Endophytic Aspergillus japonicus mediated biosynthesises of magnesium oxide nanoparticles: sustainable dye removal and in silico molecular docking evaluation of their enhanced antibacterial activity

Abstract

Sustainable biosynthesis of metal oxide nanoparticles using an eco-friendly approach is a growing research area owing to their promising environmental and biomedical applications. This work aims to biosynthesize and characterize magnesium oxide nanoparticles (MgONPS@Aj) for possible application in dye biosorption and antibacterial activity. For the first time, MgONPS@Aj was successfully synthesized by harnessing exometabolites of Aspergillus japonicus. Various parameters were statistically optimized to maximize the production of MgONPS@Aj using Plackett Burman's design and central composite design. The analysis of variance (ANOVA) revealed that pH was the most significant variable, affecting the bioproduction process followed by biomass quantity and Mg²⁺ precursor concentration. The suggested model (quadratic) was greatly significant and acceptable due to the nonsignificant lack of fit (15.10), and P-value (0.001). The optimized nanoparticles were characterized using X-ray powder diffraction, Fourier-transform infrared (FTIR) spectroscopy, transmission electron microscope (TEM), and Scanning electron microscopy. A high biosorption capacity (204.08 mg/g) of reactive black 5 dye was achieved within 40 min using a 5 mg biosorbent dose (MgONPS@Aj), 100 mg/l initial dye concentration, and pH 6.0. The biosorption process followed a pseudo-second-order (R² of 0.9842) and Langmuir isotherm (R² of 0.9422) models with a dimensionless separation factor (R_L) of 8 × 10⁴, hinting favorable and effective biosorption of dye molecules. A biosorption capacity of 81.97 mg/g after five successive cycles hints that the nanomaterial is suitable for several time utilization. Biogenic MgONPS@Aj displayed dramatic concentration-dependent antibacterial activity with the largest inhibition zones for P. aeruginosa (24.1 ± 0.8 mm, MIC: 3.125 µg/ml), followed by E. coli (22.3 ± 0.7 mm, MIC 6.25), B. subtilis (14.7 ± 0.4 mm, MIC: 12.5 µg/ml) and S. aureus (19.2 ± 0.6 mm, MIC: 6.25 µg/ml). The antibacterial activity was further interpreted using molecular simulation analysis. The lowest binding affinity was determined between MgONPS@Aj and target bacterial proteins (chloramphenicol acetyltransferase E. coli, and S. aureus MurE). The ligand (MgONPS@Aj) can bind to the active site's residues (Tyr¹⁷² and SER²²⁴), indicating a possible antibacterial mechanism. This study recommends MgONPS@Aj as an eco-friendly, and reusable alternative to traditional anionic dye sorbents and a uniquely promising candidate for antimicrobial applications.

Graphical Abstract

Highlights

• For the first time, magnesium oxide nanoparticles were produced using endophytic Aspergillus japonicus.

• Bioprocess optimization was performed to maximize nanoparticle production using Plackett Burman design and central composite design.

• Sustainable removal of azo reactive dye with high sorption capacity was achieved.

• Enhanced antibacterial activity in lower concentrations of MgO-nps was obtained.

• The inhibitory effect was explored using molecular docking for the first time.

Introduction

The industrial sector of textile, papermaking, cosmetics, plastic, and printing industries are the main consumers of synthetic dyes [1,2,3,4]. The existence of non-degradable and chemically stable dyes in water bodies in high concentrations signifies a critical threat to living beings and the environment [5,6,7]. Nearly 10–15% of synthetic dyes are directly discharged into the natural water environment without any treatment [8,9,10]. Approximately 60% of these dyes belong to the azo-reactive dyes category and are widely employed in various industries owing to their ease of application, excellent color, and bright color [11].

Reactive black 5 dye (RBL5) is one of the most commercial and synthetic textile dyes. Owing to its high solubility in water, nondegradable properties, and harmful effects on the skin and liver, RBL5 is considered a hazardous contaminant in the aquatic environment [12]. RBL5 was preferred as a model azo-reactive dye due to its incomplete fixation reaction onto cellulose fibers, indicating low efficiency in the dyeing process and highly contaminated water course [13]. Hence, the eradication of colorant dye materials from wastewater is highly desirable.

Although toxic and recalcitrant dyes can be eradicated from wastewater using many techniques, eliminating dyes from wastewater with traditional methods is difficult due to their complex structure and synthetic nature [14, 15]. The physical and chemical methods applied in the dye elimination are not economical, are poorly recyclable, have low eradication ability, and produce secondary contaminants. Hence, new efforts are being carried out to develop an efficient, eco-friendly, and cost-effective method for treating wastewater. Dye removal from wastewater can be performed using several techniques including adsorption, coagulation-flocculation, photocatalytic degradation, reverse osmosis, and ultra-filtration [16,17,18,19].

Adsorption technology has been extensively used to eradicate various hazardous wastes and pollutants from wastewater, owing to their low cost, design simplicity, low energy demand, convenient operation, and high performance. High adsorptive capacity and kinetics, ability to regenerate and reuse, and low costs in fabrication and activation are the main features of efficient adsorbent [20,21,22,23]. Therefore, the effective sorption of water pollutants using various adsorbents with different origins is currently an unlocked challenge since the existing technologies are non-environmentally costly.

Various metal oxide nanomaterials have been used in recent years as effective adsorbents for the removal of anionic and cationic dyes due to their easy production, cost-effectiveness, high stability, great absorption capacity, easily regenerated and reused [10, 20, 24,25,26]. Due to their unique chemical, physical, and catalytic features, these nanomaterials have been employed to eradicate and decompose toxic dyes from polluted water. Nanomaterials can be fabricated using various methods, including electrochemical, co-precipitation, sol–gel, hydrothermal, and micro-emulsion. However, these methods have several disadvantages such as high temperature and pressure, harsh reaction conditions, high aggregation of nanoparticles, and long reaction time [15, 27,28,29].

Magnesium oxide nanoparticles are progressively acquiring importance due to their potent pioneering applications in removing dye and hazardous pollutants from the environment and as antibacterial, antifungal, antiviral, antiprotozoal, antioxidants, and cytotoxic agents [30,31,32,33]. Nanomaterials have unique chemical and physical characteristics with higher surface area. They perform promising catalytic, electrical, optical, magnetic, and biological activities. The nanoparticle synthesis can be carried out using bottom-up and top-down methods, however, these methods are time-consuming, polluting, and inefficient [34, 35]. There is a necessity to use an unconventional synthesis method like green synthesis which delivers reduction, capping, stabilization, and nucleation processes. The biosynthesis of various nanomaterials using various kinds of microorganisms seems to be a promising approach in recent years due to its eco-friendliness, cost-effectiveness, simplicity, easily scaled-up, non-toxic by-products, and less time-consuming [36,37,38].

