Immobilization of catalase on functionalized magnetic nanoparticles: a statistical approach

Abstract

Magnetic nanoparticles (MNPs) Fe₃O₄, by virtue of easily modifiable surface, high surface-to-mass ratio and super-paramagnetic properties, are one of suitable candidates for the enzyme immobilization. Optimization of five important variables viz. concentration of 3-aminopropyl-tri-ethoxy-silane (APTES), glutaraldehyde (GA) and enzyme, time and temperature of loading was carried out using central composite type of experimental design without blocks giving 50 experiments including eight replicates at the central point. Characterization, stability and reusability studies were also carried out with optimized preparation. Results established the correlation between observed and response surface method (RSM) equation envisaged value (R² 0.99, 0.97 and 0.98 for enzyme’s activity, its loading over MNPs and corresponding specific activity, respectively. The predicted values suggested by RSM equation were 64.00 mM of APTES, 10.97 µL of GA, 14.50 mg mL⁻¹ of enzyme for 67 min at 22.6 °C, resulted in activity 32.1 U mg⁻¹ MNPs, while specific activity was 97.7 U mg⁻¹. Transmission electron microscopy (TEM) showed the sizes of MNPs (10.5 ± 1.7 nm), APTES-MNPs (10.23 ± 1.74 nm), GA-APTES-MNPs (11.84 ± 1.49 nm) and Catalase-GA-APTES-MNPs (13.32 ± 2.74 nm) were statistically similar. The enzyme MNPs preparation retained 81.65% activity after 144 h at 4 °C (free enzyme retained 7.87%) and 64.34% activity after 20 reuse cycles. Statistical optimized MNPs-based catalase preparation with high activity and magnetic strength was stable and can be used for further studies related to its application as analytical recyclable enzyme or magnetically oriented delivery in the body.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-022-03173-8.

Introduction

Catalase is one of the important enzymes which is capable of decomposing hydrogen peroxide, a reactive oxygen species (ROS). While, ROS are reported to be the main cause of oxidative stress in the body. Thus, catalase, as biopharmaceutical, has been a potential therapeutic when delivered to oxidative stressed neurons (Singhal et al. 2013; Hayes et al. 2021; Nandi et al. 2019) and has explored in Parkinson’s disease (Haney et al. 2015, 2018), diamine oxidase-treated intestinal ischemia (Calinescu et al. 2012), macrophage (ROS generating)-rich organs like liver (Saraf et al. 2013) and gene over-expression in myocardial hypertrophy and diastolic dysfunction (Qin et al. 2013), abdominal aortic aneurysms (Parastatidis et al. 2013). Immobilized enzyme preparation, a homogenous suspension, can be targeted to specific site in therapeutic situation. For instance, brain targeting of catalase via ICAM-1 antibody (Hayes et al. 2021) and intranasal extracellular vesicles (Lutton et al. 2017) was found to reduce the ROS stress generated during injury and neurodegenerative disorders, consequently. Apart from therapeutics, catalase is also being used as indicator enzyme (coupled reaction) for determination of other enzyme’s activity, e.g., glucose oxidase and/or analyte concentration, e.g., glucose (Bolivar et al. 2013), uricase (Zhao et al. 2009), amino acid oxidase (Bolivar and Nidetzky 2012). In another case, catalase was found to enhance the activity of the aldehyde-deformylating oxygenase as H₂O₂ was the inhibitor to the enzyme. Fusion of the catalase gene with the aldehyde-deformylating oxygenase gene was envisaged as solution of the above problem which is tedious, and is specific for one case (Andre et al. 2013). In such a situation, i.e., re-use during analysis, it needs a different and simple recovery mechanisms other than centrifugation.

In the era of nanotechnology/nano-biotechnology, many nano-structures have been explored for optimal and functional preparation of catalase, e.g., silver and gold nanoparticles (Pudlarz et al. 2018), polymer-grafted cellulose nano-fibrous membrane (Chen et al. 2014; Atacan and Zacar 2015) and mats (Huang et al. 2014), poly-dopamine/ polyethylenimine /titania hybrid microcapsules (Zhang et al. 2015), iron oxide and carbon nanotubes/nano-interface (Thandavan et al. 2015), epoxy resin impregnated with calcium carbonate nanoparticles (Hooda 2014), multi-walled carbon nanotube/poly (l-lysine) biocomposite (Vilian et al. 2014), titanate nanotubes (Ai et al. 2014) and so on. One of the important nanomaterials which is showing its impact in various fields ranging from storage devices to therapeutics is magnetic nanoparticles (MNPs). In recent research related to immobilization of enzymes, MNP has been the matrix of choice as it is efficient, easily recoverable, easily modifiable by surface functionalization and enzyme stabilizing immobilization matrix of high surface-to-mass ratio. In recent times, many successful cases of enzyme immobilization over MNPs, e.g., serratiopeptidase (Kumar et al. 2014a, 2013), tissue plasminogen activator (Chen et al. 2020), urokinase (Bi et al. 2009), asparaginase (Orhan and Uygun 2020), trypsin (Atacan and Zacar 2015; Sahin and Ozmen 2020) for magnetic drug delivery/ targeting; amyloglucosidase for production of high fructose corn syrup (Gupta et al. 2013), lipase for p-nitro-phenyl palmitate hydrolysis (Huang et al. 2003), glucose oxidase for glucose biosensor construction (Yang et al. 2014), have been reported. In an interesting study, catalase–iron oxide nanoparticles conjugate was capable of reducing the hypoxic conditions in breast cancer cell lines to overcome the problem of multiple drug resistance (Yen et al. 2019).

