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Rational design of a Bacillus circulans xylanase by introducing charged residue to shift the pH optimum
Process Biochemistry 47 (2012) 2487–2493
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Rational design of a Bacillus circulans xylanase by introducing charged residue to shift the pH optimum Subarna Pokhrel a , Jeong Chan Joo a , Yong Hwan Kim b , Young Je Yoo a,∗ a b
School of Chemical and Biological Engineering, Seoul National University, Seoul 151-742, Republic of Korea Department of Chemical Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea
a r t i c l e
i n f o
Article history: Received 25 July 2012 Received in revised form 4 October 2012 Accepted 22 October 2012 Available online 29 October 2012 Keywords: Bacillus circulans xylanase Electrostatic interaction Enzyme engineering pH optimum Site-directed mutagenesis
a b s t r a c t Introduction or disruption of long-range electrostatic interactions can be an effective way to change the pKa s of catalytic residues and the pH optima of enzymes. In particular, shifting the pH optima toward the acidic or basic limb is an important issue for industrial applications of xylanases, e.g., for the paper or food industries. Here, we suggest an effective strategy to shift the pH optimum of an enzyme by introducing charged residue. Our strategy is to alter the titration behavior of the strongly interacting catalytic glutamates in Bacillus circulans xylanase by introducing acidic or basic residue in juxtaposition to the natively present acidic residues surrounding the catalytic site, thereby shifting the pH-activity ˚ away from the catalytic sites. The strategy profile. Mutation sites were chosen to be long distances (>8.5 A) was verified by site-directed mutagenesis experiments. The results show that the pH optimum can be changed (−0.5 to 1.5 unit) by strategically selecting the mutation sites. The strategies developed can effectively be applied to change the pH optima of the families of enzymes harboring acidic residues as catalytic residues. © 2012 Elsevier Ltd. All rights reserved.
1. Introduction Endo--1,4-xylanases form a group of enzymes that hydrolyze xylan (a major component of hemicelluloses). Endo--1,4xylanases mainly have been grouped into families 10 and 11 of the glycoside hydrolases, although families 5, 7, 8, and 43 also contain some xylanolytic properties [1,2]. Family 10 xylanases show eightfold ␣/ barrel structures and have molecular masses > 30 kDa, whereas family 11 xylanases have an all -strand sandwich fold structure resembling a partly closed “right hand” and a lower molecular mass of ∼20 kDa [3,4]. Xylanases are widely used in many industrial applications such as pulp biobleaching in the paper industry, baking, saccharification of lignocellulosic biomass, wine making, and fruit juice clarification [5,6]. These processes are operated under harsh conditions, e.g., high temperatures or extremes of pH, and thus, the most important features required for the industrial applications of xylanases are thermostability and extreme pH optima. The strategy for improving the thermostability of xylanases has been thoroughly studied by experiments and simulations [7–10]. Still, there are few reports regarding the shift of pH optima in xylanases. Although the pH optima of xylanases can be shifted by protein engineering [11–14], shifting of the pH
∗ Corresponding author. Tel.: +82 2 880 7411; fax: +82 2 887 1659. E-mail address: [email protected] (Y.J. Yoo). 1359-5113/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.procbio.2012.10.011
optima toward desired directions (acidic- or alkaline-side pH optimum shifting) by rational design has not been fully investigated. The pH-dependence of enzymatic activity is primarily governed by the pKa values of the active site residues. The engineering of enzyme charges to shift the pH-activity profile by modulating the interaction with the active site residues has been studied by several researchers [15–17]. Most of these researchers either experienced no change or small changes in the pH-activity profiles of the enzymes studied. This result is partly because diverse families of enzymes harbor different active site residues, different reaction mechanisms and different catalytic cleft dynamics. Despite these issues, some general strategies have been proposed [17,18]. Tynon-Connolly and Nielsen [17] noted the significant role of charged-only mutations in changing the pKa s of the titratable groups by introducing desolvation and electrostatic interactions of the newly added groups. Nielsen and Vriend [18] suggested the importance of introducing neutral residues rather than charged residues to change the pKa s of catalytic groups (␣-amylase) by changing the dynamics of the active site cleft without altering the net charge of the protein. Amino acid sequence comparisons between alkaline and highly alkaline serine proteases suggested that the highly alkaline adaptation would be accompanied by an increase in the number of arginine and neutral hydrophilic residues and also by a decrease in lysine and negatively charged residues [19]. The alkalophilicity of the Bacillus sp. 41M-1 xylanase has been improved by amino acid substitutions [20]. A salt bridge in the
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catalytic cleft, and an excess of Arg residues on the protein surface were introduced to shift the optimum pH of the wild type enzyme from 8.5 to 9.5. Recently, Michaux et al. [21] compared the acidophilic family 11 xylanase from Scytalidium acidophilum (XYL1) with several mesophilic pH-dependent xylanases and found that the number of salt bridges and hydrogen bonds were decreased in acidophilic xylanases compared to the highly alkaline xylanases. According to the studies to date, larger shifts in pKa s of the catalytic groups can be achieved by mutating buried or semi buried residues or by generating mutations closer to the catalytic groups [17]. The latter approach may lead to subtle changes in the dynamics of the catalytic cleft as a result of strong repulsion or attraction by the newly introduced group or by interference with the conserved electrostatic network around the catalytic groups. The result may be a dramatic change in the catalytic performance of the enzyme. Bacillus circulans xylanase (BCX, 20.4 kDa), a family 11 glycosidase member, hydrolyzes xylan substrates with net retention of anomeric configurations. This hydrolysis proceeds via a doubledisplacement mechanism in which a covalent intermediate is formed in the glycosylation step and is subsequently hydrolyzed in the deglycosylation step [22,23]. Previous studies have confirmed that during the glycosylation reaction, Glu78 (pKa 4.6) serves as the nucleophile, which initially is negatively charged, whereas Glu172 (pKa 6.7) functions as a general acid and hence must be protonated [24–26]. In the double-displacement mechanism of BCX, Glu172 plays a dual catalytic role as a general acid in the first step and as a general base in the second. BCX [27], an alkaline xylanase, has been characterized extensively using a wide range of structural, spectroscopic, and enzymatic techniques, and is thus an excellent model system for investigating the factors that establish the pHdependence of the activity of a retaining glycosidase [24–26,28]. The active site of BCX is composed of several highly conserved residues arranged to form an intricate network of hydrogen bonds surrounding the two catalytic glutamates [29]. Here, we attempted to change the pH optimum of enzyme toward desired directions by rational design. The effect of the charged residue mutations in shifting the pH-activity profiles of enzymes was studied using BCX as a model enzyme. The aim of this study was to change the optimum pH of BCX by introducing acidic or basic residue in juxtaposition to the natively present acidic residue(s) surrounding the catalytic glutamates. 2. Materials and methods
Table 1 Primers used in this study. Xylanase
Template
Wild type BCX
Synthetic gene
Q7D
pET 23bBCX
D11R
pET 23bBCX
G34R
pET 23bBCX
V82D
pET 23bBCX
Q175R
pET 23bBCX
a b
Primera Senseb 5 -GGGCCCGGGGCTAGCATGACTGGT-3 Antisenseb 5 GGGCCCGGGCTCGAGTGCGGCCGCAGG-3 Sense 5 -CTACTGGGACAACTGGACAGACGGTG-3 Antisense5 CCAGTTGTCCCAGTAGTCTGTGCTAG-3 Sense 5 -CTGGACA CGCGGTGGCG GTATCGTTAA TG-3 Antisense5 GCCACCGCGTGTCCAGTTTTGCCAG-3 Sense 5 -CTAATACTCGGAACTTCGTAGTCGG-3 Antisense 5 -GAAGTTCCGAGTATTAGACCAATTC-3 Sense 5 TTACGTTGACGACTCTTGGGGAACGTAC-3 Antisense 5 CAAGAGTCGTCAACGTAATATTCAATCAG-3 Sense 5 GGCTACCGGAGCTCTGGTTCTTCCAACG-3 Antisense 5 -AGAGCTCCGGTAGCCTTCGGTCGCC-3
Mutation sites in the sense and antisense primers are underlined. Restriction sites in sense (NheI) and antisense primers (XhoI) are underlined.
