Search

Activation heat capacity is emerging as a crucial factor in enzyme thermoadaptation, as shown by the non-Arrhenius behaviour of many natural enzymes. However, its physical origin and relationship to the evolution of catalytic activity remain uncertain. Here we show that directed evolution of a computationally designed Kemp eliminase reshapes protein dynamics, which gives rise to an activation heat capacity absent in the original design. These changes buttress transition-state stabilization. Extensive molecular dynamics simulations show that evolution results in the closure of solvent-exposed loops and a better packing of the active site. Remarkably, this gives rise to a correlated dynamical network that involves the transition state and large parts of the protein. This network tightens the transition-state ensemble, which induces a negative activation heat capacity and non-linearity in the activity-temperature dependence. Our results have implications for understanding enzyme evolution and suggest that selectively targeting the conformational dynamics of the transition-state ensemble by design and evolution will expedite the creation of novel enzymes., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

De novo enzymes can be created by computational design and directed evolution. Here, we review recent insights into the origins of catalytic power in evolved designer enzymes to pinpoint opportunities for next-generation designs: Evolution precisely organizes active sites, introduces catalytic H-bonding networks, invokes electrostatic catalysis, and creates dynamical networks embedding the active site in a reactive protein scaffold. Such insights foster our fundamental knowledge of enzyme catalysis and fuel the future design of tailor-made enzymes., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Experiments and biomolecular simulations are revealing new and unexpected details of how enzymes are adapted to specific temperatures. These findings are elucidating enzyme evolutionary trajectories and offer great promise for design and engineering of natural and artificial enzymes. They also have implications for understanding responses of larger scale biological temperature dependence, relevant for understanding the effects of climate change on ecosystems. We review recent work on the temperature dependence of enzyme-catalysed reaction rates and the implications for enzyme evolution. Evidence from kinetic isotope effects, temperature dependent reaction rates, molecular dynamics simulations and thermodynamics provides new insights into enzyme thermoadaptation and evolution., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

We review the adaptations of enzyme activity to different temperatures. Psychrophilic (cold-adapted) enzymes show significantly different activation parameters (lower activation enthalpies and entropies) from their mesophilic counterparts. Furthermore, there is increasing evidence that the temperature dependence of many enzyme-catalyzed reactions is more complex than is widely believed. Many enzymes show curvature in plots of activity versus temperature that is not accounted for by denaturation or unfolding. This is explained by macromolecular rate theory: A negative activation heat capacity for the rate-limiting chemical step leads directly to predictions of temperature optima; both entropy and enthalpy are temperature dependent. Fluctuations in the transition state ensemble are reduced compared to the ground state. We show how investigations combining experiment with molecular simulation are revealing fundamental details of enzyme thermoadaptation that are relevant for understanding aspects of enzyme evolution. Simulations can calculate relevant thermodynamic properties (such as activation enthalpies, entropies, and heat capacities) and reveal the molecular mechanisms underlying experimentally observed behavior.

Temperature influences the reaction kinetics and evolvability of all enzymes. To understand how evolution shapes the thermodynamic drivers of catalysis, we optimized the modest activity of a computationally designed enzyme for an elementary proton-transfer reaction by nearly 4 orders of magnitude over 9 rounds of mutagenesis and screening. As theorized for primordial enzymes, the catalytic effects of the original design were almost entirely enthalpic in origin, as were the rate enhancements achieved by laboratory evolution. However, the large reductions in Δ H ⧧ were partially offset by a decrease in T and unexpectedly accompanied by a negative activation heat capacity, signaling strong adaptation to the operating temperature. These findings echo reports of temperature-dependent activation parameters for highly evolved natural enzymes and are relevant to explanations of enzymatic catalysis and adaptation to changing thermal environments.S ⧧ and unexpectedly accompanied by a negative activation heat capacity, signaling strong adaptation to the operating temperature. These findings echo reports of temperature-dependent activation parameters for highly evolved natural enzymes and are relevant to explanations of enzymatic catalysis and adaptation to changing thermal environments.

Combined quantum mechanics/molecular mechanics (QM/MM) methods are increasingly widely utilized in studies of reactions in enzymes and other large systems. Here, we apply a range of QM/MM methods to investigate the Claisen rearrangement of chorismate to prephenate, in solution, and in the enzyme chorismate mutase. Using projector-based embedding in a QM/MM framework, we apply treatments up to the CCSD(T) level. We test a range of density functional QM/MM methods and QM region sizes. The results show that the calculated reaction energetics are significantly more sensitive to the choice of density functional than they are to the size of the QM region in these systems. Projector-based embedding of a wave function method in DFT reduced the 13 kcal/mol spread in barrier heights calculated at the DFT/MM level to a spread of just 0.3 kcal/mol, essentially eliminating dependence on the functional. Projector-based embedding of correlated ab initio methods provides a practical method for achieving high accuracy for energy profiles derived from DFT and DFT/MM calculations for reactions in condensed phases.