Of the biological entities, an endophytic fungus inhabits the tissues of plants without interfering with their normal physiological activities [14, 39, 40] and plays an essential role in the synthesis of secondary metabolites in plants. These metabolites perform a variety of vital functions such as serving as antifungal, antioxidants, and antibacterial agents [10, 41, 42]. Due to their special properties, these secondary metabolites can convert metallic ions into nano-sized particles, which are widely used in the health care, medical, and industrial fields.

The misuse and overuse of various antibiotics and antimicrobials increase the potentiality of bacterial and fungal pathogens to be unresponsive and less susceptible to them in future exposures [32, 43]. Bio-fabrication of nanomaterials with potential antibacterial and antifungal activities is an open challenge due to the current increase in the rates of mortality and morbidity triggered by microbial infections [44]. The biocidal potentiality of numerous metal nanoparticles (Mg, Cu, Zn, Fe, Ag, Au) has been evaluated for combating antibiotic resistance in bacterial pathogenic diseases [30, 45, 46]; however, further research is required to develop smaller-sized nanoparticles with high antimicrobial activity and to evaluate their antimicrobial mechanism in detail.

In this context, the current study focuses on four main purposes. The first purpose is to biosynthesize eco-friendly and small-sized magnesium oxide nanoparticles for the very first-time using Aspergillus japonicus cell-free exometabolites (MgONPS@Aj). The second aim is to optimize the bioproduction process using a statistical approach to maximize MgONPS@Aj production. The third objective is to effectively decolorize reactive black 5 dye, using it as a model of anionic dye with superior adsorptive capacity. The fourth focus is to explore the aptness of MgONPS@Aj for antibacterial treatment applications using in vitro and in silico studies. The novelty of the present research consists of the biofabrication of small-sized MgONPS@Aj via a green approach for the first time which is free of ancillary contaminants with insight investigation of the inhibitory effect of the biogenic MgONPS@Aj using molecular simulation analysis for the very first time. Finally, this study concluded by delivering comparison studies and further recommendations worth exploring for RBL5 dye sorption.

Materials and methods

Isolation and molecular identification of the fungal strain

The fungus was isolated based on the methodology described previously by [41] from the healthy root of garlic (Allium sativum L.). There is no need for licenses or permission to work with A. sativum L. as it is a worldwide distributed crop, which is not at risk or endemic, based on IUCN. The root samples were pooled from local markets in Al-Qalyubia governorate, Egypt. For surface sterilization, the collected root samples were washed with running water and followed by the following steps: washing for 1 min with sterile H₂O, rinsing for 4 min with 2.5% Na hypochlorite, rinsing for 0.5 min with 70% ethanol, and lastly immersed for 2 min in sterilize water. Successful root sterilization was affirmed by the absence of any microbial growth when inoculating final washing water into Czapek’s Dox agar, nutrient agar, and starch nitrate media. The fungus was isolated by loading four segments (4.0 mm per segment) of sterilized A. sativum L. on potato dextrose agar (PDA) medium containing an antibacterial agent (chloramphenicol). These plates were incubated at 28 °C for 5 days. The fungal mycelia were regularly picked up and the hyphal tips were re-inoculated into PDA medium free of antibiotics. The pure fungus was designated as EG.AR.AJ1 and then identified according to three techniques: morphological, and optical microsocial to verify each conidial head, conidiophore, vesicle and conidia, and molecular properties. The pure fungus was identified based on the amplification of 18S rRNA gene analysis from the fungal DNA [15, 36, 45]. The ITS genes were amplified using universal primers as follows: ITS1(5’-TCC GTA GGT GAA CCT GCG G-3’), and ITS4 (5’-TCC TCC GCT TAT TGA TAT GC-3’) based on [47], the methods for DNA extraction and gene sequencing was performed as previously reported by [48, 49]. The obtained sequence was entered into the GenBank database, and the phylogenetic tree was created using MEGA-X software v11 software by the neighbor-joining method with 1000 repeats bootstrap analysis.

Biosynthesis of magnesium oxide nanoparticles (MgONPS@Aj)

The endophyte A. japonicus was inoculated into Erlenmeyer flasks containing 100 ml PDA broth. These flasks were incubated for 5 days at 28 ℃. The fungal biomass was collected by centrifugation for 5 min at 10,000 xg. The mycelia were washed several times with distilled H₂O to eliminate any remaining media constituents adhering. The pooled mycelial biomass (10 g) was incubated under shaking (100 rpm) in dark conditions in 100 ml distilled water. The cell-free filtrate was obtained via centrifugation of the preparation, and the developed supernatant was employed as a biocatalyst for the MgONPS@Aj biosynthesis. The green synthesis of MgONPS@Aj using A. japonicus biomass filtrate was conducted as follows: First, 4 g of Mg(NO₃)₂. 6H₂O was dissolved in a scale amount of distilled H₂O. The solution was mixed with cell-free supernatant (96 ml) and stirred at 100 rpm for 60 min, then the color changed from darkish yellow to light yellow. Positive control was performed under the same conditions by preparing the fungal filtrate without metal precursor. Finally, the white precipitate of the as-formed MgONPS@Aj was centrifuged for 10 min at 10,000 × g and washed thrice using distilled water. The pooled sample was further dried at 60 ℃ for 24 h [50,51,52].

Optimization of production process factors

Various input variables influencing the green production of MgONPS@Aj using A. japonicus were optimized using Plackett Burman design (PBD) and central composite design (CCD) according to the adapted methods of [10, 44]. The significance of each input factor namely, pH, biomass quantity, stirring speed, Mg²⁺ precursor concentration, and stirring time, was determined using the 2-factorial PBD (Table S1). The input variables were illustrated by two levels, i.e. the high (+ 1) and the low (−1) level for each parameter. Minitab 21 trial software was used to create 16 run PB screening experimental design. The power of each input factor on the bioprocess was detected via first-order reaction:

$${\text{Y}} = \, \beta_{0} + \, \sum \, \beta_{{\text{i}}} {\text{X}}_{{\text{i}}}$$

(1)

where Y, β_i, β₀, and X_i represent the predicted peak intensity, the linear coefficient, the model intercept, and the independent variable level, respectively. The significance of the tested factors was evaluated using the analysis of variance (ANOVA) test.