100 steps of steepest descent followed by 500 steps of conjugate gradient energy minimization after fixing the heavy atoms. The finite-difference of Poisson–Boltzmann (FDPB) equation was solved to calculate the electrostatic potentials of the functional atoms (Glu O1/O2 for BCX, His N␦1/N2 for BAS) of the catalytic residues using program Delphi under Insight II [36]. As the procedures for calculations on Asp or His are similar, we selected His residue for our calculations. We employed CHARMm19 radii with the point charge (Arg 0.5N1/0.5N2; Lys 1.0N; His 0.5N␦1/0.5N2; Asp 0.5O␦1/0.5O␦2; Glu 0.5O1/0.5O2) assigned only for the functional atoms. The X-ray structure was mapped in a 65-cubed grid, and 4 focusing runs (auto-iterations) were followed ˚ ˚ ˚ ˚ point, 1 A/grid point, 0.5 A/grid point and 0.25 A/grid point). Dielectric (2 A/grid constants of 4 and 80 were used for the protein and the solvent respectively; the ˚ and the Stern ion excluionic strength was 0.1 M, the water probe radius was 1.4 A, ˚ The pKa [37] and the pH optimum were calculated with the sion layer was 2.0 A. equations given below. pKi = i
0 − m 2.303
pH optimum =
pK1 + pK2 2
2.1. Computational 2.1.1. The effect of interaction energy and differences in intrinsic pKa s on the pH-dependent population of protonation states Alteration of the titration behavior of two strongly interacting acidic residues (two catalytic glutamates, in this study) by other acidic and basic residues was studied by using the pKa system module in pKa tool [34]. Assignment of intrinsic pKa s to the interacting groups derives from prior knowledge of the active site groups in BCX, in which, generally, the two catalytic groups differ in their intrinsic pKa s by ∼1 unit. If the residues are assumed to be placed equidistantly on a straight line and embedded in a uniform dielectric constant medium, interaction between the residues becomes distance dependent. The systems of more than one residue can be applied, for practical purposes, to shift the pKa (s) of the target residue(s). Generally, active site residues in enzymes show a more strongly coupled system, which can be modulated to shift their pKa s toward the acidic or alkaline sides by introducing or removing the charged residues surrounding them.
2.1.2. Selection of mutation sites 2.1.2.1. Data files and parameters used for pKa calculation. The crystal structures of BCX (PDB ID: 1XNB) and Bacillus amyloliquefaciens subtilisin (BAS) (PDB ID: 1GNS) were used for the calculations. Calculations using BAS were solely for the validation of the calculation parameters. All the mutant models were prepared by assigning position-specific rotamers as implemented in the WHATIF Web Interface [35]. All the crystal waters were deleted before minimization and calculation. The Biopolymer module in Insight II was used to assign hydrogens, according to the unfilled valences in each atom. Hydrogens were assigned at pH 7.0. Each structure was subjected to
where 0 = potential at site i due to the original group, m = potential at site i due to the mutated group, I = −1 or +1 for an acidic or basic group, pK1 , pK2 = change in pKa of E78 and E172. 2.2. Experimental 2.2.1. Cloning, mutagenesis and protein expression The gene construct encoding BCX was the product of our previous study [14]. Site-directed mutagenesis was performed using partially overlapping primers [30]. 2× PremixG PCR buffer (FailSafeTM PCR system, EPICENTRE Biotechnologies, Madison, USA) and nPfu-forte DNA polymerase (Enzynomics, South Korea) were used. The mutagenesis primers are shown in Table 1. Standard commercial kits were used for plasmid isolation and purification. 50–100 ng template, 2 M primer pair and 2.5 U of DNA polymerase were mixed with 25 l 2× PremixG buffer to a final volume of 50 l and thermocycled (94 ◦ C for 3 min; 16 cycles of 94 ◦ C for 1 min, 52 ◦ C for 1 min and 68 ◦ C for 8 min; followed by 1 h incubation at 68 ◦ C) to amplify the whole vector construct with the mutation site inserted. The native vector was removed after digestion with DpnI, and was then transformed into Escherichia coli BL21 by the chemical method. Transformed E. coli BL21 was grown in LB medium at 37 ◦ C overnight. Plasmid isolation and purification were performed using standard kits and were confirmed by automated sequencing (Cosmogenetech, Korea). For enzyme expression, E. coli BL21 was grown until absorbance reached to 0.5–0.6 at 600 nm, and was induced (0.75 mM IPTG) for 20 h at 20 ◦ C. Enzyme purification was performed by affinity chromatography using a Ni–chelate matrix (for His-proteins) [31] and analyzed by SDS-PAGE [32].
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2.2.2. Optimum pH and activity determination The xylanase assay was performed by the modified dinitrosalicylic acid (DNS) method [33]. The pH-dependence was determined in triplicate at different pHs (from pH 4.0 to 8.0 at 0.5 pH unit intervals) using the following buffers: CH3 COONa/CH3 COOH 0.1 M (pH 4–5.5) and K2 HPO4 /KH2 PO4 0.1 M (pH 6.0–8.0), using 0.5% birchwood xylan (SIGMA-ALDRICH CHEMIE, Gmbh, Germany) at 50 ◦ C. Half-life without substrate was measured at pH 7.0 (0.1 M Na2 HPO4 /NaH2 PO4 buffer) after preincubating the enzymes at 50 ◦ C in a JEIO TECH SBW-10 water bath (Korea) for 30–300 s. Half-lives were determined using the equation t1/2 = ln 2/k, where k is the inactivation constant. Reaction velocities were measured at pH 7 (0.1 M Na2 HPO4 /NaH2 PO4 buffer) and 40 ◦ C by using 1% birchwood xylan (SIGMAALDRICH CHEMIE Gmbh, Germany).
Table 2 Distances between the substituted groups and the active sites.
3. Results and discussion
Taking the average of these catalytically competent protonation state (CCPS) gives a significant clue to the interpretation of the shifted pH-activity profile of the enzymes, where active site residues interact strongly. The introduction of one basic residue (Fig. S1f) into the cluster of charges with three acidic residues (Fig. S1e) alters the titration behavior of the first two acidic residues, as observed based on the changes in the microstate population (Fig. S1b and S1e). The systems of 3 natively present acidic residues and 1 engineered acidic residue are shown in Fig. S1g and S1h, respectively.
3.1. Effects of interaction energy and differences in intrinsic pKa s on the pH-dependent population of protonation states To develop the rationale for shifting the pH optimum of BCX, a strategy that employs the introduction of charged residue at the periphery of the enzyme active sites was tested. The introduction of a charged residue alters the titration behavior (pKa s) of the catalytic glutamates and finally leads to a shifting of the pH optimum; these results were validated by calculating the pH-dependent population of protonation states of multiple charged residues. In a system with a single titratable group, the titration curve is sigmoidal; even in the cases of different conformations, a nonsigmoidal titration curve is not observed if the group does not show chemical shifts in different conformations [38]. However, modulation of pKa s by electrostatic interactions is in a very dynamic state of interplays among the titratable residues involved. This effect is largely dependent upon the intrinsic pKa s, the strength of interaction energies and the conformational flexibility; therefore, titration curves for systems with more than one titratable group can be nonsigmoidal. In a system with two strongly interacting groups with dissimilar intrinsic pKa s (4.0 and 6.0) and somewhat similar intrinsic pKa s of 4.5 and 5.5, four different protonation microstates can be observed under varying pH conditions. Due to the stronger interactions, the real-site titration curves have a non-sigmoidal shape. The total protonation probability is the sum of the two real-site titration curves and the two quasi-site titration curves, and one of the microstates is superseded by the other (Fig. S1a and Table S1). In the case of similar intrinsic pKa s (4.5 and 5.5), all the four microstates are clearly observed (Fig. S1b and Table S1). The pHdependent populations of different protonation states are shown in Fig. S1a (acidic/acidic), S1b (acidic/acidic, somewhat similar intrinsic pKa s), S1c (basic/basic) and S1d (acidic/basic). If the number of interacting residues is increased to more than two, the interactions become more complex to interpret [17,38]. For a globular protein, if the molecule has a uniform dielectric constant and all the interacting residues are equidistant to each other on the same straight line (Fig. S1a–h), the charge–charge interaction becomes distance dependent. In this case, the microscopic pKa shift can be explained easily. In the system with 3 and 4 interacting residues, the interactions become complex, with many microstates with varying degrees of population. All of these microstates may not be functional in a particular pH environment of a reaction. Some of the microstates become dominant over the others and only some show the functional property. Interactions among three acidic residues (Fig. S1e) and interactions among three acidic and one basic residue (Fig. S1f) are shown. If the first two residues are the catalytic glutamates E78 and E172 in BCX, any other acidic residue(s) surrounding the catalytic cleft as the third one and a mutated basic residue as the fourth one, then the alteration in titration behavior can be interpreted in terms of the catalytically functional microstates. Only the (1, 0, 0, 0), (1, 0, 0, 1), (1, 0, 1, 0) and (1, 0, 1, 1) microstates are catalytically able to act on substrates.
