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Journal of Molecular Catalysis B: Enzymatic 89 (2013) 15– 23
Contents lists available at SciVerse ScienceDirect
Journal of Molecular Catalysis B: Enzymatic
jo u rn al hom epa ge: www.elsev ier .com/ locate /molcatb
creening and isolation of an organic solvent tolerant-protease from Bacillus sp.ER02: Activity optimization by response surface methodology
rastoo Badoei-Dalfard ∗, Zahra Karamiepartment of Biology, Faculty of Sciences, Shahid Bahonar University of Kerman, Kerman, Iran
r t i c l e i n f o
rticle history:eceived 31 August 2012eceived in revised form2 November 2012ccepted 26 November 2012vailable online 7 December 2012
eywords:creening
a b s t r a c t
Organic solvent-tolerant enzymes are becoming increasingly popular in synthetic biotechnology, due totheir industrially attractive advantages over the aqueous solvents. Screening of organic solvent-tolerantmicroorganisms is a promising strategy for identification of novel enzymes that are naturally toleranttoward organic solvents. Crude proteases which have been shown to be active at high temperatures,alkaline pH and broad range of salt concentrations, are favorite proteases for industrial applications. Inthis study, Bacillus sp. JER02 was isolated from Gehver hot spring in Jiroft, Iran, and grown in mediumsupplemented with cyclohexane (30%), toluene (10%). Protease activity in the presence of different con-centrations of organic solvents showed that, this enzyme was activated about 10% and 30% in the presence
rganic solventroteaseesponse surface methodology
of 5% isopropanol and 10% DMF, respectively. In addition, this enzyme retained more than 90% of its ini-tial activity after 1 h pre-incubation with 40% organic solvent at room temperature. Moreover, responsesurface methodology was applied to optimize the assay constituents of Bacillus sp. JER02 protease. Thebest condition for protease activity was achieved in buffer containing 8 mM NaCl, 4 mM MgSO4, at 54 ◦C,and pH8. The statistical optimization by RSM resulted in 2.5 fold increase in protease activity by Bacillussp. JER02.
© 2012 Elsevier B.V. All rights reserved.
. Introduction
Organic solvent-stable proteases have been used as biocatalystsn non-aqueous media for synthetic biotechnology [1,2]. Enzy-
atic reactions in organic solvents provide numerous industriallyttractive advantages, such as increased solubility of non-polar sub-trates, reversal of the thermodynamic equilibrium of hydrolysiseactions, suppression of water-dependent side reactions, alterna-ion of substrate specificity and enantioselectivity, and eliminationf microbial contamination [3]. These applications require enzymeshat are active and stable in the presence of organic solvents. Sev-ral methods such as medium and/or protein engineering haveeen employed to improve enzymes stability in organic media4–6]. Moreover, natural organic solvent-tolerant enzymes haveeen isolated from different types of bacteria such as thermophiles,esophiles, halophiles, and organic solvent-tolerant bacteria [1,3].
n order to circumvent the toxic effect of organic solvents, organic
olvent-tolerant bacteria have made some adaptations such asapid membrane repair mechanisms, toluene efflux pumps, cis-rans isomerization of fatty acids, etc. [1]. Most of the bacteria that∗ Tel.: +98 341 3222032; fax: +98 341 3222032.E-mail addresses: [email protected], [email protected] (A. Badoei-Dalfard),
[email protected], [email protected] (Z. Karami).
381-1177/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.molcatb.2012.11.016
produce organic solvent-tolerant enzymes belong to genera Pseu-domonas [7–14], Enterobacter [15], Bacillus [7,16–21], Rhodococcus[22] and Geomicrobium [23]. Presently, a large proportion of com-mercially available alkaline proteases are derived from Bacillusstrain because of its ability to secrete large amount of alkaline pro-tease, having significant proteolytic activity and stability at high pHand temperatures [24–32]. Use of crude enzymes is preferred overpurified enzymes in industrial applications. This is to avoid the costof purification and make the processes commercially viable [23].Therefore, the crude proteases have been widely used in differ-ent biotechnological applications such as antifouling coatings [33],fish sauce preparation [34], and detergent ingredients [35]. Alka-line proteases hold a major share of the enzyme market with 2/3shares in detergent industry alone [36]. For efficient applicationin industries, alkaline proteases require to be active and stable athigh temperature, pH and in the presence of organic solvents, sur-factants and oxidizing agents [12,37,38]. In this study, we isolatedan organic solvent tolerant bacterium, from a hot spring, locatedin Jiroft, Iran, which produce organic stable protease. The tempera-ture of this hot spring is 62 ◦C and its pH is 5. The thermophilic andacidic condition of this hot spring may have provided a suitable situ-
ation for identification of organic solvent-tolerant bacteria. Resultsof 16S rDNA sequencing and phylogenetic tree show that this bac-terium belongs to genus Bacillus and is closely related to Bacilluscereus. Protease activity and stability, in presence of organic solvent,
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as measured. In addition, in order to find the best protease assayondition, protease activity was optimized using response surfaceethodology (RSM).