For designing efficient immobilization of enzymes over any matrix, researchers have to consider many factors simultaneously. Classical method for optimization, “one factor at a time (OFAT)”, makes this work not only laborious, time and funds consuming, but also neglects the mutual effect of all selected factors, thus giving no guarantee of optimal condition (Bandaru et al. 2006; Kumar and Goindi 2021a, b). Moreover, in immobilization, maximum protein loading over the matrix is not the only target but also the loading with least loss in its 3D structure. In addition, researchers want to have control over protein loading, activity (U mg⁻¹ matrix) and specific activity (U mg⁻¹ protein loaded over matrix). Therefore, statistical optimizing strategy using response surface methodology (RSM) has been adopted for designing the immobilization experiments. RSM has been largely used for optimizing the media for enzyme production and secondary metabolites (Bezerra et al. 2008). RSM has also been applied with success for optimization of critical parameters of processes in biotechnology or pharmaceutical preparation (Dwevedi and Kayastha 2009; Potumarthi et al. 2008; Kumar and Goindi 2015). This approach of mathematical model reduced the number of experiments, and time to achieve the objective to maximize efficiency (Wu et al. 2010; Kumar 2015). Recently, several studies described the effects of functionalization agents like 3-aminopropyl-tri-ethoxy-silane (APTES) (Kumar et al. 2013; Bourkaib et al. 2021), cross-linkers like glutaraldehyde (Najavand et al. 2020), enzyme amount (Kishore et al. 2012), time and temperature of process for enzyme loading (Kuo et al. 2012; Li et al. 2015) on immobilization of enzyme, but none of them has used statistical approach to achieve their optimal parameters.

Therefore, in this work, RSM methodology has been utilized for developing most efficient process with maximum enzyme loading and/or high specific activity with respect to critical immobilization conditions. RSM-assisted optimized immobilization of bovine liver catalase over MNPs has been carried out to obtain stable, magnetically active immobilized preparation of catalase. Catalase immobilized over MNPs can further be used for its applications, e.g., magnetically oriented targeted delivery to site of therapeutic action or catalytic magnetic switch for analytical reactions.

Materials and methods

Materials

Catalase enzyme (EC 3.4.24.26) of 2000–5000 IU mg⁻¹, 3-amino-propyltriethoxysilane (APTES), ferric chloride (anhyd.), methanol and hydrogen peroxide (H₂O₂) were purchased from Himedia Laboratory Pvt Ltd (Mumbai, India). Ferrous sulfate heptahydrate, acetyl acetone, glutaraldehyde (25% v/v) and bovine serum albumin (BSA) were purchased from s. d. fine chem. Ltd (Mumbai, India). Permanent magnets (Disk-shaped, dia x thickness: 25 mm × 2 mm, 200 mT strength) were purchased from FHK tools (Mumbai, India).

Enzyme activity assay

Colorimetric method was used for the measurement of enzyme activity. 10 mL chromogen was prepared by mixing 0.5 mL acetyl acetone, 3 mL methanol and 6.5 mL water. 200 µL active catalase preparation was added to 1 mL of H₂O₂ (60 mM) and 300 µL prepared chromogen (color-producing reagent). Reaction mixture was incubated at 37 °C without shaking for 20 min. 0.2 mL of 1 M HCl was added to stop the enzyme activity. Reaction mixture was centrifuged at high RPM to separate suspended enzyme materials (immobilized catalase on MNPs). Absorption of the reaction mixture was taken at 405 nm. Standard plot was constructed by performing assay with known catalase concentrations (0.1–0.001 mg mL⁻¹). Assays of unknown samples were compared with it to calculate activity (Pesce and Bodourian 1976; Lee et al. 2010).

Preparation of MNPs

Magnetic nanoparticles were prepared by ferric and ferrous ion co-precipitation in basic solution as per the method in literature (Kumar et al. 2013, 2014a, b) with little modifications. In brief, 7.5 mL, 1.5 M iron solution was prepared (ferric-Fe³⁺& ferrous-Fe²⁺ ions concentration 1 M & 0.5 M respectively). The 75 mL of 0.5 M NaOH solution was made deoxygenated by providing N₂ gas bubbling for 1 h. The iron solution was poured drop-wise to de-oxygenated NaOH with vigorous shaking by magnetic stirrer at 60 °C. The temperature was kept at 60 °C with continuous N₂ bubbling for 1 h. A magnetic separator was used to separate the prepared MNPs and washed several times using deionized water (Millipore) until wash pH becomes neutral. Resulting MNPs were suspended in 40 mL deionized water. MNPs were dried, by keeping the suspension at 70 °C overnight, and used for different characterization analysis.

Total iron contents of MNPs were assayed using potassium thiocyanate as described previously (Dandamudi and Campbell 2007) with modifications. MNPs (50 µL) were oxidized to convert the iron oxide Fe₃O₄ to its Fe³⁺ ion using concentrated HCl (100 µL). 1.0 mL appropriately diluted oxidized Fe₃O₄ solution was mixed with 1% potassium thiocyanate (3.0 mL) and H₂O₂ (40 µL), generating a red complex absorbing 490 nm of visible spectrum.

