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C-terminal truncation of a metagenome-derived detergent protease for effective expression in E. coli
(2010)
Recently, a new alkaline protease named HP70 showing highest homology to extracellular serine proteases of Stenotrophomonas maltophilia and Xanthomonas campestris was found in the course of a metagenome screening for detergent proteases (Niehaus et al., submitted for publication). Attempts to efficiently express the enzyme in common expression hosts had failed. This study reports on the realization of overexpression in Escherichia coli after structural modification of HP70. Modelling of HP70 resulted in a two-domain structure, comprising the catalytic domain and a C-terminal domain which includes about 100 amino acids. On the basis of the modelled structure the enzyme was truncated by deletion of most of the C-terminal domain yielding HP70-C477.
This structural modification allowed effective expression of active enzyme using E. coli BL21-Gold as the host. Specific activity of HP70-C477 determined with suc-l-Ala-l-Ala-l-Pro-l-Phe-p-nitroanilide as the substrate was 30 ± 5 U/mg compared to 8 ± 1 U/mg of the native enzyme. HP70-C477 was most active at 40 °C and pH 7–11; these conditions are prerequisite for a potential application as detergent enzyme. Determination of kinetic parameters at 40 °C and pH = 9.5 resulted in KM = 0.23 ± 0.01 mM and kcat = 167.5 ± 3.6 s⁻¹. MS-analysis of peptide fragments obtained from incubation of HP70 and HP70-C477 with insulin B indicated that the C-terminal domain influences the cleavage preferences of the enzyme. Washing experiments confirmed the high potential of HP70-C477 as detergent protease.
Quantifying and minimizing uncertainty is vital for simulating technically and economically successful geothermal reservoirs. To this end, we apply a stochastic modelling sequence, a Monte Carlo study, based on (i) creating an ensemble of possible realizations of a reservoir model, (ii) forward simulation of fluid flow and heat transport, and (iii) constraining post-processing using observed state variables. To generate the ensemble, we use the stochastic algorithm of Sequential Gaussian Simulation and test its potential fitting rock properties, such as thermal conductivity and permeability, of a synthetic reference model and—performing a corresponding forward simulation—state variables such as temperature. The ensemble yields probability distributions of rock properties and state variables at any location inside the reservoir. In addition, we perform a constraining post-processing in order to minimize the uncertainty of the obtained distributions by conditioning the ensemble to observed state variables, in this case temperature. This constraining post-processing works particularly well on systems dominated by fluid flow. The stochastic modelling sequence is applied to a large, steady-state 3-D heat flow model of a reservoir in The Hague, Netherlands. The spatial thermal conductivity distribution is simulated stochastically based on available logging data. Errors of bottom-hole temperatures provide thresholds for the constraining technique performed afterwards. This reduce the temperature uncertainty for the proposed target location significantly from 25 to 12 K (full distribution width) in a depth of 2300 m. Assuming a Gaussian shape of the temperature distribution, the standard deviation is 1.8 K. To allow a more comprehensive approach to quantify uncertainty, we also implement the stochastic simulation of boundary conditions and demonstrate this for the basal specific heat flow in the reservoir of The Hague. As expected, this results in a larger distribution width and hence, a larger, but more realistic uncertainty estimate. However, applying the constraining post-processing the uncertainty is again reduced to the level of the post-processing without stochastic boundary simulation. Thus, constraining post-processing is a suitable tool for reducing uncertainty estimates by observed state variables.