Catalytic Mechanism Of Enzymes Research Articles

Unraveling the mechanism: How does β-phosphoglucomutase from the haloacid dehalogenase superfamily catalyze the interconversion of β-d-glucose 1-phosphate and β-d-glucose 6-phosphate? A chemical perspective

The catalytic mechanisms of enzymes can be phylogenetically mapped corresponding to their catalytic structures. This mapping effectively elucidates the diversity of enzyme catalytic mechanisms and the emergence of new enzymatic activities within enzyme superfamilies. The haloacid dehalogenase (HAD) superfamily serves as an exemplary model system for comprehending the co-evolution of catalytic structures and mechanisms. This study delves into the mechanism underlying the functional divergence of β-phosphoglucomutase (β-PGM) from the phosphatase branch of the HAD superfamily, employing a chemical perspective. Through the construction and calculation of three models of varying scales using the Density Functional Theory method with B3LYP function, we aim to investigate the chemical mechanism driving this functional divergence of β-PGM from the HAD family. The computational results indicate that residues His20 and Lys76 in the second shell stabilize substrates and enhance the acid-base catalytic ability of Asp10. Additionally, residues Arg49, Ser116 and Asn118 facilitate substrate binding by engaging in close hydrogen bonding interactions with the substrates. Through cooperative action, these residues enable β-PGM to function as an efficient phosphoglucomutase. Through computational modeling and a chemical perspective, we unravel the mechanisms enabling β-PGM to convert β-d-glucose 1-phosphate to β-d-glucose 6-phosphate. Finally, based on the analysis of the evolutionary tree, we discussed and summarized the evolutionary relationships among different forms of metal cores of hydrolases.

"Seeing Is Believing": How Neutron Crystallography Informs Enzyme Mechanisms by Visualizing Unique Water Species.

Hydrogen is the lightest atom and composes approximately half of the atomic content in macromolecules, yet their location can only be inferred or predicted in most macromolecular structures. This is because hydrogen can rarely be directly observed by the most common structure determination techniques (such as X-ray crystallography and electron cryomicroscopy). However, knowledge of hydrogen atom positions, especially for enzymes, can reveal protonation states of titratable active site residues, hydrogen bonding patterns, and the orientation of water molecules. Though we know they are present, this vital layer of information, which can inform a myriad of biological processes, is frustratingly invisible to us. The good news is that, even at modest resolution, neutron crystallography (NC) can reveal this layer and has emerged this century as a powerful tool to elucidate enzyme catalytic mechanisms. Due to its strong and coherent scattering of neutrons, incorporation of deuterium into the protein crystal amplifies the power of NC. This is especially true when solvation and the specific participation of key water molecules are crucial for catalysis. Neutron data allow the modeling of all three atoms in water molecules and have even revealed previously unobserved and unique species such as hydronium (D3O+) and deuteroxide (OD-) ions as well as lone deuterons (D+). Herein, we briefly review why neutrons are ideal probes for identifying catalytically important water molecules and these unique water-like species, limitations in interpretation, and four vignettes of enzyme success stories from disparate research groups. One of these groups was that of Dr. Chris G. Dealwis, who died unexpectedly in 2022. As a memorial appreciation of his scientific career, we will also highlight his interest and contributions to the neutron crystallography field. As both the authors were mentored by Chris, we feel we have a unique perspective on his love of molecular structure and admiration for neutrons as a tool to query those structures.

Exploring the factors influencing the ketoenamine-enolimine tautomeric equilibrium of pyridoxal 5′-phosphate in branched-chain aminotransferases

Pyridoxal 5′-phosphate (PLP), the active form of vitamin B6, is a critical coenzyme for various enzymes. It generates a Schiff base with the substrate and exhibits ketoenamine and enolimine tautomeric forms due to intramolecular proton transfer. This study aims to ascertain the predominant tautomeric form of the PLP Schiff base in MtIlvE and analyze its influencing factors. Molecular dynamics simulations indicate that the ketoenamine tautomer has higher binding free energy than the enolimine tautomer. Density functional theory calculations suggest that, despite their ability to interconvert at a relatively low energy barrier, the ketoenamine tautomer is thermodynamically more stable. Factors affecting the keto-enol tautomeric equilibrium were investigated by constructing various QM-cluster models. Our results demonstrate that both the protonation of the pyridine nitrogen and the presence of Tyr209, which stabilizes the O3 anion, shift the tautomeric equilibrium toward the ketoenamine configuration. These findings provide a theoretical basis for investigating enzyme catalytic mechanisms.

