Results and Discussion

Computational Design of Proteins.

Two model proteins were chosen for the test: human acylphosphatase, AcPh (EC-Number 3.6.1.7), and human cell-division cycle 42 factor, Cdc42 (EC-Number 3.6.5.2). Acylphosphatase is a 98-residue protein that catalyzes hydrolysis of acylphosphates to produce carboxylate and inorganic phosphate (23). This enzyme is proposed to be involved in several metabolic pathways and, in particular, in glycolysis/gluconeogenesis, pyruvate metabolism, and benzoate degradation via CoA ligation (,23). Acylphosphatase activity has been identified in both prokaryotes and eukaryotes (,23). According to SCOP classification, this protein belongs to the structural family of α+β sandwich proteins with antiparallel β-sheet (,Fig. 1). The actual location of the substrate-binding site is not known. It is believed to be located near the sulfate-binding site observed in the 2ACY structure of the bovine protein (,Fig. 1), with residues R23, K24, N41, and K98 located in close proximity. Biochemical and mutagenesis analyses have provided further support for this notion and identified R23 and N41 (,23) as residues that are critically important for the activity of AcPh. This observation is also supported by our results on redesign of AcPh that were not concerned with maintaining activity. It was shown that a variant with 4 substitutions (K24E/E63K/N81K/Q95K), although more stable than the WT (,7), is in fact inactive.

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Fig. 1.

The diagram structures of 2 proteins studied in this paper. The locations of active sites are shown by van der Waals surfaces for the substrates. Positions of the substitutions are shown by gray circles corresponding to the backbone CA atoms. (A) Acylphosphatase (AcPh): comparison of the structures of bovine protein (PDB code 2ACY) (34) with the 3D structures of human protein AcPh-wt (blue) and its designed variant AcPh-des (green). The structures of AcPh-wt and AcPh-des were determined in this work by using multidimentional NMR spectroscopy (see Materials and Methods and SI Appendix for details). (B) Cdc42: structural model of human protein (PDB code 1AN0) as solved by x-ray crystallography in ref. 40.

Cdc42 is a eukaryotic protein and belongs to the family of small GTPases (24). It is a key enzyme that is involved in regulation of numerous vital cellular pathways such as gene expression, cell-cycle progression, and rearrangement of the actin cytoskeleton (,25). It has 198 amino acid residues arranged into a fold common for all Ras GTPases: a 6-stranded β-sheet surrounded by 5 α-helices (,Fig. 1). Eleven residues were identified to be in direct contact with the GTP molecule, of which three (K16, D57, and D118) are ionizable residues.

Distribution of surface charges in these 2 proteins was optimized by the Tanford–Kirkwood surface accessibility genetic algorithm (TKSA-GA), which is based on a GA for optimization of charge–charge interactions in proteins (26), in which the energies of charge–charge interactions are calculated by using TK formalism (,27) that includes SA correction as introduced in ref. ,28. The dependence of the energy of charge–charge interactions on the number of substitutions as identified by TKSA-GA is shown in ,Fig. 2 A and C. The favorable energies of charge–charge interactions increase with the number of substitutions until reaching saturation values. Such saturation behavior has been observed for other proteins and is consistent with 2 facts (7, ,8, ,29): (i) for a given protein topology, there is a finite number of surface sites where charged residues can be introduced, and (ii) addition of a charged residue at a new site has both favorable (with residues of the opposite charge) and unfavorable (with residues of the same charge) interactions.

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Fig. 2.

The dependence of the energy of charge–charge interactions on the number of amino acid substitutions (or the percentage of substitutions relative to the total number of amino acid residues) in AcPh (A) and Cdc42 (C). Each small dot corresponds to a different sequence. The sequences selected for experimental verification are shown in large symbols: AcPh-wt or Cdc42-wt, black circles; AcPh-des, red squares; Cdc42-des1, blue squares; and Cdc42-des2, red triangles. (B and D) The corresponding per residue energies of charge–charge interactions as calculated by TKSA model are given in B (AcPh-wt, black bars; AcPh-des, red bars) and D (Cdc42-wt, black bars; Cdc42-des1, red bars; Cdc42-des2, blue bars). Positive energies represent overall unfavorable interactions of a given residue with all other ionizable residues in the proteins, whereas negative values of ΔG_qq reflect overall favorable interactions.