One of the critical variables that determine the rate of any reaction is temperature. For biological systems, the effects of temperature are convoluted with myriad (and often opposing) contributions from enzyme catalysis, protein stability, and temperature-dependent regulation, for example. We have coined the phrase "macromolecular rate theory (MMRT)" to describe the temperature dependence of enzyme-catalyzed rates independent of stability or regulatory processes. Central to MMRT is the observation that enzyme-catalyzed reactions occur with significant values of ΔCp(‡) that are in general negative. That is, the heat capacity (Cp) for the enzyme-substrate complex is generally larger than the Cp for the enzyme-transition state complex. Consistent with a classical description of enzyme catalysis, a negative value for ΔCp(‡) is the result of the enzyme binding relatively weakly to the substrate and very tightly to the transition state. This observation of negative ΔCp(‡) has important implications for the temperature dependence of enzyme-catalyzed rates. Here, we lay out the fundamentals of MMRT. We present a number of hypotheses that arise directly from MMRT including a theoretical justification for the large size of enzymes and the basis for their optimum temperatures. We rationalize the behavior of psychrophilic enzymes and describe a "psychrophilic trap" which places limits on the evolution of enzymes in low temperature environments. One of the defining characteristics of biology is catalysis of chemical reactions by enzymes, and enzymes drive much of metabolism. Therefore, we also expect to see characteristics of MMRT at the level of cells, whole organisms, and even ecosystems.

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.

Techniques for modelling enzyme-catalyzed reaction mechanisms are making increasingly important contributions to biochemistry. They can address fundamental questions in enzyme catalysis and have the potential to contribute to practical applications such as drug development.

One of the most controversial questions in enzymology today is whether protein dynamics are significant in enzyme catalysis. A particular issue in these debates is the unusual temperature-dependence of some kinetic isotope effects for enzyme-catalysed reactions. In the present paper, we review our recent model [Glowacki, Harvey and Mulholland (2012) Nat. Chem. 4, 169-176] that is capable of reproducing intriguing temperature-dependences of enzyme reactions involving significant quantum tunnelling. This model relies on treating multiple conformations of the enzyme-substrate complex. The results show that direct 'driving' motions of proteins are not necessary to explain experimental observations, and show that enzyme reactivity can be understood and accounted for in the framework of transition state theory.

Molecular modelling and simulation methods are increasingly at the forefront of elucidating mechanisms of enzyme-catalysed reactions, and shedding light on the determinants of specificity and efficiency of catalysis. These methods have the potential to assist in drug discovery and the design of novel protein catalysts. This Tutorial Review highlights some of the most widely used modelling methods and some successful applications. Modelling protocols commonly applied in studying enzyme-catalysed reactions are outlined here, and some practical implications are considered, with cytochrome P450 enzymes used as a specific example., (This journal is © The Royal Society of Chemistry 2012)

The role of protein dynamics in enzyme catalysis is a matter of intense current debate. Enzyme-catalysed reactions that involve significant quantum tunnelling can give rise to experimental kinetic isotope effects with complex temperature dependences, and it has been suggested that standard statistical rate theories, such as transition-state theory, are inadequate for their explanation. Here we introduce aspects of transition-state theory relevant to the study of enzyme reactivity, taking cues from chemical kinetics and dynamics studies of small molecules in the gas phase and in solution--where breakdowns of statistical theories have received significant attention and their origins are relatively better understood. We discuss recent theoretical approaches to understanding enzyme activity and then show how experimental observations for a number of enzymes may be reproduced using a transition-state-theory framework with physically reasonable parameters. Essential to this simple model is the inclusion of multiple conformations with different reactivity.

The role of protein dynamics in enzyme catalysis is one of the most active and controversial areas in enzymology today. Some researchers claim that protein dynamics are at the heart of enzyme catalytic efficiency, while others state that dynamics make no significant contribution to catalysis. What is the biochemist - or student - to make of the ferocious arguments in this area? Protein dynamics are complex and fascinating, as molecular dynamics simulations and experiments have shown. The essential question is: do these complex motions have functional significance? In particular, how do they affect or relate to chemical reactions within enzymes, and how are chemical and conformational changes coupled together? Biomolecular simulations can analyse enzyme reactions and dynamics in atomic detail, beyond that achievable in experiments: accurate atomistic modelling has an essential part to play in clarifying these issues. This article is part of a Special Issue entitled: Protein Dynamics: Experimental and Computational Approaches., (Copyright © 2010 Elsevier B.V. All rights reserved.)

