Protein data set for determining geometric preferences of salt bridges

To determine the specific geometric features of salt bridges, a protein structure set was obtained from the Pisces web server with the following criteria: resolution <2.5 Å; R<0.25; sequence similarities between any pair protein <25%; and chain length ranging from 60 to 800 residues [34]. The data set comprised of 6,493 X-ray protein structures (Text S1).

Residue pairs of salt bridges

A salt bridge is formed between oppositely charged residues, such as Arg (R) or Lys (K) and Asp (D) or Glu (E). In this study, His (H) was excluded because this amino acid exhibits an ambiguous protonation state at pH 7.0. Therefore, the following four types of attractive interactions were considered: K–D; K–E; R–D; and R–E. The salt bridge is defined based on the following statements; let atom A be or of R or of K, and let atom B be or of D or or of E. A distance between atoms A and B less than 4 Å indicates the formation of a salt bridge between A and B. According to this definition and the considered types of residue pairings, 32,096 salt bridges were isolated from the 6,493 protein structures in the data set.

Relative solvent accessibility and secondary structure locations of salt bridges

The 32,096 salt bridges were classified into buried or exposed state according to the Relative Solvent Accessibility (RSA) of the salt-bridge residues. RSA is the ratio of the solvent exposed surface area of a residue [35]. To calculate the RSA of a salt-bridge residue, the residue's solvent accessibility (ASA) obtained from DSSP program was divided by a reference value of surface area for the given amino acid (X) in an extended Gly–X–Gly peptide. Salt bridges were considered at the exposed state if the RSA of one residue was >35% and that of the other residue was >25% [36], [37]. After that, the exposed salt-bridges were further categorized according to the located secondary structure and other parameters. The following types of secondary structures are defined in the DSSP program: H, G, I, B, E, T, S, and U [35]. For simplicity, these structures were categorized as helix (H = “H, G, and I”), sheet (S = “B and E”), and coil (C = “T, S, and U”).

Geometric orientation of salt bridges

The Cα and Cβ atomic coordinates of two residues (i, j) of a salt bridge were used to determine the angles (θ_1, θ₂). Two angles of a salt bridge (i–j), ∠Cβ₁Cα₁Cα₂ (θ₁) and ∠Cβ₂Cα₂Cα₁ (θ₂), were used to describe the geometric interaction of two oppositely charged groups according to the relative orientation of the two residues’ Cα–Cβ vectors (Figure 1A).

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Figure 1. Spatial orientation and Cα–Cα distances of salt bridges on protein surfaces.

(A) Statitical analysis of angles (θ₁, θ₂) of 10,556 salt bridges on the surfaces of 6,493 X-ray protein structures. Two angles of a salt bridge (i–j), ∠Cβ₁Cα₁Cα₂ (θ₁) and ∠Cβ₂Cα₂Cα₁ (θ₂), were used to describe the charge group interaction based on the relative orientation of the two residues’ Cα–Cβ vectors as indicated. All of the angles are in the range of 0° to 180° (θ₁ and θ₂ color in black and gray). The length of radius are corresponding to Cα–Cα distance (Å). (B) The scatter plot shows the angles (θ₁, θ₂) of the salt bridges at a backbone distance >7 Å, which are restrained within 0° to 110°.

https://doi.org/10.1371/journal.pone.0112751.g001

Salt-bridge pair index of weighted electrostatic attraction model

To find the effective pairings for a salt-bridge design, we developed a weighted electrostatic attraction model , which is a scoring function, to measure the attractive interactions of residue i in a protein structure. is defined as follows:(1)where represents the residues within a radius of 15 Å of ; and are the amino acids of residues and , respectively; is an electrostatic charge function that depends on the charge of the amino acid: if Lys or Arg; if Asp or Glu; and if other types of amino acids. is an empirical parameter generated by the statistical analysis of 10,556 surface salt bridges in the data set. These salt bridges were first classified based on the following parameter set: (1) Cα–Cα distance () of a salt bridge; (2) oppositely charged amino acid types (i.e., and ); and (3) the secondary structures of residues and locations. A set for each type of salt bridges depends on the frequency of occurrence in the 10,556 surface salt bridges. Therefore, these values corresponding to different frequency of specific types of salt bridges could be seen as a salt-bridge pair index to help determine the designable salt bridges with a high probability of formation. In addition, since the repulsive interactions were not taken into consideration, the frequency of repulsive pairs were not analyzed: , if .

