The mechanistic insights into different aspects of promiscuity in metalloenzymes.

Enzymes are nature's ultimate machinery to catalyze complex reactions. Though enzymes are evolved to catalyze specific reactions, they also show significant promiscuity in reactions and substrate selection. Metalloenzymes contain a metal ion or metal cofactor in their active site, which is crucial in their catalytic activity. Depending on the metal and its coordination environment, the metal ion or cofactor may function as a Lewis acid or base and a redox center and thus can catalyze a plethora of natural reactions. In fact, the versatility in the oxidation state of the metal ions provides metalloenzymes with a high level of catalytic adaptability and promiscuity. In this chapter, we discuss different aspects of promiscuity in metalloenzymes by using several recent experimental and theoretical works as case studies. We start our discussion by introducing the concept of promiscuity and then we delve into the mechanistic insight into promiscuity at the molecular level.

Gut microbial metalloproteins and its role in xenobiotics degradation and ROS scavenging.

The gut microbial metalloenzymes play an important role in maintaining the balance between gut microbial ecosystem, human physiologically processes and immune system. The metals coordinated into active site contribute in various detoxification and defense strategies to avoid unfavourable environment and ensure bacterial survival in human gut. Metallo-ß-lactamase is a potent degrader of antibiotics present in periplasmic space of both commensals and pathogenic bacteria. The resistance to anti-microbial agents developed in this enzyme is one of the global threats for human health. The organophosphorus eliminator, organophosphorus hydrolases have evolved over a course of time to hydrolyze toxic organophosphorus compounds and decrease its effect on human health. Further, the redox stress responders namely superoxide dismutase and catalase are key metalloenzymes in reducing both endogenous and exogenous oxidative stress. They hold a great importance for pathogens as they contribute in pathogenesis in human gut along with reduction of oxidative stress. The in-silico study on these enzymes reveals the importance of point mutation for the evolution of these enzymes in order to enhance their enzyme activity and stability. Various mutation studies were conducted to investigate the catalytic activity of these enzymes. By using the "directed evolution" method, the enzymes involved in detoxification and defense system can be engineered to produce new variants with enhance catalytic features, which may be used to predict the severity due to multi-drug resistance and degradation pattern of organophosphorus compounds in human gut.

Exploring metalloproteins found in the secretion of venomous species: Biological role and therapeutical applications.

Several species during evolution suffered random mutations in response to various environmental factors, which resulted in the formation of venom in phylogenetically distant species. The composition of the venom of most species is poorly known. Snake venom is well characterized while most species have poorly known composition. In contrast, snake venoms are well characterized which proteins and peptides are the main active and most abundant constituents. 42 protein families have been identified, including metalloproteins known as metalloproteinases. These macromolecules are enzymes with zinc in their active site derived from the disintegrin A and metalloproteinase (ADAM) cellular family and are categorized into three classes (PI, PII and PIII) according to their domain organization. The snake venom metalloproteinases (SVMP) are cytotoxic, neurotoxic, myotoxic and/or hematotoxic with a crucial role in the defense and restraint of prey. In this scenario envenoming represents a danger to human health and has been considered a neglected disease worldwide, particularly in tropical and subtropical countries. Nevertheless, recently advances in "omics" technologies have demonstrated interesting biological activities of SVMPs such as antimicrobial, anticancer, against cardiovascular diseases and nervous system disorders. Metalloproteins have the therapeutic potential to be converted into drugs as other components of the venom have undergone this process (e.g., captopril, tirefiban and eptifibatide). So, this chapter is focused on the metalloproteins found in the secretions of venomous species, highlight some aspects such as structure, biological activity, pharmacological therapeutic potential and on.

Metalloproteins structural and functional insights into immunological patterns.

Metalloproteins play a crucial role in regulating different aspects of the immune system in humans. They have various functions in immunity, including recognizing and presenting antigens, aiding in the movement and effectiveness of immune cells, and facilitating interactions between the host and pathogens. Understanding how these proteins work can help us develop new methods to control the immune response in different diseases. Metalloproteins contain metal ions in their structure, which allows them to perform these diverse functions. They encompass a wide range of enzymes, signaling molecules, and structural proteins that utilize metal ions as cofactors for their activities. Examples of metalloproteins include superoxide dismutase, catalase, and metalloproteases, which regulate oxidative stress, inflammation, and tissue remodelling processes associated with immune activation. By studying their functions and the effects of their dysfunction, researchers can develop strategies to improve immune function and combat various diseases. This review explores the diverse functions of metalloproteins in immune processes, highlighting their significance in both health and disease.

C-H functionalization reactions catalyzed by artificial metalloenzymes.

