Toward accurate screening in computer-aided enzyme design

Enhanced active-site electric field accelerates enzyme catalysis

The design and improvement of enzymes based on physical principles remain challenging. Here we demonstrate that the principle of electrostatic catalysis can be leveraged to substantially improve a natural enzyme's activity. We enhanced the active-site electric field in horse liver alcohol dehydrogenase by replacing the serine hydrogen-bond donor with threonine and replacing the catalytic Zn2+ with Co2+. Based on the electric field enhancement, we make a quantitative prediction of rate acceleration-50-fold faster than the wild-type enzyme-which was in close agreement with experimental measurements. The effects of the hydrogen bonding and metal coordination, two distinct chemical forces, are described by a unified physical quantity-electric field, which is quantitative, and shown here to be additive and predictive. These results suggest a new design paradigm for both biological and non-biological catalysts.

Brunner syndrome caused by point mutation explained by multiscale simulation of enzyme reaction

Brunner syndrome is a disorder characterized by intellectual disability and impulsive, aggressive behavior associated with deficient function of the monoamine oxidase A (MAO-A) enzyme. These symptoms (along with particularly high serotonin levels) have been reported in patients with two missense variants in MAO-A (p.R45W and p.E446K). Herein, we report molecular simulations of the rate-limiting step of MAO-A-catalyzed serotonin degradation for these variants. We found that the R45W mutation causes a 6000-fold slowdown of enzymatic function, whereas the E446K mutation causes a 450-fold reduction of serotonin degradation rate, both of which are practically equivalent to a gene knockout. In addition, we thoroughly compared the influence of enzyme electrostatics on the catalytic function of both the wild type MAO-A and the p.R45W variant relative to the wild type enzyme, revealing that the mutation represents a significant electrostatic perturbation that contributes to the barrier increase. Understanding genetic disorders is closely linked to understanding the associated chemical mechanisms, and our research represents a novel attempt to bridge the gap between clinical genetics and the underlying chemical physics.

An electrostatic duel: subtle differences in the catalytic performance of monoamine oxidase A and B isoenzymes elucidated at the residue level using quantum computations

The origin of the immense catalytic power of enzymes remains one of the biggest unresolved questions in biochemistry, with electrostatics being one of the main contenders. Herein, we report results that not only confirm that electrostatics is the driving force behind enzyme catalysis, but also that it is capable of tuning subtle differences in the catalytic performance between structurally similar enzymes, as demonstrated using the example of isoenzymes, monoamine oxidases A and B. Using our own computationally efficient multiscale model [A. Prah, et al., ACS Catal., 2019, 9, 1231] we analyzed the rate-limiting step of the reaction between phenylethylamine and both isoenzymes and deduced that the electrostatic environment provided by isoenzyme B has a perceivably higher catalytic influence on all the considered parameters of the reaction (energy barrier, charge transfer, dipole moment, and HOMO-LUMO gap). This is in full agreement with the available experimental kinetic data and with our own simulations of the reaction in question. In-depth analysis of individual amino acid contributions of both isoenzymes to the barrier (based on the interaction between the electric field provided by the enzyme and the dipole moment of the reacting moiety) shows that the majority of the difference between the isoenzymes can be attributed to a small number of sizable differences between the aligned amino acid pairs, whereas in most of the pairs the difference in contribution to the barrier is vanishingly small. These results suggest that electrostatics largely controls the substrate selectivity of enzymes and validates our approach as being capable of discerning fine nuances in the selectivity of structurally related isoenzymes.

The Energetic Origin of Different Catalytic Activities in Temperature-Adapted Trypsins

Psychrophilic enzymes were always observed to have higher catalytic activity (k cat) than their mesophilic homologs at room temperature, while the origin of this phenomenon remains obscure. Here, we used two different temperature-adapted trypsins, the psychrophilic Atlantic cod trypsin (ACT) and the mesophilic bovine trypsin (BT), as a model system to explore the energetic origin of their different catalytic activities using computational methods. The results reproduce the characteristic changing trends in the activation free energy, activation enthalpy, and activation entropy between the psychrophilic and mesophilic enzymes, where, in particular, the slightly decreased activation free energy of ACT is determined by its considerably reduced activation enthalpy rather than by its more negative activation entropy compared to BT. The calculated electrostatic contributions to the solvation free energies in the reactant state/ground sate (RS/GS) and transition state (TS) show that, going from BT to ACT, the TS stabilization has a predominant effect over the RS stabilization on lowering the activation enthalpy of ACT. Comparison between the solvation energy components reveals a more optimized electrostatic preorganization to the TS in ACT, which provides a larger stabilization to the TS through reducing the reorganization energy, thus resulting in the lower activation enthalpy and hence lower activation free energy of ACT. Thus, it can be concluded that it is the difference in the protein electrostatic environment, and hence its different stabilizing effects on the TS, that brings about the different catalytic activities of different temperature-adapted trypsins.

