Computational Enzymology Research Articles

GRACE: Generative Redesign in Artificial Computational Enzymology.

Designing de novo enzymes is complex and challenging, especially to maintain the activity. This research focused on motif design to identify the crucial domain in the enzyme and uncovered the protein structure by molecular docking. Therefore, we developed a Generative Redesign in Artificial Computational Enzymology (GRACE), which is an automated workflow for reformation and creation of the de novo enzymes for the first time. GRACE integrated RFdiffusion for structure generation, ProteinMPNN for sequence interpretation, CLEAN for enzyme classification, and followed by solubility analysis and molecular dynamic simulation. As a result, we selected two gene sequences associated with carbonic anhydrase from among 10,000 protein candidates. Experimental validation confirmed that these two novel enzymes, i.e., dCA12_2 and dCA23_1, exhibited favorable solubility, promising substrate-active site interactions, and achieved activity of 400 WAU/mL. This workflow has the potential to greatly streamline experimental efforts in enzyme engineering and unlock new avenues for rational protein design.

The influence of model building schemes and molecular dynamics sampling on QM-cluster models: the chorismate mutase case study.

Most QM-cluster models of enzymes are constructed based on X-ray crystal structures, which limits comparison to in vivo structure and mechanism. The active site of chorismate mutase from Bacillus subtilis and the enzymatic transformation of chorismate to prephenate is used as a case study to guide construction of QM-cluster models built first from the X-ray crystal structure, then from molecular dynamics (MD) simulation snapshots. The Residue Interaction Network ResidUe Selector (RINRUS) software toolkit, developed by our group to simplify and automate the construction of QM-cluster models, is expanded to handle MD to QM-cluster model workflows. Several options, some employing novel topological clustering from residue interaction network (RIN) information, are evaluated for generating conformational clustering from MD simulation. RINRUS then generates a statistical thermodynamic framework for QM-cluster modeling of the chorismate mutase mechanism via refining 250 MD frames with density functional theory (DFT). The 250 QM-cluster models sampled provide a mean ΔG‡ of 10.3 ± 2.6 kcal mol-1 compared to the experimental value of 15.4 kcal mol-1 at 25 °C. While the difference between theory and experiment is consequential, the level of theory used is modest and therefore "chemical" accuracy is unexpected. More important are the comparisons made between QM-cluster models designed from the X-ray crystal structure versus those from MD frames. The large variations in kinetic and thermodynamic properties arise from geometric changes in the ensemble of QM-cluster models, rather from the composition of the QM-cluster models or from the active site-solvent interface. The findings open the way for further quantitative and reproducible calibration in the field of computational enzymology using the model construction framework afforded with the RINRUS software toolkit.

Catalytic mechanism and pH dependence of a methyltransferase ribozyme (MTR1) from computational enzymology.

A methyltransferase ribozyme (MTR1) was selected in vitro to catalyze alkyl transfer from exogenous O6-methylguanine (O6mG) to a target adenine N1, and recently, high-resolution crystal structures have become available. We use a combination of classical molecular dynamics, ab initio quantum mechanical/molecular mechanical (QM/MM)and alchemical free energy (AFE) simulations to elucidate the atomic-levelsolution mechanism of MTR1. Simulations identify an active reactant state involving protonation of C10 that hydrogen bonds with O6mG:N1. The deduced mechanism involves a stepwise mechanism with two transition states corresponding to proton transfer from C10:N3 to O6mG:N1 and rate-controlling methyl transfer (19.4 kcal·mol-1 barrier). AFE simulations predict the pKa for C10 to be 6.3, close to the experimental apparent pKa of 6.2, further implicating it as a critical general acid. The intrinsic rate derived from QM/MM simulations, together with pKa calculations, enables us to predict an activity-pH profile that agrees well with experiment. The insights gained provide further support for a putative RNA world and establish new design principles for RNA-based biochemical tools.

Evaluating the active site-substrate interplay between x-ray crystal structure and molecular dynamics in chorismate mutase.

Designing realistic quantum mechanical (QM) models of enzymes is dependent on reliably discerning and modeling residues, solvents, and cofactors important in crafting the active site microenvironment. Interatomic van der Waals contacts have previously demonstrated usefulness toward designing QM-models, but their measured values (and subsequent residue importance rankings) are expected to be influenceable by subtle changes in protein structure. Using chorismate mutase as a case study, this work examines the differences in ligand-residue interatomic contacts between an x-ray crystal structure and structures from a molecular dynamics simulation. Select structures are further analyzed using symmetry adapted perturbation theory to compute ab initio ligand-residue interaction energies. The findings of this study show that ligand-residue interatomic contacts measured for an x-ray crystal structure are not predictive of active site contacts from a sampling of molecular dynamics frames. In addition, the variability in interatomic contacts among structures is not correlated with variability in interaction energies. However, the results spotlight using interaction energies to characterize and rank residue importance in future computational enzymology workflows.