Native/substituted group Arg-group mutation 11D C␥/R C 34G C␣/R C 175Q C␦/R C Asp-group mutation 7Q C␦/D C␥ 82V C/D C␥
Distance to E172 C␦ [Å]
Distance to E78 C␦ [Å]
Average distance [Å]
10.24/8.87 10.25/12.33 12.35/10.58
12.94/10.51 14.53/14.07 16.54/16.3
11.59/9.69 12.39/13.2 14.44/13.44
9.89/11.65 9.65/9.98
10.03/11.48 11.95/12.95
9.96/11.56 10.8/11.46
3.2. Selection of mutation sites As shown in Fig. S1, the pH optimum of BCX can be modulated by introducing charged residues affecting the pKa values of the catalytic glutamates (E78 and E172). All the mutation sites were selected at a distance of more than 8.5 A˚ from the catalytic glutamates (Fig. 2A). Mutant D11R serves as a control, i.e., there is no acidic residue between the mutation site and the catalytic sites (Fig. 2B). Two general strategies are presented in this work; each one is best explained by the figures (Fig. 2C and D). The Arg- and Asp-mutations were introduced at the periphery of the enzyme’s active sites to shift the pH optimum toward alkaline and acidic directions, respectively, by keeping the natively present acidic residues between the catalytic residues and the mutation sites. Mutations Q175R, G34R, Q7D and V82D are in a straight line with the catalytic glutamates. The Arg-mutations (G34R and Q175R, Fig. 2C) and Asp-mutations (Q7D and V82D, Fig. 2D) were introduced to shift the pH optimum toward the alkaline and acidic directions, respectively. Y174 is between the catalytic sites and the mutation site Q175. Y174 is present somehow between the catalytic site and the mutation site G34. Fig. 2D best explains the position of the mutant Q7D, with Y69 being between the catalytic site and the mutation site. Similarly, Y65 and Y80 are present between the catalytic site and the mutation site V82. The distances between the substituted groups and the catalytic sites are shown in Table 2. The average distance from the substi˚ In all the mutations, tuted group to the catalytic sites is 8.87–16.3 A. mutation sites are at a closer distance to the acid/base catalyst (E172) compared to the nucleophile (E78) except in the mutant Q7D where the mutation site is equidistant to both of the catalytic glutamates (Fig. 2A and Table 2). 3.3. pKa calculations for the xylanases To accurately predict the pKa changes for the catalytic glutamates caused by the Asp- and Arg-mutations, the pKa calculation method used in this study was validated by comparing the calculated and experimental pKa values for the wild type and mutant models of BAS at different ionic strengths (Table 3). The calculated pKa values for the BAS mutants are similar to the experimental results showing a good linear correlation (R2 = 0.92567, Fig. 1), which indicates that our method is reliable for predicting the pKa values of titratable residues. After standardization, the same
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Table 3 Validation of calculated pKa values with the experimental results for mutants of Bacillus amyloliquefaciens subtilisin (PDB ID: 1GNS). Calculated pKa
Ionic strength [mM]
Mutations
Target residue
a
Experimental pKa
b
1 1 1 1 1 5 10 25 100 145 500 1000 5 10 25 100 145 500 1000
D36Q K136A K170A D99S + E156S K170A + K213A D99S D99S D99S D99S D99S D99S D99S E156S E156S E156S E156S E156S E156S E156S
H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64
−0.18 +0.11 +0.20 −0.63 +0.39 −0.38 −0.42 −0.36 −0.29 N/Dc −0.1 −0.02 −0.32 −0.44 −0.41 −0.25 N/Dc N/Dc −0.06
−0.24 +0.07 +0.18 −0.74 +0.34 −0.30 −0.28 −0.36 −0.17 −0.16 −0.1 −0.08 −0.42 −0.40 −0.36 −0.26 −0.23 −0.15 −0.11
a b c
Difference 0.06 0.04 0.02 0.11 0.05 0.08 0.12 0.00 0.12 – 0.00 0.06 0.1 0.04 0.05 0.01 – – 0.05
Experimental pKa values are taken from Sternberg’s results [39]. Calculation is performed at a protein dielectric constant of 4. Not determined.
Fig. 1. Linear correlation between calculated and experimental values of pKa for the BAS shown in Table 3. Table 4 pH optima and other properties of the xylanases. Xylanase
pKa calculated E78
pKa calculated E172
pH optimum calculated
pH optimum observed
Relative activitya [mean ± SD] (%)
Wild type Q7D D11R G34R V82D Q175R
– −0.59 +0.70 +0.40 −0.58 +0.54
– −0.57 +0.82 +0.36 −1.22 +0.54
– −0.58 +0.76 +0.38 −0.90 +0.54
– −0.5 0.0 +1.0 −0.5 +1.5
100 2.82 15.6 62.63 0.75 217.1
SD, standard deviation. a Relative activities were measured at pH 7.0 and 40 ◦ C. The specific activity of the wild type was 1209.53 U/mg. b Half-lives were measured at pH 7.0 and 50 ◦ C.
± ± ± ± ± ±
3.99 0.66 0.32 0.12 0.06 23.62
Half-lifeb [mean ± SD] (s) 140 20 25 50 20 175
± ± ± ± ± ±
8 1 6 11 4 11
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Fig. 2. Schematic representation of the rationale used in this study. (A) Structure of BCX showing the mutation sites (blue), catalytic residues (red), titratable residues between catalytic residues and mutation sites (yellow) and control mutation site (black), (B) schematic representation of BCX with two acidic catalytic sites in the inner circle, a natively present acidic residue(s) surrounding the catalytic cleft and an engineered basic residue (D11R) without a titratable residue between the catalytic residues and the mutation ˚ ˚ (C) an engineered basic residue (Q175R or G34R) at the outer periphery (175R C-E78 C␦/E172 C␦, 16.3 A/10.58 ˚ ˚ 175R C/174Y A), A; site (11R C-E78 C␦/E172 C␦, 10.51 A/8.87 ˚ 174Y OH-E78 C␦/E172 C␦, 13.7 A/6.5 ˚ ˚ 34R C-E78 C␦/E172 C␦, 14.07 A/12.33 ˚ ˚ and (D) an engineered acidic residue (V82D or Q7D) at the outer periphery (82D A; A), OH, 2.8 A; ˚ ˚ 82D C␥-80Y OH/65Y OH, 9.2 A/7.4 ˚ ˚ 80Y OH-E78 C␦/E172 C␦, 3.7 A/3.5 ˚ ˚ 65Y OH-E78 C␦/E172 C␦, 10 A/6.2 ˚ ˚ (For interpretation of the A; A; A; A). C␥-E78 C␦/E172 C␦, 12.95 A/9.98 references to color in this figure legend, the reader is referred to the web version of this article.)
parameters were applied to pKa calculations for the mutant BCX models to confirm whether the selected mutants could shift the pH optimum of BCX toward desirable directions. 1 control mutant (D11R) and 4 mutations (G34R and Q175R for alkaline shift, and Q7D and V82D for acidic shift) showing comparatively larger shifts in active site pKa s were chosen as the final candidates for the sitedirected mutagenesis experiment (Table 4). 3.4. Measurement of the optimum pHs of the xylanases Among 5 mutants studied in this work, the control mutant D11R shows no change in the pH optimum and the pH-activity profile (pH optimum 5.5, Fig. 3A and Table 4). There is no titratable residue located between the mutation site and the catalytic sites. Therefore, this mutation best serves as a control. The Arg-mutations are distant from the catalytic residues and a large shift in the pH optimum is shown by the mutant Q175R (1.5 unit). Mutant Q175R has a pH optimum of 7.0 (Fig. 3A and Table 4). Q175R is located at >10.5 A˚ distance from the nearest catalytic site (E172) (Table 3). Y174 is between the interaction planes of E78, E172 and R175 ˚ NH1 of Q175R makes a favorable (R175 C/Y174 OH, 7.55 A).