Understanding the entropic effect in chorismate mutase reaction catalyzed by isochorismate-pyruvate lyase fromPseudomonas aeruginosa(PchB)

The substrate preorganization process in the entropically driven PchB-catalyzed chorismate mutase reaction leads to a remarkable apparent entropic effect.

In Silico-Directed Evolution Using CADEE

Recent years have seen an explosion of interest in both sequence- and structure-based approaches toward in silico-directed evolution. We recently developed a novel computational toolkit, CADEE, which facilitates the computer-aided directed evolution of enzymes. Our initial work (Amrein et al., IUCrJ 4:50-64, 2017) presented a pedagogical example of the application of CADEE to triosephosphate isomerase, to illustrate the CADEE workflow. In this contribution, we describe this workflow in detail, including code input/output snippets, in order to allow users to set up and execute CADEE simulations on any system of interest.

Exploring the challenges of computational enzyme design by rebuilding the active site of a dehalogenase

Rational enzyme design presents a major challenge that has not been overcome by computational approaches. One of the key challenges is the difficulty in assessing the magnitude of the maximum possible catalytic activity. In an attempt to overcome this challenge, we introduce a strategy that takes an active enzyme (assuming that its activity is close to the maximum possible activity), design mutations that reduce the catalytic activity, and then try to restore that catalysis by mutating other residues. Here we take as a test case the enzyme haloalkane dehalogenase (DhlA), with a 1,2-dichloroethane substrate. We start by demonstrating our ability to reproduce the results of single mutations. Next, we design mutations that reduce the enzyme activity and finally design double mutations that are aimed at restoring the activity. Using the computational predictions as a guide, we conduct an experimental study that confirms our prediction in one case and leads to inconclusive results in another case with 1,2-dichloroethane as substrate. Interestingly, one of our predicted double mutants catalyzes dehalogenation of 1,2-dibromoethane more efficiently than the wild-type enzyme.

Exploring the Development of Ground-State Destabilization and Transition-State Stabilization in Two Directed Evolution Paths of Kemp Eliminases

Computer-aided enzyme design presents a major challenge since in most cases it has not resulted in an impressive catalytic power. The reasons for the problems with computational design include the use of nonquantitative approaches, but they may also reflect other difficulties that are not completely obvious. Thus, it is very useful to try to learn from the trend in directed evolution experiments. Here we explore the nature of the refinement of Kemp eliminases by directed evolution, trying to gain an understanding of related requirements from computational design. The observed trend in the directed evolution refinement of KE07 and HG3 are reproduced, showing that in the case of KE07 the directed evolution leads to ground-state destabilization, whereas in the case of HG3 the directed evolution leads to transition-state stabilization. The nature of the different paths of the directed evolution is examined and discussed. The present study seems to indicate that computer-aided enzyme design may require more than calculations of the effect of single mutations and should be extended to calculations of the effect of simultaneous multiple mutations (that make a few residues preorganized effectively). However, the analysis of two known evolution paths can still be accomplished using the relevant sequences and structures. Thus, by comparing two directed evolution paths of Kemp eliminases we reached the important conclusion that the more effective path leads to transition-state stabilization.

CADEE: Computer-Aided Directed Evolution of Enzymes

The tremendous interest in enzymes as biocatalysts has led to extensive work in enzyme engineering, as well as associated methodology development. Here, a new framework for computer-aided directed evolution of enzymes (CADEE) is presented which allows a drastic reduction in the time necessary to prepare and analyze in silico semi-automated directed evolution of enzymes. A pedagogical example of the application of CADEE to a real biological system is also presented in order to illustrate the CADEE workflow.

Expanding the Catalytic Triad in Epoxide Hydrolases and Related Enzymes

Potato epoxide hydrolase 1 exhibits rich enantio- and regioselectivity in the hydrolysis of a broad range of substrates. The enzyme can be engineered to increase the yield of optically pure products as a result of changes in both enantio- and regioselectivity. It is thus highly attractive in biocatalysis, particularly for the generation of enantiopure fine chemicals and pharmaceuticals. The present work aims to establish the principles underlying the activity and selectivity of the enzyme through a combined computational, structural, and kinetic study using the substrate trans-stilbene oxide as a model system. Extensive empirical valence bond simulations have been performed on the wild-type enzyme together with several experimentally characterized mutants. We are able to computationally reproduce the differences between the activities of different stereoisomers of the substrate and the effects of mutations of several active-site residues. In addition, our results indicate the involvement of a previously neglected residue, H104, which is electrostatically linked to the general base H300. We find that this residue, which is highly conserved in epoxide hydrolases and related hydrolytic enzymes, needs to be in its protonated form in order to provide charge balance in an otherwise negatively charged active site. Our data show that unless the active-site charge balance is correctly treated in simulations, it is not possible to generate a physically meaningful model for the enzyme that can accurately reproduce activity and selectivity trends. We also expand our understanding of other catalytic residues, demonstrating in particular the role of a noncanonical residue, E35, as a "backup base" in the absence of H300. Our results provide a detailed view of the main factors driving catalysis and regioselectivity in this enzyme and identify targets for subsequent enzyme design efforts.

