5 publications

5 publications

Computational Insights on an Artificial Imine Reductase Based on the Biotin-Streptavidin Technology

Maréchal, J.-D.

ACS Catal. 2014, 4, 833-842, 10.1021/cs400921n

We present a computational study that combines protein–ligand docking, quantum mechanical, and quantum mechanical/molecular mechanical calculations to scrutinize the mechanistic behavior of the first artificial enzyme able to enantioselectively reduce cyclic imines. We applied a novel strategy that allows the characterization of transition state structures in the protein host and their associated reaction paths. Of the most striking results of our investigation is the identification of major conformational differences between the transition state geometries of the lowest energy paths leading to (R)- and (S)-reduction products. The molecular features of (R)- and (S)-transition states highlight distinctive patterns of hydrophobic and polar complementarities between the substrate and the binding site. These differences lead to an activation energy gap that stands in very good agreement with the experimentally determined enantioselectivity. This study sheds light on the mechanism by which transfer hydrogenases operate and illustrates how the change of environment (from homogeneous solution conditions to the asymmetric protein frame) affect the reactivity of the organometallic cofactor. It provides novel insights on the complexity in integrating unnatural organometallic compounds into biological scaffolds. The modeling strategy that we pursued, based on the generation of “pseudo transition state” structures, is computationally efficient and suitable for the discovery and optimization of artificial enzymes. Alternatively, this approach can be applied on systems for which a large conformational sampling is needed to identify relevant transition states.


Metal: Ir
Ligand type: Cp*; Diamine
Host protein: Streptavidin (Sav)
Anchoring strategy: Supramolecular
Optimization: Genetic
Max TON: ---
ee: 96
PDB: 3PK2
Notes: Prediction of the enantioselectivity by computational methods.

Design of an Enantioselective Artificial Metallo-Hydratase Enzyme Containing an Unnatural Metal-Binding Amino Acid

Maréchal, J.-D.; Roelfes, G.

Chem. Sci. 2017, 8, 7228-7235, 10.1039/C7SC03477F

The design of artificial metalloenzymes is a challenging, yet ultimately highly rewarding objective because of the potential for accessing new-to-nature reactions. One of the main challenges is identifying catalytically active substrate–metal cofactor–host geometries. The advent of expanded genetic code methods for the in vivo incorporation of non-canonical metal-binding amino acids into proteins allow to address an important aspect of this challenge: the creation of a stable, well-defined metal-binding site. Here, we report a designed artificial metallohydratase, based on the transcriptional repressor lactococcal multidrug resistance regulator (LmrR), in which the non-canonical amino acid (2,2′-bipyridin-5yl)alanine is used to bind the catalytic Cu(II) ion. Starting from a set of empirical pre-conditions, a combination of cluster model calculations (QM), protein–ligand docking and molecular dynamics simulations was used to propose metallohydratase variants, that were experimentally verified. The agreement observed between the computationally predicted and experimentally observed catalysis results demonstrates the power of the artificial metalloenzyme design approach presented here.


Metal: Cu
Ligand type: Bipyridine
Host protein: LmrR
Anchoring strategy: ---
Optimization: Genetic
Reaction: Hydration
Max TON: 9
ee: 64
PDB: ---
Notes: ---

Directed Evolution of an Artificial Imine Reductase

Maréchal, J.-D.; Ward, T.R.

Angew. Chem. Int. Ed. 2018, 57, 1863-1868, 10.1002/anie.201711016

Artificial metalloenzymes, resulting from incorporation of a metal cofactor within a host protein, have received increasing attention in the last decade. The directed evolution is presented of an artificial transfer hydrogenase (ATHase) based on the biotin‐streptavidin technology using a straightforward procedure allowing screening in cell‐free extracts. Two streptavidin isoforms were yielded with improved catalytic activity and selectivity for the reduction of cyclic imines. The evolved ATHases were stable under biphasic catalytic conditions. The X‐ray structure analysis reveals that introducing bulky residues within the active site results in flexibility changes of the cofactor, thus increasing exposure of the metal to the protein surface and leading to a reversal of enantioselectivity. This hypothesis was confirmed by a multiscale approach based mostly on molecular dynamics and protein–ligand dockings.