Challenges in Protein QM/MM Simulations with Intra-Backbone Link Atoms.

Hybrid quantum mechanical/molecular mechanical (QM/MM) simulations fuel discoveries in many fields of science including computational biochemistry and enzymology. Development of more convenient tools leads to an increase in the number of works in which mechanical insights into enzymes' mode of operation are obtained. Most commonly, these tools feature hydrogen-capping (link atom) approach to provide coupling between QM and MM subsystems across a covalent bond. Extensive studies were conducted to provide a solid foundation for the correctness of such an approach when a bond to a nonpolar MM atom is considered. However, not every task may be accomplished this way. Certain scenarios of using QM/MM in computational enzymology encourage or even necessitate the incorporation of backbone atoms into the QM region. Two out of three backbone atoms are polar, and in QM/MM with electrostatic embedding, a neighboring link atom will be hyperpolarized. Several schemes to mitigate this effect were previously proposed alongside a rigorous assessment of quantitative effects on model systems. However, it was not clear whether they may translate into qualitatively different results and how link atom hyperpolarization may manifest itself in a real-life enzymological scenario. Here, we show that the consequences of such an artifact may be severe and may completely overturn the conclusions drawn from the simulations. Our case advocates for the use of charge redistribution schemes whenever intra-backbone QM/MM boundaries are considered. Moreover, we addressed how different boundary types and charge redistribution schemes influence backbone dynamics. We showed that the results are heavily dependent on which boundary MM terms are retained, with charge alteration being of secondary importance. In the worst case, only three intra-backbone boundaries may be used with relative confidence in the adequacy of resulting simulations, irrespective of the hyperpolarization mitigation scheme. Thus, advances in the field are certainly needed to fuel new discoveries. As of now, we believe that issues raised in this work might encourage authors in the field to report what boundaries, boundary MM terms, and charge redistribution schemes they are using, so their results may be correctly interpreted.

QM/MM Studies on Enzyme Catalysis and Insight into Designing of New Inhibitors by ONIOM Approach: Recent Update

AbstractComputational enzymology is a rapidly developing area that uniquely provides deep insight into the fundamental processes of biological catalysis at the atomic level. Such in‐depth insight can ultimately be employed in designing potential inhibitors against the targets of interest. Computational enzymology covers a wide range of in‐silico approaches for investigating the enzyme‐catalyzed reaction mechanisms, among which combined quantum mechanics (QM) /molecular mechanics (MM) approaches have gained a lot of attention nowadays. This advanced approach generally involves a QM method (i. e. a method that estimates the electronic structure of the active site) and a simpler MM method (a method that includes the enzyme environment) to understand the enzymatic reactions. The QM/MM method has been widely tested in understanding the molecular mechanisms both at the structural and energetic levels and observed to best correlate with experimental studies of the enzymatic mechanism. It proposes a new mechanism that ultimately opens a new route for designing new potent, efficacious enzyme inhibitors. This review mainly covers wide applications of the ONIOM (Our own N‐layer Integrated molecular Orbital Molecular mechanics) method for decoding the enzymatic catalysis mechanism or designing potential small molecule inhibitors as treatment therapeutics in terms of free energy profiles. Moreover, this article also highlights employing QM/MM method in comprehending the mechanisms for drug metabolism and resistance (owing to mutations). This write‐up may encourage medicinal chemists and molecular biologists to explore this approach to propose more promising therapeutics to improve the quality of treatment.

Multiscale Computational Study on the Catalytic Mechanism of the Nonmetallo Amidase Maleamate Amidohydrolase (NicF).

