Kemp Eliminase Research Articles

Evolution Enhances Kemp Eliminase Activity by Optimizing Oxyanion Stabilization and Conformational Flexibility.

The base-promoted Kemp elimination reaction has been used as a model system for enzyme design. Among the multiple computationally designed and evolved Kemp eliminases generated along the years, the HG3-to-HG3.17 evolutionary trajectory is particularly interesting due to the high catalytic efficiency of HG3.17 and the debated role of glutamine 50 (Gln50) as potential oxyanion stabilizer. This study aims to elucidate the structural and dynamic changes along the evolutionary pathway from HG3 to HG3.17 that contribute to improved catalytic efficiency. In particular, we evaluate key variants along the HG3 evolutionary trajectory via molecular dynamics simulations coupled to non-covalent interactions and water analysis. Our computational study indicates that HG3.17 can adopt a catalytically competent conformation promoted by a water-mediated network of non-covalent interactions, in which aspartate 124 is properly positioned for proton abstraction and both glutamine 50 and mutation cysteine 84 contribute to oxyanion stabilization. We find that HG3.17 exhibits a rather high flexibility of Gln50, which is regulated by the conformation adopted by the active site residue Trp44. This interplay between Gln50 and Trp44 positioning induced by distal active site mutations affects the water-mediated network of non-covalent interactions, Gln50 preorganization, and water content of the active site pocket.

Enriching productive mutational paths accelerates enzyme evolution.

Darwinian evolution has given rise to all the enzymes that enable life on Earth. Mimicking natural selection, scientists have learned to tailor these biocatalysts through recursive cycles of mutation, selection and amplification, often relying on screening large protein libraries to productively modulate the complex interplay between protein structure, dynamics and function. Here we show that by removing destabilizing mutations at the library design stage and taking advantage of recent advances in gene synthesis, we can accelerate the evolution of a computationally designed enzyme. In only five rounds of evolution, we generated a Kemp eliminase-an enzymatic model system for proton transfer from carbon-that accelerates the proton abstraction step >108-fold over the uncatalyzed reaction. Recombining the resulting variant with a previously evolved Kemp eliminase HG3.17, which exhibits similar activity but differs by 29 substitutions, allowed us to chart the topography of the designer enzyme's fitness landscape, highlighting that a given protein scaffold can accommodate several, equally viable solutions to a specific catalytic problem.

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.

EnzyHTP Computational Directed Evolution with Adaptive Resource Allocation.

Directed evolution facilitates enzyme engineering via iterative rounds of mutagenesis. Despite the wide applications of high-throughput screening, building "smart libraries" to effectively identify beneficial variants remains a major challenge in the community. Here, we developed a new computational directed evolution protocol based on EnzyHTP, a software that we have previously reported to automate enzyme modeling. To enhance the throughput efficiency, we implemented an adaptive resource allocation strategy that dynamically allocates different types of computing resources (e.g., GPU/CPU) based on the specific need of an enzyme modeling subtask in the workflow. We implemented the strategy as a Python library and tested the library using fluoroacetate dehalogenase as a model enzyme. The results show that compared to fixed resource allocation where both CPU and GPU are on-call for use during the entire workflow, applying adaptive resource allocation can save 87% CPU hours and 14% GPU hours. Furthermore, we constructed a computational directed evolution protocol under the framework of adaptive resource allocation. The workflow was tested against two rounds of mutational screening in the directed evolution experiments of Kemp eliminase (KE07) with a total of 184 mutants. Using folding stability and electrostatic stabilization energy as computational readout, we identified all four experimentally observed target variants. Enabled by the workflow, the entire computation task (i.e., 18.4 μs MD and 18,400 QM single-point calculations) completes in 3 days of wall-clock time using ∼30 GPUs and ∼1000 CPUs.

Engineering of a P450-based Kemp eliminase with a new mechanism

Engineering of a P450-based Kemp eliminase with a new mechanism

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.

Convergence in determining enzyme functional descriptors across Kemp eliminase variants

Molecular simulations have been extensively employed to accelerate biocatalytic discoveries. Enzyme functional descriptors derived from molecular simulations have been leveraged to guide the search for beneficial enzyme mutants. However, the ideal active-site region size for computing the descriptors over multiple enzyme variants remains untested. Here, we conducted convergence tests for dynamics-derived and electrostatic descriptors on 18 Kemp eliminase variants across six active-site regions with various boundary distances to the substrate. The tested descriptors include the root-mean-square deviation of the active-site region, the solvent accessible surface area ratio between the substrate and active site, and the projection of the electric field (EF) on the breaking C–H bond. All descriptors were evaluated using molecular mechanics methods. To understand the effects of electronic structure, the EF was also evaluated using quantum mechanics/molecular mechanics methods. The descriptor values were computed for 18 Kemp eliminase variants. Spearman correlation matrices were used to determine the region size condition under which further expansion of the region boundary does not substantially change the ranking of descriptor values. We observed that protein dynamics-derived descriptors, including RMSDactive_site and SASAratio, converge at a distance cutoff of 5 Å from the substrate. The electrostatic descriptor, EFC–H, converges at 6 Å using molecular mechanics methods with truncated enzyme models and 4 Å using quantum mechanics/molecular mechanics methods with whole enzyme model. This study serves as a future reference to determine descriptors for predictive modeling of enzyme engineering.