Computational design of enzyme-ligand binding using a combined energy function and deterministic sequence optimization algorithm

Enzyme amino-acid sequences at ligand-binding interfaces are evolutionarily optimized for reactions, and the natural conformation of an enzyme-ligand complex must have a low free energy relative to alternative conformations in native-like or non-native sequences. Based on this assumption, a combined energy function was developed for enzyme design and then evaluated by recapitulating native enzyme sequences at ligand-binding interfaces for 10 enzyme-ligand complexes. In this energy function, the electrostatic interaction between polar or charged atoms at buried interfaces is described by an explicitly orientation-dependent hydrogen-bonding potential and a pairwise-decomposable generalized Born model based on the general side chain in the protein design framework. The energy function is augmented with a pairwise surface-area based hydrophobic contribution for nonpolar atom burial. Using this function, on average, 78% of the amino acids at ligand-binding sites were predicted correctly in the minimum-energy sequences, whereas 84% were predicted correctly in the most-similar sequences, which were selected from the top 20 sequences for each enzyme-ligand complex. Hydrogen bonds at the enzyme-ligand binding interfaces in the 10 complexes were usually recovered with the correct geometries. The binding energies calculated using the combined energy function helped to discriminate the active sequences from a pool of alternative sequences that were generated by repeatedly solving a series of mixed-integer linear programming problems for sequence selection with increasing integer cuts.

Simulating the catalytic effect of a designed mononuclear zinc metalloenzyme that catalyzes the hydrolysis of phosphate triesters

One of the greatest challenges in biotechnology and in biochemistry is the ability to design efficient enzymes. In fact, such an ability would be one of the most convincing manifestations of a full understanding of the origin of enzyme catalysis. Despite some progress on this front, most of the advances have been made by placing the reacting fragments in the proper places rather than by optimizing the preorganization of the environment, which is the key factor in enzyme catalysis. A rational improvement of the preorganization and a consistent assessment of the effectiveness of different design options require approaches capable of evaluating reliably the actual catalytic effect. In this work we examine the ability of the empirical valence bond (EVB) to reproduce the results of directed evolution improvements of the catalysis of diethyl 7-hydroxycoumarinyl by a designed mononuclear zinc metalloenzyme. Encouragingly, our study reproduced the catalytic effect obtained by directed evolution and offers a good start for further studies of this system.

Computer aided enzyme design and catalytic concepts

Gaining a deeper understanding of enzyme catalysis is of great practical and fundamental importance. Over the years it has become clear that despite advances made in experimental mutational studies, a quantitative understanding of enzyme catalysis will not be possible without the use of computer modeling approaches. While we believe that electrostatic preorganization is by far the most important catalytic factor, convincing the wider scientific community of this may require the demonstration of effective rational enzyme design. Here we make the point that the main current advances in enzyme design are basically advances in directed evolution and that computer aided enzyme design must involve approaches that can reproduce catalysis in well-defined test cases. Such an approach is provided by the empirical valence bond method.

Exploration of swapping enzymatic function between two proteins: a simulation study of chorismate mutase and isochorismate pyruvate lyase

The enzyme chorismate mutase EcCM from Escherichia coli catalyzes one of the few pericyclic reactions in biology, the transformation of chorismate to prephenate. The isochorismate pyruvate lyase PchB from Pseudomonas aeroginosa catalyzes another pericyclic reaction, the isochorismate to salicylate transformation. Interestingly, PchB possesses weak chorismate mutase activity as well thus being able to catalyze two distinct pericyclic reactions in a single active site. EcCM and PchB possess very similar folds, despite their low sequence identity. Using molecular dynamics simulations of four combinations of the two enzymes (EcCM and PchB) with the two substrates (chorismate and isochorismate) we show that the electrostatic field due to EcCM at atoms of chorismate favors the chorismate to prephenate transition and that, analogously, the electrostatic field due to PchB at atoms of isochorismate favors the isochorismate to salicylate transition. The largest differences between EcCM and PchB in electrostatic field strengths at atoms of the substrates are found to be due to residue side chains at distances between 0.6 and 0.8 nm from particular substrate atoms. Both enzymes tend to bring their non-native substrate in the same conformation as their native substrate. EcCM and to a lower extent PchB fail in influencing the forces on and conformations of the substrate such as to favor the other chemical reaction (isochorismate pyruvate lyase activity for EcCM and chorismate mutase activity for PchB). These observations might explain the difficulty of engineering isochorismate pyruvate lyase activity in EcCM by solely mutating active site residues.

