Role of the Charge–Charge Interactions in Defining Stability and Halophilicity of the CspB Proteins

doi:10.1016/j.jmb.2006.11.061

J. Mol. Biol. (2007) 366, 842–856

Role of the Charge–Charge Interactions in Defining Stability and Halophilicity of the CspB Proteins Alexey V. Gribenko and George I. Makhatadze⁎ Department of Biochemistry and Molecular Biology, Penn State University, College of Medicine, Hershey, PA 17033, USA

Charge–charge interactions on the surface of native proteins are important for protein stability and can be computationally redesigned in a rational way to modulate protein stability. Such computational effort led to an engineered protein, CspB-TB that has the same core as the mesophilic cold shock protein CspB-Bs from Bacillus subtilis, but optimized distribution of charge–charge interactions on the surface. The CspB-TB protein shows an increase in the transition temperature by 20 °C relative to the unfolding temperature of CspB-Bs. The CspB-TB and CspB-Bs protein pair offers a unique opportunity to further explore the energetics of charge–charge interactions as the substitutions at the same sequence positions are done in largely similar structural but different electrostatic environments. In particular we addressed two questions. What is the contribution of charge–charge interactions in the unfolded state to the protein stability and how amino acid substitutions modulate the effect of increase in ionic strength on protein stability (i.e. protein halophilicity). To this end, we experimentally measured the stabilities of over 100 variants of CspB-TB and CspB-Bs proteins with substitutions at charged residues. We also performed computational modeling of these protein variants. Analysis of the experimental and computational data allowed us to conclude that the charge–charge interactions in the unfolded state of two model proteins CspB-Bs and CspB-TB are not very significant and computational models that are based only on the native state structure can adequately, i.e. qualitatively (stabilizing versus destabilizing) and semi-quantitatively (relative rank order), predict the effects of surface charge neutralization or reversal on protein stability. We also show that the effect of ionic strength on protein stability (protein halophilicity) appears to be mainly due to the screening of the long-range charge–charge interactions. © 2006 Elsevier Ltd. All rights reserved.

*Corresponding author

Keywords: protein stability; protein halophilicity; charge–charge interactions; computational modeling; amino acid substitutions

Introduction It has become clear that the charge–charge interactions on the protein surface are important for stability.1–20 Moreover, several computational models were shown to be capable of reliably predicting the effects of amino acid substitutions on the protein stability.2,4,6–10,12,16,21,22 In most cases these computational models are largely based on native state structure and ignore or oversimplify the unfolded state contribution. However, several direct experimental measurements of the pKas of residues E-mail address of the corresponding author: [email protected]

in the unfolded state or in peptide sequences mimicking the unfolded state, indicate that for some residues the pKas are occasionally perturbed from that of free amino acid residues.23–37 It is thus unclear whether the charge–charge interactions in the unfolded state are significant only for some residues or for all residues and thus must be taken into consideration in the computational analysis of the effects of charge substitutions on the protein folding and stability. One way of addressing this question is to perform an exhaustive study of a large number of substitutions in the charged residues in a model protein. We chose for these studies a pair of proteins, CspB-Bs and CspBTB. CspB-Bs is the cold shock protein B from Bacillus subtilis that contains 67 amino acid residues arranged

0022-2836/$ - see front matter © 2006 Elsevier Ltd. All rights reserved.

843

Stability and Halophilicity of the CspB Proteins

into a five-strand β-barrel (see Figure 1). The threedimensional structure of the Csp proteins is well conserved and forms the so-called OB-fold.38,39 The CspB-TB protein was engineered from CspB-Bs and has the same amino acid sequence as CspB-Bs (Figure 1) but with optimized charge–charge interactions.4 CspB-TB is ∼20 °C more stable than CspB-Bs yet it remains active as assessed by the single-stranded (ss) DNA binding affinity assays.4 Although a three-dimensional structure of CspB-TB is not available, numerous spectroscopic probes,4 together with the overall fold conservation for CspB proteins indicate that the structure of CspB-TB is rather similar to CspB-Bs. Thus CspB-Bs and CspBTB represent a pair of proteins that are structurally similar, with identical cores but different distribution of the charged residues on the surface. Therefore substitutions at identical positions in the sequence of these two proteins will be done in a similar structural context but different charge environments. If theoretical models based on the native state structure are able to predict the effects of substitutions on stability in one background but not in the other, this will point to a difference in the interactions in the unfolded state. We therefore might be able to indirectly identify the presence of interactions in the unfolded state that are significant enough to have implications for protein stability. Another important aspect of surface charge– charge interactions is their potential role in defining

the halophilic properties of proteins. Protein halophilicity has been studied extensively by comparing properties of proteins from halophilic organisms to their mesophilic homologs. 40–47 However, only limited data on the effects of small structural perturbations (such as single site substitutions) on the halophilic behavior of proteins is available.44,45,48 It is known that ionic strength has very different effects on the stability of CspB proteins.3,49,50 The stability of CspB-Bs increases dramatically with increasing ionic strength, while the stability of the thermophilic CspB-Bc protein changes only marginally with changes in the ionic strength of the solution. Thus, by comparing the stabilities of the large number of CspB-Bs and CspB-TB variants at different ionic strengths we should be able to identify the residues that are important for the halophilic/ halophobic character of these proteins.

Results and Discussion Table 1 lists the thermodynamic parameters of unfolding for 63 variants of CspB-Bs and 42 variants of CspB-TB under selected set of conditions (the data for all six different solvent conditions are provided as an electronic supplement). Some substitutions, e.g. CspB-Bs K7E, K7Q and V20E variants, produced such dramatic destabilizing effects that no transition was observed at all studied conditions, so only

Figure 1. Sequence and structure comparison of cold-shock proteins. (a) Sequence alignment of four cold-shock proteins: CspB-TB is an engineered protein that has the same sequence of core residues as CspB-Bs but charge distribution of that of thermophilic proteins.4 The other three proteins are naturally occurring cold shock proteins from Bacillus subtilis (CspB-Bs), Bacillus caldolyticus (CspB-Bc), and Escherichia coli (CspA-Ec). Shaded are the residues identical to CspB-TB. (b) Comparison (in stereo) of the backbone structures of the CspB-Bs (1csp), CspB-Bc (1c9o) and CspA-Ec (1mjc) proteins (backbone rms <1.5 Å). Structural similarity of these three proteins, the high sequence identity, as well as similarity in spectroscopic properties4 support the notion that the structure of CspB-TB should be very similar as well.

844

Stability and Halophilicity of the CspB Proteins

Table 1. Thermodynamic parameters of unfolding of CspB variants No.

Name

A. Thermodynamic 1 WT* 2 6H-WT* 3 WT 4 E3R 5 E3R* 6 6H-E3R* 7 E3Q 8 K5E* 9 6H-K5E* 10 K5Q* 11 6H-K5Q* 12 K7E 13 K7Q 14 N10D 15 N10K* 16 E12K** 17 K13E* 18 K13Q* 19 E19K* 20 E19Q* 21 V20Q* 22 6H-V20Q* 23 V20Q/E3R* 24 6H-V20Q/ E3R* 25 V20E* 26 V20E/E3R* 27 6H-V20E/ E3R* 28 V20K* 29 6H-V20K* 30 V20K/E3R*

Tm (°C)

ΔH(Tm) (kJ/mol)

ΔG(Tref) (kJ/mol)

ΔΔG(Tref) (kJ/mol) No.

parameters of unfolding of CspB-Bs variants 55.3 (52.9) 160 (163) 0.1 (−1.1) 0.0 49.7 145 −2.5 0.0 52.3 164 −1.4 0.0 70.2 (68.6) 210 (202) 8.1 (7.1) 9.5 71.6 209 8.6 8.5 66.9 191 5.9 8.4 62.6 (60.5) 195 (193) 4.1 (3.0) 5.5 22.2 61 −12.9 −13.0 UNF <−15 38.6 125 −8.1 −8.2 37.6 (33.4) 116 (99) −8.2 (−9.6) −5.7 UNF <−15 UNF <−15 57.7 181 1.4 1.3 41.1 115 −6.2 −6.3 50.2 158 −2.5 −2.6 52.7 153 −1.1 −1.2 54.0 173 −0.5 −0.6 50.5 156 −2.3 −2.4 53.7 167 −0.7 −0.8 39.5 124 −7.5 −7.6 30.1 84 −10.4 −7.9 58.2 187 1.8 −6.8 51.6 157 −1.7 −7.6

Name

Tm (°C)

33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56

E21Q* E21Q/E19Q* D24K* D24N* D25K* D25K/E19Q* D25Q D25Q/E19Q* K39E* K39Q* E42K* E42Q* E43K* E43Q* S48K* 6H-S48K* S48E E50K* 6H-E50K* E50Q E53K* E53Q* N55K* N55D*

