Molecular Modeling for Artificial Metalloenzyme Design and Optimization

Artificial metalloenzymes (ArMs) are obtained by inserting homogeneous catalysts into biological scaffolds and are among the most promising strategies in the quest for new-tonature 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 predict the binding of transition metal-containing molecules to proteins. While this aspect is fundamental to many bioinorganic fields beyond ArMs, it has been disregarded from the molecular modeling landscape until very recently. Next we describe how to apply this computational framework to particular ArMs including systems previously characterized experimentally as well as others where computation served to guide the design. We start with the prediction of the interactions between homogeneous catalysts and biological hosts. Protein-ligand docking is pivotal at that stage, but it needs to be combined with QM/MM or MD approaches when the binding of the cofactor implies significant conformational changes of the protein or involve changes of the electronic state of the metal. Then, we summarize molecular modeling studies aimed at identifying cofactor-substrate arrangements inside the ArM active pocket that are consistent with its reactivity. These calculations stand on Theozyme-like dockings, MD-refined or not, which provide molecular rationale of the catalytic profiles of the artificial systems. In the third section, we present case studies to decode the entire catalytic mechanism of two ArMs: (1) an iridium based asymmetric transfer hydrogenase obtained by insertion of Noyori's catalyst into streptavidin and (2) a metallohydrolase achieved by including a receptor. Transition states, second coordination sphere effects, as well as motions of the cofactors are identified as drivers of the enantiomeric profiles. Finally, we report computer-aided designs of ArMs to guide experiments toward chemical and mutational changes that improve their activity and/or enantioselective profiles and expand toward future directions.