Maleamate amidohydrolase (NicF) is a key enzyme in vitamin B3 metabolism that catalyzes the hydrolysis of maleamate to produce maleic acid and ammonia. Unlike most members from the amidohydrolase superfamily it does not require a metal ion. Here, we use multiscale computational enzymology to investigate the catalytic mechanism, substrate binding, oxyanion hole, and roles of key active site residues of NicF from Bordetella bronchiseptica. In particular, molecular dynamics (MD) simulations, quantum mechanics/molecular mechanics (QM/MM) and QTAIM methods have been applied. The mechanism of the NicF-catalyzed reaction proceeds by a nucleophilic addition-elimination sequence involving the formation of a thioester enzyme intermediate (IC2 in stage 1) followed by hydrolysis of the thioester bond to form the products (stage 2). Consequently, the formation of IC2 in stage 1 is the rate-limiting step with a barrier of 88.8 kJ·mol-1 relative to the reactant complex, RC. Comparisons with related metal-dependent enzymes, particularly the zinc-dependent nicotinamidase from Streptococcus pneumonia (SpNic), have also been made to further illustrate unique features of the present mechanism. Along with -NH- donor groups of the oxyanion hole (i.e., HN-Thr146, HN-Cys150), the active site β-hydroxyl of threonine (HO-βThr146) is concluded to play a role in stabilizing the carbonyl oxygen of maleamate during the mechanism.

CaverDock: A Novel Method for the Fast Analysis of Ligand Transport.

Here we present a novel method for the analysis of transport processes in proteins and its implementation called CaverDock. Our method is based on a modified molecular docking algorithm. It iteratively places the ligand along the access tunnel in such a way that the ligand movement is contiguous and the energy is minimized. The result of CaverDock calculation is a ligand trajectory and an energy profile of transport process. CaverDock uses the modified docking program Autodock Vina for molecular docking and implements a parallel heuristic algorithm for searching the space of possible trajectories. Our method lies in between the geometrical approaches and molecular dynamics simulations. Contrary to the geometrical methods, it provides an evaluation of chemical forces. However, it is far less computationally demanding and easier to set up compared to molecular dynamics simulations. CaverDock will find a broad use in the fields of computational enzymology, drug design, and protein engineering. The software is available free of charge to the academic users at https://loschmidt.chemi.muni.cz/caverdock/.

Protocol for Computational Enzymatic Reactivity Based on Geometry Optimisation.

Enzymes play a biologically essential role in performing and controlling an important share of the chemical processes occurring in life. However, despite their critical role in nature, attaining a clear understanding of the way an enzyme acts is still cumbersome. Computational enzymology is playing an increasingly important role in this field of research. It allows the elucidation of a complete and detailed mechanism of an enzymatic reaction, including the characterization of reaction intermediates and transition states from both structural and energetic points of view, which is something that no other single experiment can produce alone. In this review, we present a general computational strategy to study enzymatic mechanisms based on adiabatic mapping and free geometry optimization. These methods allow chemical reactions to be studied with high theoretical levels, and allow a more exhaustive exploration of the chemical reactional space than other available methods, albeit being limited to the extent that they explore the enzyme conformational space. Special attention is given to the choice of the theoretical levels, as well as describing the model systems that are currently used to study enzymatic reactions. With this, we aim to provide a good introduction for non-specialised users in this field of research. We also provide a selection of hand-picked examples from our own work that illustrate the power of computational enzymology to study catalytic mechanisms. Some of these studies constitute pioneering work in the field that were later validated by experimental means.

Computational enzymology for degradation of chemical warfare agents: promising technologies for remediation processes

Computational enzymology for degradation of chemical warfare agents: promising technologies for remediation processes

Computational enzymology for degradation of chemical warfare agents: promising technologies for remediation processes.

Chemical weapons are a major worldwide problem, since they are inexpensive, easy to produce on a large scale and difficult to detect and control. Among the chemical warfare agents, we can highlight the organophosphorus compounds (OP), which contain the phosphorus element and that have a large number of applications. They affect the central nervous system and can lead to death, so there are a lot of works in order to design new effective antidotes for the intoxication caused by them. The standard treatment includes the use of an anticholinergic combined to a central nervous system depressor and an oxime. Oximes are compounds that reactivate Acetylcholinesterase (AChE), a regulatory enzyme responsible for the transmission of nerve impulses, which is one of the molecular targets most vulnerable to neurotoxic agents. Increasingly, enzymatic treatment becomes a promising alternative; therefore, other enzymes have been studied for the OP degradation function, such as phosphotriesterase (PTE) from bacteria, human serum paraoxonase 1 (HssPON1) and diisopropyl fluorophosphatase (DFPase) that showed significant performances in OP detoxification. The understanding of mechanisms by which enzymes act is of extreme importance for the projection of antidotes for warfare agents, and computational chemistry comes to aid and reduce the time and costs of the process. Molecular Docking, Molecular Dynamics and QM/MM (quantum-mechanics/molecular-mechanics) are techniques used to investigate the molecular interactions between ligands and proteins.

