Misunderstanding the preorganization concept can lead to confusions about the origin of enzyme catalysis

Long-Range Electrostatics in Serine Proteases: Machine Learning-Driven Reaction Sampling Yields Insights for Enzyme Design

Computational enzyme design is a promising technique for producing novel enzymes for industrial and clinical needs. A key challenge that this technique faces is to consistently achieve the desired activity. Fundamental studies of natural enzymes revealed critical contributions from second-shell - and even more distant - residues to their remarkable efficiency. In particular, such residues organize the internal electrostatic field to promote the reaction. Engineering such fields computationally proved to be a promising strategy, which, however, has some limitations. Charged residues necessarily form specific patterns of local interactions that may be exploited for structural integrity. As a result, it is impossible to probe the electrostatic field alone by substituting amino acids. We hypothesize that an approach that isolates the influences of residues' charges from other influences could yield deeper insights. We use molecular modeling with AI-enhanced QM/MM reaction sampling to implement such an approach and apply it to a model serine protease subtilisin. We find that the negative charge 8 Å away from the catalytic site is crucial to achieving the enzyme's catalytic efficiency, contributing more than 2 kcal/mol to lowering the barrier. In contrast, a positive charge from the second-closest charged residue opposes the efficiency of the reaction by raising the barrier by 0.8 kcal/mol. This result invites discussion into the role of this residue and trade-offs that might have taken place in the evolution of such enzymes. Our approach is transferable and can help investigate the evolution of electrostatic preorganization in other enzymes. We believe that the study and engineering of electrostatic fields in enzymes is a promising direction to advance both fundamental and applied enzymology and lead to the design of new powerful biocatalysts.

Statistical Coupling Analysis Predicts Correlated Motions in Dihydrofolate Reductase

Dihydrofolate reductase (DHFR), due to its universality and the depth with which it has been studied, is a model system in the study of protein dynamics. Myriad previous works have identified networks of residues in positions near to and remote from the active site that are involved in the dynamics. For example, specific mutations on the Met20 loop in Escherichia coli DHFR (N23PP/S148A) are known to disrupt millisecond-time scale motions as well as reduce catalytic activity. However, how and if networks of dynamically coupled residues influence the evolution of DHFR is still an unanswered question. In this study, we first identify, by statistical coupling analysis and molecular dynamic simulations, a network of coevolving residues that possesses increased correlated motions. We then go on to show that allosteric communication in this network is knocked down in N23PP/S148A mutant E. coli DHFR. We also identify two sites in the human DHFR sector which may accommodate the Met20 loop double proline motif. Finally, we demonstrate a concerted evolutionary change in the human DHFR allosteric networks, which maintains dynamic communication. These findings strongly implicate protein dynamics as a driving force for evolution.

Statistical Coupling Analysis Predicts Correlated Motions in Dihydrofolate Reductase

The role of dynamics in enzymatic function is a highly debated topic. Dihydrofolate reductase (DHFR), due to its universality and the depth with which it has been studied, is a model system in this debate. Myriad previous works have identified networks of residues in positions near to and remote from the active site that are involved in dynamics and others that are important for catalysis. For example, specific mutations on the Met20 loop in E. coli DHFR (N23PP/S148A) are known to disrupt millisecond-timescale motions and reduce catalytic activity. However, how and if networks of dynamically coupled residues influence the evolution of DHFR is still an unanswered question. In this study, we first identify, by statistical coupling analysis and molecular dynamic simulations, a network of coevolving residues, which possess increased correlated motions. We then go on to show that allosteric communication in this network is selectively knocked down in N23PP/S148A mutant E. coli DHFR. Finally, we identify two sites in the human DHFR sector which may accommodate the Met20 loop double proline mutation while preserving dynamics. These findings strongly implicate protein dynamics as a driving force for evolution.

Design of Efficient Artificial Enzymes Using Crystallographically Enhanced Conformational Sampling

The ability to create efficient artificial enzymes for any chemical reaction is of great interest. Here, we describe a computational design method for increasing the catalytic efficiency of de novo enzymes by several orders of magnitude without relying on directed evolution and high-throughput screening. Using structural ensembles generated from dynamics-based refinement against X-ray diffraction data collected from crystals of Kemp eliminases HG3 (kcat/KM 125 M-1 s-1) and KE70 (kcat/KM 57 M-1 s-1), we design from each enzyme ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display kcat/KM values improved by 100-250-fold, comparable to mutants obtained after screening thousands of variants in multiple rounds of directed evolution. Crystal structures show excellent agreement with computational models, with catalytic contacts present as designed and transition-state root-mean-square deviations of ≤0.65 Å. Our work shows how ensemble-based design can generate efficient artificial enzymes by exploiting the true conformational ensemble to design improved active sites.

