https://orcid.org/0000-0002-1065-5727
Figures
Abstract
The total phenolic content and flavonoid content of the Usnea cornuta extract were evaluated as 210.31 ± 2.87 mg GAE/g and 22.42 ± 0.78 mg QE/g, respectively. The crude extract exhibited strong antioxidant activity (IC50: 32.91 ± 1.27 µg/mL) and notable anti-diabetic effects via α-glucosidase inhibition, with IC50 values of 2.59 ± 2.23 µg/mL for the dichloromethane extract. LC-MS analysis identified eleven metabolites like D-mannitol (1), galbinic acid (2), conhypoprotocetraric acid (3), roccellaric acid (4), diffractatic acid (5), haemathamnolic acid isomer (6), conprotocetraric acid (7), constictic acid I (8), salazinic acid II (9), menegazziaic acid (10), and one unknown compound (11). Among these, menegazziaic acid exhibited the strongest binding affinity of −9.7 kcal/mol with the target (PDB ID 3A4A), favorable molecular dynamics, binding free energy (MM/GBSA, and pharmacokinetic profiles. Furthermore, the extract showed strong antimicrobial activity, with inhibition zones of 23 mm and 26 mm at 10 mg/mL against Staphylococcus aureus ATCC 29213 and ATCC 245, respectively. These findings highlight the therapeutic potential of Usnea cornuta, specifically for managing oxidative stress, microbial infections, and type 2 diabetes.
Citation: Karki D, Phunyal A, Lamichhane TR, Karki D, Adhikari A (2026) Antioxidant, antibacterial, in vitro, and in silico α-glucosidase inhibition activities and chemical profiling of Usnea cornuta Korb. PLoS One 21(6): e0351423. https://doi.org/10.1371/journal.pone.0351423
Editor: Jorddy Neves Cruz, Universidade Federal do Para, BRAZIL
Received: July 17, 2025; Accepted: May 26, 2026; Published: June 12, 2026
Copyright: © 2026 Karki et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and publicly available in the Supporting Information files, as well as https://doi.org/10.6084/m9.figshare.30964633 for figure and https://doi.org/10.6084/m9.figshare.25788243.
Funding: The University Grant Commission provides financial support to carry out the research work with the reference number MRS-78-79-S&T-38. The funder had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
1 Introduction
Diabetes mellitus, a global public health crisis, encompasses metabolic disorders characterized by persistent hyperglycemia due to insufficient insulin production, resistance, or both [1]. Affecting 537 million adults (10.5% of the 20–79 age group) in 2021, cases may rise to 643 million by 2030 and 783 million by 2045 [2]. The disease manifests as Type 1, involving autoimmune β-cell destruction, and Type 2, comprising over 90% of cases, marked by insulin resistance and inadequate secretion [3,4]. Emerging research suggests natural inhibitors, particularly from botanical sources, as potential modulators of α-amylase and α-glucosidase, offering promising, low-side-effect therapies [5].
Lichens, which are intricate life forms, showed a mutual relationship of fungi and algae, used for both nutritional and remedial purposes since antiquity [6]. They produce a wide range of secondary metabolites, particularly phenolic compounds that exhibit notable biological activity, including antioxidant properties [1,7–10]. Pharmacological interest in lichens is predominantly focused on their bioactive secondary metabolites, especially phenolic compounds, which are characterized by a reactive –OH group [11,12]. Furthermore, secondary metabolites derived from lichens have demonstrated diverse biological activities, including anti-oxidant, anti-inflammatory, analgesic, plant growth inhibition, enzyme inhibition, anti-proliferative, anti-cancer, anti-microbial, and cytotoxic effects [13–18].
The original specimen of U. cornuta (sensu stricto), characterized by a cornuta-type central mechanical axis (CMA) was selected for this study based on its ethnobotanical significance and therapeutic potential [19,20]. The presence of usnic acid in Usnea species, a bioactive compound traditionally used to manage metabolic disorders, including diabetes, suggests the potential antidiabetic relevance of this lichen [21]. Previous phytochemical investigations of U. cornuta have identified flavonoids, phenolics, and terpenoids, compounds well known for their antioxidant properties [22]. However, despite its traditional applications, scientific evidence supporting the antidiabetic and antioxidant activities of U. cornuta remains limited. Therefore, the present study aims to evaluate the antidiabetic potential of U. cornuta through integrated experimental and computational approaches.
Therefore, previous studies on U. cornuta have largely focused on taxonomic classification and preliminary phytochemical characterization with limited experimental and computational evidence linking its metabolites to diabetes-related biological activities [23,24]. This study presents the first systematic evaluation of the antibacterial, and α-glucosidase inhibitory activities, along with the identification of its bioactive compounds using LC-MS. It further reports the first application of molecular docking and molecular dynamics simulations to assess the interaction and stability of these compounds with α-glucosidase (PDB ID: 3A4A). The findings highlight U. cornuta as a promising natural source of digestive enzyme inhibitors relevant to hyperglycemia and diabetes management.
2 Experimental materials and methods
2.1 Chemicals required
High-quality organic solvents, Folin-Ciocalteu reagent (FCR), gallic acid, DPPH, quercetin, acarbose, DMSO, phosphate salts, Na2CO3, AlCl3, and other chemicals were obtained from Fisher Scientific. The α-glucosidase enzyme (S. cerevisiae) and substrate p-NPG were sourced from Sigma-Aldrich.
2.2 Assemble of lichen materials
Drawing upon literature references and expert knowledge, lichens identified as U. cornuta were collected from Champadevi (S1 Fig in S1 File) in Kathmandu, Nepal in January 2023, emphasizing their ethnobotanical significance. The specimens were examined by Research Officer Amrit KC at the National Herbarium and Plant Laboratories (KATH), Godawari, Lalitpur, Nepal, where a voucher specimen was deposited under voucher number D. Karki, 20UC. Following collection, lichen materials were meticulously shaded during drying, finely powdered, and subsequently stored in tightly sealed containers.
2.3 Preparation and fractionation of crude extracts
65 g of shade-dried U. cornuta powder was soaked in 90% methanol for three days with periodic agitation. The mixture was filtered, and the filtrates were concentrated using a rotary evaporation at 50 °C, followed by further drying in a water bath to obtain the crude extract. The extraction yield was calculated as a percentage of the initial material. The extract was fractionated into hexane, DCM, ethyl acetate, and water fractions. All solvent fractions were transferred to sterile glass vials, tightly sealed, and stored at −4 °C until further phytochemical and biological analyses.
2.4 Total phenolic content
The TPC in U. cornuta, was quantified using the FC reagent colorimetric method with slight modification [25]. In summary, 20 µL of the lichen sample, 100 µL of Folin-Ciocalteu reagent and 80 µL of Na2CO3 were mixed in a 96-well plate and incubated in the dark for 15 minutes, measured at 765 nm using a microplate reader (Epoch2, BioTek Instruments, Inc., USA).