For experimental tests, we selected sequences that have the largest increase in the energy of charge–charge interactions but have the number of substitutions at 5–6% of the total number of amino acid residues. These sequences are shown in Fig. 2 B and D. For AcPh, the substitutions were made at 5 positions, 3 of which led to charge reversal (H60E, E63K, and K72E) and 2 of which introduced new charges (Q50K and N81K). For Cdc42, we fully characterized 2 variants with 7 (Cdc42-des1) and 8 (Cdc42-des2) substitutions. Cdc42-des1 has 4 charge-reversal substitutions (E95K, K107E, D121K, and K131E) and 3 new charges (Q74E, N167K, and V189K). Cdc42-des2 has an additional charge-reversal substitution (E178K). Calculations show that the designed variants should have more favorable energies of charge–charge interactions, in particular because of a decrease in the number of amino acid residues with overall unfavorable energies of charge–charge interactions, ΔG_qq (see Fig. 2 B and D for TKSA calculations and Fig. S1 for calculations using the MCCE software package) (30).

Experimental Characterization of Designed Proteins.

These designed proteins were cloned, expressed, and purified (see Material and Methods), and their physico–chemical properties were examined and compared with the properties of the corresponding WT proteins. First, we compared the oligomerization properties of the WT and designed proteins. Substitutions on the protein surface can influence the oligomerization state in solution by increasing the size of oligomer, by creating complementary surfaces, or by inducing domain swapping (31, ,32). Any of these mechanisms would increase the association constant, thereby making the oligomerization stronger. Such changes in the quaternary structure can lead to changes in stability. However, this increase/decrease in stability will not be directly relevant to the main hypothesis of this work, which is the demonstration that protein stability can be enhanced by optimization of charge–charge interactions on the protein surface. Both WT AcPh and Cdc42 are monomeric in solution as measured by equilibrium analytical ultracentrifugation. Importantly, all of the designed proteins were also found to be monomeric in solution (Fig. S2). This finding suggests that if the designed AcPh and Cdc42 are more stable, the increase in stability would not be due to changes in the oligomerization state of the proteins.

Changes in the structure of proteins upon amino acid substitutions can be another reason for changes in stability and thus create additional challenges for testing the hypothesis. Such substitutions can sometimes lead to dramatic structural changes (33). These changes in the structure of the native-state protein could, in turn, alter stability in a way that could compromise experimental proofs of enhancing protein stability solely by optimization of charge–charge interactions on the protein surface. To address this issue, structures of proteins were examined first at the low level of resolution by using circular-dichroism spectroscopy. Comparison showed no changes in the far-UV CD spectra between WT and designed proteins, suggesting that there are no large structural changes in these proteins with substitutions (Fig. S3). To further validate this conclusion, at higher structural resolution, we solved solution structures of AcPh-wt and AcPh-des by using multidimensional NMR spectroscopy (see SI Appendix for procedures on structure determination and detailed statistics of the final structures). Fig. 1A shows the overlay of the averaged structures of these 2 proteins, AcPh-wt and AcPh-des, together with the reported X-ray structure of bovine AcPh (34) that was used for the TKSA-GA optimization protocol. The rms deviation of the backbone atoms between bovine, ensemble-averaged AcPh-wt, and AcPh-des is <1.1 Å. This observation further supports the notion that, structurally, the designed protein is very similar to the WT AcPh.

Because there are no changes in the oligomerization state of the proteins and there are no dramatic changes in their 3D structure, all changes in stability are probably directly related to differences in charge–charge interactions in the WT and designed proteins.

Stability of the AcPh-wt and AcPh-des proteins in solution was estimated from temperature-induced reversible unfolding by using 2 different methods: differential scanning calorimetry (DSC) and monitoring the changes in ellipticity at 222 nm by using CD spectroscopy (Fig. 3 A and B). Temperature-induced unfolding was fully reversible, and the transitions were analyzed by using a 2-state unfolding model (see SI Appendix). Importantly, both methods (DSC and CD) produced similar thermodynamic parameters, suggesting that there are no changes in the unfolding mechanism upon substitutions in AcPh-des (Table 1). Notably, AcPh-des has an unfolding temperature 10 °C higher than AcPh-wt, which translates into an increase in Gibbs energy of 9 ± 1 kJ/mol. This increase in stability is rather substantial (,4), suggesting that indeed, the TKSA-GA protein-stabilization approach can identify protein sequences that will produce more stable proteins.

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Fig. 3.

