Statistical optimization of chemical modification of chitosan-magnetic nano-particles beads to promote Bacillus subtilis MK1 α-amylase immobilization and its application
Bulletin of the National Research Centre volume 44, Article number: 40 (2020)
α-Amylase randomly hydrolyzes starch molecule to produce oligosaccharides of different chain length. It is among the most significant hydrolytic enzymes used in industrial applications. Enzyme immobilization is the simplest way to solve the stability problem of protein under industrial harsh conditions. Magnetic nano-particles considered suitable for immobilization due to their unique characteristics. The polymer nanocarriers still the feature of modifiable surfaces of carriers for further conjugation with biomolecule. This study aims to promote the immobilization of Bacillus subtilis MK1 α-amylase using the statistical optimization of the chemical modification of the chitosan-magnetic nano-magnetic particle beads and their ability to apply.
B. subtilis MK1 α-amylase was successfully immobilized on chitosan-magnetic nano-particles using a method combining the advantages of both physical adsorption and covalent binding. The beads were chemically modified using polyethyleneimine (PEI) followed by glutaraldehyde (GA). Aminated beads by (PEI), activated beads by (GA), and immobilized enzyme on activated beads were characterized using FTIR. Morphological examinations of the beads surface before and after conjugation with the α-amylase enzyme were carried out using scanning electron microscope (SEM). Chemical modification parameters of the beads were optimized using response surface methodology based on central composite design. Statistical approach enhanced the immobilization yield (IY%) by 1.5-fold. The application of immobilized enzyme in the baking process enhanced dough-raising about 2.3-fold and can be reused for 5 cycles with 100% activity.
Statistical methods are an important way to improve immobilization yield and efficiency. The ANOVA data confirmed the fitness of the model which possessed R2 value (0.975) and the adjusted R2 value (0.940). The results confirm the ability to reuse the immobilized enzyme in industrial processes.
Enzymes have numerous characteristics, and they are more preferable over chemical catalysts in many areas of industry ranging from food to pharmaceuticals (Zdarta et al. 2018). α-Amylase (EC 220.127.116.11) randomly hydrolyzes α-1,4-glycosidic bonds in the interior of starch molecule (Fig. 1) to produce branched and linear oligosaccharides of different chain length (Simair et al. 2017; Frantz et al. 2019). Amylases are among the most significant hydrolytic enzymes that are used in various industrial applications including food production (fruit juice clarification, bread, baking, and beer industries), textiles, detergents, pharmaceutical, pulp and paper, ethanol, and biofuel production (Yang et al. 2014; Tambekar et al. 2016; Pandey et al. 2017; Frantz et al. 2019).
High stability of enzyme under industrial conditions is considered an economic advantage due to low enzyme loss. Enzyme immobilization is the simplest way to solve the stability problem of protein and reduce the expensive cost of applying them on an industrial scale (Ahmed et al. 2019a, 2019b). Also, immobilization improves enzyme properties as activity, reduces the inhibition, increases stability, specificity to substrates, and avoids contamination of product by enzyme (Ahmed et al. 2019a, 2019b).
Immobilization of enzymes can offer many benefits as reusability and recovery from their products enhance stability under both operational and storage conditions (Souza et al. 2019). Enzymes immobilization on nano-carriers results in stabilization of active conformation, which enhances the interfacial reactions between the enzyme active sites and its substrate. The nano-environment surrounding enzyme molecules prevent enzyme deactivation (Misson et al. 2015). Nano-particles have high adsorption capacities, large specific surface areas, high mobility in porous media because of their specific functionality, surface area per unit mass, and smaller size than the relevant pore spaces and the ease of modifying their surface functionality (Swelam et al. 2019). Also, magnetic nano-particles as Fe3O4, γ-Fe2O3, ZnO, and TiO2 are considered significant carriers that enhance immobilization efficiency because they have high stability and high electron conductivity.
Fe3O4 nano-particles are considered suitable for immobilization due to their unique characteristics (small size, super-paramagnetism, tailored surface chemistry, low toxicity, biocompatibility, and biodegradability). The magnetic Fe3O4 nano-materials are generally unstable under acidic solutions and undergo leaching which leads to reduction of its lifetime (Sojitra et al. 2017).
