For optimum production of protease enzyme, initially, five different media combinations were screened to find a suitable nutrient medium for protease enzyme production, which are as follows: nutrient broth (peptone—5 g, beef extract—3 g, and NaCl—4 g per liter) supplemented with skim milk (1%) and gelatin (1%) separately, nutrient broth half strength (peptone—2.5 g, beef extract—1.5 g, and NaCl—2 g per liter) supplemented with skim milk (1%) and gelatin (1%) separately, soya casein digest medium (tryptone—17 g, NaCl—5 g, soya peptone—3 g, dipotassium phosphate—2.5 g, and dextrose—2.5 g per liter) (all chemicals were purchased from HiMedia Laboratories Pvt. Ltd., India).
The media were inoculated with 2% (v/v) seed culture into 50 mL of respective medium and incubated at 28°C, 150 ×g rpm for 24 h. Samples were withdrawn at intervals of 3 h up to 24 h and centrifuged at 10,000 rpm for 5 min. The cell pellet was discarded, and the supernatant was preserved at 4°C for enzyme analysis.
Tryptone, soya peptone, dextrose, NaCl, and dipotassium phosphate were considered as chemical components while pH, temperature, incubation time, and inoculum percentage were considered as physical parameters for protease production from
In preliminary studies, “one-factor-at-a-time” approach was used to evaluate various factors for their suitability to sustain good production of protease by
(a) Plackett-Burman experimental design for screening of important chemical determinants of protease production by
Variable | Unit | Code | Experimental value | ||
---|---|---|---|---|---|
Lower (−1) | Center (0) | Higher (+1) | |||
Tryptone | g/L |
|
3.0 | 5.0 | 7.0 |
Soya peptone | g/L |
|
4.5 | 5.0 | 5.5 |
Dextrose | g/L |
|
1.0 | 1.5 | 2.0 |
NaCl | g/L |
|
5.0 | 6.0 | 7.0 |
Dipotassium phosphate | g/L |
|
0.75 | 1.0 | 1.25 |
Standard order | Experimental values |
Protease production | ||||
---|---|---|---|---|---|---|
|
|
|
|
| ||
1 | 7 | 5.5 | 1 | 7 | 1.25 |
|
2 | 3 | 5.5 | 2 | 5 | 1.25 |
|
3 | 7 | 4.5 | 2 | 7 | 0.75 |
|
4 | 3 | 5.5 | 1 | 7 | 1.25 |
|
5 | 3 | 4.5 | 2 | 5 | 1.25 |
|
6 | 3 | 4.5 | 1 | 7 | 0.75 |
|
7 | 7 | 4.5 | 1 | 5 | 1.25 |
|
8 | 7 | 5.5 | 1 | 5 | 0.75 |
|
9 | 7 | 5.5 | 2 | 5 | 0.75 |
|
10 | 3 | 5.5 | 2 | 7 | 0.75 |
|
11 | 7 | 4.5 | 2 | 7 | 1.25 |
|
12 | 3 | 4.5 | 1 | 5 | 0.75 |
|
All five chemical components,
(a) Experimental ranges and levels of the independent variable used in RSM. (b) Central composite design matrix for the experimental design and respective response for protease activity.
