The optimization of physiochemical parameters for alkaline protease production using
The protease is ubiquitous in nature. It is found in all living organisms and required for cell growth and differentiation. Alkaline proteases are one of the most important groups of industrial enzymes. They are extensively used in leather, food, pharmaceutical, textile, organic chemical synthesis, wastewater treatment, and other industries [
Only large-scale production of alkaline protease can fulfill the demand and usefulness of the proteases in the industry. In industry, microbial protease production was carried out by fermentative process. It is necessary to improve the yield of protease without increasing the process cost through fermentative process. Rapid enzyme production can be achieved by manipulation of media composition and culture conditions. Thus, optimization of fermentation conditions is the most important step in the development of a cost-effective fermentation process [
In this study, a systematic and sequential optimization strategy was applied to enhance the production of alkaline protease from
Bradford Reagent (Sigma, USA), bovine serum albumin (BSA) (Himedia, India), Dialysis sacks (Sigma, USA), Methanol for HPLC (E-Merck, Germany), Acetonitrile for HPLC (E-Merck, Germany), Triton X-100 (E-Merck, Germany), 2-propanol (Merck, India), benzene (Merck, India), toluene (Merck, India), hexane (Merck, India), Tween-80 (Merck, India), PEG 4000 (Merck, India), PEG 600 (Merck, India), Trichloroacetic acid (Merck, India), Mannitol (Himedia, India), Glycerol (Himedia, India), SDS (Himedia, India), DMSO (Himedia, India), and Casein (Himedia, India) were used in this study. Water used for the HPLC analysis was prepared by Ultrapure Water System (Arium, 611UF, Sartorius, Germany). All other chemicals used were of analytical grade and commercially available in India. The statistical software package “Design Expert” 7.0.0 (Stat-Ease Inc., Minneapolis, USA) was used to analyze the experimental design and the regression analysis of the experimental data.
The protease producing
A 2% fresh culture (OD550
Protease activity was determined by a modified method of Folin and Ciocalteu [
Inoculum percentage, temperature, pH, agitation, and incubation time [
The screening of the physical parameters was done by Plackett-Burman design with respect to their main effects and not to their interaction effects [
Plackett-Burman experimental design for screening of important physical parameters of alkaline protease production by
Factor | Name | Low level (−1) | High level (+1) |
---|---|---|---|
Inoculum percentage (%) | 2 | 3 | |
Temperature (°C) | 30 | 40 | |
pH | 7 | 8 | |
Agitation (RPM) | 120 | 180 | |
Incubation time (h) | 72 | 96 |
RSM is employed for multiple regression analysis. It solves polynomial equations using quantitative data obtained from properly designed experiments [
Experimental range of the five numerical variables studied using rotatable CCD in terms of actual and coded factors.
Factor | Name | Range of variables | ||||
− | Low (−1) | Mid (0) | High (+1) | + | ||
Inoculum Percentage (%) | 1.3 | 2 | 2.5 | 3 | 3.7 | |
Temperature (°C) | 23 | 30 | 35 | 40 | 47 | |
pH | 6.3 | 7 | 7.5 | 8 | 8.7 | |
Agitation (RPM) | 79 | 120 | 150 | 180 | 221 | |
Incubation time (h) | 55.5 | 72 | 84 | 96 | 112.5 |
Analysis of variance (ANOVA) was used for analysis of regression coefficient, prediction equations, and case statistics. The experimental results of RSM were fitted via the response surface regression procedure, using the following second order polynomial equation:
In this polynomial equation,
However, in this study, the independent variables were coded as
The second-order polynomial coefficients and Response surface plots were obtained using the Design Expert software.
The statistical model was validated with respect to all the three variables within the design space. A random set of 6 experimental combinations was used to study protease production in 250 mL shake flasks.
The fermented broth was centrifuged at 10000 rpm for 30 min at 4°C. At first, solid ammonium sulphate was added to the supernatant for 30% saturation and centrifuged at 4°C for 30 min. Again, solid ammonium sulphate was added to the supernatant for 60% saturation and centrifuged at 4°C for 30 min. The precipitate was resuspended in 50 mM phosphate buffer (pH 9). The precipitated sample was desalted by dialysis through semipermeable membrane (molecular weight cutoff 12 kD, Sigma) against same buffer for overnight.