Formation of a Stable Iminol Intermediate in the Redox Regulation Mechanism of Protein Tyrosine Phosphatase 1B (PTP1B)

Protein tyrosine phosphatase 1B (PTP1B) is a key enzyme in a variety of physiological processes including insulin and leptin signaling. Experimentally it has been previously suggested to form an enzyme-derived sulfenyl-amide intermediate as a means of protecting an active site cysteinyl against overoxidation. In this study, key aspects of the mechanism by which PTP1B mediates against overoxidation of its active site cysteinyl has been examined via multiscale computational enzymology (e.g., molecular dynamics simulations and high-level hybrid quantum mechanics/molecular mechanics). Several possible initial reactive complexes containing an active site sulfenic acid (oxidized cysteinyl) were considered, as well as possible reaction pathways and intermediates. Importantly, the only enzymatically feasible mechanism for formation of a putative sulfenyl-amide intermediate occurs via a stepwise pathway. The only feasible mechanism was found to occur in a stepwise fashion, in which a stable iminol intermediate is ...

Mathematical and computational modeling in biology at multiple scales.

A variety of topics are reviewed in the area of mathematical and computational modeling in biology, covering the range of scales from populations of organisms to electrons in atoms. The use of maximum entropy as an inference tool in the fields of biology and drug discovery is discussed. Mathematical and computational methods and models in the areas of epidemiology, cell physiology and cancer are surveyed. The technique of molecular dynamics is covered, with special attention to force fields for protein simulations and methods for the calculation of solvation free energies. The utility of quantum mechanical methods in biophysical and biochemical modeling is explored. The field of computational enzymology is examined.

Challenges in computational studies of enzyme structure, function and dynamics

In this review we give an overview of the field of Computational enzymology. We start by describing the birth of the field, with emphasis on the work of the 2013 chemistry Nobel Laureates. We then present key features of the state-of-the-art in the field, showing what theory, accompanied by experiments, has taught us so far about enzymes. We also briefly describe computational methods, such as quantum mechanics-molecular mechanics approaches, reaction coordinate treatment, and free energy simulation approaches. We finalize by discussing open questions and challenges.

Computational investigations on the catalytic mechanism of maleate isomerase: the role of the active site cysteine residues.

The maleate isomerase (MI) catalysed isomerization of maleate to fumarate has been investigated using a wide range of computational modelling techniques, including small model DFT calculations, QM-cluster approach, quantum mechanical/molecular mechanical approach (QM/MM in the ONIOM formalism) and molecular dynamics simulations. Several fundamental questions regarding the mechanism were answered in detail, such as the activation and stabilization of the catalytic Cys in a rather hydrophobic active site. The two previously proposed mechanisms were considered, where either enediolate or succinyl-Cys intermediate forms. Small model calculations as well as an ONIOM-based approach suggest that an enediolate intermediate is too unstable. Furthermore, the formation of succinyl-Cys intermediate via the nucleophilic attack of Cys76(-) on the substrate C2 (as proposed experimentally) was found to be energetically unfeasible in both QM-cluster and ONIOM approaches. Instead, our results show that Cys194, upon activation via the substrate, acts as a nucleophile and Cys76 acts as an acid/base catalyst, forming a succinyl-Cys intermediate in a concerted fashion. Indeed, the calculated PA of Cys76 is always higher than that of Cys194 before or upon substrate binding in the active site. Furthermore, the mechanism proceeds via multiple steps by substrate rotation around C2-C3 with the assistance of the now negatively charged Cys76, leading to the formation of fumarate. Finally, our calculated barrier is in good agreement with experiment. These findings represent a novel mechanism in the racemase superfamily.

Multi-Scale Computational Enzymology: Enhancing Our Understanding of Enzymatic Catalysis

Elucidating the origin of enzymatic catalysis stands as one the great challenges of contemporary biochemistry and biophysics. The recent emergence of computational enzymology has enhanced our atomistic-level description of biocatalysis as well the kinetic and thermodynamic properties of their mechanisms. There exists a diversity of computational methods allowing the investigation of specific enzymatic properties. Small or large density functional theory models allow the comparison of a plethora of mechanistic reactive species and divergent catalytic pathways. Molecular docking can model different substrate conformations embedded within enzyme active sites and determine those with optimal binding affinities. Molecular dynamics simulations provide insights into the dynamics and roles of active site components as well as the interactions between substrate and enzymes. Hybrid quantum mechanical/molecular mechanical (QM/MM) can model reactions in active sites while considering steric and electrostatic contributions provided by the surrounding environment. Using previous studies done within our group, on OvoA, EgtB, ThrRS, LuxS and MsrA enzymatic systems, we will review how these methods can be used either independently or cooperatively to get insights into enzymatic catalysis.