CH functionalization, a promising frontier in modern organic chemistry, facilitates the direct conversion of inert CH bonds into many valuable functional groups. Despite its merits, traditional homogeneous catalysis, often faces challenges in efficiency, selectivity, and sustainability towards this transformation. In this context, artificial metalloenzymes (ArMs), resulting from the incorporation of a catalytically-competent metal cofactor within an evolvable protein scaffold, bridges the gap between the efficiency of enzymatic transformations and the versatility of transition metal catalysis. Accordingly, ArMs have emerged as attractive tools for various challenging catalytic transformations. Additionally, the coming of age of directed evolution has unlocked unprecedented avenues for optimizing enzymatic catalysis. Taking advantage of their genetically-encoded protein scaffold, ArMs have been evolved to catalyze various CH functionalization reactions. This review delves into the recent developments of ArM-catalyzed CH functionalization reactions, highlighting the benefits of engineering the second coordination sphere around a metal cofactor within a host protein.

Perspective: multi-configurational methods in bio-inorganic chemistry.

Transition metal ions play crucial roles in the structure and function of numerous proteins, contributing to essential biological processes such as catalysis, electron transfer, and oxygen binding. However, accurately modeling the electronic structure and properties of metalloproteins poses significant challenges due to the complex nature of their electronic configurations and strong correlation effects. Multiconfigurational quantum chemistry methods are, in principle, the most appropriate tools for addressing these challenges, offering the capability to capture the inherent multi-reference character and strong electron correlation present in bio-inorganic systems. Yet their computational cost has long hindered wider adoption, making methods such as density functional theory (DFT) the method of choice. However, advancements over the past decade have substantially alleviated this limitation, rendering multiconfigurational quantum chemistry methods more accessible and applicable to a wider range of bio-inorganic systems. In this perspective, we discuss some of these developments and how they have already been used to answer some of the most important questions in bio-inorganic chemistry. We also comment on ongoing developments in the field and how the future of the field may evolve.

Computational modelling of supramolecular metallopeptide assemblies.

Among the important questions in supramolecular peptide self-assemblies are their interactions with metallic compounds and ions. In the last decade, intensive efforts have been devoted to understanding the structural properties of these interactions including their dynamical and catalytic impact in natural and de novo systems. Since structural insights from experimental approaches could be particularly challenging, computational chemistry methods are interesting complementary tools. Here, we present the general multiscale strategies we developed and applied for the study of metallopeptide assemblies. These strategies include prediction of metal binding site, docking of metallic moieties, classical and accelerated molecular dynamics and finally QM/MM calculations. The systems of choice for this chapter are, on one side, peptides involved in neurodegenerative diseases and, on the other, de novo fibrillar systems with catalytic properties. Both successes and remaining challenges are highlighted so that the protocol could be apply to other system of this kind.

Redox-reversible siderophore-based catalyst anchoring within cross-linked artificial metalloenzyme aggregates enables enantioselectivity switching.

The immobilisation of artificial metalloenzymes (ArMs) holds promise for the implementation of new biocatalytic reactions. We present the synthesis of cross-linked artificial metalloenzyme aggregates (CLArMAs) with excellent recyclability, as an alternative to carrier-based immobilisation strategies. Furthermore, iron-siderophore supramolecular anchoring facilitates redox-triggered cofactor release, enabling CLArMAs to be recharged with alternative cofactors for diverse selectivity.

Machine Learning-Enhanced Quantum Chemistry-Assisted Refinement of the Active Site Structure of Metalloproteins.

Understanding the fine structural details of inhibitor binding at the active site of metalloenzymes can have a profound impact on the rational drug design targeted to this broad class of biomolecules. Structural techniques such as NMR, cryo-EM, and X-ray crystallography can provide bond lengths and angles, but the uncertainties in these measurements can be as large as the range of values that have been observed for these quantities in all the published structures. This uncertainty is far too large to allow for reliable calculations at the quantum chemical (QC) levels for developing precise structure-activity relationships or for improving the energetic considerations in protein-inhibitor studies. Therefore, the need arises to rely upon computational methods to refine the active site structures well beyond the resolution obtained with routine application of structural methods. In a recent paper, we have shown that it is possible to refine the active site of cobalt(II)-substituted MMP12, a metalloprotein that is a relevant drug target, by matching to the experimental pseudocontact shifts (PCS) those calculated using multireference ab initio QC methods. The computational cost of this methodology becomes a significant bottleneck when the starting structure is not sufficiently close to the final one, which is often the case with biomolecular structures. To tackle this problem, we have developed an approach based on a neural network (NN) and a support vector regression (SVR) and applied it to the refinement of the active site structure of oxalate-inhibited human carbonic anhydrase 2 (hCAII), another prototypical metalloprotein target. The refined structure gives a remarkably good agreement between the QC-calculated and the experimental PCS. This study not only contributes to the knowledge of CAII but also demonstrates the utility of combining machine learning (ML) algorithms with QC calculations, offering a promising avenue for investigating other drug targets and complex biological systems in general.