Enzyme Tunnel Dynamics and Catalytic Mechanism of Norcoclaurine Synthase: Insights from a Combined LiGaMD and DFT Study.

This study conducts a systematic investigation into the catalytic mechanism of norcoclaurine synthase (NCS), a key enzyme in the biosynthesis of tetrahydroisoquinolines (THIQs) with therapeutic applications. By integration of LiGaMD and DFT calculations, the reaction pathway of NCS is mapped, providing detailed insights into its catalytic activity and selectivity. Our findings underscore the critical role of E103 in substrate capture and reveal the hitherto unappreciated influence of nonpolar residues M183 and L76 on tunnel dynamics. A prominent discovery is the identification of a high-energy barrier (44.2 kcal/mol) associated with the aromatic electrophilic attack, which pinpoints the rate-limiting step. Moreover, we disclose the existence of dual transition states leading to different products with the energetically favored six-membered ring formation consistent with experimental evidence. These mechanistic revelations not only refine our understanding of NCS but also advocate for a renewed emphasis on enzyme tunnel engineering for optimizing THIQs biosynthesis. The research sets the stage for translating these findings into practical enzyme modifications. Our results highlight the potential of NCS as a biocatalyst to overcome the limitations of current synthetic methodologies, such as low yields and environmental impacts, and provide a theoretical contribution to the efficient, eco-friendly production of THIQs-based pharmaceuticals.

Identify Regioselective Residues of Ginsenoside Hydrolases by Graph-Based Active Learning from Molecular Dynamics.

Identifying the catalytic regioselectivity of enzymes remains a challenge. Compared to experimental trial-and-error approaches, computational methods like molecular dynamics simulations provide valuable insights into enzyme characteristics. However, the massive data generated by these simulations hinder the extraction of knowledge about enzyme catalytic mechanisms without adequate modeling techniques. Here, we propose a computational framework utilizing graph-based active learning from molecular dynamics to identify the regioselectivity of ginsenoside hydrolases (GHs), which selectively catalyze C6 or C20 positions to obtain rare deglycosylated bioactive compounds from Panax plants. Experimental results reveal that the dynamic-aware graph model can excellently distinguish GH regioselectivity with accuracy as high as 96-98% even when different enzyme-substrate systems exhibit similar dynamic behaviors. The active learning strategy equips our model to work robustly while reducing the reliance on dynamic data, indicating its capacity to mine sufficient knowledge from short multi-replica simulations. Moreover, the model's interpretability identified crucial residues and features associated with regioselectivity. Our findings contribute to the understanding of GH catalytic mechanisms and provide direct assistance for rational design to improve regioselectivity. We presented a general computational framework for modeling enzyme catalytic specificity from simulation data, paving the way for further integration of experimental and computational approaches in enzyme optimization and design.

Crystal structure of activating sulfotransferase SgdX2 involved in biosynthesis of secondary metabolite sungeidine

Microorganisms synthesize a plethora of complex secondary metabolites, many of which are beneficial to human health, such as anticancer agents and antibiotics. Among these, the Sungeidines are a distinct class of secondary metabolites known for their bulky and intricate structures. They are produced by a specific biosynthetic gene cluster within the genome of the soil-dwelling actinomycete Micromonospora sp. MD118. A notable enzyme in the Sungeidine biosynthetic pathway is the activating sulfotransferase SgdX2. In this pathway, SgdX2 mediates a key sulfation step, after which the product undergoes spontaneous dehydration to yield a Sungeidine compound. To delineate the structural basis for SgdX2's substrate recognition and catalytic action, we have determined the crystal structure of SgdX2 in complex with its sulfate donor product, 3′-phosphoadenosine 5′-phosphate (PAP), at a resolution of 1.6 Å. Although SgdX2 presents a compact overall structure, its core elements are conserved among other activating sulfotransferases. Our structural analysis reveals a unique substrate-binding pocket that accommodates bulky, complex substrates, suggesting a specialized adaptation for Sungeidine synthesis. Moreover, we have constructed a substrate docking model that provides insights into the molecular interactions between SgdX2 and Sungeidine F, enhancing our understanding of the enzyme's specificity and catalytic mechanism. The model supports a general acid-base catalysis mechanism, akin to other sulfotransferases, and underscores the minor role of disordered regions in substrate recognition. This integrative study of crystallography and computational modeling advances our knowledge of microbial secondary metabolite biosynthesis and may facilitate the development of novel biotechnological applications.