Basic rationale of our approach

To design salt bridges on a protein by using our approach, the following procedures were performed. The residues with the following characteristics were isolated: RSA >35%; Z-score of B-factor (z_B-factor) >0; Z-score of rWCN (z_rWCN) >0; and conservation <4. The idea of this pre-filtering was to select the potential residues that can be designed to improve protein thermal stability. Note that only polar residues (Ser, Thr, Cys, Asn, and Gln) and charged residues (Lys, Arg, Asp, and Glu) were considered because polar residues are scarcer in thermophilic proteins compared with their homologous counterparts [38]. The WCN of an atom is the sum of the inverse squares of the distances between the atom and other atoms; rWCN is the reciprocal of WCN [27], [28]. The rWCN value was utilized in the selection of potential residues for designing salt bridges. The rWCN and B-factor of a residue were computed using those values of the Cα atom only. The values were further normalized to their respective Z-scores (z_B-factor and z_rWCN). The normalization was calculated as , where and are the mean and standard deviation of . The value of is either the B-factor or the rWCN value. The evolutionary conservation of a residue was also taken into consideration because low conservation residue may indicate less importance in determining the structural conformation and function of proteins [39]. Therefore, low conservation residues can be mutated to design salt bridges without affecting protein folding and activity. The conservation scores obtained from the Consurf data set were used in the selection [39]. Each position fit all of the above criteria was then independently assumed as K, R, D, and E and scored by Eq. (1) to determine the most efficient pairing of each position with one of the surrounding charge residues within a radius of 15 Å. If a multimeric protein complex was obtained, the residues across the interface of another inter-subunit within a radius of 15 Å were also considered. Before the scoring, the surrounding charge residues within 15-Å radius of each position were located. Therefore, the putative pairs of each position can be obtained. If the putative pairs of a position were all at an interval of <5 residues of the primary sequence, they were not scored because the pairings may not be influential for increasing thermal stability. Besides, to ensure that the side chains of the two pairing residues were oriented toward each other, the angles of one pair were restricted to a limited degree. The angular criteria were established from the statistical analysis of the surface salt bridges in the data set and discussed in the results and discussion section. Only the pairs that fit the angular criteria were scored by Eq. (1). The most efficient type of charged amino acid for each position to form the highest-scoring salt bridge was identified to perform site-directed mutagenesis.

Site-directed mutagenesis and Protein overexpression

A plasmid encoding the BglA (pBglA) was used as a site-directed mutagenesis template. Fifteen pBglA plasmid amplification cycles were performed with the primers containing the mutated nucleotides and KOD DNA polymerase (Takara Inc.). The amplified material was digested with Dpn I and the digested mixtures were used to transform Escherichia coli DH5α. Kanamycin-resistant colonies were then isolated on a LB plate (MDBio Inc.) containing 25 µg mL⁻¹ kanamycin. The mutated plasmids were extracted and used to transform E. coli BL21 Star (DE3) for protein overexpression. Cells were grown in LB broth containing kanamycin (25 µg mL⁻¹) at 37°C with shaking to optical density at 600°nm (OD₆₀₀) of 0.4. Isopropyl β-D-1-thiogalactopyranoside (IPTG) of 0.5 mM was added to induce the BglA overexpression overnight at 25°C.

Protein purification

Cell destruction was performed using an Avestin Emulsiflex Homogenizer EF-C3 (Utek Process) under 20,000 psi pressure for five cycles. Insoluble cell debris was removed by centrifugation at 16,000×g for 15°min at 4°C. The supernatant was passed through a HisTrap column (GE Healthcare Inc.). After the column was washed with a phosphate binding buffer (10 mL, 20°mM) containing imidazole (20 mM), elution was performed using 20°mM phosphate buffer containing imidazole (100 mM). The elution buffer was replaced with 20°mM PBS buffer by using a HiTrap desalting column (GE Healthcare Inc.). His-tag was removed by incubating the purified protein with thrombin (GE Healthcare Inc.) at 20°C for 10°h. The resulting sample was reloaded on the HisTrap column. The eluted protein was collected and concentrated using a 10,000 MWCO centrifugal filter (Millipore Inc.). The concentrated protein was purified by gel filtration (HiLoad 26/60 Superdex 200 prep-grade, GE Healthcare Inc.) using citrate phosphate buffer (50 mM, pH 7) to elute 400 kDa BglA.