To evaluate the importance of the single and mutual interaction of variables, the selected three significant variables affecting the investigated process were further optimized using the Response Surface Methodology (Table S2), and the obtained results are clearly illustrated in (Table S3). The second-order mathematical equation was utilized to evaluate the correlation among the variation in the response and variables:

$${\text{Y}} = \, \beta_{0} + \, \sum \beta_{{\text{i}}} {\text{X}}_{{\text{i}}} + \, \sum \beta_{{{\text{ii}}}} {\text{X}}_{{\text{i}}} + \, \sum \, \beta_{{\text{i}}} {\text{X}}_{{\text{j}}}$$

(2)

where Y, β₀, β_i, β_ii, and X_i, are the response, the regression coefficient, the linear model intercept, the calculated coefficient, and the coded variable of an independent variable, respectively. Twenty-five trials were generated in one block with a 95% confidence interval. The significance of each variable was explored through the application of the ANOVA test and the Surface plots response was carried out to elucidate the single and interactive response of the dependent and independent variables.

Characterization

The fungal filtrate and the as-synthesized MgONPS@Aj were characterized using Fourier-transform infrared (FTIR) spectroscopy, transmission electron microscope (TEM), and X-ray powder diffraction (XRD). The functional groups in the cell-free supernatant were determined by FTIR (PerkinElmer, USA) and compared with the functional groups in the green synthesized MgO-nps. The spectra were investigated from 450 to 4000 cm⁻¹. The crystalline structure of the green synthesized MgONPS@Aj was determined by X-ray diffraction spectroscopy analysis (XRD, X’PERT PRO, MiniFlex 300/600 X-ray, USA). The XRD patterns were examined in the range of 5°- 80° of 2θ. The TEM micrographs were attained by JEOL JEM 1010 transmission electron microscope (TEM) operating at accelerating voltage (100 kV). The preparation was dispersed in water, droplet was deposited on a carbon-coated copper grid and dried at room temperature for 24 h. The morphology and elemental analysis of the biogenic MgONPS@Aj were determined using a scanning electron microscope (JEOL JEM-1010 SEM) attached to an energy-dispersive X-ray detector (SEM–EDX) at 10 keV.

Biosorption assay

The biosorption experiments of MgONPS@Aj against Reactive black 5 (RBL5) were performed at 25 ℃ under aeration and constant stirring (150 rpm). 50 ml of an aqueous dye solution of RBL5 with an initial concentration of 100 mg/l and 5 mg of biosorbent nanoparticles were used for all preparations.

To evaluate the influence of different pH levels on the biosorption efficiency of the green MgONPS@Aj synthesized by harnessing the exometabolites of A. japonicus, the adsorption experiments were conducted in a pH range from 4 to 10. The pH level of the prepared dye solutions was adjusted using NaOH and/or HCl. Before the biosorption experiments, nano powder (5 mg) was added to the suspensions, subsequently, the mixtures were slowly stirred for 30 min in the dark to achieve adsorption–desorption equilibrium. The decolorization capacity was calculated by withdrawing 1.0 ml from each preparation, centrifuged for 3 min at 5000 × g, and the residual dye concentration was monitored at λ_max of 597 nm using a spectrophotometer.

The biosorption capacity (q_e in mg/g) was calculated using the following Eq. (3):

$${\text{q}}_{{\text{e}}} \, = \,\frac{{(C_{i} - C_{e} ) V}}{{M_{ads} }}$$

(3)

where C_i, and C_e (mg/l) are the initial, and equilibrium concentrations of the dye, respectively; V (L) and ${M}_{ads}$ (g) represent the aqueous solution volume, and the adsorbent mass used in the reaction, respectively.

Biosorption kinetics and isotherms

The experimental kinetic data were fitted using pseudo-first-order and pseudo-second-order models (35). Both models theorize that biosorption is a pseudo-chemical reaction, whereby biosorption rate can be described by the following Eqs. (4) and (5):

$${\text{PF}}:\,\ln \left( {q_{e} - q_{t} } \right) = \ln q_{e} - K_{1} t$$

(4)

$$PS:\frac{t}{{q_{e} }} = \frac{1}{{K_{2} q_{e}^{2} }} + \frac{1}{{q_{e} }}$$

(5)

Where ${K}_{1}$ (min) and ${K}_{2}$ (g/mg/min) are the biosorption rate constants of pseudo-first–order, and pseudo-second–order at equilibrium, respectively.

Langmuir and Freundlich's isotherms were employed to evaluate the experimental data and to clarify the interactive performance between absorbent and adsorbate. The Langmuir isotherm deduces that biosorption occurs at homogenous sites on the adsorbent [53]. In this circumstance, no additional biosorption can occur at these sites and the biosorption capacity should be directly related to adsorbent specific area and adsorbate concentration [45]. On the contrary, the Freundlich model assumes a heterogenous surface of adsorbent with an adsorbate multilayer developed onto adsorbent. Equations (6) and (7) represent Langmuir and Freundlich isotherms, respectively:

$${\text{q}}\, = \,\frac{{q_{m} b C_{f} }}{{1 + b C_{f} }}$$

(6)

$${\text{q }} = {\text{ K}}_{F} {\text{C}}^{{{1}/{\text{n}}}}$$

(7)

in Eq. (5), maximum biosorption capacity, and the sorption equilibrium constant are signified by ${q}_{m}$ (mg/g) and b, respectively; in Eq. (6) Freundlich equilibrium capacity and the biosorption intensity are represented by K_F (mg/g) and n, respectively.

The dimensionless constant (R_L) is the main feature of Langmuir isotherm and can be determined according to Eq. (8):

$${\text{R}}_{{\text{L}}} \, = \,\frac{1}{{1 + b C_{i} }}$$

(8)

The isotherm type can be interpreted based on the values of R_L as R_L > 1 (unfavorable), R_L = 1 (linear), and 0 < R_L < 1 (favorable).

Antibacterial activity

The antibacterial activity of the green synthesized MgONPS@Aj was evaluated through agar-well diffusion method assay [36, 54] against different pathogenic bacteria including Gram-negative bacteria (Escherichia coli, pseudomonas aeruginosa) and Gram-positive bacteria (Bacillus subtilis, Staphylococcus aureus). An overnight fresh culture of each tested bacterium was prepared in sterile distilled H₂O to match 10⁸ cfu/ml (OD ~ 0.2 nm). A sterilized swab of each bacterium was spread on the surface of the Muller-Hinton agar (MHA) plate and subsequently, wells of 0.6 mm diameter were dung in each plate. Five various concentrations of the MgONPS@Aj (100, 50, 25, 12.5, 6.25, and 3.125 µg/ml) were prepared from stock solution (1 mg/ml in 50% DMSO), followed by adding 100 µl of each concentration to a consistent well. The standard antibiotic (chloramphenicol 100 µg/ml) and 50% DMSO were employed as the positive and negative controls. The inoculated plats were kept for 60 min in the refrigerator and then incubated for 24 h at 37 ℃. The inhibitory zones around each well were measured. The lowest concentration displaying a zone of clearance is determined as minimum inhibitory concentration (MIC). The previous procedure was performed based on the recommended standards of Clinical and Laboratory Standards Institute (CLSI) guidelines (2023) [55]. The most susceptible bacterial strain was selected based on the lowest MIC assay and then examined using SEM (JEOL- JSM-6510LV microscope Japan) after incubation under MIC concentration at 37 °C for 24 h to evaluate their morphological changes in cell structure.