Amino functionalization (A-MNPs) and glutaraldehyde activation (G-A-MNPs) of MNPs

Surface functionalization of MNPs with 3-amino-propyltriethoxysilane (APTES) was performed as per the method described earlier (Kumar et al. 2013; Nosrati et al. 2018) with some modifications (Fig. 1). 0.2 mL of 3-amino-propyltriethoxysilane (APTES) of different concentrations (given in the experimental design) was added to 8 mL methanol. 5 mL MNP suspension (30 mg mL⁻¹) was added to it and kept for 4 h incubation at 50 °C and 250 RPM. Suspension was washed using deionized pure water, many times with the help of the magnetic separator and again suspended in 5 mL water. Dry form was prepared, by keeping A-MNPs suspension at 70 °C overnight, for different characterization analysis.

Fig. 1.

Step-wise preparation of magnetic catalase after immobilization with APTES-functionalized glutaraldehyde-activated MNPs (MNPs: Magnetic nanoparticles, APTES: 3-amino-propyltriethoxysilane, A-MNPs: APTES-functionalized MNPs, G-A-MNPs: Glutaraldehyde-modified A-MNPs, C-G-A-MNPs: Catalase-immobilized G-A-MNPs)

Glutaraldehyde activation was performed (Fig. 1) with different amounts (given in the experimental design) added to 5 mL A-MNPs suspension, and incubated for 4 h at 37 °C and 250 RPM. With the help of external magnetic field, the MNPs suspension was washed with deionized water many times, and finally suspended in 2.5 mL buffer solution (phosphate, 50 mM, 7.0 pH). Dry form was prepared by keeping G-A-MNPs suspension at 70 °C overnight for different characterization analysis.

Immobilization of catalase (C-G-A-MNPs)

Different concentration solutions of catalase were prepared (double the concentration of the enzyme as given in the experimental design). G-A-MNPs suspension and enzyme solution were mixed to get the desired concentration of catalase (Fig. 1). The solution was incubated with 250 RPM at various temperatures (as given in “Experimental Design” section) for a particular time (as given in “Experimental Design” section). The C-G-A-MNPs suspension was washed with phosphate buffer two times and finally re-suspended in 5 mL buffer (phosphate, 50 mM, 7.0 pH). Suspension was stored and used for activity determination while supernatant and wash were used for protein loading estimation as per the method given in literature (Kecskemeti and Gaspar 2017; Waterborg and Matthews 1994).

$$\mathrm{CL}=\frac{C_i-C_f}{M},$$ 1

where CL is catalase protein loading (mg g⁻¹) on MNPs, which can be calculated from amount of MNPs (in g) added in the reaction mixture for enzyme immobilization (M), the amount of enzyme protein in mg added in reaction mixture at zero time (C_i) and final protein (mg) present after completion of enzyme loading process (C_f).

C-G-A-MNPs were separated from the enzyme loading mixture using an externally applied magnet, washed several times and re-suspended in buffer 50 mL (phosphate, 50 mM, 7.0 pH) and were checked for its activity. The residual activity of C-G-A-MNPs was calculated by

$$\mathrm{C}-\mathrm{RA}\%=(\left[\mathrm{C},-,\mathrm{G},-,\mathrm{A},-,\mathrm{MNPs}\right]/{([C]}_i-{\left[C\right]}_f))\times 100,$$ 2

where C-RA is catalase residual activity which has been loaded over G-A-MNPs, [C-G-A-MNPs] enzyme activity of C-G-A-MNPs, [C]_i and [C]_f are the enzyme activities of catalase immobilized on G-A-MNPs, free catalase activity initially present in solution and after immobilization, respectively.

Dry form was prepared, by keeping C-G-A-MNPs suspension at 70 °C overnight, for different characterization analysis.

Experimental design

Five variables and three outcomes central composite design (CCD) without blocks have been used for its optimization of immobilization process with activity, specific activity and protein loading as optimized outcomes. Five variables were selected as critical dependents viz. enzyme concentration (X₁), APTES concentration (X₂), glutaraldehyde concentration (X₃), temperature of loading (X₄) and time of loading (X₅). Decision of range was based on available literature survey or general assumptions. Range of selected critical parameters was converted into codes (+1 for upper level, −1 for lower level and 0 for central) to bring each value of different parameters at the comparable platform (Table 1). The employed CCD design proposed fifty experiments in total which included eight replicates at the central point after giving a range of each parameter as input (Table 2). All experiments were carried out in duplicate and the average activity, specific activity and protein loading were taken as the responses.

Table 1.

Coded levels and independent variables for catalase immobilization on magnetic nanoparticles statistical analysis and validation of the experimental model

Coded level	X1	X2	X3	X4	X5
	Enzyme concentration (mg mL−1)	APTES Concentration (mM)	Glutaraldehyde concentration (mM)	Temperature of loading (°C)	Time of loading (minutes)
−1	6.5	32.5	7	18	40
0	10.5	52.5	12	25	65
+1	14.5	72.5	17	32	90

Table 2.

Central composite design matrix in coded and un-coded terms with experimental values for immobilization of catalase on MNPs for specific activity of catalase (U mg⁻¹ MNP), protein loading (mg g⁻¹ MNP) and specific activity of catalase w.r.t protein (U mg⁻¹ protein)

