3.1. Characterization of chemically Synthesized Ag NPs
3.1.1. X-ray diffraction
Figure 2 reveals the X-ray diffraction (XRD) peaks of the produced nano material. This technique generally employed to give definite information about the grain size and the type of material prepared either it was amorphous or crystalline (i.e. silver nanoparticles). It can be seen only one peak in XRD spectrum shown in at 2θ = 38o which indicates to polycrystalline of silver nanoparticles according to the ASTM standards and card No. (JCPDS File No. 89-3722) (Landage et al., 2014). The crystallite size in nm (D) was determined using the Debye–Scherer Equation (Shankar et al., 2017).
$$D=\frac{K\lambda }{{\beta }_{0 }cos\theta }$$
1
Where K, λ, βo, and θ are Scherer constant (0.95), X-ray wavelength (0.154 nm), FWHM (full width at half maximum), and Bragg’s angle in radians, respectively. The average crystallite size using the assigned peak in Fig. 1 was nearly 7.2 nm.
3.1.2. UV-Vis spectrum
Due to the potential application UV-Vis spectra is considered one of the characterization techniques revealing the properties of silver nanoparticles. The surface plasmon resonance (SPR) appeared at Fig. 3 at wavelength 425 nm. As reported previously by (Landage et al., 2014) the peaks of silver nanoparticles at this range indicates that the nanoparticles are rounded in shapes and these results are confirmed by our results in the SEM graphs.
3.1.3. FT-IR spectrum
Fourier Transform Infrared (FTIR) spectra, Fig. 4, could effectively be applied to express the particle formation. As reported previously both the width and intensity of peaks in an IR spectrum possess explicit dependence on the particle size. With particle size increases, one can find that the width of the peak decreases while the intensity increases (Pacios et al., 2007). Both beaks shift as well as changing in the intensity and broadness of the peaks related to silver nitrate which located between 1200 to 1500 cm− 1 are generally referred to evidence to the formation of silver nanoparticles from its precursor silver nitrate. Moreover, the broadness and intensity of the peaks reflects the small sizes of silver nanoparticles formed by the chemical reduction method.
3.1.4. SEM images
Figure 5 reveals the SEM images of silver nano particles at different magnification powers. The images reveal some kind of agglomeration which is normal result due the preparation method. Also, the spherical shape is clearly shown at all magnification powers which is strong evidence on the formation of silver nano particles. All results lie in line with other researches (Kordy et al., 2022; Muhi, 2017; Sodha et al., 2015).
3.1.5. DLS and Zeta potential of Ag nanoparticles
Both DLS as well as Zeta potential are considered a good proof for the presence of well-prepared silver nanoparticles, for this reason a colloidal solution was prepared in order to analyze quantitative size distribution. Figure 6A illustrates two distinct peaks with different intensities located between 3 and 15 nanometers, moreover, the value of zeta potential appeared at Fig. 6B which is − 39.3 mV. The negative value of zeta potential indicating the well polydispersity and stability of prepared silver nanoparticles. This data supports our data obtained from the SEM.
3.2. In vitro antibacterial evaluation
3.2.1. Preliminary antimicrobial activity evaluation:
The AgNPs were first screened for their in vitro antimicrobial activity against standard strains of E. coli (ATCC25922), K. pneumoniae (ATCC700603), P. aeruginosa (ATCC27853), Staphylococcus aureus (ATCC43300), B. subtilis (ATCC6633), and against the fungus C. albicans (ATCC60193). Imipenem and Azithromycin were used as a reference standard for antibacterial activity while fluconazole was used as a reference for antifungal activity. Results in Table 1 illustrate that AgNPs has a high antimicrobial activity against E. coli, K. pneumoniae, and C. albicans with moderate antimicrobial activity against S. aureus, B. subtilis and low antimicrobial activity against P. aeruginosa.
Table 1
Zone of growth inhibition (mm) of the tested microorganisms caused by the prepared AgNPs.
Compound | E. coli | K. pneumoniae | P. aeruginosa | S. aureus | B. subtilis | C. albicans |
AgNPs | 18 | 19 | 13 | 17 | 15 | 25 |
Imipenem | 17 | 16 | 15 | 18 | 16 | - |
Azithromycin | 21 | 21 | 18 | 25 | 32 | - |
Fluconazole | - | - | - | - | - | 30 |
3.2.2. Determination of the minimum inhibitory concentrations (MIC) and growth curve analysis:
AgNPs were then screened for their MIC against the bacterial and fungal strains. The MIC shown in Table 2 was determined by the standard Clinical and Laboratory Standards Institute (CLSI) broth microdilution method. Growth curve analysis was performed to detect the effect of subinhibitory concentrations (Sub-MIC) of the synthesized and natural compounds on the growth of bacterial and fungal strains. From Table 2, it can be observed that the tested compounds did not expose a significant difference (P > 0.05) in the growth of strains treated at their Sub-MIC (1/2 MIC) compared with untreated cultures.
Table 2
MIC and sub-MIC (1/2 MIC) of the tested compounds against P. aeruginosa and C. albicans.
