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We present an artificial metalloenzyme based on the transcriptional regulator LmrR that exhibits dynamics involving the positioning of its abiological metal cofactor. The position of the cofactor, in turn, was found to be related to the preferred catalytic reactivity, which is either the enantioselective Friedel-Crafts alkylation of indoles with β-substituted enones or the tandem Friedel-Crafts alkylation/enantioselective protonation of indoles with α-substituted enones. The artificial metalloenzyme could be specialized for one of these catalytic reactions introducing a single mutation in the protein. The relation between cofactor dynamics and activity and selectivity in catalysis has not been described for natural enzymes and, to date, appears to be particular for artificial metalloenzymes.

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 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.

Altres ajuts: acords transformatius de la UAB Many biological systems obtain their activity by the inclusion of metalloporphyrins into one or several binding pockets. However, decoding the molecular mechanism under which these compounds bind to their receptors is something that has not been widely explored and is a field with open questions. In the present work, we apply computational techniques to unravel and compare the mechanisms of two heme-binding systems, concretely the HasA hemophores from Gram negative bacteria Serratia marcescens (HasAsm) and Yersinia pestis (HasAyp). Despite the high sequence identity between both systems, the comparison between the X-ray structures of their apo and holo forms suggests different heme-binding mechanisms. HasAyp has extremely similar structures for heme-free and heme-bound forms, while HasAsm presents a very large displacement of a loop that ultimately leads to an additional coordination to the metal with respect to HasAyp. We combined Gaussian accelerated molecular dynamics simulations (GaMDs) in explicit solvent and protein-ligand docking optimized for metalloligands. GaMDs were first carried out on heme-free forms of both hemophores. Then, protein-ligand dockings of the heme were performed on cluster representatives of these simulations and the best poses were then subjected to a new series of GaMDs. A series of analyses reveal the following: (1) HasAyp has a conformational landscape extremely similar between heme-bound and unbound states with no to limited impact on the binding of the cofactor, (2) HasAsm presents as a slightly broader conformational landscape in its apo state but can only visit conformations similar to the X-ray of the holo form when the heme has been bound. Such behavior results from a complex cascade of changes in interactions that spread from the heme-binding pocket to the flexible loop previously mentioned. This study sheds light on the diversity of molecular mechanisms of heme-binding and discusses the weight between the pre-organization of the receptor as well as the induced motions resulting in association. Heme-containing enzymes and proteins are important for many biological and biotechnological processes. However, very little is known about heme-binding mechanisms. To shed light on this, we report a multi-level approach combining Gaussian accelerated molecular dynamics and protein−ligand dockings optimized for metallic moieties. The protocol unveils the difference in heme recruitment between HasAsm and HasAyp hemophores and shows its possible applicability to other heme-binding proteins.

Natural proteins often present binding or functional promiscuity. In biocatalysis, this promiscuity has been exploited for accessing new-to-nature reactions. Here, we report an unexpected catalytic reactivity for the regulatory protein QacR from the TetR family of multidrug resistance regulators. QacR is able to catalyze the enatioselective tandem Friedel-Crafts / enantioselective protonation reaction of indoles with alpha substituted conjugated enones with up to 40% yield and 83% ee. Mutagenesis and computational studies support the hypothesis that an acidic residue in the binding pocket of the protein is responsible for protonating the enolate intermediate.

We present an artificial metalloenzyme based on the transcriptional regulator LmrR that exhibits a unique form of structural dynamics involving the positioning of its abiological cofactor. The position of the cofactor was found to relate to the preferred catalytic activity, which is either the enantioselective Friedel-Crafts alkylation of indoles with beta-substituted indoles or the tandem Friedel-Crafts alkylation / enantioselective protonation of indoles with alpha-substituted enones. The artificial metalloenzyme could be specialized for one of these reactions by introducing a single mutation in the protein.

Altres ajuts: PhD grant from the Generalitat de Catalunya An artificial heme enzyme was created through self-assembly from hemin and the lactococcal multidrug resistance regulator (LmrR). The crystal structure shows the heme bound inside the hydrophobic pore of the protein, where it appears inaccessible for substrates. However, good catalytic activity and moderate enantioselectivity was observed in an abiological cyclopropanation reaction. We propose that the dynamic nature of the structure of the LmrR protein is key to the observed activity. This was supported by molecular dynamics simulations, which showed transient formation of opened conformations that allow the binding of substrates and the formation of pre-catalytic structures.