Design											Response
	APTES (mM) (X2)		Glutaraldehyde (µ L) (X3)		Enzyme (mg/mL) (X1)		Time (min) (X5)		Temperature (°C) (X4)		Activity	Protein loading	Specific activity
Run	Level	Mm	Level	Mm	Level	mg/ml	Level	min	Level	°C	U mg−1 MNP	mg g−1	U mg−1 protein
1	1	72.5	1	17	1	14.5	1	90	−1	18	23.90	320.55	74.56
2	2.38	100	0	12	0	10.5	0	65	0	25	16.45	355.90	46.23
3	1	72.5	1	17	1	14.5	1	90	1	32	20.16	332.55	60.62
4	0	52.5	0	12	2.38	20	0	65	0	25	23.92	361.65	66.14
5	−1	32.5	1	17	−1	6.5	−1	40	1	32	19.67	401.55	48.99
6	1	72.5	−1	7	−1	6.5	−1	40	1	32	18.59	364.55	50.99
7	0	52.5	−2.38	0.11	0	10.5	0	65	0	25	18.73	321.20	58.31
8	−2.38	4.93	0	12	0	10.5	0	65	0	25	13.37	396.40	33.72
9	0	52.5	0	12	0	10.5	0	65	−2.37	8.35	24.49	330.70	74.04
10	−1	32.5	−1	7	1	14.5	1	90	1	32	16.02	329.55	48.61
11	0	52.5	0	12	0	10.5	0	65	0	25	31.57	308.50	102.33
12	−1	32.5	1	17	−1	6.5	−1	40	−1	18	19.21	369.55	51.99
13	0	52.5	0	12	−2.38	0.97	0	65	0	25	23.86	328.35	72.67
14	−1	32.5	1	17	−1	6.5	1	90	−1	18	21.73	306.55	70.87
15	−1	32.5	1	17	1	14.5	1	90	−1	18	16.32	337.55	48.35
16	0	52.5	2.38	23.89	0	10.5	0	65	0	25	17.03	368.80	46.18
17	−1	32.5	1	17	1	14.5	1	90	1	32	13.62	349.55	38.98
18	0	52.5	0	12	0	10.5	0	65	0	25	31.25	344.00	90.84
19	−1	32.5	−1	7	−1	6.5	−1	40	1	32	19.20	381.55	50.33
20	1	72.5	1	17	1	14.5	−1	40	1	32	22.69	398.55	56.92
21	1	72.5	1	17	−1	6.5	−1	40	1	32	19.12	384.55	49.72
22	1	72.5	−1	7	−1	6.5	1	90	1	32	18.14	298.55	60.76
23	0	52.5	0	12	0	10.5	0	65	2.38	41.65	21.26	359.30	59.17
24	1	72.5	−1	7	1	14.5	1	90	1	32	22.66	312.55	72.49
25	1	72.5	1	17	1	14.5	−1	40	−1	18	24.49	386.55	63.34
26	1	72.5	−1	7	1	14.5	−1	40	−1	18	25.42	366.55	69.35
27	−1	32.5	−1	7	−1	6.5	1	90	1	32	20.12	315.55	63.76
28	−1	32.5	−1	7	1	14.5	−1	40	−1	18	16.52	383.55	43.08
29	−1	32.5	−1	7	−1	6.5	−1	40	−1	18	17.81	369.55	48.19
30	0	52.5	0	12	0	10.5	2.38	124.46	0	25	12.26	269.65	45.45
31	0	52.5	0	12	0	10.5	−2.38	5.54	0	25	11.74	426.75	27.50
32	1	72.5	1	17	−1	6.5	1	90	1	32	17.96	318.55	56.37
33	1	72.5	−1	7	−1	6.5	1	90	−1	18	19.90	286.55	69.47
34	1	72.5	1	17	−1	6.5	−1	40	−1	18	19.90	372.55	53.41
35	−1	32.5	1	17	1	14.5	−1	40	1	32	14.21	415.55	34.19
36	−1	32.5	1	17	−1	6.5	1	90	1	32	19.96	335.55	59.38
37	0	52.5	0	12	0	10.5	0	65	0	25	31.86	306.00	104.11
38	−1	32.5	−1	7	1	14.5	1	90	−1	18	18.03	317.55	56.77
39	0	52.5	0	12	0	10.5	0	65	0	25	32.21	329.50	97.75
40	1	72.5	−1	7	−1	6.5	−1	40	−1	18	18.59	352.55	52.73
41	1	72.5	−1	7	1	14.5	−1	40	1	32	24.33	378.55	64.29
42	−1	32.5	1	17	1	14.5	−1	40	−1	18	15.15	403.55	37.55
43	0	52.5	0	12	0	10.5	0	65	0	25	32.56	335.00	97.22
44	1	72.5	−1	7	1	14.5	1	90	−1	18	25.27	300.55	84.11
45	−1	32.5	−1	7	1	14.5	−1	40	1	32	16.23	395.55	41.04
46	0	52.5	0	12	0	10.5	0	65	0	25	31.82	304.50	104.49
47	1	72.5	1	17	−1	6.5	1	90	−1	18	20.25	306.55	66.07
48	0	52.5	0	12	0	10.5	0	65	0	25	31.72	330.00	96.13
49	−1	32.5	−1	7	−1	6.5	1	90	−1	18	20.99	303.55	69.13
50	0	52.5	0	12	0	10.5	0	65	0	25	31.46	309.50	101.64

Analysis of variance (ANOVA) method was utilized to establish the statistical decisions of the established model with Design Expert® 7.0.0 software (Stat-Ease Inc., Minneapolis, USA). The outcomes of Fisher’s F test and corresponding p values represent significance of the proposed model, while correlation coefficient (R²) represents the goodness-of-fit of regression line generated by the model and corresponding contour plots. The model was validated using the proposed design space for independent variables to get desired results (maximum specific activity).

Characterization of MNPs and C-G-A-MNPs

Transmission electron microscope (TEM, 120 kV, Hitachi-H 7500, Japan), was used to take photomicrograph of the MNPs and C-G-A-MNPs at nanoscale. A tiny drop of diluted MNPs suspension, with optical density approx. 0.5 at 630 nm after bath sonication was placed and dried on a copper grid coated with a carbon layer and examined.