2.5 Total flavonoid content
For the assessment of TFC, the aluminum trichloride method was utilized [26]. This entailed pipetting 20 µL of lichen extracts, 110 µL of distilled water, 60 µL of ethanol, 5 µL of potassium acetate, and 5 µL of AlCl3 into 96-well plates. The plates were then incubated for 30 minutes under light exclusion, and the absorbance was recorded at 415 nm.
2.6 DPPH scavenging activity
Various volumes of lichen fraction samples, increased in 100 µL increments, were added to individual wells of a 96-well plate, along with 100 µL of DPPH solution. The DPPH solution (0.1 mM) was prepared by dissolving 0.975 mg of DPPH in 25 mL of methanol and a stock solution of quercetin (100 µg/mL) for standard was prepared by dissolving 1 mg of quercetin in 10 mL of methanol. The plate was then incubated in the dark for 30 minutes, and the absorbance was measured at 517 nm. All measurements were performed in triplicate (n = 3) and analyzed using the following formula [27]. Additionally, the inhibition curve was generated using linear regression analysis in GraphPad Prism 8.0.
Acontrol = absorbances of the control
Asample = absorbances of the sample solution
The software GraphPad Prism was employed to calculate the IC50 value.
2.7 In-vitro alpha-glucosidase inhibition activity
The inhibitory effect of U. cornuta on α-glucosidase was determined using p-NPG as the substrate, with slight modifications to the previous procedure [28]. In a 96-well plate, 20 µL of lichen extract, 10 µL of α-glucosidase (1 mg/mL; 0.625 Units/mL), and 130 µL of buffer were incubated at 37°C for 15 minutes, and the initial absorbance was measured at 405 nm. After the addition of 20 µL of 5 mM pNPG (3 mg in 2 mL buffer), incubation continued for 15 minutes, followed by the addition of 20 µL of Na2CO3 to develop a yellow color. The final absorbance was measured at 405 nm. Acarbose (1 mg/mL) was used as the standard inhibitor at concentrations ranging from 10 to 100 µg/mL to construct the inhibition curve, with all measurements performed in triplicate. The inhibition percentage was calculated using a standard formula and IC₅₀ values were determined using nonlinear regression analysis in GraphPad Prism 8.0.
Acontrol = absorbance of the control
Asample = absorbance of the sample
GraphPad Prism was used to calculate IC50 value.
2.8 Anti-bacterial assay
This assay was tested against Staphylococcus aureus (ATCC 29213 and ATCC 245) and Escherichia coli (ATCC 25922) using the well-diffusion method. Bacterial cultures were grown in nutrient broth at 37 °C for 24 hours to reach 0.5 McFarland turbidity and were then spread onto Mueller-Hinton agar plates. Wells (6 mm) were filled with U. cornuta extract and incubated at 37°C for 18–24 hours. Zones of inhibition (ZOI) were measured in millimeters after observing clear inhibition zones. Cefoxitin and azithromycin were used as positive controls, while DMSO served negative control. Sensitivity was determined by inhibition zone diameters, with ≥23 mm (cefoxitin) and ≥25 mm (azithromycin) considered sensitive, 15–20 mm intermediate, and ≤15 mm resistant.
2.9 Instrumental LC-MS conditions
The ethyl acetate fraction of Usnea cornuta was subjected to liquid chromatography–mass spectrometry (LC-MS) analysis using a Shimadzu LC-MS 2020 system (Shimadzu Corporation, Japan). Chromatographic separation was carried out using a reversed-phase column housed in a CTO-20A column oven, maintained at 40°C, and samples were introduced via SIL-20A autosampler. The mobile phase consisted of 0.2% (v/v) formic acid in water (solvent A) and methanol (solvent B). A linear gradient elution program was employed over a total run time of 35 minutes flow rate of 0.4 mL/min(0–10 min, 90% A to 60% A; 10–25 min, 60% A to 10% A;25–30 min, held at 10% A; and 30–35 min, returned to initial conditions (90%A) for column re-equilibration). The injection volume for each sample was 1 µL. Mass spectrometric detection was performed using an electrospray ionization (ESI) source operated in both positive and negative ionization modes to ensure comprehensive metabolite profiling. The mass analyzer was set to scan a mass-to-charge (m/z) range of 50–700. The nebulizing gas flow rate was maintained at 1.5 L/min, and data acquisition was conducted under optimized default source parameters provided by the manufacturer. The chemical constituents present in the ethyl acetate fraction were tentatively identified based on a combination of retention times, mass spectral data, and high-resolution m/z values, followed by spectral interpretation and validation using MestReNova software. The obtained mass spectral features were compared with reported literature data to support compound annotation.
2.10 Computational experiment
2.10.1 Preparation of ligands.
Based on LC-MS analysis, a total of 11 compounds (with one unidentified compound) were detected from Usnea cornuta extract. Among these, only 6 compounds were selected for docking due to the availability of their chemical structures in PubChem database portal [29]. The three-dimensional (3D) conformers of the selected ligands, along with acarbose (used as a standard compound for antidiabetic activity), were downloaded in SDF format. Energy minimization was performed for all ligands using the Avogadro v1.2 with the UFF force field, and the minimized structures were subsequently saved in PDB format for docking [30,31].
2.10.2 Preparation of protein.
The crystal structure of alpha-glucosidase enzyme (PDB ID: 3A4A; resolution: 1.60 Å) was obtained from Protein Data Bank (www.rcsb.org) in PDB format. To ensure the structural integrity of the protein, validation was performed using SAVES v6.0 (https://saves.mbi.ucla.edu/) server [32]. Before molecular docking, all water molecules and co-crystallized ligands were removed. Kollman charges and polar hydrogens were incorporated using the AutoDock Tools v1.5.7 program [33]. The processed protein structure was saved in PDBQT format for docking analysis.
2.10.3 Molecular docking.
Molecular docking was performed to investigate the interactions between selected inhibitors with alpha-glucosidase. The AutoDock Vina v1.2 was employed for molecular docking, and resulting complexes were visualized through the PyMOL v2.4.0 software [34,35]. The grid box was 35 Å × 35 Å × 35 Å, with its center at the coordinates X = 23.244385, Y = −7.482308, and Z = 23.570923. All docking parameters were kept at their default values, except for exhaustiveness, which was increased to 48 to improve search accurate, and the number of modes was set to 20. The detailed procedure outlined by Karki et al. [36].
2.10.4 Validation of docking protocol.
The protocol was validated by re-docking the co-crystalized ligand into protein’s active site using PyMOL. Literally, a low root mean square deviation (RMSD) (below 2 Å) between re-docked and native ligand value indicates the reliability of docking protocol [37]. Furthermore, the protein’s active site was identified using the CASTp server [38].