Biophysical characterization of the designed and WT acylphosphatase (AcPh) and Cdcd42. (A) Comparison of the experimental partial molar heat capacity profiles of AcPh-wt (white circles) and AcPh-des (red squares). Solid lines are the results of the fit according to a 2-state unfolding model. The results of the fit are given in Table 1. (B) Comparison of the experimental temperature-induced unfolding profiles of AcPh-wt (black thin line) and AcPh-des (red thin line) as monitored by the changes in ellipticity at 222 nm. Thick solid lines are the results of the fit according to a 2-state unfolding model. The results of the fit are given in Table 1. (C) Comparison of the experimental temperature induced unfolding profiles of Cdc42-wt (thin black line), Cdc42-des1 (thin blue line), and Cdc42-des2 (thin red line) as monitored by the changes in ellipticity at 222 nm. (D) Dependence of specific activity of Cdc42 variants (Cdc42-wt, black circles; Cdc42-des1, blue squares; Cdc42-des2, red triangles) on temperature. Lines are drawn to guide the eye. (E) Resistance of Cdc42 variants to aggregation after exposure to high temperature. The fraction of soluble monomer remained in solution after exposure to elevated temperatures was measured by using size-exclusion chromatography as described in Materials and Methods. Shown are Cdc42-wt (black circles), Cdc42-des1 (blue squares), and Cdc42-des2 (red triangles), and lines are drawn to guide the eye.

How does this increase in stability affect the enzymatic activity of AcPh? The activity of AcPh proteins was compared by using synthetic substrate benzoylphosphate (35, ,36). The results of activity measurements at different temperatures are given in ,Table 1 (actual kinetic traces are shown in Fig. S4). It is remarkable that not only does the AcPh-des protein remain active, it also has very similar kinetic parameters as the WT. At all temperatures, the catalytic constant K_cat is the same for both enzymes (AcPh-wt and AcPh-des). Although there is some decrease in Michaelis-Menten constant K_m at room temperature, the difference becomes very small at 55 °C (Table 1). Thus, for AcPh, the amino acid substitutions that were introduced by design via optimization of charge–charge interactions on the protein surface produced an enzyme that is not only 10 °C more stable but also equally catalytically active.

Temperature-induced unfolding of Cdc42 is irreversible because of aggregation in the unfolded state of the protein (see below). To compare the transition profiles, temperature-induced unfolding was monitored by using changes in ellipticity at 222 nm at different protein concentrations and solvent conditions (2 M urea) (Fig. 3C and Table 2). Melting profiles for Cdc42-wt, Cdc42-des1, and Cdc42-des2, when compared at identical concentrations (,Fig. 3C and Table 2), show that the designed variants have ≈8 °C and ≈10 °C higher midpoints of transition, respectively, than the Cdc42-wt protein.

The enzymatic activity of all 3 proteins (Cdc42-wt, Cdc42-des1, and Cdc42-des2) at room temperature remains unchanged (Table 2). The temperature dependence of the specific activity for these 3 proteins is compared in ,Fig. 3D. It is clear that both designed Cdc42 proteins remain active at temperatures higher than the WT protein. Estimates show that the difference in midpoint of temperature inactivation for the Cdc42-des1 and Cdc42-des2 proteins is ≈10 ± 2 °C, which is consistent with the estimates based on the CD-melting profiles (Fig. 3D and Table 2). The changes in CD signal and loss-of-activity are due to the protein unfolding followed by irreversible protein aggregation. To test whether substitutions in the designed Cdc42 variants led to offset in the temperature of aggregation, the temperature dependence of aggregation was determined by using size-exclusion chromatography to quantify the fraction of protein that remains as monomer in solution. ,Fig. 3E shows the percentage of the monomeric Cdc42 remaining in solution after exposure to different temperatures. The midpoint temperatures of aggregation for both designed Cdc42 variants are ≈9 ± 2 °C higher than that of the WT protein, which is also consistent with the data from CD-monitored unfolding and temperature dependence of the specific activity (Fig. 3 C and D). These results suggest that the offset in the aggregation temperature in designed Cdc42 variants is due to the higher stability of their native states that start to unfold at higher temperatures, and thus the onset of aggregation is at higher temperatures as well.

Implications for Protein Evolution.