The crystal size and structures of the MNPs and enzyme-loaded MNPs were estimated by wide angle X-ray diffraction instrument (Panalytical'sX'Pert Pro, Netherland) with radiation source of CuK_α, geniometric plate angle moved from 20° to 70° within 25 min and ICCD database was used to confirm the Fe₃O₄ phase.

Vibrating Sample Magnetometer (PARC, 155, USA) provided a relationship between the magnetic moment (emu g⁻¹) of MNPs and its enzymatic preparations and an external magnetic field (Oe) to express the magnetic properties.

Thermogravimetric analysis (TGA) of powder samples of MNPs, A-MNPs and C-G-A-MNPs was performed with model TG/DTA 6300, SII EXSTAR 6000, UK. Exactly nearly 10 mg of MNPs and enzyme immobilized MNPs samples was taken on heating and weighing pan with temperature range 30–700 °C with 10 °C min⁻¹ rate of heating.

Stability and recyclability studies

The magnetic preparation of catalase (C-G-A-MNPs) was stored in a buffer (2 mg MNPs eq. mL⁻¹) at various temperatures (4 and 25 °C), and at different times (0, 12, 24, 48, 72, 96 and 144 h), 0.5 mL of C-G-A-MNPs preparation was taken and checked for its activity retention, and free enzyme at 4 °C was taken as reference. For the reusability study, the same sample of the C-G-A-MNPs was recycled with help of external application of permanent magnet for 20 consecutive cycles of activity reactions and was checked for its activity retention (Kumar et al. 2010).

Results and discussion

Optimization of immobilized catalase magnetic nanoparticles

With the given conditions of catalase activity assay, plot of catalase concentration and absorbance at 405 nm showed linear relation with good correlation coefficient (R²-0.966) (Fig. 2). This established correlation was used to estimate the catalase activity equivalents in unknown sample of enzyme. Similar correlation of enzyme units with respect to the assay has been established in the literature. In one study, H₂O₂ degradation with catalase in presence of non-ionic surfactant Triton X-100 resulted in frothing, and height of froth was directly correlated with catalase activity units (Iwase et al. 2013).

Fig. 2.

Linear plot of enzyme concentration and absorbance at 405 nm following the reaction of catalase with enzyme reaction mixture (reaction mixture constituted of 200 µL catalase, 1 mL of H₂O₂ of 60 mM concentration and 300 µL chromogen at 37 °C. After 20 min, 0.2 mL of 1 M HCl was added)

The primary objectives of enzyme immobilization on different matrices are to re-use the enzyme preparation for catalyst application and stability. In a study, it was also observed that the activity lipase over polyurethane retained 50% of its activity even after 5 cycles of its re-use (Facin et al. 2018). While, the crosslinked enzyme aggregates of nitrilase retained 50% activity at 45 °C storage for 140 h in comparison to nearly loss of activity of free enzyme even in storage at 4 °C for same time (Kumar et al. 2010). In case of enzyme as drug, its immobilization over specialized matrix has been used for their delivery to the target site for therapeutic application (Kumar et al. 2013, 2014a, 2014b). In a study, asymmetric immobilization of urease over platelet cells imparted it as micro-motor which is a potential target delivery system (Tang et al. 2020). In any matrix-associated catalyst system, the matrix is the major part, so to achieve the high enzyme activity, per gram of MNPs catalase was immobilized with a statistical approach. The statistical procedure of response surface methodology has been reported to be a helpful tool and used in processes where optimization is an important economic point. With application of this method, it is easy and convenient to spot optimum conditions and also minimize the amount of experiments needed for a particular response. Results of CCD experiments with different combinations of factors (APTES, glutaraldehyde and enzyme concentration, and time and temperature of the process for enzyme loading) are presented along with mean predicted and experimental responses for specific activity of catalase with respect to MNPs (U mg⁻¹ MNP), protein loading (mg g⁻¹ MNP) and specific activity of catalase with respect to protein (U mg⁻¹ protein) (Table 2). There was a considerable variation in the responses for selected variables. The maximum specific activity for C-G-A-MNPs wrt MNPs and protein was obtained up to 31.7 ± 1.42 U mg⁻¹ and 99.1 ± 3.1 U mg⁻¹, respectively with 52.5 mM of APTES, 12 µL mL⁻¹ of glutaraldehyde, 10.5 mg mL⁻¹ of enzyme, 65 min for time for enzyme loading and 25 °C of temperature for enzyme loading reaction. On the other hand, maximum protein loading was 426.7 ± 19.2 mg g⁻¹ MNPs with 52.5 mM of APTES, 12 µL mL⁻¹ of glutaraldehyde, 10.5 mg mL⁻¹ of enzyme, 5.54 min for time for enzyme loading and 25 °C of temperature for enzyme loading reaction. APTES act as covalent linker between glutaraldehyde and MNPs, and also responsible for protein’s conformational plasticity at active site, which is critical for catalysis in both the ways, i.e., negative or positive (Kumar et al. 2013). In this study, addition of both the linkers resulted in C-G-A-MNPs with high specific activity of catalase per gram of MNPs as well as protein which is evident from respective correlation coefficients, which are summarized in Table 3.

Table 3.