In silico molecular docking

A molecular simulation study was performed to explore molecular insight and endorse the antibacterial activity obtained. The 3D crystal structure of the target proteins namely, chloramphenicol acetyltransferase type III (PDB: 6X7Q) for E. coli and Staphylococcus aureus MurE domain (PDB: 4F13) were retrieved from the Protein Data Bank (http://www.rcsb.org). The protein crystal structures were prepared by removing water molecules, hetero atoms, and co-crystallized ligands, merging nonpolar hydrogens, calculating Gasteiger charges, and finally localizing protein active pockets. The MgONPS@Aj crystal structure has been generated using the Molecular Operating Environment (MOE, vs. 10) program tool and optimized before running the molecular docking process. Docking was carried out using the default program settings of the MOE [10] software considering the optimized ligands and the prepared target proteins. The docked pose with the lowest binding energy of the ligands against the active sites was employed for further investigation [56, 57].

Statistical analysis

The data were performed in three independent replicates. The production process was optimized using PBD and CCD using a statistical software package (MINTAB vs. 21.0, USA). Data were statistically processed and analyzed through package SPSS version 13.0 (2004) to detect the significant behaviors between treatments. The mean comparisons were carried out using One-way ANOVA, followed by Tukey’s post hoc at the probability of P < 0.05.

Results

Fungal identification and biosynthesis of MgO-nps using endophytic fungal strain

The endophytic fungus was isolated from the garlic/healthy root and examined for its capability to produce MgONPS@Aj. The fungal isolate (EG.AR.AJ1) was selected for further work owing to the color peak intensity produced after mixing the metal precursor with the fungal cell-free extract. The fungus identification was performed by examining its cultural, and microscopical characters related to A. japonicus. This strain identity was confirmed by detecting its internal transcribed space (ITS) sequence and then validated by a non-redundant BLAST search in the NCBI database (www.ncbi.nlm.nih.gov) accessed on (23 Oct 2024). The phylogenetic tree of A. japonicus (accession number PQ481709.1) displayed a similarity percentage of 99% with closely related isolates deposited in the NCBI database (Fig. 1). In the current study, the rapid biosynthesis of MgONPS@Aj by harnessing the fungal biomass extract as an eco-friendly method was successfully performed. Our findings are consistence with [30, 58] who biosynthesized the MgONPs using different fungal strains isolated from soil water. However, nanoparticle fabrication using endophytic fungi, particularly those obtained from various medicinal plants is of novel interest. This may have been attributed to this high production of bioactive materials, and potential medicinal activities [30, 32]. Endophytic strains were employed for MgONPs synthesis as an alternative approach for the eco-friendly production of nanomaterials [30, 53]. The fungal biomass filtrate contains various bioactive materials including enzymes, organic acids, carbohydrates, proteins, and other secondary metabolites. These bioactive components can perform several functions during the fabrication of various nanoparticles. For example, various enzymes can reduce metal ions to fabricate various nanoparticles, while carbohydrates and protons can reduce and stabilize the nanomaterials, preventing them from aggregation [15, 23, 25, 32, 45]. Organic acids (oxalic and citric acids), terpenoids, and alkaloids in the fungal extract can potentially displayed in reducing, a metal precursor to the nanoscale form, capping, and stabilizing the developed nanomaterials [23, 32, 45, 59, 60].

Fig. 1

Phylogenetic analysis of ITS sequence of Aspergillus japonicus with the sequences retrieved from NCBI using MEGA X-11 through the Neighbor-joining method. The symbol indicates the ITS sequence of the strain under investigation

Bioprocess optimization using response surface methodology

The process, including various input factors, can benefit significantly from PBD and CCD since they assist in exploring the process variables that influence the bioproduction process [41, 45, 61, 62]. Response surface methodology diminishes the data complication required to determine the highest output of the bioprocess, making the bioprocess further economical [15, 63,64,65]. The significance of five independent factors on the biosynthesis of MgONPS@Aj using A. japonicus was determined using PB sixteen trial design [10]. The input variables, the output experimental, and the predicted values are shown in Table S1. The significance of the bioprocess variables, i.e. pH (X₁), biomass quantity (X₂), Mg²⁺ precursor concentration (X₃), stirring speed (X₄), and stirring time (X₅) on the production was examined using PB sixteen-trial design. Trial # 11 with pH (+ 1), biomass quantity (−1), stirring speed (−1), Mg²⁺ precursor concentration (−1), and stirring time (-1) displayed the highest production of MgONPS@Aj, however, the lowest production have been detected at the trial number # 8 with the incorporation of pH (+ 1), biomass quantity (+ 1), stirring speed (+ 1), Mg precursor concentration (+ 1), and stirring time (+ 1). The input factors were analyzed using multiple regression statistics and variance analysis (ANOVA). Figure 2a, b showed the significance of each variable, whereby pH showed the highest effect on the bioproduction process, followed by biomass quantity and Mg²⁺ precursor concentration. The normal distribution of the residual points nearby the diagonal line hints at the precise fitting of the predicted and actual values as shown in Fig. 2c. The model showed a significant behavior based on the Fishers F-test (6.62) and the P-value (0.008).

Fig. 2

The impact of various variables on the MgO-nps@Aj biosynthesized using A. japonicus. a Normal probability plot of the input variables, b Pareto chart plot showing the significance of each factor, c Standardized effect plot of the individual variables based on the Plackett–Burman design, d–f 3D-response surface plots showing the interaction impact of different variables on the bioproduction of MgO-nps@Aj based on CCD