Metal: Ir
Ligand type: Amino-sulfonamide; Cp*
Host protein: Streptavidin (Sav)
Anchoring strategy: Supramolecular
Optimization: Chemical & genetic
Max TON: 380
ee: 95
PDB: 6ESS
Notes: Salsolidine formation; Sav mutant S112A-N118P-K121A-S122M: (R)-selective

Metal: Ir
Ligand type: Amino-sulfonamide; Cp*
Host protein: Streptavidin (Sav)
Anchoring strategy: Supramolecular
Optimization: Chemical & genetic
Max TON: 220
ee: 85
PDB: 6ESU
Notes: Salsolidine formation; Sav mutant S112R-N118P-K121A-S122M-L124Y: (S)-selective

Structural, Kinetic, and Docking Studies of Artificial Imine Reductases Based on Biotin−Streptavidin Technology: An Induced Lock-and-Key Hypothesis

Maréchal, J.-D.; Ward, T.R.

J. Am. Chem. Soc. 2014, 136, 15676-15683, 10.1021/ja508258t

An artificial imine reductase results upon incorporation of a biotinylated Cp*Ir moiety (Cp* = C5Me5–) within homotetrameric streptavidin (Sav) (referred to as Cp*Ir(Biot-p-L)Cl] ⊂ Sav). Mutation of S112 reveals a marked effect of the Ir/streptavidin ratio on both the saturation kinetics as well as the enantioselectivity for the production of salsolidine. For [Cp*Ir(Biot-p-L)Cl] ⊂ S112A Sav, both the reaction rate and the selectivity (up to 96% ee (R)-salsolidine, kcat 14–4 min–1 vs [Ir], KM 65–370 mM) decrease upon fully saturating all biotin binding sites (the ee varying between 96% ee and 45% ee R). In contrast, for [Cp*Ir(Biot-p-L)Cl] ⊂ S112K Sav, both the rate and the selectivity remain nearly constant upon varying the Ir/streptavidin ratio [up to 78% ee (S)-salsolidine, kcat 2.6 min–1, KM 95 mM]. X-ray analysis complemented with docking studies highlight a marked preference of the S112A and S112K Sav mutants for the SIr and RIr enantiomeric forms of the cofactor, respectively. Combining both docking and saturation kinetic studies led to the formulation of an enantioselection mechanism relying on an “induced lock-and-key” hypothesis: the host protein dictates the configuration of the biotinylated Ir-cofactor which, in turn, by and large determines the enantioselectivity of the imine reductase.


Metal: Ir
Ligand type: Amino-sulfonamide; Cp*
Host protein: Streptavidin (Sav)
Anchoring strategy: Supramolecular
Optimization: Genetic
Max TON: ---
ee: 93
PDB: ---
Notes: ---

Toward the Computational Design of Artificial Metalloenzymes: From Protein–Ligand Docking to Multiscale Approaches

Review

Maréchal, J.-D.

ACS Catal. 2015, 5, 2469-2480, 10.1021/acscatal.5b00010

The development of artificial enzymes aims at expanding the scope of biocatalysis. Over recent years, artificial metalloenzymes based on the insertion of homogeneous catalysts in biomolecules have received an increasing amount of attention. Rational or pseudorational design of these composites is a challenging task because of the complexity of the identification of efficient complementarities among the cofactor, the substrate, and the biological partner. Molecular modeling represents an interesting alternative to help in this task. However, little attention has been paid to this field so far. In this manuscript, we aim at reviewing our efforts in developing strategies efficient to computationally drive the design of artificial metalloenzymes. From protein–ligand dockings to multiscale approaches, we intend to demonstrate that modeling could be useful at the different steps of the design. This Perspective ultimately aims at providing computational chemists with illustration of the applications of their tools for artificial metalloenzymes and convincing enzyme designers of the capabilities, qualitative and quantitative, of computational methodologies.


Notes: ---