Is it fortuitous that the selected sequences are more stable? Is it that the reference (i.e., WT) proteins chosen were not very stable to begin with? Both AcPh-wt and Cdc42-wt have midpoint-transition temperatures >55 °C, which suggests that they are rather stable proteins. Are the introduced substitutions more frequent in the corresponding sequence positions of the homologous series of AcPh and Cdc42? If so, can they be identified by using a consensus-design approach that uses evolutionary conservation as a means to engineer more stable proteins? To answer these questions, we analyzed the normalized probabilities of the residues at the substitution sites. The normalized probability of a residue at a given position in sequence (P_i) indicates how many times over what would be expected by random chance does a given residue type occur at that position (see Materials and Methods for details). Interestingly, of 13 substitutions in the 2 proteins, 12 are substitutions from more frequent (P_i > 1) to less frequent (P_i < 1) amino acid residues. The ratio P_i,wt>/P_i,des, ranges from 2 to 75 with an average value of 13 (Table S1). The only exception is the H60E substitution in AcPh, for which WT histidine is 1.6 times less frequent than glutamic acid. Overall, for all 12 substitution positions, the WT residues have normalized P_i, > 1. Moreover, in many cases, WT-residues have the maximal or second maximal P_i, and yet the substitutions in these residues lead to increase of protein stability without changes in enzymatic activity. One possible explanation of these observations is that the interactions with other proteins and ligands in the cell dictate the preference for the residues at these positions, i.e., activity-for-stability tradeoff (37, ,38). Although cellular partners interacting with AcPh are not known, proteins interacting with Cdc42 are well characterized, and disruption of these interactions frequently leads to lethal phenotype. For example, a set of alanine-scanning mutations has been generated in the yeast homolog of Cdc42, and the effect of these mutations on the growth of yeast has been examined (,39). A number of mutations were found to be detrimental. However, for the mutations in positions E95, D121, K131, and E178 (i.e., site of substitutions in our Cdc42-des1 and Cdc42-des2), the growth of haploid cells on YPD plates had a WT phenotype (,39). Further support that the site of the substitutions in Cdc42-des1 and Cdc42-des2 does not affect the function of this protein was obtained from the analysis of the Cdc42 interactions with Cdc42 GTPase-activating protein Cdc42GAP. Cdc42GAP binds Cdc42 and activates GTPase activity of Cdc42 (,40). We analyzed the enzymatic activity (e.g., GTPase activity) enhancement of Cdc42 in the presence of Cdc42GAP protein. For all 3 proteins (Cdc42-wt, Cdc42-des1, and Cdc42-des2), it was observed that the addition of Cdc42GAP leads to an enhancement of GTPase activity in a concentration-dependent manner (Fig. S5). Moreover, the enhancement in activity among the WT and designed Cdc42 variants is practically indistinguishable.

To further characterize the interactions of the Cdc42 variants with Cdc42GAP, we performed 2 types of experiments. First, we measured the GTPase activity of the Cdc42 variants at a fixed concentration of GTP (100 μM) and different concentrations of Cdc42GAP. The results of these measurements are shown in Fig. 4 and suggest that all 3 Cdc42 proteins have similar enhancement of activity by Cdc42GAP. Analysis shows that the K_d,_app is similar for Cdc42-wt, Cdc42-des1, and Cdc42-des2 and is on the order of 0.3 μM (Table 2). Interestingly, the same dissociation constant of Cdc42-wt and Cdc42GAP was measured by Cerione et al. (41), who used fluorescence polarization spectroscopy to monitor Cdc42 to Cdc42GAP binding in the presence of GTP. Second, we used isothermal titration calorimetry to measure Cdc42 binding to Cdc42GAP in the presence of GDP (Fig. S6). These experiments were performed in the presence of excess GDP, and thermodynamic analysis of the binding isotherms shows that under these conditions, the apparent dissociation constant for Cdc42 binding to Cdc42GAP is on the order of 10 ± 3 μM and is the same for all 3 Cdc42 variants. This estimate for Cdc42 binding to Cdc42GAP in the presence of GDP agrees well with the dissociation constant of 2.8–2.9 μM (24, ,42) and is in reasonable agreement with the 50 μM value reported by Hoffman et al. (41). These experiments establish the feasibility of engineering a protein with enhanced stability without perturbing its enzymatic or signaling functions.

Fig. 4.

Stimulation of the GTPase activity of the Cdc42 proteins by the increasing concentrations of Cdc42GAP. Experiments were performed at the initial GTP concentration of 100 μM. Shown are Cdc42-wt (black circles), Cdc42-des1 (blue squares), and Cdc42-des2 (red triangles). Concentration of all 3 Cdc42 proteins was kept constant at 0.5 μM. Data were fitted to a binding equation where y is the fractional saturation, L_T is the total Cdc42GAP concentration, M_T is the total Cdc42 concentration, and K_d,_app is the apparent dissociation constant of Cdc42 to Cdc42GAP. Dashed lines are the results of the fit for each individual protein, and the solid line is the fit of all datapoints to a single K_d,_app. See the results of the fit in Table 2.