2.10.5 Molecular dynamics procedure.
Molecular dynamics simulations of the protein-ligand complexes were performed using GROMACS 2019.6 program [39]. The CHARMM36 force field was applied to receptor topology [40]. Ligand topologies were generated via SwissParam server [41]. The system was solvated in a cubic TIP3P water box. NaCl (0.15 M) ions were added to neutralize the system. Energy minimization was conducted using the steepest descent algorithm. Subsequently, the system was equilibrated in two phases: 100 ps NVT ensemble (310 K) using a modified Berendsen thermostat, followed by 100 ps NPT ensemble using the Berendsen method. Long-range electrostatic interactions were treated using the Particle Mesh Ewald (PME) method with 30 Å cutoff [42]. After equilibration, a 100 ns production run was conducted with 1000 frames at 2 fs time step. Geometrical parameters, including RMSD, RMSF, Rg, SASA, hydrogen bonding, and FELs were analyzed using GROMACS modules, while PCA and DCCM were assessed through the R programming package Bio3D. A detailed protocol was mentioned by Gyawali et al [43].
2.10.6 Binding free energy calculations.
The binding free energies for protein-ligand complexes were calculated by using the MM/GBSA method facilitated by the gmx-MMPBSA program v1.5.7 [44,45]. For each system, 200 frames extracted from the final, stable 20 ns of 100 ns production trajectory for energy calculations. The methodology closely followed the protocols described by Genheden et al. [46].
3 Results
3.1 Percentage yield
The percentage yield of U. cornuta in 90% methanol was 16.21% and the yield of fractions like hexane, DCM, ethyl acetate and water extracted from the crude extract of ethanol were 40.32%, 27.41%, 29.79% and 9.01% respectively (Table 1).
3.2 Estimation of total phenolic content and total flavonoid content
The total phenolic content (TPC) in U. cornuta extracts was quantified as 210.31 ± 2.87 mg GAE/g using gallic acid calibration (Y = 0.0054x + 0.0188, R2 = 0.9901). A 96-well plate method facilitated this analysis. The total flavonoid content (TFC) was determined as 222.42 ± 0.782 mg QE/g using quercetin calibration (Y = 0.0183x + 0.0037, R2 = 0.9926) (Table 2).
3.3 Calculation of anti-oxidant and α-glucosidase inhibition activity
The free radical scavenging activity of U. cornuta lichen extract showed an IC50 of 32.91 ± 1.26 µg/mL, comparable to quercetin (31.44 ± 0.21 µg/mL) with standard curve (S2 Fig in S1 File). Among fractions, ethyl acetate fraction exhibited the highest activity (IC50 = 12.75 ± 1.13 µg/mL), whereas water showed the weakest activity (IC50 = 77.05 ± 0.90 µg/mL) followed by hexane (IC50 = 44.04 ± 0.93 µg/mL) and DCM (IC50 = 13.81 ± 0.60 µg/mL).
Similarly, α-glucosidase inhibition revealed an IC50 of 13.15 ± 1.24 µg/mL for the crude extract. Fraction IC50 values ranged from hexane (0.37 ± 0.17 µg/mL), DCM (2.59 ± 2.23 µg/mL) and ethyl acetate (3.72 ± 0.64 µg/mL), with acarbose used as the standard with curve (S3 Fig in S1 File) (3.11 ± 0.22 µg/mL), presented in Table 3 and the activities were performed in triplicate form (n = 3) (Table 3). These values are tally in 95% confidence interval which showed p values are within the range (0.001–0.0175), reflects p value are below 0.05.
3.4 Antibacterial assay
Following treatment and incubation of the inoculated plates for 24 hours, antibacterial activity of the methanolic extract of U. cornuta was observed against Staphylococcus aureus ATCC 29213 and Staphylococcus aureus ATCC 245. However, no activity was observed against Escherichia coli ATCC 25922. The antibacterial screening results are shown (Fig 1).
Both concentrations of the methanolic extract, 10 mg/mL (L1) and 5 mg/mL (L2), exhibited activity against the microorganism strains. The crude extract inhibited the growth of S. aureus ATCC 29213 with a zone of inhibition (ZOI) of 23 mm at 10 mg/mL and 19 mm at 5 mg/mL, while S. aureus ATCC 245 showed ZOI of 26 mm and 16.5 mm at the same concentrations. However, no activity was recorded against E. coli ATCC 25922 (S1 Table in S1 File).
3.5 Identification of metabolites in lichen Usnea cornuta by LC-MS
The LC-MS analysis of U. cornuta hexane extract identified metabolites in positive and negative ESI modes, generating total ion chromatograms (TICs) (Figs 2 and 3). Based on accurate masses, retention times, standards, and database searches (Mest ReNova), presence of 11 compounds was confirmed in the ethyl acetate extract (S2 Table in S1 File). In positive ion mode, a peak at m/z 181.0708 (C6H13O6) with a retention time of 32.885 minutes was identified as d-mannitol, a major component in Coffea Arabica L. pulp [49]. Another peak at m/z 429.0459 (C20H13O11) with a retention time of 20.314 minutes was identified as galbinic acid, compound found in E. trulla (S3 Table in S1 File) [50,51].
Additionally, in this study, conhypoprotocetraric acid (m/z 359.0762, C18H15O8, Rt. 13.250 min) was identified as a depsidone, and has been previously reported in Ramalina siliquosa [52]. Roccellaric acid (m/z 325.2380, C19H33O4, 32.995 min) was detected in negative ion mode and classified as a cycloaliphatic acid [53], while diffractaic acid (m/z 373.1292, C20H21O7, 20.339 min) was detected as depsides [54]. Another compound, menegazziaic acid (C18H13O9), was also detected with m/z 373.0555 and retention time of 20.657 minutes. This discovery aligns with information reported in a previous research document regarding Usnea longissima and the genus Usnea [50,55]. Additionally, a peak with a mass-to-charge ratio (m/z) of 403.0660, retention time of 15.482 minutes, and molecular formula of C19H15O10 was identified as haemathamnolic acid isomer, categorized as a depside compound.
This compound has been reported as a bioactive compound of Pseudevernia furfuracea [56]. In addition, a depsidones compound, salazinic acid II (C18H11O10), was detected with an m/z of 387.0348 and a retention time of 16.525 minutes in negative mode [19]. Conprotocetraric acid, classified as a depsidones compound with a molecular formula of C18H13O9, was eluted at 19.893 minutes and detected at m/z 375.0719. This compound has been found similarly in Parmotrema species [57]. Additionally, constictic acid I, with a molecular formula of C19H13O10, appeared with a retention time of 22.415 minutes at an m/z peak of 401.0513 in negative ion mode. These identifications were made through comparison with previously recognized reported compounds [19]. Furthermore, an unidentified compound with the molecular formula C20H13O12 was also detected as a peak at m/z 445.0410, with a retention time of 23.186 minutes in positive ion mode [19].
3.6 Validation of protein structure and docking protocol
The overall quality factor of 93.7716 indicates good quality of protein model, and this was further supported by the Ramachandran plot generated through the PROCHECK module, which showed acceptable stereo-chemical properties (S4 Fig in S1 File). On other hand, RMSD between docked pose and original crystallographic position was found to be 1.943 Å, hinting that the parameters were valid for other ligands [58] (Fig 4).