interaction with Y174. Such a favorable interaction of NH2 /NH1 of R175 with the Y174 in the vicinity of the catalytic cleft is the major contributing factor to increasing the pKa of the catalytic sites. Sternberg et al. [39] have reported the distance-dependent change in pKa of H64 in BAS by the charged groups; the effect is observed when the ˚ Ravi Rajagopalan et al. [40] have reported group is placed at 17.6 A. the change in enzyme activity when the mutating group is placed 15 A˚ from the catalytic sites. Turunen et al. [41] introduced a series of multiple arginines into the Ser/Thr surface of Trichoderma reesei xylanase II (TRXII) (PDB ID: 1XYP). Five neutral residues were substituted (S186R, N67R, T26R, Q34R and S40R; S186R, N67R, T26R, Q34R and N69R), and the result was a shift in the pH optimum by 0.5–1.0 units. The mutation sites of TRXII are >15 A˚ from the catalytic glutamates (E86 and E177). The mutation G34R in our study ˚ from the active site residues. Somehow, Y174 is is distant (>13 A) located in between the mutation site and the catalytic sites. The mutant G34R shows an alkaline-side shifting of the optimum pH by 1 unit (Fig. 3A and Table 4). Mutant Q7D shows a sharp decline in the alkaline-side activity. It has a pH optimum of 5.0 (Fig. 3B and Table 4). Q7D is located at almost equidistance to the catalytic residues (Table 2). Y69 is between the catalytic sites and Q7D; Y69 is involved in substrate
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the mutant V82D seems to be due to charge repulsion between the tyrosines and the catalytic glutamates. Microscopic pKa values of the amino acid residues depend upon desolvation and background interaction effects. The macroscopic pKa values are dependent upon interaction with the titratable groups in the proteins, which are largely influenced by the orientations and the distance between the titratable residues. Therefore, the active site pKa values can be designed by carefully placing the mutated residue(s) around the catalytic cleft. A long-standing challenge is to define experimentally and theoretically the factors that establish the precise pKa values of these catalytically essential groups along a given reaction pathway and thereby set the conditions of pH under which an enzyme is maximally active. The rational design strategy used in this study could give a better understanding of these factors that would aid in the engineering of enzymes with tailored pH optima. 4. Conclusion The results of introducing Arg and Asp in juxtaposition to the natively present acidic residue(s) show that the pH optimum of the enzyme can be significantly changed. The shift in pH optimum seems to be due to charge repulsion; the charge repulsion may be direct, between the Asp inserted and the catalytic glutamates, or it may be indirect. Introducing Arg at sites located far from the catalytic sites shows that the alkaline-side shift in the pH optimum can be achieved when the pKa of the acidic residue (Tyr) in the immediate surroundings of the catalytic glutamates is decreased by the interaction with the newly placed Arg, thereby resulting in charge repulsion between the tyrosine and the catalytic glutamate. In the case of Asp-mutations, the pKa of the natively present acidic residue (Tyr) is increased; such a shift plays a role by decreasing the pKa s of the catalytic glutamates. The strategies presented in this study can be applied to shift the pH optima of the families of enzymes harboring acidic residues as their catalytic residues. Fig. 3. pH-activity profile of wild type () and (A) mutants D11R (), G34R () and Q175R (䊉), as well as (B) mutants Q7D () and V82D (䊉).
Acknowledgements binding, and forms hydrogen bonds with the catalytic nucleophile E78 [29]. There is direct interaction (charge repulsion) between E172 and D7, but the interaction with E78 is obstructed by Y69. The accessible surface area of Q7D calculated with VADAR [42] is 9.5 A´˚ 2 . Generally, the hydrophobic environment tends to increase the pKa of negatively charged residues, and the decrease in the pH optimum in the mutant Q7D seems to be due to charge repulsion between Q7D and the catalytic glutamate E172 (direct interaction). The interaction of Q7D with Y69 (charge repulsion) cannot be ignored, as it results in decreased pKa s of the catalytic glutamates. Generally, the interactions between acidic residues tend to lower the pKa of the residue with the lower intrinsic pKa . In Bacillus agaradhaerens (PDB ID: 1QH7), E17 (equivalent to Q7 in BCX) is involved in the binding of thesubstrate and in a salt bridge network with the residues R49, K53, and E178. The charge modification of this residue in Bacillus sp. 41M-1 (PDB ID: 2DCJ) may redistribute the electronic cloud in the catalytic cleft. In regard to the broad shape of the activity profile of Bacillus pumilus [43], this salt-bridge network may partly contribute to the alkaline activity adaptation. V82 (BCX) is highly conserved among the family 11 xylanases; its mutant V82D shows the optimum pH of 5.0 (Fig. 3B and Table 4). D82 is far ˚ ˚ Y80 (a from the catalytic sites (D82 C␥/E78 C␦/E172 C␦, 13 A/10 A). substrate-binding residue that forms an H-bond with the acid/base catalyst E172) and Y65 are between the mutation site and the catalytic sites. V82D interacts with Y65 and Y80, and the shifts in the pKa s of these two tyrosines are the determinants in lowering the pKa of the catalytic glutamates. The decrease in the pH optimum of
This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (MEST) (NRF2011-C1AAA001-2011-0030345). Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/ j.procbio.2012.10.011. References [1] Henrissat B, Davies G. Structural and sequence-based classification of glycoside hydrolases. Curr Opin Struct Biol 1997;7:637–44. [2] Collins T, Gerday C, Feller G. Xylanases, xylanase families and extremophilic xylanases. FEMS Microbiol Rev 2005;29:3–23. [3] Törrönen A, Harkki A, Rouvinen J. Three-dimensional structure of endo-1,4-xylanase II from Trichoderma reesei: two conformational states in the active site. EMBO J 1994;13:2493–501. [4] Dominguez R, Souchon H, Spinelli S, Dauter Z, Wilson KS, Chauvaux S, et al. A common protein fold and similar active site in two distinct families of glycanases. Nat Struct Biol 1995;2:569–76. [5] Antranikian G. Xylanolytic enzymes from fungi and bacteria. Crit Rev Biotechnol 1997;17:39–67. [6] Wouters J, Georis J, Engher D, Vandenhaute J, Dusart J, Frere JM, et al. Crystallographic analysis of family 11 endo--l,4-xylanase Xyl1 from Streptomyces sp. S38. Acta Crystallogr, Sect D: Biol Crystallogr 2001;57:1813–9. [7] Joo JC, Pohkrel S, Pack SP, Yoo YJ. Thermostabilization of Bacillus circulans xylanase via computational design of a flexible surface cavity. J Biotechnol 2010;146:31–9.
S. Pokhrel et al. / Process Biochemistry 47 (2012) 2487–2493 [8] Joo JC, Pack SP, Kim YH, Yoo YJ. Thermostabilization of Bacillus circulans xylanase: computational optimization of unstable residues based on thermal fluctuation analysis. J Biotechnol 2011;151:56–65. [9] Purmonen M, Valjakka J, Takkinen K, Laitinen T, Rouvinen J. Molecular dynamics studies on the thermostability of family 11 xylanases. Protein Eng Des Sel 2007;20:551–9. [10] Verma D, Satyanarayana T. Molecular approaches for ameliorating microbial xylanases. Bioresour Technol 2012;117:360–7. [11] Yang JH, Park JY, Kim SH, Yoo YJ. Shifting pH optimum of Bacillus circulans xylanase based on molecular modeling. J Biotechnol 2008;133:294–300. [12] Liu L, Wang B, Chen H, Wang S, Wang M, Zhang S, et al. Rational pH-engineering of the thermostable xylanase based on computational model. Process Biochem 2009;44:912–5. [13] Kongsted J, Ryde U, Wydra J, Jensen JH. Prediction and rationalization of the pH dependence of the activity and stability of family 11 xylanases. Biochemistry 2007;46:13581–92. [14] Kim SH, Pokhrel S, Yoo YJ. Mutation of non-conserved amino acids surrounding catalytic site to shift pH optimum of Bacillus circulans xylanase. J Mol Catal B: Enzym 2008;55:130–6. [15] Russell AJ, Fersht AR. Rational modification of enzyme catalysis by engineering surface charge. Nature 1987;328:496–500. [16] Thomas PG, Russell AJ, Fersht AR. Tailoring the pH dependence of enzyme catalysis using protein engineering. Nature 1985;318:375–6. [17] Tynan-Connolly BM, Nielsen JE. Redesigning protein pKa values. Protein Sci 2007;16:239–49. [18] Nielsen JE, Vriend G. Optimizing the hydrogen-bond network in Poisson–Boltzmann equation-based pKa calculations. Proteins: Struct Funct Genet 2001;43:403–12. [19] Shirai T, Suzuki A, Yamane T, Ashida T, Kobayashi T, Hitomi J, et al. Highresolution crystal structure of M-protease: phylogeny aided analysis of the high-alkaline adaptation mechanism. Protein Eng 1997;10:627–34. [20] Umemoto H, Ihsanawati Inami M, Yatsunami R, Fukui T, Kumasaka T, Tanaka N, et al. Improvement of alkaliphily of Bacillus alkaline xylanase by introducing amino acid substitutions both on catalytic cleft and protein surface. Biosci Biotechnol Biochem 2009;73:965–7. [21] Michaux C, Pouyez J, Mayard A, Vandurm P, Housen I, Wouters J. Structural insights into the acidophilic pH adaptation of a novel endo-1,4--xylanase from Scytalidium acidophilum. Biochimie 2010;92:1407–15. [22] Sinnott ML. Catalytic mechanisms of enzymic glycosyl transfer. Chem Rev 1990;90:1171–202. [23] Gebler J, Gilkes NR, Claeyssens M, Wilson DB, Beguin P, Wakarchuk WW, et al. Stereoselective hydrolysis catalyzed by related -1,4-glucanases and -1,4xylanases. J Biol Chem 1992;267:12559–61. [24] McIntosh LP, Hand G, Johnson PE, Joshi MD, Korner M, Plesniak LA, et al. The pK(a) of the general acid/base carboxyl group of a glycosidase cycles during catalysis: a 13C-NMR study of Bacillus circulans xylanase. Biochemistry 1996;35:9958–66. [25] Miao S, Ziser L, Aebersold R, Withers SG. Identification of glutamic acid 78 as the active site nucleophile in Bacillus subtilis xylanase using electrospray tandem mass spectrometry. Biochemistry 1994;33:7027–32.