Thermal denaturation and kinetic studies of BglA and mutants

The thermal stability and kinetics of the purified BglA and mutants were measured. The melting temperature (T_m) was determined by differential scanning calorimeter (VP-DSC, GE Healthcare Inc.) with protein samples (2.0 mg/mL) in a solution of 50°mM citrate phosphate buffer at pH 7.0. The scanning temperature ranged from 20°C to 70°C at a heating rate of 1°C/min. The resulting data were fitted to non-2-state model. 4-Nitrophenyl β-D-glucopyranoside (pNPG; Sigma) was used as an artificial substrate for detecting β-glucosidase activity. The kinetic assays were conducted at 37°C for 5.5 minutes in 50°mM citrate phosphate buffer, pH 7.0, using 1 µg.800 µl⁻¹ enzyme and 0.02−3 mM pNPG, modified based on the previously described method [40]. The results were fit to the Michaelis-Menten equation for determination of the K_m and k_cat by the software of GraphPad Prism 5.

Geometry preferences of salt bridges

In total, the data set consisted of 32,096 salt bridges from 6,493 X-ray protein structures. To analyze the specific geometry of salt bridges on the protein surface, we further classified these salt bridges as a buried or exposed state based on the RSA value (Table 1). The analysis revealed that the K–E pairs favored the formation of exposed salt bridges, whereas R–D pairs were more common in buried salt bridges. The result may suggest that stability of K–E pairings were higher on protein surfaces than in buried regions. Hence, the analyses were consistent with those in previous reports, in which K and E are more common on the surface of thermophilic proteins than on mesophilic homologues [38]. The 10,556 salt bridges at the exposed state were regarded as surface salt bridges and used for the following statistical analysis (Table 1).

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Table 1. Relative solvent accessibility of salt bridges in 6,493 X-ray protein structures.

https://doi.org/10.1371/journal.pone.0112751.t001

Figure 1A shows the Cα–Cα distance and angular distributions of the 10,556 surface salt bridges. The angles of the surface salt bridges ranged from 0° to 180°, which could be used as an angle constraint. The longest Cα–Cα distance was observed in the R–E pair in PDB 1HBN (R535–E39 coil–helix at 14.235 Å, θ₁∶27.16°, and θ₂∶9.45°), whereas the shortest was observed in the K–D pair in PDB 1D2T (K90–D91 coil–coil at 3.719 Å, θ₁∶97.08°, and θ₂∶119.25°). The results suggested that the range of possible interaction in a salt-bridge extended to 15 Å of Cα–Cα distance approximately. Hence, we set a 15-Å radius of a position as the considered area in the search for the surrounding charge residues. Figure 1B shows the angles (θ₁, θ₂) of the salt bridges with Cα–Cα distance >7 Å. Based on statistical analysis, the two angles (θ₁, θ₂) of 89.7% salt bridges were both restrained within 0° to 110° when the Cα–Cα distance (r) was >7 Å. For the remaining 10.2% salt bridges, when one of the angles was >110°, then the second angle should be <110°, with only one exception. Therefore, we set the angular restraint of <110° for at least one angle (θ₁ or θ₂) if the Cα–Cα distance is >7 Å to ensure the close proximity of the ionic groups of the salt bridges to each other.