Superimposition of native ligand (from PDB, grey) and the re-docked one (yellow) ensures an RMSD of less than 2 Å (ball and stick model).
3.7 Molecular docking analysis
Molecular docking results identified menegazziaic acid as a potent alpha-glucosidase inhibitor, with a binding affinity of −9.7 kcal/mol against protein (PDB ID: 3A4A), surpassing acarbose, which has an affinity of −8.9 kcal/mol (Table 4). These negative binding affinity values indicate the formation of stable protein-ligand complexes within the enzyme’s active site. Interaction analysis performed using the Biovia Discovery 2021, which revealed that menegazziaic acid formed multiple interactions in orthosteric and allosteric sites, including hydrogen bonds, π-π stacking, and van der Waals’ interactions (involvement of active site residues) with key amino acid residues such as TYR158 and GLU411, ARG442 (Table 5) [59]. The results supported reported article [60,61]. Similarly, acarbose showed diverse interactions with the enzyme, involving residues such as GLU411, ARG442, and LYS156 (Fig 5). The binding affinities and docking poses suggested that both compounds effectively bind to the active and allosteric sites of protein; however, menegazziaic forms a more stable interactions, particularly at allosteric site, highlighting its potential as a promising anti-diabetic candidate, warranting further stability testing and clinical validation.
(a) menegazziaic acid and (b) acarbose with protein (PDB ID 3A4A).
3.8 Molecular dynamics simulation (MDS)
3.8.1 Root mean square deviation (RMSD).
The RMSD curve, derived from molecular dynamics simulations trajectory, was used to assess conformational stability of the protein-ligand complexes (Fig 6a) [62]. The protein with menegazziaic acid adduct showed an average RMSD of 3.37 ± 0.77 Å, whereas the protein- acarbose complex exhibited a slightly higher value of 3.71 ± 0.51 Å. Both adducts reached equilibrium at around 20 ns. After equilibration, the RMSD values stabilized at approximately 3.5 Å, with minor fluctuations of about 0.5 Å. Notably, similar results have been reported in previous studies [63]. These results indicate that the ligands consistently maintained their docked positions throughout the molecular dynamics simulation period in both complexes.
(a) RMSD plot of menegazziaic acid and acarbose relative to the protein backbone, (b) RMSF curves of alpha carbon atoms, (c) Rg curves of protein, (d) SASA plot of the protein, and (e) the count of hydrogen bonds that bind ligands to the protein. Red curve = menegazziaic acid, blue curve = acarbose.
3.8.2 Root mean square fluctuation (RMSF).
RMSF analysis was performed to assess the flexibility of amino acid residues during MDS [64]. In both protein-menegazziaic acid and protein-acarbose adducts, the majority of amino acid residues exhibited RMSF values below 2 Å, reflecting limited structural fluctuations (Fig 6b). In contrast, residues located between PRO400 to ARG450 displayed higher peaks around 3 Å to 5 Å, hinting at increased mobility in this region. This elevated flexibility may be attributed to a deficiency of alpha or beta-pleated sheet structures in this region [37].
Comparative analysis revealed that the protein-menegazziaic acid exhibited an average RMSF of 1.40 ± 1.01 Å, while the protein-acarbose complex showed less fluctuation compared to protein-menegazziaic acid complex, with a slightly lower value of 1.36 ± 1.07 Å, suggesting reduced flexibility of the protein-acarbose complex and indicating its higher stability [65]. Similar trend have been reported in previous article [66].
3.8.3 Radius of gyration (Rg).
Rg profiles of both protein-menegazziaic acid and protein-acarbose complexes were monitored throughout the simulation period to assess structural compactness [67]. The average values were 2.49 ± 0.03 nm and 2.52 ± 0.01 nm, respectively. The protein-menegazziaic acid complex stabilized around 20 ns, whereas the protein-acarbose complex stabilized earlier, about 10 ns. Both complexes exhibited minor fluctuations of approximately 0.5 nm, indicating minimal structural expansion or contraction (Fig 6c). throughout the simulation period, consistent with previously reported studies (Fig 6c) [68,69]. Top of FormBottom of FormTop of FormBottom of Form
3.8.4 Solvent accessible surface area (SASA).
The SASA of protein in manegazziaic acid adduct was around 261.25 ± 7.04 nm2, while the protein-acarbose complex showed a slightly higher value of 263.95 ± 4.98 nm2. The complex values ranged from 265 to 270 nm2 after 10 ns of simulation (Fig 6d). The relatively smooth SASA profiles inferred the condensed structure of protein-ligand adducts [70].
Furthermore, the SASA analysis suggests that both menegazziaic and acarbose remained stable within the protein’s orthosteric and allosteric sites without exposing the hydrophobic portion [71].
3.8.5 Hydrogen bonds.
The number of hydrogen bonds was monitored over 1,000 frames during the molecular dynamics simulations (Fig 6e). The protein-menegazziaic acid adduct maintained about two hydrogen bonds beyond 500 frames, whereas the protein-acarbose complex sustained around six hydrogen bonds beyond 200 frames, indicating greater stability of the acarbose at the orthosteric and allosteric sites. Overall, the results suggest stable protein-ligand interactions with minimal RMSD fluctuations throughout the simulation period [72].
3.8.6 PCA, DCCM, and FEL analysis.
Principle component analysis (PCA) and dynamic cross-correlation matrix (DCCM) analyses were performed using Bio3D-R Package taking 100 ns MDS trajectories to evaluate the collective motion and correlated residue dynamics of the protein in complex with ligands. In PCA analysis, the protein-menegazziaic acid showed the highest contribution to structural variance in PC1 cluster (48.76%), followed by PC2 (13.7%), and PC3 (5.66%) clusters (Fig 7a). This high concentration of variance suggests that the protein moves in a specific, large-scale manner when bound to menegazziaic acid.
(a) menegazziaic acid, (b) acarbose complexes. Different colors (blue, white, and red indicate different stages (initial, intermediate, and final).
In contrast, the protein-acarbose complex showed significantly lower variance along PC1 (29.81%), PC2 (18.35%), and PC3 (10.23%) clusters, respectively (Fig 7b), indicating that acarbose binding makes the protein more flexible in many ways. In addition, the eigenvalue distribution confirmed that PC1 dominates in capturing the major conformational dynamics of both complexes.
Residue-residue correlation effects of the amino acid were analyzed using by DCCM plots. The protein-menegazziaic acid complex showed a maximum positive interaction distribution (0–1) across functional regions more clearly defined anti-correlated motions between distant domains. This indicates that menegazziaic acid stabilizes a tightly coupled dynamic state, which may facilitate allosteric transitions more smoothly (Fig 8a). In contrast, protein-acarbose complex displayed a mixture of strong positive and negative correlations (−1–0), suggesting more constrained and coordinated pattern of residue motion (Fig 8b).
