Redesigning protein pKa values

Abstract

The ability to re-engineer enzymatic pH-activity profiles is of importance for industrial applications of enzymes. We theoretically explore the feasibility of re-engineering enzymatic pH-activity profiles by changing active site pK(a) values using point mutations. We calculate the maximum achievable DeltapK(a) values for 141 target titratable groups in seven enzymes by introducing conservative net-charge altering point mutations. We examine the importance of the number of mutations introduced, their distance from the target titratable group, and the characteristics of the target group itself. The results show that multiple mutations at 10A can change pK(a) values up to two units, but that the introduction of a requirement to keep other pK(a) values constant reduces the magnitude of the achievable DeltapK(a). The algorithm presented shows a good correlation with existing experimental data and is available for download and via a web server at http://enzyme.ucd.ie/pKD.

Figure 1.

The importance of the intrinsic pK_a when redesigning protein pK_a values. In the top panel an acid with an intrinsic pK_a of 4.0 is inserted so that it interacts with the target group (intrinsic pK_a 6.0) with an interaction energy of 2.3 kT/e. This results in the pK_a of the target residue being elevated 1.0 unit. Similarly, in the bottom panel we insert an acid with an intrinsic pK_a value of 8.0 that interacts with the target acid with an interaction energy of 2.3 kT/e. However, since the intrinsic pK_a value of the inserted group is larger than that of the target residue, the pK_a value of the target group remains 6.0, whereas the pK_a value of the inserted group is elevated by 1.0 units.

Figure 2.

The correlation between the ΔpK_a value calculated with ΔΦ/ln(10) compared with the ΔpK_a value calculated with the Monte Carlo method for all single mutations in the test set. ΔΦ/ln(10) (Equation 1) gives highly inaccurate results for a number of mutations due to the lack of description of effects related to the intrinsic pK_a differences for both the inserted residue and the target residue.

Figure 3.

(A) The distance dependence of the effect of single mutations. Above 12.5 Å the effect of a point is rarely above 0.5 units. (B) ɛ_apparent as a function of distance between the formally charged atoms of mutated residue and the target group. The large variation in ɛ_apparent for small distances is an effect of the dielectric properties of the protein molecule and the appropriateness of the pKa values for inducing a ΔpKa. At larger distances ɛ_apparent becomes representative of the “average” dielectric properties of the protein and its surrounding solvent.

Figure 4.

The average maximum abs(ΔpK_a value) obtainable for all targets as a function of a number of mutations and minimum distance allowed between any atom of the mutated residue and any atom of the target residue. Above 13Å, pK_a shifts are generally quite small even when using multiple mutations.

Figure 5.

The correlation between the ΔpK_a value obtainable for a target and its solvent accessibility. Circles show the maximum abs(ΔpK_a value) obtained for targets, whereas the squares show the average abs(ΔpK_a value) for the targets.

Figure 6.

Plot of calculated ΔpK_a values vs. experimentally determined ΔpK_a values for the values shown in Table 3. The calculations are able to reproduce the experimental values reasonably well, with a R ² of 0.79.