Model coefficient estimated by response surface regression

Model term	Coefficient estimate			p-value
	Activity	Protein loading	Specific activity	Activity	Protein loading	Specific activity
Intercept	31.77	321.09	99.11	<0.0001	<0.0001	<0.0001
X1	0.09	7.85	−1.00	0.3646	<0.0001	0.0605
X2	1.48	−7.65	5.16	<0.0001	<0.0001	<0.0001
X3	−0.31	9.15	−2.37	0.0046	<0.0001	<0.0001
X4	−0.66	6.86	−3.16	<0.0001	<0.0001	<0.0001
X5	0.12	−32.94	5.24	0.2582	<0.0001	<0.0001
X1X1	−1.44	4.46	−5.47	<0.0001	0.0003	<0.0001
X2X2	−3.02	9.96	−10.67	<0.0001	<0.0001	<0.0001
X3X3	−2.50	4.46	−8.50	<0.0001	0.0003	<0.0001
X4X4	−1.62	4.46	−5.97	<0.0001	0.0003	<0.0001
X5X5	−3.54	5.02	−11.29	<0.0001	<0.0001	<0.0001
X1 X2	2.16	−1.16	6.26	<0.0001	0.4315	<0.0001
X1 X3	−0.57	1.16	−1.77	<0.0001	0.4315	0.0060
X1 X4	−0.30	−1.16	−0.58	0.0178	0.4315	0.3423
X2 X3	−0.31	−0.09	−1.08	0.0137	0.9489	0.0797
X2 X4	0.02	1.16	−0.39	0.0099	0.4315	0.5173
X3 X4	−0.23	−1.16	−0.63	0.0634	0.4315	0.2994
X1X5	−0.43	−0.09	−0.53	0.0012	0.9489	0.3860
X2X5	−0.20	1.16	−0.64	0.1049	0.0015	0.2943
X3X5	−0.16	0.09	−0.81	0.1989	0.0089	0.1844
X4X5	−0.46	−0.09	−1.72	0.0005	0.9489	0.0073
Model of fitness	A	a	a	a	a	A
Lack of fit	B	b	b	b	b	B
R2	0.98	0.95	0.97

a significant, b non-significant

Model fitting for optimized parameters

The response surface regression procedure for statistical analysis system was employed to fit the second-order polynomial equation (Eq. 3) to the experimental data, represented as specific activity with respect to MNPs and protein and as protein loading. Table 3 shows the polynomial model of second order as given in the equation (Eq. 3) with respect to correlation coefficients (with R² = 0.98, 0.97 and 0.95 for specific activity per gram MNPs, per milligram protein and protein loading, respectively) and statistical significance obtained from analysis of variance (ANOVA), i.e., very small p value (0.0001) suggesting the established relationship between the responses and the significant variables. In addition, the cumulative effect of the selected immobilization variables (APTES, glutaraldehyde and enzyme concentration, and time and temperature of the process for enzyme loading) analyzed by a joint test (Table 3), revealing that all the selected variables are statistically critical factors with high level of significance (p < 0.001). The results mentioned above validate the suitability of second-order quadratic equation as in situ relationship of different factors with target outcome which was either unknown or cumbersome to understand.

$$\mathit{Y}={\mathit{\beta }}_1{\mathit{X}}_1{+\mathit{\beta }}_2{\mathit{X}}_2+{\mathit{\beta }}_3{\mathit{X}}_3{+\mathit{\beta }}_4{\mathit{X}}_4+{\mathit{\beta }}_5{\mathit{X}}_5+{\mathit{\beta }}_1^2{\mathit{X}}_1^2+{\mathit{\beta }}_2^2{\mathit{X}}_2^2+{\mathit{\beta }}_3^2{\mathit{X}}_3^2+{\mathit{\beta }}_4^2{\mathit{X}}_4^2+{\mathit{\beta }}_5^2{\mathit{X}}_5^2+{\mathit{\beta }}_1{{\mathit{\beta }}_2\mathit{X}}_1{\mathit{X}}_2+{\mathit{\beta }}_1{{\mathit{\beta }}_3\mathit{X}}_1{\mathit{X}}_3+{\mathit{\beta }}_1{{\mathit{\beta }}_4\mathit{X}}_1{\mathit{X}}_4+{\mathit{\beta }}_1{{\mathit{\beta }}_5\mathit{X}}_1{\mathit{X}}_5+{\mathit{\beta }}_2{{\mathit{\beta }}_3\mathit{X}}_2{\mathit{X}}_3+{\mathit{\beta }}_2{{\mathit{\beta }}_4\mathit{X}}_2{\mathit{X}}_4+{\mathit{\beta }}_3{{\mathit{\beta }}_4\mathit{X}}_3{\mathit{X}}_4+{\mathit{\beta }}_2{{\mathit{\beta }}_5\mathit{X}}_2{\mathit{X}}_5+{\mathit{\beta }}_3{{\mathit{\beta }}_5\mathit{X}}_3{\mathit{X}}_5+{\mathit{\beta }}_4{{\mathit{\beta }}_5\mathit{X}}_4{\mathit{X}}_5.$$ 3

Additionally, if the model attribute, i.e., lack of fit is found insignificant, the model is said to predict the data in the experimental conditions or vice versa. The ANOVA showed that model with both F value (174.76, 84.05 and 48.68 for catalase activity, its specific activity and protein loading, respectively) and p value (0.06, 0.96 and 1.00 for catalase activity per gram MNPs, specific activity per milligram protein and protein loading, respectively) for the lack of fit was not significant, which means that the model has represented the satisfactorily data. Furthermore, coefficient of variation (CV) is another statistical attribute to numerically express the variation in the results, low value of CV represents high precision and high value (CV > 10) signifies the difference in average value is by chance, leading to rejection of the response model. Results of designed experiments showed low CV values (3.16, 5.38 and 2.38% for specific activity per gram MNPs, per milligram protein and protein loading, respectively), and indicated the experimental precision and reliability. One should find a way to present the confidence in the proposed model’s performance in the real world. Design of experiment has the facility to present diagnostic plots of the predicted versus actual value in visual and instant conclusive regression line graphs (Fig. 3). The correlation coefficients (R²) between actual and predicted values for catalase activity, protein loading and specific activity were 0.99, 0.97 and 0.98, respectively. R² values approaching 1.0 demonstrate a concord between the model predicted and observed results.