Furthermore, magnetic nano-particles coated with organic/inorganic molecules as polymers are more effective for the free functional groups present on the surface due to providing a large number of active sites (Swelam et al. 2019). The polymer nano-carriers still the feature of modifiable surfaces of carriers for further conjugation with biomolecule (Misson et al. 2015).
Chitosan is a natural polymer known as an ideal carrier for enzyme immobilization. It is cheap, non-toxic, and has good (hydrophilicity, biocompatibility and adhesion) and high affinity towards proteins (Bindu et al. 2018). In addition, amino groups of chitosan are useful for conjugation with enzyme protein via cross-linking agents such as glutaldehyde (Kuo et al. 2012).
Enzyme immobilization includes different methods such as physical adsorption, entrapment, covalent binding, and cross-linking to solid carrier. Physical adsorption is the simplest immobilization method showing low stability after repeated use due to the weak bonds between enzyme and carrier surface as van der Waals and hydrogen bonding.
However, the immobilization by covalent binding is considered the most effective method because of increasing the enzyme stabilities. Also, covalent immobilization may be an encouraging procedure in establishing enzymes and inhibiting their leakage because of the formation of covalent bonds between enzyme and carrier surface (Eskandarloo and Abbaspourrad 2018). A method was developed that combines the advantages of both physical adsorption and covalent bonding methods (Wang et al. 2015). The immobilization yield (IY%) is the key parameter since it represents the general output of the efficiency of the immobilization process (Ahmed et al. 2008).
Response surface methodology (RSM) is a statistical and mathematical mechanism used for constructing models, designing experiments, and examining the optimum conditions affecting the IY%. It was performed through combination of some independent variables involved in the experimental design for promoting the IY of enzyme. Furthermore, RSM based on central composite design (CCD) reduces the number of individual experiments desired for providing information on the interactions between different variables to define the most significant factors (Abdel Wahab and Ahmed 2018).
In the current study, α-amylase enzyme was immobilized onto chitosan-magnetic nano-particles (Ch-MNP) beads using physical adsorption and covalent binding in order to increase its activity and stability. Ch-MNP beads modification to promote IY% was optimized using statistical methods. Finally, the chitosan-magnetic nano-particles/polyethyleneimine/glutaraldehyde/enzyme (Ch-MNP/PEI/GA/Enz) was applied in the baking industry.
Materials and methods
Chitosan (Mw 50–90 kDa with DD of 80%) was supplied from Sigma Chemical, Co., St. Louis, USA.
Polyethyleneimine (PEI, 50%, w/v), glutaraldehyde (GA, 50%, w/v), 3,5-dinitrosalicylic acid (DNS98%), and sodium hydroxide 97% (Mw 40 g/mol) were obtained from Sigma-Aldrich, Chemie GmbH, Riedstr. 2, D-89555 Steinheim, Germany. Soluble starch 99% (Mw 342.3 g/mol) and potassium sodium tartrate (Rochelle salt, 99%, Mw 282.23 g/mol) were obtained from WINLAB, UK. Other chemicals were of analytical grade.
α-Amylase enzyme was produced under submerged fermentation from isolated strain B. subtilis strain-MK1 as reported in the previous work by Ahmed et al. 2019a, 2019b. The enzyme was partially purified using 60% (v/v) ethanol precipitation; the precipitate was collected by centrifugation, dried, weighed, and used for α-amylase immobilization.
Preparation of chitosan-magnetic nano-particles (Ch-MNP) beads
Magnetic nano-particles (Fe3O4) were prepared according to Mehta et al. (2006) with some modifications. The method depended on mixing ferric and ferrous ions in a 1:2 molar ratio in highly basic solution at elevated temperature. In brief, iron (III) chloride hexahydrate (0.0551 mol) was dissolved in 150 mL of ammoniated water while 0.0275 mol of iron (II) chloride tetrahydrate was dissolved in 150 mL of ammoniated water. Then, the two solutions were mixed in a 500 mL conical flask and placed in a temperature-controlled water bath equipped with a magnetic stirrer. A sodium hydroxide aqueous solution (12.8 g in 120 mL of distilled water) was then added with a flow rate of 10 mL/min while continuously stirring at 80 °C, and the reaction was continued for 60 min under the same conditions. The resulting Fe3O4 particles were washed 3 times repeatedly with 500 mL of distilled water until neutral pH using magnetic field separation and finally kept in 150 mL distilled water in the fridge for further treatment.
