Molecular Modeling for Artificial Metalloenzyme Design and Optimization

Nanoscale chemical reaction exploration with a quantum magnifying glass

Nanoscopic systems exhibit diverse molecular substructures by which they facilitate specific functions. Theoretical models of them, which aim at describing, understanding, and predicting these capabilities, are difficult to build. Viable quantum-classical hybrid models come with specific challenges regarding atomistic structure construction and quantum region selection. Moreover, if their dynamics are mapped onto a state-to-state mechanism such as a chemical reaction network, its exhaustive exploration will be impossible due to the combinatorial explosion of the reaction space. Here, we introduce a "quantum magnifying glass" that allows one to interactively manipulate nanoscale structures at the quantum level. The quantum magnifying glass seamlessly combines autonomous model parametrization, ultra-fast quantum mechanical calculations, and automated reaction exploration. It represents an approach to investigate complex reaction sequences in a physically consistent manner with unprecedented effortlessness in real time. We demonstrate these features for reactions in bio-macromolecules and metal-organic frameworks, diverse systems that highlight general applicability.

Rationalization of a Streptavidin Based Enantioselective Artificial Suzukiase: An Integrative Computational Approach

An Artificial Metalloenzyme (ArM) built employing the streptavidin-biotin technology has been used for the enantioselective synthesis of binaphthyls by means of asymmetric Suzuki-Miyaura cross-coupling reactions. Despite its success, it remains a challenge to understand how the length of the biotin cofactors or the introduction of mutations to streptavidin leads the preferential synthesis of one atropisomer over the other. In this study, we apply an integrated computational modeling approach, including DFT calculations, protein-ligand dockings and molecular dynamics to rationalize the impact of mutations and length of the biotion cofactor on the enantioselectivities of the biaryl product. The results unravel that the enantiomeric differences found experimentally can be rationalized by the disposition of the first intermediate, coming from the oxidative addition step, and the entrance of the second substrate. The work also showcases the difficulties facing to control the enantioselection when engineering ArM to catalyze enantioselective Suzuki-Miyaura couplings and how the combination of DFT calculations, molecular dockings and MD simulations can be used to rationalize artificial metalloenzymes.

Is Metal Stabilization of the Leaving Group Required or Can Lysine Facilitate Phosphodiester Bond Cleavage in Nucleic Acids? A Computational Study of EndoV

Endonuclease V (EndoV) is a single-metal-dependent enzyme that repairs deaminated DNA nucleobases in cells by cleaving the phosphodiester bond, and this enzyme has proven to be a powerful tool in biotechnology and medicine. The catalytic mechanism used by EndoV must be understood to design new disease detection and therapeutic solutions and further exploit the enzyme in interdisciplinary applications. This study has used a mixed molecular dynamics (MD) and quantum mechanics/molecular mechanics (QM/MM) approach to compare eight distinct catalytic pathways and provides the first proposed mechanism for bacterial EndoV. The calculations demonstrate that mechanisms involving either direct or indirect metal coordination to the leaving group of the substrate previously proposed for other nucleases are unlikely for EndoV, regardless of the general base (histidine, aspartate, and substrate phosphate moiety). Instead, distinct catalytic pathways are characterized for EndoV that involve K139 stabilizing the leaving group, a metal-coordinated water stabilizing the transition structure, and either H214 or a substrate phosphate group activating the water nucleophile. In silico K139A and H214A mutational results support the newly proposed roles of these residues. Although this is a previously unseen combination of general base, general acid, and metal-binding architecture for a one-metal-dependent endonuclease, our proposed catalytic mechanisms are fully consistent with experimental kinetic, structural, and mutational data. In addition to substantiating a growing body of literature, suggesting that one metal is enough to catalyze P-O bond cleavage in nucleic acids, this new fundamental understanding of the catalytic function will promote the exploration of new and improved applications of EndoV.

Transesterification of RNA model induced by novel dinuclear copper (II) complexes with bis-tridentate imidazole derivatives

Two novel bis-tridentate imidazole derivatives were conveniently synthesized using a 'one-pot' method. Their dinuclear (Cu₂L¹Cl₄, Cu₂L²Cl₄) and mononuclear (CuL¹Cl₂, CuL²Cl₂∙H₂O) copper (II) complexes were synthesized to comparably evaluate their reactivities in the hydrolytic cleavage of 2-hydroxypropyl p-nitrophenyl phosphate (HPNP) as a classic RNA model. Single crystals of Cu₂L¹Cl₄ and Cu₂L²Cl₄ indicate that both of them are centrosymmetric, and each central copper ion is penta-coordinated. Regarding the transesterification of HPNP, both of dinuclear ones exhibited excess one order of magnitude rate enhancement in contrast with auto-hydrolysis reaction. Under comparable conditions, dinuclear complexes displayed no more than twofold increase in activity over their mononuclear analogues, which verifies the lack of binuclear cooperation effect due to long Cu-to-Cu space.

