration (MIC) of CLO Using the Micro Dilution Method
The MIC of CLO against tested fungal strains was determined using the micro dilution method according to the Clinical and Laboratory Standards Institute (CLSI) protocol [55]. The experiment was performed in a 96-well microtiter plate, and geometric dilutions (50-0.098 µg/mL) of CLO were prepared. Then, equal numbers of fungal cells (2 × 10 5 CFU mL −1 , 0.5 McFarland) were inoculated to each well, and the plate was incubated for 48 h at 28 ± 2 • C. Fluconazole was used as the positive control, and DMSO was used as the negative control. After incubation, resazurin dye (1 mg/mL) was added, and a change in color of resazurin dye was observed in each well. The lowest concentration at which color changed from purple to pink was considered as the MIC value.

Analysis of Antioxidant Potential of CLO
The antioxidant capacity of CLO was evaluated using two different antioxidant assays, namely, DPPH and FRAP assays. For both assays, L-ascorbic acid (2.5-10 µg/mL) was used as the standard control [56][57][58]. The antioxidant capacity of CLO and ascorbic acid was expressed in terms of IC 50 value (half maximal inhibitory concentration).

DPPH Radical Scavenging Activity
The DPPH radical scavenging activity of CLO was measured by the method described by Torres-Martínez et al. [59]. In this procedure, 100 µL of CLO or ascorbic acid (10-80 µg/mL) was mixed with 900 µL of 0.004% DPPH solution, and the absorbance of the reaction mixture was measured at 517 nm after incubation of 30 min in the dark at room temperature using an ultraviolet-visible (UV-VIS) spectrophotometer. The capability of scavenging the DPPH radical was calculated from the following equation: where A (control) is the absorbance of the control, and A (sample) is the absorbance of the test/standard.

FRAP Assay
The FRAP activity of CLO was expressed as Fe (II) equivalents per gram of the extract calculated from the linear calibration curve of FeSO 4 (10 to 80 µM) as described by Kumar et al. [54] and Kumar et al. [58]. To 100 µL of CEO or ascorbic acid (10-80 µg/mL), 900 µL of freshly prepared FRAP solution was added. The FRAP reagent was prepared by mixing 300 mM acetate buffer (pH-3.6), 10 mM TPTZ in 40 mM HCl, and 20 mM FeCl 3 at a ratio of 10:1:1 (v/v/v). The reaction mixture was incubated at room temperature for 30 min, and then absorbance was recorded at 593 nm using a UV-VIS spectrophotometer.

Evaluation of Anti-Diabetic Potential of CLO
The anti-diabetic potential of CLO was evaluated using an in vitro α-amylase inhibition method. In this method, the enzyme solution was prepared by dissolving α-amylase in 20 mM phosphate buffer (pH-6.9) at a concentration of 0.5 mg/mL. Then, 1 mL of CLO of various concentrations (10-80 µg/mL) was mixed with 1 mL of enzyme solution and incubated at 25 • C for 10 min. After incubation, 1 mL of starch (0.5%) solution was added to the mixture, and further reaction mixture was incubated at 25 • C for 10 min. The reaction was terminated by adding 2 mL of dinitrosalicylic acid (DNS, color reagent) and heating the reaction mixture in a boiling water bath for 5 min. Then, absorbance was measured after cooling at 540 nm [60,61]. Metformin was used as the standard drug in this experiment. The inhibition percentage was calculated using the following formula: where A (control) is the absorbance of the control reaction (containing all reagents except the test sample) and A (sample) is the absorbance of the test sample. The experiment was performed in triplicate, and results were calculated as mean ± S.D.

Identification of Chemical Components of CLO using GC-MS Analysis
The chemical composition of CLO was conducted using the GC-MS technique using Thermo Trace 1300 GC coupled with a Thermo TSQ 800 Triple Quadrupole mass spectrometer fitted with a BP 5MS capillary column (30 m 0.25 mm, 0.25 mm film thickness). Helium was used as the carrier gas at a flow rate of 1 mL/min. The oven program started with an initial temperature of 50 • C and was then held for 5 min; following this, the temperature was heated at rate of 5 • C/min to 280 • C and finally held isothermally for 2 min. The run time was 34.09 min. The MS operated at a flow speed of 1 mL/min, with an ionization voltage of 70 eV, at an interface temperature of 250 • C, in a SCAN mode, and at a mass interval of m/z 35-650. The essential oil constituents were identified in relation to the reference on the basis of their retention time (R T ). The compounds were identified on the basis of matching unknown peaks with the MS data bank (NIST 2.0 Electronic Library).
The crystal structures of target proteins were prepared for binding analysis using Autodock Tools (ADT). Protein preparation included the addition of Gasteinger charges, polar hydrogen atoms, and optimizing the rotatable bonds. Prepared proteins were then saved in pdbqt format for further analysis. Further, binding sites of target proteins were obtained from the previous literature, and the grid box was created on the basis of the above information [53,[66][67][68][69]. The details of target proteins, number of amino acids, chain selected for docking, and grid box coordinates are shown in Table 8.