Stability of the enzyme in organic solvents was studied by incubating the enzyme solution (3 mL) with various organic solvents (1 mL), namely, DMSO, methanol, ethanol, ACN, 2-propanol, benzene, toluene, and hexane at 37°C with shaking at 150 rpm for 30 min. The remaining activity of the enzyme was measured. The activity of the enzyme solution without organic solvent was considered as control (100%).
The enzyme was incubated with some nonionic detergents (5% v/v Tween-20, Tween-80, and Triton X-100), surfactants (0.5% w/v SDS), bleaches (5% v/v H2O2, 0.5% w/v sodium perborate), and anti-redeposition agents (10% w/v Na2CMC, 100 mM Na2CO3) for 60 min at room temperature and remaining activity was measured. The activity of the enzyme solution without modifier was considered as control (100%).
Thermal stability was determined by incubating the partially purified enzyme at 60°C (pH 9), in the presence of 2.5 and 5 mM CaCl2 and in the presence of various polyols (5, 10% w/v). Polyols used in this study were PEG-4000, PEG-600, mannitol, and glycerol. Aliquots were withdrawn at 15 min intervals and the residual activity was determined. The activity of the enzyme solution kept at 4°C was considered as control (100%).
The surfactant stability was determined by incubating the partial purified enzyme in the presence of surfactants (0.5% w/v SDS) and in the presence or absence of various polyols (10% w/v) for 60 min at room temperature. Polyols used in this study were PEG-4000, PEG-600, mannitol, and glycerol. Remaining activity of the enzyme was measured. The activity of the enzyme solution without modifier was considered as control (100%).
For detergent stability study, the purified alkaline protease (0.05 mg/mL) was incubated at 40°C using Surf excel (7 mg/mL) in the presence of various polyols (10% w/v). At every 15 min interval, the residual protease activity was determined up to 1 hour. The purified alkaline protease without detergent was considered as control (100%).
Clean cotton cloth pieces were stained with 25
Partially purified enzyme was qualitatively analyzed using HPLC. For sample preparation, the sample was centrifuged at 10000 rpm for 10 min at 4°C and the supernatant filtered through 0.45
Plackett-Burman design was adopted to select most significant physical components. The studentized effect corresponding sum of square, standard error, percentage of contribution,
Estimated effect and analysis of variables for protease activity from Plackett-Burman design experiment.
Factor | Name | Studentized effect | Sum square | Percentage of contribution | Standard error | ||
---|---|---|---|---|---|---|---|
Inoculum percentage (%) | 15.92 | 760.29 | 2.72 | 0.34 | 47.44 | <0.0001 | |
Temperature (°C) | 29.35 | 2585.08 | 9.26 | 0.34 | 87.48 | <0.0001 | |
pH | 27.49 | 2267.32 | 8.12 | 0.34 | 81.93 | <0.0001 | |
Agitation (RPM) | 82.11 | 20228.37 | 72.46 | 0.34 | 244.71 | <0.0001 | |
Incubation time (h) | 26.25 | 2067.06 | 7.40 | 0.34 | 78.23 | <0.0001 |
Plackett-Burman design for five variables with actual values along with the observed protease activity.
Sl. number | Protease activity (U) | |||||
---|---|---|---|---|---|---|
1 | 3 | 40 | 7 | 180 | 96 | 257.23 |
2 | 2 | 40 | 8 | 120 | 96 | 166.03 |
3 | 3 | 30 | 8 | 180 | 72 | 176.52 |
4 | 2 | 40 | 7 | 180 | 96 | 275.47 |
5 | 2 | 30 | 8 | 120 | 96 | 134.25 |
6 | 2 | 30 | 7 | 180 | 72 | 218.98 |
7 | 3 | 30 | 7 | 120 | 96 | 147.30 |
8 | 3 | 40 | 7 | 120 | 72 | 150.14 |
9 | 3 | 40 | 8 | 120 | 72 | 122.58 |
10 | 2 | 40 | 8 | 180 | 72 | 219.67 |
11 | 3 | 30 | 8 | 180 | 96 | 201.53 |
12 | 2 | 30 | 7 | 120 | 72 | 136.41 |
As Plackett-Burman design is inappropriate to study the mutual interaction of process variables, therefore the level of significant factors needed further optimization. In this investigation, RSM was applied for the optimization of significant factors in protease production to study the importance of screening factors at different levels. The RCCD design plan of RSM was used in the present study and the physicochemical components were optimized for maximum protease production.