Methods used to determine the structure of the oxygenase component of naphthalene 1,2 dioxygenase.

Rieske non-heme iron oxygenases are ubiquitously expressed in prokaryotes. These enzymes catalyze a wide variety of reactions, including cis-dihydroxylation, mono-hydroxylation, sulfoxidation, and demethylation. They contain a Rieske-type [2Fe-2S] cluster and an active site with a mono-nuclear iron bound to a 2-His carboxylate triad. Naphthalene 1,2 dioxygenase, a representative of this family, catalyzes the conversion of naphthalene to (+)-cis-(1R,2S)-dihydroxy-1,2-dihydronaphthalene. This transformation requires naphthalene, two electrons, and an oxygen molecule. The first structure of the terminal oxygenase component of a Rieske non-heme iron oxygenase to be determined was naphthalene 1,2 dioxygenase (NDO-O). In this article, we describe in detail the methods used to recombinantly express and purify NDO-O in rich and minimal salts media, the crystallization of NDO-O for structure determination by X-ray crystallography, the challenges faced, and the methods used for the preparation of enzyme ligand complexes. The methods used here resulted in the determination of several NDO-O complexes with aromatic substrates, nitric oxide, oxygen molecule, and products, leading to an initial understanding of the mechanism of enzyme catalysis and the molecular determinants of the regio- and stereo-specificity of this class of enzymes.

Design and Mechanism Insight of Monodispersed AuCuPt Alloy Nanozyme with Antitumor Activity.

The abrogation of the self-adaptive redox evolution of tumors is promising for improving therapeutic outcomes. In this study, we designed a trimetallic alloy nanozyme AuCuPt-PpIX (ACPP), which mimics up to five naturally occurring enzymes: glucose oxidase (GOD), superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and glutathione peroxidase (GPx). Facilitated by these enzyme-mimicking traits, the constructed ACPP nanozymes can not only disrupt the established redox homeostasis in tumors through a series of enzymatic cascade reactions but also achieve cyclic regeneration of the relevant enzyme substrates. Density functional theory (DFT) calculations have theoretically explained the synergistic effect of multimetallic doping and the possible mechanism of enzymatic catalysis. The doped Cu and Pt sites are conducive to the adsorption, activation, and dissociation of reactant molecules, whereas the Au sites are conducive to desorption, which significantly improves catalytic efficiency via a synergistic effect. Additionally, ACPP nanozymes can improve the effect of protoporphyrin (PpIX)-enabled sonodynamic therapy (SDT) by alleviating hypoxia and initiating ferroptosis by inducing lipid peroxidation (LPO) and inhibiting GPX4 activity, thus achieving multimodal synergistic therapy. This study presents a typical paradigm to enable the use of multimetallic alloy nanozymes for the treatment of tumor cells with self-adaptive properties.

A semiempirical method optimized for modeling proteins

ContextIn recent years, semiempirical methods such as PM6, PM6-D3H4, and PM7 have been increasingly used for modeling proteins, in particular enzymes. These methods were designed for more general use, and consequently were not optimized for studying proteins. Because of this, various specific errors have been found that could potentially cast doubt on the validity of these methods for modeling phenomena of biochemical interest such as enzyme catalytic mechanisms and protein-ligand interactions. To correct these and other errors, a new method specifically designed for use in organic and biochemical modeling has been developed.MethodsTwo alterations were made to the procedures used in developing the earlier PMx methods. A minor change was made to the theoretical framework, which affected only the non-quantum theory interatomic interaction function, while the major change involved changing the training set for optimizing parameters, moving the focus to systems of biochemical significance. This involved both the selection of reference data and the weighting factors, i.e., the relative importance that the various data were given. As a result of this change of focus, the accuracy in prediction of heats of formation, hydrogen bonding, and geometric quantities relating to non-covalent interactions in proteins was improved significantly.