52.5 52.0 50.9 48.4 35.0 39.4 44.2 43.6 52.8 (50.7) 54.9 55.3 54.1 (50.5) 56.3 55.5 61.6 56.8 53.0 (49.3) 49.7 49.7 42.6 53.7 56.6 (54.7) 54.3 59.2 (57.0)

ΔH(Tm) (kJ/mol)

ΔG(Tref) (kJ/mol)

146 −1.2 171 −1.6 143 −1.9 130 −2.9 56 −5.9 87 −5.7 122 −4.8 106 −4.5 150 (143) −1.0 (−2.0) 148 0.0 179 0.2 149 (142) 0.4 (−2.1) 193 0.8 181 0.3 174 3.2 161 0.9 159 (143) −1.0 (−2.7) 132 −2.3 131 −2.3 143 −6.5 168 −0.7 183 (187) 0.9 (−0.2) 158 −0.3 185 (194) 2.2. (1.2)

ΔΔG(Tref) (kJ/mol) −1.3 −1.7 −2.0 −3.0 −6.0 −5.8 −3.4 −4.6 −1.1 −0.1 0.1 −0.5 0.7 0.2 3.1 3.4 0.4 −2.4 0.2 −5.1 −0.8 0.8 −0.4 2.1

UNF 43.0 34.7

124 87

−5.5 −8.0

<−15 −14.1 −13.9

57 58 59

R56Q K65E* 6H−K65E*

55.3 38.2 33.4 (33.2)

178 101 88 (74)

0.2 −7.0 −8.8 (−7.9)

1.6 −7.1 −6.3

37.2 (29.1) 29.3 54.6

108 (69) 78 164

−8.0 (9.7) −10.4 −0.2

−8.1 −7.9 −8.8

60 61 62

48.0 44.4 44.0

131 121 135

−3.1 −4.7 −5.3

−3.2 −2.2 0.0

47.9

142

−3.4

−9.3

63

39.0

116

−7.4

−2.1

54.3

181

−0.4

−0.5

64

K65Q* 6H−K65Q* E3R/F15A/ F27A* E3R/F15A/ E19K/F27A* E3R/F15A/ D25K/F27A*

23.0

43

−10.5

−5.2

B. Thermodynamic parameters of unfolding of CspB-TB variants 1 WT* 73.0 (70.0) 187 (186) 8.0 (7.0) 0.0 2 6H-WT* 66.7 (63.0) 165 (158) 5.0 (3.4) 0.0 3 WT 69.3 186 6.7 0.0 4 R3E 29.7 68 −9.3 −16.0 5 R3Q 60.8 156 2.5 −4.2 6 K5E* 55.1 136 0.0 −8.0 7 6H-K5E* 49.0 115 −2.3 −7.3 8 K5Q* 66.1 181 5.3 −2.7 9 6H-K5Q* 59.9 142 2.0 −3.0 10 K7E* 50.1 127 −2.1 −10.1 11 K7Q* 61.7 157 2.9 −5.1 12 D10K* 46.0 106 −3.4 −11.4 13 K12E* 76.0 201 9.8 1.8 14 K12Q* 73.5 224 10.2 2.2 15 K13E* 68.9 190 6.7 −1.3 16 K13Q* 73.0 194 8.4 0.4 17 K20E 60.8 156 2.5 −5.5 18 K20Q 70.3 206 8.0 0.0 19 K20V* 86.1 253 17.1 9.1 20 6H-K20V* 81.0 241 14.3 9.3 21 E21K* 64.1 163 4.0 −4.0

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

E21Q* D24K* D24N* D25K* D25N K39E* K39Q* K42E* K42Q* E43K* E43Q E48K* 6H-E48K* E48Q* E50K* 6H-E50K* E50Q E53K* E53Q* K55E* K55Q*

68.7 69.9 69.5 65.1 64.1 72.0 72.9 69.7 71.3 69.1 70.4 61.1 54.8 66.3 70.3 69.8 70.6 69.8 73.3 77.8 73.9

184 179 188 158 154 208 221 192 205 198 193 140 124 163 189 178 198 188 208 216 242

6.4 6.6 6.9 4.2 3.7 8.8 9.8 7.1 8.3 7.1 7.4 2.4 −0.1 4.8 7.2 6.6 7.7 7.0 9.3 11.4 11.3

−1.6 −1.4 −1.1 −3.8 −3.0 0.8 1.8 −0.9 0.3 −0.9 −0.6 −5.6 −5.1 −1.9 −0.8 1.6 1.0 0.3 1.3 3.4 3.3

31 32

6H-V20K/ E3R* E21K*

Tm is the transition temperature, ΔH(Tm) is the enthalpy of unfolding at Tm, ΔG(Tref) is the Gibbs energy of unfolding at a reference temperature, taken to be 55 °C, ΔΔG(Tref) is the changes in stability relative to the wt protein. All data are from the analysis of the CD melting profiles while the values in parenthesis are obtained from the analysis of DSC profiles. UNF, under these conditions protein appears to be unfolded, and thus their relative stability is estimated to be less than −15 kJ/mol. Estimated uncertainties in Tm ±0.5 °C, ΔH (Tm) ±7 kJ/mol, ΔG(Tref) ±0.6 kJ/mol, ΔΔG(Tref) ±1.3 kJ/mol. Solvent conditions: 50 mM sodium cacodylate, 100 mM NaCl pH 7.5. The data for other solvent conditions are available as an Electronic Supplement.

Stability and Halophilicity of the CspB Proteins

estimates for the upper limit of stability for these variants can be made. Figure 2 shows the dependence of the enthalpy of unfolding, ΔH(Tm), on the transition temperature, Tm. They were obtained from the analysis of the temperature-induced unfolding monitored by the far-UV circular dichroism (CD) spectroscopy and from the analysis of the differential scanning calorimetry (DSC) melting profiles for a selected set of variants. Three notable observations can be made. First, the enthalpies of unfolding for a given protein (CspB-Bs or CspB-TB) follow the same functional dependence. This suggests that all variants in each protein background have rather similar enthalpy functions and thus share similarities in the mechanism of stabilization.4 Second, the enthalpies obtained from the DSC experiments are in good agreement with the enthalpies determined from the analysis of the CD melting profiles. Similarity of thermodynamic parameters obtained from DSC and CD suggests that all variants follow a two-state unfolding and justify the use of CD data for further analysis. Third, the slope of the dependence of ΔH on the transition temperature, which represents the heat capacity change upon unfolding, ΔCp, is the same for both the CspB-Bs and CspB-TB protein variants (3.5 (±0.6) kJ/(mol K) and 3.7(±0.5) kJ/(mol K) for CspBBs and CspB-TB, respectively). This is similar to the ΔCp determined from the DSC melting profiles 3.5 (±0.6) kJ/(mol K). Such similarity of the ΔCp for these two proteins is expected. Indeed, CspB-Bs and CspBTB share the sequence of the buried (core) residues, and it is the exposure of the buried groups to solvent that largely defines the ΔCp.4,51,52 The amino acid substitutions made in both CspB-Bs and CspB-TB backgrounds affect the transition temperature (see Table 1). The range of changes is dramatic and covers over 50 °C. For

845 CspB-Bs the observed changes in transition temperature vary from −32 °C to +20 °C from the corresponding “wild-type” protein, while the CspB-TB variants had changes in transition temperature from −42 °C to +16 °C. These changes in the transition temperatures translate into the changes in stability (ΔΔG) from −15 kJ/mol to +10 kJ/ mol relative to the corresponding wild-type proteins (see Table 1). Effects of ionic strength on the thermodynamic parameters of unfolding of the CspB-Bs and CspB-TB variants For the analysis of the effects of charge–charge interactions on the stability of a protein, substitutions that affect only the charge of the side-chain but not the size or hydrophobicity will be ideal. However, this requires the use of non-natural amino acid residues, which is possible via total chemical synthesis of a protein or native chemical ligation,2,53–55 but technically challenging for large proteins and a large number of variants to be generated. For the natural amino acid residues, the choices are rather limited. Arguably, for basic residues K and R the most compatible substitution for charge neutralization appears to be Q and for charge reversal E. Similarly, for neutralization of negative charge on E or D the best substitution will be Q or N and for charge reversal substitution with K is the best option. As a result, charge neutralization or charge reversal using naturally occurring amino acid residues might alter not only the charge–charge interactions but other interactions as well. Therefore the stabilization or destabilization of CspB variants as a result of surface charged residue substitutions could be due to several reasons. It is conceivable that in some cases,

Figure 2. Dependence of the enthalpy of unfolding, ΔH(Tm) on the transition temperature, Tm, for the CspB-Bs variants (a) and CspB-TB variants (b). Different open symbols show the results obtained from the analysis of the temperature-induced CD melting profiles for six different solvent conditions, containing in addition to 50 mM sodium cacodylate (pH 7.5), 0 M NaCl (circle), 50 mM NaCl (square), 100 mM NaCl (triangle up), 200 mM NaCl (triangle down), 500 mM NaCl (diamond), or 1 M NaCl (hexagon). Filled red circles represent the results of DSC experiments obtained with 100 mM NaCl. Thick continuous line represents a linear fit of all data points. The slopes that represent the heat capacity change upon unfolding, ΔCp, are 3.5(±0.6) kJ/mol and 3.7(±0.5) kJ/mol, for CspB-Bs and CspB-TB, respectively. Thin continuous lines represent the prediction interval at 95 % confidence.

