2.5.1. Qualitative preliminary phytochemical screening.

A range of standard colorimetric techniques was utilized to detect different phytochemical groups present in the respective extracts, following previously established protocols [15]. For flavonoids, 25 mg of each extract was dissolved in 2.5 mL of methanol. The addition of 5% NaOH produced a yellow color, which became colorless upon adding a few drops of 10% HCl, indicating the presence of flavonoids. Tannins were detected using both ferric chloride and lead acetate assays. In the ferric chloride test, 2 mL of a 10 mg/mL extract solution was mixed with 5% FeCl₃, producing a greenish-black or reddish-black color. In the lead acetate test, the extract solution was combined with 10% Pb(C₂H₃O₂)₂ solution, forming a grayish-white or creamy precipitate. Terpenoids were identified by dissolving 10 mg of extract in 8 mL chloroform, followed by the gentle layering of concentrated H₂SO₄ along the tube; the formation of a brownish ring at the interface confirmed terpenoids. Steroids were detected by treating 10 mg of extract with concentrated H₂SO₄ and gently shaking, resulting in a reddish or dark lower layer. Saponins were assessed by mixing 0.5 mg/mL extract solution with 3 mL distilled water, shaking vigorously for 2–3 minutes, and allowing the mixture to stand for 10 minutes; persistent foam indicated the presence of saponins. Reducing sugars were tested by mixing 25 mg of extract with equal volumes of Fehling’s solutions A and B, then heating for 5–10 minutes; a yellowish or reddish-orange precipitate confirmed their presence. Alkaloids were detected by dissolving 50 mg of extract in 5 mL of 2% HCl, boiling briefly, and filtering; the addition of 1 mL Mayer’s reagent produced a creamy-white precipitate. Cardiac glycosides were identified using the Keller–Killiani method, where 25 mg of extract was treated with 4 mL glacial acetic acid, 1 mL 5% FeCl₃, and 1 mL 1 N HCl, producing a brown ring at the interface. All tests were performed in triplicate to ensure reproducibility.

2.5.2. FT-IR spectroscopic analysis.

FT-IR was conducted on the respective extract based on a previously described method [7]. In short, the extract was pressed into a KBr disc (1 mg of extract blended with 100 mg of spectroscopic-grade KBr), which was subsequently loaded into the FT-IR instrument’s sample chamber. The IR spectrum was captured within the wavenumber range of 4500–400 cm ⁻ ¹, with a spectral resolution of 4 cm ⁻ ¹. Each spectrum was obtained after 32 scans under ambient temperature (25 ± 1 °C).

2.5.3. GC-MS analysis.

The GC-MS examination was carried out following an established protocol [16]. Phytocompounds present in the respective extract were identified using a Shimadzu triple quadrupole GC-MS-TQ8040 system. Helium served as the carrier gas (99.999% purity), while an Rtx-5MS capillary column (30 m in length, 0.25 mm internal diameter, and 0.25 µm film thickness) functioned as the stationary phase. The column oven was programmed to start at 50°C for 1 minute, ramped up to 200°C for 2 minutes, and finally held at 300°C for 7 minutes. During the 40-minute analysis, the injector temperature was steadily maintained at 250°C. A 1 µL aliquot of the sample was introduced into the GC inlet at a constant flow rate of 1 mL/min using spitless injection mode. Detector parameters were set as follows: Q3 full scan in mass spectrometry, scanning from 50 to 600 m/z, with a scan speed setting of 2000, interface temperature at 250°C, ion source temperature at 230°C, scan duration of 0.3 seconds per scan, and an ionization energy of 70 eV. Phytochemical identification was based on retention time and spectral data comparisons, which were further validated by matching spectra to the NIST database for verification of compound identity.

2.6.1. Antibacterial activity assessment by agar-well diffusion assay.

The antibiotic activity of MEECF, EEECF, EAEECF, MEECL, EEECL, and EAEECL against MRSA was evaluated using agar-well diffusion assay, according to previously established protocols [16]. Briefly, the cryopreserved strain of MRSA was thawed and plated separately on LB agar medium. The plates were incubated at 37°C to allow bacterial colony development. One selected colony was then transferred to 25 mL of LB broth and cultured at 37°C under constant shaking at 250 rpm until its optical density (OD) at 600 nm wavelength reached 0.4, as measured with a Multiskan Sky Microplate Spectrophotometer (Multiskan Sky with Cuvette and Touch Screen, Cat #A51119700C, Thermo Fisher Scientific, USA). 50 µL of this culture was uniformly spread over LB agar plates, and four wells were punched with a sterile cork borer. The MEECF, EEECF, EAEECF, MEECL, EEECL, and EAEECL stock solutions (1000 µg/mL) were dissolved in their respective solvent and serially diluted to obtain concentrations of 500, 250, 125, and 62.5 µg/mL. Each concentration was introduced into separate wells on the LB agar plates. A standard antibacterial disc containing 10 µg of methicillin was placed centrally on each plate and used as the positive control. Plates were then incubated at 37°C for 16 ± 1 hours, and the inhibition zones were measured to determine antibacterial efficacy. Each test was performed in triplicate.