Structure-Guided Evolution Modulate Alcohol Oxidase to Improve Ethanol Oxidation Performance.

A high ethanol usage of alcohol oxidase (AOX) was required in industry. In this study, a "expand substrate pocket" strategy achieved a high activity AOX from Hansenula polymorpha (H. polymorpha) by Phe to Val residue (F/V) site-directed mutation to enlarge ethanol channel. Although H. Polymorpha AOX (HpAOX) possessed respectively 71.3% and 76.1% similarity with AOX (PpAOX) from Pichia pastoris (P. pastoris) in DNA and protein sequences, their active site structures including catalytic site and substrate channel were similar according to computer-aided analysis. After 3D structure analysis, Phe99 residue of their substrate channels was the most important residue to impact enzyme activity because of its large aromatic side chains. F99V mutation of HpAOX (HpAOXF99V) was designed and executed based on the enzyme catalytic mechanism and molecular computation in order to allow more larger size ethanol into active site. The highest enzyme activity of the fourth strains of HpAOXF99V mutant strain exhibited 12.06-folds increase than that of the host GS115 strain. Furthermore, kinetic studies indicated that the HpAOXF99V significantly promoted catalytic efficiency of ethanol than HpAOX, including Km, Vmax, kcat and kcat/Km. We also provided a new insight that the cofactor FAD irritated both active AOX octamer biosynthesis production and enzyme-catalysed ability due to help enzyme assembly and redox potential.

Photoenzymatic Decarboxylation to Produce Hydrocarbon Fuels: A Critical Review.

Photoenzymatic decarboxylation shows great promise as a pathway for the generation of hydrocarbon fuels. CvFAP, which is derived from Chlorella variabilis NC64A, is a photodecarboxylase capable of converting fatty acids into hydrocarbons. CvFAP is an example of coupling biocatalysis and photocatalysis to produce alkanes. The catalytic process is mild, and it does not yield toxic substances or excess by-products. However, the activity of CvFAP can be readily inhibited by several factors, and further enhancement is required to improve the enzyme yield and stability. In this article, we will examine the latest advancements in CvFAP research, with a particular focus on the enzyme's structural and catalytic mechanism, summarized some limitations in the application of CvFAP, and laboratory-level methods for enhancing enzyme activity and stability. This review can serve as a reference for future large-scale industrial production of hydrocarbon fuels.

Unravelling the complexity of enzyme catalysis

The study of enzymes never disappoints. Despite its long history-almost 150 years following the first documented use of the word enzyme in 1878-the field of enzymology advances apace. This long journey has witnessed landmark developments that have defined modern enzymology as a broad discipline, leading to improved understanding at the molecular level, as we aspire to discover the complex relationships between enzyme structures, catalytic mechanisms and biological function. How enzymes are regulated at the gene and post-translational levels and how catalytic activity is modulated by interactions with small ligands and macromolecules, or the broader enzyme environment, are topical areas of study. Insights from such studies guide the exploitation of natural and engineered enzymes in biomedical or industrial processes; for example, in diagnostics, pharmaceuticals manufacture and processing technologies that use immobilised enzymes and enzyme reactor-based systems. In this Focus Issue, The FEBS Journal seeks to highlight breaking science and informative reviews, as well as personal reflections, to illustrate the breadth and importance of contemporary molecular enzymology research.

Binding order and apparent binding affinity in the bisubstrate activity of strictosidine synthase

The Rauvolfia serpentina strictosidine synthase (RsSTR) enzyme with a bisubstrate activity is central to monoterpenoid indole alkaloid (MIA) biosynthesis pathways, as it stereoselectively condenses the terpenoid and indole metabolites, secologanin and tryptamine, respectively, into strictosidine. Here, cooperativity was aimed to be deciphered by proxy with help of a non-substrate tryptamine analog (decoy compound) to allow a bisubstrate binding without reaction, facilitating an isothermal titration calorimetry (ITC)-based analysis of the effect of the presence of one substrate on the binding of the other. Tryptamine and tryptamine analog bound to RsSTR with similar binding affinities (Kd). On the contrary, ITC revealed an exothermic titration of secologanin to RsSTR but could not fully quantify it because of weak binding. Interestingly, secologanin bound to RsSTR with an apparent binding affinity (Kd,app) of 212.1 μM in the presence of the decoy compound, as opposed to a lack of binding to RsSTR alone, strongly suggesting a “tryptamine-first” mode of binding. Conversely, binding of tryptamine analog in the presence of secologanin was enhanced >3-fold. Further, molecular dynamics simulation (MDS) analyses revealed the conformational flexibility needed for such cooperativity. Our binding studies complemented with the computational analyses suggested cooperativity in the ordered bisubstrate binding to RsSTR. Therefore, understanding thermodynamics and cooperativity in the binding of substrates or ligands would help to unravel the mechanism of enzyme catalysis and ligand-receptor interactions, and would guide the redesign of enzymes for enhanced properties and the design of inhibitors against enzymes and receptors. Communicated by Ramaswamy H. Sarma

