Contribution of Oxyanion Stabilization to Kemp Eliminase Efficiency

Epistasis arises from shifting the rate-limiting step during enzyme evolution of a β-lactamase

Epistasis, the non-additive effect of mutations, can provide combinatorial improvements to enzyme activity that substantially exceed the gains from individual mutations. Yet the molecular mechanisms of epistasis remain elusive, undermining our ability to predict pathogen evolution and engineer biocatalysts. Here we reveal how directed evolution of a β-lactamase yielded highly epistatic activity enhancements. Evolution selected four mutations that increase antibiotic resistance 40-fold, despite their marginal individual effects (≤2-fold). Synergistic improvements coincided with the introduction of super-stochiometric burst kinetics, indicating that epistasis is rooted in the enzyme's conformational dynamics. Our analysis reveals that epistasis stemmed from distinct effects of each mutation on the catalytic cycle. The initial mutation increased protein flexibility and accelerated substrate binding, which is rate-limiting in the wild-type enzyme. Subsequent mutations predominantly boosted the chemical steps by fine-tuning substrate interactions. Our work identifies an overlooked cause for epistasis: changing the rate-limiting step can result in substantial synergy that boosts enzyme activity.

Using High-Throughput Molecular Dynamics Simulation to Enhance the Computational Design of Kemp Elimination Enzymes

Computational enzyme design has been successfully applied to identify new alternatives to natural enzymes for the biosynthesis of important compounds. However, the moderate catalytic activities of de novo designed enzymes indicate that the modeling accuracy of current computational enzyme design methods should be improved. Here, high-throughput molecular dynamics simulations were used to enhance computational enzyme design, thus allowing the identification of variants with higher activities in silico. Different time schemes of high-throughput molecular dynamics simulations were tested to identify the catalytic features of evolved Kemp eliminases. The 20 × 1 ns molecular dynamics simulation scheme was sufficiently accurate and computationally viable to screen the computationally designed massive variants of Kemp elimination enzymes. The developed hybrid computational strategy was used to redesign the most active Kemp eliminase, HG3.17, and five variants were generated and experimentally confirmed to afford higher catalytic efficiencies than that of HG3.17, with one double variant (D52Q/A53S) exhibiting a 55% increase. The hybrid computational enzyme design strategy is general and computationally economical, with which we anticipate the efficient creation of practical enzymes for industrial biocatalysis.

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

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

Probing the effect of microenvironment on the enzyme-like behavior of catalytic peptide assemblies

As bridging species between short peptides and macromolecular proteins, peptide assemblies not only provide a supramolecular approach for the fabrication of controllable molecular machines with enzyme-like functions, but also a simplified model for understanding the catalytic mechanism of natural enzymes. In this study, we focused on probing the effect of microenvironment on the catalytic behavior of peptide assemblies. Upon simply replacing the X residue in Fmoc-FFXAH-CONH2, we realized the modulation of the microenvironment of the amyloid assemblies, which thus appeared esterase-like function with different catalytic abilities. The chemistry, structure and activity were analyzed to explore the principles that how the hydrophobic, charged, polar and chiral microenvironment deciding the catalytic behavior of the esterase mimic. In addition, we also presented the potential of the catalytic assemblies in the encapsulation, delivery and enzymatic metabolization of a mutual prodrug. This work sheds new insights for understanding the structure-function relationship of catalytic peptide assemblies and natural enzymes, and also provides a new avenue for the designing of artificial enzymes with better functions.

Decoded fingerprints of hyperresponsive, expanding product space: polyether cascade cyclizations as tools to elucidate supramolecular catalysis

Simple enough to be understood and complex enough to be revealing, cascade cyclizations of diepoxides are introduced as new tools to characterize supramolecular catalysis. Decoded product fingerprints are provided for a consistent set of substrate stereoisomers, and shown to report on chemo-, diastereo- and enantioselectivity, mechanism and even autocatalysis. Application of the new tool to representative supramolecular systems reveals, for instance, that pnictogen-bonding catalysis is not only best in breaking the Baldwin rules but also converts substrate diastereomers into completely different products. Within supramolecular capsules, new cyclic hemiacetals from House–Meinwald rearrangements are identified, and autocatalysis on anion–π catalysts is found to be independent of substrate stereochemistry. Decoded product fingerprints further support that the involved epoxide-opening polyether cascade cyclizations are directional, racemization-free, and interconnected, at least partially. The discovery of unique characteristics for all catalysts tested would not have been possible without decoded cascade cyclization fingerprints, thus validating the existence and significance of privileged platforms to elucidate supramolecular catalysis. Once decoded, cascade cyclization fingerprints are easily and broadly applicable, ready for use in the community.

Hyperresponsive XL product space identifies polyether cascade fingerprinting as an attractive tool to elucidate supramolecular catalysis, including pnictogen-bonding, capsule and anion–π catalysts.