Chitosan was dissolved in distilled water to produce 2% (w/v) solution then mixed with a solution of magnetic nano-particles (Fe3O4). After that, the polymer solution was sprayed into cross-linking solution of NaOH 5% (w/v), through a nozzle of 300 μm using the Inotech Encapsulator. The prepared beads were hardened in cross-linking solutions for 3 h. To modify the gel beads for covalent immobilization of enzyme, the gel beads were soaked in a solution of 4% (v/v) polyethylenimine (PEI) at pH 9.5 for 3 h, followed by soaking in glutaraldehyde (GA) 2.5% for 3 h, and after washing, the gel beads was ready for immobilization (Yuan et al. 2016).
Enzyme binding to activated beads
Partially purified α-amylase from B. subtilis strain-MK1 was immobilized by covalent binding on Ch-MNP. This was performed by mixing 2 mL of the partially purified α-amylase (200 U) with 1 g of the activated beads. The mixture was left for 24 h at 4 °C, and then the beads were washed twice with distilled H2O and were used for enzyme assay (Abdel Wahab et al. 2018).
Determination of α-amylase activity
α-Amylase activity was done according to Sajjad and Choudhry (2012) by mixing 0.5 ml of 1% soluble starch in 0.1 M phosphate buffer (pH 7.0) with 0.5 mL of the partially purified enzyme or 0.2 g of immobilized enzyme and was incubated for 30 min at 40 °C. The reaction was stopped by adding 1 mL of dinitrosalicylic acid (DNS) reagent and kept on boiling water bath for 10 min (Miller 1959), and the color absorbance was read at 540 nm. One unit of enzyme activity (U) is defined as the amount of enzyme that liberated 1 μmol of reducing sugar as glucose/min under assay conditions. All the experiments were performed in triplicate, and the results were expressed as mean values. Immobilization yield (IY%) was calculated according to Wang et al. (2015) as following:
where I is the total activity of immobilized enzyme, A is the total activity offered for immobilization, and B is the total activity of unbounded enzyme.
Optimization of beads modifications using statistical design
Response surface methodology (RSM) based on central composite design (CCD) was used to determine the optimum level of four important factors for beads modification of Ch-MNP beads. These factors include PEI percent (%) (W), PEI activation time (X), GA percent (%) (Y), and GA activation time (Z). These factors were tested at three levels as, low (− 1), central (0), and high (+ 1), resulting in experimental design of 25 experiments with respect to mean of IY (%) of α-amylase on Ch-MNP beads as response. The experimental data were analyzed by the response surface regression procedure to fit the second order polynomial of the equation:
where IY represents response, β0 is the interception coefficient, βi is the coefficient of the linear effect, βii is the coefficient of quadratic effect, βij is the coefficient of the interaction effect, and XiXj are the independent variables which influence the response variable (IY). All the experiments were performed in triplicate, and the results were expressed as mean values. The independent variables of the experimental design were optimized and interpreted using the (JMP) statistical software.
Statistical analysis of data
Statistical analysis of the model was carried out according to the analysis of variance (ANOVA). The quality of the fit of the polynomial model equation was assessed by determining the R2 coefficient and the adjusted R2 coefficient. Also, the significance of statistical and regression coefficient were checked with F test and P value, respectively. The validation of the model was checked by the comparison of experimentally obtained data with the predicted values, and the prediction error was calculated. Three-dimensional (3D) surface plots and corresponding contour plots were constructed to explain the effect of the independent variables on the responses (IY).
Fourier transforms infrared (FTIR) spectroscopy analysis
The FTIR absorption spectra of Ch-MNP/PEI/GA (beads), free α-amylase enzyme, and Ch-MNP/PEI/GA/Enz (immobilized enzyme) were measured by FTIR spectroscopy attenuated total reflection (ATR) mode Bruker VERTEX 70/70v model using the KBr disk technique. This test was performed to detect the presence of the new functional group and carbonyl group formed at all different formulas. The reaction began by mixing 2% (w/w) of the sample with dry KBr. The mixture was ground into a fine powder using an agate mortar before it was compressed into a KBr disk under a hydraulic press at 10,000 psi. Each KBr disk was scanned over a wave number range of 400–4000 cm−1, with a resolution of 4 cm−1, and the characteristic peaks were recorded.