. Material and methods
.1. Materials
Casein was purchased from Sigma (St. Louis, MO, USA). Tryp-one, yeast extract and organic solvents were a product of MerckWhitehouse Station, New Jersey, United States). All chemicals usedere of analytical grade.
.2. Screening of organic solvent-tolerant bacteria strain androtease producers
Water samples were gathered from Gehver hot spring locatedn Jiroft, Iran. For initial screening, the organic solvent-toleranttrains were screened by toluene and cyclohexane enrichment inuria–Bertani (LB) medium containing: tryptone (10.0 g/L); yeastxtract (5.0 g/L); NaCl (10 g/L); and MgSO4 (0.5 g/L), as previouslyescribed [6,39]. After autoclaving, toluene and cyclohexane weredded to the medium at final concentration of 10% and 30% (v/v),espectively. The cultivation flask (20 ml of medium in 250 ml shakeasks) was blocked with a chloroprene-rubber stopper to avoideparture of the organic solvent [7]. The shake flasks were incu-ated for 48 h, at 30 ◦C and shaking at 180 rpm. Cultures werehen acclimated by repeated transfer in the same culture condi-ions with organic solvents. Samples of repeated cultures wereiluted and spread on Skim milk-plates (SMA), which contained:ryptone (5.0 g/L); yeast extract 3.0 (g/L); skimmed milk powder00 (g/L); and bacteriological agar 12 (g/L), and growing coloniesere purified by repeated streaking. Finally, the purified coloniesere compared qualitatively for their protease production area on
kim-milk agar plates, which were incubated at 37 ◦C for 48 h. Theacteria showing high ratios of clear zone width to colony widthere chosen as potential high-yield protease producers. These
olonies were grown on liquid culture in the presence of differentrganic solvents and protease activity was measured as describedelow. The bacterium that showed the best activity in the presencef organic solvents was chosen for subsequent experiments.
.3. Production of protease
JER02 isolate was grown-up in pre-culture medium contain-ng nutrient broth (8 g/L), starch (10 g/L), yeast extract (10 g/L),nd NaCl (5 g/L). Incubations were performed with agitation at88 rpm for 16 h at 37 ◦C. The liquid medium used for the produc-ion of protease was composed of sucrose (5 g/L), citric acid (5 g/L),east extract (10 g/L), K2HPO4 (1 g/L), MgSO4·7H2O (0.1 g/L) and ofaCl2·2H2O (0.1 g/L) [40]. The pH of the medium was adjusted to.0. The medium was inoculated at 10% (v/v) with a 16 h old culturend incubated at 37 ◦C with 200 rpm shaking for 48 h. After that,he culture medium was centrifuged at 10,000 × g for 10 min at◦C and subsequently supernatant was precipitated with 0–100%
(NH4)2SO4) to fraction the proteins at 4 ◦C. After centrifuging at2,000 × g for 20 min at 4 ◦C, the pellet was dissolved in a smallmount of 10 mM Tris–HCl buffer, pH 8.0, and dialyzed overnightgainst the same buffer. This partially purified enzyme was usedor further studies.