We further classified the surface salt bridges based on the secondary structure locations. Figure 2 shows the frequency distributions of K–D, K–E, R–D, and R–E salt-bridges corresponding to different secondary structure pairs. The highest frequency occurred at the helix-helix structure for all pairings of the salt bridges except the R–D pairings. Thus, such salt bridge formation at the helix-helix structure may efficiently stabilize protein structures. Based on previous studies, the formation of R–E salt-bridge at the helix−helix structure may account for much of the stabilization of certain thermophilic proteins [41]. To determine the exact value standing for the preference of salt bridge formation of a particular type of ion pair, we constructed a matrix of empirical weights () based on the frequency of each type of salt bridges. To calculate the frequency of each type of salt bridges, the 10,556 surface salt-bridge pairs were first classified based on pairing residues, Cα–Cα distances, and secondary structure locations. Table S1 shows the empirical weight of each salt bridge type depending on the frequency. The weight matrix was then used in the weighted electrostatic attraction model to score each pairings. Finally, the angular constraints and the weighted electrostatic attraction model could be used in the last steps of identifying the pairings and the substitutions after the selection of potential positions for salt-bridge design (Figure 3).

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Figure 2. Frequency distributions of surface salt bridges at different pairs of secondary structures.

Four types of salt-bridge pairings, Arg/Asp (R/D), Arg/Glu (R/E), Lys/Asp (K/D), and Lys/Glu (K/E), were considered in the statistical analysis.

https://doi.org/10.1371/journal.pone.0112751.g002

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Figure 3. Flowchart of salt-bridge design in this study.

Identification of potential positions and mutation types for a given protein structure are demonstrated.

https://doi.org/10.1371/journal.pone.0112751.g003

Potential pairings in intra- and inter-subunits

The BglA obtained from B. polymyxa was used as a polymeric model for the design of inter- and intra-salt bridges to enhance thermal stability. Polar and charge residues were considered as the pre-filtering criterion in the isolation of potential positions for different reasons. Polar residues are reported to be scarcer in thermophilic proteins, and charged residues can be selected for charge optimization; for example, to avoid electrostatic repulsion [38], [42]. Proteins from thermophiles and hyper-thermophiles exhibit more frequently networked salt bridges than proteins from the mesophilic counterparts [43]. Hence, increasing the thermal stability of proteins by optimization of charge–charge interactions is a good strategy for an evolutionary solution. Besides, the criteria of RSA >35%; z_B-factor >0; z_rWCN >0; and conservation <4 were also used as pre-filters for isolation of residues on the surface of a protein, conformational fluctuation above the average, packing density below the average, and low conservation, respectively [27], [28], [36], [39]. The characteristics used in the pre-filtering of all the 447 BglA residues are shown in Table S2. Thirty four positions that fit the pre-filtering criteria in the BglA were isolated (Pre-filters: STCNQ/KRDE: 185, RSA >35%: 67, z_B-factor >0∶50, z_rWCN >0∶49, and conservation <4∶34) as shown in Table S3. To determine the potential pairings for each 34 positions, we located the charge residues within a radius of 15 Å of the position. The putative pairs of the 34 positions were separately identified as in Table S4. Only 11 positions that could be paired with the surrounded residues were found according to the angular constrains of θ₁ and θ₂ generated from the statistical analysis of the surface salt-bridges (Figure 1) and at least one pair for each position that met the sequence separation criteria (≥5 residues). The putative pairs for each 11 positions were separately identified and scored using Eq. (1). To demonstrate how to identify the charge amino acids for substitutions at the 11 positions, Table S5 shows the theoretical scores of the putative pairings between each of the 11 positions and their surrounded charge residues. The predicted pair equivalent to the highest-score pair of each position was the pair with the highest preference for salt-bridge formation. The suggested amino acids for mutations were the one that fit the predicted pair at each position. For example, the suggested mutations would be E96K because E96K–D28 is the predicted pair of E96 position. However, if a position’s highest-scoring pair exhibited a short sequence separation (i.e. <5 residues), the corresponding mutation was not adopted. Moreover, an alternative charged substitution was performed when that substitution for alternative pair formation (i.e., sequence separation ≥5 residues) was scored higher (i.e., higher preference for forming the salt bridge) than the same substitution for inefficient pair formation (i.e., sequence separation <5 residues). The highest-scoring pair was inferred when two or more of such alternative pairs were found. One example in Table S5 was the S440E–R436 pair. The pair exhibits a pair interval <5 residues of the primary sequence and was the highest-scoring pair for the R436 position. Moreover, the S440R–E70 pair’s score was higher than that of the S440R–R436 pair. Therefore, the suggested S440 mutation was S440R for the S440R–E70 pair formation.