Scanning electron microscope (SEM)
These investigations were performed in order to describe the morphological changes on the beads surface before and after conjugation with the enzyme. Morphological examinations on the surface of Ch-MNP and Ch-MNP/PEI/GA/Enz were carried out using scanning electron microscopy (SEM, Quanta 250 FEG, accelerating voltage 200 V–30 kV, FEI Company, Thermo Fisher Scientific).
Application of Ch-MNP/PEI/GA/enzyme in baking process
For studying application of immobilized α-amylase in baking process, dough was prepared by using 50 g wheat flour, 1.0 g baker’s yeast, 1.0 g Ch-MNP/PEI/GA/Enz (200 U), and 5 mL of H2O (Ahmed et al. 2016). Dough-raising was observed carefully after incubation at room temperature (~ 35 °C) for 2 h and was compared with control (dough without enzyme). After each run, the beads were washed with sodium phosphate buffer (0.1 M, pH 8.0) to remove any residual substrate and reused to start a new run.
Ch-MNP beads were chemically modified using PEI followed by GA. B. subtilis MK1 α-amylase was successfully immobilized on activated Ch-MNP beads with high immobilization yield 55% (Fig. 2). The activated beads reacted with the enzyme molecule as shown in Fig. 3. The reaction happened between the NH2 group found in the enzyme protein molecule, and the free C=O group located on GA forming C=N–bond.
Central composite statistical design (CCD) for optimization of beads modifications
The CCD of the coded variables and the corresponding experimental results of the IY% are displayed in Table 1. By using a multi-regression analysis for the experimental results, the predicted IY (%) of α-amylase was acquired from the following second-order polynomial equation:
where I is the predicted IY (%); W, X, Y, and Z are the code values of PEI percent (%), PEI activation time, GA percent (%), and GA activation time, respectively.
The highest IY of α-amylase (81.6%) was obtained in trail no.23 under optimizing conditions of 6% PEI, 4 h PEI activation time, 4% GA, and 2 h GA activation time as shown in Table 1. The ANOVA data is displayed in Table 2 and confirmed the great significance of the model which possessed F value of 27.914, low P value of < 0.0001. According to the results, the value of R2 was 0.975, and the adjusted R2 was 0.940 which is close to R2 value. The estimation coefficients, standard errors, t test values, and P values are illustrated in Table 3, and the data displayed the significance of linear, interaction, and quadratic terms. As shown in Fig. 4, the Pareto chart interpreted that GA (%) possessed the most significant influence flowed by PEI activation time and PEI (%) that affected IY of α-amylase positively, whereas GA activation time exhibited negatively significant effect. The closing between the actual values and predicted values of IY indicated the good precision and validation of the model (Fig. 5). The normality assumption was satisfied as the residual plot approximated along a straight line as given in Fig. 6. The results reported in Fig. 7a–f of the 3D surface interactions investigated that figures of interaction terms (WY, WZ, and YZ) showed significant effect, while figures of interaction terms (WX, XY and XZ) showed insignificant effect on IY of α-amylase. Finally, optimization of beads modifications enhanced the IY by 1.5-fold compared with un-optimized.
Scanning electron microscope (SEM) characterization
Scanning electron microscopy showed the modification which occurred during gel beads formation, activation, and immobilization. A remarkable change in pore size before and after immobilization is displayed in Fig. 8a–b. Noticeable changes were recognized along the steps of gel beads formation and accumulation of enzyme on the gel beads surface after immobilization.
FTIR spectroscopic analysis of activated beads (Ch-MNP/PEI/GA) (A), immobilized α-amylase on activated beads (Ch-MNP/PEI/GA/Enz) (B), and free α-amylase enzyme (C) were carried out in a range varied from 400–4000 cm−1. As seen in Fig. 9, the typical characteristic absorption bands of modified activated chitosan beads and free α-amylase enzyme in accordance with the literature appeared. Activated beads showed two peaks, one of them at 1730 cm−1 corresponding to the (C=O) group of GA free end, and the second one is at 1650 cm−1 corresponding to the (C=N–) group that was produced from the reaction with GA (curve A). While immobilized enzyme on beads give broader peak at 3457 cm−1, pointed to increasing NH2 group’s concentration (curve B). When comparing with free α-amylase enzyme (curve C), we can notice that the characteristic peaks of enzyme, at 3434 cm−1 and at 1117 cm−1, are found in curve (B).