The Quantum Chemical Cluster Approach in Biocatalysis

ConspectusThe quantum chemical cluster approach has been used for modeling enzyme active sites and reaction mechanisms for more than two decades. In this methodology, a relatively small part of the enzyme around the active site is selected as a model, and quantum chemical methods, typically density functional theory, are used to calculate energies and other properties. The surrounding enzyme is modeled using implicit solvation and atom fixing techniques. Over the years, a large number of enzyme mechanisms have been solved using this method. The models have gradually become larger as a result of the faster computers, and new kinds of questions have been addressed. In this Account, we review how the cluster approach can be utilized in the field of biocatalysis. Examples from our recent work are chosen to illustrate various aspects of the methodology. The use of the cluster model to explore substrate binding is discussed first. It is emphasized that a comprehensive search is necessary in order to identify the lowest-energy binding mode(s). It is also argued that the best binding mode might not be the productive one, and the full reactions for a number of enzyme-substrate complexes have therefore to be considered to find the lowest-energy reaction pathway. Next, examples are given of how the cluster approach can help in the elucidation of detailed reaction mechanisms of biocatalytically interesting enzymes, and how this knowledge can be exploited to develop enzymes with new functions or to understand the reasons for lack of activity toward non-natural substrates. The enzymes discussed in this context are phenolic acid decarboxylase and metal-dependent decarboxylases from the amidohydrolase superfamily. Next, the application of the cluster approach in the investigation of enzymatic enantioselectivity is discussed. The reaction of strictosidine synthase is selected as a case study, where the cluster calculations could reproduce and rationalize the selectivities of both the natural and non-natural substrates. Finally, we discuss how the cluster approach can be used to guide the rational design of enzyme variants with improved activity and selectivity. Acyl transferase from Mycobacterium smegmatis serves as an instructive example here, for which the calculations could pinpoint the factors controlling the reaction specificity and enantioselectivity. The cases discussed in this Account highlight thus the value of the cluster approach as a tool in biocatalysis. It complements experiments and other computational techniques in this field and provides insights that can be used to understand existing enzymes and to develop new variants with tailored properties.

Expanding the Synthetic Toolbox through Metal-Enzyme Cascade Reactions

The combination of metal-, photo-, enzyme-, and/or organocatalysis provides multiple synthetic solutions, especially when the creation of chiral centers is involved. Historically, enzymes and transition metal species have been exploited simultaneously through dynamic kinetic resolutions of racemates. However, more recently, linear cascades have appeared as elegant solutions for the preparation of valuable organic molecules combining multiple bioprocesses and metal-catalyzed transformations. Many advantages are derived from this symbiosis, although there are still bottlenecks to be addressed including the successful coexistence of both catalyst types, the need for compatible reaction media and mild conditions, or the minimization of cross-reactivities. Therefore, solutions are here also provided by means of catalyst coimmobilization, compartmentalization strategies, flow chemistry, etc. A comprehensive review is presented focusing on the period 2015 to early 2022, which has been divided into two main sections that comprise first the use of metals and enzymes as independent catalysts but working in an orchestral or sequential manner, and later their application as bionanohybrid materials through their coimmobilization in adequate supports. Each part has been classified into different subheadings, the first part based on the reaction catalyzed by the metal catalyst, while the development of nonasymmetric or stereoselective processes was considered for the bionanohybrid section.

Convergence in determining enzyme functional descriptors across Kemp eliminase variants

Molecular simulations have been extensively employed to accelerate biocatalytic discoveries. Enzyme functional descriptors derived from molecular simulations have been leveraged to guide the search for beneficial enzyme mutants. However, the ideal active-site region size for computing the descriptors over multiple enzyme variants remains untested. Here, we conducted convergence tests for dynamics-derived and electrostatic descriptors on 18 Kemp eliminase variants across six active-site regions with various boundary distances to the substrate. The tested descriptors include the root-mean-square deviation of the active-site region, the solvent accessible surface area ratio between the substrate and active site, and the projection of the electric field (EF) on the breaking C-H bond. All descriptors were evaluated using molecular mechanics methods. To understand the effects of electronic structure, the EF was also evaluated using quantum mechanics/molecular mechanics methods. The descriptor values were computed for 18 Kemp eliminase variants. Spearman correlation matrices were used to determine the region size condition under which further expansion of the region boundary does not substantially change the ranking of descriptor values. We observed that protein dynamics-derived descriptors, including RMSD_{active_site} and SASA_ratio, converge at a distance cutoff of 5 Å from the substrate. The electrostatic descriptor, EF_C-H, converges at 6 Å using molecular mechanics methods with truncated enzyme models and 4 Å using quantum mechanics/molecular mechanics methods with whole enzyme model. This study serves as a future reference to determine descriptors for predictive modeling of enzyme engineering.