Functional metalloenzymes based on myoglobin and neuroglobin that exploit covalent interactions.

Globins, such as myoglobin (Mb) and neuroglobin (Ngb), are ideal protein scaffolds for the design of functional metalloenzymes. To date, numerous approaches have been developed for enzyme design. This review presents a summary of the progress made in the design of functional metalloenzymes based on Mb and Ngb, with a focus on the exploitation of covalent interactions, including coordination bonds and covalent modifications. These include the construction of a metal-binding site, the incorporation of a non-native metal cofactor, the formation of Cys/Tyr-heme covalent links, and the design of disulfide bonds, as well as other Cys-covalent modifications. As exemplified by recent studies from our group and others, the designed metalloenzymes have potential applications in biocatalysis and bioconversions. Furthermore, we discuss the current trends in the design of functional metalloenzymes and highlight the importance of covalent interactions in the design of functional metalloenzymes.

AcrIIA28 is a metalloprotein that specifically inhibits targeted-DNA loading to SpyCas9 by binding to the REC3 domain.

CRISPR-Cas systems serve as adaptive immune systems in bacteria and archaea, protecting against phages and other mobile genetic elements. However, phages and archaeal viruses have developed countermeasures, employing anti-CRISPR (Acr) proteins to counteract CRISPR-Cas systems. Despite the revolutionary impact of CRISPR-Cas systems on genome editing, concerns persist regarding potential off-target effects. Therefore, understanding the structural and molecular intricacies of diverse Acrs is crucial for elucidating the fundamental mechanisms governing CRISPR-Cas regulation. In this study, we present the structure of AcrIIA28 from Streptococcus phage Javan 128 and analyze its structural and functional features to comprehend the mechanisms involved in its inhibition of Cas9. Our current study reveals that AcrIIA28 is a metalloprotein that contains Zn2+ and abolishes the cleavage activity of Cas9 only from Streptococcus pyrogen (SpyCas9) by directly interacting with the REC3 domain of SpyCas9. Furthermore, we demonstrate that the AcrIIA28 interaction prevents the target DNA from being loaded onto Cas9. These findings indicate the molecular mechanisms underlying AcrIIA28-mediated Cas9 inhibition and provide valuable insights into the ongoing evolutionary battle between bacteria and phages.

A database overview of metal-coordination distances in metalloproteins.

Metalloproteins are ubiquitous in all living organisms and take part in a very wide range of biological processes. For this reason, their experimental characterization is crucial to obtain improved knowledge of their structure and biological functions. The three-dimensional structure represents highly relevant information since it provides insight into the interaction between the metal ion(s) and the protein fold. Such interactions determine the chemical reactivity of the bound metal. The available PDB structures can contain errors due to experimental factors such as poor resolution and radiation damage. A lack of use of distance restraints during the refinement and validation process also impacts the structure quality. Here, the aim was to obtain a thorough overview of the distribution of the distances between metal ions and their donor atoms through the statistical analysis of a data set based on more than 115 000 metal-binding sites in proteins. This analysis not only produced reference data that can be used by experimentalists to support the structure-determination process, for example as refinement restraints, but also resulted in an improved insight into how protein coordination occurs for different metals and the nature of their binding interactions. In particular, the features of carboxylate coordination were inspected, which is the only type of interaction that is commonly present for nearly all metals.

Electron-Transferring Metalloenzymes and their Potential Biotechnological Applications.

Modern societies rely heavily on centralized industrial processes to generate a multitude of products ranging from electrical energy to synthetic chemical building blocks to construction materials. To date, these processes have relied extensively on energy produced from fossil fuels, which has led to dramatically increased quantities of greenhouse gases (including carbon dioxide) being released into the atmosphere; the effects of the ensuing change to our climate are easily observed in day-to-day life. Some of the reactions catalyzed by these industrial processes can be catalyzed in nature by metal-containing enzymes (metalloenzymes) that have evolved over the course of up to 3.8 billion years to do so under mild physiological conditions using Earth-abundant metals. While such metalloenzymes could in principle facilitate the implementation of carbon-neutral processes around the globe, either in "bio-inspired" catalyst design or even by direct exploitation, many remaining questions surrounding their mechanisms often preclude both options. Here, our recent efforts in understanding and applying metalloenzymes that catalyze reactions such as dinitrogen reduction to ammonia or proton reduction to molecular hydrogen are discussed. In closing, an opinion on the question: "Can these types of enzymes really be used in new biotechnologies?" is offered.