Modeling catalytic promiscuity in the alkaline phosphatase superfamily

In recent years, it has become increasingly clear that promiscuity plays a key role in the evolution of new enzyme function. This finding has helped to elucidate fundamental aspects of molecular evolution. While there has been extensive experimental work on enzyme promiscuity, computational modeling of the chemical details of such promiscuity has traditionally fallen behind the advances in experimental studies, not least due to the nearly prohibitive computational cost involved in examining multiple substrates with multiple potential mechanisms and binding modes in atomic detail with a reasonable degree of accuracy. However, recent advances in both computational methodologies and power have allowed us to reach a stage in the field where we can start to overcome this problem, and molecular simulations can now provide accurate and efficient descriptions of complex biological systems with substantially less computational cost. This has led to significant advances in our understanding of enzyme function and evolution in a broader sense. Here, we will discuss currently available computational approaches that can allow us to probe the underlying molecular basis for enzyme specificity and selectivity, discussing the inherent strengths and weaknesses of each approach. As a case study, we will discuss recent computational work on different members of the alkaline phosphatase superfamily (AP) using a range of different approaches, showing the complementary insights they have provided. We have selected this particular superfamily, as it poses a number of significant challenges for theory, ranging from the complexity of the actual reaction mechanisms involved to the reliable modeling of the catalytic metal centers, as well as the very large system sizes. We will demonstrate that, through current advances in methodologies, computational tools can provide significant insight into the molecular basis for catalytic promiscuity, and, therefore, in turn, the mechanisms of protein functional evolution.

Why nature really chose phosphate

Phosphoryl transfer plays key roles in signaling, energy transduction, protein synthesis, and maintaining the integrity of the genetic material. On the surface, it would appear to be a simple nucleophile displacement reaction. However, this simplicity is deceptive, as, even in aqueous solution, the low-lying d-orbitals on the phosphorus atom allow for eight distinct mechanistic possibilities, before even introducing the complexities of the enzyme catalyzed reactions. To further complicate matters, while powerful, traditional experimental techniques such as the use of linear free-energy relationships (LFER) or measuring isotope effects cannot make unique distinctions between different potential mechanisms. A quarter of a century has passed since Westheimer wrote his seminal review, 'Why Nature Chose Phosphate' (Science 235 (1987), 1173), and a lot has changed in the field since then. The present review revisits this biologically crucial issue, exploring both relevant enzymatic systems as well as the corresponding chemistry in aqueous solution, and demonstrating that the only way key questions in this field are likely to be resolved is through careful theoretical studies (which of course should be able to reproduce all relevant experimental data). Finally, we demonstrate that the reason that nature really chose phosphate is due to interplay between two counteracting effects: on the one hand, phosphates are negatively charged and the resulting charge-charge repulsion with the attacking nucleophile contributes to the very high barrier for hydrolysis, making phosphate esters among the most inert compounds known. However, biology is not only about reducing the barrier to unfavorable chemical reactions. That is, the same charge-charge repulsion that makes phosphate ester hydrolysis so unfavorable also makes it possible to regulate, by exploiting the electrostatics. This means that phosphate ester hydrolysis can not only be turned on, but also be turned off, by fine tuning the electrostatic environment and the present review demonstrates numerous examples where this is the case. Without this capacity for regulation, it would be impossible to have for instance a signaling or metabolic cascade, where the action of each participant is determined by the fine-tuned activity of the previous piece in the production line. This makes phosphate esters the ideal compounds to facilitate life as we know it.

Computational Enzyme Design: Advances, hurdles and possible ways forward

This mini review addresses recent developments in computational enzyme design. Successful protocols as well as known issues and limitations are discussed from an energetic perspective. It will be argued that improved results can be obtained by including a dynamic treatment in the design protocol. Finally, a molecular dynamics-based approach for evaluating and refining computational designs is presented.

Computer-Aided Protein Directed Evolution: a Review of Web Servers, Databases and other Computational Tools for Protein Engineering

The combination of computational and directed evolution methods has proven a winning strategy for protein engineering. We refer to this approach as computer-aided protein directed evolution (CAPDE) and the review summarizes the recent developments in this rapidly growing field. We will restrict ourselves to overview the availability, usability and limitations of web servers, databases and other computational tools proposed in the last five years. The goal of this review is to provide concise information about currently available computational resources to assist the design of directed evolution based protein engineering experiment.