The significant variables were further optimized via a central composite design (CCD) [14, 44]. The CCD proposes restricted experimental runs to analyze the critical influence of each variable and their synergistic interactions on bioprocess optimization. The CCD creates a regression model based on five levels, i.e. −α, −1, 0, + 1, and + α, α = 1.68 to evaluate the impact of three input factors on the MgONPS@Aj production. According to the F-test (8.39) and P-value (0.001), the model is greatly significant with a nonsignificant lack of fit (15.10), indicating the model acceptance. pH was the highest significant factor (P = 0.000) with the variables’ interaction of (pH)², (precursor concentration)², and pH × biomass quantity (Table 1). Three-dimensional graphs showed that the central values of each examined variable significantly enhanced the production of the nanoparticles (Fig. 2d–f). Our findings are in line with [15, 44, 53] who optimize various input variables for maximizing the nanomaterials bioproduction using PBD and CCD. The green synthesis of MgONPS was characterized by its cost-effectiveness and ease of performance. However, the development of nanoparticles with high stability, and small sizes, signifies the optimization of input variables during the production process [14, 15, 51, 66]. The fluctuations between the actual and predicted values in the PBD clearly showed the importance of statistical optimization for maximizing the biosynthesis of nanomaterials [3, 11, 36, 37]. Different input variables, i.e. temperature, pH, biomass quantity, production time, stirring speed, stirring time, and precursor concentration are modified to produce the nanoparticles with desired characters [7, 50, 58, 65, 67, 68]. pH is the major factor affecting the bioproduction of nanoparticles as reported by [10]. The preparation is highly influenced by high and low pH values of the reaction mixtures due to their negative effect on the reducing and capping agents in the cell-free filtrate [15, 21, 25, 44].

Table 1 ANOVA analysis of central composite design for optimizing various variables interaction on the green production of MgO-nps@Aj using A. japonicus

The MgONPS@Aj characterizations

Fourier transform infrared spectroscopy (FTIR)

FTIR analysis was carried out to explore the functional groups in the fungal extract and the biosynthesized MgONPS@Aj as shown in Fig. 3a. The FTIR spectra pattern unveiled the presence of the N–H, O–H, C=O, C–N, and C–O functional groups. The band at 3433.63 cm⁻¹ was assigned to the overlapping stretching vibration of O–H and N–H groups [14]. The bands at 2915.84 cm⁻¹, and 2847.38 cm⁻¹ can be related to the O–H and C–H stretching vibrations of carboxylic, alcohol, and alkane groups, respectively. The bands at 1631.48 cm⁻¹, 1379.81 cm⁻¹, and 1064.51 cm⁻¹ can be referred to as stretching vibrations of C=O, C–N, and C–O groups, respectively. These peaks were also detected in the spectra of the green synthesized MgONPS@Aj, showing a slight displacement. The peaks around 626.71 cm⁻¹ and 420.37 cm⁻¹ can be attributed to the stretching vibration of MgONPS@Aj, hinting at the successful fabrication of magnesium oxide nanoparticles. Our results are consistent with [3, 30, 32, 44] who clearly detected the presence of different functional groups after the synthesis of various nanomaterials using green approach, which play an important role during the biosorption of dye molecules.

Fig. 3

a FTIR chart of fungal filtrate (a) and the biosynthesized magnesium oxide b obtained by harnessing the exometabolites of Aspergillus japonicus, b X-ray diffraction pattern, c Transmission electron microscope of MgO-nps@Aj, d Histogram illustrating the nanoparticle size distribution developed from TEM picture using Image J software. e SEM images of MgO-nps@Aj, (f) elemental analysis by EDX

XRD spectroscopy

The high crystallinity of the green synthesized MgONPS@Aj was assessed using XRD spectroscopy. Figure 3b clearly illustrates the existence of sharp XRD peaks, indicating the crystallinity of MgONPS@Aj. The diffraction peaks at 2 $\theta$ of 18.97°, 38.34°, 51.13°, 58.95°, 62.38°, 68.44°, and 72.29° were respectively attributed to the (101), (111), (200), (211), (220), (311), and (222) diffraction planes of the MgO nanopowder [44]. The presence of other diffraction peaks provides evidence for the role of fungal metabolites as capping agents, which is coherent with the FTIR analysis [10, 69].

TEM

The morphology and size of the green synthesized MgONPS@Aj are investigated by transmission electron microscopy. TEM image showed unsatisfactory dispersibility owing to agglomeration via stack forming (Fig. 3c). The MgONPS@Aj displayed a well-arranged irregularly spherical shape with an average size of 8.7 ± 3.2 nm (Fig. 3d). Similarly, the cell-free extract of Rhizopus oryzae was employed for the biosynthesis of MgO-nps with an average size of 20.38 ± 9.9 nm [44]. The activity of various nanoparticles is highly dependent on their structural morphologies, size, and shape characteristics [39]. The smaller-size nanomaterials exhibited higher toxicity over larger sizes owing to their capability to release Mg²⁺ ions faster than large ones [58, 60, 70].

SEM–EDX

The morphological observation and elemental analyses of the biogenic MgONPS@Aj were tested using SEM. It would be noted that the MgONPS@Aj exhibited a uniform and homogenous distributed spherical structure as illustrated by Fig. 3e. The biosynthesized nanoparticle has a heterogenous surface with pores of different shapes and sizes, ledges, and cavities, indicating better biosorption capability. The biosynthesis process enhances the surface area, exposing different functional groups to the biosorption process. The chemical validation or the elemental analysis of the as-prepared nanomaterials was determined using EDX. Figure 3f clearly showed intense peaks of oxygen and magnesium. Similar findings for the morphology and elemental distribution of the green synthesized MgONPS were reported by [30, 44, 71].

Biosorption assay

The biosorption experiments of MgONPS@Aj against Reactive black 5 were performed at 25 ℃ under aeration and constant stirring (150 rpm). For all preparations, 50 ml of an aqueous dye solution of RBL5 with an initial concentration of 100 mg/l and 5 mg of biosorbent nanoparticles were used.

Effect of pH

The pH level of the dye solution is considered an essential factor in the biosorption process as it can affect the biosorption capacity and alter the interaction degree between adsorbate and adsorbent. In the current study, the effect of various pH values i.e. 4, 6, 8, and 10 on the reactive black 5 adsorption have been scrutinized and the results are illustrated in Fig. 4a. The biosorption efficiency was sharply increased when the pH rose from 4 to 6. This may be attributed to the protonation of amino groups that can interact with sulfonic groups (SO₃⁻, anionic groups) in dye molecules [27]. Further increase in the pH value to 10 can reduce the biosorption capacity of MgONPS@Aj (Zero point charge = 6.7, Fig. S1) due to the change in the adsorbent surface charge from positive to negative which leads to a competition between dye molecules and hydroxyl groups toward biosorption active sites [45]. At relatively high pH, the RBL5 (PKa = 3.8) dye molecules had a high negative charge. The charge of the biosorbent surface becomes positively charged at pH < PZC owing to the protonation of functional groups, resulting in high biosorption of anionic dye. The charge sorbent surface was negatively charged at pH > PZC, which reduces the RBL5 biosorption capacity due to the electrostatic repulsion of anionic dye molecules [16, 20, 45].