2.6.2. Antibacterial activity assessment by MIC and MBC assay.

The MIC of MEECF, EEECF, EAEECF, MEECL, EEECL, and EAEECL was determined through a two-fold serial dilution method, following a previously described protocol with slight adjustments [7]. Briefly, the initial stock solutions of MEECF, EEECF, EAEECF, MEECL, EEECL, and EAEECL were prepared at a concentration of 2000 µg/mL and subsequently serially diluted in LB broth using 25 mL glass tubes to obtain final concentrations of 1000, 500, 250, and 125 µg/mL. These tubes were then inoculated with a bacterial suspension adjusted to the 0.5 McFarland standards (~1 × 10⁸ CFU/mL). One glass tube containing only LB broth (equivalent in volume to the extract-containing tubes) served as the negative control, and another tube containing LB broth with the bacterial suspension adjusted to the 0.5 McFarland standard (~1 × 10⁸ CFU/mL) served as the growth control. All tubes were incubated at 37°C for 24 h, and turbidity was monitored visually. The lowest concentration of each extract that inhibited visible bacterial growth was recorded as the MIC. For the determination of MBC, 50 µL from each bacterial culture was spread onto LB agar plates and incubated at 37°C for 16 h. The minimum concentration of the respective extracts that completely inhibited bacterial colony growth on the agar plate was recorded as the MBC. The MIC and MBC values reported in the Results section were determined strictly following the procedures described above, and all reported values are fully consistent with the experimental methodology. All experiments were conducted in triplicate to ensure reproducibility.

2.7.1. Protein structure retrieval and preparation.

Although 1VQQ offers higher resolution, it is an apo (ligand-free) structure and does not reflect ligand-induced conformations of the active and allosteric sites. 3ZG0, in contrast, is ligand-bound with ceftaroline and a peptidoglycan fragment at the allosteric site, providing a biologically relevant conformation. This makes 3ZG0 more appropriate for docking studies and for predicting phytocompound interactions with functionally relevant PBP2a sites. Therefore, the three-dimensional tertiary structures of the PBP2a (PDB ID: 3ZG0) and β-Lactamase (PDB ID: 3BLM) proteins in PDB format were downloaded via the RCSB protein data bank (RCSB PDB: Homepage). The crystallographic structures of β-Lactamase and PBP2a were resolved with a resolution of 2.00 Å and 2.60 Å, respectively. Initially, the structure of these two proteins was prepared using the Discovery Studio software. In this preparation method, the following criteria, including water molecules, different associated ions, and several cofactors, were removed from the crude protein. The non-polar hydrogen bonds were merging, and polar hydrogen bonds were added. Finally, the energy was minimized by the Swiss PDB viewer tools for preparing the protein molecules.

2.7.2. Preparation of ligand.

A total of 383 instinct compounds were detected in the GC-MS output, which were screened using preliminary drug likeness approaches, including RO5 violation and gastrointestinal permeability checks, two crucial factors for selecting drug candidates. One hundred and one (101) compounds were obtained, which exhibited pharmacologically acceptable properties. These phytochemicals were obtained in 3D SDF format from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and prepared using AutoDock 4. During ligand preparation, aromatic carbon atoms were first detected, followed by the generation of the torsion tree. The correct atom types were assigned, and non-polar hydrogens were merged. Finally, all the compounds were energy-minimized using the MMFF94 force field and saved in PDBQT format.

2.7.3. Molecular docking studies.

The in silico docking analysis study is among the key strategies used to determine the active site pose of a protein with potential ligand molecules. The prepared ligands were docked with the prepared protein by utilizing PyRx software via the AutoDock Vina wizard. The exhaustiveness was set to 8, and the energy range was maintained at 4 kcal/mol during the docking procedure to ascertain optimal binding affinities between the ligands and the protein. For docking the two target proteins (β-Lactamase and PBP2a), a grid box was generated for the docked ligand in the proteins’ active pockets mentioned in S1 Table. For the β-Lactamase, the grid box center coordinates were defined as X = 59.4384 Å, Y = 42.9518 Å, Z = 48.4556 Å, where the center was chosen at the position of X = 59.4384 Å, Y = 42.9518 Å, Z = 48.4556 Å, respectively. Whereas the PBP2a, its grid box size was assigned as X = 18.1968 Å, Y = 19.1843 Å, Z = 51.9170 Å, and the center was set at X = 69.8303 Å, Y = 59.6052 Å, Z = 130.9461 Å, correspondingly. The interactions among the proteins (β-Lactamase and PBP2a) and their respective ligands were interpreted by estimating their docking scores in Kcal/mol. The different chemicals and ligand-binding residues were identified using the Maestro Schrodinger viewer from the protein-ligand docking complex pose.

2.7.4. Molecular dynamics (MD) simulation.

MD simulations were carried out to assess the structural persistence of the protein-ligand complexes under simulated physiological conditions. To evaluate atomic-level motions and dynamics of the ligands within the protein-bound environment, 200 ns long MD simulations with a 2 fs integration time step were performed to examine the dynamic stability of the protein-ligand complexes. The physical motions of atoms in protein molecules were examined using Schrödinger’s Desmond module (Release 2020−3) operating in a Linux environment. A cubic simulation box with Simple Point Charge (SPC) water molecules was created for each complex, which had dimensions of 10 × 10 × 10 ų, to maintain a constant system volume. Na⁺ and Cl^- ions were randomized into the simulation environment to achieve a physiological salt content level of approximately 0.15 M. The OPLS3e force field enabled the stabilization and relaxation of the system. During the simulation, temperature was maintained at 300 K and pressure at 1.01 bar within the NPT (constant pressure-constant temperature) ensemble, summarized in S1 Table. Through the computation of parameters such as RMSD, RMSF, Rg, SASA, and protein-ligand contacts, overall structural stability and dynamic properties of each complex were assessed.

