Molecular Modeling for Artificial Metalloenzyme Design and Optimization
Abstract
Artificial metalloenzymes (ArMs) are obtained by inserting homogeneous catalysts into biological scaffolds and are among the most promising strategies in the quest for new-to-nature biocatalysts. The quality of their design strongly depends on how three partners interact: the biological host, the "artificial cofactor," and the substrate. However, structural characterization of functional artificial metalloenzymes by X-ray or NMR is often partial, elusive, or absent. How the cofactor binds to the protein, how the receptor reorganizes upon the binding of the cofactor and the substrate, and which are the binding mode(s) of the substrate for the reaction to proceed are key questions that are frequently unresolved yet crucial for ArM design. Such questions may eventually be solved by molecular modeling but require a step change beyond the current state-of-the-art methodologies.Here, we summarize our efforts in the study of ArMs, presenting both the development of computational strategies and their application. We first focus on our integrative computational framework that incorporates a variety of methods such as protein-ligand docking, classical molecular dynamics (MD), and pure quantum mechanical (QM) methods, which, when properly combined, are able to depict questions that range from host-cofactor binding predictions to simulations of entire catalytic mechanisms. We also pay particular attention to the protein-ligand docking strategies that we have developed to accurately…
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