Variable | Experimental ranges and levels | ||||
---|---|---|---|---|---|
−2.378 | −1 | 0 | +1 | +2.378 | |
Tryptone | 0.24 | 3.00 | 5.00 | 7.00 | 9.76 |
Soya peptone | 3.81 | 4.50 | 5.00 | 5.50 | 6.19 |
Dextrose | 0.31 | 1.00 | 1.50 | 2.00 | 2.69 |
NaCl | 3.62 | 5.00 | 6.00 | 7.00 | 8.38 |
Dipotassium phosphate | 0.41 | 0.75 | 1.00 | 1.25 | 1.59 |
Std. order | Experimental coded values of different variables | Protease activity (U/mL) | |||||
---|---|---|---|---|---|---|---|
|
|
|
|
|
Actual | Predicted | |
1 | −1 | −1 | −1 | −1 | −1 |
|
1187.04 |
2 | 1 | −1 | −1 | −1 | −1 |
|
1327.02 |
3 | −1 | 1 | −1 | −1 | −1 |
|
1329.21 |
4 | 1 | 1 | −1 | −1 | −1 |
|
1221.26 |
5 | −1 | −1 | 1 | −1 | −1 |
|
1128.77 |
6 | 1 | −1 | 1 | −1 | −1 |
|
1390.44 |
7 | −1 | 1 | 1 | −1 | −1 |
|
1167.55 |
8 | 1 | 1 | 1 | −1 | −1 |
|
1181.28 |
9 | −1 | −1 | −1 | 1 | −1 |
|
1321.81 |
10 | 1 | −1 | −1 | 1 | −1 |
|
1432.92 |
11 | −1 | 1 | −1 | 1 | −1 |
|
1488.54 |
12 | 1 | 1 | −1 | 1 | −1 |
|
1351.72 |
13 | −1 | −1 | 1 | 1 | −1 |
|
1152.72 |
14 | 1 | −1 | 1 | 1 | −1 |
|
1385.53 |
15 | −1 | 1 | 1 | 1 | −1 |
|
1216.07 |
16 | 1 | 1 | 1 | 1 | −1 |
|
1200.94 |
17 | −1 | −1 | −1 | −1 | 1 |
|
1173.58 |
18 | 1 | −1 | −1 | −1 | 1 |
|
1272.75 |
19 | −1 | 1 | −1 | −1 | 1 |
|
1342.36 |
20 | 1 | 1 | −1 | −1 | 1 |
|
1193.59 |
21 | −1 | −1 | 1 | −1 | 1 |
|
1260.05 |
22 | 1 | −1 | 1 | −1 | 1 |
|
1480.9 |
23 | −1 | 1 | 1 | −1 | 1 |
|
1325.44 |
24 | 1 | 1 | 1 | −1 | 1 |
|
1298.36 |
25 | −1 | −1 | −1 | 1 | 1 |
|
1178.03 |
26 | 1 | −1 | −1 | 1 | 1 |
|
1248.34 |
27 | −1 | 1 | −1 | 1 | 1 |
|
1371.38 |
28 | 1 | 1 | −1 | 1 | 1 |
|
1193.75 |
29 | −1 | −1 | 1 | 1 | 1 |
|
1153.68 |
30 | 1 | −1 | 1 | 1 | 1 |
|
1345.68 |
31 | −1 | 1 | 1 | 1 | 1 |
|
1243.64 |
32 | 1 | 1 | 1 | 1 | 1 |
|
1187.7 |
33 | −2.378 | 0 | 0 | 0 | 0 |
|
1289.12 |
34 | 2.378 | 0 | 0 | 0 | 0 |
|
1389.05 |
35 | 0 | −2.378 | 0 | 0 | 0 |
|
1267.98 |
36 | 0 | 2.378 | 0 | 0 | 0 |
|
1249.18 |
37 | 0 | 0 | −2.378 | 0 | 0 |
|
1335.33 |
38 | 0 | 0 | 2.378 | 0 | 0 |
|
1258.84 |
39 | 0 | 0 | 0 | −2.378 | 0 |
|
1138.25 |
40 | 0 | 0 | 0 | 2.378 | 0 |
|
1166.92 |
41 | 0 | 0 | 0 | 0 | −2.378 |
|
1141.46 |
42 | 0 | 0 | 0 | 0 | 2.378 |
|
1109.71 |
43 | 0 | 0 | 0 | 0 | 0 |
|
1569.75 |
44 | 0 | 0 | 0 | 0 | 0 |
|
1569.75 |
45 | 0 | 0 | 0 | 0 | 0 |
|
1569.75 |
46 | 0 | 0 | 0 | 0 | 0 |
|
1569.75 |
47 | 0 | 0 | 0 | 0 | 0 |
|
1569.75 |
48 | 0 | 0 | 0 | 0 | 0 |
|
1569.75 |
49 | 0 | 0 | 0 | 0 | 0 |
|
1569.75 |
50 | 0 | 0 | 0 | 0 | 0 |
|
1569.75 |
The data obtained from RSM were subjected to analysis of variance (ANOVA) for analysis of regression coefficient, prediction equations, and case statistics. The experimental results of RSM were fitted using the following second order polynomial equation:
The culture broth centrifuged at 10,000 rpm for 5 min was used as enzyme source. Protease activity was measured by incubating 150
Extracellular protease enzyme was extracted from cell suspension grown under optimized soya casein digest medium after 18 h of incubation by centrifugation at 10,000 rpm. Purification and characterization of protease enzyme were carried out as described previously [
Fibrin degradation analysis was performed by modified method of Datta et al. (1995) and Mahajan et al. (2012) [