Substrate Positioning Dynamics Involves a Non-Electrostatic Component to Mediate Catalysis

Substrate positioning dynamics (SPD) orients the substrate in the active site, thereby influencing catalytic efficiency. However, it remains unknown whether SPD effects originate primarily from electrostatic perturbation inside the enzyme or can independently mediate catalysis with a significant non-electrostatic component. In this work, we investigated how the non-electrostatic component of SPD affects transition state (TS) stabilization. Using high-throughput enzyme modeling, we selected Kemp eliminase variants with similar electrostatics inside the enzyme but significantly different SPD. The kinetic parameters of these mutants were experimentally characterized. We observed a valley-shaped, two-segment linear correlation between the TS stabilization free energy (converted from kinetic parameters) and substrate positioning index (a metric to quantify SPD). The energy varies by approximately 2 kcal/mol. Favorable SPD was observed for the distal mutant R154W, increasing the proportion of reactive conformations and leading to the lowest activation free energy. These results indicate the substantial contribution of the non-electrostatic component of SPD to enzyme catalytic efficiency.

Anisotropic dynamics of an interfacial enzyme active site observed using tethered substrate analogs and ultrafast 2D IR spectroscopy

Enzymes accelerate the rates of biomolecular reactions by many orders of magnitude compared to bulk solution, and it is widely understood that this catalytic effect arises from a combination of polar pre-organization and electrostatic transition state stabilization. A number of recent reports have also implicated ultrafast (femtosecond-picosecond) timescale motions in enzymatic activity. However, complications arising from spatially-distributed disorder, the occurrence of multiple substrate binding modes, and the influence of hydration dynamics on solvent-exposed active sites still confound many experimental studies. Here we use ultrafast two-dimensional infrared (2D IR) spectroscopy and covalently-tethered substrate analogs to examine dynamical properties of the promiscuous Pyrococcus horikoshii ene-reductase (PhENR) active site in two binding configurations mimicking proposed "inactive" and "reactive" Michaelis complexes. Spectral diffusion measurements of aryl-nitrile substrate analogs reveal an end-to-end tradeoff between fast (sub-ps) and slow (>5 ps) motions. Fermi resonant aryl-azide analogs that sense interactions of coupled oscillators are described. Lineshape and quantum beat analyses of these probes reveal characteristics that correlate with aryl-nitrile frequency fluctuation correlation functions parameters, demonstrating that this anisotropy is an intrinsic property of the water-exposed active site, where countervailing gradients of fast dynamics and disorder in the reactant ground state are maintained near the hydration interface. Our results suggest several plausible factors leading to state-selective rate enhancement and promiscuity in PhENR. This study also highlights a strategy to detect perturbations to vibrational modes outside the transparent window of the mid-IR spectrum, which may be extended to other macromolecular systems.

Evidence for a short-lived resonance state in enzyme catalysis via rate-equation convolution

At the cellular level, all biological function relies on enzymes to provide catalytic acceleration of essential biochemical processes driving cellular metabolism. The enzyme is presumed to lower the activation energy barrier separating reactants from products, but the precise mechanism remains unresolved. Here we examine the temperature dependence of the enzyme-catalyzed dissociation of p-nitrophenyl-α-D-glucopyranoside (pNPG), a chromogenic analog for maltose, isomaltose, and sucrose disaccharide sugars, into p-nitrophenol (pNP) and glucose (monosaccharide). The enzymes of interest are the wild type and mutant forms of glucosidase MalL produced by the probiotic bacterium Bacillus subtilis. The per-enzyme production rates k(T) for the pNPG→ glucose reaction all show a characteristic temperature profile with an Arrhenius-like (approximately exponential) slow acceleration at low temperatures, rising through a point of inflexion to reach a maximum, then turning over to decline steeply towards zero production at high temperatures. This asymmetric profile is found to be well fitted by convolving an exponential growth function f(T) with a Gaussian temperature distribution g(T) to produce an exponentially modified Gaussian function h(T). To give a physical interpretation of the convolution components, we make the temperature mapping Θ≡T_{ref}-T where T_{ref} marks the temperature at which a given mutant becomes fully denatured (unfolded) and therefore inactive, then convert the convolution components to probability density functions which obey the convolution theorem of statistics. Working in Θ space, we identify f(Θ) as the density function for an Arrhenius-like transition from ground-state A to metastable-state B, and g(Θ) as the Gaussian distribution of offset-temperature fluctuations for the metastable state. By mapping the standard thermodynamic relations for temperature and energy fluctuations to the enzyme frame of reference, we are able to derive an expression for the lifetime for the metastable B state. For the 15 enzyme experiments, we obtain a mean value 〈Δt〉≳(29.0±1.3)×10^{-15}s, in remarkably good agreement with the ∼30-fs estimate for the period of glycosidic bond oscillations extracted from published infrared spectroscopy. We suggest that the metastable B state provides a low-energy target that has the effect of lowering the activation energy barrier by presenting an alternative axis for the reaction coordinate.