Computational protein engineering: bridging the gap between rational design and laboratory evolution

Enzymes are tremendously proficient catalysts, which can be used as extracellular catalysts for a whole host of processes, from chemical synthesis to the generation of novel biofuels. For them to be more amenable to the needs of biotechnology, however, it is often necessary to be able to manipulate their physico-chemical properties in an efficient and streamlined manner, and, ideally, to be able to train them to catalyze completely new reactions. Recent years have seen an explosion of interest in different approaches to achieve this, both in the laboratory, and in silico. There remains, however, a gap between current approaches to computational enzyme design, which have primarily focused on the early stages of the design process, and laboratory evolution, which is an extremely powerful tool for enzyme redesign, but will always be limited by the vastness of sequence space combined with the low frequency for desirable mutations. This review discusses different approaches towards computational enzyme design and demonstrates how combining newly developed screening approaches that can rapidly predict potential mutation “hotspots” with approaches that can quantitatively and reliably dissect the catalytic step can bridge the gap that currently exists between computational enzyme design and laboratory evolution studies.

Examining the promiscuous phosphatase activity of Pseudomonas aeruginosa arylsulfatase: a comparison to analogous phosphatases

Pseudomonas aeruginosa arylsulfatase (PAS) is a bacterial sulfatase capable of hydrolyzing a range of sulfate esters. Recently, it has been demonstrated to also show very high proficiency for phosphate ester hydrolysis. Such proficient catalytic promiscuity is significant, as promiscuity has been suggested to play an important role in enzyme evolution. Additionally, a comparative study of the hydrolyses of the p-nitrophenyl phosphate and sulfate monoesters in aqueous solution has demonstrated that despite superficial similarities, the two reactions proceed through markedly different transition states with very different solvation effects, indicating that the requirements for the efficient catalysis of the two reactions by an enzyme will also be very different (and yet they are both catalyzed by the same active site). This work explores the promiscuous phosphomonoesterase activity of PAS. Specifically, we have investigated the identity of the most likely base for the initial activation of the unusual formylglycine hydrate nucleophile (which is common to many sulfatases), and demonstrate that a concerted substrate-as-base mechanism is fully consistent with the experimentally observed data. This is very similar to other related systems, and suggests that, as far as the phosphomonoesterase activity of PAS is concerned, the sulfatase behaves like a "classical" phosphatase, despite the fact that such a mechanism is unlikely to be available to the native substrate (based on pK(a) considerations and studies of model systems). Understanding such catalytic versatility can be used to design novel artificial enzymes that are far more proficient than the current generation of designer enzymes.

Validating the vitality strategy for fighting drug resistance

The current challenge in designing effective drugs against HIV-1 is to find novel candidates with high potency, but with a lower susceptibility to mutations associated with drug resistance. Trying to address this challenge, we developed in our previous study (Ishikita and Warshel, Angew Chem Int Ed Engl 2008; 47:697-700) a novel computational strategy for fighting drug resistance by predicting the likely moves of the virus through constraints on binding and catalysis. This has been based on calculating the ratio between the vitality values ((K(i) k(cat)/K(M))(mutant)/(K(i) k(cat)/K(M))(wild-type)) and using it as a guide for predicting the moves of the virus. The corresponding calculations of the binding affinity, K(i), were carried out using the semi-macroscopic version of the protein dipole Langevin dipole (PDLD/S) in its linear response approximation (LRA) in its β version (PDLD/S-LRA/β). We also calculate the proteolytic efficiency, k(cat)/K(M), by evaluating the transition state (TS) binding free energies using the PDLD/S-LRA/β method. Here we provide an extensive validation of our strategy by calculating the vitality of six existing clinical and experimental drug candidates. It is found that the computationally determined vitalities correlate reasonably well with those derived from the corresponding experimental data. This indicates that the calculated vitality may be used to identify mutations that would be most effective for the survival of the virus. Thus, it should be possible to use our approach in screening for mutations that would provide the most effective resistance to any proposed antiviral drug. This ability should be very useful in guiding the design of drug molecules that will lead to the slowest resistance.