2.7.5. Pharmacokinetics (PK) and toxicity analysis.

The body’s interaction with an administered drug depends on its chemical and physical characteristics, including lipophilicity, water solubility, pharmacokinetics (such as GI absorption and BBB permeability), adherence to the Rule of Five for drug-likeness, synthetic accessibility, and toxicity prediction. These factors are crucial criteria in determining the effectiveness of a drug. Diverse computational tools were used to predict the initial potential of selected compounds with multi-targeting capabilities, showing higher molecular docking scores than the control drug methicillin. Thus, an online tool named SwissADME (SwissADME) was utilized to evaluate these parameters, excluding those related to toxicity and renal clearance. Admetlab2 (ADMETlab 2.0) and Admetsar2 (admetSAR) were employed for toxicity and excretion (renal clearance) analyses, respectively. All predictions were performed using default server parameters.

3.1.1. PPS of Eichhornia crassipes flowers and leaves.

The preliminary phytochemical screening (PPS) of methanolic, ethanolic, and ethyl acetate extracts of E. crassipes flowers and leaves revealed the presence of diverse classes of bioactive compounds, including flavonoids, tannins, alkaloids, cardiac glycosides, saponins, terpenoids, steroids, and reducing sugars. Detailed qualitative test outcomes are summarized in Table 1, highlighting the intensity of color change and test confirmation results for each extract. As illustrated in S1 Fig, most extracts showed strong positive reactions for flavonoids, tannins, terpenoids, and alkaloids, which are known to contribute to antibacterial potential. Flavonoids were confirmed by yellow coloration in the alkaline reagent test, while tannins showed greenish-black coloration with FeCl₃. Similarly, saponins were indicated by stable foam formation, and alkaloids were identified by creamy precipitate formation in Mayer’s test. These findings indicate that both flowers and leaves of E. crassipes are rich in phytochemical constituents linked to antimicrobial, antioxidant, and anti-inflammatory properties.

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Table 1. Preliminary phytochemical screening of MEECF, EEECF, EAEECF, MEECL, EEECL, and EAEECL showed the co-existence of different classes of phytochemicals.

https://doi.org/10.1371/journal.pone.0349750.t001

3.1.2. FT-IR spectroscopic functional group analysis.

As shown in Table 2 and S2 and S3 Figs, the FT-IR spectra confirmed the presence of multiple functional groups characteristic of biologically active compounds. The major absorption peaks corresponded to –OH, –NH, C–H, C = O, and C = C stretching vibrations, indicating the presence of alcohols, phenols, amides, alkanes, ketones, esters, and aromatic compounds. Broad absorption bands around 3700−2970 cm ⁻ ¹ represented O–H and N–H stretching, suggesting alcohols, phenols, and amines. Peaks between 1710−1650 cm ⁻ ¹ corresponded to C = O stretching in carbonyl compounds (esters, ketones, and amides). Aromatic C = C and N–O vibrations appeared around 1630−1460 cm ⁻ ¹, while bands at 1440−1260 cm ⁻ ¹ indicated aliphatic C–H bending and C–O stretching. The 1070−520 cm ⁻ ¹ region reflected C–O, C–N, and C–X stretching, confirming esters, amines, and halide groups. FT-IR results revealed that E. crassipes extracts contain multiple functional groups typically associated with antimicrobial phytochemicals such as phenolics, terpenoids, and fatty acids.

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Table 2. FT-IR spectral evaluation of MEECF, EEECF, EAEECF, MEECL, EEECL, and EAEECL. The distinct peak positions observed in the spectrum typically indicate the vibrational modes of specific chemical bonds.

https://doi.org/10.1371/journal.pone.0349750.t002

3.1.3. GC-MS analysis.

The GC-MS chromatograms for MEECF, EEECF, and EAEECF revealed 74, 65, and 65 peaks, respectively, while MEECL, EEECL, and EAEECL exhibited 72, 63, and 44 peaks. Each peak corresponds to a specific phytochemical, and the relative abundance of the compounds was determined by comparing each peak’s average area against the total area beneath the retention time (RT) curve (S4 and S5 Figs). The detailed identification of compounds’ chemical name, molecular formula, RT, peak area (%), PubChem CID, and chemical nature is provided in S2-S7 Tables. Among the detected phytochemicals, several bioactive constituents known for antibacterial potential were predominant. In MEECF, key compounds included 9-Octadecenamide, (Z)- (26.14%), cholest-5-en-3-ol, (3α)-, TMS derivative (17.44%), 9,12,15-Octadecatrienoic acid, methyl ester (3.18%), allocryptopine (2.81%), and phytol (1.85%). In EEECF, the major compounds were n-Hexadecanoic acid (17.17%), 9,12,15-Octadecatrienoic acid (12.82%), β-Sitosterol (7.20%), stigmasterol (5.20%), and 9-Octadecenamide, (Z)- (5.72%). The EAEECF extract primarily contained (Z)6,(Z)9-Pentadecadien-1-ol (11.80%), n-Hexadecanoic acid (8.04%), stigmasterol (7.32%), and γ-Sitosterol (4.54%). In MEECL, predominant compounds were 9-Octadecenamide, (Z)- (30.02%), stigmasterol (11.37%), and β-Sitosterol (2.29%). For EEECL, the major compounds were 9-Octadecenamide, (Z)- (25.21%), stigmasterol (16.71%), n-Hexadecanoic acid (6.54%), and neophytadiene (4.24%). Finally, EAEECL exhibited high contents of stigmasterol (29.85%), 9-Octadecenamide, (Z)- (23.95%), and eicosyl isopropyl ether (3.00%). Collectively, the predominance of fatty acids (n-Hexadecanoic acid, linoleic acid derivatives), sterols (stigmasterol, β-Sitosterol), and fatty acid amides (9-Octadecenamide, (Z)-) across all extracts suggests a consistent presence of compounds with well-documented antibacterial properties, correlating with their observed bioactivities. The differences observed in the chemical profiles of the extracts can be explained by the varying polarities of the solvents used for extraction. Methanol is a highly polar solvent capable of extracting a broad spectrum of polar and moderately polar phytochemicals, including compounds containing polar functional groups such as ester (–COO–), ketone/oxo (C = O), and hydroxyl (–OH) groups. Ethanol, slightly less polar than methanol, can also efficiently extract these compounds, though with slightly reduced efficiency. Ethyl acetate, being moderately polar, is more suitable for semi-polar to non-polar compounds and is less efficient at isolating highly oxygenated molecules. As a result, each solvent enriched a distinct subset of metabolites from E. crassipes, which is consistent with the GC–MS results obtained in this study.