Directed evolution generated an enzyme for the enantioselective synthesis of α-trifluoromethylated organoborons—potentially attractive synthons for fluorinated compounds.

Despite the increasing efforts in the last few years, the identification of efficient catalysts able to perform the enantioselective addition of water to double bonds has not been achieved yet. Natural hydratases represent an interesting pool of biocatalysts to generate chiral alcohols, but modifying their substrate scope remains an issue. The use of artificial metalloenzymes (ArMs) appears as a promising solution in this field. In the last few years, Roelfes and co-workers have been designing a variety of DNA- and protein-based ArMs able to carry out the copper-mediated addition of water to conjugated alkenes with promising enantioselective levels. Still, from a mechanistic point of view, the copper-mediated hydration reaction remains unclear and a matter of debate. This lack of information greatly hampers further designs and optimizations of the LmrR-based copper hydratases in terms of substrates and/or enantioselective profiles. In this study, we aim to provide a better understanding of the copper-catalyzed hydration of alkenes occurring both in water solvent and into the context of the LmrR protein as designed by Roelfes and co-workers. For that purpose, we make use of an integrated computational protocol that combines quantum mechanics (QM) (including small and large cluster models as well as ab initio molecular dynamics (AIMD)) and force-field approaches (including protein-ligand docking and classical molecular dynamics (MD) simulation). This integrative study sheds light on the general doubts around the copper-catalyzed hydration mechanism and also paves the way toward more conscious designs of ArMs able to efficiently catalyze the enantioselective addition of water to double bonds.

The design of Artificial Metalloenzymes (ArMs), which result from the incorporation of organometallic cofactors into biological structures, has grown steadily in the last two decades and important new-to-Nature reactions have been reached. These type of exercises could greatly benefit from an understanding of the structural impact that the inclusion of organometallic moieties may have on the biological host. To date though, our understanding of this phenomenon is highly partial. This lack of knowledge is one of the elements that condition that first-generation ArMs generally display relatively poor catalytic profiles. In this work, we approach this matter by assessing the dynamics and stability of a series of ArMs resulting from the inclusion, via different anchoring strategies, of a variety of organometallic cofactors into the Lactococcal multidrug resistance regulator (LmrR) protein. To this aim, we coupled standard force field-based techniques such as Protein-Ligand Docking and Molecular Dynamics simulations with a variety of trajectory convergence analyses, capable of assessing both the stability and flexibility of the different systems under study upon the binding of cofactors. Together with the experimental evidence obtained in other studies, we provide an overview on how these changes can affect the catalytic outcomes obtained from the different ArMs. Fundamentally, our results show that the convergence analysis used in this work can assess how the inclusion of synthetic metallic cofactors in proteins can condition different structural modulations of their host. Those conformational modifications are key to the success of the desired catalytic activity and their proper identification can be wisely used to improve the quality and the rate of success of the ArMs.

OpenMM is a free and GPU-accelerated Molecular Dynamics (MD) engine written as a layered and reusable library. This approach allows maximum flexibility to configure MD simulations and develop new molecular mechanics (MM) methods. However, this powerful versatility comes at a cost: the user is expected to write Python scripts to run a simulation. OMMProtocol aims to fill this gap by stitching OpenMM and additional third-party modules together, providing an easy way to create an input file to configure a full multi-stage simulation protocol, from minimization to equilibration and production. OMMProtocol is LGPL-licensed and freely available at https://github.com/insilichem/ommprotocol.

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.

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 too...

Starting from biochemical knowledge followed by computational design, an artificial metallo-hydratase comprising an unnatural metal binding amino acid was created., 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.

The design of enzymes is an exciting field of research that aims at expanding the scope of biocatalysis and to ease the transition of chemical industries to greener practices. At the frontiers between organic and inorganic chemistry, protein engineering and structural biology, this exercise challenges in many ways our molecular intuitions. In this context, computation, and more particularly molecular modelling, presents a series of valuable tools. From tuning of naturally occurring enzymes to the discovery of new active scaffolds, computation offers molecular predictions and insights that could be useful for designers. This chapter aims to present how computational chemistry can be involved in enzyme design. After a general overview of the main schemes used nowadays in the development of novel enzymes, we recap on the pros and cons of modelling tools when applying them to this particular field. Finally, we provide a deeper analysis of the modelling works that have been executed so far. We contemplate situations where computation has shown predictive power in successfully identifying new biocatalysts and those representing proof of concept in enzyme design.