The full experimental plan of CCD design for studying the effects of five independent variables, namely, inoculum percentage (
Rotatable CCD matrix for five variables with actual protease activity.
Sl. number | Protease activity (U) | |||||
---|---|---|---|---|---|---|
1 | 2 | 30 | 7 | 120 | 72 | 132.49 |
2 | 3 | 30 | 7 | 120 | 72 | 122.78 |
3 | 2 | 40 | 7 | 120 | 72 | 157.69 |
4 | 3 | 40 | 7 | 120 | 72 | 152.79 |
5 | 2 | 30 | 8 | 120 | 72 | 115.42 |
6 | 3 | 30 | 8 | 120 | 72 | 102.48 |
7 | 2 | 40 | 8 | 120 | 72 | 136.70 |
8 | 3 | 40 | 8 | 120 | 72 | 131.90 |
9 | 2 | 30 | 7 | 180 | 72 | 233.79 |
10 | 3 | 30 | 7 | 180 | 72 | 215.84 |
11 | 2 | 40 | 7 | 180 | 72 | 261.54 |
12 | 3 | 40 | 7 | 180 | 72 | 249.97 |
13 | 2 | 30 | 8 | 180 | 72 | 213.10 |
14 | 3 | 30 | 8 | 180 | 72 | 193.58 |
15 | 2 | 40 | 8 | 180 | 72 | 239.87 |
16 | 3 | 40 | 8 | 180 | 72 | 223.89 |
17 | 2 | 30 | 7 | 120 | 96 | 170.54 |
18 | 3 | 30 | 7 | 120 | 96 | 152.69 |
19 | 2 | 40 | 7 | 120 | 96 | 200.84 |
20 | 3 | 40 | 7 | 120 | 96 | 190.84 |
21 | 2 | 30 | 8 | 120 | 96 | 134.84 |
22 | 3 | 30 | 8 | 120 | 96 | 116.60 |
23 | 2 | 40 | 8 | 120 | 96 | 164.75 |
24 | 3 | 40 | 8 | 120 | 96 | 151.32 |
25 | 2 | 30 | 7 | 180 | 96 | 242.52 |
26 | 3 | 30 | 7 | 180 | 96 | 215.75 |
27 | 2 | 40 | 7 | 180 | 96 | 276.16 |
28 | 3 | 40 | 7 | 180 | 96 | 257.23 |
29 | 2 | 30 | 8 | 180 | 96 | 203.78 |
30 | 3 | 30 | 8 | 180 | 96 | 177.30 |
31 | 2 | 40 | 8 | 180 | 96 | 236.54 |
32 | 3 | 40 | 8 | 180 | 96 | 216.43 |
33 | 1.3 | 35 | 7.5 | 150 | 84 | 286.35 |
34 | 3.7 | 35 | 7.5 | 150 | 84 | 250.07 |
35 | 2.5 | 23 | 7.5 | 150 | 84 | 101.30 |
36 | 2.5 | 47 | 7.5 | 150 | 84 | 174.75 |
37 | 2.5 | 35 | 6.3 | 150 | 84 | 160.24 |
38 | 2.5 | 35 | 8.7 | 150 | 84 | 88.85 |
39 | 2.5 | 35 | 7.5 | 79 | 84 | 103.75 |
40 | 2.5 | 35 | 7.5 | 221 | 84 | 300.87 |
41 | 2.5 | 35 | 7.5 | 150 | 55.5 | 131.02 |
42 | 2.5 | 35 | 7.5 | 150 | 112.5 | 163.67 |
43 | 2.5 | 35 | 7.5 | 150 | 84 | 273.31 |
44 | 2.5 | 35 | 7.5 | 150 | 84 | 274.10 |
45 | 2.5 | 35 | 7.5 | 150 | 84 | 272.82 |
46 | 2.5 | 35 | 7.5 | 150 | 84 | 132.49 |
47 | 2.5 | 35 | 7.5 | 150 | 84 | 122.78 |
48 | 2.5 | 35 | 7.5 | 150 | 84 | 157.69 |
49 | 2.5 | 35 | 7.5 | 150 | 84 | 152.79 |
50 | 2.5 | 35 | 7.5 | 150 | 84 | 115.42 |
Regression analysis for the production of alkaline protease by
Source | Sum of squares | Degree of freedom | Mean square | Coefficient | Standard error | |||
---|---|---|---|---|---|---|---|---|
Model* | 181834.9 | 20 | 9091.75 | — | — | 5602.23 | <0.0001 | Significant |
