Designing Artificial Metalloenzymes by Tuning of the Environment beyond the Primary Coordination Sphere

Abstract

Metalloenzymes catalyze a variety of reactions using a limited number of natural amino acids and metallocofactors. Therefore, the environment beyond the primary coordination sphere must play an important role in both conferring and tuning their phenomenal catalytic properties, enabling active sites with otherwise similar primary coordination environments to perform a diverse array of biological functions. However, since the interactions beyond the primary coordination sphere are numerous and weak, it has been difficult to pinpoint structural features responsible for the tuning of activities of native enzymes. Designing artificial metalloenzymes (ArMs) offers an excellent basis to elucidate the roles of these interactions and to further develop practical biological catalysts. In this review, we highlight how the secondary coordination spheres of ArMs influence metal binding and catalysis, with particular focus on the use of native protein scaffolds as templates for the design of ArMs by either rational design aided by computational modeling, directed evolution, or a combination of both approaches. In describing successes in designing heme, nonheme Fe, and Cu metalloenzymes, heteronuclear metalloenzymes containing heme, and those ArMs containing other metal centers (including those with non-native metal ions and metallocofactors), we have summarized insights gained on how careful controls of the interactions in the secondary coordination sphere, including hydrophobic and hydrogen bonding interactions, allow the generation and tuning of these respective systems to approach, rival, and, in a few cases, exceed those of native enzymes. We have also provided an outlook on the remaining challenges in the field and future directions that will allow for a deeper understanding of the secondary coordination sphere a deeper understanding of the secondary coordintion sphere to be gained, and in turn to guide the design of a broader and more efficient variety of ArMs.

Copper-Containing Artificial Polyenzymes as a Clickase for Bioorthogonal Chemistry

“…Given the abundance of metal catalysts, many other metal-containing ArPoly can be reasonably expected. These include biological metal cofactors, e.g., iron, zinc, and cobalt, and also abiological catalytic centers of palladium, rhodium, and ruthenium …”

“…These include biological metal cofactors, e.g., iron, zinc, and cobalt, and also abiological catalytic centers of palladium, rhodium, and ruthenium. 44…”

Design of a Flexible, Zn-Selective Protein Scaffold that Displays Anti-Irving–Williams Behavior

“…Up to a third of all proteins are associated with metal ions, − which are often necessary for the proper folding/assembly of proteins − and enable them to fulfill essential functions such as signaling, − small molecule transport, − and catalysis. − The correct pairing of metalloproteins with their cognate metal ions is essential for these cellular functions . In fact, there are very few known cases where the natural metal component of a protein can be substituted by another metal ion in vivo without deleterious consequences. − Despite the fact that there is a limited set of metal-coordinating amino acid residues ( e.g ., His, Asp, Glu, Cys, Met, Tyr), three-dimensional protein structures can achieve some extent of metal selectivity through a suitable combination of such residues in the primary coordination sphere in addition to the further tuning of metal–ligand interactions through secondary sphere effects. ,− Of particular significance is steric selection by proteins, which describes the precise spatial arrangement of the primary-sphere residues to discriminate between metal ions (particularly, those of the d-block) based on their ionic radii and their electronic preferences for specific coordination geometries .…”

Engineering a Conformationally Switchable Artificial Metalloprotein

“…The preparation and study of artificial metalloproteins (ArMs) have emerged as powerful techniques to model, define, and mimic enzymatic structure, function, and reactivity relationships. − ArMs can be broadly classified into two groups: those that serve as structural models enabling structure–function correlations and those that serve as functional models facilitating catalytic transformations. The focus in this work is the former.…”