A novel 3D Zn-coordination polymer based on a multiresponsive fluorescent sensor demonstrating outstanding sensitivities and selectivities for the efficient detection of multiple analytes

A novel and unusual 3D luminescent coordination polymer (CP) [Zn₂(3-bpah)(bpta)(H₂O)]·3H₂O (1), where 3-bpah denotes N,N'-bis(3-pyridinecarboxamide)-1,2-cyclohexane and H₄bpta denotes 2,2',4,4'-biphenyltetracarboxylic acid, was successfully synthesized via hydrothermal methods from Zn(II) ions and 3-bpah and bpta ligands. The structure of this CP was investigated via powder X-ray diffraction (PXRD) analysis along with single crystal X-ray diffraction. Notably, 1 exhibits remarkable fluorescence behavior and stability over a wide pH range and in various pure organic solvents. More importantly, 1 can become an outstanding candidate for the selective and sensitive sensing of Fe³⁺, Mg²⁺, Cr₂O₇^2-, MnO₄^-, nitrobenzene (NB) and nitromethane (NM), at an extremely low detection limit. The changes in the fluorescence intensity exhibited by these six analytes in the presence of 1 over a wide pH range indicate that this polymer can be an excellent luminescent sensor. To the best of our knowledge, 1 is a rare example of a CP-based multiresponsive fluorescent sensor for metal cations, anions, and toxic organic solvents.

Designing better enzymes: Insights from directed evolution

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.

How directed evolution reshapes the energy landscape in an enzyme to boost catalysis

The advent of biocatalysts designed computationally and optimized by laboratory evolution provides an opportunity to explore molecular strategies for augmenting catalytic function. Applying a suite of nuclear magnetic resonance, crystallography, and stopped-flow techniques to an enzyme designed for an elementary proton transfer reaction, we show how directed evolution gradually altered the conformational ensemble of the protein scaffold to populate a narrow, highly active conformational ensemble and accelerate this transformation by nearly nine orders of magnitude. Mutations acquired during optimization enabled global conformational changes, including high-energy backbone rearrangements, that cooperatively organized the catalytic base and oxyanion stabilizer, thus perfecting transition-state stabilization. The development of protein catalysts for many chemical transformations could be facilitated by explicitly sampling conformational substates during design and specifically stabilizing productive substates over all unproductive conformations.

Catalytic Effect of Electric Fields on the Kemp Elimination Reactions with Neutral Bases

The effect of solvent reaction fields and oriented electric fields on the Kemp elimination reaction between methylamine or imidazole and 5-nitrobenzisoxazole has been theoretically studied. The Kemp reaction is the most widely used for the design of new enzymes. Our results, using the SMD continuous model for solvents, are in quite good agreement with the experimental fact that the rate of the analogous reaction with butylamine is one order of magnitude smaller in water than in acetonitrile. In the case of external electric fields, our results show that they can increase or decrease the energy barrier depending on the magnitude and orientation of the field. A duly oriented electric field may have a notable catalytic effect on the reaction. So, external electric fields and reaction fields due to the medium can contribute to the design of new enzymes. Several factors that must be taken into account to increase the catalytic effect are discussed.

Ensemble-based enzyme design can recapitulate the effects of laboratory directed evolution in silico

The creation of artificial enzymes is a key objective of computational protein design. Although de novo enzymes have been successfully designed, these exhibit low catalytic efficiencies, requiring directed evolution to improve activity. Here, we use room-temperature X-ray crystallography to study changes in the conformational ensemble during evolution of the designed Kemp eliminase HG3 (k_cat/K_M 146 M⁻¹s⁻¹). We observe that catalytic residues are increasingly rigidified, the active site becomes better pre-organized, and its entrance is widened. Based on these observations, we engineer HG4, an efficient biocatalyst (k_cat/K_M 103,000 M⁻¹s⁻¹) containing key first and second-shell mutations found during evolution. HG4 structures reveal that its active site is pre-organized and rigidified for efficient catalysis. Our results show how directed evolution circumvents challenges inherent to enzyme design by shifting conformational ensembles to favor catalytically-productive sub-states, and suggest improvements to the design methodology that incorporate ensemble modeling of crystallographic data.

Kemp eliminases are artificial enzymes that catalyze the concerted deprotonation and ring-opening of benzisoxazoles. Here, the authors use room-temperature X-ray crystallography to investigate changes to the conformational ensemble of the Kemp eliminase HG3 along a directed evolutionary trajectory, and develop an experimentally guided, ensemble-based computational enzyme design procedure.

Enhancing a de novo enzyme activity by computationally-focused ultra-low-throughput screening

Directed evolution has revolutionized protein engineering. Still, enzyme optimization by random library screening remains sluggish, in large part due to futile probing of mutations that are catalytically neutral and/or impair stability and folding. FuncLib is a novel approach which uses phylogenetic analysis and Rosetta design to rank enzyme variants with multiple mutations, on the basis of predicted stability. Here, we use it to target the active site region of a minimalist-designed, de novo Kemp eliminase. The similarity between the Michaelis complex and transition state for the enzymatic reaction makes this system particularly challenging to optimize. Yet, experimental screening of a small number of active-site variants at the top of the predicted stability ranking leads to catalytic efficiencies and turnover numbers (∼2 × 104 M-1 s-1 and ∼102 s-1) for this anthropogenic reaction that compare favorably to those of modern natural enzymes. This result illustrates the promise of FuncLib as a powerful tool with which to speed up directed evolution, even on scaffolds that were not originally evolved for those functions, by guiding screening to regions of the sequence space that encode stable and catalytically diverse enzymes. Empirical valence bond calculations reproduce the experimental activation energies for the optimized eliminases to within ∼2 kcal mol-1 and indicate that the enhanced activity is linked to better geometric preorganization of the active site. This raises the possibility of further enhancing the stability-guidance of FuncLib by computational predictions of catalytic activity, as a generalized approach for computational enzyme design.

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.