Molecular Docking
Molecular docking of phytocompounds with selected proteins was performed using the Glide (grid-based ligand docking) program incorporated in the Schrödinger molecular modelling package with extra precision (XP). Extra-precision (XP) docking and scoring is a more powerful and discriminating procedure that requires more time to execute than SP. XP is intended for use on ligand postures that have been demonstrated to be highscoring using standard precision (SP) docking. XP also has a more complicated scoring methodology that is "harder" than the SP GlideScore, with stricter ligand-receptor form complementarity criteria. This eliminates false positives that SP allows through. Because XP penalizes ligands that do not match well to the specific receptor conformation used, we recommend docking to many receptor conformations whenever possible. The best pose based on binding energies for each ligand-protein interaction was further analyzed in Discovery Studio (DS) Visualizer (Accelrys, San Diego, CA, USA). From the interaction profile, the ligands showing high binding energy were further considered for the molecular dynamic simulations.

MD Simulations
Structural stability of the receptor-ligand complexes was investigated using MD simulations with the help of the academic version of the Desmond program [70,71]. For this, the system was designed using the TIP3P water model with a cubic periodic box containing simple point charge (SPC) (10 Å × 10 Å × 10 Å) and optimized potentials for liquid simulations (OPLS) all-atom force field 2005 [72]. Then, the appropriate amount of sodium ions was added for the system neutralization process. The receptor-ligand complex was provided for the initial energy minimization step and pre-equilibration in various restrained steps.
MD simulations were carried out using OPLS 2005 force field parameters with periodic boundary conditions in the NPT ensemble system [73,74], with a relaxation duration of 1 ps at a constant temperature of 300 K and a constant volume. The smooth particle mesh Ewald (PME) approach (with a 10 −9 tolerance limit and a cut off distance of 9.0 Å) was used to analyze protein structures every 1 ns. An average structure from the MD simulation corresponding to the production period was used to determine the stability. Furthermore, the root means square deviation (RMSD), the root means square fluctuation (RMSF), the hydrogen bond, the radius of gyration (R g ), and the histogram for torsional bonds were investigated for the analysis of structural changes with the dynamic role of the receptor-ligand complexes [75][76][77] were employed for the calculation of the binding free energies of protein-ligand complexes [78,79]. Hence, the PRIME module of Maestro 11.4 and the OPLS-2005 force field were used for the determination of the binding energy of best-docked ligand-receptor complex, and the following equation was used for the calculation of binding energy: ∆G Bind = ∆E MM + ∆G Solv + ∆G SA where ∆E MM represents the difference of the minimized energies of the protein-ligand complex, while ∆G Solv is the difference between GBSA solvation energy of the proteinligand complexes and the sum of the solvation energies for the protein and ligand. ∆G SA represents the surface area energies in the protein-ligand complexes and the difference in the surface area energies for the complexes [80,81]. The protein-ligand complexes were minimized using a local optimization feature of PRIME.

Drug Likeness, ADME/Toxicity Prediction
Lipinski's rule (rule of five, RO5) was considered the primary factor for screening of the molecules, and it was evaluated using the SWISS ADME web server (http://www. swissadme.ch/, accessed on 6 October 2022). Further, the toxicity of selected compounds was analyzed using the Protox-II tool to ascertain their risk of drugability [82]. PROTOX is a server that predicts the LD 50 value and toxicity class of a question molecule in rodents. The SMILES format of the selected compounds was submitted to a Swiss ADME web server and Protox-II tool.

Statistical Analysis
The results are represented as mean ± standard deviation (SD) wherever applicable. The statistical comparisons were conducted using two-way analysis of variance (ANOVA) (p < 0.05) using Graph Pad Prism 8.0 (GraphPad Software, San Diego, CA-92108, USA).

Conclusions
Traditional medicinal herbs offer a wealth of phytocompounds, including essential oils (EOs), which can be explored for antifungal activities. Essential oil of C. longa (CLO) leaves showed antifungal, antioxidant, and anti-diabetic activity that was further validated by in silico studies. Among selected phytocompounds, 3,7-cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene) of CLO showed higher interaction towards the antifungal, antioxidant, and anti-diabetic receptors, which was further validated with MD simulations. Further, 3,7-Cyclodecadien-1-one, 3,7-dimethyl-10-(1-methylethylidene) was found to be safer for drug formulation as it follows Lipinski's rule and lacks hepatotoxicity, immunogenicity, carcinogenicity, and cytotoxicity. In light of these findings, we can say that the essential oil of C. longa (CLO) leaves can be exploited for its broad-spectrum therapeutic applications.
Supplementary Materials: The following supporting information can be downloaded at https: //www.mdpi.com/article/10.3390/molecules27227664/s1, Figure S1: Antifungal activity of CLO. Figure S2: 3-D structure of major phytocompounds identified in the GC-MS analysis of CLO selected for docking. Figure