Six of the eleven positions in Table S5 (i.e., N47, N88, Q95, T273, E360, and N437) exhibited their highest-scoring pairs to be the pairs with a short sequence separation (<5 residues). Moreover, no alternative substitutions were found for these positions except for the N437 position. Besides, two positions, E96 and Q277, each was found with two suggested mutations for two predicted pairs that showed equal scores (Table S5). Hence, eight mutations for the eight predicted pairs consisted of two inter-subunit pairs (i.e., T231K–D388 and N437K–D49) and six intra-subunit pairs (i.e., E96K–D28, E96R–D28, Q141K–E148, Q277D–R137, Q277E–R137, and S440K–E70) were constructed to perform the experimental thermal stability test. In addition, to validate the pre-filtering criteria set for the salt-bridge design, the Q216 was used as a negative control position because the RSA, z_B-factor and z_rWCN values of Q216 are not qualified but close to the criteria. The z_B-factor and z_rWCN of the Q216 are close but less than 0 and the RSA is less than 35% as shown in Table 2. The D289 was the qualified charge residues to bridge with the Q216R (Cα–Cα distance: 5.52 Å; scores of D289–Q216K/R/D/E: 0.04/0.07/0/0; θ₁ and θ₂ of Q216R–D289 are 60.36°°and 11.61°) as shown in Table S5. Therefore, the T_m resulted from the Q216R–D289 pair was expected not to be higher than that of the wild type enzyme. Figure 4 shows the locations of the eight predicted pairs and Q216R–D289 pair in the BglA structure. Two residues (T231, N437) at the A/D and A/E interfaces and four residues (E96, Q141, Q277, and S440) of the intra-subunit pairs, as well as Q216, were subjected to site-directed mutagenesis. The eight suggested mutations of the six positions (E96, Q141, T231, Q277, N437, and S440) were E96K, E96R, Q141K, T231K, Q277D, Q277E, N437K, and S440K. The structural characteristics of the seven positions (E96, Q141, T231, Q277, N437, S440, and Q216) are summarized in Table 2. All the six positions (E96, Q141, T231, Q277, N437, and S440) qualify the following characteristics of the pre-filtering criteria: polar or charged residue, RSA >35%, z_B-factor >0, z_rWCN >0, and conservation <4. Figure S1 shows the z_rWCN and the z_B-factor profiles of 1BGA: chain A, which demonstrate that the z_rWCN and z_B-factor of the six positions are higher than the average. On the contrary, the Q216, used as a negative control position, exhibits the z_B-factor and z_rWCN slightly lower than the average. The profiles also demonstrate that Q216 is a relatively stable and buried position around the local sequence region.

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Figure 4. Locations of eight predicted pairs and one control pair in the octameric structure (PDB: 1BGA).

A, D, E subunits are represented in green, yellow, and purple, respectively. The N437–D49 pair is between subunits A and D; the T231–D388 pair is between subunits A and E; whereas E96–D28, Q141–E148, Q277–R137, S440–E70 and control pair Q216-D289 are intra-subunit pairs. The predictive pairs as well as T231D–D388K and Q216R–D289 pairs were simulated by Pymol program. Each pair is magnified in an independent window showing secondary structure elements and the paired residues (red for negatively charged residues and blue for positively charged residues). The neighboring charge residue of each position (E96, Q141, T231, Q277, N437, S440, and Q216) suggested the oppositely charged substitutions for forming salt bridges.

https://doi.org/10.1371/journal.pone.0112751.g004

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Table 2. Characteristics of positions in BglA for mutations.

