Catalytic Efficiency Of Enzyme Research Articles

Enzyme catalytic efficiency prediction: employing convolutional neural networks and XGBoost.

In the intricate realm of enzymology, the precise quantification of enzyme efficiency, epitomized by the turnover number (k cat), is a paramount yet elusive objective. Existing methodologies, though sophisticated, often grapple with the inherent stochasticity and multifaceted nature of enzymatic reactions. Thus, there arises a necessity to explore avant-garde computational paradigms. In this context, we introduce "enzyme catalytic efficiency prediction (ECEP)," leveraging advanced deep learning techniques to enhance the previous implementation, TurNuP, for predicting the enzyme catalase k cat. Our approach significantly outperforms prior methodologies, incorporating new features derived from enzyme sequences and chemical reaction dynamics. Through ECEP, we unravel the intricate enzyme-substrate interactions, capturing the nuanced interplay of molecular determinants. Preliminary assessments, compared against established models like TurNuP and DLKcat, underscore the superior predictive capabilities of ECEP, marking a pivotal shift in silico enzymatic turnover number estimation. This study enriches the computational toolkit available to enzymologists and lays the groundwork for future explorations in the burgeoning field of bioinformatics. This paper suggested a multi-feature ensemble deep learning-based approach to predict enzyme kinetic parameters using an ensemble convolution neural network and XGBoost by calculating weighted-average of each feature-based model's output to outperform traditional machine learning methods. The proposed "ECEP" model significantly outperformed existing methodologies, achieving a mean squared error (MSE) reduction of 0.35 from 0.81 to 0.46 and R-squared score from 0.44 to 0.54, thereby demonstrating its superior accuracy and effectiveness in enzyme catalytic efficiency prediction. This improvement underscores the model's potential to enhance the field of bioinformatics, setting a new benchmark for performance.

Detection of urea in milk by urease-inorganic hybrid nanoflowers combined with portable colorimetric microliter tube.

Asimple one-pot green synthesis method was used to prepare urease-inorganic hybrid nanoflowers (UE-HNFs), which had a high surface-to-volume ratio to improve enzyme catalytic efficiency and make urease reusable. A portable colorimetric microliter tube based on urease-inorganic hybrid nanoflowers (UE-HNFs-PCMT), as an urea colorimetric biosensor, was developed for determining urea concentration in milk. The combination of urea colorimetric biosensor and asmartphone is usedfor capturingthe colour change of milk after reaction. There was a good linear relationship between colour intensity of the image (Δ intensity) and urea concentration (43-600mg L-1), with a detection limit of 12.81mg L-1. UE-HNFs-PCMT has the advantages of no need for complex equipment, easy operation, reusability, low detection cost, good portability, and environmental friendliness and can achieve urea detection in milk.

Insecticide binding mode analysis and biological effects of acetylcholinesterase target-site resistance mutations in Spodoptera frugiperda

It is urgent to solve insecticide resistance issues for fall armyworm (FAW), Spodoptera frugiperda. Some acetylcholinesterase-1 (Ace-1) mutations (A201S, G227A and F290V) have been identified as a cause of FAW resistance to organophosphates (OPs) and carbamates insecticides (CXs). However, the structural biological mechanisms on the relationship between the Ace-1 mutations and resistance to OPs and CXs still remain elusive. In this study, the A201S and F290V mutaions were found in eight fields populations of FAW except the G227A. Molecular docking revealed that the four Ace-1 proteins (Ace1-WT, Ace1-A201S, Ace1-G227A and Ace1-F290V) had the same binding modes and the same binding energies with acetylcholine (Ach), trichlorfon, chlorpyrifos, methomyl, carbaryl and chlorpyrifos oxide. The structural biological analysis revealed that the A201S mutations can enhance enzyme catalytic efficiency by introducing the hydroxyl group (-OH) from serine which performed the same function as the main-chain -NH and enhanced the interaction with the carboxy oxygen of acetylcholine (Ach), and the F290V mutation can effectively improve FAW resistance to insecticides by increasing the likelihood of Ach to enter the enzyme's active center for phenylalanine replaced by smaller valine under insecticide inhibition conditions. The bioassays and age-stage-specific life table analysis of FAW-SS and FAW-F290V populations revealed that F290V mutation effectively contributed to FAW resistance with a low fitness cost. This study provides a theoretical basis for future pest resistance management.