Fig. 3.

Diagnostic plots of correlation between actual and predicted values for A Catalase activity, B Protein loading and C Specific activity during catalase immobilization over G-A-MNPs.

Individual and mutual effect of parameter on specific activity of catalase

Among all the studied variables, enzyme concentration, APTES and immobilization time had a strong positive effect on the activity of immobilized catalase per gram MNPs. Furthermore, APTES and immobilization time had the highest linear effect on the activity of immobilized catalase per milligram protein. On the other hand, the effect of enzyme and glutaraldehyde concentration and temperature had a significant positive effect on protein loading. It is also interesting to note that immobilization reaction time and APTES concentration had a negative effect on protein loading, as evident by an increase in overall response with respect to its decreasing value. The above observation can be attributed to the possibility of intermolecular cross-linking/adsorption stacking of APTES as higher concentration and not available for attachment (An et al. 2020). The quadratic coefficients of all variables were statistically significant but in negative direction for immobilized catalase activity per MNPs and protein. Interestingly, these parameters showed a positive effect for protein loading.

Most significant interaction and validation of model

The contour plots generated from the regression model in the design of experiment are the most interactive way to illustrate combined effects of independent variables upon the response. Figure 4A, B provide the profile of immobilized catalase activity under positive interaction of APTES concentration with enzyme concentration and temperature of enzyme loading. A quadratic effect was observed for both variables in this plot. Interestingly, it was observed that APTES concentration alone showed a significant positive effect on immobilized catalase activity and specific activity of enzyme. In its quadratic interaction with enzyme concentration and temperature, it showed positive combined effects of independent variable upon the response variable. Figure 4A, B show that with increase in APTES concentration, activity of immobilized catalase increases, which could be due to availability of more free functional groups for attachment of free enzymes with more free exposed sites of immobilized catalase. It was observed that APTES concentration showed negative correlation coefficient for protein loading (Table 3) and with its positive correlation with time of loading, more protein loading was observed (Fig. 4C). This could be attributed to more availability of functional groups exposed for catalase binding during immobilization. The mutual interaction of APTES concentration and enzyme concentration for loading also positively affected the specific activity of immobilized catalase (Fig. 4D). It was interesting to note that glutaraldehyde concentration as an independent variable significantly increased the protein loading but did not result in positive effect on catalase activity and its specific activity (Table 3).

Fig. 4.

Contour plots showing significant design space for Catalase activity (A, B), Protein loading (C) and Specific activity (D) with respect to dependent variables viz. concentration of APTES, glutaraldehyde, enzyme concentration, time and temperature for enzyme loading

Validation of the any model is also a mandatory step (Gawas et al. 2018) and can be executed with an additional experiment utilizing the values of parameters as predicted by model to obtain maximum catalase activity (U mg⁻¹ MNP) and specific activity (U mg⁻¹ protein loaded). The optimal condition suggested by model was 64.00 mM of APTES, 10.97 µL of Glutaraldehyde, 14.50 mg mL⁻¹ of catalase enzyme, 67 min of time and 22.6 °C of temperature. Under these conditions, the predicted catalase activity was 31.70 U mg⁻¹ MNP and specific activity was 97.16 U mg⁻¹ loaded protein, according to the RSM model. It was observed that predicted value was significantly similar to the mean value of both catalase activity and its specific activity of three experimental tests, supporting that the RSM model was adequate for the expected optimization of catalase immobilization on MNPs. Catalase activity was 32.1 ± 1.1 U mg⁻¹ MNPs and specific activity was observed to be 97.7 ± 3.4 U mg⁻¹ of loaded protein content under optimized conditions. This result of analysis suggested that the experimental responses were in good agreement with the predicted ones, and also signifies the validation of the RSM model. The optimized condition has resulted in high catalase activity during optimized conditions.

Characterization of MNPs and catalase-immobilized MNPs

Co-precipitation of ferric (FeCl₃) and ferrous (FeSO₄) ions in presences of base was carried out to prepare the MNPs. The particles prepared by this method were having hydroxyl group at their surface which makes it amenable to silanization reaction with 3 amino propyl-triethoxy silane (APTES) rendering it with free surface –NH₂ (Kumar et al. 2013). This free surface -NH₂ group can further be activated by glutaraldehyde due to its bidentate chemistry. The characterization of MNPs, intermediate, i.e., APTES-functionalized MNPs (A-MNPs) and glutaraldehyde-activated A-MNPs (G-A-MNPs) and final, i.e., catalase-immobilized G-A-MNPs (C-G-A-MNPs) was performed. Transmission electron microscope (TEM) images of prepared MNPs showed the average size of 10.5 nm which were monodisperse (SD 1.7 nm), while the shape of the particles was almost spherical (Fig. 5A). The sizes of APTES-functionalized MNPs (A-MNPs), glutaraldehyde-activated A-MNPs (G-A-MNPs) were numerically close and statistically same (10.23 ± 1.74 and 11.84 ± 1.49, respectively) to that of pure MNPs. On the other hand, C-G-A-MNPs (Supplementary Material 1) showed the average size 13.32 ± 2.74 nm which was numerically different but not of statistical significance in comparison to core MNPs size. X-ray diffraction (XRD) of all preparations, i.e., MNPs, its surface engineered preparations (A-MNPs and G-A-MNPs) and C-G-A-MNPs showed peaks at 30.28, 35.657, 43.39, 53.7, 57.31, and 62.91 of 2θ (eq. to (220), (311), (400), (422), (511), (440) planes of a cubic cell). Presence of peaks in each preparation suggests its crystalline structure (no peaks indicate amorphous nature). Moreover, its pattern was the characteristics of the Fe₃O₄ MNPs (Fig. 5B and Supplementary Material 2) which are confirmed by ICDD database (reference code 01-076-0955), an indication of 100% purity with respect to crystal material, i.e., Fe₃O₄ (magnetite). This result ruled out the possible formation of Fe₂O₃ (maghemite) or other form of iron oxide while in the process of surface functionalization and enzyme immobilization (Pavani et al. 2016).