Protein Dynamics and Enzymatic Catalysis

This Perspective presents a review of our work and that of others in the highly controversial topic of the coupling of protein dynamics to reaction in enzymes. We have been involved in studying this topic for many years. Thus, this perspective will naturally present our own views, but it also is designed to present an overview of the variety of viewpoints of this topic, both experimental and theoretical. This is obviously a large and contentious topic.

Analyzing the Reaction of Orotidine 5'-Phosphate Decarboxylase as a Way to Examine Some Key Catalytic Proposals

This study analyzes the origin of enzyme catalysis by focusing on the reaction of orotidine 5'-phosphate decarboxylase (ODCase). This reaction involves an enormous catalytic effect of 23 kcal/mol that has been attributed to reactant state destabilization associated with the use of binding energy through the so-called Circe effect. However, our early studies and subsequent key experiments have shown that the presumed effect of the binding energy (namely, the strain exerted by a bond to a phosphate group) does not contribute to the catalysis. In this study, we perform quantitative empirical valence bond calculations that reproduce the catalytic effect of ODCase and the effect of removing the phosphate side chain. The calculations demonstrate that the effect of the phosphate is due to a change in reorganization energy and should not be described as an induced fit effect. Similarly, we show that the overall catalytic effect is due to electrostatic transition state stabilization, which again reflects the smaller reorganization energy in the enzyme than in water. We also elaborate on the problems with the induced fit proposal, including the fact that it does not serve to tell us what the actual origin of the action of the catalytic effect is. In addition to the above points, we use this paper to discuss misconceptions about the meaning of the preorganization effect, as well as other misunderstandings of what is being done in consistent calculations of enzyme catalysis.

Perspective: Path Sampling Methods Applied to Enzymatic Catalysis

This Perspective reviews the use of Transition Path Sampling methods to study enzymatically catalyzed chemical reactions. First applied by our group to an enzymatic reaction over 15 years ago, the method has uncovered basic principles in enzymatic catalysis such as the protein promoting vibration, and it has also helped harmonize such ideas as electrostatic preorganization with dynamic views of enzyme function. It is now being used to help uncover principles of protein design necessary to artificial enzyme creation.

High throughput and quantitative enzymology in the genomic era

Accurate predictions from models based on physical principles are the ultimate metric of our biophysical understanding. Although there has been stunning progress toward structure prediction, quantitative prediction of enzyme function has remained challenging. Realizing this goal will require large numbers of quantitative measurements of rate and binding constants and the use of these ground-truth data sets to guide the development and testing of these quantitative models. Ground truth data more closely linked to the underlying physical forces are also desired. Here, we describe technological advances that enable both types of ground truth measurements. These advances allow classic models to be tested, provide novel mechanistic insights, and place us on the path toward a predictive understanding of enzyme structure and function.

Cryo-kinetics Reveal Dynamic Effects on the Chemistry of Human Dihydrofolate Reductase

Effects of isotopic substitution on the rate constants of human dihydrofolate reductase (HsDHFR), an important target for anti-cancer drugs, have not previously been characterized due to its complex fast kinetics. Here, we report the results of cryo-measurements of the kinetics of the HsDHFR catalyzed reaction and the effects of protein motion on catalysis. Isotopic enzyme labeling revealed an enzyme KIE (kHLE /kHHE ) close to unity above 0 °C; however, the enzyme KIE was increased to 1.72±0.15 at -20 °C, indicating that the coupling of protein motions to the chemical step is minimized under optimal conditions but enhanced at non-physiological temperatures. The presented cryogenic approach provides an opportunity to probe the kinetics of mammalian DHFRs, thereby laying the foundation for characterizing their transition state structure.