(a) menegazziaic acid, (b) acarbose complexes.
The free energy landscapes (FELs) of the protein-ligand complexes were evaluated using the first two principal components (PC1 and PC2). The protein-menegazziaic acid complex has multiple low-energy basins, which implies that protein moves across conformational sub-states separated by energy barriers of approximately 8.02 kJ/mol (Fig 9a). In contrast, the protein-acarbose complex showed more compact and well-defined FEL with low-energy basins, indicating conformational ensemble or a transition towards prevailing state. The minimum free energy reached roughly 7.54 kJ/mol, suggesting that its energy surface is a little flatter than protein-menegazziaic acid complex (Fig 9b).
Different colors represent varying energy levels. (a) menegazziaic acid and (b) acarbose complexes.
Overall, the PCA, DCCM, and FEL analyses demonstrate that menegazziaic acid promotes greater conformational flexibility and dynamic residue correlations, whereas acarbose induces more rigid and dynamically coordinated protein structure.
3.8.7 Result from MM/GBSA calculations.
The MM/GBSA binding free energy was determined as −15.79 ± 4.39 kcal/mol for the protein- menegazziaic acid complex and −28.64 ± 4.34 kcal/mol for the protein-acarbose complex (S3 Table in S1 File). The negative values indicate a spontaneous binding process, consistent with the docking results, and favorable product formation [73,74]. Heat maps (200 frames) highlighted the influence of ligands on both complexes and showed significant ligand influence on both complexes, with dark blue regions indicating key residues, while lighter blue represents other amino acids (Fig 10). Furthermore, last 200 frames of equilibrated trajectories and per-residue energy components are depicted (S5 and S6 Figs in S1 File). These results confirm the correlation between docking interactions and binding energy calculations.
(a) menegazziaic acid and (b) acarbose with amino acid during the final 200 frames.
3.8.8 Drug-likeness Properties.
The menegazziaic acid lies in toxicity class IV compounds with predicted LD50 962 kg/mg using ProTox 3.0 server. Various parameters are crucial for the selection of an optimal drug candidate, with physicochemical properties playing a significant role in reliable design of drugs [75] (S4 Table in S1 File). Its total polar surface area (131.75 Å), significantly lower than acarbose (321.17 Å), indicating better cell membrane permeability. Menegazziaic acid adheres to Lipinski’s rule of five and has a bioavailability score of 0.55, suggesting favorable oral absorption. It does not cross the blood-brain barrier (BBB) and exhibits low gastrointestinal absorption. These attributes, along with its physicochemical properties, position menegazziaic acid as a potential candidate for diabetes treatment (Fig 11).
4 Discussion
Usnea species are well known for producing a wide range of secondary metabolites with diverse biological activities [76]. In the present study, Usnea cornuta exhibited notably high total phenolic content (TPC) and moderate total flavonoid content (TFC), which may partly explain its strong antioxidant and enzyme-inhibitory activities. While previous reports on other Usnea species have shown wide variability in phenolic and flavonoid levels, these differences are likely attributable to species-specific metabolic profiles and environmental factors rather than direct indicators of biological potency [72,73].
Among the solvent fractions, ethyl acetate and hexane fractions demonstrated the strongest antioxidant and α-glucosidase inhibitory activities, respectively. This pattern is consistent with the preferential extraction of lipophilic lichen metabolites, such as usnic acid, salazinic acid, and related depsides and depsidones, which are known to exhibit antioxidant, enzyme-inhibitory properties, and antimicrobial [74–79]. The remarkably low IC₅₀ value of hexane fraction against α-glucosidase suggests that non-polar constituents play major role in enzyme inhibition, likely through favorable interactions with hydrophobic regions of the enzyme active site.
An antibacterial activity of U. cornuta also correlated well with its chemical profile. The pronounced inhibition zones observed against Staphylococcus aureus strain are in line with the presence of well-established lichen acids, such as usnic acid and salazinic acid, which have been widely reported to disrupt bacterial cell membranes and metabolic processes [80,81]. Important chemical constituents are present in Usnea species, LC-MS profiling identified multiple metabolites, including a tentatively assigned unknown compound, further supporting chemical diversity contributing to the observed antimicrobial effects, antioxidant and α-glucosidase inhibitory activities [82].
Molecular docking analysis further supported these findings, as selected lichen metabolites demonstrated strong binding affinities toward α-glucosidase, indicating potential competitive inhibition. Compounds were selected for docking based on confident LC-MS identification, documented occurrence in Usnea species, and structural features relevant to enzyme inhibition. Menegazziaic acid was highlighted in this study based on prior literature identifying it as an important secondary metabolite in several Usnea species and its favorable in silico interaction with α-glucosidase [83]]. Its presence in U. cornuta was tentatively suggested by LC–MS mass analysis; however, it was not quantitatively determined. Therefore, menegazziaic acid should be regarded as a computationally prioritized candidate rather than a confirmed major constituent.
Although direct interactions with catalytic dyad (only Van der Waals’) were not observed, the strong binding affinity and engagement of functionally important residues suggested a potential allosteric inhibition mechanism, which warrants further experimental validation.
5 Conclusions
This study evaluated methanol extracts of U. cornuta for antioxidant, antidiabetic, and antibacterial activities. Analyses of total phenolic and flavonoid content revealed significant inhibitory effects against DPPH radicals, α-glucosidase, and bacterial strains. Among the solvent fractions, ethyl acetate fraction showed the strongest antioxidant activity, while hexane fraction demonstrated the best α-glucosidase inhibition. LC-MS analysis identified several bioactive compounds, including menegazziaic acid, which in silico analysis suggests as a promising therapeutic option for diabetes. These findings highlight U. cornuta’s potential for managing oxidative stress, hyperglycemia, and bacterial infections. Future work will isolate and characterize active compounds, explore mechanisms of action, and conduct in vivo and clinical studies.
Supporting information
S1 File. S1 Fig. Geographical map of collection area, Champadevi, Kathmandu, Nepal.
S2 Fig. Standard curve of DPPH inhibition by Quercetin. S3 Fig. α-Glucosidase inhibition activity of Acarbose. S4 Fig. Ramachandran plot of protein (PDB ID: 3A4A). S5 Fig. Changes in the binding free energy of different protein adducts with (a) protein-menegazziaic acid and (b) acarbose, red indicates the moving average. S6 Fig. Residues contribution in binding energies (kcal/mol) of complexs. (a) menegazziaic acid and (b) acarbose complexes. S1 Table. Zone of inhibition of crude extract of U. cornuta against bacteria strain. S2 Table. Identification of metabolites in lichen Usnea cornuta by LC-MS. S3 Table. Mass spectra chromatogram of 11 compounds present in U. cornuta. S4 Table. Change in binding energies (kcal/mol) of complex with different components. (a) menegazziaic acid and (b) acarbose complexes. S5 Table. Drug-Likeness Properties of menegazziaic acid and acarbose through the Swiss ADME Portal.
https://doi.org/10.1371/journal.pone.0351423.s001
(ZIP)
Acknowledgments
Authors thank the Department of Food Technology and Quality Control for LC-MS analysis and the Central Department of Microbiology, Tribhuvan University, Kathmandu, Nepal, for antibacterial activity assays.
