Computational enzyme design challanges our understanding of molecular enzymology and recognition and has previously been used to generate functional biocatalysts for a hand full of reactions. However, thus far all computationally designed enzymes showed low catalytic efficiency when compared to naturally occurring ones. Evolutionary, new enzyme functions are introduced into nature by amino acid sequence optimization. This is facilitated via repurposing the catalytic machinery of an existing active site from an enzyme with a specific function, for a new/different reaction. Here I propose the utilization of already characterized catalytic geometries derived from natural enzymes for computational enzyme design. Using these machineries for the design of non-natural reactions, I want to redesign mono- and dinuclear metalloenzymes for the catalysis of a nucleophilic aromatic substitution reaction. In particular, we want to design an enzyme that catalyzes the dechlorination of the herbicidal compound atrazine, which was shown to accumulate in soil and ground water and was correlated to increased risk of cancer. The project will involve the construction of a structural database exclusively comprising scaffolds of mononuclear zinc enzymes. The rmaining stages will include calculation and construction of a substrate model, which recapitulates the transition state of the reaction, the actual design calculations as well as computational and rational evaluations thereof. Subsequently, the best designs as judged from calculated energy and chemical intuition, will be experimentally tested. Two already established assays to test for atrazine dechlorination activity are available and will be used to complete this task. If necessary, directed evolution methods will be used to enhance the levels of catalytic efficiency of the designed enzymes. During the return phase, structure determination of active designs in an apo and substrate bound form will be performed.
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