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[26] Sidhu G, Withers SG, Nguyen NT, McIntosh LP, Ziser L, Brayer GD. Sugar ring distortion in the glycosyl-enzyme intermediate of a family G/11 xylanase. Biochemistry 1999;38:5346–54. [27] Sung WL, Luk CK, Zahab DM, Wakarchuk W. Overexpression of the Bacillus subtilis and circulans xylanases in Escherichia coli. Protein Exp Purif 1993;4: 200–6. [28] Joshi MD, Sidhu G, Pot I, Brayer GD, Withers SG, McIntosh LP. Hydrogen bonding and catalysis: a novel explanation for how a single amino acid substitution can change the pH optimum of a glycosidase. J Mol Biol 2000;299:255–79. [29] Wakarchuk WW, Campbell RL, Sung WL, Davoodi J, Yaguchi M. Mutational and crystallographic analyses of the active site residues of the Bacillus circulans xylanase. Protein Sci 1994;3:467–75. [30] Zheng L, Baumann U, Reymond JL. An efficient one-step site-directed and sitesaturation mutagenesis protocol. Nucleic Acids Res 2004:32. [31] Li ZL, Bricker TM, Burnap R. Kinetic characterization of His-tagged CP47 photosystem II in Synechocystis sp. PCC6803. Biochim Biophys Acta – Bioenergetics 2000;1460:384–9. [32] Laemmli UK. Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature 1970;227:680–5. [33] Miller GL. Use of dinitrosalicylic acid reagent for determination of reducing sugar. Anal Chem 1959;31:426–8. [34] Nielsen JE. Analysing the pH-dependent properties of proteins using pKa calculations. J Mol Graph Model 2007;25:691–9. [35] Chinea G, Padron G, Hooft RWW, Sander C, Vriend G. The use of positionspecific rotamers in model building by homology. Proteins: Struct Funct Genet 1995;23:415–21. [36] Nicholls A, Honig B. A rapid finite difference algorithm, utilizing successive over-relaxation to solve the Poisson–Boltzmann equation. J Comput Chem 1991;12:435–45. [37] Honig B, Sharp K, Yang AS. Macroscopic models of aqueous solutions: biological and chemical applications. J Phys Chem 1993;97:1101–9. [38] Klingen AR, Bombarda E, Ullmann GM. Theoretical investigation of the behavior of titratable groups in proteins. Photochem Photobiol Sci 2006;5: 588–96. [39] Sternberg MJE, Hayes FRF, Russell AJ, Thomas PG, Fersht AR. Prediction of electrostatic effects of engineering of protein charges. Nature 1987;330:86–8. [40] Ravi Rajagopalan PT, Lutz S, Benkovic SJ. Coupling interactions of distal residues enhance dihydrofolate reductase catalysis: mutational effects on hydride transfer rates. Biochemistry 2002;41:12618–28. [41] Turunen O, Vuorio M, Fenel F, Leisola M. Engineering of multiple arginines into the Ser/Thr surface of Trichoderma reesei endo-1,4--xylanase II increases the thermotolerance and shifts the pH optimum towards alkaline pH. Protein Eng 2002;15:141–5. [42] Willard L, Ranjan A, Zhang H, Monzavi H, Boyko RF, Sykes BD, et al. VADAR. A web server for quantitative evaluation of protein structure quality. Nucleic Acids Res 2003;31:3316–9. [43] Liu X, Qu Y, You F, Liu Y. Studies on the key amino acid residues responsible for the alkali-tolerance of the xylanase by site-directed or random mutagenesis. J Mol Catal B: Enzym 2002;18:307–13.
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Process Biochemistry journal homepage: www.elsevier.com/locate/procbio
Rational design of a Bacillus circulans xylanase by introducing charged residue to shift the pH optimum Subarna Pokhrel a , Jeong Chan Joo a , Yong Hwan Kim b , Young Je Yoo a,∗ a b
School of Chemical and Biological Engineering, Seoul National University, Seoul 151-742, Republic of Korea Department of Chemical Engineering, Kwangwoon University, Seoul 139-701, Republic of Korea
a r t i c l e
i n f o
Article history: Received 25 July 2012 Received in revised form 4 October 2012 Accepted 22 October 2012 Available online 29 October 2012 Keywords: Bacillus circulans xylanase Electrostatic interaction Enzyme engineering pH optimum Site-directed mutagenesis
a b s t r a c t Introduction or disruption of long-range electrostatic interactions can be an effective way to change the pKa s of catalytic residues and the pH optima of enzymes. In particular, shifting the pH optima toward the acidic or basic limb is an important issue for industrial applications of xylanases, e.g., for the paper or food industries. Here, we suggest an effective strategy to shift the pH optimum of an enzyme by introducing charged residue. Our strategy is to alter the titration behavior of the strongly interacting catalytic glutamates in Bacillus circulans xylanase by introducing acidic or basic residue in juxtaposition to the natively present acidic residues surrounding the catalytic site, thereby shifting the pH-activity ˚ away from the catalytic sites. The strategy profile. Mutation sites were chosen to be long distances (>8.5 A) was verified by site-directed mutagenesis experiments. The results show that the pH optimum can be changed (−0.5 to 1.5 unit) by strategically selecting the mutation sites. The strategies developed can effectively be applied to change the pH optima of the families of enzymes harboring acidic residues as catalytic residues. © 2012 Elsevier Ltd. All rights reserved.