846 observed changes in the stability of CspB-Bs and CspB-TB are dominated by factors such as changes in secondary structure propensities, formation or disruption of hydrogen bonds, hydrophobic effect, etc. Introduction of a positive or a negative charge may result in formation or disruption of short-range charge–charge interactions, such as salt-bridges. Alternatively, modification of surface charges may, in turn, significantly affect only long-range charge– charge interactions. Distinguishing between the short-range effects and long-range charge–charge interactions by simple visual inspection of the structure is not always trivial and requires additional data. Experimental and theoretical studies suggest that long-range and short-range charge–charge interactions are affected differently upon increase in the ionic strength: salt efficiently screens long-range interactions, while short-range interactions start to weaken at 1 M ionic strength but may persist even at 1.5 M ionic strength. 43,56,57 Thus thermodynamic analysis of the halophilicity of proteins upon substitutions in the charged residues can distinguish between the effects on the long-range charge–charge interactions on one hand, and the effects on the short range charge–charge interactions or interactions other then charge–charge, on the other. Proteins can be classified as halophilic (“salt-loving”) and halophobic (“salt-hating”) with respect to the effects of the ionic strength on protein thermodynamics, and in particular, thermodynamic stability.43 Increasing the ionic strength leads to the increased stability of halophilic proteins, while in halophobic proteins, increasing the ionic strength produces the opposite effect. Changes in ionic strength produce dramatically different effects on the stability of the wild-type CspB-Bs and CspB-TB proteins (Figure 3). CspB-Bs is clearly a halophilic protein as can be seen from the increase of stability upon increase of ionic strength: in 500 mM NaCl CspB-Bs is 3.0 kJ/mol more stable than in the absence of salt while in 1 M NaCl CspBBs is 5.0 kJ/mol more stable. This increase in stability of the CspB-Bs protein with increasing ionic strength has been observed previously.43,58,59 In contrast, CspB-TB shows 1.0 kJ/mol decrease in stability up to 500 mM NaCl, with a moderate increase in stability observed between 500 mM and 1 M NaCl, presumably due to Hoffmeister effects.60 CspB-TB is likely to behave similarly to CspB from the thermophilic organism Bacillus caldolyticus (CspB-Bc), which also shows an initial decrease in stability upon increase in ionic strength, followed by increasing stability at higher salt concentrations.3,43 Based on these observations, it is expected that analysis of the effects of ionic strength on the stability of the CspB-Bs and CspB-TB single site substitution variants would make it possible to distinguish between the effects of substitutions on long-range charge–charge interactions and the effects on short-range interactions in these two proteins. For example, in CspB-Bs, there are several residues that have unfavorable charge–charge inter-

Stability and Halophilicity of the CspB Proteins

Figure 3. Changes in stability, ΔΔG = ΔG(XM)–ΔG (0M), upon increaseing ionic strength for a representative set of substitutions in the CspB-Bs (a) and CspB-TB (b) proteins.

actions. Thus, introducing an unfavorable longrange interaction or removing a favorable one in CspB-Bs through a site-directed substitution would likely result in greater stabilization of the new variant upon increasing ionic strength than was observed for the wild-type protein, i.e. the substitutions would increase halophilicity of the protein. This is indeed observed, for example, for the K5Q variant of CspB-Bs (Figure 3) that shows 6.9 kJ/mol relative increase in stability upon addition of 500 mM NaCl, twice more than the 3.0 kJ/mol relative increase in stability of the wild-type CspBBs. Alternatively, introduction of a charge that would improve long-range charge–charge interactions or elimination of a charge that was negatively contributing to the long-range interactions, will lead to a decreased dependence of protein stability on the ionic strength, i.e. will decrease CspB-Bs halophilicity. An example of such an effect is the E3R substitution (see Figure 3). In CspB-TB, on the other hand, all charged residues are largely optimized and thus introduction of a favorable or removal of an unfavorable charge–charge interaction will likely enhance halophobic behavior of the protein (e.g. K13Q variant of CspB-TB, Figure 3). Similarly, introduction of an unfavorable or removal of a favorable long-range interaction in CspB-TB will reduce halophobicity of the protein and may even turn it into a halophilic one (e.g. R3E and E43K variants of CspB-TB, Figure 3). The idea that the long-range charge–charge interactions are, by and large, defining the halophilicity of a protein implies that identical substitutions made in structurally similar positions but in different electrostatic

847

Stability and Halophilicity of the CspB Proteins

contexts should produce different outcomes. For the CspB-Bs and CspB-TB proteins this indeed seems to be true (e.g. compare E43Q and E43K in Figure 3). What if no changes in halophilic or halophobic behavior of the protein upon site-directed substitutions are observed, yet there are significant changes in stability? This can be an indication that no longrange charge–charge interactions were affected and that the observed changes in stability of the protein are likely due to other reasons, such as changes in secondary structure propensities, changes in hydrogen bonding, hydrophobic effects or changes in short-range charge–charge interactions (formation or disruption of salt-bridges, for example). To test this in a more quantitative way, we introduced a parameter ΔΔΔGHAL that describes the changes in halophilicity relative to the wild-type protein: DDDGHAL ¼ ðDGvar ð1MÞ  DGWT ð1MÞÞ  ðDGvar ð0MÞ  DGWT ð0MÞÞ

ð1Þ

If observed changes in stability of a variant (ΔΔG = ΔGvar–ΔGwt) are indeed due to changes in long-range charge–charge interactions, they should inversely correlate with changes in halophilicity of this variant ΔΔΔGHAL. Significant deviation of a data point for a given variant from the correlation plot would suggest that the observed effects are either dominated by the short-range charge–charge interactions (salt-bridges, for example) or nonelectrostatic factors (such as changes in secondary structure propensities, hydrogen bonds, hydrophobic effect). Based on the above considerations, one would expect that most (if not all) data points of such correlation plot would cluster in the upper left (increased halophilicity and decreased stability) and bottom right (decreased halophilicity and increased stability) quadrants of the correlation plot. This indeed appears to be the case (Figure 4). There are, however, a few exceptions that support the notion that stability versus halophilicity plot helps to identify other factors contributing to the changes in stability upon substitutions introducing or removing charged residues. For example, substitutions at position V20 in CspB-Bs with neutral polar (V20Q), acidic (V20E) or basic (V20K) residues show significant deviation from the general trend for ΔΔG versus ΔΔΔGHAL dependences exhibited by other substitutions (Figure 4). This argues that the observed substitution effects are dominated by non-electrostatic factors (presumably, changes in hydrophobicity). The effects of substitutions at this position in the CspB-TB sequence on stability further support such a conclusion. When positively charged K20 in the sequence of CspB-TB is substituted by hydrophobic residue (K20V), the increase in stability does not follow the general trend observed for all other residues including other substitutions at this position: neutral (K20Q) or acidic (K20E) residues. This is clearly an indication that hydrophobic interactions at position 20 of CspB proteins have a larger contribution to protein stability than the charge–charge interactions, and thus should be

Figure 4. Correlation between changes in stability, ΔΔGvar = ΔGvar–ΔGWT, and changes in halophilicity, ΔΔΔGHAL (see equation (1)) relative to the corresponding wild-type proteins CspB-Bs (a) and CspB-TB (b). Identities of selected data points are shown on the plot. Data points that significantly deviate from the overall trend are shown as squares. Continuous lines are linear fits of the data and serve to guide the eye.

excluded from the analysis of the role of charge– charge interactions on the protein stability. Experimental versus theoretical changes in stability Thermodynamic data available for a large number of CspB-Bs and CspB-TB variants with substitutions involving charged residues combined with the computational models predicting the effects of substitutions of charged residues on protein stability can provide insight into the magnitude of the charge–charge interactions in the unfolded state. In the case of the CspB-Bs and CspB-TB proteins, substitutions are done in largely similar structural but different electrostatic environments (Figure 5). We hypothesize that, without taking into account charge–charge interactions in the unfolded state, “native-centric” computational models might be able to predict the effects of substitutions at certain positions on stability in one of the proteins but not effects of the identical substitutions at the same position in the other protein. Such deviations will be indicative of possible charge–charge interactions in the unfolded state. For example, introducing an amino acid side-chain that would stabilize the unfolded state of a protein to a greater extent than

848

Stability and Halophilicity of the CspB Proteins

Figure 5. (a) Comparison of the surface charge distribution on CspB-Bs (left) and CspB-TB (right). Coloring is done according to atoms Gasteiger charge and is assigned using ViewerLite 5.0 (Accelrys). (b) Comparison of the pairwise interaction energies of charge–charge interactions between ionizable residues in CspB-Bs (left) and CspB-TB (right) wildtype proteins as calculated by TK-SA. Different shades of red represent unfavorable interactions, while different shades of blue represent favorable pairwise charge–charge interactions.