.4. Protease assay
Protease activity was determined by the modified method ofembhavi and Kulkami [41]. Partially purified enzyme (500 �L)as added to a tube containing 200 �L of 2% casein (dissolved
lar Catalysis B: Enzymatic 89 (2013) 15– 23
in 50 mM Tris–HCl buffer, pH 8.0) and 250 �L of 50 mM Tris–HCl,pH 8.0, and incubated at 50 ◦C for 10 min. Afterwards, 500 �L of10% trichloroacetic acid (TCA) was added to stop the reaction andwas incubated at 4 ◦C for 30 min. After incubation, the tubes werecentrifuged at 12,000 × g for 15 min, and the absorbance of thesupernatant was measured at 280 nm. One unit of enzyme activitywas defined as the amount of enzyme releasing 1 �mol prod-uct/min. For characterization of the protease activity in the organicsolvent, water/organic solvent mixtures were prepared by com-bining the required amounts of the components and the enzyme[6]. After preparing different concentrations of organic solvent, thepH was adjusted to the required value. The effect of increasingorganic solvent up to 30% (v/v) on the enzyme activity was studiedunder standard assay condition [6]. The remaining protease activ-ity of sample without any organic solvent was expressed as control(100%). The protease stability in the presence of organic solventwas examined by incubating filter-sterilized protease with organicsolvent at 30 ◦C, 160 rpm for 48 h [6]. The final concentration oforganic solvent in the incubation mixture was 40% (v/v). At eachtime interval 50 �l from each sample was picked up, kept on icefor 30 min, microfuged and finally the remaining protease activitywas determined in standard condition, as described above. The finalconcentration of protease and organic solvent in the assay mixturewas 4% (v/v) and 35 �g ml−1, respectively. All experiments weredone in triplicate.
2.5. PCR amplification and 16S rDNA sequencing
Genomic DNA of Bacillus sp. JER02 was extracted accord-ing to Sambrook and Russell protocols [42] and its purity waschecked by the A260/A280. Universal 16S rDNA PCR forwardprimer (5-AGTTTGATCCTGGCTCAG-3) and reverse primer (5-GGC/T ACCTTGTTACGACTT-3) were used for the amplification of16S rDNA genes [43]. PCR program was performed as follows: (1)94◦C for 5 min as initial temperature, (2) a run of 30 cycles witheach cycle consisting of 45 s at 94 ◦C, 45 s at 48 ◦C and 90 s at 72 ◦Cand (3) 5 min at 72 ◦C to permit for the extension of any incompleteproducts [43]. PCR products were electrophoresed on agarose gel(0.7%) and subsequently amplified 16S rDNA bands were purifiedby DNA extraction kit (Cinaclone) and then DNA sequencing wasperformed on both strands directly by SEQ-LAB (Germany).
2.6. Phylogenetic tree analysis
The phylogenetic tree was made based on comparisonof 16S rDNA sequences of Bacillus sp. JER02 strain withother strains of Bacillus species that were obtained fromGene Bank database (http://www.ncbi.nlm.nih.gov). All sequenceswere aligned with Clustal Omega that was obtained from:http://www.seqtool.sdsc.edu/CGI/Omega.cgi [44] and phyloge-netic tree was made in MEGA4 [45]. The obtained 16S rDNAsequence has been deposited in GeneBank under accession no.JX441868 for isolate Bacillus sp. JER02.
2.7. Casein-SDS-PAGE
Zymogram was performed as described by Garcia-Carreno et al.[46] with few modifications. Partially purified protease was com-bined with non-reducing loading buffer and subsequently loadedon 10% gel electrophoresis without being heated. After elec-trophoresis, the gels were rinsed in 2.5% Triton X-100 for 30 min,with constant shaking to remove SDS [47]. Then, Triton X-100 was
removed by washing the gel three times with 50 mM Tris/HCl buffer(pH 8.0). The gel was subsequently incubated with 1% (w/v) caseinin 50 mM Tris/HCl buffer (pH 8.0) for 30 min at 50 ◦C. Finally, thegel was stained with coomassie brilliant blue R250 and destained.
A. Badoei-Dalfard, Z. Karami / Journal of Molecular Catalysis B: Enzymatic 89 (2013) 15– 23 17
Table 12-level design experimental design for evaluating factors influencing protease activity.
Run A: Temperature B: pH C: NaCl D: CaCl2 E: MgSO4 F: ZnSO4 Protease activity (�mol/min)
1 70 5 2 2 2 8 12.32 70 7 8 8 8 8 12.13 50 5 2 8 8 8 17.34 50 5 8 8 2 2 26.35 50 7 2 2 8 2 29.3
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he development of clear zones on the blue background of the gelndicated the protease activity.