Fig. 4

Effect of different pH levels (a), contact time (b), and regeneration (c) on the biosorption capacity of reactive black 5 using MgO-nps@Aj

Effect of contact time

The biosorption capacity using MgONPS@Aj for RBL5 dye as a time function at pH 6.0 and 100 mg/l of initial dye concentration was clearly illustrated in Fig. 4b. The biosorbent nanomaterial achieved equilibrium at 40 min with a removal efficiency of 90.4% of the dye. The kinetic profile of biosorption capacity implies greater access to the biosorption sites on the nanomaterial surface, whereby the molecular size of RBL5 (2.9 nm). The production of nano-MgO-based adsorbent was performed by [72] who reported that the equilibrium time of biosorption is attained after 60 min, hinting at a slower biosorption profile when compared to our work [44]. Produced MgO-nps with an average size of 8 − 38 nm that showed a decolorization efficiency of 67.4% when using 1 µg/ml biosorbent dose, and a contact time of 60 min. Nano-MgO with average particle sizes in the range from 39.19 nm to 65.64 nm was fabricated by extract of brown seaweeds which displayed a removal efficiency of 43.4% using acid black [31]. The removal efficiency of methylene blue was 98% after 110 min contact time using the produced nano-MgO [37].

Regeneration of the biosorbent

The stability, efficiency, cost-effectiveness, regeneration, and reusability of a biosorbent are often important issues for developing biosorption technology for industrial and commercial applications [10, 31]. The reusability of MgONPS@Aj was investigated during the progress of five consecutive cycles at initial dye concentration (100 mg/l), pH (6.0), sorbent dosage (5 mg), time (40 min), volume (100 ml). After each cycle, the green synthesized nano-adsorbent was regenerated and desorbed using 0.1 M NaOH. Figure 4c exhibits the biosorption capacity of MgONPS@Aj for 5 five consecutive cycles. Noteworthy, the biosorption capacity of MgONPS@Aj for RBL5 was gradually reduced to reach 81.97 mg/g for up to five treatment cycles, suggesting that the recovered biosorbent still has a potential biosorption capacity during the performance of adsorption–desorption experiments. This may be due to the loss of free active adsorbent sites after the regeneration of nano biomaterials [15] and [37]. Confirmed that different kinds from the developed biosorbent nanocomposites can be regenerated and further employed in the removal of dye pollutants from aqueous solutions.

Adsorption kinetics and isotherms modeling

The experimental data were investigated using pseudo-first-order (PF) and pseudo-second-order (PS) models to evaluate the biosorption mechanism for RBL5 dye. The analyzed kinetic parameters are clearly illustrated in Table 2a and Fig. S2a, b. The kinetic models were adjusted based on the linear fit models’ correlation coefficient (R²). The results clearly showed that the PS order model is more adequate for illustrating the sorption mechanism, due to a high R² that implies a good fit. Hence, the biosorption mechanism is regulated via chemical adsorption [72]. The adsorption process of azo dyes using environmentally friendly MgO nanoparticles seems to follow a PS model kinetics [68].

Table 2 Kinetic and isotherm parameters for the biosorption of Reactive black 5 onto magnesium oxide biosynthesized using exometabolites of Aspergillus japonicus

Isotherm modeling is important in understanding the connection between the dye molecules and the biosorbent’s surface. In the current study, Langmuir and Freundlich's models were employed to scrutinize the isothermal biosorption of RBL5 on MgONPS@Aj as an adsorbent. The isothermal models of RBL5 on the biosynthesized MgONPS@Aj are represented in Fig. S2c, d and the isothermal parameters are tabulated in Table 2b. The linear plot of 1/C_e versus 1/q_e displayed that the biosorption of RBL5 on MgONPS@Aj follows Langmuir isotherm as confirmed by the closer biosorption capacities developed from the PS and the experimental data and the higher R² value of 0.9422. The calculated dimensionless separation factor (R_L) was 8 × 10⁴, affirming that the biosorption of RBL5 dye molecules on MgONPS@Aj is favorable. The better fitting of the Langmuir isotherm signifies a similar distribution of binding sites on the biosorbent surface, whereby a uniform enthalpy and activation energy are suggested for each adsorption site. The Freundlich model displays high K_F and n values, suggesting an excellent sorption strength. The adsorption capacity of MgONPS@Aj for reactive black 5 was compared with previous sorbent-engineered materials in the literature (Table S4). In the current study, the biosorption capacity (204.08 mg/g) was superior to other sorbent nanocomposites that exhibited a sorption capacity, indicating that MgONPS@Aj can be effectively employed in the removal of RBL5 dye molecules from the contaminated systems.

Proposed adsorption mechanism of RBL5 by MgONPS@Aj

The charges of various functional groups namely, carbonyl, amine, and hydroxyl, on the MgONPS@Aj surface were significantly altered by the fluctuations in the solution pH [3]. Fig. S2-e clearly illustrates the possible interaction mechanism for RBL5 dye on the surface of MgONPS@Aj in an aqueous solution. According to the above results, the functional groups of the biosynthesized nano-magnesium oxide are positively charged and can interact with the anionic reactive black 5 dye. Additionally, the point of zero charge for the green synthesized nanomaterial was 6.7 and the sorption system was carried out at pH 6.0 (pH < PZC), whereby the sorbent surface charge was positively charged due to the presence of the protonated –NH₃ groups. The anionic dye RBL5 has negatively charged sulfonic groups (SO₃⁻) and the dissociation constant (PKa) was = 3.8, at relatively high pH the dye molecules become highly negative. Strong electrostatic attraction is created between cationic surfaces and anionic dyes. Oxygen groups of RBL5 can interact with functional groups containing nitrogen of MgONPS@Aj via dipole–dipole interaction (hydrogen bond). Further investigations are required to explore the biosorption mechanism of RBL5 on MgONPS@Aj surface using SEM, FTIR, and XRD analyses. Hence, it is proposed that possible interaction between the produced MgONPS@Aj has mainly interacted with the investigated anionic dye through electrostatic interaction and chemical adsorption. The possible mechanism for dye removal using various nanomaterials synthesized by different methods is reported by several researchers [3, 73,74,75].