3.2.1. Antibacterial activity assessment in agar well diffusion assay.

Given the persistent threat of MRSA, an evaluation was performed to determine the antibacterial efficacy of MEECF, MEECL, EEECF, EAEECF, EEECL, and EAEECL against MRSA. Our results demonstrated that the respective extracts prevented the growth of MRSA proliferation in a concentration-dependent fashion, producing inhibition zones ranging between 14 ± 1–21 ± 1 mm by MEECF, 8.666 ± 0.577 to 18.666 ± 0.577 mm by MEECL, 9.333 ± 0.577 to 19.333 ± 0.577 mm by EEECF, 11.66666667 ± 0.577350269 to 20 ± 1 mm by EEECL, 8 ± 1–18 ± 1 mm by EAEECF and 9.333 ± 0.577 to 19.333 ± 0.577 mm by EAEECF in the agar well diffusion method, using extract concentrations between 125–1000 µg/mL (Figs 1A-C).

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Fig 1. Antibacterial efficacy of different extracts derived from Eichhornia crassipes (Mart.) Solms flowers and leaves against methicillin-resistant Staphylococcus aureus.

(A) Methanol extracts of flowers (MEECF) and leaves (MEECL). (B) Ethanol extracts of flowers (EEECF) and leaves (EEECL). (C) Ethyl acetate extracts of flowers (EAEECF) and leaves (EAEECL). (D) The zones of inhibition recorded through agar-well diffusion were measured in millimeters (mm), indicating the antibacterial impact of MEECF, EEECF, EAEECF, MEECL, EEECL, and EAEECL. As a positive control, the commercial antibiotic methicillin did not inhibit the growth of MRSA. The data were presented as mean ± standard deviation (STDEV, n = 3).

https://doi.org/10.1371/journal.pone.0349750.g001

Notably, the reference antibiotic methicillin, typically ineffective against MRSA due to resistance mechanisms, failed to exhibit antibacterial activity. The apparent antibacterial activity in the agar well diffusion assay highlighted comparative antibacterial activity of MEECF, MEECL, EEECF, EAEECF, EEECL, and EAEECL depicted in Fig 1D.

3.2.2. Antibacterial activity assessment by MIC and MBC.

The minimum inhibitory concentration (MIC) refers to the lowest amount of an antimicrobial agent that completely inhibits visible microbial growth (indicated by the absence of turbidity) following overnight incubation. In contrast, the minimum bactericidal concentration (MBC) denotes the lowest concentration that achieves bacterial eradication after subculturing onto antibiotic-free solid agar media. As shown in S6-S11 Figs, our antibacterial experiment, some of the dilutions of the test extract exhibited no turbidity. In the MBC assay, the respective extracts at final concentrations corresponding to the MIC or higher resulted in the killing of 99.9% of the bacteria being tested The MIC of MEECF, MEECL, EEECF, EAEECF, EEECL, and EAEECL was determined to be 416.666 ± 144.337 µg/mL for MEECF, 666.666 ± 288.675 µg/mL for MEECL, 833.333 ± 288.675 µg/mL for EEECF, 583.333 ± 381.881 µg/mL for EEECL, 666.666 ± 288.675 µg/mL for EAEECF, and 500 ± 0 µg/mL for EAEECL Meanwhile, MBC values were observed as 666.666 ± 288.675 µg/mL for MEECF, 833.333 ± 288.675 µg/mL for MEECL, 1000 ± 0 µg/mL for EEECF, 833.333 ± 288.675 µg/mL for EEECL, 1000 ± 0 µg/mL for EAEECF, and 833.333 ± 288.675 µg/mL for EAEECL (Fig 2). It is worth noting that higher concentrations of the extracts were required during the MBC test to ensure complete bacterial killing, whereas lower concentrations were sufficient to halt bacterial growth in the MIC assay. The bactericidal potential of the extracts was further evaluated by calculating the ratio of MBC to MIC using the following formula:

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Fig 2. MIC and MBC of the six extracts of Eichhornia crassipes (Mart.) Solms flowers and leaves against MRSA in an antibacterial activity assay.