https://doi.org/10.1371/journal.pone.0112751.t002

Thermal stability and kinetics of BglA mutants

The ΔT_m of the enzymes was obtained from DSC experiments, in which the ΔT_m values and the melting curves of the wild type and the mutants are compared in Table 3 and Figure S2, respectively. The irreversible denaturation of the enzymes could cause the uneven baselines of the melting curves (Figure S2). Therefore, the melting temperatures described below were apparent melting temperatures. Six of the eight suggested mutants showed greater stability than that of the wild type based on the ΔT_m value (i.e., E96K, E96R, T231K, Q277E, N437K, and S440K) while the two mutants (Q141K and Q277D) showed similar Tm as that of the wild type. The ΔT_m caused by the six suggested mutations were 3.7, 4.8, 1.2, 0.7, 8.8, and 1.3°C (E96K, E96R, T231K, Q277E, N437K, and S440K, respectively). Each predicted position that successfully increased the Tm of the BglA through the suggested mutations was individually mutated to all the four charged amino acids, K, R, D, and E, via site-directed mutagenesis. These additional mutations were conducted to further confirm the predicted accuracy and investigate the relation between the increase of melting temperature (ΔT_m) and the predicted score of each mutant. The ΔT_m values resulted from the suggested charged mutations that leaded to increased T_m and the same type of charge mutation at each of the E96, T231, Q277, N437, and S440 positions were higher than that of the opposite type of charge mutations except the Q277D and Q277R mutations (Table 3). The latter two mutations displayed a similar Tm. These findings suggested that the prediction of the pairings according to the scores calculated by Eq. (1) of the K, R, D, and E substitutions at each five positions were in agreement with the ΔT_m results. Besides, the k_cat/K_m values of the thermostable mutants were also measured as in Table 3. Moreover, the mechanisms of thermal adaptation of each position (E96, Q216, T231, N437, Q277, and S440) were varied and discussed in the following sections.

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Table 3. ΔT_m and kinetic parameters of wild-type BglA and mutant enzymes.

https://doi.org/10.1371/journal.pone.0112751.t003

Case 1: E96, reversing the negatively charged residue

The E96 residue exhibits high B-factor and rWCN values (z_B-factor: 2.46, z_rWCN: 0.81), which indicate a large fluctuation and a low packing density, respectively (Table 2). Based on Table S5, E96 in a helix revealed the highest probability of forming an intra-molecular salt bridge with a neighboring D28 residue in a loop structure by reversing from negatively to positively charged K or R residues. The D28 residue is also in a flexible region as indicated by the z_B-factor. Therefore, the bridging of K96 or R96 with D28 could stabilize two flexible regions to increase protein rigidity. In previous studies, the E96K mutation was also detected by random mutation [44]. The mutant was crystallized to demonstrate the formation of a link between K96 and D28 [45]. This crystalline structure also indicates a twist conformation caused by E96K mutation, where the K96 side chain turns to the D28 side chain. We also found that ΔT_m caused by E96R mutation was even higher than that of E96K mutation. The E96R mutant showed a ΔT_m of 4.8°C compared with 3.1°C of E96K (Table 3). On the contrary, when a negatively charged Aspartate was introduced at E96, the T_m of the BglA dropped 1°C. Thus, thermal adaptation occurred more effectively in this environment than in the original environment in the event of reversal of a negatively charged residue (E96) to its positively charged residues (K96 or R96). Previous research has also mentioned that charge reversal of a residue could convert a destabilizing effect to a stabilizing effect because charge reversal alters the core of the interactions; as a result, such destabilized interactions are stabilized [42]. A four-fold increase of k_cat/K_m value was found in E96K mutant, whereas that of E96R mutant remained almost unchanged.