References
- 1. Gerich JE. Contributions of insulin-resistance and insulin-secretory defects to the pathogenesis of type 2 diabetes mellitus. Mayo Clin Proc. 2003;78(4):447–56. pmid:12683697
- 2. Kumar A, Gangwar R, Zargar AA, Kumar R, Sharma A. Prevalence of Diabetes in India: A Review of IDF Diabetes Atlas 10th Edition. Curr Diabetes Rev. 2024;20(1):e130423215752. pmid:37069712
- 3. Atkinson MA, Eisenbarth GS. Type 1 diabetes: new perspectives on disease pathogenesis and treatment. Lancet. 2001;358(9277):221–9. pmid:11476858
- 4. Kahn SE. Type 1 diabetes: new perspectives on disease pathogenesis and treatment. Diabetologia. 2003;46(1):3–19.
- 5. Tarling CA, Woods K, Zhang R, Brastianos HC, Brayer GD, Andersen RJ, et al. The Search for Novel Human Pancreatic α‐Amylase Inhibitors: High‐Throughput Screening of Terrestrial and Marine Natural Product Extracts. ChemBioChem. 2008;9(3):433–8.
- 6. Crawford SD. Lichens Used in Traditional Medicine. Lichen Secondary Metabolites. Springer International Publishing. 2014. 27–80.
- 7. Elečko J, Vilková M, Frenák R, Routray D, Ručová D, Bačkor M, et al. A Comparative Study of Isolated Secondary Metabolites from Lichens and Their Antioxidative Properties. Plants. 2022;11(8):1077. pmid:35448805
- 8. Ureña-Vacas I, González-Burgos E, Divakar PK, Gómez-Serranillos MP. Lichen Depsidones with Biological Interest. Planta Med. 2022;88(11):855–80. pmid:34034351
- 9. Moyo CE, Minibayeva F, Liers C, Beckett RP. Quinone reductase activity is widespread in lichens. The Lichenologist. 2021;53(3):265–9.
- 10. Shukla V, Joshi GP, Rawat MSM. Lichens as a potential natural source of bioactive compounds: a review. Phytochem Rev. 2010;9(2).
- 11. Fernández-Moriano C, Gómez-Serranillos MP, Crespo A. Antioxidant potential of lichen species and their secondary metabolites. A systematic review. Pharm Biol. 2016;54(1):1–17. pmid:25885942
- 12. González-Burgos E, Fernández-Moriano C, Gómez-Serranillos MP. Current knowledge on Parmelia genus: Ecological interest, phytochemistry, biological activities and therapeutic potential. Phytochemistry. 2019;165:112051. pmid:31234093
- 13. Aravind SR, Sreelekha TT, Dileep Kumar BS, Kumar SN, Mohandas C. Characterization of three depside compounds from a Western Ghat lichen Parmelia erumpens Kurok with special reference to antimicrobial and anticancer activity. RSC Adv. 2014;4(65):34632.
- 14. Brandão LFG, Alcantara GB, Matos M de FC, Bogo D, Freitas D dos S, Oyama NM, et al. Cytotoxic evaluation of phenolic compounds from lichens against melanoma cells. Chem Pharm Bull (Tokyo). 2013;61(2):176–83. pmid:23207680
- 15. Hong J-M, Suh S-S, Kim T, Kim J, Han S, Youn U, et al. Anti-Cancer Activity of Lobaric Acid and Lobarstin Extracted from the Antarctic Lichen Stereocaulon alpnum. Molecules. 2018;23(3):658.
- 16. Huneck S. The significance of lichens and their metabolites. Naturwissenschaften. 1999;86(12):559–70. pmid:10643590
- 17. Choudhary MI, Ali M, Wahab A, Khan A, Rasheed S, Shyaula SL, et al. New antiglycation and enzyme inhibitors from Parmotrema cooperi. Sci China Chem. 2011;54(12):1926–31.
- 18. Mendili M, Khadhri A, Mediouni-Ben Jemâa J, Andolfi A, Tufano I, Aschi-Smiti S, et al. Anti-Inflammatory Potential of Compounds Isolated from Tunisian Lichens Species. Chem Biodivers. 2022;19(8):e202200134. pmid:35789537
- 19. Castañeta G, Sepulveda B, Vargas R, Garcia-Beltran O, Simirgiotis M, Areche C. A sustainable application for the extraction of lichen metabolites from Usnea cornuta: nontargeted metabolomics and antioxidant activity. Nat Prod Res. 2023;37(12):2076–82. pmid:36008873
- 20. Prateeksha P, Paliya BS, Bajpai R, Jadaun V, Kumar J, Kumar S, et al. The genus Usnea: a potent phytomedicine with multifarious ethnobotany, phytochemistry and pharmacology. RSC Adv. 2016;6(26):21672–96.
- 21. Maulidiyah M, Darmawan A, Wahyu W, Musdalifah A, Salim LOA, Nurdin M. Potential of Usnic Acid Compound from Lichen Genus Usnea sp. as Antidiabetic Agents. J Oleo Sci. 2022;71(1):127–34. pmid:35013035
- 22. Cetin Cakmak K, Gülçin İ. Anticholinergic and antioxidant activities of usnic acid-an activity-structure insight. Toxicol Rep. 2019;6:1273–80. pmid:31832335
- 23.
Phytochemical Methods: A Guide to Modern Techniques of Plant Analysis.
- 24. Gerlach A, da Silveira RM, Rojas C, Clerc P. Naming and describing the diversity in the Usnea cornuta aggregate (lichenized Ascomycota, Parmeliaceae) focusing on Brazilian specimens. Plant and Fungal Systematics. 2020;65(2):272–302.
- 25. Lu X, Ross CF, Powers JR, Aston DE, Rasco BA. Determination of total phenolic content and antioxidant activity of garlic (Allium sativum) and elephant garlic (Allium ampeloprasum) by attenuated total reflectance-Fourier transformed infrared spectroscopy. J Agric Food Chem. 2011;59(10):5215–21. pmid:21506613
- 26. Chang CC, Yang MH, Wen HM, Chern JC. Estimation of total flavonoid content in propolis by two complementary colometric methods. J Food Drug Anal. 2020;10(3).
- 27. Mensor LL, Menezes FS, Leitão GG, Reis AS, dos Santos TC, Coube CS, et al. Screening of Brazilian plant extracts for antioxidant activity by the use of DPPH free radical method. Phytother Res. 2001;15(2):127–30. pmid:11268111
- 28. Telagari M, Hullatti K. In-vitro α-amylase and α-glucosidase inhibitory activity of Adiantum caudatum Linn. and Celosia argentea Linn. extracts and fractions. Indian J Pharmacol. 2015;47(4):425–9. pmid:26288477
- 29. Kim S, Bolton EE. PubChem: A Large‐Scale Public Chemical Database for Drug Discovery. Methods and Principles in Medicinal Chemistry. Wiley. 2023. 39–66.