NMR-guided directed evolution.

Directed evolution is a powerful tool for improving existing properties and imparting completely new functionalities to proteins1-4. Nonetheless, its potential ineven small proteins is inherently limited by the astronomical number of possible amino acid sequences. Sampling the complete sequence space of a 100-residue protein would require testing of 20100 combinations, which is beyond any existing experimental approach. In practice, selective modification of relatively few residues is sufficient for efficient improvement, functional enhancement and repurposing of existing proteins5. Moreover, computational methods have been developed to predict the locations and, in certain cases, identities of potentially productive mutations6-9. Importantly, all current approaches for prediction of hot spots and productive mutations rely heavily on structural information and/or bioinformatics, which is not always available for proteins of interest. Moreover, they offer a limited ability to identify beneficial mutations far from the active site, even though such changes may markedly improve the catalytic properties of an enzyme10. Machine learning methods have recently showed promise in predicting productive mutations11, but they frequently require large, high-quality training datasets, which are difficult to obtain in directed evolution experiments. Here we show that mutagenic hot spots in enzymes can be identified using NMR spectroscopy. In a proof-of-concept study, we converted myoglobin, a non-enzymatic oxygen storage protein, into a highly efficient Kemp eliminase using only three mutations. The observed levels of catalytic efficiency exceed those of proteins designed using current approaches and are similar with those of natural enzymes for the reactions that they are evolved to catalyse. Given the simplicity of this experimental approach, which requires no a priori structural or bioinformatic knowledge, we expect it to be widely applicable and to enable the full potential of directed enzyme evolution.

Efficient Base-Catalyzed Kemp Elimination in an Engineered Ancestral Enzyme.

The routine generation of enzymes with completely new active sites is a major unsolved problem in protein engineering. Advances in this field have thus far been modest, perhaps due, at least in part, to the widespread use of modern natural proteins as scaffolds for de novo engineering. Most modern proteins are highly evolved and specialized and, consequently, difficult to repurpose for completely new functionalities. Conceivably, resurrected ancestral proteins with the biophysical properties that promote evolvability, such as high stability and conformational diversity, could provide better scaffolds for de novo enzyme generation. Kemp elimination, a non-natural reaction that provides a simple model of proton abstraction from carbon, has been extensively used as a benchmark in de novo enzyme engineering. Here, we present an engineered ancestral β-lactamase with a new active site that is capable of efficiently catalyzing Kemp elimination. The engineering of our Kemp eliminase involved minimalist design based on a single function-generating mutation, inclusion of an extra polypeptide segment at a position close to the de novo active site, and sharply focused, low-throughput library screening. Nevertheless, its catalytic parameters (kcat/KM~2·105 M−1 s−1, kcat~635 s−1) compare favorably with the average modern natural enzyme and match the best proton-abstraction de novo Kemp eliminases that are reported in the literature. The general implications of our results for de novo enzyme engineering are discussed.

Natural Evolution Provides Strong Hints about Laboratory Evolution of Designer Enzymes

Laboratory evolution combined with computational enzyme design provides the opportunity to generate novel biocatalysts. Nevertheless, it has been challenging to understand how laboratory evolution optimizes designer enzymes by introducing seemingly random mutations. A typical enzyme optimized with laboratory evolution is the abiological Kemp eliminase, initially designed by grafting active site residues into a natural protein scaffold. Here, we relate the catalytic power of laboratory-evolved Kemp eliminases to the statistical energy ([Formula: see text]) inferred from their natural homologous sequences using the maximum entropy model. The [Formula: see text] of designs generated by directed evolution is correlated with enhanced activity and reduced stability, thus displaying a stability-activity trade-off. In contrast, the [Formula: see text] for mutants in catalytic-active remote regions (in which remote residues are important for catalysis) is strongly anticorrelated with the activity. These findings provide an insight into the role of protein scaffolds in the adaption to new enzymatic functions. It also indicates that the valley in the [Formula: see text] landscape can guide enzyme design for abiological catalysis. Overall, the connection between laboratory and natural evolution contributes to understanding what is optimized in the laboratory and how new enzymatic function emerges in nature, and provides guidance for computational enzyme design.