Application of Ch-MNP/PEI/GA/Enz in baking process
As shown in Fig. 10, treated dough with 1 g beads of Ch-MNP/PEI/GA/Enz enhanced dough-raising about 2.3-fold as compared to the control (without enzyme). Ch-MNP/PEI/GA/Enz could be easily separated from the dough and reused for 5 consecutive cycles with 100% residual activity.
Immobilized enzyme is required for the development of industrial applications in order to minimize the cost of the biocatalyst. As shown in Fig. 2, α-amylase from B. subtilis MK1 was immobilized on Ch-MNP/PEI/GA beads and exhibited 55% IY. Ahmed et al. (2018) reported that α-amylase was immobilized on phosphosilicate glass with lower IY (27.9%). The activated beads reacted with the enzyme molecule as shown in Fig. 3. The reaction happened between the NH2 group found in the enzyme protein molecule and the free C=O group located on GA (the cross-linker) forming C=N– bond as reported by Yuan et al. (2016). Magnetic nano-particles have unique properties as high affinity to proteins and ability to be chemically modified with reactive functional groups (Atacan et al. 2016). Misson et al. (2015) reported that, polymeric nano-carrier can be fabricated easily in nanometer scale with a large surface area in the range of 30–500 nm.
Statistical optimization of carrier modification by CCD of the coded variables and the corresponding experimental results of the IY% are displayed in Table 1. It was found that, trail no.23 gives the highest IY of α-amylase (81.6%) at optimizing conditions of 6% PEI, 4 h PEI activation time, 4% GA, and 2 h GA activation time. Lower result was obtained by Ahmed et al. (2018) after statistical optimization of the immobilization conditions (79.9% IY). Hassan et al. (2019) found that lower concentration of PEI (1%) and GA (0.5) was suitable to activate κ-carrageenan gel beads.
The analysis of variance (ANOVA) was performed to examine the effectiveness of the quadratic model equation for the immobilization as presented in Table 2. The low probability value (Prob > F or P value ≤ 0.05) and F value proved that the model is potent and highly significant. The ANOVA data confirmed the great significance of the model which possessed F value of 27.914 and low P value of < 0.0001, and there is only 0.01 % chance that this value might occur due to noise. Furthermore, the determination coefficient (R2) evaluated the fineness of the model where the R2 value becomes nearer to 1.0. According to the results, the R2 value was 0.975 indicating that 97.5 % of the response variability could be analyzed by the statistical model. A regression model, with R2 (0.975) was considered to have very high correlation (Jaya et al. 2010). Also, the adjusted R2 value was 0.940 and closer to R2 value which demonstrated the suitability of this model. On the other hand, the estimation coefficients, standard errors, t test values, and P values are illustrated in Table 3, and the data displayed the significance of linear, interaction, and quadratic terms. The data indicated that, linear terms (W, X, Y, Z), interaction terms (WY, WZ, YZ), and quadratic terms (X2, Z2) were significant (P ≤ 0.05), while interaction terms (WX, XY, XZ) and quadratic terms (W2, Y2) were insignificant (P > 0.05). As shown in Fig. 4 the Pareto chart interpreted that GA (%) possessed the most significant influence flowed by PEI activation time and PEI (%) that affected IY of α-amylase positively, whereas GA activation time exhibited negatively significant effect. The validation of the model was achieved from the closing between the actual and predicted IY values as presented in Fig. 5. The normality assumption was satisfied as the residual plot approximated along a straight line as given in Fig. 6. Demonstrating the fitness and influence of CCD statistical model to optimize beads modification conditions for α-amylase immobilization agrees with the findings of Singh et al. (2015). The 3D response surface plots supply an explanation of the interaction between two factors while preserving the other variables at zero level as observed in Fig. 7a–f. The 3D surface interactions determined the optimum levels of independent variables that gave maximum immobilization yield of α-amylase and also revealed the significance of these interactions. Finally, optimization of beads modifications enhanced the IY by 1.5-fold compared with un-optimized.