Intercept | — | — | — | 271.59 | 0.45 | — | <0.0001 | |
2598.525 | 1 | 2598.53 | −7.75 | 0.19 | 1601.18 | <0.0001 | ||
10664.44 | 1 | 10664.44 | 15.69 | 0.19 | 6571.31 | <0.0001 | ||
9597.122 | 1 | 9597.12 | −14.89 | 0.19 | 5913.64 | <0.0001 | ||
74093.66 | 1 | 74093.66 | 41.36 | 0.19 | 45655.68 | <0.0001 | ||
2104.942 | 1 | 2104.94 | 6.97 | 0.19 | 1297.04 | <0.0001 | ||
77.25183 | 1 | 77.25 | 1.55 | 0.23 | 47.60 | <0.0001 | ||
5.974906 | 1 | 5.97 | −0.43 | 0.23 | 3.68 | 0.0649 | ||
133.704 | 1 | 133.70 | −2.04 | 0.23 | 82.39 | <0.0001 | ||
92.57182 | 1 | 92.57 | −1.70 | 0.23 | 57.04 | <0.0001 | ||
8.381578 | 1 | 8.38 | −0.51 | 0.23 | 5.16 | 0.0306 | ||
22.72784 | 1 | 22.73 | 0.84 | 0.23 | 14.00 | 0.0008 | ||
95.25979 | 1 | 95.26 | 1.73 | 0.23 | 58.70 | <0.0001 | ||
14.67836 | 1 | 14.68 | −0.68 | 0.23 | 9.04 | 0.0054 | ||
569.8502 | 1 | 569.85 | −4.22 | 0.23 | 351.14 | <0.0001 | ||
1741.185 | 1 | 1741.19 | −7.38 | 0.23 | 1072.90 | <0.0001 | ||
19.17653 | 1 | 19.18 | −0.59 | 0.17 | 11.82 | 0.0018 | ||
30951.91 | 1 | 30951.91 | −23.60 | 0.17 | 19072.22 | <0.0001 | ||
37519.93 | 1 | 37519.93 | −25.98 | 0.17 | 23119.36 | <0.0001 | ||
8321.356 | 1 | 8321.36 | −12.24 | 0.17 | 5127.53 | <0.0001 | ||
26782.87 | 1 | 26782.87 | −21.95 | 0.17 | 16503.30 | <0.0001 | ||
Residual | 47.0635 | 29 | 1.62 | |||||
Lack of Fit | 8.692591 | 22 | 0.40 | 0.07 | 1 | Not significant | ||
Pure Error | 38.37091 | 7 | 5.48 | |||||
Cor Total | 181882 | 49 |
*SD: 1.27; Mean: 198.51; R-Squared: 0.9997; Adj R-Squared: 0.9996; C.V.%: 0.64; PRESS: 81.03.
The normal probability plot given in Figure
(a) Normal plot of residual; (b) residual versus predicted plot; (c) Predicted versus Actual Response plot for protease production (U) by
Perturbation plot for the production of alkaline protease by
The special features of the RSM tool are contour plot generation and point prediction from, where we can determine the optimum value of the combination of the five physical parameters for the maximum production of alkaline protease. The contour plot (Figures
Contour plot for protease production (U) by
In our previous experiment 184.27 U of protease was found after media optimization (data not shown). A significant improvement (1.71-fold) in the alkaline protease production by
The model was validated for all five variables within the design space. A random set of six production combinations was prepared and tested for protease production (given in Table
Validation of quadratic model within the design space.