Assessing the Extent of Potential Inversion by Cyclic Voltammetry: Theory, Pitfalls, and Application to a Nickel Complex with Redox-Active Iminosemiquinone Ligands.

Potential inversion refers to the situation where a protein cofactor or a synthetic molecule can be oxidized or reduced twice in a cooperative manner; that is, the second electron transfer is easier than the first. This property is very important regarding the catalytic mechanism of enzymes that bifurcate electrons and the properties of bidirectional redox molecular catalysts that function in either direction of the reaction with no overpotential. Cyclic voltammetry is the most common technique for characterizing the thermodynamics and kinetics of electron transfer to or from these molecules. However, a gap in the literature is the absence of analytical predictions to help interpret the values of the voltammetric peak potentials when potential inversion occurs; the cyclic voltammograms are therefore often analyzed by simulating the data, with no discussion of the possibility of overfitting and often no estimation of the error on the determined parameters. Here we formulate the theory for the voltammetry of freely diffusing or surface-confined two-electron redox species in the experimentally relevant irreversible limit where the peak separation depends on the scan rate. We explain why the model is intrinsically underdetermined, and we illustrate this conclusion by analysis of the voltammetry of a nickel complex with redox-active iminosemiquinone ligands. Being able to characterize the thermodynamics of two-electron electron-transfer reactions will be crucial for designing more efficient catalysts.

Rational design of enzyme activity and enantioselectivity.

The strategy of rational design to engineer enzymes is to predict the potential mutants based on the understanding of the relationships between protein structure and function, and subsequently introduce the mutations using the site-directed mutagenesis. Rational design methods are universal, relatively fast and have the potential to be developed into algorithms that can quantitatively predict the performance of the designed sequences. Compared to the protein stability, it was more challenging to design an enzyme with improved activity or selectivity, due to the complexity of enzyme molecular structure and inadequate understanding of the relationships between enzyme structures and functions. However, with the development of computational force, advanced algorithm and a deeper understanding of enzyme catalytic mechanisms, rational design could significantly simplify the process of engineering enzyme functions and the number of studies applying rational design strategy has been increasing. Here, we reviewed the recent advances of applying the rational design strategy to engineer enzyme functions including activity and enantioselectivity. Five strategies including multiple sequence alignment, strategy based on steric hindrance, strategy based on remodeling interaction network, strategy based on dynamics modification and computational protein design are discussed and the successful cases using these strategies are introduced.

Ensemble-function relationships to dissect mechanisms of enzyme catalysis.

Decades of structure-function studies have established our current extensive understanding of enzymes. However, traditional structural models are snapshots of broader conformational ensembles of interchanging states. We demonstrate the need for conformational ensembles to understand function, using the enzyme ketosteroid isomerase (KSI) as an example. Comparison of prior KSI cryogenic x-ray structures suggested deleterious mutational effects from a misaligned oxyanion hole catalytic residue. However, ensemble information from room-temperature x-ray crystallography, combined with functional studies, excluded this model. Ensemble-function analyses can deconvolute effects from altering the probability of occupying a state (P-effects) and changing the reactivity of each state (k-effects); our ensemble-function analyses revealed functional effects arising from weakened oxyanion hole hydrogen bonding and substrate repositioning within the active site. Ensemble-function studies will have an integral role in understanding enzymes and in meeting the future goals of a predictive understanding of enzyme catalysis and engineering new enzymes.