.8. Optimization of protease activity
At first, we evaluated the effect of different factors which wereelected according to the previous articles, including NaCl, MgSO4,aCl2, ZnSO4, pH and temperature on protease activity. RSM, thatolves polynomial equations using quantitative data obtained fromroperly designed experiments, has been used in this study toetermine the optimum assay condition for the protease activity48]. The first step of the optimization is screening of the factorsnfluencing the response using Half-Fractional Factorial Design (H-FD). Then in order to obtain optimal values for selected factors,entral composite design was employed [48,49]
.8.1. Half-Fractional Factorial Designs (H-FFD)At the first step of the experimental design, a Half-Fractional
actorial Design (24−1), H-FFD, was used at 2 levels in order to get general view of the significant factors and the possible mean-ngful interactions in the range of variables were studied [48,49].aCl, MgSO4, CaCl2, ZnSO4, pH and temperature were selected toisplay the most important factors before optimization (Table 1).he experiments were conducted in triplicate and the results werenalyzed using Design Expert software (version 7.0, Stat-Ease,nc., Minneapolis, MN). Therefore, this is a preliminary screening
ethod and evidently using this data to estimate the pattern ofrotease activity is far from being a qualified model [49–51].
.8.2. Central composite design (CCD)Central composite design is an experimental design that has
een applied to determine the best conditions for protease activity48]. The range and the levels of experimental variables investi-ated in this study are presented in Table 2. The actual values ofndependent variables (Xi) were coded as xi according to this equa-ion by assigning the lowest values listed in Table 2 as −2 and theighest values as +2:
Xi − xi
xi =�xj, i = 1, 2, 3, . . . , k
n this equation xi states the actual values of an indepen-ent variable at the core point and �xj indicates the step
able 2oded levels and range of independent variables for experimental design.
Level Codedlevel
Uncoded level
(xi) Temperature(◦C) (i = 1, x1)
pH(i = 2, x2)
NaCl (mM)(i = 3, x3)
MgSO4 (mM)(i = 4, x4)
Lowest −2 42 5 2 2Low −1 50 6 4 4Mid 0 58 7 6 6High +1 66 8 8 8Highest +2 74 9 10 10
2 2 35.32 8 23.38 2 44
change. Xi represents the accurate significance of the indepen-dent variable, in which, xi is the dimensionless significance of anindependent variable. The second-order model utilized to fit theresponse to the independent variables is revealed in the followingequation:
Y = ˇ0 +4∑
i=1
ˇiXi +4∑
i=1
ˇiiX2i +
4∑
i=1
4∑
j=i+1
ˇiiXjXj
In this equation Y is the predicted response, Xi and Xj representingthe independent coded variables. ˇ0 and ˇi are the model constantand coefficients of the linear effects, respectively. Furthermore,ˇii and ˇij signify the coefficients of the quadratic effects and thecoefficients of interaction between these factors, respectively. i andj indicated coded factors of system [48,52].
The coefficients were calculated by regression analysis and theirsignificance was verified using analysis of variance (ANOVA) withthe Statistic (version 7.0) software program. The model accuracywas validated by comparing the model results with the experimen-tal data which were not included in the model estimation. Finally,the optimum values of the selected variables were obtained bycalculating the regression equation and by analyzing the responsesurface contour plots [53].
3. Results and discussion
3.1. Selection of the most potential solvent-stable proteaseproducer
Organic-solvent-tolerant bacteria can tolerate the toxic anddestructive effects of organic solvents, due to their various adaptivemechanisms [1]. Therefore, they are highly valuable in envi-ronmental biotechnology applications for the production of finechemicals [9]. Since both toluene (10%, v/v) and cyclohexane (30%,v/v) were added to the medium, at the beginning of inoculation,only solvent-tolerant strains were able to grow [6,39]. Cultureswere then acclimated by repeated transfer in the same cultureconditions with organic solvents. 0.1 ml of each culture mediumwas picked up and distributed on a SKM plate, separately. TheseSKM plates were incubated at 37 ◦C for 48 h. Protease producerswere isolated among bacteria that showed clear zones of hydroly-sis around the colonies on SKM plate. These colonies were grown onliquid culture in the presence of different organic solvents and pro-tease activity was measured in assay condition (data not shown).Bacillus sp. JER02 showed the best protease activity in the presenceof organic solvents (Fig. 1).