The mean+STDEV (n = 3) was used to represent the data.

https://doi.org/10.1371/journal.pone.0349750.g002

The calculated MBC/MIC ratios were 1.6 for MEECF, 1.25 for MEECL, 1.2 for EEECF, 1.43 for EEECL, 1.5 for EAEECF, and 1.67 for EAEECL. As all ratios were below 4, these findings indicate that all tested extracts exhibited bactericidal activity against the examined bacterial strains. The MIC and MBC values presented here are fully consistent with the methodology described in the Materials and Methods section.

3.3.1. Multi-modal molecular docking interaction analysis.

Molecular docking is a vital approach for identifying the fundamental interaction mode of a ligand to a macromolecule with a 3D structure in computer-aided drug design and structural molecular biology [17]. Binding affinity studies were conducted between phytochemicals obtained from GC-MS and the target proteins β-lactamase and PBP2a for MRSA. Among 383 phytocompounds, 3 exhibited multi-modal binding affinities greater than methicillin with β-lactamase (Table 3), and 41 displayed multi-modal binding affinities greater than methicillin with PBP2a (Table 4). However, two phytocompounds, protopine (CID 4970) and propanoic acid, 2-methyl-,[(3aS,6R,6aR,9aS,9bR)-dodecahydro-6a-hydroxy-9a-methyl-3-methylene-2,9dioxoazuleno[4,5-b]furan-6-yl]-methyl ester (CID 615944), exhibited multi-modal and notably higher negative binding affinities for both target proteins (β-lactamase and PBP2a) compared to the control drug, methicillin (CID 6087). CID 4970 exhibited molecular binding within the active pocket, with a docking energy score of −8.1 kcal/mol for β-lactamase and −7.7 kcal/mol for PBP2a. In comparison, CID 615944 showed binding energy scores of −7.7 kcal/mol for β-lactamase and −7.6 kcal/mol for PBP2a. Since both phytocompounds demonstrated multi-modal binding and better binding poses for all targets compared to the control drug methicillin, they were selected as hit phytocompounds for further evaluation through a rational drug design process.

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Table 3. The molecular docking interaction score between phytochemicals and the β-lactamase receptor (PDB ID: 3BLM) of methicillin-resistant Staphylococcus aureus was determined. Phytocompounds that exhibited a docking score of ≥ −7.6 are listed here, as this is the docking score of the control drug methicillin.

https://doi.org/10.1371/journal.pone.0349750.t003

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Table 4. The molecular docking interaction score between phytochemicals and the PBP 2a receptor (PDB ID: 3ZG0) of methicillin-resistant Staphylococcus aureus was determined. Phytocompounds that exhibited a docking score of ≥ −5.7 are listed here, as this is the docking score of the control drug methicillin.

https://doi.org/10.1371/journal.pone.0349750.t004

3.3.2. Interpretations of 3D, and 2D structure of protein-ligands interaction.

The molecular docking interactions of CID 4970, CID 615944, and control drug methicillin (CID 6087) with the target proteins (β-actamase and PBP2a) have been illustrated by the Maestro module of the Schrödinger suite, depicted in Figs 3, 4. Different kinds of noncovalent bonds, like hydrophobic, polar, hydrogen bonds, and other bonds, contributed to the binding of ligands to the active pockets of target proteins to make an interaction between protein and ligands. Non-covalent bond formation in drug discovery plays a crucial role because ligands don’t alter the structure of the protein; rather, they bind with the target protein efficiently. The formation of hydrogen bonds is a crucial component of drug binding and appropriate metabolism. As indicated in the figures, CID 4970, when interacting with the β-lactamase receptor, formed one hydrogen bond, nine hydrophobic bonds, and several other bonds. Similarly, CID 615944 formed one hydrogen bond, seven hydrophobic bonds, and other bonds with the β-actamase protein. Methicillin (CID 6087) exhibited four hydrogen bonds, thirteen hydrophobic bonds, and additional bonds when binding to the β-lactamase receptor. Additionally, CID 4970 interacted with the PBP2a receptor, forming ten hydrophobic bonds, one hydrogen bond, and other bonds, while CID 615944, when binding to PBP2a, formed no hydrogen bonds, fourteen hydrophobic bonds, and additional bonds. Methicillin also formed ten hydrophobic bonds, one hydrogen bond, and other bonds with PBP2a.

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Fig 3. The molecular docking interactions of Beta-Lactamase with two selected phytocompounds and the control drug methicillin are illustrated in 3D (left) and 2D (right) representations.

Panels (A-C) show the binding of CID 4970, CID 615944, and CID 6087 (control drug, methicillin), respectively, within the active pocket of the β-Lactamase protein.

https://doi.org/10.1371/journal.pone.0349750.g003

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Fig 4. The molecular docking interactions of PBP2a with two selected compounds and the control drug methicillin are presented in 3D (left) and 2D (right) views.

