The Case for Enzymatic Competitive Metal Affinity Methods

Tutorial Review on the Set-Up and Running of Quantum Mechanical Cluster Models for Enzymatic Reaction Mechanisms

Enzymes turnover substrates into products with amazing efficiency and selectivity and as such have great potential for use in biotechnology and pharmaceutical applications. However, details of their catalytic cycles and the origins surrounding the regio- and chemoselectivity of enzymatic reaction processes remain unknown, which makes the engineering of enzymes and their use in biotechnology challenging. Computational modelling can assist experimental work in the field and establish the factors that influence the reaction rates and the product distributions. A popular approach in modelling is the use of quantum mechanical cluster models of enzymes that take the first- and second coordination sphere of the enzyme active site into consideration. These QM cluster models are widely applied but often the results obtained are dependent on model choice and model selection. Herein, we show that QM cluster models can give highly accurate results that reproduce experimental product distributions and free energies of activation within several kcal mol-1, regarded that large cluster models with >300 atoms are used that include key hydrogen bonding interactions and charged residues. In this tutorial review, we give general guidelines on the set-up and applications of the QM cluster method and discuss its accuracy and reproducibility. Finally, several representative QM cluster model examples on metal-containing enzymes are presented, which highlight the strength of the approach.

Synthesis of metallic nanoparticles using biometabolites: mechanisms and applications

Bio-metabolites have played a crucial role in the recent green synthesis of nanoparticles, resulting in more versatile, safer, and effective nanoparticles. Various primary and secondary metabolites, such as proteins, carbohydrates, lipids, nucleic acids, enzymes, vitamins, organic acids, alkaloids, flavonoids, and terpenes, have demonstrated strong metal reduction and stabilization properties that can be utilized to synthesize nanomaterials and influence their characters. While physical and chemical methods were previously used to synthesize these nanomaterials, their drawbacks, including high energy consumption, elevated cost, lower yield, and the use of toxic chemicals, have led to a shift towards eco-friendly, rapid, and efficient alternatives. Biomolecules act as reducing agents through deprotonation, nucleophilic reactions, transesterification reactions, ligand binding, and chelation mechanisms, which help sequester metal ions into stable metal nanoparticles (NPs). Engineered NPs have potential applications in various fields due to their optical, electronic, and magnetic properties, offering improved performance compared to bulkier counterparts. NPs can be used in medicine, food and agriculture, chemical catalysts, energy harvesting, electronics, etc. This review provides an overview of the role of primary and secondary metabolites in creating effective nanostructures and their potential applications.

β-Lactoglobulin Enhances Clay and Activated Carbon Binding and Protection Properties for Cadmium and Lead

The removal of heavy metals from wastewater remains a challenge due to the limitations of current remediation methods. This study aims to develop multicomponent composites as inexpensive and environmentally friendly sorbents with enhanced capture of cadmium (Cd) and lead (Pb). The composites are based on calcium montmorillonite (CM) and activated carbon (AC) because of their proven effectiveness as sorbents for diverse toxins in environmental settings. In this study, we used a combination of computational and experimental methods to delineate that β-lactoglobulin enhances CM and AC binding and protection properties for Cd and Pb. Modeling and molecular dynamics simulations investigated the formation of material systems formed by CM and AC in complex with β-lactoglobulin and predicted their capacity to bind heavy metal ions at neutral pH conditions. Our simulations suggest that the enhanced binding properties of the material systems are attributed to the presence of several binding pockets formed by β-lactoglobulin for the two heavy metal ions. At neutral pH conditions, divalent Cd and Pb shared comparable binding propensities in all material systems, with the former being consistently higher than the latter. To validate the interactions depicted in simulations, two ecotoxicological models (L. minor and H. vulgaris) were exposed to Cd, Pb, and a mixture of the two. The inclusion of CM-lactoglobulin (β-lactoglobulin amended CM) and AC-lactoglobulin (β-lactoglobulin amended AC) at 0.05-0.2% efficiently and dose-dependently reduced the severe toxicity of metals and increased the growth parameters. This high efficacy of protection shown in the ecotoxicological models may result from the numerous possible interaction pockets of the β-lactoglobulin-amended materials depicted in simulations. The ecotoxicological models support the agreement with computations. This study serves as a proof of concept on how computations in tandem with experiments can be used in the design of multicomponent clay- and carbon-based sorbent amended systems with augmented functionalities for particular toxins.

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.

Microbes vary strategically in their metalation of mononuclear enzymes

Studies have determined that nonredox enzymes that are cofactored with Fe(II) are the most oxidant-sensitive targets inside Escherichia coli. These enzymes use Fe(II) cofactors to bind and activate substrates. Because of their solvent exposure, the metal can be accessed and oxidized by reactive oxygen species, thereby inactivating the enzyme. Because these enzymes participate in key physiological processes, the consequences of stress can be severe. Accordingly, when E. coli senses elevated levels of H2O2, it induces both a miniferritin and a manganese importer, enabling the replacement of the iron atom in these enzymes with manganese. Manganese does not react with H2O2 and thereby preserves enzyme activity. In this study, we examined several diverse microbes to identify the metal that they customarily integrate into ribulose-5-phosphate 3-epimerase, a representative of this enzyme family. The anaerobe Bacteroides thetaiotaomicron, like E. coli, uses iron. In contrast, Bacillus subtilis and Lactococcus lactis use manganese, and Saccharomyces cerevisiae uses zinc. The latter organisms are therefore well suited to the oxidizing environments in which they dwell. Similar results were obtained with peptide deformylase, another essential enzyme of the mononuclear class. Strikingly, heterologous expression experiments show that it is the metal pool within the organism, rather than features of the protein itself, that determine which metal is incorporated. Further, regardless of the source organism, each enzyme exhibits highest turnover with iron and lowest turnover with zinc. We infer that the intrinsic catalytic properties of the metal cannot easily be retuned by evolution of the polypeptide.