1. Introduction Endo--1,4-xylanases form a group of enzymes that hydrolyze xylan (a major component of hemicelluloses). Endo--1,4xylanases mainly have been grouped into families 10 and 11 of the glycoside hydrolases, although families 5, 7, 8, and 43 also contain some xylanolytic properties [1,2]. Family 10 xylanases show eightfold ␣/ barrel structures and have molecular masses > 30 kDa, whereas family 11 xylanases have an all -strand sandwich fold structure resembling a partly closed “right hand” and a lower molecular mass of ∼20 kDa [3,4]. Xylanases are widely used in many industrial applications such as pulp biobleaching in the paper industry, baking, saccharification of lignocellulosic biomass, wine making, and fruit juice clarification [5,6]. These processes are operated under harsh conditions, e.g., high temperatures or extremes of pH, and thus, the most important features required for the industrial applications of xylanases are thermostability and extreme pH optima. The strategy for improving the thermostability of xylanases has been thoroughly studied by experiments and simulations [7–10]. Still, there are few reports regarding the shift of pH optima in xylanases. Although the pH optima of xylanases can be shifted by protein engineering [11–14], shifting of the pH
∗ Corresponding author. Tel.: +82 2 880 7411; fax: +82 2 887 1659. E-mail address: [email protected] (Y.J. Yoo). 1359-5113/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.procbio.2012.10.011
optima toward desired directions (acidic- or alkaline-side pH optimum shifting) by rational design has not been fully investigated. The pH-dependence of enzymatic activity is primarily governed by the pKa values of the active site residues. The engineering of enzyme charges to shift the pH-activity profile by modulating the interaction with the active site residues has been studied by several researchers [15–17]. Most of these researchers either experienced no change or small changes in the pH-activity profiles of the enzymes studied. This result is partly because diverse families of enzymes harbor different active site residues, different reaction mechanisms and different catalytic cleft dynamics. Despite these issues, some general strategies have been proposed [17,18]. Tynon-Connolly and Nielsen [17] noted the significant role of charged-only mutations in changing the pKa s of the titratable groups by introducing desolvation and electrostatic interactions of the newly added groups. Nielsen and Vriend [18] suggested the importance of introducing neutral residues rather than charged residues to change the pKa s of catalytic groups (␣-amylase) by changing the dynamics of the active site cleft without altering the net charge of the protein. Amino acid sequence comparisons between alkaline and highly alkaline serine proteases suggested that the highly alkaline adaptation would be accompanied by an increase in the number of arginine and neutral hydrophilic residues and also by a decrease in lysine and negatively charged residues [19]. The alkalophilicity of the Bacillus sp. 41M-1 xylanase has been improved by amino acid substitutions [20]. A salt bridge in the
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catalytic cleft, and an excess of Arg residues on the protein surface were introduced to shift the optimum pH of the wild type enzyme from 8.5 to 9.5. Recently, Michaux et al. [21] compared the acidophilic family 11 xylanase from Scytalidium acidophilum (XYL1) with several mesophilic pH-dependent xylanases and found that the number of salt bridges and hydrogen bonds were decreased in acidophilic xylanases compared to the highly alkaline xylanases. According to the studies to date, larger shifts in pKa s of the catalytic groups can be achieved by mutating buried or semi buried residues or by generating mutations closer to the catalytic groups [17]. The latter approach may lead to subtle changes in the dynamics of the catalytic cleft as a result of strong repulsion or attraction by the newly introduced group or by interference with the conserved electrostatic network around the catalytic groups. The result may be a dramatic change in the catalytic performance of the enzyme. Bacillus circulans xylanase (BCX, 20.4 kDa), a family 11 glycosidase member, hydrolyzes xylan substrates with net retention of anomeric configurations. This hydrolysis proceeds via a doubledisplacement mechanism in which a covalent intermediate is formed in the glycosylation step and is subsequently hydrolyzed in the deglycosylation step [22,23]. Previous studies have confirmed that during the glycosylation reaction, Glu78 (pKa 4.6) serves as the nucleophile, which initially is negatively charged, whereas Glu172 (pKa 6.7) functions as a general acid and hence must be protonated [24–26]. In the double-displacement mechanism of BCX, Glu172 plays a dual catalytic role as a general acid in the first step and as a general base in the second. BCX [27], an alkaline xylanase, has been characterized extensively using a wide range of structural, spectroscopic, and enzymatic techniques, and is thus an excellent model system for investigating the factors that establish the pHdependence of the activity of a retaining glycosidase [24–26,28]. The active site of BCX is composed of several highly conserved residues arranged to form an intricate network of hydrogen bonds surrounding the two catalytic glutamates [29]. Here, we attempted to change the pH optimum of enzyme toward desired directions by rational design. The effect of the charged residue mutations in shifting the pH-activity profiles of enzymes was studied using BCX as a model enzyme. The aim of this study was to change the optimum pH of BCX by introducing acidic or basic residue in juxtaposition to the natively present acidic residue(s) surrounding the catalytic glutamates. 2. Materials and methods
Table 1 Primers used in this study. Xylanase
Template
Wild type BCX
Synthetic gene
Q7D
pET 23bBCX
D11R
pET 23bBCX
G34R
pET 23bBCX
V82D
pET 23bBCX
Q175R
pET 23bBCX
a b
Primera Senseb 5 -GGGCCCGGGGCTAGCATGACTGGT-3 Antisenseb 5 GGGCCCGGGCTCGAGTGCGGCCGCAGG-3 Sense 5 -CTACTGGGACAACTGGACAGACGGTG-3 Antisense5 CCAGTTGTCCCAGTAGTCTGTGCTAG-3 Sense 5 -CTGGACA CGCGGTGGCG GTATCGTTAA TG-3 Antisense5 GCCACCGCGTGTCCAGTTTTGCCAG-3 Sense 5 -CTAATACTCGGAACTTCGTAGTCGG-3 Antisense 5 -GAAGTTCCGAGTATTAGACCAATTC-3 Sense 5 TTACGTTGACGACTCTTGGGGAACGTAC-3 Antisense 5 CAAGAGTCGTCAACGTAATATTCAATCAG-3 Sense 5 GGCTACCGGAGCTCTGGTTCTTCCAACG-3 Antisense 5 -AGAGCTCCGGTAGCCTTCGGTCGCC-3
Mutation sites in the sense and antisense primers are underlined. Restriction sites in sense (NheI) and antisense primers (XhoI) are underlined.
100 steps of steepest descent followed by 500 steps of conjugate gradient energy minimization after fixing the heavy atoms. The finite-difference of Poisson–Boltzmann (FDPB) equation was solved to calculate the electrostatic potentials of the functional atoms (Glu O1/O2 for BCX, His N␦1/N2 for BAS) of the catalytic residues using program Delphi under Insight II [36]. As the procedures for calculations on Asp or His are similar, we selected His residue for our calculations. We employed CHARMm19 radii with the point charge (Arg 0.5N1/0.5N2; Lys 1.0N; His 0.5N␦1/0.5N2; Asp 0.5O␦1/0.5O␦2; Glu 0.5O1/0.5O2) assigned only for the functional atoms. The X-ray structure was mapped in a 65-cubed grid, and 4 focusing runs (auto-iterations) were followed ˚ ˚ ˚ ˚ point, 1 A/grid point, 0.5 A/grid point and 0.25 A/grid point). Dielectric (2 A/grid constants of 4 and 80 were used for the protein and the solvent respectively; the ˚ and the Stern ion excluionic strength was 0.1 M, the water probe radius was 1.4 A, ˚ The pKa [37] and the pH optimum were calculated with the sion layer was 2.0 A. equations given below. pKi = i
0 − m 2.303
pH optimum =
pK1 + pK2 2
2.1. Computational 2.1.1. The effect of interaction energy and differences in intrinsic pKa s on the pH-dependent population of protonation states Alteration of the titration behavior of two strongly interacting acidic residues (two catalytic glutamates, in this study) by other acidic and basic residues was studied by using the pKa system module in pKa tool [34]. Assignment of intrinsic pKa s to the interacting groups derives from prior knowledge of the active site groups in BCX, in which, generally, the two catalytic groups differ in their intrinsic pKa s by ∼1 unit. If the residues are assumed to be placed equidistantly on a straight line and embedded in a uniform dielectric constant medium, interaction between the residues becomes distance dependent. The systems of more than one residue can be applied, for practical purposes, to shift the pKa (s) of the target residue(s). Generally, active site residues in enzymes show a more strongly coupled system, which can be modulated to shift their pKa s toward the acidic or alkaline sides by introducing or removing the charged residues surrounding them.
2.1.2. Selection of mutation sites 2.1.2.1. Data files and parameters used for pKa calculation. The crystal structures of BCX (PDB ID: 1XNB) and Bacillus amyloliquefaciens subtilisin (BAS) (PDB ID: 1GNS) were used for the calculations. Calculations using BAS were solely for the validation of the calculation parameters. All the mutant models were prepared by assigning position-specific rotamers as implemented in the WHATIF Web Interface [35]. All the crystal waters were deleted before minimization and calculation. The Biopolymer module in Insight II was used to assign hydrogens, according to the unfilled valences in each atom. Hydrogens were assigned at pH 7.0. Each structure was subjected to
where 0 = potential at site i due to the original group, m = potential at site i due to the mutated group, I = −1 or +1 for an acidic or basic group, pK1 , pK2 = change in pKa of E78 and E172. 2.2. Experimental 2.2.1. Cloning, mutagenesis and protein expression The gene construct encoding BCX was the product of our previous study [14]. Site-directed mutagenesis was performed using partially overlapping primers [30]. 2× PremixG PCR buffer (FailSafeTM PCR system, EPICENTRE Biotechnologies, Madison, USA) and nPfu-forte DNA polymerase (Enzynomics, South Korea) were used. The mutagenesis primers are shown in Table 1. Standard commercial kits were used for plasmid isolation and purification. 50–100 ng template, 2 M primer pair and 2.5 U of DNA polymerase were mixed with 25 l 2× PremixG buffer to a final volume of 50 l and thermocycled (94 ◦ C for 3 min; 16 cycles of 94 ◦ C for 1 min, 52 ◦ C for 1 min and 68 ◦ C for 8 min; followed by 1 h incubation at 68 ◦ C) to amplify the whole vector construct with the mutation site inserted. The native vector was removed after digestion with DpnI, and was then transformed into Escherichia coli BL21 by the chemical method. Transformed E. coli BL21 was grown in LB medium at 37 ◦ C overnight. Plasmid isolation and purification were performed using standard kits and were confirmed by automated sequencing (Cosmogenetech, Korea). For enzyme expression, E. coli BL21 was grown until absorbance reached to 0.5–0.6 at 600 nm, and was induced (0.75 mM IPTG) for 20 h at 20 ◦ C. Enzyme purification was performed by affinity chromatography using a Ni–chelate matrix (for His-proteins) [31] and analyzed by SDS-PAGE [32].