Enzyme Models-From Catalysis to Prodrugs

Enzymes are highly specific biological catalysts that accelerate the rate of chemical reactions within the cell. Our knowledge of how enzymes work remains incomplete. Computational methodologies such as molecular mechanics (MM) and quantum mechanical (QM) methods play an important role in elucidating the detailed mechanisms of enzymatic reactions where experimental research measurements are not possible. Theories invoked by a variety of scientists indicate that enzymes work as structural scaffolds that serve to bring together and orient the reactants so that the reaction can proceed with minimum energy. Enzyme models can be utilized for mimicking enzyme catalysis and the development of novel prodrugs. Prodrugs are used to enhance the pharmacokinetics of drugs; classical prodrug approaches focus on alternating the physicochemical properties, while chemical modern approaches are based on the knowledge gained from the chemistry of enzyme models and correlations between experimental and calculated rate values of intramolecular processes (enzyme models). A large number of prodrugs have been designed and developed to improve the effectiveness and pharmacokinetics of commonly used drugs, such as anti-Parkinson (dopamine), antiviral (acyclovir), antimalarial (atovaquone), anticancer (azanucleosides), antifibrinolytic (tranexamic acid), antihyperlipidemia (statins), vasoconstrictors (phenylephrine), antihypertension (atenolol), antibacterial agents (amoxicillin, cephalexin, and cefuroxime axetil), paracetamol, and guaifenesin. This article describes the works done on enzyme models and the computational methods used to understand enzyme catalysis and to help in the development of efficient prodrugs.

QM/MM Study of the Fosfomycin Resistance Mechanism Involving FosB Enzyme

Multidrug-resistant organisms contain antibiotic-modifying enzymes that facilitate resistance to a variety of antimicrobial compounds. Particularly, the fosfomycin (FOF) drug can be structurally modified by several FOF-modifying enzymes before it reaches the biological target. Among them, FosB is an enzyme that utilizes l-cysteine or bacillithiol in the presence of a divalent metal to open the epoxide ring of FOF and, consequently, inactivate the drug. Here, we have used hybrid quantum mechanics/molecular mechanics (QM/MM) and molecular dynamics (MD) simulations to explore the mechanism of the reaction involving FosB and FOF. The calculated free-energy profiles show that the cost to open the epoxide ring of FOF at the C2 atom is ∼3.0 kcal/mol higher than that at the C1 atom. Besides, our QM/MM MD results revealed the critical role of conformation change of Cys9 and Asn50 to release the drug from the active site. Overall, the present study provides insights into the mechanism of FOF-resistant proteins.

Supramolecular kinetic effects by pillararenes: the synergism between spatiotemporal and preorganization concepts in decarboxylation reactions

Spatiotemporal and preorganization factors were both responsible for the catalytic and inhibitory supramolecular effects in decarboxylation reactions.

Enhancing the enthalpic contribution of hydrogen bonds by solvent shielding

In biological systems, polar interactions are heavily burdened by high desolvation penalties resulting from strong solute-solvent interactions. As a consequence thereof, enthalpic contributions of hydrogen bonds to the free energy of binding are severely diminished. However, this effect is strongly attenuated for interactions within solvent-shielded areas of proteins. In microcalorimetric experiments, we show that the bacterial lectin FimH utilizes conformational adaptions to effectively shield its binding site from solvent. The transition into a lower dielectric environment results in an enthalpic benefit of approximately -13 kJ mol-1 for mannoside binding. However, this effect can be abrogated, if the hydrogen bond network within the binding site is disturbed by deoxygenation of the ligand. Conformational adaption leading to reduced local dielectric constants could represent a general mechanism for proteins to enable enthalpy-driven recognition of polar ligands.

Combinatorial Approach for Exploring Conformational Space and Activation Barriers in Computer-Aided Enzyme Design

Computer-aided enzyme design is a field of great potential importance for biotechnological applications, medical advances, and a fundamental understanding of enzyme action. However, reaching a predictive ability in this direction is extremely challenging. It requires both the ability to predict quantitatively the activation barriers in cases where the structure and sequence are known and the ability to predict the effect of different mutations. In this work, we propose a protocol for predicting reasonable starting structures of mutants of proteins with known structures and for calculating the activation barriers of the generated mutants. Our approach also allows us to use the predicted structures of the generated mutant to predict structures and activation barriers for subsequent set of mutations. This protocol is used to examine the reliability of the in silico directed evolution of Kemp eliminase and haloalkane dehalogenase. We also used the results of single and double mutations as a base for predicting the effect of transition-state stabilization by multiple concurrent mutations. This strategy seems to be useful in creating an activity funnel that provides a qualitative ranking of the catalytic power of different mutants.