Biosynthesis of phloroglucinol compounds in microorganisms--review

Phloroglucinol derivatives are a major class of secondary metabolites of wide occurrence in biological systems. In the bacteria kingdom, these compounds can only be synthesized by some species of Pseudomonads. Pseudomonas spp. could produce 2,4-diacetylphloroglucinol (DAPG) that plays an important role in the biological control of many plant pathogens. In this review, we summarize knowledge about synthesis of phloroglucinol compounds based on the DAPG biosynthetic pathway. Recent advances that have been made in understanding phloroglucinol compound biosynthesis and regulation are highlighted. From these studies, researchers have identified the biosynthesis pathway of DAPG. Most of the genes involved in the biosynthesis pathway have been cloned and characterized. Additionally, heterologous systems of the model microorganism Escherichia coli are constructed to produce phloroglucinol. Although further work is still required, a full understanding of phloroglucinol compound biosynthesis is almost within reach. This review also suggests new directions and attempts to gain some insights for better understanding of the biosynthesis and regulation of DAPG. The combination of traditional biochemistry and molecular biology with new systems biology and synthetic biology tools will provide a better view of phloroglucinol compound biosynthesis and a greater potential of microbial production.

Coarse-grained (multiscale) simulations in studies of biophysical and chemical systems

Recent years have witnessed an explosion in computational power, leading to attempts to model ever more complex systems. Nevertheless, there remain cases for which the use of brute-force computer simulations is clearly not the solution. In such cases, great benefit can be obtained from the use of physically sound simplifications. The introduction of such coarse graining can be traced back to the early usage of a simplified model in studies of proteins. Since then, the field has progressed tremendously. In this review, we cover both key developments in the field and potential future directions. Additionally, particular emphasis is given to two general approaches, namely the renormalization and reference potential approaches, which allow one to move back and forth between the coarse-grained (CG) and full models, as these approaches provide the foundation for CG modeling of complex systems.

Converting structural information into an allosteric-energy-based picture for elongation factor Tu activation by the ribosome

The crucial process of aminoacyl-tRNA delivery to the ribosome is energized by the GTPase reaction of the elongation factor Tu (EF-Tu). Advances in the elucidation of the structure of the EF-Tu/ribosome complex provide the rare opportunity of gaining a detailed understanding of the activation process of this system. Here, we use quantitative simulation approaches and reproduce the energetics of the GTPase reaction of EF-Tu with and without the ribosome and with several key mutants. Our study provides a novel insight into the activation process. It is found that the critical H84 residue is not likely to behave as a general base but rather contributes to an allosteric effect, which includes a major transition state stabilization by the electrostatic effect of the P loop and other regions of the protein. Our findings have general relevance to GTPase activation, including the processes that control signal transduction.

Computational protein design with a generalized Born solvent model: application to Asparaginyl-tRNA synthetase

Computational Protein Design (CPD) is a promising method for high throughput protein and ligand mutagenesis. Recently, we developed a CPD method that used a polar-hydrogen energy function for protein interactions and a Coulomb/Accessible Surface Area (CASA) model for solvent effects. We applied this method to engineer aspartyl-adenylate (AspAMP) specificity into Asparaginyl-tRNA synthetase (AsnRS), whose substrate is asparaginyl-adenylate (AsnAMP). Here, we implement a more accurate function, with an all-atom energy for protein interactions and a residue-pairwise generalized Born model for solvent effects. As a first test, we compute aminoacid affinities for several point mutants of Aspartyl-tRNA synthetase (AspRS) and Tyrosyl-tRNA synthetase and stability changes for three helical peptides and compare with experiment. As a second test, we readdress the problem of AsnRS aminoacid engineering. We compare three design criteria, which optimize the folding free-energy, the absolute AspAMP affinity, and the relative (AspAMP-AsnAMP) affinity. The sequences and conformations are improved with respect to our previous, polar-hydrogen/CASA study: For several designed complexes, the AspAMP carboxylate forms three interactions with a conserved arginine and a designed lysine, as in the active site of the AspRS:AspAMP complex. The conformations and interactions are well maintained in molecular dynamics simulations and the sequences have an inverted specificity, favoring AspAMP over AsnAMP. The method is not fully successful, since experimental measurements with the seven most promising sequences show that they do not catalyze at a detectable level the adenylation of Asp (or Asn) with ATP. This may be due to weak AspAMP binding and/or disruption of transition-state stabilization.

Multiscale modeling of biological functions

Recent years have witnessed a tremendous explosion in computational power, which in turn has resulted in great progress in the complexity of the biological and chemical problems that can be addressed by means of all-atom simulations. Despite this, however, our computational time is not infinite, and in fact many of the key problems of the field were resolved long before the existence of the current levels of computational power. This review will start by presenting a brief historical overview of the use of multiscale simulations in biology, and then present some key developments in the field, highlighting several cases where the use of a physically sound simplification is clearly superior to a brute-force approach. Finally, some potential future directions will be discussed.