Panels (A-C) illustrate the binding of CID 4970, CID 615944, and CID 6087 (control drug, methicillin), respectively, bound within the active site of PBP2a.

https://doi.org/10.1371/journal.pone.0349750.g004

3.3.3. Protein-ligand binding interaction.

The molecular interactions between the selected ligands and the target proteins, β-lactamase and PBP2a, were explored using the Maestro interface of the Schrödinger suite, as represented in Figs 3, 4. The 2D and 3D visualizations revealed how the compounds are accommodated within the respective active sites through hydrogen bonding and hydrophobic interactions, contributing to their binding stability and specificity, as tabulated in S8 Table. For β-lactamase, both lead compounds (CID 4970 and CID 615944) and the control (CID 6087) exhibited strong affinity within the catalytic pocket. CID 4970 interacted with several key residues, including ALA 69, SER 70, THR 71, LYS 73, and GLU 166, establishing a single hydrogen bond with ASN 132. Hydrophobic contacts with ALA 69, TYR 105, TYR 129, ILE 167, TYR 171, and LEU 220 stabilized its binding conformation. Similarly, CID 615944 demonstrated extensive residue engagement involving ALA 69, SER 70, THR 71, TYR 105, and GLU 166, forming one hydrogen bond with ARG 244. Its hydrophobic network encompassed ALA 69, TYR 105, TYR 129, ILE 167, LEU 169, and ALA 238, indicating a strong anchoring within the hydrophobic cavity. The control compound, CID 6087, established multiple hydrogen bonds with SER 130, SER 235, GLN 237, and ARG 244, confirming its stable orientation in the active pocket. It also maintained hydrophobic interactions with residues TYR 68, ALA 69, TYR 105, MET 127, TYR 171, and ILE 239. Notably, all three ligands shared common hydrophobic contacts with ALA 69, TYR 105, TYR 129, and ILE 239, suggesting occupation of a conserved hydrophobic region crucial for β-lactamase inhibition. In the case of PBP2a, the binding patterns indicated a similar trend of strong noncovalent interactions. CID 4970 exhibited a stable conformation through one hydrogen bond with THR 600 and multiple hydrophobic contacts with TYR 446, VAL 448, ILE 459, ALA 601, and TRP 616. CID 615944, though lacking hydrogen bonding, maintained stability through hydrophobic interactions with residues such as ILE 171, LEU 190, TYR 223, PHE 227, TYR 369, and MET 372, implying its binding is primarily driven by van der Waals forces and π–π stacking. The control compound, CID 6087, formed a hydrogen bond with ALA 642 and numerous hydrophobic interactions involving TYR 446, ILE 459, TRP 616, PHE 617, and MET 641. Importantly, residues TYR 446, TRP 616, PHE 617, MET 641, and ALA 642 were common interaction points among the three ligands, signifying a conserved hydrophobic core at the PBP2a active site. The interaction profiles revealed that both lead compounds exhibited comparable or even stronger binding patterns than the control, primarily through stable hydrophobic interactions complemented by occasional hydrogen bonding. These conserved interactions suggest that CID 4970 and CID 615944 may effectively block the functional domains of β-lactamase and PBP2a, thereby interfering with bacterial resistance mechanisms.

3.3.4. Examination of the protein-ligand complexes’ structural stability of the best-hit compound through molecular dynamics simulation.

Molecular dynamics (MD) simulation is a robust method to observe the conformational changes where ligands and proteins are both permitted to run within a defined period under simulated physiological conditions. A 200 ns simulation was conducted to examine the flexibility, conformational dynamics, and binding interactions of the complex, addressing metrics such as RMSD, RMSF, Rg, SASA, and protein-ligand interaction profiling.

RMSD Analysis: The RMSD analysis evaluates the overall conformational stability of proteins during the simulation by measuring the average atomic displacement over time. Deviations within 1–3 Å are generally considered stable, indicating that the protein-ligand complex maintains equilibrium throughout the simulation period. The RMSD values for protein Cα atoms were calculated across the 200-ns simulation trajectory. As illustrated in Figs 5A and 5B and Table 5, the RMSD profiles of the β-lactamase and PBP2a complexes demonstrated acceptable fluctuations during ligand binding compared to their respective apo forms and the reference drug methicillin (CID 6087). For β-lactamase, the calculated Cα RMSD values were 1.188 Å for the apoprotein, 1.422 Å for CID 4970, 1.240 Å for CID 615944, and 1.373 Å for the methicillin complex. These values remained well within the acceptable range, indicating stable conformations throughout the simulation period. Among the ligand-bound systems, the CID 615944-β-lactamase complex exhibited the lowest average deviation, suggesting strong structural rigidity and minimal conformational drift. The CID 4970-β-lactamase complex also maintained a stable trajectory, comparable to the control complex, confirming that both compounds effectively stabilized the protein backbone during dynamic motion. In the case of PBP2a, higher RMSD fluctuations were observed compared to β-lactamase, consistent with its larger structural domain and inherent flexibility. The average RMSD values were 6.070 Å for the apoprotein, 2.973 Å for CID 4970, 2.858 Å for CID 615944, and 2.686 Å for the methicillin complex. Despite slightly elevated deviations, both CID 4970 and CID 615944 complexes displayed consistent RMSD plateaus after the initial equilibration phase, reflecting structural convergence and stable protein-ligand binding over the 200-ns trajectory. Taken together, these results confirm that both CID 4970 and CID 615944 maintained stable interactions with β-lactamase and PBP2a throughout the 200-ns simulation. The RMSD trends of both protein-ligand complexes closely resembled those of the control drug and stabilized within the acceptable range, indicating that the selected ligands were able to preserve structural integrity and stable binding orientations within the active pockets of both target proteins.

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Table 5. Values of simulation parameters of beta-lactamase, PBP2a in complex with CID 4970, CID 615944 and CID 6087 (Control) and apo form.

https://doi.org/10.1371/journal.pone.0349750.t005

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Fig 5. MD simulations of the selected phytocompounds and target protein complexes were conducted over a 200-nanosecond trajectory timeframe.