Stability and Halophilicity of the CspB Proteins

it stabilizes the native state would result in a net destabilization of the protein. Alternatively, destabilization of the unfolded state to a greater degree than destabilization of the native state would result in an increase in the overall stability of the protein. Ignoring unfolded state interactions should result in predictions of the effects of these substitutions that are inconsistent with experimental measurements. It is important to note that this approach will be able to detect residual structure in the unfolded state but would not be able to distinguish whether the charge–charge interactions in the unfolded state are the cause or consequence of this residual structure. The correlation plots comparing the experimental, ΔΔGexp, and predicted, ΔΔGqq, changes in stability are shown in Figure 6(a) and (b). Since the results of calculations using different compu-

849 tational models show qualitatively similar results (Figure 7), we are limiting the comparison to the TKSA model. It is important to emphasize that we will be evaluating not the absolute values for the changes in the energy of charge–charge interactions obtained from the computational model but the qualitative predictions. This is because the model ignores changes in other factors that may contribute to protein stability such as secondary structure propensities, hydrophobic interactions between long side-chains, hydrogen bonds and others. As long as a substitution is predicted to produce either a stabilizing or destabilizing effect on the proteins and experimental results confirm that stability does change by more than 1.3 kJ/ mol (∼0.3 kcal/mol), the prediction is considered to be correct, disregarding absolute values of the observed stabilization or destabilization. As a

Figure 6. Comparison between experimental and calculated changes in the stability of the CspB-Bs (a) and CspB-TB (b) variants. Red circles are for the calculated changes in charge–charge interactions based on the native state only, ΔΔGqq. Blue squares depict data points in which the charges in charge–charge interactions were corrected for the changes in the charge–charge interactions in the unfolded state as calculated by Gaussian chain model.24 Green triangles show data points in which the charges in charge–charge interactions were corrected for the half of the charge–charge interactions per residue that originated from the residues ±6 in sequence in the native state context. (c) Expansion of the lower right quadrant of the plot shown in (a) showing the data that corresponds to the filled circles. (d) Effects of substitutions in the positions 19, 21 and 25 incorporated in different backgrounds (E19Q is the E19Q background, and AA is the F15A/F27A/ E3R background) for the calculated and experimental stabilities of CspB-Bs variants. Shaded parts on all four plots are the areas for the data point locations when computational models fail to predict the experimentally measured changes in stability. All data are for 100 mM NaCl.

850

Stability and Halophilicity of the CspB Proteins

Figure 7. Comparison of the energies of charge–charge interactions for CspB-Bs (a) and CspB-TB (b) calculated using different computational models: TK-SA (black bars), MM_SCP (red bars), UHBD (green bars), MCCE (yellow bars). Calculations using each individual computational model were done on 11 different structures generated MODELLER81 and the averaged results are shown. The error bars represent the standard deviation of the mean.

consequence, we expected that data points corresponding to the correct predictions would cluster in the upper right (predicted to be stabilizing and determined to be stabilizing) and bottom left (predicted to be destabilizing and confirmed to be destabilizing) quadrants of the correlation plots, but will not necessarily produce perfect correlations. As can be seen from Figure 6(a) and (b), the majority of the data points on the correlation plot are indeed located in the upper right (predicted to be stabilizing and determined to be stabilizing) and bottom left (predicted to be destabilizing and confirmed to be destabilizing) quadrants of the correlation plots for CspB-Bs and CspB-TB. Such qualitative correlations have been previously observed for several other proteins.5–8 However, in the case of the CspB-Bs and CspB-TB pair, there are exceptions: for several sequence positions, substitutions that are not correctly predicted in CspB-Bs background do follow predictions in CspB-TB. For these variants the calculated energy changes do not correlate with the experimentally measured changes in stability beyond the ±1.3 kJ/mol estimated errors in ΔΔGexp and ΔΔGqq. Interestingly, most of those substitutions are clustered between positions 19 through 25 (Figure 6(c)) located in the β2–β3

hairpin, suggesting that charge–charge interactions involving these residues could be modulating the structure of the unfolded state. Can the correlation between ΔΔGexp and ΔΔGqq be improved by taking into account charge–charge interactions in the unfolded state? The calculations described above are all based on the native state and assume that the charge–charge interactions in the unfolded state are the same for all protein variants. This might be an oversimplification, as there are currently several reports documenting perturbations in the pKas of ionizable residues in the unfolded state.29–37 There are two potential sources for these unfolded state effects. First, the local sequence context can affect the intrinsic pKa of a residue that usually is taken to be the same for all residues of a given type. It has been shown that in some cases, local sequence context and/or local conformational preferences can perturb pKas by as much as 0.3–0.5 units, that will translate into 1.5–2.5 kJ/mol in ΔΔGqq (more than the estimated error of ±1.3 kJ/mol, see Figure 6(a) and (b)). Second, the unfolded state of a protein can retain some structural details that will influence the

Stability and Halophilicity of the CspB Proteins

charge–charge interactions in the unfolded state. We must recognize that since we are comparing relative (to the wild-type) changes in stability, the substitution must perturb this unfolded state structure in order to manifest itself in ΔΔGexp. There are several models of the unfolded state that have been introduced previously to account for the local sequence context and/or local conformational preferences. These models are rather simple, but arguably can capture certain properties of the unfolded proteins such as the fact that they are linear polymers of finite length24 or that the unfolded state can retain some of the topological features of the native state. 23 The Gaussian chain model introduced by Zhou24 treats the distances between charged residues in a linear protein sequence as a Gaussian distribution and uses the Debye-Huckel model to calculate the corresponding interaction energy between the charges. However, correcting ΔΔGqq with our implementation of ΔΔGGauss does not improve the overall correlation between experimental and calculated values (see Figure 6(a) and (b)). A structural model to account for interactions between charges in the unfolded state was introduced by Elcock.23 In this model, the structure of the unfolded molecule is obtained by “expanding” the native state. This structural model retains some properties of the native state, and particularly in the turn segments. However, in such structural model all residues are solvent-exposed and some of the computational models, TKSA in particular, are not applicable. Nevertheless, the native-like topology of Elcock unfolded state inspired another empirical approach to estimate the interactions in the unfolded state. For example, one can count only the ΔΔGqq energy per residue that originated from the residues ±6 in sequence. If half of these values are used to correct the ΔΔGqq, a rather satisfactory qualitative result can be observed (Figure 6(a) and (b)). For both CspB-Bs and CspB-TB variants this does not have any significant effect on the correlation coefficient. What is more significant, however, is that there are fewer data points in the lower right quadrant of the correlation plot for CspB-Bs (Figure 6(a)). This empirical correlation can be easily rationalized. The unfolded state might retain some of the native state topology. As such, the topology of the residues in the 19–25 segment of the β2–β3 hairpin might be retained in the unfolded state but the magnitude of charge–charge interactions between residues in this hairpin will be weaker due to partial solvent exposure of this segment and some conformational changes. Close inspection of the sequence in the β2–β3 hairpin reveals that there are four negative residues in the turn (Figure 1). The stem of the hairpin appears to be stabilized by four Phe residues that have been shown to be important for the global stability of CspB-Bs.61–63 It is also known that cross-strand side-chain to side-chain interactions of Phe residues are important for β-sheet stability.64 Furthermore, several studies have suggested possibilities of aromatic cluster formation in the unfolded state.65–70 Based on these observations