In vitro Antibacterial activity

The increasing distribution of antibiotic-resistant genes among bacteria strains leads to severe human deaths worldwide. Hence, the production of new active nanostructure materials based on green, simple, cost-effective, environmentally friendly procedures, is becoming increasingly desirable. In the current study, the MgONPS@Aj was prepared by fungal-free biomolecules and examined for its ability to combat the growth of gram-negative and gram-positive pathogenic strains. The antibacterial activity of six doses of the green synthesized nano-magnesium oxide on the growth of various bacterial pathogens was assessed and then compared to a standard antibiotic (chloramphenicol) at a dose of 100 µg/ml. The results displayed that the antibacterial activity of the biosynthesized MgONPS@Aj exhibited a dose-dependent manner against various tested bacterial species. Interestingly, P. aeruginosa displayed the maximum susceptibility to MgONPS@Aj, followed by E. coli and S. aureus, while the least sensitivity was observed for B. subtilis. At the doses of 100 µg/ml and 50 µg/ml of MgONPS@Aj, significant inhibitions on the bacterial growth were observed, while the growth of B. subtilis was not affected at 6.25 µg/ml concentration. The inhibition zones on E. coli, and P. aeruginosa were respectively found to be 12.3 ± 0.3 mm and 17.1 ± 0.4 mm at a concentration of 12.5 µg/ml (Table 3). Overall, the maximum inhibition zones were found in the order: P. aeruginosa (24.1 ± 0.8 mm, MIC: 3.125 µg/ml), E. coli (22.3 ± 0.7 mm, MIC 6.25), B. subtilis (14.7 ± 0.4 mm, MIC: 12.5 µg/ml) and S. aureus (19.2 ± 0.6 mm, MIC: 6.25 µg/ml). Gram-negative bacteria showed greater susceptibility to the bio-MgO-nps than Gram-positive bacteria [32, 53]. This fluctuation in the bacterial susceptibility to the MgO-nps may be partially ascribed to the intense peptidoglycan layer found in the Gram-positive bacterial cell wall, which limits or hinders the entry of nanoparticles into the bacterial cells [14, 32, 36, 44].

Table 3 Antibacterial effect of magnesium oxide nanoparticles synthesized by entophytic Aspergillus japonicus on the growth of gram-negative (Escherichia coli, Pseudomonas aeruginosa) and gram-positive (Bacillus subtilis, Staphylococcus aureus) bacteria strains

The possible integration of green synthesized nanomaterials into medication mainly depends on a reliable MIC value [32]. Low MIC values were observed for the investigated bacterial strains, hinting at the treatment strategy's reliability for effective infection control and prevention. The MIC and MBC values for various gram-negative were determined and illustrated in Table 3. In the current work, the MIC values were 6.25, 3.125, 12.5, and 6.25 µg/ml for E. coli, P. aeruginosa, B. subtilis, and S. aureus, respectively. The highest susceptible strain with the minimal MIC value (3.125 µg/ml) was P. aeruginosa, whereas the highest value of MIC (12.5 µg/ml) was recorded for B. subtilis, hinting the least susceptible strain. Notably, the obtained MICs in the current study were lower than those observed for S. aureus (25 µg/ml) [36], E. coli, and B. subtilis (100 µg/ml) [34]. The MIC values were 25 µg/ml for S. aureus, P. aeruginosa, and E. coli with inhibitory zones in the ranges from 16.3 ± 0.6 to 7.2 ± 0.2, whereas, the MIC for B. subtilis was 12.5 µg/ml [36]. The MIC values of MgO-nps synthesized were in the range of 0.5–1.2 mg/ml against various pathogenic yeasts and bacteria [34]. The MgO-nps synthesized using Aspergillus terreus exhibited an inhibition zone of 8.0–9.3 mm with 100 µg/ml MIC against E. coli, B. subtilis, S. aureus, and Candida albicans and MIC of 50 µg/ml against P. aeruginosa [46]. The MBC values of bacterial strains (E. coli and B. subtilis) were twice the MIC values, except for P. aeruginosa and S. aureus which displayed MBC values of 3.125 and 25 µg/ml, respectively. Our findings highlight that the green synthesized MgONPS@Aj in the current work showed higher antibacterial efficiency in lower concentrations when compared with the antibacterial efficiency of np-MgO documented by [32, 34, 44, 53]. Additionally, it supports the finding that Gram-negative strains are more vulnerable to the biogenic nano-MgO than Gram-positive strains, as appeared from the lower MIC values for Gram-negative bacteria. The tolerance level of the biogenic green synthesized MgONPS@Aj in the current work was detected based on its respective MBC and MIC (Table 3). It showed higher antibacterial efficiency in lower concentrations when compared with the antibacterial efficiency for each tested bacterial strain. When the tolerance level (MBC/MIC ratio) is ≤ 4, the antibacterial agent is considered bactericidal [32]. The tolerance levels of the investigated bacterial strains are in the range from 1 to 4, indicating the bactericidal activity of the biogenic MgONPS@Aj.

Time-kill assay

A kill-time assay of MgONPS@Aj on the investigated bacterial strains was analyzed and the obtained results were illustrated in Fig. S3A-D. The growth rate was dramatically reduced over 8 h for all tested strains in the presence of the biosynthetic MgONPS@Aj when compared to the control. The maximum reduction was determined after 6 h of incubation for P. aeruginosa when plotting the log₁₀ CFU ml⁻¹ concentration with incubation time Fig. S3B. Complete inhibition observed for E. coli using np-MgO after 7 h of incubation (Fig. S3A), whereas the bactericidal endpoint for B. subtilis, and S. aureus was determined after 8 h of incubation (Fig. S3C, D). The data confirmed that the green synthesized MgONPS@Aj at a shorter contact time was capable of killing the tested strains at low concentrations. This may be due to the unique physicochemical characteristics of the developed nanomaterials using a green approach based on microorganisms. The biogenic MgONPS@Aj displayed a broad-spectrum antibacterial activity since it exhibited analogs influence on both Gram-positive strains and Gram-negative strains. The fluctuations in nanoparticles’ concentrations are directly related to the viability of bacterial cells [36]. This variation in the killing-time kinetics among the tested strains could be attributed to the fluctuations in the bacterial cell structure and metabolism, as well as a mode of action for various nanoparticles. Our findings are consistence with [14, 32, 44, 53] who mentioned that the viability of bacterial cells was significantly reduced during the elevation in contact time with MgONPS@Aj.

Morphological analysis using SEM

SEM imaging was employed to evaluate the antibacterial efficiency of MgONPS@Aj against P. aeruginosa, the most vulnerable strain. The broth culture of the bacterial strain was supplemented with 3.125 µg/ml (MIC) of MgONPS@Aj and then examined by using SEM, compared to the control strain. In untreated bacterial cells, uniform and intact bacterial cells with well-defined bacillus-shaped cells have been observed (Fig. 5a), however, the treated bacterial cells displayed a cytoplasmic leakage, bacterial cell rupture, alterations in bacterial cell shape, and a significant decrease in the cell number upon exposure to np-MgO (Fig. 5b).