- 30. Rappe AK, Casewit CJ, Colwell KS, Goddard WA, Skiff WM. UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations. J Am Chem Soc. 1992;114(25):10024–35.
- 31. Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminform. 2012;4(1):17. pmid:22889332
- 32. Yamamoto K, Miyake H, Kusunoki M, Osaki S. Crystal structures of isomaltase from Saccharomyces cerevisiae and in complex with its competitive inhibitor maltose. FEBS J. 2010;277(20):4205–14. pmid:20812985
- 33. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009;30(16):2785–91. pmid:19399780
- 34.
Schrödinger LLC, D L W. PyMOL.
- 35. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455–61. pmid:19499576
- 36. Karki D, Phunyal A, Rayamajhi A, Gyawali N, Sapkota A, Adhikari A. In vitro Anti-oxidant, Alpha-Amylase, and Alpha-Glucosidase Inhibition Studies of Physica aipolia (Ehrh.ex Humb.) Furnr. CEI. 2025;21(1):40–51.
- 37. Phunyal A, Adhikari A, Adhikari Subin J. In silico exploration of potent flavonoids for dengue therapeutics. PLOS ONE. 2024;19(12):e0301747. pmid:39666626
- 38. Tian W, Chen C, Lei X, Zhao J, Liang J. CASTp 3.0: computed atlas of surface topography of proteins. Nucleic Acids Research. 2018;46(W1):W363-7. pmid:29860391
- 39. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1–2:19–25.
- 40. Lee J, Cheng X, Jo S, MacKerell AD, Klauda JB, Im W. CHARMM-GUI Input Generator for NAMD, Gromacs, Amber, Openmm, and CHARMM/OpenMM Simulations using the CHARMM36 Additive Force Field. Biophysical Journal. 2016;110(3):641a. pmid:26631602
- 41. Zoete V, Cuendet MA, Grosdidier A, Michielin O. SwissParam: a fast force field generation tool for small organic molecules. J Comput Chem. 2011;32(11):2359–68. pmid:21541964
- 42. Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML. Comparison of simple potential functions for simulating liquid water. J Chem Phys. 1983;79(2):926–35.
- 43. Darden T, Perera L, Li L, Pedersen L. New tricks for modelers from the crystallography toolkit: the particle mesh Ewald algorithm and its use in nucleic acid simulations. Structure. 1999;7(3):R55–60.
- 44. Gyawali K, Maharjan R, Acharya A, Khanal M, Ghimire MP, Lamichhane TR. Identification of catechin as main protease inhibitor of SARS-CoV-2 Omicron variant using molecular docking, molecular dynamics, PCA, DCCM, MM/GBSA and ADMET profiling. Nat Prod Res. 2024;1–8.
- 45. Wang E, Sun H, Wang J, Wang Z, Liu H, Zhang JZH, et al. End-Point Binding Free Energy Calculation with MM/PBSA and MM/GBSA: Strategies and Applications in Drug Design. Chemical Reviews. 2019;119(16):9478–508. pmid:31244000
- 46. Valdés-Tresanco MS, Valdés-Tresanco ME, Valiente PA, Moreno E. gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. J Chem Theory Comput. 2021;17(10):6281–91. pmid:34586825
- 47. Genheden S, Ryde U. The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov. 2015;10(5):449–61. pmid:25835573
- 48. Daina A, Michielin O, Zoete V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7(1):42717. pmid:28256516
- 49. Duangjai A, Suphrom N, Wungrath J, Ontawong A, Nuengchamnong N, Yosboonruang A. Comparison of antioxidant, antimicrobial activities and chemical profiles of three coffee (Coffea arabica L.) pulp aqueous extracts. Integr Med Res. 2016;5(4):324–31. pmid:28462135
- 50. Salgado F, Albornoz L, Cortéz C, Stashenko E, Urrea-Vallejo K, Nagles E, et al. Secondary Metabolite Profiling of Species of the Genus Usnea by UHPLC-ESI-OT-MS-MS. Molecules. 2017;23(1):54. pmid:29280946
- 51. Castro ON, Benites J, Rodilla J, Santiago JC, Simirgiotis M, Sepulveda B, et al. Metabolomic Analysis of the Lichen Everniopsis trulla Using Ultra High Performance Liquid Chromatography-Quadrupole-Orbitrap Mass Spectrometry (UHPLC-Q-OT-MS). Chromatographia. 2017;80(6):967–73.
- 52. Parrot D, Jan S, Baert N, Guyot S, Tomasi S. Comparative metabolite profiling and chemical study of Ramalina siliquosa complex using LC-ESI-MS/MS approach. Phytochemistry. 2013;89:114–24. pmid:23489575
- 53. Manassov N, Samy MN, Datkhayev U, Avula B, Adams SJ, Katragunta K, et al. Ultrastructural, energy-dispersive X-ray spectroscopy, chemical study and LC DAD-QToF chemical characterization of Cetraria islandica (L.) Ach. Molecules. 2023;28(11):4493. pmid:37298969
- 54. Karagoz ID, Ozaslan M, Kilic IH, Guler I, Uyar C, Tuter D, et al. Hepatoprotective effect of diffractaic acid on carbon tetrachloride-induced liver damage in rats. Biotechnol Biotechnol Equip. 2015;29(5):1011–6.
- 55. Sun C, Liu F, Sun J, Li J, Wang X. Optimisation and establishment of separation conditions of organic acids from Usnea longissima Ach. by pH-zone-refining counter-current chromatography: Discussion of the eluotropic sequence. J Chromatogr A. 2016;1427:96–101. pmid:26686561
- 56. Kalra R, Conlan XA, Areche C, Dilawari R, Goel M. Metabolite Profiling of the Indian Food Spice Lichen, Pseudevernia furfuracea Combined With Optimised Extraction Methodology to Obtain Bioactive Phenolic Compounds. Front Pharmacol. 2021;12:629695. pmid:34040518
- 57. Torres-Benítez A, Rivera-Montalvo M, Sepúlveda B, Castro O, Nagles E, Simirgiotis M, et al. Metabolomic analysis of two Parmotrema lichens: P. robustum (Degel.) Hale and P. andinum (Mull. Arg.) Hale using UHPLC-ESI-OT-MS-MS. Molecules. 2017;22(11):1861. pmid:29084151
- 58. Karki D, Phunyal A, Lamichhane TR, Rayamajhi A, Sapkota A, Nyaupane H, et al. Chemical profiling, in-vitro and in silico α-glucosidase inhibition, antioxidant and antibacterial activities of Hypotrachyna cirrhata (Fr.) Hale ex Sipman. Life. 2024;17(1).