Case 2: Q216, with low B-factor and WCN at a slightly buried state

The Q216 residue at a slightly buried state exhibits lower RSA, B-factor, and rWCN values (RSA: 0.16, z_B-factor: −0.14, and z_rWCN: −0.19) than that prescribed in the pre-filtering criteria (Table 2); the surrounded D289 could be paired with the Q216R according to the scores calculated by Eq. (1). Thus, Q216R–D289 was used as a negative control pair. The ΔT_m of Q216 mutants were lower or similar than that of the wild type enzyme, which was consistent with our predictions (Table 3). Thermal stability of the Q216D and Q216K variants showed 0.1 and −0.5°C of ΔT_m while a large decrease in T_m of Q216E and Q216R (−2.2 and −3.5°C) mutants were found, which can be explained by the repulsion that arised from the larger side chains of E and R than that of D and K. The large decrease in T_m caused by the Q216E and Q216R mutations could be expected from the slightly buried and crowded position indicated by the low RSA and z_rWCN of Q216. Therefore, the ΔT_m of the Q216R mutant was in agreement with the pre-filtering criteria set for the isolation of designable positions. Bleicher et°al. have proposed that the existence of salt bridges in the hydrophobic core of a mesophilic laminarinase (versus its absence in a hyperthermophilic laminarinase) may be responsible for the differences in the thermal stability between the two enzymes [46]. In the mesophilic enzyme, the existence of charge-charge interactions permeating the hydrophobic core of the enzyme actually destabilizes the structure by facilitating water penetration into hydrophobic cavity. These findings could support the ΔT_m result caused by the putative pairing of Q216R–D289 in this study.

Case 3: T231, polarity reversal of a putative electrostatic pair

The T231 residue locates at a coiled structure between two subunits of A and E subunits shows a large rWCN value (z_rWCN: 3.28) in Table 2. When an exposed polar T231 residue was mutated into a positively charged residue (T231K), T_m was slightly increased (ΔT_m: 1.2°C), whereas the T231R mutant showed a similar thermal stability as that of the wild type enzyme (Table 3). On the contrary, the T_m of T231D and T231E mutants was 3.8 and 3.2°C lower than that of the wild type. Originally, K231 or R231 was expected to form a salt bridge with D388 across the interface (Table S5). We suspected that the small increase in T_m caused by T231K mutation was triggered by an intra-subunit electrostatic interaction instead of an inter-subunit electrostatic interaction. Further considering the local environment, we found that the positively charged residue K231 could stabilize the negatively charged environment and interact with one of the three negatively charged residues (E233, E234, and D235) from the neighboring helix of the same subunit at Cα–Cα distances of 6.6, 8.5 and 7.0 Å, respectively. The three negatively charged residues are also closely located to 231 position in the primary sequence. Hence, these negatively charged residues could interfere the expected inter-subunit attraction (T231K–D388) by drawing the positive charge of K231 side chain away from the negative charge of D388 side chain in the opposite direction. Therefore, we hypothesized that the reversal of the charges of the two residues, that is, K231 and D388 into D231 and K388, is more likely to promote the salt bridge formation at the interface due to the interferences are dismissed. Thus, the double mutant (T231D–D388K) was constructed to investigate the hypothesis, which resulted in a large increase in T_m of 4.8°C. This change was significantly greater than that of T231K mutation (Table 3). The results suggested that the negatively charged residues closely located at the helix next to residue D231, may hinder the flexibility of D231 and drive the side chain of D231 to rotate to cross the interface, which formed a bridge with the newly reverse-charged variant K388 at another subunit. Therefore, the T_m of T231K and T231D–D388K mutants may also potentially demonstrate that an inter-subunit electrostatic interaction could increase protein thermal stability more efficiently than an intra-subunit interaction of polymeric proteins. A five-fold increase in the k_cat/K_m value caused by the T231D–D388K mutation was observed, whereas the k_cat/K_m value of T231K remained almost unchanged (Table 3). The results suggested that the inter-subunit interaction may have influenced the enzyme kinetics of the BglA.

Case 4: N437, elongating the side chain across the interface

The N437 residue locates in a helix structure was predicted to form an inter-subunit salt bridge with the D49 residue in a coil structure between the A and D subunits by the N437K substitution (Table S5). The N437K and N437R mutations resulted in an increase in T_m of 8.8 and 5.3°C, respectively (Table 3). The N437K mutation was also found as a thermostable mutation by random mutation in a previous study [44]. Based on the BglA structure [33], there is no direct interaction between the N437 residue of subunit A and D49 of subunit D. In addition, the distance between the amide group of the N437 side chain and the carboxyl group of the D49 side chain is 4.5 Å which is longer than 4 Å required for the salt-bridge formation. Therefore, the substitution of R437 or K437 for N437 may shorten the distance by elongating the length of the residue side chain. Therefore, the putative salt bridge between K437 or R437 and D49 may form across the interface by extending the range of the side chain interaction. By contrast, the T_m values of N437D and N437E mutants were lower than that of the wild type. During the BglA purification process, the protein gel-filtration data revealed that the N437D and N437E mutants both eluted differently sized tetramers from the native octamer (data not shown). Hence, this position was suggested to function crucially in protein oligomerization.