Gaussian field-based 3D-QSAR and molecular simulation studies to design potent pyrimidine-sulfonamide hybrids as selective BRAFV600E inhibitors

The "RAS-RAF-MEK-ERK" pathway is an important signaling pathway in melanoma. BRAF^V600E (70-90%) is the most common mutation in this pathway. BRAF inhibitors have four types of conformers: type I (αC-IN/DFG-IN), type II (αC-IN/DFG-OUT), type I_1/2 (αC-OUT/DFG-IN), and type I/II (αC-OUT/DFG-OUT). First- and second-generation BRAF inhibitors show resistance to BRAF^V600E and are ineffective against malignancies induced by dimer BRAF mutants causing 'paradoxical' activation. In the present study, we performed molecular modeling of pyrimidine-sulfonamide hybrids inhibitors using 3D-QSAR, molecular docking, and molecular dynamics simulations. Previous reports reveal the importance of pyrimidine and sulfonamide moieties in the development of BRAF^V600E inhibitors. Analysis of 3D-QSAR models provided novel pyrimidine sulfonamide hybrid BRAF^V600E inhibitors. The designed compounds share similarities with several structural moieties present in first- and second-generation BRAF inhibitors. A total library of 88 designed compounds was generated and molecular docking studies were performed with them. Four molecules (T109, T183, T160, and T126) were identified as hits and selected for detailed studies. Molecular dynamics simulations were performed at 900 ns and binding was calculated. Based on molecular docking and simulation studies, it was found that the designed compounds have better interactions with the core active site [the nucleotide (ADP or ATP) binding site, DFG motif, and the phospho-acceptor site (activation segment) of BRAF^V600E protein than previous inhibitors. Similar to the FDA-approved BRAF^V600E inhibitors the developed compounds have [αC-OUT/DFG-IN] conformation. Compounds T126, T160 and T183 interacted with DIF (Leu505), making them potentially useful against BRAF^V600E resistance and malignancies induced by dimer BRAF mutants. The synthesis and biological evaluation of the designed molecules is in progress, which may lead to some potent BRAF^V600E selective inhibitors.

Copper-Containing Artificial Polyenzymes as a Clickase for Bioorthogonal Chemistry

Artificial polyenzymes (ArPoly) are tailored combinations of universal protein scaffolds and polymers newly proposed as promising alternatives to natural enzymes to expand the biocatalyst toolbox. The concept of ArPoly has been continuously extended to metal-containing ArPoly to overcome the drawbacks faced by conventional artificial metalloenzymes. Herein, we present a sustainable route to synthesize a novel water-soluble metalloenzyme for copper-catalyzed azide-alkyne cycloadditions in water with remarkable selectivity. In this case, synthetic l-proline monomers were polymerized onto bovine serum albumen in an aqueous medium via copper-mediated "grafting-from" atom-transfer radical polymerization, resulting in protein-polymer-copper conjugates named ArPoly_clickase. The copper in ArPoly_clickase plays pivotal bifunctional roles, not only as the catalyst for polymerization but also as the coordinated active site for alkyne-azide click catalysis. ArPoly_clickase showcases high efficiency, substrate generality, regioselectivity, and ease of product separation for "click chemistry" in water. Notably, ArPoly_clickase displays good biocompatibility without imposing copper toxicity on living cells, which offers the prospect for the upcoming bioorthogonal chemistry.