Flexibility of Binding Site is Essential to the Ca2+ Selectivity in EF-Hand Calcium-Binding Proteins.

High binding affinity and selectivity of metal ions are essential to the function of metalloproteins. Thus, understanding the factors that determine these binding characteristics is of major interest for both fundamental mechanistic investigations and guiding of the design of novel metalloproteins. In this work, we perform QM cluster model calculations and quantum mechanics/molecular mechanics (QM/MM) free energy simulations to understand the binding selectivity of Ca2+ and Mg2+ in the wild-type carp parvalbumin and its mutant. While a nonpolarizable MM model (CHARMM36) does not lead to the correct experimental trend, treatment of the metal binding site with the DFTB3 model in a QM/MM framework leads to relative binding free energies (ΔΔGbind) comparable with experimental data. For the wild-type (WT) protein, the calculated ΔΔGbind is ∼6.6 kcal/mol in comparison with the experimental value of 5.6 kcal/mol. The good agreement highlights the value of a QM description of the metal binding site and supports the role of electronic polarization and charge transfer to metal binding selectivity. For the D51A/E101D/F102W mutant, different binding site models lead to considerable variations in computed binding affinities. With a coordination number of seven for Ca2+, which is shown by QM/MM metadynamics simulations to be the dominant coordination number for the mutant, the calculated relative binding affinity is ∼4.8 kcal/mol, in fair agreement with the experimental value of 1.6 kcal/mol. The WT protein is observed to feature a flexible binding site that accommodates a range of coordination numbers for Ca2+, which is essential to the high binding selectivity for Ca2+ over Mg2+. In the mutant, the E101D mutation reduces the flexibility of the binding site and limits the dominant coordination number of Ca2+ to be seven, thereby leading to reduced binding selectivity against Mg2+. Our results highlight that the binding selectivity of metal ions depends on both the structural and dynamical properties of the protein binding site.

Discovery of Novel Metalloenzyme Inhibitors Based on Property Characterization: Strategy and Application for HDAC1 Inhibitors.

Metalloenzymes are ubiquitously present in the human body and are relevant to a variety of diseases. However, the development of metalloenzyme inhibitors is limited by low specificity and poor drug-likeness associated with metal-binding fragments (MBFs). A generalized drug discovery strategy was established, which is characterized by the property characterization of zinc-dependent metalloenzyme inhibitors (ZnMIs). Fifteen potential Zn2+-binding fragments (ZnBFs) were identified, and a customized pharmacophore feature was defined based on these ZnBFs. The customized feature was set as a required feature and applied to a search for novel inhibitors for histone deacetylase 1 (HDAC1). Ten potential HDAC1 inhibitors were recognized, and one of them (compound 9) was a known potent HDAC1 inhibitor. The results demonstrated the effectiveness of our strategy to identify novel inhibitors for zinc-dependent metalloenzymes.

Unlocking Precision Docking for Metalloproteins.

Metalloproteins play a fundamental role in molecular biology, contributing to various biological processes. However, the discovery of high-affinity ligands targeting metalloproteins has been delayed due, in part, to a lack of suitable tools and data. Molecular docking, a widely used technique for virtual screening of small-molecule ligand interactions with proteins, often faces challenges when applied to metalloproteins due to the particular nature of the ligand metal bond. To address these limitations associated with docking metalloproteins, we introduce a knowledge-driven docking approach known as "metalloprotein bias docking" (MBD), which extends the AutoDock Bias technique. We assembled a comprehensive data set of metalloprotein-ligand complexes from 15 different metalloprotein families, encompassing Ca, Co, Fe, Mg, Mn, and Zn metal ions. Subsequently, we conducted a performance analysis of our MBD method and compared it to the conventional docking (CD) program AutoDock4, applied to various metalloprotein targets within our data set. Our results demonstrate that MBD outperforms CD, significantly enhancing accuracy, selectivity, and precision in ligand pose prediction. Additionally, we observed a positive correlation between our predicted ligand free energies and the corresponding experimental values. These findings underscore the potential of MBD as a valuable tool for the effective exploration of metalloprotein-ligand interactions.

On the hunt for metalloenzyme inhibitors: Investigating the presence of metal-coordinating compounds in screening libraries and chemical spaces.