851 it is reasonable to propose that the wild-type CspBBs could retain, to some degree, the structure of β2–β3 hairpin in the unfolded state, which is mainly stabilized by interactions between F15, F17, F27 and F30, while the repulsive interactions between negatively charged E19, E21, D24 and D25 are destabilizing. Upon charge neutralization (i.e. E to Q or D to N substitutions) or charge reversal (i.e. E to K substitution) in E19, E21, D24 and D25, the postulated structure will be further stabilized because of the removal of some of the unfavorable interactions. We thus hypothesize that in the unfolded state the β2–β3 hairpin could be stabilized by interactions between phenylalanine side-chains and thus lead to misprediction of the effect of substitution on charge–charge interactions in positions 19, 21, 24 and 25. To test this hypothesis, we generated E19K and D25K substitutions in the F15A/F27A background (because these substitutions lead to a significant destabilization, they were combined with a stabilizing substitution E3R). Alasubstitutions were chosen because they dramatically decrease the size and hydrophobicity of the sidechain, yet are not as dramatic as changing polarity by introducing ionizable groups.27,28 The overall effect however is expected to be significant51,52 because these substitutions are introduced at two positions simultaneously. The F15A/F27A substitutions should disrupt the postulated structure of the β2–β3 hairpin in the unfolded state. An increase in stability upon E19K and D25K substitutions in this background (in accord with the prediction of the native state based calculations) will suggest that indeed β2–β3 is structured in the unfolded state of CspB-Bs. However, experiments show that E19K and D25K substitutions in the F15A/F27A/E3R background decrease stability by 2.2 kJ/mol and 5.2 kJ/mol, respectively, which is practically identical to the decrease in stability upon the same substitutions in the wild-type background. Furthermore, analysis of the urea-induced unfolding profiles (data not shown) using linear extrapolation method71–73 produces free energies of unfolding of 8.6(±1.0) kJ/mol for CspB-Bs-Lwt, 7.9(±0.8) kJ/mol for CspB-Bs-E19K, which is comparable with the stability estimates from the thermal melts (9.7 and 8.7 kJ/mol, for CspB-Bs-Lwt and CspB-Bs-E19K, respectively). Furthermore, m-values for the two proteins are the same within experimental error: 2.5(±0.2) kJ/mol2, 2.6(±0.2) kJ/mol2 for CspB-BsLwt and E19K, respectively, suggesting that these two proteins unfold to the same extent.74,75 These observations provide no evidence for the existence of a Phe-stabilized β2–β3 hairpin in the unfolded state, and thus do not explain the deviations of the experimental and predicted changes in the stability upon substitutions in positions 19, 21, 24 and 25. We can conclude that stabilization of the unfolded state is an unlikely explanation for the observed deviation of the experimental results from the computational predictions. Another possibility to explain this deviation between the experimental and predicted changes

852 in stability upon substitutions in positions 19, 21, 24 and 25 is related to the fact that all these residues, and in particular E19 and D25 have strong interactions with K7 (see Figure 5). Substitutions at K7 are detrimental for CspB-Bs stability (see Table 1). Thus it is possible that the substitutions at positions 19 and 25, and to a lesser degree 21 and 24, affect and compete with K7. The best way to test this will be to measure stability of variants at positions 19, 21, 24 and 25 in the background of K7Q and K7E substitution. This is impossible however, because both K7Q and K7E substitutions are so destabilizing that they render unfolded proteins. Therefore, we made substitutions and compared stabilities of the following three variants: E21Q/E19Q, D25Q/E19Q and D25K/E19Q. The hypothesis is that E19Q substitution perturbs/disrupts the complex network of charge–charge interactions (which can have some cooperative or anticooperative behavior) and thus substitutions at the other positions can be adequately predicted. This is indeed true. The E21Q, D25Q and D25K substitutions in the wild-type background are destabilizing while native state based calculations of charge–charge interactions predict just the opposite, an increase in stability. In the E19Q background, these three substitutions are now predicted to be destabilizing and this is what is observed experimentally. It is important to emphasize that the experimental decrease in stability is independent of whether the substitution is made in the wild-type or E19Q background (Figure 6(c)). This once again suggests that the mispredictions of the effects of substitutions at positions 19, 21, 24 and 25 cannot be attributed to the unfolded state effects. It is simply the inability of the computational model to account for the side-chain dynamics in the highly dense network of charges.

Concluding Remarks Based on the results presented here we can conclude that the charge–charge interactions in the unfolded state of two model proteins CspB-Bs and CspB-TB are not significant. It is not easy to generalize this finding, particularly in relation to the relatively small size of CspBs. One can argue that the stability of smaller globular proteins, due to their smaller surface-to-volume ratio, might actually be more prone to the influence of charge–charge interactions in the unfolded state: smaller surfaceto-volume ratios lead to a higher fraction of ionizable residues in the sequence of small proteins and thus higher probability for them to interact in the unfolded state. In this case it is conceivable that negative design has been used during the evolution such that sequences with sub-optimal charge–charge interactions in the native state were selected because they had even less favorable interactions in the unfolded state. Nevertheless, computational models that are based only on the native state structure can adequately, i.e. qualitatively (stabilizing versus destabilizing) and semi-quantitatively (relative

Stability and Halophilicity of the CspB Proteins

rank order), predict the effects of surface charge neutralization or reversal on the protein stability in CspB and in several other proteins.2,4–10,16,21,43 Most certainly, in order to improve the predictive power to a quantitative level, the interactions in the unfolded state must be accounted for but none of the current models appear to be adequate, mainly because of the lack of detailed understanding of the unfolded state ensemble. Another interesting observation is that the effect of ionic strength on protein stability (protein halophilicity) appears to be mainly due to the screening of the long-range charge–charge interactions. This conclusion has been reached previously from both experimental and computational studies. 43,56,57 However, to the best of our knowledge, the data for CspB-Bs and CspB-TB variants provide arguably the most detailed study to date of the molecular mechanisms of protein halophilicity. More experimental data on a large set of different proteins will probably be needed to gain a detailed insight into this phenomenon.

Materials and Methods Cloning, expression and purification of CspB-Bs and CspB-TB variants In addition to the original constructs for expression of CspB-Bs and CspB-TB described by us previously,4 we also made removable 6xHis-tags constructs that facilitate subsequent recombinant protein purification. For this the N-terminal methionine was replaced with the MHHHHHHML sequence. This allows CNBr cleavage at methionine residues and in our case renders both CspBBs and CspB-TB proteins with Leu as the first amino acid residue. Since the original CspB-TB construct also contained additional methionine in position 36, it was substituted with glycine. These constructs were made using standard cloning protocols involving PCR amplification of corresponding templates. Presence of the target sequences encoding CspB-Bs or CspB-TB with 6xHis-tags was confirmed by automated dideoxynucleotide sequencing on ABI 50 genetic analyzer at the Molecular Genetics Core Facility of the Section of Research Resources, Penn State College of Medicine. Resulting constructs were designated as 6H-CspB-Bs* and 6H-CspB-TB*. Site-directed substitutions in the CspB-Bs and CspB-TB sequences were introduced using the QuickChange site-directed mutagenesis kit (Stratagene) according to the manufacturer's protocol. Multiple substitutions were introduced by sequential PCR step mutagenesis.76 The following CspB-Bs variants E3R(M1), E3Q, K7E, K7Q, N10D, D25Q, S48E, E50Q, R56Q were cloned using original CspB-Bs construct as template.4 CspB-Bs variants E3R(L1), K5Q, K5E, N10K, E12K, E12Q, K13E, K13Q, E19K, E19Q, V20K, V20Q, V20E, E21K, E21Q, D24N, D24K, D25K, K39E, K39Q, E42K, E42Q, E43K, E43Q, S48K, E50K, E53K, E53Q, N55K, N55D, K65E, K65Q, E3R/ V20K, E3R/V20Q, E3R/V20E, F15A/F27A/E3R, E19K/ F15A/F27A/E3R and D25K/F15A/F27A/E3R were produced using 6H-CspB-Bs* as template. Likewise, CspB-TB variants R3E, R3Q, K20E, K20Q, D25N, E48Q, E50Q were constructed using original CspB-TB template,4 while K5E, K5Q, K7E, K7Q, D10K, D10N, K12E, K12Q, K13E, K13Q,