Computationally driven design of an artificial metalloenzyme using supramolecular anchoring strategies of iridium complexes to alcohol dehydrogenase

Artificial metalloenzymes (ArMs) confer non-biological reactivities to biomolecules, whilst taking advantage of the biomolecular architecture in terms of their selectivity and renewable origin. In particular, the design of ArMs by the supramolecular anchoring of metal catalysts to protein hosts provides flexible and easy to optimise systems. The use of cofactor dependent enzymes as hosts gives the advantage of both a (hydrophobic) binding site for the substrate and a cofactor pocket to accommodate the catalyst. Here, we present a computationally driven design approach of ArMs for the transfer hydrogenation reaction of cyclic imines, starting from the NADP⁺-dependent alcohol dehydrogenase from Thermoanaerobacter brockii (TbADH). We tested and developed a molecular docking workflow to define and optimize iridium catalysts with high affinity for the cofactor binding site of TbADH. The workflow uses high throughput docking of compound libraries to identify key structural motifs for high affinity, followed by higher accuracy docking methods on smaller, focused ligand and catalyst libraries. Iridium sulfonamide catalysts were selected and synthesised, containing either a triol, a furane, or a carboxylic acid to provide the interaction with the cofactor binding pocket. IC₅₀ values of the resulting complexes during TbADH-catalysed alcohol oxidation were determined by competition experiments and were between 4.410 mM and 0.052 mM, demonstrating the affinity of the iridium complexes for either the substrate or the cofactor binding pocket of TbADH. The catalytic activity of the free iridium complexes in solution showed a maximal turnover number (TON) of 90 for the reduction of salsolidine by the triol-functionalised iridium catalyst, whilst in the presence of TbADH, only the iridium catalyst with the triol anchoring functionality showed activity for the same reaction (TON of 36 after 24 h). The observation that the artificial metalloenzymes developed here lacked stereoselectivity demonstrates the need for the further investigation and optimisation of the ArM. Our results serve as a starting point for the design of robust artificial metalloenzymes, exploiting supramolecular anchoring to natural NAD(P)H binding pockets.

Successes and challenges in multiscale modelling of artificial metalloenzymes: the case study of POP-Rh2 cyclopropanase

Molecular modelling applications in metalloenzyme design are still scarce due to a series of challenges. On top of that, the simulations of metal-mediated binding and the identification of catalytic competent geometries require both large conformational exploration and simulation of fine electronic properties. Here, we demonstrate how the incorporation of new tools in multiscale strategies, namely substrate diffusion exploration, allows taking a step further. As a showcase, the enantioselective profiles of the most outstanding variants of an artificial Rh₂-based cyclopropanase (GSH, HFF and RFY) developed by Lewis and co-workers (Nat. Commun., 2015, 6, 7789 and Nat. Chem., 2018, 10, 318-324) have been rationalized. DFT calculations on the free-cofactor-mediated process identify the carbene insertion and the cyclopropanoid formation as crucial events, the latter being the enantiodetermining step, which displays up to 8 competitive orientations easily altered by the protein environment. The key intermediates of the reaction were docked into the protein scaffold showing that some mutated residues have direct interaction with the cofactor and/or the co-substrate. These interactions take the form of a direct coordination of Rh in GSH and HFF and a strong hydrophobic patch with the carbene moiety in RFY. Posterior molecular dynamics sustain that the cofactor induces global re-arrangements of the protein. Finally, massive exploration of substrate diffusion, based on the GPathFinder approach, defines this event as the origin of the enantioselectivity in GSH and RFY. For HFF, fine molecular dockings suggest that it is likely related to local interactions upon diffusion. This work shows how modelling of long-range mutations on the catalytic profiles of metalloenzymes may be unavoidable and software simulating substrate diffusion should be applied.

Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis

Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system.

Establishing the allosteric mechanism in CRISPR-Cas9

Allostery is a fundamental property of proteins, which regulates biochemical information transfer between spatially distant sites. Here, we report on the critical role of molecular dynamics (MD) simulations in discovering the mechanism of allosteric communication within CRISPR-Cas9, a leading genome editing machinery with enormous promises for medicine and biotechnology. MD revealed how allostery intervenes during at least three steps of the CRISPR-Cas9 function: affecting DNA recognition, mediating the cleavage and interfering with the off-target activity. An allosteric communication that activates concerted DNA cleavages was found to led through the L1/L2 loops, which connect the HNH and RuvC catalytic domains. The identification of these "allosteric transducers" inspired the development of novel variants of the Cas9 protein with improved specificity, opening a new avenue for controlling the CRISPR-Cas9 activity. Discussed studies also highlight the critical role of the recognition lobe in the conformational activation of the catalytic HNH domain. Specifically, the REC3 region was found to modulate the dynamics of HNH by sensing the formation of the RNA:DNA hybrid. The role of REC3 was revealed to be particularly relevant in the presence of DNA mismatches. Indeed, interference of REC3 with the RNA:DNA hybrid containing mismatched pairs at specific positions resulted in locking HNH in an inactive "conformational checkpoint" conformation, thereby hampering off-target cleavages. Overall, MD simulations established the fundamental mechanisms underlying the allosterism of CRISPR-Cas9, aiding engineering strategies to develop new CRISPR-Cas9 variants for improved genome editing.