Among different growth media, the highest proteolytic activity (955.68 U/mL) was observed in soya casein digest broth after 18 h, and 832 U/mL activity was observed with skim milk supplemented nutrient broth. Therefore, soya casein digest broth was selected for further optimization study using statistical approaches. Physical parameters were optimized first (optimized physical factors: pH—7.7, temperature—28.0°C, incubation time—18 h, and inoculum percentage—3%) followed by optimization of different chemical components of soya casein digest medium using COVT method, Plackett-Burman design, and response surface methodology (RSM).
Standard chemical composition of soya casein digest broth in g/L was tryptone—17.0, soya peptone—3.0, sodium chloride—5.0, dextrose—2.5, and dipotassium phosphate—2.5. The effect of the five factors, namely, tryptone, soya peptone, sodium chloride, dextrose, and dipotassium phosphate, was studied on protease production using COVT method. Protease production was observed over a broad tryptone range (2.5 to 22.5 g/L). The optimum tryptone concentration for protease production was determined to be 5.0 g/L. Protease production increased consistently with the increase in tryptone concentration from 2.5 to 5.0 g/L and decreased with further increase in tryptone concentration (Figure
Optimization of chemical components under COVT approach for maximum production of protease by
The effect of salinity on protease production in
A total of five variables were analyzed for their effects on protease production using Plackett-Burman design. The PB design matrix selected for the screening of chemical components for protease production and their respective response are shown in Table
Estimated effect, contribution, and ANOVA for Plackett-Burman design experiment for the five chemical components.
Source | % Contribution | Studentized effect | Ranking | Sum of squares | Mean squares | df |
|
|
Coefficient estimate | |
---|---|---|---|---|---|---|---|---|---|---|
Model |
|
|
5 | 19.00 | 0.0013 | Significant | ||||
|
0.87 | 38.14 | 4 | 4720.33 | 4720.33 | 1 | 0.89 | 0.3817 | 19.83 | |
|
11.96 | 141.81 | 3 | 60208.33 | 60208.33 | 1 | 11.36 | 0.0150 | 70.83 | |
|
24.89 | 204.53 | 2 |
|
|
1 | 23.57 | 0.0028 | 102.00 | |
|
58.71 | 314.14 | 1 |
|
|
1 | 55.71 | 0.0003 | 156.83 | |
|
3.57 | −77.47 | 5 | 18408.33 | 18408.33 | 1 | 3.47 | 0.1116 | −39.17 | |
Residual | 31787.67 | 5297.94 | 6 | |||||||
|
||||||||||
Corrected total |
|
11 |
RSM was used to evaluate the relationship between different independent variables and their interactive effects on protease production by
Following quadratic model of response equation in terms of coded variable and actual variables was obtained as follows:
The ANOVA showed suitability of the model for protease production. The quadratic type model was produced, and all the effects were considered significant (
Regression analysis for the production of alkaline metalloprotease from
Source | Sum of squares | df | Mean squares |
|
|
|
---|---|---|---|---|---|---|
Model |
|
17 | 58540.22 | 72.44 | <0.0001 | Significant |
|
16990.30 | 1 | 16990.30 | 21.03 | <0.0001 | |
|
179.38 | 1 | 179.38 | 0.22 | 0.6407 | |
|
14027.31 | 1 | 14027.31 | 17.36 | 0.0002 | |
|
2732.31 | 1 | 2732.31 | 3.38 | 0.0752 | |
|
3196.94 | 1 | 3196.94 | 3.96 | 0.0553 | |
|
|
1 |
|
139.68 | <0.0001 | |
|
24780.95 | 1 | 24780.95 | 30.67 | <0.0001 | |
|
5240.32 | 1 | 5240.32 | 6.48 | 0.0159 | |
|
17302.65 | 1 | 17302.65 | 21.41 | <0.0001 | |
|
20175.38 | 1 | 20175.38 | 24.97 | <0.0001 | |
|