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.

Transfer of hydrogen as a proton, hydride or hydrogen atom is an important step in many enzymic reactions. Experiments show kinetic isotope effects (KIEs) for some enzyme-catalysed hydrogen transfer reactions that deviate significantly from the limits imposed by considering the differences in mass of the isotopes alone (i.e. the semiclassical limit). These KIEs can be explained if the transfer of the hydrogen species occurs via a quantum mechanical tunnelling mechanism. The unusual temperature dependence of some KIEs has led to suggestions that enzymes have evolved to promote tunnelling through dynamics - a highly controversial hypothesis. Molecular simulations have a vital role in resolving these questions, providing a level of detail of analysis not possible through experiments alone. Here, we review computational molecular modelling studies of quantum tunnelling in enzymes, in particular focusing on the enzymes soybean lipoxygenase-1 (SLO-1), dihydrofolate reductase (DHFR), methylamine dehydrogenase (MADH) and aromatic amine dehydrogenase (AADH) to illustrate the current controversy regarding the importance of quantum effects in enzyme catalysis.

Molecular simulation is increasingly demonstrating its practical value in the investigation of biological systems. Computational modelling of biomolecular systems is an exciting and rapidly developing area, which is expanding significantly in scope. A range of simulation methods has been developed that can be applied to study a wide variety of problems in structural biology and at the interfaces between physics, chemistry and biology. Here, we give an overview of methods and some recent developments in atomistic biomolecular simulation. Some recent applications and theoretical developments are highlighted.

Molecular modelling and simulation can give atomic-level understanding of the fundamental mechanisms of enzyme catalysis. For example, modelling can identify likely enzyme reaction mechanisms, analyse catalytic interactions, and identify determinants of reactivity and specificity. Combined quantum mechanics/molecular mechanics (QM/MM) methods are an important technique in this maturing field of computational enzymology. By coupling quantum chemical (electronic structure) calculations on the active site with a simpler, empirical 'molecular mechanics' treatment of the rest of the protein, QM/MM methods allow the modelling of reactions in enzymes. In this Highlight, QM/MM techniques are outlined and some recent applications are discussed. These applications illustrate how calculations of this type can be used to interpret and complement experiments, with important potential implications for practical developments such as drug and catalyst design.

Simulations and modelling [e.g. with combined QM/MM (quantum mechanics/molecular mechanics) methods] are increasingly important in investigations of enzyme-catalysed reaction mechanisms. Calculations offer the potential of uniquely detailed, atomic-level insight into the fundamental processes of biological catalysis. Highly accurate methods promise quantitative comparison with experiments, and reliable predictions of mechanisms, revolutionizing enzymology.

Modern modelling methods can now give uniquely detailed understanding of enzyme-catalyzed reactions, including the analysis of mechanisms and the identification of determinants of specificity and catalytic efficiency. A new field of computational enzymology has emerged that has the potential to contribute significantly to structure-based design and to develop predictive models of drug metabolism and, for example, of the effects of genetic polymorphisms. This review outlines important techniques in this area, including quantum-chemical model studies and combined quantum-mechanics and molecular-mechanics (QM/MM) methods. Some recent applications to enzymes of pharmacological interest are also covered, showing the types of problems that can be tackled and the insight they can give.

An overview of the combined quantum mechanical/molecular mechanical (QM/MM) approach and its application to studies of biotransformation enzymes and drug metabolism is given. Theoretical methods to simulate enzymatic reactions have rapidly developed during the last decade. In particular, QM/MM methods provide detailed insights into enzyme catalyzed reactions, which can be extremely valuable in complementing experimental research. QM/MM methods allow the reacting groups in the active site of an enzyme to be studied at a quantum mechanical level, while the surrounding protein and solvent is included at a classical (and computationally less expensive) molecular mechanical level. Existing QM/MM implementations vary in the level of interaction between the QM and MM regions and in the way the partitioning into QM and MM regions is setup. Some general considerations concerning reaction modeling are discussed and a number of QM/MM studies related to drug metabolism are described. These studies illustrate that theoretical modeling of important metabolic reactions provides detailed insights into mechanisms of reaction and specific catalytic effects of enzyme residues as well as explaining variation in rates of conversion of different metabolites. Such information is essential in the development of methods to predict metabolism of drugs and to understand metabolic effects of genetic polymorphism in biotransformation enzymes.