Metalloenzymes play vital roles in various biological processes, requiring the search for inhibitors to develop treatment options for diverse diseases. While compound library screening is a conventional approach, the exploration of virtual chemical spaces housing trillions of compounds has emerged as an alternative strategy. In this study, we investigated the suitability of selected screening libraries and chemical spaces for discovering inhibitors of metalloenzymes featuring common ions (Mg2+, Mn2+, and Zn2+). First, metal-coordinating groups from ligands interacting with ions in the Protein Data Bank were extracted. Subsequently, the prevalence of these groups in two focused screening libraries (Life Chemicals' chelator library, comprising 6,428 compounds, and Otava's chelator fragment library, with 1,784 fragments) as well as two chemical spaces (GalaXi and REAL space, containing billions of virtual products) was investigated. In total, 1,223 metal-coordinating groups were identified, with about a quarter of these groups found within the examined libraries and spaces. Our results indicate that these can serve as valuable starting points for drug discovery targeting metalloenzymes. In addition, this study suggests ways to improve libraries and spaces for better success in finding potential inhibitors for metalloenzymes.

Structural, Biochemical, and Computational Characterization of Sulfamides as Bimetallic Peptidase Inhibitors.

The sulfonamide function is used extensively as a general building block in various inhibitory scaffolds and, more specifically, as a zinc-binding group (ZBG) of metalloenzyme inhibitors. Here, we provide biochemical, structural, and computational characterization of a metallopeptidase in complex with inhibitors, where the mono- and bisubstituted sulfamide functions are designed to directly engage zinc ions of a bimetallic enzyme site. Structural data showed that while monosubstituted sulfamides coordinate active-site zinc ions via the free negatively charged amino group in a canonical manner, their bisubstituted counterparts adopt an atypical binding pattern divergent from expected positioning of corresponding tetrahedral reaction intermediates. Accompanying quantum mechanics calculations revealed that electroneutrality of the sulfamide function is a major factor contributing to the markedly lower potency of bisubstituted compounds by considerably lowering their interaction energy with the enzyme. Overall, while bisubstituted uncharged sulfamide functions can bolster favorable pharmacological properties of a given inhibitor, their use as ZBGs in metalloenzyme inhibitors might be less advantageous due to their suboptimal metal-ligand properties.

Bridging the Coordination Chemistry of Small Compounds and Metalloproteins Using Machine Learning.

Metalloproteins require metal ions as cofactors to catalyze specific reactions with remarkable efficiency and specificity. In various electron transfer reactions, metals in the active sites change their oxidation states to facilitate the biochemical reactions. Cryogenic electron microscopy, X-ray, and X-ray free electron laser (XFEL) crystallography are used to image metalloproteins to understand the reaction mechanisms. However, radiation damage in cryoEM and X-ray crystallography, and the challenge of generating homogeneous crystals and keeping the appropriate experimental conditions for all the crystals in XFEL crystallography, may alter the oxidation states. Here, we build machine learning models trained on a large data set from the Cambridge Crystallographic Data Center to evaluate the metal oxidation states. The models yield high accuracy scores (from 82% to 94%) for all metals in the small molecules. Then, they were used to predict the oxidation states of more than 30 000 metal clusters in metalloproteins with Fe, Mn, Co, and Cu in their active sites. We found that most of the metals exist in the lower oxidation states (Fe2+ 77%, Mn2+ 85%, Co2+ 65%, and Cu+ 64%), and these populations correlate with the standard reduction potentials of the metal ions. Furthermore, we found no clear correlation between these populations and the resolution of the structures, which suggests no significant dependence of these predictions on the resolution. Our models represent a valuable tool for evaluating the oxidation states of the metals in metalloproteins imaged with different techniques. The data files and the machine learning code are available in a public GitHub repository: https://github.com/mamin03/OxitationStatesMetalloprotein.git.

Protein3D: Enabling analysis and extraction of metal-containing sites from the Protein Data Bank with molSimplify.

Metalloenzymes catalyze a wide range of chemical transformations, with the active site residues playing a key role in modulating chemical reactivity and selectivity. Unlike smaller synthetic catalysts, a metalloenzyme active site is embedded in a larger protein, which makes interrogation of electronic properties and geometric features with quantum mechanical calculations challenging. Here we implement the ability to fetch crystallographic structures from the Protein Data Bank and analyze the metal binding sites in the program molSimplify. We show the usefulness of the newly created protein3D class to extract the local environment around non-heme iron enzymes containing a two histidine motif and prepare 372 structures for quantum mechanical calculations. Our implementation of protein3D serves to expand the range of systems molSimplify can be used to analyze and will enable high-throughput study of metal-containing active sites in proteins.