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2.2.2. Optimum pH and activity determination The xylanase assay was performed by the modified dinitrosalicylic acid (DNS) method [33]. The pH-dependence was determined in triplicate at different pHs (from pH 4.0 to 8.0 at 0.5 pH unit intervals) using the following buffers: CH3 COONa/CH3 COOH 0.1 M (pH 4–5.5) and K2 HPO4 /KH2 PO4 0.1 M (pH 6.0–8.0), using 0.5% birchwood xylan (SIGMA-ALDRICH CHEMIE, Gmbh, Germany) at 50 ◦ C. Half-life without substrate was measured at pH 7.0 (0.1 M Na2 HPO4 /NaH2 PO4 buffer) after preincubating the enzymes at 50 ◦ C in a JEIO TECH SBW-10 water bath (Korea) for 30–300 s. Half-lives were determined using the equation t1/2 = ln 2/k, where k is the inactivation constant. Reaction velocities were measured at pH 7 (0.1 M Na2 HPO4 /NaH2 PO4 buffer) and 40 ◦ C by using 1% birchwood xylan (SIGMAALDRICH CHEMIE Gmbh, Germany).
Table 2 Distances between the substituted groups and the active sites.
3. Results and discussion
Taking the average of these catalytically competent protonation state (CCPS) gives a significant clue to the interpretation of the shifted pH-activity profile of the enzymes, where active site residues interact strongly. The introduction of one basic residue (Fig. S1f) into the cluster of charges with three acidic residues (Fig. S1e) alters the titration behavior of the first two acidic residues, as observed based on the changes in the microstate population (Fig. S1b and S1e). The systems of 3 natively present acidic residues and 1 engineered acidic residue are shown in Fig. S1g and S1h, respectively.
3.1. Effects of interaction energy and differences in intrinsic pKa s on the pH-dependent population of protonation states To develop the rationale for shifting the pH optimum of BCX, a strategy that employs the introduction of charged residue at the periphery of the enzyme active sites was tested. The introduction of a charged residue alters the titration behavior (pKa s) of the catalytic glutamates and finally leads to a shifting of the pH optimum; these results were validated by calculating the pH-dependent population of protonation states of multiple charged residues. In a system with a single titratable group, the titration curve is sigmoidal; even in the cases of different conformations, a nonsigmoidal titration curve is not observed if the group does not show chemical shifts in different conformations [38]. However, modulation of pKa s by electrostatic interactions is in a very dynamic state of interplays among the titratable residues involved. This effect is largely dependent upon the intrinsic pKa s, the strength of interaction energies and the conformational flexibility; therefore, titration curves for systems with more than one titratable group can be nonsigmoidal. In a system with two strongly interacting groups with dissimilar intrinsic pKa s (4.0 and 6.0) and somewhat similar intrinsic pKa s of 4.5 and 5.5, four different protonation microstates can be observed under varying pH conditions. Due to the stronger interactions, the real-site titration curves have a non-sigmoidal shape. The total protonation probability is the sum of the two real-site titration curves and the two quasi-site titration curves, and one of the microstates is superseded by the other (Fig. S1a and Table S1). In the case of similar intrinsic pKa s (4.5 and 5.5), all the four microstates are clearly observed (Fig. S1b and Table S1). The pHdependent populations of different protonation states are shown in Fig. S1a (acidic/acidic), S1b (acidic/acidic, somewhat similar intrinsic pKa s), S1c (basic/basic) and S1d (acidic/basic). If the number of interacting residues is increased to more than two, the interactions become more complex to interpret [17,38]. For a globular protein, if the molecule has a uniform dielectric constant and all the interacting residues are equidistant to each other on the same straight line (Fig. S1a–h), the charge–charge interaction becomes distance dependent. In this case, the microscopic pKa shift can be explained easily. In the system with 3 and 4 interacting residues, the interactions become complex, with many microstates with varying degrees of population. All of these microstates may not be functional in a particular pH environment of a reaction. Some of the microstates become dominant over the others and only some show the functional property. Interactions among three acidic residues (Fig. S1e) and interactions among three acidic and one basic residue (Fig. S1f) are shown. If the first two residues are the catalytic glutamates E78 and E172 in BCX, any other acidic residue(s) surrounding the catalytic cleft as the third one and a mutated basic residue as the fourth one, then the alteration in titration behavior can be interpreted in terms of the catalytically functional microstates. Only the (1, 0, 0, 0), (1, 0, 0, 1), (1, 0, 1, 0) and (1, 0, 1, 1) microstates are catalytically able to act on substrates.
Native/substituted group Arg-group mutation 11D C␥/R C 34G C␣/R C 175Q C␦/R C Asp-group mutation 7Q C␦/D C␥ 82V C/D C␥
Distance to E172 C␦ [Å]
Distance to E78 C␦ [Å]
Average distance [Å]
10.24/8.87 10.25/12.33 12.35/10.58
12.94/10.51 14.53/14.07 16.54/16.3
11.59/9.69 12.39/13.2 14.44/13.44
9.89/11.65 9.65/9.98
10.03/11.48 11.95/12.95
9.96/11.56 10.8/11.46
3.2. Selection of mutation sites As shown in Fig. S1, the pH optimum of BCX can be modulated by introducing charged residues affecting the pKa values of the catalytic glutamates (E78 and E172). All the mutation sites were selected at a distance of more than 8.5 A˚ from the catalytic glutamates (Fig. 2A). Mutant D11R serves as a control, i.e., there is no acidic residue between the mutation site and the catalytic sites (Fig. 2B). Two general strategies are presented in this work; each one is best explained by the figures (Fig. 2C and D). The Arg- and Asp-mutations were introduced at the periphery of the enzyme’s active sites to shift the pH optimum toward alkaline and acidic directions, respectively, by keeping the natively present acidic residues between the catalytic residues and the mutation sites. Mutations Q175R, G34R, Q7D and V82D are in a straight line with the catalytic glutamates. The Arg-mutations (G34R and Q175R, Fig. 2C) and Asp-mutations (Q7D and V82D, Fig. 2D) were introduced to shift the pH optimum toward the alkaline and acidic directions, respectively. Y174 is between the catalytic sites and the mutation site Q175. Y174 is present somehow between the catalytic site and the mutation site G34. Fig. 2D best explains the position of the mutant Q7D, with Y69 being between the catalytic site and the mutation site. Similarly, Y65 and Y80 are present between the catalytic site and the mutation site V82. The distances between the substituted groups and the catalytic sites are shown in Table 2. The average distance from the substi˚ In all the mutations, tuted group to the catalytic sites is 8.87–16.3 A. mutation sites are at a closer distance to the acid/base catalyst (E172) compared to the nucleophile (E78) except in the mutant Q7D where the mutation site is equidistant to both of the catalytic glutamates (Fig. 2A and Table 2). 3.3. pKa calculations for the xylanases To accurately predict the pKa changes for the catalytic glutamates caused by the Asp- and Arg-mutations, the pKa calculation method used in this study was validated by comparing the calculated and experimental pKa values for the wild type and mutant models of BAS at different ionic strengths (Table 3). The calculated pKa values for the BAS mutants are similar to the experimental results showing a good linear correlation (R2 = 0.92567, Fig. 1), which indicates that our method is reliable for predicting the pKa values of titratable residues. After standardization, the same
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Table 3 Validation of calculated pKa values with the experimental results for mutants of Bacillus amyloliquefaciens subtilisin (PDB ID: 1GNS). Calculated pKa
Ionic strength [mM]
Mutations
Target residue
a
Experimental pKa
b
1 1 1 1 1 5 10 25 100 145 500 1000 5 10 25 100 145 500 1000
D36Q K136A K170A D99S + E156S K170A + K213A D99S D99S D99S D99S D99S D99S D99S E156S E156S E156S E156S E156S E156S E156S
H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64 H64
−0.18 +0.11 +0.20 −0.63 +0.39 −0.38 −0.42 −0.36 −0.29 N/Dc −0.1 −0.02 −0.32 −0.44 −0.41 −0.25 N/Dc N/Dc −0.06
−0.24 +0.07 +0.18 −0.74 +0.34 −0.30 −0.28 −0.36 −0.17 −0.16 −0.1 −0.08 −0.42 −0.40 −0.36 −0.26 −0.23 −0.15 −0.11
a b c
Difference 0.06 0.04 0.02 0.11 0.05 0.08 0.12 0.00 0.12 – 0.00 0.06 0.1 0.04 0.05 0.01 – – 0.05
Experimental pKa values are taken from Sternberg’s results [39]. Calculation is performed at a protein dielectric constant of 4. Not determined.