The long-awaited structure of human fucosidase FucA1 opens novel avenues for the treatment of fucosidosis

In this issue of Structure, Armstrong and colleagues probe the structure of human fucosidase FucA1. Their work resolves an ongoing debate around the enzyme's catalytic mechanism and provides a valid structural template to guide the design of drugs alleviating the rare, yet severe, lysosomal storage disease fucosidosis.

EnzyHTP: A High-Throughput Computational Platform for Enzyme Modeling.

Molecular simulations, including quantum mechanics (QM), molecular mechanics (MM), and multiscale QM/MM modeling, have been extensively applied to understand the mechanism of enzyme catalysis and to design new enzymes. However, molecular simulations typically require specialized, manual operation ranging from model construction to data analysis to complete the entire life cycle of enzyme modeling. The dependence on manual operation makes it challenging to simulate enzymes and enzyme variants in a high-throughput fashion. In this work, we developed a Python software, EnzyHTP, to automate molecular model construction, QM, MM, and QM/MM computation, and analyses of modeling data for enzyme simulations. To test the EnzyHTP, we used fluoroacetate dehalogenase (FAcD) as a model system and simulated the enzyme interior electrostatics for 100 FAcD mutants with a random single amino acid substitution. For each enzyme mutant, the workflow involves structural model construction, 1 ns molecular dynamics (MD) simulations, and quantum mechanical calculations in 100 MD-sampled snapshots. The entire simulation workflow for 100 mutants was completed in 7 h with 10 GPUs and 160 CPUs. EnzyHTP improves the efficiency of computational enzyme modeling, setting a basis for high-throughput identification of function-enhancing enzymes and enzyme variants. The software is expected to facilitate the fundamental understanding of catalytic origins across enzyme families and to accelerate the optimization of biocatalysts for non-native substrates.

Structure and Cooperativity in Substrate-Enzyme Interactions: Perspectives on Enzyme Engineering and Inhibitor Design.

Enzyme-based synthetic chemistry provides a green way to synthesize industrially important chemical scaffolds and provides incomparable substrate specificity and unmatched stereo-, regio-, and chemoselective product formation. However, using biocatalysts at an industrial scale has its challenges, like their narrow substrate scope, limited stability in large-scale one-pot reactions, and low expression levels. These limitations can be overcome by engineering and fine-tuning these biocatalysts using advanced protein engineering methods. A detailed understanding of the enzyme structure and catalytic mechanism and its structure-function relationship, cooperativity in binding of substrates, and dynamics of substrate-enzyme-cofactor complexes is essential for rational enzyme engineering for a specific purpose. This Review covers all these aspects along with an in-depth categorization of various industrially and pharmaceutically crucial bisubstrate enzymes based on their reaction mechanisms and their active site and substrate/cofactor-binding site structures. As the bisubstrate enzymes constitute around 60% of the known industrially important enzymes, studying their mechanism of actions and structure-activity relationship gives significant insight into deciding the targets for protein engineering for developing industrial biocatalysts. Thus, this Review is focused on providing a comprehensive knowledge of the bisubstrate enzymes' structure, their mechanisms, and protein engineering approaches to develop them into industrial biocatalysts.

Enzyme Inhibitors: The Best Strategy to Tackle Superbug NDM-1 and Its Variants.

Multidrug bacterial resistance endangers clinically effective antimicrobial therapy and continues to cause major public health problems, which have been upgraded to unprecedented levels in recent years, worldwide. β-Lactam antibiotics have become an important weapon to fight against pathogen infections due to their broad spectrum. Unfortunately, the emergence of antibiotic resistance genes (ARGs) has severely astricted the application of β-lactam antibiotics. Of these, New Delhi metallo-β-lactamase-1 (NDM-1) represents the most disturbing development due to its substrate promiscuity, the appearance of variants, and transferability. Given the clinical correlation of β-lactam antibiotics and NDM-1-mediated resistance, the discovery, and development of combination drugs, including NDM-1 inhibitors, for NDM-1 bacterial infections, seems particularly attractive and urgent. This review summarizes the research related to the development and optimization of effective NDM-1 inhibitors. The detailed generalization of crystal structure, enzyme activity center and catalytic mechanism, variants and global distribution, mechanism of action of existing inhibitors, and the development of scaffolds provides a reference for finding potential clinically effective NDM-1 inhibitors against drug-resistant bacteria.