Challenges and advances in validating enzyme design proposals: the case of kemp eliminase catalysis

One of the fundamental challenges in biotechnology and biochemistry is the ability to design effective enzymes. Despite recent progress, most of the advances on this front have been made by placing the reacting fragments in the proper places, rather than by optimizing the preorganization of the environment, which is the key factor in enzyme catalysis. Thus, rational improvement of the preorganization would require approaches capable of evaluating reliably the actual catalytic effect. This work considers the catalytic effects in different Kemp eliminases as a benchmark for a computer-aided enzyme design. It is shown that the empirical valence bond provides a powerful screening tool, with significant advantages over current alternative strategies. The insights provided by the empirical valence bond calculations are discussed with an emphasis on the ability to analyze the difference between the linear free energy relationships obtained in solution and those found in the enzymes. We also point out the trade-off between the reliability and speed of the calculations and try to determine what it takes to realize reliable computer-aided screening.

Evaluation and ranking of enzyme designs

In 2008, a successful computational design procedure was reported that yielded active enzyme catalysts for the Kemp elimination. Here, we studied these proteins together with a set of previously unpublished inactive designs to determine the sources of activity or lack thereof, and to predict which of the designed structures are most likely to be catalytic. Methods that range from quantum mechanics (QM) on truncated model systems to the treatment of the full protein with ONIOM QM/MM and AMBER molecular dynamics (MD) were explored. The most effective procedure involved molecular dynamics, and a general MD protocol was established. Substantial deviations from the ideal catalytic geometries were observed for a number of designs. Penetration of water into the catalytic site and insufficient residue-packing around the active site are the main factors that can cause enzyme designs to be inactive. Where in the past, computational evaluations of designed enzymes were too time-extensive for practical considerations, it has now become feasible to rank and refine candidates computationally prior to and in conjunction with experimentation, thus markedly increasing the efficiency of the enzyme design process.

On catalytic preorganization in oxyanion holes: highlighting the problems with the gas-phase modeling of oxyanion holes and illustrating the need for complete enzyme models

Oxyanion holes play a major role in catalyzing enzymatic reactions, yet the corresponding energetics is frequently misunderstood. The main problem may be associated with the nontrivial nature of the electrostatic preorganization effect, without following the relevant formulation. That is, although the energetics of oxyanion holes have been fully quantified in early studies (which include both the enzymatic and reference solution reactions), the findings of these studies are sometimes overlooked, and, in some cases, it is assumed that gas-phase calculations with a fixed model of an oxyanion hole are sufficient for assessing the corresponding effect in the protein. Herein, we present a systematic analysis of this issue, clarifying the problems associated with modeling oxyanions by means of two fixed water molecules (or related constructs). We then re-emphasize the point that the effect of the oxyanion hole is mainly due to the fact that the relevant dipoles are already set in an orientation that stabilizes the TS charges, whereas the corresponding dipoles in solution are randomly oriented, resulting in the need to pay a very large reorganization energy. Simply calculating interaction energies with relatively fixed species cannot capture this crucial point, and considering it may help in advancing rational enzyme design.

Catalytic reaction mechanism of acetylcholinesterase determined by Born-Oppenheimer ab initio QM/MM molecular dynamics simulations

Acetylcholinesterase (AChE) is a remarkably efficient serine hydrolase responsible for the termination of impulse signaling at cholinergic synapses. By employing Born-Oppenheimer molecular dynamics simulations with a B3LYP/6-31G(d) QM/MM potential and the umbrella sampling method, we have characterized its complete catalytic reaction mechanism for hydrolyzing neurotransmitter acetylcholine (ACh) and determined its multistep free-energy reaction profiles for the first time. In both acylation and deacylation reaction stages, the first step involves the nucleophilic attack on the carbonyl carbon, with the triad His447 serving as the general base, and leads to a tetrahedral covalent intermediate stabilized by the oxyanion hole. From the intermediate to the product, the orientation of the His447 ring needs to be adjusted very slightly, and then, the proton transfers from His447 to the product, and the break of the scissile bond happens spontaneously. For the three-pronged oxyanion hole, it only makes two hydrogen bonds with the carbonyl oxygen at either the initial reactant or the final product state, but the third hydrogen bond is formed and stable at all transition and intermediate states during the catalytic process. At the intermediate state of the acylation reaction, a short and low-barrier hydrogen bond (LBHB) is found to be formed between two catalytic triad residues His447 and Glu334, and the spontaneous proton transfer between two residues has been observed. However, it is only about 1-2 kcal/mol stronger than the normal hydrogen bond. In comparison with previous theoretical investigations of the AChE catalytic mechanism, our current study clearly demonstrates the power and advantages of employing Born-Oppenheimer ab initio QM/MM MD simulations in characterizing enzyme reaction mechanisms.