The Organization of Active Site Side Chains of Glycerol-3-phosphate Dehydrogenase Promotes Efficient Enzyme Catalysis and Rescue of Variant Enzymes

A comparison of the values of k_cat/K_m for reduction of dihydroxyacetone phosphate (DHAP) by NADH catalyzed by wild type and K120A/R269A variant glycerol-3-phosphate dehydrogenase from human liver (hlGPDH) shows that the transition state for enzyme-catalyzed hydride transfer is stabilized by 12.0 kcal/mol by interactions with the cationic K120 and R269 side chains. The transition state for the K120A/R269A variant-catalyzed reduction of DHAP is stabilized by 1.0 and 3.8 kcal/mol for reactions in the presence of 1.0 M EtNH₃⁺ and guanidinium cation (Gua⁺), respectively, and by 7.5 kcal/mol for reactions in the presence of a mixture of each cation at 1.0 M, so that the transition state stabilization by the ternary E·EtNH₃⁺·Gua⁺ complex is 2.8 kcal/mol greater than the sum of stabilization by the respective binary complexes. This shows that there is cooperativity between the paired activators in transition state stabilization. The effective molarities (EMs) of ∼50 M determined for the K120A and R269A side chains are ≪10⁶ M, the EM for entropically controlled reactions. The unusually efficient rescue of the activity of hlGPDH-catalyzed reactions by the HP_i/Gua⁺ pair and by the Gua⁺/EtNH₃⁺ activator pair is due to stabilizing interactions between the protein and the activator pieces that organize the K120 and R269 side chains at the active site. This “preorganization” of side chains promotes effective catalysis by hlGPDH and many other enzymes. The role of the highly conserved network of side chains, which include Q295, R269, N270, N205, T264, K204, D260, and K120, in catalysis is discussed.

Cations in motion: QM/MM studies of the dynamic and electrostatic roles of H+ and Mg2+ ions in enzyme reactions

Here we discuss current trends in the simulations of enzymatic reactions focusing on phosphate catalysis. The mechanistic details of the proton transfers coupled to the phosphate cleavage is one of the key challenges in QM/MM calculations of these and other enzyme catalyzed reactions. The lack of experimental information offers both an opportunity for computations as well as often unresolved controversies. We discuss the example of small GTPases including the important human Ras protein. The high dimensionality and chemical complexity of these reactions demand carefully chosen computational techniques both in terms of the underlying quantum chemical theory and the sampling of the conformational ensemble. We also point out the important role of Mg²⁺ ions, and recent advances in their transient involvement in the catalytic mechanisms.

Interaction vs Preorganization in Enzyme Catalysis. A Dispute That Calls for Resolution

This essay focuses on the debate between Warshel et al. (proponents of preorganization) and Menger and Nome (proponents of spatiotemporal effects) over the source of fast enzyme catalysis. The Warshel model proposes that the main function of enzymes is to push the solvent coordinate toward the transition state. Other physical-organic factors (e.g., desolvation, entropic effects, ground state destabilization, etc.) do not, ostensibly, contribute substantially to the rate. Indeed, physical organic chemistry in its entirety was claimed to be "irrelevant to an enzyme's active site". Preorganization had been applied by Warshel to his "flagship" enzyme, ketosteroid isomerase, but we discuss troubling issues with their ensuing analysis. For example, the concepts of "general acid" and "general base", known to play a role in this enzyme's mechanism, are ignored in the text. In contrast, the spatiotemporal theory postulates that enzyme-like rates (i.e., accelerations >10⁸) occur when two functionalities are held rigidly at contact distances less than ca. 3 Å. Numerous diverse organic systems are shown to bear this out experimentally. Many of these are intramolecular systems where distances between functionalities are known. Among them are fast intramolecular systems where strain is actually generated during the reaction, thereby excluding steric compression as a source of the observed enzyme-like rates. Finally, the account ends with structural data from four active sites of enzymes, obtained by others, all showing contact distances between substrate analogues and enzyme. To our knowledge, contact distances less than the diameter of water are found universally among enzymes, and it is to this fact that we attribute their extremely fast rates given the assumption that enzymes, whatever their particular mechanism, obey elementary chemical principles.

Fast Amide Bond Cleavage Assisted by a Secondary Amino and a Carboxyl Group-A Model for yet Unknown Peptidases?

Unconstrained amides that undergo fast hydrolysis under mild conditions are valuable sources of information about how amide bonds may be activated in enzymatic transformations. We report a compound possessing an unconstrained amide bond surrounded by an amino and a carboxyl group, each mounted in close proximity on a bicyclic scaffold. Fast amide hydrolysis of this model compound was found to depend on the presence of both the amino and carboxyl functions, and to involve a proton transfer in the rate-limiting step. Possible mechanisms for the hydrolytic cleavage and their relevance to peptide bond cleavage catalyzed by natural enzymes are discussed. Experimental observations suggest that the most probable mechanisms of the model compound hydrolysis might include a twisted amide intermediate and a rate-determining proton transfer.