36106.56 | 1 | 36106.56 | 44.68 | <0.0001 | |
|
28770.01 | 1 | 28770.01 | 35.60 | <0.0001 | |
|
98473.27 | 1 | 98473.27 | 121.86 | <0.0001 | |
|
|
1 |
|
209.37 | <0.0001 | |
|
|
1 |
|
160.90 | <0.0001 | |
|
|
1 |
|
375.71 | <0.0001 | |
|
|
1 |
|
425.80 | <0.0001 | |
Residual | 25858.54 | 32 | 808.08 | |||
Lack of fit | 19313.66 | 25 | 772.55 | 0.83 | 0.6663 | Not significant |
Pure error | 6544.87 | 7 | 934.98 | |||
|
||||||
Corrected total |
|
49 |
To analyze the effects of the independent variables and interactive effects of each independent variable for maximum protease production, 3D response surface curves and the 2D contour plots were drawn against two experimental variables while the other variables were maintained constant at its central level (Figure
Response surface plot and respective contour plot. (a) The combined effect of tryptone and dextrose, (b) the combined effect of tryptone and dipotassium phosphate, and (c) the combined effect of dextrose and dipotassium phosphate on protease production of
Figure
Figures
Optimization of enzyme production was carried out numerically by using Design Expert software, version 8.0.7.1, to evaluate the optimum values for each variable from the model. In optimization process, the goal for each variable was to select a range which could provide the highest protease activity. On the basis of experimental design and developed model, the experiment was carried out under optimal conditions for maximum protease production by
Residual diagnostics of contour surface of the quadratic model by predicted versus actual protease production of
For validation of overall model and optimized condition, experiments were carried out under predicted optimal conditions. The experimental protease activity 1589.42 U/mL under optimized condition was in close agreement with the predicated protease activity. Therefore, the model developed was reliable for predicting the optimal conditions for variables influencing the protease production by
Fibrin plate method for fibrinolytic activity was tested and found positive for purified protease enzyme. After incubation at 37°C, plates of fibrin inoculated with protease showed relatively bigger zone (15 mm) of hydrolysis than that of plasmin (Figure
Fibrinolytic activity of (a) purified and (b) crude protease in fibrin degradation assay.
Further purification and characterization of protease were carried out and approximately 8-fold purification was achieved. Characterization of protease, based on different pH and temperature, showed optimum protease activity at 34°C at pH 8.2. Different protease inhibitor study and MALDI-TOF/TOF analysis revealed its resemblance with serralysin type alkaline metalloproteases from
Fibrinolytic activity confirms the serralysin nature of purified alkaline metalloproteases of
To the best of our knowledge, this is the first report on optimization of medium components for protease production by
The authors declare that there is no conflict of interests regarding the publication of this paper.
The authors acknowledge the World Bank funded National Agriculture Innovation Projects (NAIP-ICAR) Component 4 (IARI-70:13) for sponsoring this research project. The authors would like to thank Gargi Goswami, Research Scholar, Department of Biotechnology, National Institute of Technology, Duragapur, for her precious suggestions throughout the research work.