(A and B) show the RMSD values from the Cα atoms of β-lactamase and PBP2a as apoproteins, as well as for the phytocompounds CID 4970, CID 615944, and the control drug methicillin (CID 6087) in the docked complexes. (C and D) present the RMSF values obtained from the Cα atoms within the protein-ligand docked complexes. The RMSF values for the β-lactamase and PBP2a as apoproteins are illustrated in blue, the control drug (methicillin) in violet, and the two selected phytocompounds, CID 4970 in red and CID 615944 in green, complexed with the β-lactamase and PBP2a proteins, respectively. (E and F) Illustrate the radius of gyration (Rg) of the protein-ligand complexes. The Rg values of the selected phytocompounds CID 4970 and CID 615944, along with the control drug (ampicillin), in complex with β-lactamase and PBP2a are delineated in red, green, and violet, respectively. (G and H) Provide a graphical presentation of the protein-ligand complex’s SASA values. The SASA values for the two phytocompounds (CID 4970 and CID 615944) and the control drug (methicillin) in complex with β-lactamase and PBP2a are represented by red, green, and violet, respectively.

https://doi.org/10.1371/journal.pone.0349750.g005

RMSF Analysis: RMSF profiling provides insight into the flexibility and localized movements of individual amino acid residues throughout the protein chain during simulation. This analysis helps identify regions of high mobility that may contribute to ligand accommodation or conformational shifts. As illustrated in Figs 5C and 5D and Table 5, RMSF values were determined for β-lactamase and PBP2a proteins complexed with CID 4970 and CID 615944, compared to their apoproteins and the control drug methicillin (CID 6087). The β-lactamase receptor exhibited average RMSF values of 0.730 Å, 0.666 Å, 0.645 Å, and 0.760 Å for the apoprotein, CID 4970, CID 615944, and methicillin complex, respectively. These results indicate minimal fluctuations within the β-lactamase binding site, suggesting that both ligands contributed to the maintenance of structural stability during the 200-ns simulation. For the PBP2a receptor, the average RMSF values were recorded as 2.393 Å for the apoprotein, 2.004 Å for the CID 4970 complex, 1.700 Å for the CID 615944 complex, and 1.751 Å for the control drug. The slightly reduced fluctuation observed in both CID 4970 and CID 615944 complexes indicates that ligand binding improved the rigidity of the protein structure, stabilizing the flexible loops within the active site. The highest atomic fluctuations were noted around specific residues, particularly ASP 120, TYR 272, SER 306, and GLN 607 for the PBP2a complex, while SER 53, LYS 99, GLY 156, LYS 175, and GLY 254 showed elevated peaks in β-lactamase. Overall, all ligand-bound systems exhibited well-controlled fluctuations within the acceptable range, reflecting stable and compact protein-ligand complexes over the 200-ns simulation period.

Radius of Gyration Analysis: The spatial distribution of atomic coordinates with respect to the protein’s center of mass, defined as the radius of gyration (Rg), provides critical insight into the overall compactness and stability of protein-ligand complexes. This parameter reflects whether ligand binding influences the structural folding or expansion of the protein macromolecule. The Rg values of β-lactamase and PBP2a complexes were analyzed over the 200-ns simulation period to assess the dynamic stability of CID 4970, CID 615944, and the control drug (methicillin, CID 6087), as illustrated in Figs 5E and 5F and Table 5. For β-lactamase, the average Rg values for the CID 4970- and CID 615944-bound complexes were 3.830 Å and 3.864 Å, respectively, while the control methicillin complex showed an average Rg of 4.009 Å. The observed Rg for the CID 4970 complex was slightly lower than both the CID 615944 and control complexes, suggesting that CID 4970 induced tighter packing and greater compactness of the protein structure. This implies that ligand binding contributed to an overall stable conformation during the simulation. Similarly, for PBP2a, the mean Rg values were 3.460 Å for CID 4970, 3.821 Å for CID 615944, and 4.002 Å for the control drug. The lower Rg values of the CID 4970 and CID 615944 complexes relative to the control indicate that the binding of both phytocompounds enhanced structural compactness and reduced molecular flexibility. Among the two, CID 4970 again demonstrated the most compact structure, suggesting superior stabilizing potential and stronger intramolecular packing. Collectively, these findings suggest that both phytocompounds promoted a more compact and stable conformation of β-lactamase and PBP2a compared to the reference drug methicillin. The consistently lower Rg values of CID 4970 across both target proteins further indicate its stronger structural stabilization effect during the 200-ns simulation period.