Scanning electron microscopy showed the modification that happened before and after enzyme conjugation (immobilization). As shown in Fig. 8 a and b, a remarkable change in pore size can be recognized as the gel beads exhibited pores before coupling with enzyme molecule. While after immobilization, the enzyme particles were accumulated on the bead surfaces and the pores were decreased in size and tend to disappeared (Ahmed et al. 2018). The changes in morphology of composites in SEM structural analysis indicate their interaction as pointed by Nasir et al. (2017).
FTIR spectroscopic analysis determined the typical characteristic absorption bands of activated beads (Ch-MNP/PEI/GA) (A), immobilized α-amylase on activated beads (Ch-MNP/PEI/GA/Enz) (B), and free α-amylase enzyme (C). As displayed in Fig. 9, activated beads showed two peaks, one of them at 1730 cm−1 corresponding to the (C=O) group of GA free end, and the second one is at 1650 cm−1 corresponding to the (C=N–) group that was produced from the reaction with GA (curve A). Whereas, Ch-MNP/PEI/GA/Enz gives broader peak at 3457 cm−1, pointed to increasing NH2 group’s concentration (curve B). When comparing with free α-amylase enzyme (curve C) we can notice that, the characteristic peaks of enzyme, at 3434 cm−1 and at 1117 cm−1, are found in curve (B). From these data, it was revealed that the processes of amination, activation, and immobilization were successful. These results were in agreement with the results obtained by other published results (Yuan et al. 2016). Díaz-Hernández et al. (2018) reported that the change in intensity as well as the displacement of the NH2, C–O, and Fe bands after cross-linking suggests the formation of covalent bonds between enzymes and the magnetic beads. From characterization steps, FTIR and SEM, it was concluded that the process of activation and immobilization takes place successfully as pointed by Hassan et al. (2019).
By applying immobilized α-amylase (Ch-MNP/PEI/GA/Enz) in baking process it was found that, Ch-MNP/PEI/GA/Enz enhanced dough-raising about 2.3-fold as compared to the control (without enzyme) as shown in Fig. 10. Also, Ch-MNP/PEI/GA/Enz could be reused for 5 consecutive cycles with 100% residual activity. The results pointed to the possibility of safely using of Ch-MNP/PEI/GA/α-amylase enzyme for application in the baking industry. This result might be due to reducing the staling rate of the crumb and increasing the bread volume by α-amylase (Kaltsa et al. 2013).
α-Amylase was successfully immobilized on chitosan-magnetic nano-particles (Ch-MNP/PEI/GA) with high IY%. SEM and FTIR studies confirm the linkage between Ch-MNP/PEI/GA beads and the α-amylase enzyme. Optimized conditions using statistical methods (CCD) enhanced IY% from 55 to 81.6%. The highest IY of α-amylase was obtained at 6% PEI for 4 h activation time and 4% GA for 2 h activation time. The ANOVA data confirmed the great significance of the model which possessed F value of 27.914. Also, the adjusted R2 value was 0.940 and closer to R-square value which demonstrated the suitability of this model which improved IY% by 1.5-fold compared to un-optimized process. The Ch-MNP/PEI/GA/Enz is suitable for application in baking industry and enhanced dough-raising about 2.3-fold as compared to the control.
Availability of data and materials
All data generated or analyzed during this study are included in this manuscript.
Response surface methodology
Analysis of variance
Central composite design
Degree of freedom
Fourier transforms infrared
Scanning electron microscope
- R2 :
Coefficient of determination
Response surface methodology
Degree of deacetylation
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The authors are very thankful to the National Research Centre for providing financial assistance to carry out this research.
This research was a part of PhD thesis that was funded by the National Research Centre.
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Abdella, M.A.A., El-Sherbiny, G.M., El-Shamy, A.R. et al. Statistical optimization of chemical modification of chitosan-magnetic nano-particles beads to promote Bacillus subtilis MK1 α-amylase immobilization and its application. Bull Natl Res Cent 44, 40 (2020). https://doi.org/10.1186/s42269-020-00301-3
- Chitosan-magnetic nano-particles
- Beads modification
- Central Composite design