Number | Inoculum percentage % | Temperature °C | pH | Agitation RPM | Incubation time h | Actual response (U) | Predict response (U) |
---|---|---|---|---|---|---|---|
1 | 2 | 35 | 7 | 120 | 72 | 165.24 | 168.68 |
2 | 2.5 | 37 | 7.5 | 150 | 72 | 242.03 | 244.48 |
3 | 3 | 40 | 8 | 160 | 96 | 199.17 | 205.25 |
4 | 2.5 | 30 | 7.8 | 180 | 84 | 139.65 | 242.2 |
5 | 3 | 37 | 7.4 | 150 | 74 | 248.21 | 247.53 |
6 | 2.0 | 36.6 | 7.35 | 180 | 85 | 315.28 | 314.65 |
Partial purification of crude enzyme was done by salting out technique using ammonium sulphate as the salt. The specific activity of the partially purified enzyme was found to be 158.52 (U/mg). The purification fold and yield of protease from the crude enzyme solution were 17.34 fold and 82.43%, respectively (data not shown).
The organic solvents are also used as the media for enzymatic reactions. But the enzymes are easily deactivated in organic solvent. Usually, presence of organic solvent reduces the structural flexibility of enzyme which is required for catalysis. There are published reports of protease inactivation in the presence of water immiscible organic solvent (benzene, toluene, xylene, and hexane) [
The effects of organic solvents on protease activity differ among proteases. Organic solvents with a different log
Effect of (a) organic solvent and (b) detergent, surfactant, bleach, and anti-redeposition on protease activity.
This study showed that the enzyme was more than 94% stable at 5% v/v of non ionic detergent (Tween-80, Triton X-100) and 60% residual activity was obtained after incubation with 0.5% w/v SDS. Such effect variation of detergent can be explained due to their different hydrophilic/lipophilic balance (HLB) number. HLB of detergent is defined as the way a detergent distributes between polar and nonpolar phases [
Thermostability profile of purified enzyme was determined by incubating the enzyme at various temperatures that is, 40, 50, 60, 65, 70, 75, and 80°C for 1 hr. The residual activity of the enzyme was determined at standard assay condition. Results showed that enzyme was stable and retained almost its full activity after 60 min incubation at 40 and 50°C. The enzyme was found to be 57.22, 23.7, 13.45, and 1.07% stable at 60, 70, 75 and 80°C respectively after 60 min incubation (data not shown). So further study was done to evaluate the effect of different additives on thermal stability. The data presented in Figure
Stabilizing effect of additives on protease stability in presence of (a) high temperature, (b) surfactant, and (c) local detergent.
The influence of some polyols on the stability of enzyme against SDS was also examined by incubating with SDS (0.5% w/v) at 37°C in the presence or absence of polyols (10%, w/v) for 60 minutes, and residual enzyme activity was measured. The data presented in Figure
The detergents stability study was done in the presence of locally available detergents (Tide, Surf Excel, Ariel, and Rin), and results showed that the maximum protease stability was observed with Rin (91%) after incubation with this detergent at 40°C for 1 hr, followed by Tide (89.65%), Ariel (81%), and Surf Excel (31.89%) (data not shown). So, further study was done to evaluate the effect of polyols on detergent stability. In present study, the purified alkaline protease (0.05 mg/mL) was incubated at 40°C using Surf Excel (7 mg/mL) in the presence of various polyols (10%). At every 15 min interval, the residual protease activity was determined up to 1 hour.
The data presented in Figure
Result showed that the better wash performance was found in combinations of commercial detergents (7 mg/ml) with purified enzyme preparations (2000 U). Result showed that significant removal of the blood stains was found in the presence of the detergent and the protease enzyme (Figure
Washing performance analysis of enzyme in the presence of the commercial detergent RIN. (a) Cloth stained with blood, (b) cloth stained with blood and washed with tap water; (b) blood-stained cloth and washed with RIN; (c) blood-stained cloth and washed with RIN added with crude enzyme.
From the HPLC chromatogram of dialysed 60% ammonium sulphate fraction, it was evident that the retention time of our target protein is 1.84 min. The chromatogram represented that this protein is 73.5% pure.
Due to efficiency and economic concern, the present study was focused on optimization of a variety of culture conditions, for maximal alkaline protease production through microbial fermentation. This is important for obtaining higher alkaline protease production as well as reduction of operating cost in processing. The mathematical analysis has been carried out by standard software, and the procedure followed is user friendly. Taking the characteristics of protease together our protease might be an interesting candidate for the detergent industry in combination with polyols. Use of proteases in detergent can also reduce the volume of detergents which indirectly reduce pollution load in the world.
This project work was financially supported by the Department of Biotechnology, National Institute of Technology, Durgapur. The authors have expressed their sincere thanks and gratitude to the Director of National Institute of Technology Durgapur.