Exploring challenges in rational enzyme design by simulating the catalysis in artificial kemp eliminase

One of the fundamental challenges in biotechnology and in biochemistry is the ability to design effective enzymes. Doing so would be a convincing manifestation of a full understanding of the origin of enzyme catalysis. Despite an impressive progress, most of the advances on this front have been made by placing the reacting fragments in the proper places, rather than by optimizing the environment preorganization, which is the key factor in enzyme catalysis. Rational improvement of the preorganization would require approaches capable of evaluating reliably the actual catalytic effect. This work takes previously designed kemp eliminases as a benchmark for a computer aided enzyme design, using the empirical valence bond as the main screening tool. The observed absolute catalytic effect and the effect of directed evolution are reproduced and analyzed (assuming that the substrate is in the designed site). It is found that, in the case of kemp eliminases, the transition state charge distribution makes it hard to exploit the active site polarity, even with the ability to quantify the effect of different mutations. Unexpectedly, it is found that the directed evolution mutants lead to the reduction of solvation of the reactant state by water molecules rather that to the more common mode of transition state stabilization used by naturally evolved enzymes. Finally it is pointed out that our difficulties in improving Kemp eliminase are not due to overlooking exotic effect, but to the challenge in designing a preorganized environment that would exploit the small change it charge distribution during the formation of the transition state.

At the dawn of the 21st century: Is dynamics the missing link for understanding enzyme catalysis?

Enzymes play a key role in almost all biological processes, accelerating a variety of metabolic reactions as well as controlling energy transduction, the transcription, and translation of genetic information, and signaling. They possess the remarkable capacity to accelerate reactions by many orders of magnitude compared to their uncatalyzed counterparts, making feasible crucial processes that would otherwise not occur on biologically relevant timescales. Thus, there is broad interest in understanding the catalytic power of enzymes on a molecular level. Several proposals have been put forward to try to explain this phenomenon, and one that has rapidly gained momentum in recent years is the idea that enzyme dynamics somehow contributes to catalysis. This review examines the dynamical proposal in a critical way, considering basically all reasonable definitions, including (but not limited to) such proposed effects as "coupling between conformational and chemical motions," "landscape searches" and "entropy funnels." It is shown that none of these proposed effects have been experimentally demonstrated to contribute to catalysis, nor are they supported by consistent theoretical studies. On the other hand, it is clarified that careful simulation studies have excluded most (if not all) dynamical proposals. This review places significant emphasis on clarifying the role of logical definitions of different catalytic proposals, and on the need for a clear formulation in terms of the assumed potential surface and reaction coordinate. Finally, it is pointed out that electrostatic preorganization actually accounts for the observed catalytic effects of enzymes, through the corresponding changes in the activation free energies.

Multiscale simulations of protein landscapes: using coarse-grained models as reference potentials to full explicit models

Evaluating the free-energy landscape of proteins and the corresponding functional aspects presents a major challenge for computer simulation approaches. This challenge is due to the complexity of the landscape and the enormous computer time needed for converging simulations. The use of simplified coarse-grained (CG) folding models offers an effective way of sampling the landscape but such a treatment, however, may not give the correct description of the effect of the actual protein residues. A general way around this problem that has been put forward in our early work (Fan et al., Theor Chem Acc 1999;103:77-80) uses the CG model as a reference potential for free-energy calculations of different properties of the explicit model. This method is refined and extended here, focusing on improving the electrostatic treatment and on demonstrating key applications. These applications include: evaluation of changes of folding energy upon mutations, calculations of transition-states binding free energies (which are crucial for rational enzyme design), evaluations of catalytic landscape, and evaluations of the time-dependent responses to pH changes. Furthermore, the general potential of our approach in overcoming major challenges in studies of structure function correlation in proteins is discussed.

The EVB as a quantitative tool for formulating simulations and analyzing biological and chemical reactions

Recent years have seen dramatic improvements in computer power, allowing ever more challenging problems to be approached. In light of this, it is imperative to have a quantitative model for examining chemical reactivity, both in the condensed phase and in solution, as well as to accurately quantify physical organic chemistry (particularly as experimental approaches can often be inconclusive). Similarly, computational approaches allow for great progress in studying enzyme catalysis, as they allow for the separation of the relevant energy contributions to catalysis. Due to the complexity of the problems that need addressing, there is a need for an approach that can combine reliability with an ability to capture complex systems in order to resolve long-standing controversies in a unique way. Herein, we will demonstrate that the empirical valence bond (EVB) approach provides a powerful way to connect the classical concepts of physical organic chemistry to the actual energies of enzymatic reactions by means of computation. Additionally, we will discuss the proliferation of this approach, as well as attempts to capture its basic chemistry and repackage it under different names. We believe that the EVB approach is the most powerful tool that is currently available for studies of chemical processes in the condensed phase in general and enzymes in particular, particularly when trying to explore the different proposals about the origin of the catalytic power of enzymes.