Case 5: Q277 and S440, introducing intra-subunit pairings at long distances

The Q277 in a coil structure was predicted to form an intra-subunit salt bridge with the R137 in a helix structure through the Q277D or Q277E mutation (Table S5). The predicted pairs (Q277D–R137 and Q277E–R137) exhibit a 10.96 Å Cα–Cα distance. The Q277E mutant showed a higher Tm (ΔT_m: 0.7°C) than that of the Q277D mutant (ΔT_m: −0.1°C) (Table 3). Hence, the results suggested that instead of aspartate, a longer glutamate side-chain was more suitable to reach the R137 side chain.

The S440 and E70 residues at a long interval (Cα–Cα: 10.31 Å) were also predicted to interact with each other through the S440K mutation (Table S5). Both the S440K and S440R mutations resulted in increased ΔT_m values (i.e., 1.3 and 2.4°C, respectively) (Table 3). However, both mutations caused a 0.5-fold drop of kcat/Km. A decrease of kcat/Km caused by the Q277E and Q277D mutations was also observed.

Case 6: Combining the thermostable mutations

The combination of the E96R, T231D–D388K, and N437K mutations with each showed an increased kcat/Km value compared with that of the wild type provided a maximum ΔT_m of approximately 15.7°C (Table 3). The large increase in T_m observed in the combined mutant emphasized the importance of increasing the number of salt bridges in enhancing protein thermal stability [38]. The k_cat/K_m value was only slightly less than that of the wild type at 37°C.

Previous simulation studies of mesophilic and hyper-thermophilic proteins have shown that protein unfolding may be initiated at sites prone to large thermal fluctuations; an example of such sites include the loop region in rubredoxin [47], [48], whose flexibility allows water to access and unfold the protein structure. In the present study, the novel salt bridges were constructed within the flexible region to minimize fluctuations; hence, the development of resistance to high temperatures was promoted [49]. Previous reports have also indicated the relevance of high rigidity as a factor that influences the integrity of the native folded structure by increasing the packing density on a protein surface [27]–[29], which is consistent with the strategy of constructing salt bridges at positions of low contact number and high solvent accessibility in this study.

The two mutation pairs, T231D–D388K and N437K–D49, in the A/E and A/D inter-subunit could increase multimeric stabilization. The ΔTm of T231D–D388K mutant was 4.8°C, and that of N437K–D49 was 8.8°C higher than that of the wild type. According to the previous studies, inter-subunit interactions are known to preserve oligomer structures and increase resistance protein thermal stability [50], [51]. Besides, the k_cat/K_m values of these two mutants were approximately five- and two-fold greater than that of the wild-type, respectively (Table 3). A similar observation was reported that the inter-subunit salt bridge of quinone reductase obtained from Bacillus subtilis affects not only protein oligomerization but also catalytic activity [51].

In the present study, the weighted electrostatic attraction model could be used to calculate the interaction between two oppositely charged residues to identify the preferred pairings and the possible substitution in a salt-bridge design. The most stable electrostatic pair at each of the three identified positions increased the BglA thermal stability by charge addition, charge reversion, and polarity reversal. The computational prediction scores clearly identified the positively or negatively charged substitutions with increased T_m, which suggested that the model could be useful in the design of thermostable proteins.

Accordingly, various rational techniques have been employed to improve protein thermal stability, including consensus-guided mutagenesis and the comparison of thermophiles and mesophilic homologues [52]–[55]. Most of these methods adopt the evolutionary analysis of multiple sequence alignment to substitute the conserved residue for the non-conserved ones at an aligned position or to substitute a residue from thermophiles for a residue from mesophiles. The bioinformatics method used in this study managed to recognize the potential charge substitutions for the low conservation residues from the surrounding electrostatic environment.