3.2. Production of crude protease
Generally, synthesis and secretion of the protease is initiated
during the exponential growth phase, with a substantial increasenear the end of the growth phase and maximum protease pro-duction in the stationary growth phase [54,55]. The productionof extra cellular proteases during the stationary phase of growth
18 A. Badoei-Dalfard, Z. Karami / Journal of Molecular Catalysis B: Enzymatic 89 (2013) 15– 23
hTessJftiTTic(
Fs
Fig. 1. The zone of hydrolysis of Bacillus sp. JER02 on skim-milk agar.
as been described as characteristic of many bacterial species.he effect of different pHs and temperatures on production ofnzyme was checked and the optimum pH and temperature werehown to be approximately 7.5 and 37 ◦C, respectively (data nothown). Results showed that protease production by Bacillus sp.ER02 was the highest after 48 h of inoculation. Supernatant wasractionated and concentrated with 0–100% ((NH4)2SO4) to fractionhe proteins. Results showed that this protease was precipitatedn 70% ammonium sulphate. The pellet was dissolved in 50 mMris–HCl buffer, pH 8.0 and dialyzed against the same buffer.his partially purified enzyme was characterized in the follow-
ng experiments. Zymogram activity staining showed a band oflear zone of proteolytic activity against the blue backgroundFig. 2).ig. 3. 16S rDNA based phylogenetic analysis of Bacillus sp. JER02. Each organism is pubstitution rate per site are indicated. The phylogenetic tree constructed by the neighbo
Fig. 2. Gel electrophoresis and zymogram of crude protease: crude extract (left);zymogram of the crude extract (right).
3.3. Identification of bacteria with 16S rDNA sequence
Bacterial identification was done using its 16S rRNA genesequence. The 16S rRNA gene is adequately different from itseukaryotic, archaeal, mitochondrial and chloroplastic homologs,and is enough conserved so that almost universal bacterial ampli-fication can be done using specific primers. PCR product of 16SrRNA gene was about 1500 bp and it’s sequence was edited to atotal length 1018 bp direct sequencing. For designing of phylogenictree, 16S rDNA sequence from Bacillus species (19 type strains)were obtained from the national center for biotechnology informa-
tion (NCBI, http://www.ncbi.nlm.nih.gov/) and multiple sequencealignment was performed with Clustal Omega [45]. The phylo-genetic tree was created by neighbor-joining method by MEGA4receded by its NCBI accession number. Bootstrap values and scale bar depictingr-joining method showing the position of isolate JER02.
olecular Catalysis B: Enzymatic 89 (2013) 15– 23 19
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Fig. 4. Effects of different percentages of organic solvents were considered on the
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A. Badoei-Dalfard, Z. Karami / Journal of M
oftware (Fig. 3) [46]. Result showed that Bacillus sp. JER02 istrongly related to B. cereus with 97% homology.
.4. Activity and stability of crude protease in presence of organicolvents
Generally, organic solvents unfavorably affect enzyme activityue to their direct interaction with the essential water that sur-ounds the enzyme [6,13]. Since, organic solvent-stable proteasesan be used in organic solvent media without any need for stabi-izing modifications, they have been widely used as biocatalystsn non-aqueous media for synthetic biotechnology applications.ased on previous reports, these organic solvents (DMF, methanol,
sopropanol and n-propanol) were selected for the investigation6,56–58]. These organic solvents have log P 1.0, 0.76, 0.14 and 0.28,espectively. DMF and methanol have lower log P than isopropanolnd n-propanol; therefore, these solvents have more tendencieso strip water from the essential layer, thereby inactivating orenaturing the biocatalyst. In addition, protease activity stronglyecreases in the presence of isopropanol and n-propanol in spitef the higher log P for isopropanol and n-propanol, compared toMF and methanol, because protease activity was inhibited in the
ow concentrations of mentioned organic solvents [6]. Bacillus sp.ER02 protease was shown to be activated about 10% and 30% in theresence of 5% isopropanol and 10% DMF, respectively. In addition,rotease activity was also increased about 10%, in the presence of0% methanol (Fig. 4). Moreover, it was shown that the proteaserom Bacillus sp. JER02 was activated about 20%, after incubationor 48 h in the presence of 40% (v/v) of DMF, toluene and methanol,hile this proteolytic enzyme was partially inactivated in presence
f the other organic solvents (Fig. 5). Results show that this proteasectivity was maintained at least 90% of its initial activity compareo control. Bacillus sp. HR-08 protease maintained more than 90%f its initial activity after pre-incubation 48 h at room temperaturen the presence of 20% of DMSO, DMF and isopropanol. In compari-on, the protease of B. cereus was found to be stable in the presencef isopropanol (0.1%, 0.5% v/v) and retained more than 80% of itsctivity [59].