NMR‐Guided Directed Evolution of a Kemp Eliminase

Directed evolution has emerged as a powerful tool for improving protein properties and imparting completely new functionalities onto existing proteins. Several computational methods have been successful in predicting potential hotspots for directed evolution. However, they rely heavily on prior structural and/or functional information. To address these fundamental limitations, we focused on Nuclear Magnetic Resonance (NMR) as a tool to rapidly evolve enzymes without structural characterization. AlleyCat, a computationally designed Kemp eliminase produced by introducing a single mutation on C‐terminal domain of a non‐enzymatic metalloprotein Calmodulin, has recently been evolved into AlleyCat7. This traditional directed evolution process required seven rounds of mutagenesis and screening. A retrospective analysis of the evolved variants of AlleyCat series showed a remarkable correlation between chemical shift perturbation and probability of finding a productive mutation in the vicinity of that residue. Using our novel approach, AlleyCat7 has been rapidly evolved into AlleyCat10 with an improvement of about four‐fold in catalytic efficiency after three successive rounds of evolution. NMR‐guided evolution strategy eliminates excessive mutagenesis and has the potential to unlock exciting avenues in directed evolution of protein catalysts.

Tuning the Catalytic Activity of Synthetic Enzyme KE15 with DNA.

Efficiency improvement of synthetic enzymes through scaffold modifications suffers from limitations in terms of effectiveness, cost, and potential devastating consequences for protein structural stability. Here, we propose an alternative to scaffold modification, within electrostatic preorganization theory, where the enzyme's greater environment is designed to support the evolution of the reaction in the active site. We demonstrate the feasibility of such an approach by placing a (polar) DNA fragment in the surroundings of the Kemp eliminase enzyme KE15 (structure from Houk's group) and computing the resulting change in catalytic activity. We find that the introduction of a DNA fragment magnifies the contribution of protein residues to the stabilization of the transition state, estimated from electric field calculations with polarizable molecular dynamics. Our randomly generated test systems reveal a 2.0 kcal/mol reduction in activation energy, suggesting that even more significant catalytic improvements could be made by optimizing DNA size, sequence, and orientation with respect to the enzyme, validating our approach.

Evolution of dynamical networks enhances catalysis in a designer enzyme.

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

Structural characterization and Kemp eliminase activity of the Mycobacterium smegmatis Ketosteroid Isomerase

The Kemp elimination reaction, involving the ring-opening of benzoxazole and its derivatives under the action of natural enzymes or chemical catalysts, has been of interest to researchers since its discovery. Because this reaction does not exist in all currently known metabolic pathways, the computational design of Kemp eliminases has provided valuable insights into principles of enzymatic catalysis. However, it was discovered that the naturally occurring promiscuous enzymes ydbC, xapA and ketosteroid isomerase also can catalyze Kemp elimination. Here, we report the crystal structure of ketosteroid isomerase (KSI) from Mycobacterium smegmatis MC2 155. MsKSI crystallizes in the P212121 space group with two molecules in an asymmetric unit, and ultracentrifugation data confirms that it forms a stable dimer in solution, consistent with the 1.9 Å-resolution structure. Our assays confirm that MsKSI accelerates the Kemp elimination of 5-nitrobenzoxazole (5NBI) with an optimal pH of 5.5. A 2.35 Å resolution crystal structure of the MsKSI-5NBI complex reveals that the substrate 5NBI is bound in the active pocket of the enzyme composed of hydrophobic residues. In addition, the Glu127 residue is proposed to play an important role as a general base in proton transfer and breaking weak O–N bonds to open the five-membered ring. This work provides a starting point for exploring the artificial modification of MsKSI using the natural enzyme as the backbone.

Backbone 1H, 15N, and 13C Chemical Shift Assignments for the Apo-State of Kemp Eliminase HG3.17

Backbone 1H, 15N, and 13C Chemical Shift Assignments for the Apo-State of Kemp Eliminase HG3.17

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 (kcat/KM 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 (kcat/KM 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.

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.

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.

Kemp Eliminases of the AlleyCat Family Possess High Substrate Promiscuity

AbstractThe front cover artwork for Issue 05/2019 is provided by the Korendoych and the Makhlynets Labs at Syracuse University (USA) in collaboration with the VIB Centre for Structural Biology (Belgium), College of Charleston (USA) and Kyiv National University (Ukraine). The image shows AlleyCat2, a minimalist allosterically regulated Kemp eliminase, efficiently promoting ring opening of leflunomide, an immunosuppresant. Small libraries of designed catalysts provide fertile ground for discovering new reactivities. See the Communication itself at https://doi.org/10.1002/cctc.201801994.

The evolution of multiple active site configurations in a designed enzyme

Developments in computational chemistry, bioinformatics, and laboratory evolution have facilitated the de novo design and catalytic optimization of enzymes. Besides creating useful catalysts, the generation and iterative improvement of designed enzymes can provide valuable insight into the interplay between the many phenomena that have been suggested to contribute to catalysis. In this work, we follow changes in conformational sampling, electrostatic preorganization, and quantum tunneling along the evolutionary trajectory of a designed Kemp eliminase. We observe that in the Kemp Eliminase KE07, instability of the designed active site leads to the emergence of two additional active site configurations. Evolutionary conformational selection then gradually stabilizes the most efficient configuration, leading to an improved enzyme. This work exemplifies the link between conformational plasticity and evolvability and demonstrates that residues remote from the active sites of enzymes play crucial roles in controlling and shaping the active site for efficient catalysis.