- 59.
Biovia DS. Discovery studio visualizer. San Diego, CA, USA: Dassault Systèmes. 2021.
- 60. Nipun TS, Khatib A, Ibrahim Z, Ahmed QU, Redzwan IE, Saiman MZ, et al. Characterization of α-Glucosidase Inhibitors from Psychotria malayana Jack Leaves Extract Using LC-MS-Based Multivariate Data Analysis and In-Silico Molecular Docking. Molecules. 2020;25(24):5885. pmid:33322801
- 61. Shulgau Z, Palamarchuk I, Sergazy S, Urazbayeva A, Gulyayev A, Ramankulov Y, et al. Synthesis, Computational Study, and In Vitro α-Glucosidase Inhibitory Action of Thiourea Derivatives Based on 3-Aminopyridin-2(1H)-Ones. Molecules. 2024;29(15):3627. pmid:39125032
- 62. Rudrapal M, Celik I, Khan J, Ansari MA, Alomary MN, Yadav R, et al. Identification of bioactive molecules from Triphala (Ayurvedic herbal formulation) as potential inhibitors of SARS-CoV-2 main protease (Mpro) through computational investigations. J King Saud Univ Sci. 2022;34(3):101826. pmid:35035181
- 63. Xu Y, Liang X, Hyun C-G. Discovery of α-glucosidase inhibitors from Paenibacillus sp. JNUCC 31 via genome mining, fatty acid profiling, and in silico analysis. Sci Rep. 2025;15(1):38133. pmid:41173923
- 64. Shrestha RLS, Tamang A, Poudel Chhetri S, Parajuli N, Poudel M, M C S, et al. AI-assisted discovery of potent FGFR1 inhibitors via virtual screening and in silico analysis. PLoS One. 2025;20(9):e0331837. pmid:40934169
- 65. Kudin KN, Scuseria GE. Linear-scaling density-functional theory with Gaussian orbitals and periodic boundary conditions: efficient evaluation of energy and forces via the fast multipole method. Phys Rev B. 2000;61(24):16440–53.
- 66. Wang D, Wei G, Huang A. Identification and molecular mechanisms of novel α-glucosidase inhibitory peptides from Xundian cured beef: In vitro, molecular docking, and molecular dynamics studies. LWT. 2026;239:118979.
- 67. Khanal M, Acharya A, Maharjan R, Gyawali K, Adhikari R, Mulmi DD, et al. Identification of potent inhibitors of HDAC2 from herbal products for the treatment of colon cancer: Molecular docking, molecular dynamics simulation, MM/GBSA calculations, DFT studies, and pharmacokinetic analysis. PLoS One. 2024;19(7):e0307501. pmid:39037973
- 68. Acharya A, Khanal M, Maharjan R, Gyawali K, Luitel BR, Adhikari R, et al. Quantum chemical calculations on calcium oxalate and dolichin A and their binding efficacy to lactoferrin: An <i>in silico</i> study using DFT, molecular docking, and molecular dynamics simulations. AIMSBPOA. 2024;11(2):142–65.
- 69. Shrestha RLS, Neupane P, Dhital S, Parajuli N, Maharjan B, Shrestha T, et al. Inhibitory effects of phytochemicals of spices on human monoamine oxidase B through molecular docking, molecular dynamics simulation, and DFT study. Discover Chemistry. 2025;2(1):42.
- 70. Abdalla M, Mohapatra RK, Sarangi AK, Mohapatra PK, Eltayb WA, Alam M, et al. In silico studies on phytochemicals to combat the emerging COVID-19 infection. Journal of Saudi Chemical Society. 2021;25(12):101367.
- 71. Stroganov OV, Novikov FN, Stroylov VS, Kulkov V, Chilov GG. Lead Finder: An Approach to Improve Accuracy of Protein−Ligand Docking, Binding Energy Estimation, and Virtual Screening. J Chem Inf Model. 2008;48(12):2371–85. pmid:19007114
- 72. Waiba A, Phunyal A, Lamichhane TR, Ghimire MP, Nyaupane H, Phuyal A, et al. PLOS One. 2025;20(7):e0327862. pmid:40632786
- 73. Meanwell NA. Improving drug candidates by design: a focus on physicochemical properties as a means of improving compound disposition and safety. Chem Res Toxicol. 2011;24(9):1420–56. pmid:21790149
- 74. Das AK, Sarkar S, Devi P. Overview of Lichen. Chemistry, Biology and Pharmacology of Lichen. Wiley. 2024. 1–12.
- 75. Bharti S, Nayaka S, Kumar R. Evaluation of some traditional therapeutic properties of usnea longissima (ascomycota, lichenized fungi): antimicrobial, antiquorum and antioxidant. J Microb Biotech Food Sci. 2022;11(4):e3163.
- 76. Karki D, Phunyal A, Adhikari A. Estimation of total phenolic content, total flavonoid content, anti-oxidant activity and molecular docking studies of Stereocaulon piluliferum Th. J Plant Resour. 2024;22(1):104–12.
- 77. Maulidiyah M, Rachman F, Mulkiyan LOMZ, Natsir M, Nohong N, Darmawan A, et al. Antioxidant Activity of Usnic Acid Compound from Methanol Extract of Lichen Usnea sp. J Oleo Sci. 2023;72(2):179–88. pmid:36631103
- 78. Wa E. Lichentherapy: Highlights on the Pharmaceutical Potentials of Lichens. OAJMB. 2021;6(2):1–10.
- 79. Gupta VK, Verma S, Gupta S, Singh A, Pal A, Srivastava SK, et al. Membrane-damaging potential of natural L-(−)-usnic acid in Staphylococcus aureus. Eur J Clin Microbiol Infect Dis. 2012;31(12):3375–83. pmid:22865029
- 80. Ohmura Y. A synopsis of the lichen genus Usnea (Parmeliaceae, Ascomycota) in Taiwan. Mem Natl Mus Nat Sci. 2012;48:91–137.
- 81. Ismed F, Dévéhat FL-L, Rouaud I, Ferron S, Bakhtiar A, Boustie J. NMR reassignment of stictic acid isolated from a Sumatran lichen Stereocaulon montagneanum (Stereocaulaceae) with superoxide anion scavenging activities. Z Naturforsch C J Biosci. 2017;72(1–2):55–62. pmid:27770605
- 82. Hirayama T, Fujikawa F, Yosioka I, Kitagawa I. On the constituents of the lichen in the genus Menegazzia. Chem Pharm Bull (Tokyo). 1976;24(10):2340–4.
- 83. Gerlach ADCL, Toprak Z, Naciri Y, Caviró EA, Da Silveira RMB, Clerc P. New insights into the Usnea cornuta aggregate (Parmeliaceae, lichenized Ascomycota): Molecular analysis reveals high genetic diversity correlated with chemistry. Mol Phylogenet Evol. 2019 Feb;131:125–37. pmid:30385309