853

Stability and Halophilicity of the CspB Proteins E21K, E21Q, D24N, D24K, D25K, K39E, K39Q, K42E, K42Q, E43K, E43Q, E48K, E50K, E53K, E53Q, K55E and K55Q substitutions were incorporated into 6H-CspB-TB*. Expression and purification of untagged CspB-Bs and CspB-TB variants was done essentially as described for the wild-type proteins.4 6xHis tagged proteins were purified by Ni2+ affinity chromatography on Ni-NTA His-Bind resin (Novagen), as recommended by the manufacturer. The final purification step included gel-filtration chromatography on Sephadex G-75 (2.5 cm × 100 cm) equilibrated in 5% acetic acid. CspB-containing fractions were pooled together and lyophilized. Purity of the recombinant proteins was better than 95% as judged by the Coomassie staining of SDS-polyacrylamide gels. For both 6H-CspB-Bs* and 6H-CspB-TB* protein variants, 6xHis-tags were removed by cyanogen bromide cleavage reaction as described.77 5–10 mg of lyophilized protein powder was dissolved in 1 ml of 80% formic acid. 0.5 ml of ∼300 mg/ml cyanogen bromide solution in 80% formic acid was added to the protein and mixed. The solution was purged with nitrogen gas for 5–10 min, tubes were capped, wrapped in aluminium foil and left at room temperature for 24 h. CNBr-cleaved samples were dialyzed against water with a small amount of 1% NH4OH for 6–12 h (changed once), followed by dialysis against 50 mM NaH2PO4 (pH 8.0), 300 mM NaCl, 10 mM imidazole (native lysis buffer, Novagen Ni-NTA His-Bind resins instruction manual) for an additional 12–18 h (changed once). To remove any remaining 6xHis-tagged protein, samples were passed three times through ∼0.25 ml (bed volume) of Ni-NTA resin equilibrated with at least 3 ml of the native lysis buffer (Novagen). NiNTA resin was washed with an additional 1 ml of the native lysis buffer. Flow through and wash fractions were combined and extensively dialyzed against 50 mM sodium cacodylate (pH 7.5). Efficiency of 6 × His-tag removal was verified by polyacrylamide gel electrophoresis and matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectroscopy. Protein concentration was determined spectrophotometrically using the following extinction coefficients at 280 nm: 6H-CspBBs* variants, 0.675 (mg/ml)−1cm−1; 6H-CspB-TB* variants, 0.659 (mg/ml)−1cm−1; CspB-Bs variants, 0.774 (mg/ ml)−1cm−1; CspB-TB variants, 0.753 (mg/ml)−1cm−1. Light scattering was corrected as described by Winder and Gent.78 Stability measurements Circular dichroism measurements were performed on a Jasco J-715 automatic recording spectropolarimeter as described.4 Structural integrity of the CspB-Bs and CspBTB variants was verified by recording far-UV CD spectra. As expected, substitutions of the surface residues did not produce significant changes in the far-UV CD spectra of the proteins, indicating that substitutions did not produce dramatic structural changes. Temperature-induced protein unfolding was monitored by recording ellipticity values at 222 nm in a Jasco PTC-351S Peltier type cell holder using six 1 cm rectangular quartz cells. Protein concentration ranged from 0.035 mg/ml to 0.05 mg/ml. Each of the six cells contained 2.5–3 ml of protein solution in 50 mM sodium cacodylate (pH 7.5) at increasing ionic strength (NaCl concentrations of 0, 0.05, 0.1, 0.2, 0.5 and 1 M). Ellipticity values were recorded every 1 deg.C between 2 and 95 °C at a heating rate of 0.5 deg.C per minute. Upon completion of the experiment, protein solutions were allowed to equilibrate at 2 °C for at least

1 h to check reversibility of the unfolding by recording farUV CD spectra of the refolded protein. The ellipticity changes for thermal denaturation curves was fitted to a two-state model using the equation: QðTÞ ¼ FN ðTÞQN ðTÞ þ FU ðTÞQN ðTÞ

ð2Þ

where Θ(T) is the measured ellipticity at any given temperature, ΘN(T) and ΘU(T) are ellipticity values of the native and unfolded protein at a given temperature, FN(T) and FU(T) are fractions of the native and unfolded protein at this temperature. Fractions of the native and unfolded proteins were calculated as: FN ðTÞ ¼ 1  FU ðT Þ ¼

1 1 þ Kd ðTÞ

ð3Þ

where K is the equilibrium constant of the two-state unfolding reaction calculated as:   DGðTÞ Kd ðT Þ ¼ exp  ð4Þ RT ΔG of the unfolding reaction was calculated as: DGðT Þ ¼ DH ðTÞ  TDSðTÞ Tm  T DH ðTm Þ þ ðT  Tm ÞDCp ¼ Tm þ DCp TlnðTm =T Þ

ð5Þ

where Tm is the transition temperature, ΔH(Tm) is the enthalpy change at the transition temperature, ΔCp is the heat capacity change. For the least stable proteins when FU was significant even at 2 °C no Tm could be accurately estimated, therefore unfolding free energy changes were also estimated from fitting experimental results to the equation: DGðTÞ ¼ DHðTÞ  TDSðTÞ ¼ DHðTs Þ þ ðT  Ts ÞDCp þ DCp TlnðTs =TÞ

ð6Þ

where Ts is the temperature of maximum stability of the protein. Differential scanning calorimetry (DSC) analysis of selected CspB variants was performed using VP-DSC (Microcal, Northhampton) instrument. All experiments were done at a heating rate of 1 deg.C/min. Reversibility was checked by recording a second scan and comparing with the first one. Partial specific volumes for the determination of the partial heat capacity were calculated from the amino acid composition of the proteins as described.79 Data fitting to a two-state model was performed as described.80 Analysis of the thermal denaturation profiles was done using the non-linear regression software package NLREG using in-house written scripts. Structural modeling and calculations of the charge–charge interactions All structural modeling was done using the Modeller version 7.7 software package.81 The template for all modeling was the X-ray structure of CspB-Bs, 1csp.82 An ensemble of 11 structures was generated for each variant and used in all consequent calculations. Calculations were done on the individual structures in the ensemble, the results were averaged, and the mean value with standard deviation from the mean is reported. The energies of charge–charge interactions were determined from the changes in the pKas relative to the model

854 compound values. The pKas of ionizable residues were calculated using several different computational models. (i) The TK-SA procedure, the implementation of which is described elsewhere.6,83 In this model, the energy of pairwise interactions between unit charges was calculated according to the Tanford-Kirkwood algorithm84 with the solvent accessibility correction as proposed by Gurd.85,86 This energy of pairwise interactions was then used to calculate the effect of charge–charge interactions on the perturbations of pKas of ionizable groups from their model compound values (Asp, 4.0; Glu, 4.5; Lys, 10.6; Arg, 12.0; His, 6.3; Tyr, 10.5; N –terminus, 7.7; C – terminus, 3.6). (ii) The Microenvironment Modulated Screened Coulomb Potential (MM_SCP) approach described by Mehler et al.69,87 (iii) The finite difference Poisson–Boltzmann (FDPB) method as implemented in the UHBD software package 88,89 was described previously.4,10,57 (iv) The Multi Conformer Continuum Electrostatics (MCCE) software package that uses MonteCarlo sampling of different side-chain rotamers in conjunction with the FDPB calculations using the DELPHI software package and PARSE solvation.90,91 Figure 7 compares the results of these different computational models that were used to describe the charge–charge interactions in CspB-Bs and CspB-TB. It is apparent that despite different degrees of the detailing that are included in these models the results are qualitatively the same. The qualitative similarity of different computational models to account for the interactions between charged residues on the surface of several different proteins has been shown previously.4,7–9

Stability and Halophilicity of the CspB Proteins

5. 6.

7.

8.

9.

10.

11.

12.

Acknowledgements We thank Vakhtang Loladze, John Richardson, Marimar Lopez, Jessica Wolgemuth and Samantha Strickler for help with protein cloning and purification at the initial stages of this project and Katrina Schweiker and Marimar Lopez for comments on the manuscript. This work was supported by a grant from the National Science Foundation (MCB 0416746).

13.

14.

15.

Supplementary Data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/ j.jmb.2006.11.061

16. 17.

References 1. Xiao, L. & Honig, B. (1999). Electrostatic contributions to the stability of hyperthermophilic proteins. J. Mol. Biol. 289, 1435–1444. 2. Spector, S., Wang, M., Carp, S. A., Robblee, J., Hendsch, Z. S., Fairman, R. et al. (2000). Rational modification of protein stability by the mutation of charged surface residues. Biochemistry, 39, 872–879. 3. Perl, D. & Schmid, F. X. (2001). Electrostatic stabilization of a thermophilic cold shock protein. J. Mol. Biol. 313, 343–357. 4. Makhatadze, G. I., Loladze, V. V., Gribenko, A. V. &

18.

19. 20. 21.