SASA Analysis: SASA serves as a vital structural parameter that reflects the degree of surface exposure of amino acid residues and provides insight into the solvation behavior, hydrophobicity, and functional dynamics of a protein during ligand interaction. Residues positioned on the outer protein surface often participate in molecular recognition and binding events, thereby influencing the conformational adaptability of macromolecules. As represented in Figs 5G and 5H and Table 5, the SASA profiles for β-lactamase and PBP2a were monitored throughout the 200-ns simulation for the apoprotein, the control drug (methicillin, CID 6087), and the lead compounds (CID 4970 and CID 615944). For β-lactamase, the SASA values fluctuated between 129.668 Ų and 414.899 Ų for the CID 4970 complex, and from 115.002 Ų to 269.314 Ų for the CID 615944 complex. In contrast, the methicillin-bound structure exhibited a range of 96.883 Ų to 511.286 Ų. The average SASA values were recorded as 280.534 Ų for CID 4970, 160.742 Ų for CID 615944, and 252.802 Ų for the control complex. These findings suggest that the β-lactamase–CID 4970 complex exhibited higher solvent exposure relative to CID 615944 but remained well within the stable solvation range, indicating compact folding and reduced flexibility. Similarly, in the PBP2a complexes, the SASA values ranged between 74.669 Ų and 417.692 Ų for CID 4970, and from 79.673 Ų to 586.920 Ų for CID 615944. The methicillin complex displayed values between 105.794 Ų and 292.749 Ų. The average SASA values were 230.223 Ų for CID 4970, 313.182 Ų for CID 615944, and 201.831 Ų for the control drug. The slightly elevated SASA observed for CID 615944–PBP2a indicates enhanced solvent exposure compared to the CID 4970 complex, which demonstrated better compactness and stability during the 200-ns trajectory. The SASA analysis revealed that both phytocompounds maintained favorable solvent accessibility compared to methicillin. Among them, CID 4970 displayed reduced surface area exposure, reflecting stronger packing interactions and a more stable protein–ligand interface for both β-lactamase and PBP2a during the entire simulation period.

Protein-ligand contact analysis: Using the 100 ns simulation timeframe, the configurations of the target proteins complexed with the selected compounds and their interaction patterns were observed. Protein-ligand contacts within the receptors (β-lactamase and PBP2a) and the phytocompounds (CID 4970 and CID 615944) and the control drug were examined and illustrated in Fig 6, considering factors such as ionic bonding, water bridges, hydrogen bonding, and noncovalent interactions (hydrophobic bonding). Both compounds interacted during the 100 ns simulation, including ionic bonds, water bridges, hydrogen bonds, and hydrophobic interactions. These interactions facilitated stable binding between the compounds and the target proteins, which persisted throughout the simulation. CID 4970 exhibited water bridges, hydrophobic interactions, and hydrogen bonding with both PBP2a and β-lactamase. Similarly, CID 615944 interacted with β-lactamase and PBP2a through ionic bonds, water bridges, hydrophobic interactions, and hydrogen bonds. In contrast, the control ligand methicillin interacted with both receptors (β-lactamase and PBP2a) through consistent hydrogen bonds, hydrophobic contacts, solvent-mediated bridges, and electrostatic interactions.

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Fig 6. Bonding in the molecular dynamic simulation of CID 4970, CID 615944, and the control drug methicillin (CID 6087) with the target proteins (β-lactamase and PBP2a) was calculated from a 200-ns simulation.

(A) β-Lactamase and CID 4970 interaction. (B) β-Lactamase and CID 615944 interaction. (C) β-Lactamase and CID 6087 association. (D) PBP2a and CID 4970 interaction. (E) PBP2a and CID 615944 interaction. (F) PBP2a and CID 6087 interconnection.

https://doi.org/10.1371/journal.pone.0349750.g006

3.3.5. Pharmacokinetics, drug-likeness, and toxicity analysis.

The pharmacokinetics (PK), drug-likeness, and toxicity assessment are critical in drug development to ensure both safety and effectiveness for regulatory clearance. Accordingly, the PK, drug-likeness, and toxicity profiles of the two hit phytocompounds were evaluated. The PK properties of drug candidates are significantly influenced by their physicochemical parameters, including MW, HBD, HBA, RB, and TPSA. As shown in Table 6, both phytocompounds displayed molecular weights (≤500 g/mol), HBD (≤5), HBA (≤10), and TPSA values (≤140 Ų) within the optimal range, suggesting their potential for efficient oral absorption. The ideal range for RB is 0–11, and our phytocompounds fall within this, with RB values ranging from 0 to 4, suggesting favorable absorption characteristics. A cLogP value between 1 and 5 typically reflects a high likelihood of absorption, and both phytocompounds satisfy this requirement, with cLogP values ranging from 2.02 to 2.67. LogS, a measure of solubility, is ideally low, with a suitable range of −4.0 to 0.5. Both phytocompounds exhibited LogS values from −2.52 to −4.0, fitting well within the desired range. HIA predictions were also performed since they greatly impact systemic availability of a drug. CID 4970, CID 615944, and methicillin (CID 6087) exhibited high GI absorption. Renal excretion serves as a major pathway for eliminating both metabolized and unmetabolized compounds. Potential drug candidates are expected to demonstrate efficient and favorable excretion characteristics during drug development. We observed that protopine demonstrated high clearance, whereas CID 615944 and methicillin exhibited moderate clearance. Lipinski’s Rule of Five (RO5) provides a framework for evaluating the drug-like properties of compounds. Using the SwissADME server, we assessed the RO5 compliance of both hit phytocompounds and confirmed that they satisfied the criteria, indicating their potential as drug-like molecules. Evaluating the synthetic accessibility of potential drug candidates is a fundamental aspect of computational and medicinal chemistry. Both phytocompounds exhibited favorable synthetic accessibility scores, ranging from 3.48 to 4.65. An in-silico toxicity profiling was performed using the AdmetSAR2 web server, revealing non-hepatotoxic, non-carcinogenic, and non-mutagenic characteristics for both compounds. Based on these combined properties, the two hit phytocompounds were selected as the most promising candidates.

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Table 6. The physicochemical characteristics, lipophilicity, water solubility, toxicity profiles, drug-likeness, and synthetic accessibility properties of the phytoconstituents.

https://doi.org/10.1371/journal.pone.0349750.t006