Fig. 1. Linear correlation between calculated and experimental values of pKa for the BAS shown in Table 3. Table 4 pH optima and other properties of the xylanases. Xylanase
pKa calculated E78
pKa calculated E172
pH optimum calculated
pH optimum observed
Relative activitya [mean ± SD] (%)
Wild type Q7D D11R G34R V82D Q175R
– −0.59 +0.70 +0.40 −0.58 +0.54
– −0.57 +0.82 +0.36 −1.22 +0.54
– −0.58 +0.76 +0.38 −0.90 +0.54
– −0.5 0.0 +1.0 −0.5 +1.5
100 2.82 15.6 62.63 0.75 217.1
SD, standard deviation. a Relative activities were measured at pH 7.0 and 40 ◦ C. The specific activity of the wild type was 1209.53 U/mg. b Half-lives were measured at pH 7.0 and 50 ◦ C.
± ± ± ± ± ±
3.99 0.66 0.32 0.12 0.06 23.62
Half-lifeb [mean ± SD] (s) 140 20 25 50 20 175
± ± ± ± ± ±
8 1 6 11 4 11
S. Pokhrel et al. / Process Biochemistry 47 (2012) 2487–2493
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Fig. 2. Schematic representation of the rationale used in this study. (A) Structure of BCX showing the mutation sites (blue), catalytic residues (red), titratable residues between catalytic residues and mutation sites (yellow) and control mutation site (black), (B) schematic representation of BCX with two acidic catalytic sites in the inner circle, a natively present acidic residue(s) surrounding the catalytic cleft and an engineered basic residue (D11R) without a titratable residue between the catalytic residues and the mutation ˚ ˚ (C) an engineered basic residue (Q175R or G34R) at the outer periphery (175R C-E78 C␦/E172 C␦, 16.3 A/10.58 ˚ ˚ 175R C/174Y A), A; site (11R C-E78 C␦/E172 C␦, 10.51 A/8.87 ˚ 174Y OH-E78 C␦/E172 C␦, 13.7 A/6.5 ˚ ˚ 34R C-E78 C␦/E172 C␦, 14.07 A/12.33 ˚ ˚ and (D) an engineered acidic residue (V82D or Q7D) at the outer periphery (82D A; A), OH, 2.8 A; ˚ ˚ 82D C␥-80Y OH/65Y OH, 9.2 A/7.4 ˚ ˚ 80Y OH-E78 C␦/E172 C␦, 3.7 A/3.5 ˚ ˚ 65Y OH-E78 C␦/E172 C␦, 10 A/6.2 ˚ ˚ (For interpretation of the A; A; A; A). C␥-E78 C␦/E172 C␦, 12.95 A/9.98 references to color in this figure legend, the reader is referred to the web version of this article.)
parameters were applied to pKa calculations for the mutant BCX models to confirm whether the selected mutants could shift the pH optimum of BCX toward desirable directions. 1 control mutant (D11R) and 4 mutations (G34R and Q175R for alkaline shift, and Q7D and V82D for acidic shift) showing comparatively larger shifts in active site pKa s were chosen as the final candidates for the sitedirected mutagenesis experiment (Table 4). 3.4. Measurement of the optimum pHs of the xylanases Among 5 mutants studied in this work, the control mutant D11R shows no change in the pH optimum and the pH-activity profile (pH optimum 5.5, Fig. 3A and Table 4). There is no titratable residue located between the mutation site and the catalytic sites. Therefore, this mutation best serves as a control. The Arg-mutations are distant from the catalytic residues and a large shift in the pH optimum is shown by the mutant Q175R (1.5 unit). Mutant Q175R has a pH optimum of 7.0 (Fig. 3A and Table 4). Q175R is located at >10.5 A˚ distance from the nearest catalytic site (E172) (Table 3). Y174 is between the interaction planes of E78, E172 and R175 ˚ NH1 of Q175R makes a favorable (R175 C/Y174 OH, 7.55 A).
interaction with Y174. Such a favorable interaction of NH2 /NH1 of R175 with the Y174 in the vicinity of the catalytic cleft is the major contributing factor to increasing the pKa of the catalytic sites. Sternberg et al. [39] have reported the distance-dependent change in pKa of H64 in BAS by the charged groups; the effect is observed when the ˚ Ravi Rajagopalan et al. [40] have reported group is placed at 17.6 A. the change in enzyme activity when the mutating group is placed 15 A˚ from the catalytic sites. Turunen et al. [41] introduced a series of multiple arginines into the Ser/Thr surface of Trichoderma reesei xylanase II (TRXII) (PDB ID: 1XYP). Five neutral residues were substituted (S186R, N67R, T26R, Q34R and S40R; S186R, N67R, T26R, Q34R and N69R), and the result was a shift in the pH optimum by 0.5–1.0 units. The mutation sites of TRXII are >15 A˚ from the catalytic glutamates (E86 and E177). The mutation G34R in our study ˚ from the active site residues. Somehow, Y174 is is distant (>13 A) located in between the mutation site and the catalytic sites. The mutant G34R shows an alkaline-side shifting of the optimum pH by 1 unit (Fig. 3A and Table 4). Mutant Q7D shows a sharp decline in the alkaline-side activity. It has a pH optimum of 5.0 (Fig. 3B and Table 4). Q7D is located at almost equidistance to the catalytic residues (Table 2). Y69 is between the catalytic sites and Q7D; Y69 is involved in substrate
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the mutant V82D seems to be due to charge repulsion between the tyrosines and the catalytic glutamates. Microscopic pKa values of the amino acid residues depend upon desolvation and background interaction effects. The macroscopic pKa values are dependent upon interaction with the titratable groups in the proteins, which are largely influenced by the orientations and the distance between the titratable residues. Therefore, the active site pKa values can be designed by carefully placing the mutated residue(s) around the catalytic cleft. A long-standing challenge is to define experimentally and theoretically the factors that establish the precise pKa values of these catalytically essential groups along a given reaction pathway and thereby set the conditions of pH under which an enzyme is maximally active. The rational design strategy used in this study could give a better understanding of these factors that would aid in the engineering of enzymes with tailored pH optima. 4. Conclusion The results of introducing Arg and Asp in juxtaposition to the natively present acidic residue(s) show that the pH optimum of the enzyme can be significantly changed. The shift in pH optimum seems to be due to charge repulsion; the charge repulsion may be direct, between the Asp inserted and the catalytic glutamates, or it may be indirect. Introducing Arg at sites located far from the catalytic sites shows that the alkaline-side shift in the pH optimum can be achieved when the pKa of the acidic residue (Tyr) in the immediate surroundings of the catalytic glutamates is decreased by the interaction with the newly placed Arg, thereby resulting in charge repulsion between the tyrosine and the catalytic glutamate. In the case of Asp-mutations, the pKa of the natively present acidic residue (Tyr) is increased; such a shift plays a role by decreasing the pKa s of the catalytic glutamates. The strategies presented in this study can be applied to shift the pH optima of the families of enzymes harboring acidic residues as their catalytic residues. Fig. 3. pH-activity profile of wild type () and (A) mutants D11R (), G34R () and Q175R (䊉), as well as (B) mutants Q7D () and V82D (䊉).
Acknowledgements binding, and forms hydrogen bonds with the catalytic nucleophile E78 [29]. There is direct interaction (charge repulsion) between E172 and D7, but the interaction with E78 is obstructed by Y69. The accessible surface area of Q7D calculated with VADAR [42] is 9.5 A´˚ 2 . Generally, the hydrophobic environment tends to increase the pKa of negatively charged residues, and the decrease in the pH optimum in the mutant Q7D seems to be due to charge repulsion between Q7D and the catalytic glutamate E172 (direct interaction). The interaction of Q7D with Y69 (charge repulsion) cannot be ignored, as it results in decreased pKa s of the catalytic glutamates. Generally, the interactions between acidic residues tend to lower the pKa of the residue with the lower intrinsic pKa . In Bacillus agaradhaerens (PDB ID: 1QH7), E17 (equivalent to Q7 in BCX) is involved in the binding of thesubstrate and in a salt bridge network with the residues R49, K53, and E178. The charge modification of this residue in Bacillus sp. 41M-1 (PDB ID: 2DCJ) may redistribute the electronic cloud in the catalytic cleft. In regard to the broad shape of the activity profile of Bacillus pumilus [43], this salt-bridge network may partly contribute to the alkaline activity adaptation. V82 (BCX) is highly conserved among the family 11 xylanases; its mutant V82D shows the optimum pH of 5.0 (Fig. 3B and Table 4). D82 is far ˚ ˚ Y80 (a from the catalytic sites (D82 C␥/E78 C␦/E172 C␦, 13 A/10 A). substrate-binding residue that forms an H-bond with the acid/base catalyst E172) and Y65 are between the mutation site and the catalytic sites. V82D interacts with Y65 and Y80, and the shifts in the pKa s of these two tyrosines are the determinants in lowering the pKa of the catalytic glutamates. The decrease in the pH optimum of
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