Combined Quantum Mechanics/Molecular Mechanics (QM/MM) Methods in Computational Enzymology

Computational enzymology is a rapidly maturing field that is increasingly integral to understanding mechanisms of enzyme-catalyzed reactions and their practical applications. Combined quantum mechanics/molecular mechanics (QM/MM) methods are important in this field. By treating the reacting species with a quantum mechanical method (i.e., a method that calculates the electronic structure of the active site) and including the enzyme environment with simpler molecular mechanical methods, enzyme reactions can be modeled. Here, we review QM/MM methods and their application to enzyme-catalyzed reactions to investigate fundamental and practical problems in enzymology. A range of QM/MM methods is available, from cheaper and more approximate methods, which can be used for molecular dynamics simulations, to highly accurate electronic structure methods. We discuss how modeling of reactions using such methods can provide detailed insight into enzyme mechanisms and illustrate this by reviewing some recent applications. We outline some practical considerations for such simulations. Further, we highlight applications that show how QM/MM methods can contribute to the practical development and application of enzymology, e.g., in the interpretation and prediction of the effects of mutagenesis and in drug and catalyst design.

Car–Parrinello simulation of hydrogen bond dynamics in sodium hydrogen bissulfate

We studied proton dynamics of a short hydrogen bond of the crystalline sodium hydrogen bissulfate, a hydrogen-bonded ferroelectric system. Our approach was based on the established Car-Parrinello molecular dynamics (CPMD) methodology, followed by an a posteriori quantization of the OH stretching motion. The latter approach is based on snapshot structures taken from CPMD trajectory, calculation of proton potentials, and solving of the vibrational Schrodinger equation for each of the snapshot potentials. The so obtained contour of the OH stretching band has the center of gravity at about 1540 cm(-1) and a half width of about 700 cm(-1), which is in qualitative agreement with the experimental infrared spectrum. The corresponding values for the deuterated form are 1092 and 600 cm(-1), respectively. The hydrogen probability densities obtained by solving the vibrational Schrodinger equation allow for the evaluation of potential of mean force along the proton transfer coordinate. We demonstrate that for the present system the free energy profile is of the single-well type and features a broad and shallow minimum near the center of the hydrogen bond, allowing for frequent and barrierless proton (or deuteron) jumps. All the calculated time-averaged geometric parameters were in reasonable agreement with the experimental neutron diffraction data. As the present methodology for quantization of proton motion is applicable to a variety of hydrogen-bonded systems, it is promising for potential use in computational enzymology.

Computational enzymology

Molecular simulations and modelling are changing the science of enzymology. Calculations can provide detailed, atomic-level insight into the fundamental mechanisms of biological catalysts. Computational enzymology is a rapidly developing area, and is testing theories of catalysis, challenging 'textbook' mechanisms, and identifying novel catalytic mechanisms. Increasingly, modelling is contributing directly to experimental studies of enzyme-catalysed reactions. Potential practical applications include interpretation of experimental data, catalyst design and drug development.

Gas-Phase Geometry Optimization of Biological Molecules as a Reasonable Alternative to a Continuum Environment Description: Fact, Myth, or Fiction?

Gas-phase optimization of single biological molecules and of small active-site biological models has become a standard approach in first principles computational enzymology. The important role played by the surrounding environment (solvent, enzyme, both) is normally only accounted for through higher-level single point energy calculations performed using a polarizable continuum model (PCM) and an appropriate dielectric constant with the gas-phase-optimized geometries. In this study we analyze this widely used approximation, by comparing gas-phase-optimized geometries with geometries optimized with different PCM approaches (and considering different dielectric constants) for a representative data set of 20 very important biological molecules--the 20 natural amino acids. A total of 323 chemical bonds and 469 angles present in standard amino acid residues were evaluated. The results show that the use of gas-phase-optimized geometries can in fact be quite a reasonable alternative to the use of the more computationally intensive continuum optimizations, providing a good description of bond lengths and angles for typical biological molecules, even for charged amino acids, such as Asp, Glu, Lys, and Arg. This approximation is particularly successful if the protonation state of the biological molecule could be reasonably described in vacuum, a requirement that was already necessary in first principles computational enzymology.