.5. Optimization of protease activity by response surfaceethodology
In order to select the factors that have the highest impact onhe protease activity, the effect of six factors (temperature, pH,aCl, MgCl2, CaCl2, and ZnCl2) were evaluated in 12 runs usinglackett–Burman (PB) design. Variation ranging from 12.1 to 44
ig. 5. Effects of organic solvents were evaluated on the protease stability of Bacillus sp.
emperature in the presence of 40% (v/v) organic solvents for 48 h. The remaining activityhe experimental values.
activity protease of Bacillus sp. JER02. For evaluation of the activity, different concen-trations of organic solvents were added to the assay reaction. Activity was measuredat standard condition. Standard deviations were within 6% of the experimentalvalues.
(�mol/min) in protease activity was observed. Based on theseresponses, the main effects and two-factor interactions were calcu-lated. To choose the factors with major contribution in the proteaseactivity, a “Normal Probability Plot” was applied. If the data werenormally distributed, the plot should have estimated a straight line.Hence, considerable effects which diverge from straight line areexplained as outliers in the graph. Consequently, it can be provedthat the main effects of X1, X2, X3 and X4 are the ones that influ-ence the protease activity in the ranges investigated. Based on thePlackett–Burman design, the following factors temperature, pH,MgSO4 and NaCl were chosen for further optimization by RSM.To check the combined effect of these factors on protease activ-ity, a central composite design (CCD) was employed. The centralcomposite design for 4 variables and the corresponding experimen-tal data are shown in Table 3. Regression analysis of the data wasachieved for testing the sufficiency of the proposed quadratic modeland the following second-order polynomial equation was derived).
Protease activity = +53.17 + 11.94X1 + 9.80X2 + 4.93X3 + 1.92X4
− 1.44X1X2 + 5.16X1X3 + 3.72X1X4 + 7.43X2X3
+ 7.46X2X4 − 7.21X3X4 − 2.03X21 + 8.28X2
2
+ 1.12X23 + 1.03X2
4
JER02. For evaluation of the protease stability, the enzyme was incubated at room was then measured at standard condition. Standard deviations were within 6% of
20 A. Badoei-Dalfard, Z. Karami / Journal of Molecular Catalysis B: Enzymatic 89 (2013) 15– 23
Table 3Experimental plan for optimization of protease activity using central composite design.
Run no. X1 X2 X3 X4 Protease activity (�mol/min)
Experimental Predicted
1 0 0 0 2 43.8 46.592 −1 −1 1 −1 75.9 72.523 0 0 −2 0 49.6 50.084 −1 −1 −1 1 15.8 23.065 0 0 0 0 63.3 64.826 1 1 1 1 48.8 32.87 −2 0 0 0 35.5 34.118 1 −1 −1 1 101.8 87.379 −1 1 1 1 43.5 49.5910 1 −1 −1 −1 70.8 61.7911 0 0 0 0 62.4 64.8212 −1 −1 1 1 58.1 47.6013 0 0 0 0 61.3 64.8214 1 1 −1 1 58.8 66.9615 1 −1 1 1 63.5 71.1116 −1 1 −1 −1 90.4 79.8717 0 0 −2 −2 35.5 43.2018 0 −2 0 0 60.9 68.1819 1 1 −1 −1 85.1 80.3320 −1 1 1 −1 106.6 113.4621 0 2 0 0 85.5 88.7122 0 0 2 0 65.1 67.4123 1 1 1 −1 75.6 73.1224 −1 −1 −1 −1 20.3 21.0325 0 0 0 0 63.6 64.8226 1 −1 1 −1 75.8 72.4827 0 0 0 0 65.5 6482
wXM
d(tccXF
TA
28 −1 1 −1
29 0 0 0
30 2 0 0
here Y is the response value, that is, the protease activity, and X1,2, X3 and X4 are the coded levels of temperature, pH, NaCl andgSO4, respectively.Fisher’s test for the analysis of variance on the experimental
ata indicated that the model was significant and F-value was 8.82Table 4). This implies that the model is significant, which meanshat there is only 0.01% chance that a model with a large F value
ould occur due to noise. Values of “Prob. > F” less than 0.0500 indi-ates that model terms are significant. In this case X1, X2, X3, X1X2,1X3, X1X4, X2X4, X3X4 are significant model terms. The “Lack of Fit-value” of 54.78 implies the Lack of Fit is significant. There is only aable 4nova for response surface quadratic model of protease activity.