Fig. 5.

A TEM Image of MNPs and particle size distribution (figure inset) in the sample of ~950 particles giving average size 10.5 ± 1.7 nm; B XRD pattern of prepared MNPs; C VSM of pure MNPs, G-A-MNPs and C-G-A-MNPs; D Weight loss study with increasing temperature (TGA) of MNPs, G-A-MNPs and C-G-A-MNPs

Pure MNPs (Supplementary Material 3) and C-G-A-MNPs (Fig. 5C (inset)), showed magnetic attraction, i.e., attracted to the wall of vial within maximum 30 s using permanent magnet (200 mT), and becomes freely suspended as soon as applied magnetic field released and with little shaking. While, quantitative measurement (Fig. 5C) of saturation magnetization (M_s) with vibrating sample magnetometer (VSM) performed in external magnetic field (H) ranging from −11 KOe to +11 KOe, showed 57, 51 and 44 emu g⁻¹ MNPs for MNPs, G-A-MNPs and C-G-A-MNPs, respectively. This reduced M_s with processed particles can be attributed to coating of functionalization and enzyme catalase. Moreover, absence of hysteresis in the curve (anhysteric loop), i.e., zero magnetic moment at zero magnetic field showed the particles to be super-paramagnetic in nature which made it suitable candidate of targeted drug delivery matrix (Lamichhane et al. 2022).

Figure 5D showed the thermo-gravimetric analysis of MNPs, G-A-MNPs and C-G-A-MNPs. Initial loss of weight in MNPs and G-A-MNPs sample (4.3 and 1.2%, respectively) may be attributed to the adsorbed water on their surfaces (Cao et al. 2009). The difference of weight loss between C-G-A-MNPs and G-A-MNPs clearly depicted the presence of 89.5 mg organic matter g⁻¹ MNPs (8.95%) over C-G-A-MNPs.

Stability and re-usability

The stability of optimized magnetic preparation of catalase (C-G-A-MNPs) was studied for 144 h at 4 and 25 °C along with free enzyme at 4 °C. The results (Fig. 6A) clearly showed the retention of activity 81.65 ± 7.65 and 68.12 ± 3.158% after 144 h at 4 and 25 °C, respectively, while that of free enzyme was 7.87 ± 2.15% after 144 h at 4 °C. Thus, immobilization of enzymes over G-A-MNPs showed a high level of stability. The increase in the storage and thermo-stability can be attributed to molecular confined and stable cross-linking structure with APTES and glutaraldehyde that retain the conformational integrity or catalytic activity even at higher temperature and storage time (Secundo 2013; Kumar et al. 2010; Motamedi et al. 2021).

Fig. 6.

A Activity retention of free enzymes at 4 °C and C-G-A-MNPs at 4 and 25 °C. B Activity retention of C-G-A-MNPs with each cycle of reaction upto 20 cycles. The data points are an average of 3 copies of a parallel experiment with an error bar representing the standard deviation

The prepared C-G-A-MNPs are amenable to be recycled by separation from reaction mixture by application of external magnetic field (300 mT). The activity was found to be decreasing with each cycle and was retaining 64.34 ± 8.15% activity after the 20th cycle (Fig. 6B). In a study of nitrilase immobilization as cross-linked nitrilase aggregates also showed recyclability studies. It was observed that the activity of cross-linked nitrilase aggregates increased after initial cycles and it reached maximum values in five cycles and started decreasing afterward (Kumar et al. 2010).

Conclusion

The MNP is one of many important nanomaterials. Its beauty relies on its ease in preparation, functionalization and involvement in vast applications. Catalase needs to be recycled when used as an analytical reagent for determination of activity. Statistical immobilization optimization method (RSM) has been observed to be equivalent with respect to effectiveness along with significant reduction of number of experiments in comparison to OFAT method. So, this statistically organized method is proven to reduce time and money. Correlation coefficients obtained from the experiments showed insignificant difference between actual and predicted value for both outcomes. Its characterization showed the evidence of size, constituent, magnetic strength and organic coating over inorganic material. The preparation also shows better stability than free enzymes and good number of recyclability. Finally, we achieved an efficient magnetic preparation of catalase enzyme which can be separated from the reaction mixture by magnetic separation (easier method and better control) and can be re-used further by carrying out a lesser number of experiments. Biological evaluation, i.e., target delivery and efficacy of such preparation is the end left open for grooming and interested researchers.

Supplementary Information

Below is the link to the electronic supplementary material.

Declarations

Conflict of interest

We, the authors, declare that we have no conflict of interest in the publication.