Compound | MIC (E. coli) (µg/ml or µl/ml) | MIC (K. pneumoniae) (µg/ml or µl/ml) | MIC (P. aeruginosa) (µg/ml or µl/ml) | MIC (S. aureus) (µg/ml or µl/ml) | MIC (B. subtilis) (µg/ml or µl/ml) | MIC (C. albicans) (µg/ml or µl/ml) | Sub-MIC = 1/2 MIC (P. aeruginosa) (µg/ml or µl/ml) | Sub-MIC = 1/2 MIC (C. albicans) (µg/ml or µl/ml) |
AgNPs | 37.5 | 18.75 | 37.5 | 37.5 | 37.5 | 37.5 | 18.75 | 18.75 |
Imipenem | 37.5 | 37.5 | 37.5 | 37.5 | 37.5 | - | 18.75 | - |
Fluconazole | - | - | - | - | - | 46.87 | - | 23.4 |
3.2.3. Inhibitory effect of AgNPs on biofilm formation
Crystal violet assay was performed in 96-well polystyrene plates to decide the impact of the tested compounds on biofilm formation. P. aeruginosa and C. albicans was cultured with sub-MIC of each compound, sub-MIC of Imipenem (18.75 µg/ml) and sub-MIC of Fluconazole (23.4 µg/ml). AgNPs inhibited the biofilm formation of P. aeruginosa by 43%. Additionally, it inhibited the biofilm formation of C. albicans by 50.7%. Sub-MIC of Fluconazole inhibits the biofilm formation of C. albicans by 56.1% and sub-MIC of Imipenem inhibits the biofilm formation of P. aeruginosa by 55.2%, Table 3.
Table 3
Percent inhibition of biofilm formation.
Compound | Absorbance at 570 nm ± SD (P. aeruginosa) | Percent of Inhibition (%) (P. aeruginosa) | Absorbance at 570 nm ± SD (C. albicans) | Percent of Inhibition (%) (C. albicans) |
Control | 0.424 ± 0.0139 | - | 0.578 ± 0.00119 | - |
AgNPs | 0.242 ± 0.0102 | 43 | 0.285 ± 0.0082 | 50.7 |
Imipenem | 0.190 ± 0.00613 | 55.2 | - | - |
Fluconazole | - | - | 0.254 ± 0.0094 | 56.1 |
3.3. In silico investigation
To investigate the possible mode of action of the prepared AgNPs against E. coli, we conducted a series of MD simulation-based investigations using E. coli-derived glutathione (GSH) reductase enzyme as a key target for silver atoms.
Several previous reports have indicated that the key mode of action of Ag metal against microorganisms is its ability to elevate the microbial intracellular oxidative stress (Zou et al. 2018; Ameh et al. 2022; Quinteros et al. 2016), however, the exact mechanism of action remained elusive.
Previously, Baiocco and his co-worker have reported how can Ag metal can inhibit Leishmania parasite via its interaction with the parasite’s trypanothione reductase (Baiocco et al. 2011).
Being structurally homologs to Leishmanial trypanothione reductase, we proposed that the bacterial GSH reductase can also interact and be inhibited by Ag metal. As shown in Fig. 7A, 7B, and 7C both Leishmanial trypanothione reductase (PDB ID: 2X50; Baiocco et al. 2011)d coli-derived GSH reductase (PDB ID: 1GER; Mittl et al. 1994) are perfectly aligned with each other with low RMSD of 2.02 Å.
Accordingly, we conducted a number of 300 ns-long MD simulations to investigate if modelled Ag atoms can interact with the modelled E. coli-derived GSH reductase. We added in each simulation trial 5 Ag atoms inside the simulation box.
After careful analysis of 5 independent MD simulations, we found the following results:
-
In two out of five simulations, a single Ag silver atom was able to reach the GSH reductase catalytic site (after ~ 180 ns ± 30 ns) and stay there until the end of simulations (Fig. 8A).
-
In one out of the three remaining simulations, double Ag atoms were captured inside the GSH reductase dimeric interface (after ~ 140 ns) and remained there until the end of the simulation (Fig. 8B). In this MD trajectory, a single Ag atom was come first and bound inside the enzyme’s dimeric interface (at 170 ns Fig. 8) and then another Ag atom came to bind in line with the first Ag atom (at 190 ns).
A deeper investigation of the produced MD trajectories revealed that Ag was able to achieve a stable binding inside the enzyme’s catalytic site, where it was coordinated by: two cysteine amino acid residues (CYS-42 and CYS-47), THR-311, and HIS-439 (Fig. 9A and 9C).
Interestingly, this binding mode was almost identical to that of Ag inside the crystallized structure of Leishmanial trypanothione reductase (PDB ID: 2X50), where Ag was also coordinated by the same amino acid residues (CYS-52, CYS-57, THR-33, and HIS-461) (Fig. 9B).
On the other hand, Ag atoms (i.e., two atoms) were able to bind inside an allosteric site located at the interface of two GSH reductase subunits (Fig. 10A), where Ag-B was coordinated by two methionine amino acid residues (i.e., MET-425A and MET-425B), while Ag-A was coordinated by GLY-418B. This binding mode (i.e., of Ag-A) was slightly different from the corresponding one in the leishmanial trypanothione reductase (PDB ID: 2X50), where Ag-A was coordinated by two cysteine amino acid residues (CYS-444A and CYS-444B) the same amino acid residues (CYS-52, CYS-57, THR-33, and HIS-461) (Fig. 10B).
Upon 100 ns-long MDS, it was found that Ag-B was more stable than Ag-A inside the binding site (Fig. 10C), where Ag-A started to unbound out of the binding site at 48 ns, while Ag-B remained stable until the end of the simulation.
According to the previous MD simulation-based findings, it can be concluded that Ag atoms desorbed from the AgNPs have tentatively the potential to interact and inhibit E. coli GSH reductase causing a lethal elevated oxidative stress inside the bacterial cell (Fig. 1). To our knowledge, this is the first MD simulation study that investigates such a mode of action of Ag metal with the bacterial GSH reductase. Hence, we believe such MD simulation findings will open the door for further studies about metal-proteins interactions that can help develop new metal-based therapeutics.