Lopez, M. M. (2004). Mechanism of thermostabilization in a designed cold shock protein with optimized surface electrostatic interactions. J. Mol. Biol. 336, 929–942. Sanchez-Ruiz, J. M. & Makhatadze, G. I. (2001). To charge or not to charge? Trends Biotechnol. 19, 132–135. Loladze, V. V., Ibarra-Molero, B., Sanchez-Ruiz, J. M. & Makhatadze, G. I. (1999). Engineering a thermostable protein via optimization of charge-charge interactions on the protein surface. Biochemistry, 38, 16419–16423. Permyakov, S. E., Makhatadze, G. I., Owenius, R., Uversky, V. N., Brooks, C. L., Permyakov, E. A. & Berliner, L. J. (2005). How to improve nature: study of the electrostatic properties of the surface of alphalactalbumin. Protein Eng. Des. Sel. 18, 425–433. Lee, C. F., Makhatadze, G. I. & Wong, K. B. (2005). Effects of charge-to-alanine substitutions on the stability of ribosomal protein L30e from Thermococcus celer. Biochemistry, 44, 16817–16825. Strickler, S. S., Gribenko, A. V., Keiffer, T. R., Tomlinson, J., Reihle, T., Loladze, V. V. & Makhatadze, G. I. (2006). Protein stability and surface electrostatics: a charged relationship. Biochemistry, 45, 2761–2766. Schwehm, J. M., Fitch, C. A., Dang, B. N., GarciaMoreno, E. B. & Stites, W. E. (2003). Changes in stability upon charge reversal and neutralization substitution in staphylococcal nuclease are dominated by favorable electrostatic effects. Biochemistry, 42, 1118–1128. Marshall, S. A., Morgan, C. S. & Mayo, S. L. (2002). Electrostatics significantly affect the stability of designed homeodomain variants. J. Mol. Biol. 316, 189–199. Zollars, E. S., Marshall, S. A. & Mayo, S. L. (2006). Simple electrostatic model improves designed protein sequences. Protein Sci. 15, 2014–2018. Grimsley, G. R., Shaw, K. L., Fee, L. R., Alston, R. W., Huyghues-Despointes, B. M., Thurlkill, R. L. et al. (1999). Increasing protein stability by altering longrange coulombic interactions. Protein Sci. 8, 1843–1849. Laurents, D. V., Huyghues-Despointes, B. M., Bruix, M., Thurlkill, R. L., Schell, D., Newsom, S. et al. (2003). Charge-charge interactions are key determinants of the pK values of ionizable groups in ribonuclease Sa (pI = 3.5) and a basic variant (pI = 10.2). J. Mol. Biol. 325, 1077–1092. Koide, A., Jordan, M. R., Horner, S. R., Batori, V. & Koide, S. (2001). Stabilization of a fibronectin type III domain by the removal of unfavorable electrostatic interactions on the protein surface. Biochemistry, 40, 10326–10333. Zhou, H. X. & Dong, F. (2003). Electrostatic contributions to the stability of a thermophilic cold shock protein. Biophys. J. 84, 2216–2222. Bae, E. & Phillips, G. N., Jr (2005). Identifying and engineering ion pairs in adenylate kinases. Insights from molecular dynamics simulations of thermophilic and mesophilic homologues. J. Biol. Chem. 280, 30943–30948. Forsyth, W. R. & Robertson, A. D. (2000). Insensitivity of perturbed carboxyl pK(a) values in the ovomucoid third domain to charge replacement at a neighboring residue. Biochemistry, 39, 8067–8072. Kumar, S., Tsai, C. J. & Nussinov, R. (2000). Factors enhancing protein thermostability. Protein Eng. 13, 179–191. Kumar, S. & Nussinov, R. (2002). Close-range electrostatic interactions in proteins. Chembiochem. 3, 604–617. Makhatadze, G. I., Loladze, V. V., Ermolenko, D. N.,

Stability and Halophilicity of the CspB Proteins

22. 23.

24.

25. 26.

27.

28.

29.

30.

31.

32.

33.

34.

35. 36.

37.

Chen, X. & Thomas, S. T. (2003). Contribution of surface salt-bridges to protein stability: guidelines for protein engineering. J. Mol. Biol. 327, 1135–1148. Li, H., Robertson, A. D. & Jensen, J. H. (2005). Very fast empirical prediction and rationalization of protein pKa values. Proteins: Struct. Funct. Genet. 61, 704–721. Elcock, A. H. (1999). Realistic modeling of the denatured states of proteins allows accurate calculations of the pH dependence of protein stability. J. Mol. Biol. 294, 1051–1062. Zhou, H. X. (2002). A Gaussian-chain model for treating residual charge-charge interactions in the unfolded state of proteins. Proc. Natl Acad. Sci. USA, 99, 3569–3574. Zhou, H. X. (2002). Residual charge interactions in unfolded staphylococcal nuclease can be explained by the Gaussian-chain model. Biophys. J. 83, 2981–2986. Zhou, H. X. (2003). Direct test of the Gaussian-chain model for treating residual charge-charge interactions in the unfolded state of proteins. J. Am. Chem. Soc. 125, 2060–2061. Robic, S., Guzman-Casado, M., Sanchez-Ruiz, J. M. & Marqusee, S. (2003). Role of residual structure in the unfolded state of a thermophilic protein. Proc. Natl Acad. Sci. USA, 100, 11345–11349. Guzman-Casado, M., Parody-Morreale, A., Robic, S., Marqusee, S. & Sanchez-Ruiz, J. M. (2003). Energetic evidence for formation of a pH-dependent hydrophobic cluster in the denatured state of Thermus thermophilus ribonuclease H. J. Mol. Biol. 329, 731–743. Trefethen, J. M., Pace, C. N., Scholtz, J. M. & Brems, D. N. (2005). Charge-charge interactions in the denatured state influence the folding kinetics of ribonuclease Sa. Protein Sci. 14, 1934–1938. Cho, J. H. & Raleigh, D. P. (2006). Electrostatic interactions in the denatured state and in the transition state for protein folding: effects of denatured state interactions on the analysis of transition state structure. J. Mol. Biol. 359, 1437–1446. Cho, J. H. & Raleigh, D. P. (2005). Mutational analysis demonstrates that specific electrostatic interactions can play a key role in the denatured state ensemble of proteins. J. Mol. Biol. 353, 174–185. Kuhlman, B., Luisi, D. L., Young, P. & Raleigh, D. P. (1999). pKa values and the pH dependent stability of the N-terminal domain of L9 as probes of electrostatic interactions in the denatured state. Differentiation between local and nonlocal interactions. Biochemistry, 38, 4896–4903. Pace, C. N., Alston, R. W. & Shaw, K. L. (2000). Charge-charge interactions influence the denatured state ensemble and contribute to protein stability. Protein Sci. 9, 1395–1398. Oliveberg, M., Arcus, V. L. & Fersht, A. R. (1995). pKA values of carboxyl groups in the native and denatured states of barnase: the pKA values of the denatured state are on average 0.4 units lower than those of model compounds. Biochemistry, 34, 9424–9433. Tan, Y. J., Oliveberg, M., Davis, B. & Fersht, A. R. (1995). Perturbed pKA-values in the denatured states of proteins. J. Mol. Biol. 254, 980–992. Tollinger, M., Crowhurst, K. A., Kay, L. E. & FormanKay, J. D. (2003). Site-specific contributions to the pH dependence of protein stability. Proc. Natl Acad. Sci. USA, 100, 4545–4550. Hammack, B. N., Smith, C. R. & Bowler, B. E. (2001). Denatured state thermodynamics: residual structure, chain stiffness and scaling factors. J. Mol. Biol. 311, 1091–1104.

855 38. Theobald, D. L., Mitton-Fry, R. M. & Wuttke, D. S. (2003). Nucleic acid recognition by OB-fold proteins. Annu. Rev. Biophys. Biomol. Struct. 32, 115–133. 39. Arcus, V. (2002). OB-fold domains: a snapshot of the evolution of sequence, structure and function. Curr. Opin. Struct. Biol. 12, 794–801. 40. Mevarech, M., Frolow, F. & Gloss, L. M. (2000). Halophilic enzymes: proteins with a grain of salt. Biophys. Chem. 86, 155–164. 41. Elcock, A. H. & McCammon, J. A. (1998). Electrostatic contributions to the stability of halophilic proteins. J. Mol. Biol. 280, 731–748. 42. Szilagyi, A. & Zavodszky, P. (2000). Structural differences between mesophilic, moderately thermophilic and extremely thermophilic protein subunits: results of a comprehensive survey. Struct. Fold. Des. 8, 493–504. 43. Dominy, B. N., Perl, D., Schmid, F. X. & Brooks, C. L., 3rd (2002). The effects of ionic strength on protein stability: the cold shock protein family. J. Mol. Biol. 319, 541–554. 44. Madern, D., Ebel, C. & Zaccai, G. (2000). Halophilic adaptation of enzymes. Extremophiles, 4, 91–98. 45. Madern, D., Pfister, C. & Zaccai, G. (1995). Mutation at a single acidic amino acid enhances the halophilic behaviour of malate dehydrogenase from Haloarcula marismortui in physiological salts. Eur. J. Biochem. 230, 1088–1095. 46. Premkumar, L., Greenblatt, H. M., Bageshwar, U. K., Savchenko, T., Gokhman, I., Sussman, J. L. & Zamir, A. (2005). Three-dimensional structure of a halotolerant algal carbonic anhydrase predicts halotolerance of a mammalian homolog. Proc. Natl Acad. Sci. USA, 102, 7493–7498. 47. Lanyi, J. K. (1974). Salt-dependent properties of proteins from extremely halophilic bacteria. Bacteriol. Rev. 38, 272–290. 48. Jaenicke, R. & Bohm, G. (1998). The stability of proteins in extreme environments. Curr. Opin. Struct. Biol. 8, 738–748. 49. Wunderlich, M., Martin, A. & Schmid, F. X. (2005). Stabilization of the cold shock protein CspB from Bacillus subtilis by evolutionary optimization of Coulombic interactions. J. Mol. Biol. 347, 1063–1076. 50. Mueller, U., Perl, D., Schmid, F. X. & Heinemann, U. (2000). Thermal stability and atomic-resolution crystal structure of the Bacillus caldolyticus cold shock protein. J. Mol. Biol. 297, 975–988. 51. Makhatadze, G. I. & Privalov, P. L. (1995). Energetics of protein structure. Advan. Protein Chem. 47, 307–425. 52. Loladze, V. V., Ermolenko, D. N. & Makhatadze, G. I. (2002). Thermodynamic consequences of burial of polar and non-polar amino acid residues in the protein interior. J. Mol. Biol. 320, 343–357. 53. Kent, S. (2004). Novel forms of chemical protein diversity – in nature and in the laboratory. Curr. Opin. Biotechnol. 15, 607–614. 54. Bang, D., Makhatadze, G. I., Tereshko, V., Kossiakoff, A. A. & Kent, S. B. (2005). Total chemical synthesis and X-ray crystal structure of a protein diastereomer: [DGln 35]ubiquitin. Angew Chem. Int. Ed. Engl. 44, 3852–3856. 55. Bang, D., Gribenko, A. V., Tereshko, V., Kossiakoff, A. A., Kent, S. B. & Makhatadze, G. I. (2006). Dissecting the energetics of protein alpha-helix C-cap termination through chemical protein synthesis. Nature Chem. Biol. 2, 139–143. 56. Kao, Y. H., Fitch, C. A., Bhattacharya, S., Sarkisian, C. J., Lecomte, J. T. & Garcia-Moreno, E. B. (2000). Salt