Source Sum of squares DF
Model 11514.23 14 822.44X1 2049.22 1 2049.2X2 454.31 1 454.31X3 2488.36 1 2488.3X4 152.80 1 152.80X1X2 1624.09 1 1624.0X1X3 1664.64 1 1664.6X1X4 554.60 1 554.60X2X3 320.41 1 320.41X2X4 1517.101 1 1517.1X3X4 988.63 1 988.63X2
1 586.89 1 586.89X2
2 310.84 1 310.84X2
3 66.14 1 66.14
X24 1.15 1 1.15
Residual 1399.07 15 93.27Lack of Fit 1386.41 10 138.64Pure error 12.65 5 2.53Cor. total 12913.29 29Std. dev. 9.66
Mean 61.38
C.V.% 15.74
Press 8151.10
1 47.2 42.950 65.1 64.820 46.2 58.08
0.02% chance that such a large “Lack of Fit F-value” could occur dueto noise. The statistical significance of the model was also confirmedby the coefficient of determination (R2 = 0.89) which implies thatonly 11% of the variability of the response was not explained by themodel (Table 3). The “Pred R-Squared” of 0.3688 is not as close tothe “Adj R-Squared” of 0.7905 as one might normally expect. Thismay indicate a large block effect or a possible problem with this
model and/or data. Our ratio of 13.535 indicates an adequate signal.This model can be utilized to navigate the design space. There-fore, this is a suitable model for prediction of the protease activityin the investigated conditions. Response of the predicted andMean square F value Prob. > F
8.82 <0.0001 Significant2 21.97 0.0003
4.87 0.04336 26.68 0.0001
1.64 0.22009 17.41 0.00084 17.85 0.0007
5.95 0.0277 3.44 0.08360 16.27 0.0011
10.60 0.0053 6.29 0.0241
3.33 0.08790.71 0.41300.012 0.9130
54.78 0.0002 Significant
R-Squared 0.8917Adj R-Squared 0.7905Pred R-Squared 0.3688Adeq Precision 13.535
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A. Badoei-Dalfard, Z. Karami / Journal of M
xperimental data of these conditions on protease activity arehown in Table 4.
The good response for this regression model was designed inig. 6. 3D graphs were plotted for the pair-wise combination ofhe three factors while keeping the other one at its optimal valuesor protease activity. As shown in Fig. 6a, an increase in the tem-erature improved the protease activity at low pH level. It is alsoevealed that increasing the pH in low levels of temperature leadso an increase in the protease activity, but, at highest temperaturehis effect was diminished. Fig. 6b also shows that the responseurface plot obtained as function of temperature vs. NaCl, while allther variables are maintained at zero level. Results indicated that,ncreasing of NaCl concentration has remarkable effect on protease
ctivity, at low level of temperature. Furthermore, increasing ofemperature value also enhanced the protease activity in low levelf NaCl, but this effect was reduced in high level of NaCl concen-ration. As shown in Fig. 6c, an increase in MgSO4 concentrationig. 6. (a) Response surface plot for protease activity as a function of protease activity shrations of 6 mM and 6 mM respectively; (b) response surface plot for protease activity asonstant pH and MgSO4 concentration of 7 and 6 mM respectively; (c) response surface pemperature and MgSO4, as constant pH and NaCl concentration of 7 and 6 mM respectivhowed the interaction of pH and NaCl, as constant temperature and MgSO4 concentratio
lar Catalysis B: Enzymatic 89 (2013) 15– 23 21
decreased the protease activity at low temperature level. It is alsoshown that increasing the temperature in the presence of highMgSO4 level leads to an increase in the protease activity and at lowMgSO4 level this effect was also increased. The 3D surface graphrevealed that increasing the pH in the presence of low level of NaClleads to an increase in protease activity (Fig. 6d). In addition, rais-ing the NaCl concentration increased protease activity at low andhighest levels of pH.