Integrated experimental/computational approaches to characterize the systems formed by vanadium with proteins and enzymes

An integrated instrumental/computational approach to characterize metallodrug–protein adducts at the molecular level is reviewed. A series of applications are described, focusing on potential vanadium drugs with a generalization to other metals.

BioMetAll: Identifying Metal-Binding Sites in Proteins from Backbone Preorganization

With a large amount of research dedicated to decoding how metallic species bind to proteins, in silico methods are interesting allies for experimental procedures. To date, computational predictors mostly work by identifying the best possible sequence or structural match of the target protein with metal-binding templates. These approaches are fundamentally focused on the first coordination sphere of the metal. Here, we present the BioMetAll predictor that is based on a different postulate: the formation of a potential metal-binding site is related to the geometric organization of the protein backbone. We first report the set of convenient geometric descriptors of the backbone needed for the algorithm and their parameterization from a statistical analysis. Then, the successful benchmark of BioMetAll on a set of more than 90 metal-binding X-ray structures is presented. Because BioMetAll allows structural predictions regardless of the exact geometry of the side chains, it appears extremely valuable for systems whose structures (either experimental or theoretical) are not optimal for metal-binding sites. We report here its application on three different challenging cases: (i) the modulation of metal-binding sites during conformational transition in human serum albumin, (ii) the identification of possible routes of metal migration in hemocyanins, and (iii) the prediction of mutations to generate convenient metal-binding sites for de novo biocatalysts. This study shows that BioMetAll offers a versatile platform for numerous fields of research at the interface between inorganic chemistry and biology and allows to highlight the role of the preorganization of the protein backbone as a marker for metal binding. BioMetAll is an open-source application available at https://github.com/insilichem/biometall.

An Artificial Hemoprotein with Inducible Peroxidase- and Monooxygenase-Like Activities

A novel inducible artificial metalloenzyme obtained by covalent attachment of a manganese(III)-tetraphenylporphyrin (MnTPP) to the artificial bidomain repeat protein, (A3A3')Y26C, is reported. The protein is part of the αRep family. The biohybrid was fully characterized by MALDI-ToF mass spectrometry, circular dichroism and UV/Vis spectroscopies. The peroxidase and monooxygenase activities were evaluated on the original and modified scaffolds including those that have a) an additional imidazole, b) a specific αRep bA3-2 that is known to induce the opening of the (A3A3') interdomain region and c) a derivative of the αRep bA3-2 inducer extended with a His₆ -Tag (His₆ -bA3-2). Catalytic profiles are highly dependent on the presence of co-catalysts with the best activity obtained with His₆ -bA3-2. The entire mechanism was rationalized by an integrative molecular modeling study that includes protein-ligand docking and large-scale molecular dynamics. This constitutes the first example of an entirely artificial metalloenzyme with inducible peroxidase and monooxygenase activities, reminiscent of allosteric regulation of natural enzymatic pathways.

Unlocking the Full Evolutionary Potential of Artificial Metalloenzymes Through Direct Metal-Protein Coordination : A review of recent advances for catalyst development

Generation of artificial metalloenzymes (ArMs) has gained much inspiration from the general understanding of natural metalloenzymes. Over the last decade, a multitude of methods generating transition metal-protein hybrids have been developed and many of these new-to-nature constructs catalyse reactions previously reserved for the realm of synthetic chemistry. This perspective will focus on ArMs incorporating 4d and 5d transition metals. It aims to summarise the significant advances made to date and asks whether there are chemical strategies, used in nature to optimise metal catalysts, that have yet to be fully recognised in the synthetic enzyme world, particularly whether artificial enzymes produced to date fully take advantage of the structural and energetic context provided by the protein. Further, the argument is put forward that, based on precedence, in the majority of naturally evolved metalloenzymes the direct coordination bonding between the metal and the protein scaffold is integral to catalysis. Therefore, the protein can attenuate metal activity by positioning ligand atoms in the form of amino acids, as well as making non-covalent contributions to catalysis, through intermolecular interactions that pre-organise substrates and stabilise transition states. This highlights the often neglected but crucial element of natural systems that is the energetic contribution towards activating metal centres through protein fold energy. Finally, general principles needed for a different approach to the formation of ArMs are set out, utilising direct coordination inspired by the activation of an organometallic cofactor upon protein binding. This methodology, observed in nature, delivers true interdependence between metal and protein. When combined with the ability to efficiently evolve enzymes, new problems in catalysis could be addressed in a faster and more specific manner than with simpler small molecule catalysts.