856

57. 58.

59.

60. 61.

62. 63. 64. 65.

66.

67.

68. 69.

70. 71. 72. 73.

74.

Stability and Halophilicity of the CspB Proteins

effects on ionization equilibria of histidines in myoglobin. Biophys. J. 79, 1637–1654. Lee, K. K., Fitch, C. A. & Garcia-Moreno, E. B. (2002). Distance dependence and salt sensitivity of pairwise, coulombic interactions in a protein. Protein Sci. 11, 1004–1016. Perl, D., Mueller, U., Heinemann, U. & Schmid, F. X. (2000). Two exposed amino acid residues confer thermostability on a cold shock protein. Nature Struct. Biol. 7, 380–383. Perl, D., Welker, C., Schindler, T., Schroder, K., Marahiel, M. A., Jaenicke, R. & Schmid, F. X. (1998). Conservation of rapid two-state folding in mesophilic, thermophilic and hyperthermophilic cold shock proteins. Nature Struct. Biol. 5, 229–235. Baldwin, R. L. (1996). How Hofmeister ion interactions affect protein stability. Biophys. J. 71, 2056–2063. Schindler, T., Perl, D., Graumann, P., Sieber, V., Marahiel, M. A. & Schmid, F. X. (1998). Surfaceexposed phenylalanines in the RNP1/RNP2 motif stabilize the cold-shock protein CspB from Bacillus subtilis. Proteins: Struct. Funct. Genet. 30, 401–406. Hillier, B. J., Rodriguez, H. M. & Gregoret, L. M. (1998). Coupling protein stability and protein function in Escherichia coli CspA. Fold. Des. 3, 87–93. Rodriguez, H. M., Vu, D. M. & Gregoret, L. M. (2000). Role of a solvent-exposed aromatic cluster in the folding of Escherichia coli CspA. Protein Sci. 9, 1993–2000. Smith, C. K. & Regan, L. (1995). Guidelines for protein design: the energetics of beta sheet side chain interactions. Science, 270, 980–982. Koepf, E. K., Petrassi, H. M., Sudol, M. & Kelly, J. W. (1999). WW: an isolated three-stranded antiparallel beta-sheet domain that unfolds and refolds reversibly; evidence for a structured hydrophobic cluster in urea and GdnHCl and a disordered thermal unfolded state. Protein Sci. 8, 841–853. Crowhurst, K. A. & Forman-Kay, J. D. (2003). Aromatic and methyl NOEs highlight hydrophobic clustering in the unfolded state of an SH3 domain. Biochemistry, 42, 8687–8695. Klein-Seetharaman, J., Oikawa, M., Grimshaw, S. B., Wirmer, J., Duchardt, E., Ueda, T. et al. (2002). Longrange interactions within a nonnative protein. Science, 295, 1719–1722. Baldwin, R. L. (2002). Making a network of hydrophobic clusters. Science, 295, 1657–1658. Mehler, E. L., Fuxreiter, M., Simon, I. & GarciaMoreno, E. B. (2002). The role of hydrophobic microenvironments in modulating pKa shifts in proteins. Proteins: Struct. Funct. Genet. 48, 283–292. Dill, K. A. & Shortle, D. (1991). Denatured states of proteins. Annu. Rev. Biochem. 60, 795–825. Pace, C. N. (1990). Measuring and increasing protein stability. Trends Biotechnol. 8, 93–98. Pace, C. N. (1986). Determination and analysis of urea and guanidine hydrochloride denaturation curves. Methods Enzymol. 131, 266–280. Santoro, M. M. & Bolen, D. W. (1992). A test of the linear extrapolation of unfolding free energy changes over an extended denaturant concentration range. Biochemistry, 31, 4901–4907. Shortle, D. (1995). Staphylococcal nuclease: a show-

75.

76.

77. 78. 79.

80. 81. 82.

83.

84. 85. 86.

87.

88. 89. 90.

91.

case of m-value effects. Advan. Protein Chem. 46, 217–247. Pace, C. N., Laurents, D. V. & Thomson, J. A. (1990). pH dependence of the urea and guanidine hydrochloride denaturation of ribonuclease A and ribonuclease T1. Biochemistry, 29, 2564–2572. Cormack, B. (1992). Mutagenesis of cloned DNA. In Short Protocols in Molecular Biology (Ausubel, F. M., Brent, R., Kingston, R. E., Moore, D. D., Seidman, J. G., Smith, J. A. & Struhl, K., eds), pp. 8.23–8.24, John Wiley & Sons, New York. Gross, E. (1967). The cyanogen bromide cleavage reaction. Methods Enzymol. 11, 238–255. Winder, A. F. & Gent, W. L. (1971). Correction of lightscattering errors in spectrophotometric protein determinations. Biopolymers, 10, 1243–1251. Makhatadze, G. I., Medvedkin, V. N. & Privalov, P. L. (1990). Partial molar volumes of polypeptides and their constituent groups in aqueous solution over a broad temperature range. Biopolymers, 30, 1001–1010. Lopez, M. M. & Makhatadze, G. I. (2002). Differential scanning calorimetry. Methods Mol. Biol. 173, 113–119. Sali, A. & Blundell, T. L. (1993). Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815. Schindelin, H., Marahiel, M. A. & Heinemann, U. (1993). Universal nucleic acid-binding domain revealed by crystal structure of the B. subtilis major cold-shock protein. Nature, 364, 164–168. Ibarra-Molero, B., Loladze, V. V., Makhatadze, G. I. & Sanchez-Ruiz, J. M. (1999). Thermal versus guanidineinduced unfolding of ubiquitin. An analysis in terms of the contributions from charge-charge interactions to protein stability. Biochemistry, 38, 8138–8149. Tanford, C. & Kirkwood, J. G. (1957). Theory of protein titration curves. I. General equations for impenetrable spheres. J. Am. Chem. Soc. 79, 5333–5339. Matthew, J. B. & Gurd, F. R. (1986). Calculation of electrostatic interactions in proteins. Methods Enzymol. 130, 413–436. Matthew, J. B., Gurd, F. R., Garcia-Moreno, B., Flanagan, M. A., March, K. L. & Shire, S. J. (1985). pH-dependent processes in proteins. CRC Crit. Rev. Biochem. 18, 91–197. Mehler, E. L. & Guarnieri, F. (1999). A self-consistent, microenvironment modulated screened coulomb potential approximation to calculate pH-dependent electrostatic effects in proteins. Biophys. J. 77, 3–22. Antosiewicz, J., McCammon, J. A. & Gilson, M. K. (1994). Prediction of pH-dependent properties of proteins. J. Mol. Biol. 238, 415–436. Antosiewicz, J., McCammon, J. A. & Gilson, M. K. (1996). The determinants of pKas in proteins. Biochemistry, 35, 7819–7833. Alexov, E. G. & Gunner, M. R. (1997). Incorporating protein conformational flexibility into the calculation of pH-dependent protein properties. Biophys. J. 72, 2075–2093. Georgescu, R. E., Alexov, E. G. & Gunner, M. R. (2002). Combining conformational flexibility and continuum electrostatics for calculating pK(a)s in proteins. Biophys. J. 83, 1731–1748.

Edited by J. E. Ladbury (Received 5 September 2006; received in revised form 20 October 2006; accepted 17 November 2006) Available online 22 November 2006