Distal Mutations in the β-Clamp of DNA Polymerase III* Disrupt DNA Orientation and Affect Exonuclease Activity

DNA polymerases are responsible for the replication and repair of DNA found in all DNA-based organisms. DNA Polymerase III is the main replicative polymerase of E. coli and is composed of over 10 proteins. A subset of these proteins (Pol III*) includes the polymerase (α), exonuclease (ϵ), clamp (β), and accessory protein (θ). Mutations of residues in, or around the active site of the catalytic subunits (α and ϵ), can have a significant impact on catalysis. However, the effects of distal mutations in noncatalytic subunits on the activity of catalytic subunits are less well-characterized. Here, we investigate the effects of two Pol III* variants, β-L82E/L82'E and β-L82D/L82'D, on the proofreading reaction catalyzed by ϵ. MD simulations reveal major changes in the dynamics of Pol III*, which extend throughout the complex. These changes are mostly induced by a shift in the position of the DNA substrate inside the β-clamp, although no major structural changes are observed in the protein complex. Quantum mechanics/molecular mechanics (QM/MM) calculations indicate that the β-L82D/L82'D variant has reduced catalytic proficiency due to highly endoergic reaction energies resulting from structural changes in the active site and differences in the electric field at the active site arising from the protein and substrate. Conversely, the β-L82E/L82'E variant is predicted to maintain proofreading activity, exhibiting a similar reaction barrier for nucleotide excision compared with the WT system. However, significant differences in the reaction mechanism are obtained due to the changes induced by the mutations on the β-clamp.

Contrasting Effects of Inhibitors Li+ and Be2+ on Catalytic Cycle of Glycogen Synthase Kinase-3β

Ionic lithium shows rare effectiveness for treating bipolar disorder and is a potential drug for neurodegenerative diseases. Unfortunately, lithium suffers from significant drawbacks, mainly a narrow therapeutic window. Among the targets of lithium, glycogen synthase kinase 3β (GSK-3β) may be responsible for its therapeutic effects. The development of alternative, selective inhibitors of this kinase could prevent lithium side effects, but such efforts have met little success so far. An atomistic understanding of Li⁺ inhibition and the GSK-3β phosphorylation reaction would therefore facilitate the development of new drugs. In this study, we use extensive sampling of catalytic states with our mixed quantum-classical dynamics method QM/DMD and binding affinities from a competitive metal affinity (CMA) approach to expand the atomistic picture of Li⁺ GSK-3β inhibition. We compare Li⁺ action with Be²⁺ and find our results in agreement with in vitro kinetics studies. Ultimately, our simulations show that Li⁺ inhibition is driven by decreasing the phosphorylation reaction rate, rather than reducing catalytic turnover through tight binding to different GSK-3β states like Be²⁺ inhibition. The effect of these metals derive from electrostatic differences and especially their smaller atomic radii compared to the native Mg²⁺ and thus provide insight for the development of GSK-3β inhibitors based on other paradigms.

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.

Theoretical characterization of Al(III) binding to KSPVPKSPVEEKG: Insights into the propensity of aluminum to interact with key sequences for neurofilament formation

Classical molecular dynamic simulations and density functional theory are used to unveil the interaction of aluminum with various phosphorylated derivatives of the fragment KSPVPKSPVEEKG (NF13), a major multiphosphorylation domain of human neurofilament medium (NFM). Our calculations reveal the rich coordination chemistry of the resultant structures with a clear tendency of aluminum to form multidentate structures, acting as a bridging agent between different sidechains and altering the local secondary structure around the binding site. Our evaluation of binding energies allows us to determine that phosphorylation has an increase in the affinity of these peptides towards aluminum, although the interaction is not as strong as well-known chelators of aluminum in biological systems. Finally, the presence of hydroxides in the first solvation layer has a clear damping effect on the binding affinities. Our results help in elucidating the potential structures than can be formed between this exogenous neurotoxic metal and key sequences for the formation of neurofilament tangles, which are behind of some of the most important degenerative diseases.

Exploring the Mechanism of Catalysis with the Unified Reaction Valley Approach (URVA)—A Review

The unified reaction valley approach (URVA) differs from mainstream mechanistic studies, as it describes a chemical reaction via the reaction path and the surrounding reaction valley on the potential energy surface from the van der Waals region to the transition state and far out into the exit channel, where the products are located. The key feature of URVA is the focus on the curving of the reaction path. Moving along the reaction path, any electronic structure change of the reacting molecules is registered by a change in their normal vibrational modes and their coupling with the path, which recovers the curvature of the reaction path. This leads to a unique curvature profile for each chemical reaction with curvature minima reflecting minimal change and curvature maxima, the location of important chemical events such as bond breaking/forming, charge polarization and transfer, rehybridization, etc. A unique decomposition of the path curvature into internal coordinate components provides comprehensive insights into the origins of the chemical changes taking place. After presenting the theoretical background of URVA, we discuss its application to four diverse catalytic processes: (i) the Rh catalyzed methanol carbonylation—the Monsanto process; (ii) the Sharpless epoxidation of allylic alcohols—transition to heterogenous catalysis; (iii) Au(I) assisted [3,3]-sigmatropic rearrangement of allyl acetate; and (iv) the Bacillus subtilis chorismate mutase catalyzed Claisen rearrangement—and show how URVA leads to a new protocol for fine-tuning of existing catalysts and the design of new efficient and eco-friendly catalysts. At the end of this article the pURVA software is introduced. The overall goal of this article is to introduce to the chemical community a new protocol for fine-tuning existing catalytic reactions while aiding in the design of modern and environmentally friendly catalysts.