Fig. 5

SEM images of control (a) and treated (b) P. aeruginosa after 24 h of incubation with MIC concentration of MgO-nps@Aj

The biogenic nano-MgO are well known for their strong antibacterial efficacy, however, their antibacterial efficiency is highly dependent on various variables including temperature, nanoparticles’ surface areas, agglomeration, and contact time among microbial cells and nanoparticles. In the present study, the strong antibacterial efficacy at relatively low concentrations of MgO-nps may be ascribed to their smaller size, enabling them to quickly diffuse and penetrate bacterial cells over larger ones. Numerous antibacterial mechanisms of the developed nanomaterials have been suggested (Fig. S4). The liberation of Mg²⁺ ions inside the bacterial cell inhibits enzyme functions, and destroys the cytoplasmic membrane, enhancing the transcription of various ROS which displayed a deleterious effect on the bacterial cell constituents such as nucleic acid, proteins, ribosomes, and amino acids and ultimately causing the cell death [36, 44, 45, 60]. Although several researchers reported the therapeutic effects of the green synthesized MgONPS, their cytotoxicity on vital organs and normal human cells needs to be evaluated to prevent unwanted side effects [76,77,78].

The therapeutic effects of bioderived MgONPS are diverse, but evaluating their potential toxicity on normal human cells and vital organs is important to avoid unwanted side effects. Several studies have demonstrated the relative safety of biogenic MgONPS for use with normal human cell lines. The MgONPS@Aj revealed excellent antibacterial potential in vitro models; however, their optimal dosage and toxicity require careful concern. Future research might focus on the evaluation of the cytotoxicity, ecotoxicity and phytotoxicity of MgONPS.

Molecular docking

The binding affinity of MgONPS@Aj with bacterial proteins was explored using in silico molecular docking analysis. The target proteins, chloramphenicol acetyltransferase type III (PDB: 6X7Q) and Staphylococcus aureus MurE domain (PDB: 4F13) were used in the current study due to their significant roles in E. coli and S. aureus pathogenicity, respectively. The addition of _L-lysine of the peptidoglycan moiety as the third residue catalyzed by the enzyme Staphylococcus aureus MurE; hence reported as a potential target for novel antibiotic development. The MgONPS@Aj is docking with the selected proteins as possible targets for the new therapeutic agent’s development via Molecular Operating Environment (MOE, vs. 10) program tools. The possible docking mode of MgONPS@Aj and chloramphenicol against chloramphenicol acetyltransferase type III (PDB: 6X7Q) and Staphylococcus aureus MurE domain (PDB: 4F13) are illustrated in Fig. 6. The values of the binding energy and interaction were employed for forecasting the best-docked pose. The docking process was performed at a root mean square deviation (RMSD) of 0.3. The lower the binding energy, the higher the ligands' stability, bond strength, and affinity toward the target proteins [57, 64]. The minimum binding affinity of − 5.4 kcal/mol and − 5.9 kcal/mol were determined for MgONPS@Aj and chloramphenicol as standard antibiotics toward chloramphenicol acetyltransferase type III (PDB: 6X7Q), respectively. The formation of two hydrogen bonds with the residues (TYR²⁰, and HIS¹⁹⁸) at the active site (Fig. 6A–f). While the MgONPS@Aj was bound to the residue (Tyr¹⁷²) via hydrogen bond (3.02 Å) (Fig. 6A-e). The formation of such hydrogen bonding interactions is necessary for the inhibitor stabilization in the active pocket of the target protein. The more negative the docking score, the greater the ligand's affinity for binding to the receptor. The docking score(S) shows that the MgONPS@Aj can tightly bind to the investigated proteins (Table S5). Additionally, the MgONPS@Aj engaged the same pose of the inherent ligand, hinting that the biosynthetic nanomaterial is effectively bound to the catalytic site of the target proteins. In a study on the Staphylococcus aureus MurE domain (PDB: 4F13), hydrogen bonds were formed between the ligands (chloramphenicol and MgONPS@Aj) with the residues (SER²²⁴) at the active binding site (Fig. 6B-e, f). The MOE program cannot monitor the Van der Walls, hydrophobic, ionic, and aromatic interactions. However, it still influences the ligands' binding energy in the active site. The developed molecular simulation models are in harmony with [56] who used the molecular simulation to observe the possible interaction of zinc oxide nanoparticles against microbial proteins for E. coli and S. aureus (PDB: 4F6C and PDB: 6X7Q), respectively.

Fig. 6

The confirmational illustration for the interaction of a biogenic MgO-nps@Aj and (b) chloramphenicol against A Staphylococcus aureus MurE domain (PDB: 4F13) and B chloramphenicol acetyltransferase type III (PDB: 6X7Q). The depiction of 3D (c, d) and 2D (e, f) of the tested ligands with the amino acids residues in the catalytic site

Conclusions and future perspectives

The exometabolites of A. japonicus were used to successfully fabricate non-toxic, eco-friendly, and cost-effective magnesium oxide nanoparticles for the very first time. The bioproduction process was statistically optimized via PBD and CCD to maximize nanoparticle production. The developed model is highly significant (P = 0.000) and acceptable, showing that pH was the highest significant factor influencing the bioprocess. The MgONPS@Aj exhibited a high biosorption capacity (204.08 mg/g dye sorption) within 40 min. The kinetic data were more tailored to the pseudo-second-order model, indicating the role of the ion exchange/chemisorption mechanism. The biosorption process greatly matched the Langmuir isotherm, suggesting the monolayer sorption of RBL5 onto MgONPS@Aj. The biosorption efficiency was significantly influenced by the pH value of the medium, hinting that the electrostatic interactions had a remarkable effect on the biosorption mechanism. The developed biosorbent can be regenerated and retain its performance for up to five cycles with a biosorption capacity of 81.97 mg/g. The green synthesized MgONPS@Aj exhibited dramatic time and dose-dependent antibacterial activity with the largest inhibition zones for P. aeruginosa (24.1 ± 0.8 mm, MIC: 3.125 µg/ml). The docking simulation analysis shows that MgONPS@Aj can efficiently inhibit the target bacterial proteins (chloramphenicol acetyltransferase E. coli, and S. aureus MurE), indicating high antibacterial activity. Hence, the MgONPS@Aj can be applied to antibacterial pathogens with significant killing rates. Based on the above results, the green synthesized MgONPS@Aj is a superior, efficient, and stable adsorbent with a high biosorption capacity, which signifies their promising application for treating wastewater-containing dyes. Future research is required to reduce the fabrication cost, enhance reusability, and evaluate the effect of secondary contaminants and incorporation of nanoparticles within natural polymers on the biosorption process to be widely used in wastewater treatment. Furthermore, the MgONPS@Aj revealed excellent antibacterial potential in vitro models; however, their optimal dosage and toxicity require careful concern.