The response surface model was validated with a random set ofexperiments designed by the model (Table 5). Result show that, thevalues observed were close to the predicted value. Thus, the modelwas successfully validated and the optimized condition obtainedfor the protease activity was as follow: temperature (54 ◦C), pH (8),
NaCl (8 mM) and MgSO4 (4 mM) in assay buffer. The statistical opti-mization by RSM resulted in 2.5 fold increase in protease activityby Bacillus sp. JER02.owed the interaction of temperature and pH, as constant NaCl and MgSO4 concen- a function of protease activity showed the interaction of temperature and NaCl, aslot for protease activity as a function of protease activity showed the interaction ofely; (d) response surface plot for protease activity as a function of protease activityn of 58 and 6 mM respectively.
22 A. Badoei-Dalfard, Z. Karami / Journal of Molecular Catalysis B: Enzymatic 89 (2013) 15– 23
Table 5Results of validation conditions and experimental/predicted values of them.
Run no. X1 X2 X3 X4 Protease activity (�mol/min)
Experimental Predicted
1 50.00 8.00 8.00 4.00 110.1 109.52 52.5 8.00 7.90 4.1 104.2 108.43 54.0 8.00 8.00 4.00 111.3 113.54 50.10 8.00 7.7 4.1 108.8 110.15 50 7.9 8.00 4.1 109 111.06 65.9 8.00 4.2 4.0 83.1 79.57 66.0 8.0 4.0 4.0 82.0 80.4
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8 66.0 8.0 4.2
9 66.0 8.0 6.4
10 66.0 8.0 5.3
Although optimization of the microbial protease production,sing RSM, has been described before [60–63], this is the first reporto optimize the protease activity in assay conditions using RSM. Inhis study, a systematic and sequential optimization strategy waspplied to improve the protease activity from Bacillus sp. JER02nd it was shown that high temperature and alkaline pH are twoaluable factors to improve the protease activity. Our results alsondicated that activity of Bacillus sp. JER02 protease at the opti-
al situation was improved with temperature (54 ◦C) and pH (8),aCl (8 mM) and MgSO4 (4 mM). Although high salt concentrationecreases water activity, our results showed that Bacillus sp. JER02rotease was stable and active in different levels of MgSO4 andaCl. Taken together, the above mentioned properties and opti-ized assay conditions show that Bacillus sp. JER02 protease can be
onsidered an attractive protease for biotechnology applications.
. Conclusion
This report expresses the screening of an organic solvent-olerant bacterium and characterization of its secreted solvent-table protease. This protease retained more than 95% of its initialctivity after incubation in the presence of 40% (v/v) cyclohexanend isopropanol. In addition, protease activity was also improvedore than 20% after 48 h incubation in the presence of 40% DMF,
oluene and methanol. These results make this organic solvent-table protease a promising biocatalyst for enzymatic synthesisn the presence of organic solvents. To our knowledge, this is therst report of isolation of organic solvent-tolerant B. cereus. More-ver, RSM was employed to study the modeling and interactionf the parameters for the optimization of protease activity from
strain of Bacillus sp. JER02, that was isolated from hot spring.lthough, there are some reports to optimize protease produc-
ion by RSM, but, to our knowledge, this is the first report toemonstrate use of RSM to optimize the protease activity in assayondition. The results obtained in this study indicated that RSMs a reliable method for developing the model, optimizing fac-ors, and analyzing interaction effects, before using a protease inndustry.
cknowledgment
The authors express their gratitude to the Research Council ofhe Shahid Bahonar University of Kerman for financial support dur-ng the course of this project.
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