Computational redesign of metalloenzymes for catalyzing new reactions.

The ability to design novel activities in existing metalloenzyme active sites is a stringent test of our understanding of enzyme mechanisms, sheds light on enzyme evolution, and would have many practical applications. Here, we describe a computational method in the context of the macromolecular modeling suite Rosetta to repurpose active sites containing metal ions for reactions of choice. The required inputs for the method are a model of the transition state(s) for the reaction and a set of crystallographic structures of proteins containing metal ions. The coordination geometry associated with the metal ion (Zn(2+), for example) is automatically detected and the transition state model is aligned to the open metal coordination site(s) in the protein. Additional interactions to the transition state model are made using RosettaMatch and the surrounding amino acid side chain identities are optimized for transition state stabilization using RosettaDesign. Validation of the design is performed using docking and molecular dynamics simulations, and candidate designs are generated for experimental validation. Computational metalloenzyme repurposing is complementary to directed evolution approaches for enzyme engineering and allows large jumps in sequence space to make concerted sequence and structural changes for introducing novel enzymatic activities and specificities.

Engineering V-type nerve agents detoxifying enzymes using computationally focused libraries.

VX and its Russian (RVX) and Chinese (CVX) analogues rapidly inactivate acetylcholinesterase and are the most toxic stockpile nerve agents. These organophosphates have a thiol leaving group with a choline-like moiety and are hydrolyzed very slowly by natural enzymes. We used an integrated computational and experimental approach to increase Brevundimonas diminuta phosphotriesterase's (PTE) detoxification rate of V-agents by 5000-fold. Computational models were built of the complex between PTE and V-agents. On the basis of these models, the active site was redesigned to be complementary in shape to VX and RVX and to include favorable electrostatic interactions with their choline-like leaving group. Small libraries based on designed sequences were constructed. The libraries were screened by a direct assay for V-agent detoxification, as our initial studies showed that colorimetric surrogates fail to report the detoxification rates of the actual agents. The experimental results were fed back to improve the computational models. Overall, five rounds of iterating between experiment and model refinement led to variants that hydrolyze the toxic SP isomers of all three V-agents with kcat/KM values of up to 5 × 10(6) M(-1) min(-1) and also efficiently detoxify G-agents. These new catalysts provide the basis for broad spectrum nerve agent detoxification.