Mining and characterization of a novel cytochrome P450 MaCYP71BG22 involved in the C4-stereoselective hydroxylation of 1-deoxynojirimycin biosynthesis in mulberry leaves

1-Deoxynojirimycin (DNJ), a primary active component in mulberry leaves, has garnered significant attention due to its unique structure and notable pharmacological properties. Our previous investigations have elucidated the biosynthetic pathways of DNJ from lysine to 2-methylpiperidine. However, the hydroxylation process and its underlying mechanisms remain elusive. In this study, five CYP450s hydroxylase genes significantly correlated (P < 0.05) with DNJ content in mulberry leaves at various time were screened through transcriptome profile. MaCYP71BG22 was first cloned and functionally characterized. This gene was shown to specifically catalyze the stereoselective hydroxylation of (R)-2-methylpiperidine at the C4-position to produce (2R, 4R)-2-methylpiperidin-4-ol. In hairy roots of mulberry, overexpression of MaCYP71BG22 increased DNJ accumulation, while virus-induced gene silencing (VIGS) decreased its production. Furthermore, structural-function analysis pinpointed a critical residue, G460, in MaCYP71BG22, mutation of this residue to G460E enhanced the enzyme's catalytic efficiency. This study represents the first report of a CYP450 hydroxylase involved in the biosynthesis of piperidine alkaloids in mulberry leaves, and demonstrates that MaCYP71BG22 selectively catalyzes the C4-stereoselective hydroxylation of (R)-2-methylpiperidine in DNJ biosynthesis. These findings further elucidate the DNJ biosynthetic pathway and provide new insights into the stereo- and regio-selective hydroxylation abilities of CYP450s hydroxylase in DNJ biosynthesis.

Site-directed mutagenesis enhances the activity of dextranase from Arthrobacter oxidans KQ11

Dextranase is an enzyme that specifically hydrolyzes the α-1, 6 glucoside bond. In order to improve the activity of dextranase from Arthrobacter oxidans KQ11, site-directed mutagenesis was used to modify the amino acids involved in the "tunnel-like binding site". A saturating mutation at position 507 was carried out on this basis. The mutant enzymes A356G, S357W, W507Y, and W507F with improved enzyme activities and catalytic efficiency were successfully obtained. Compared with wild type (WT), W507Y showed the specific activity increasing by 3.00 times, the kcat value increasing by 3.62 times, the Km value decreasing by 54%, and the catalytic efficiency (kcat/Km) increasing by 8.98 times. The three-dimensional structure analysis showed that the increase of the number of hydrogen bonds and the distance between "tunnel-like binding sites" were important factors affecting enzyme activity. Compared with WT, W507Y had a shortened distance from the residues on the other side of the "tunnel-like binding site", which made it easier to generate hydrogen binding forces. Accordingly, the substrate hydrolysis and product efflux were accelerated, which dramatically increased the enzyme activity and catalytic efficiency.

Production of γ-aminobutyric acid by immobilization of two Yarrowia lipolytica glutamate decarboxylases on electrospun nanofibrous membrane

This study harnesses glutamate decarboxylase (GAD) from Yarrowia lipolytica to improve the biosynthesis of γ-aminobutyric acid (GABA), focusing on boosting the enzyme's catalytic efficiency and stability by immobilizing it on nanofibrous membranes. Through recombinant DNA techniques, two GAD genes, YlGAD1 and YlGAD2, were cloned from Yarrowia lipolytica and then expressed in Escherichia coli. Compared to their soluble forms, the immobilized enzymes exhibited significant improvements in thermal and pH stability and increased resistance to chemical denaturants. The immobilization notably enhanced substrate affinity, as evidenced by reduced Km values and increased kcat values, indicating heightened catalytic efficiency. Additionally, the immobilized YlGAD1 and YlGAD2 enzymes showed substantial reusability, maintaining 50% and 40% of their activity, respectively, after six consecutive cycles. These results underscore the feasibility of employing immobilized YlGAD enzymes for cost-effective and environmentally sustainable GABA production. This investigation not only affirms the utility of YlGADs in GABA synthesis but also underscores the advantages of enzyme immobilization in industrial settings, paving the way for scalable biotechnological processes.

Comprehensive Analysis of Allulose Production: A Review and Update.

Advancements in D-allulose production have seen significant strides in recent years, focusing on enzymatic conversion methods. Key developments include traditional immobilization techniques, the discovery of novel enzymes, directed evolution studies, and biosynthesis through metabolic pathway modification. Enzymatic conversion, particularly utilizing D-allulose 3-epimerase, remains fundamental for industrial-scale production. Innovative immobilization strategies, such as functionalized nano-beads and magnetic MOF nanoparticles, have significantly enhanced enzyme stability and reusability. Directed evolution has led to improved enzyme thermostability and catalytic efficiency, while synthetic biology methods, including phosphorylation-driven and thermodynamics-driven pathways, have optimized production processes. High-throughput screening methods have been crucial in identifying and refining enzyme variants for industrial applications. Collectively, these advancements not only enhance production efficiency and cost-effectiveness but also adhere to sustainable and economically viable manufacturing practices. The past five years have witnessed critical developments with significant potential impact on the commercial viability and global demand for allulose.

DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures.

Turnover numbers (kcat), which indicate an enzyme's catalytic efficiency, have a wide range of applications in fields including protein engineering and synthetic biology. Experimentally measuring the enzymes' kcat is always time-consuming. Recently, the prediction of kcat using deep learning models has mitigated this problem. However, the accuracy and robustness in kcat prediction still needs to be improved significantly, particularly when dealing with enzymes with low sequence similarity compared to those within the training dataset. Herein, we present DeepEnzyme, a cutting-edge deep learning model that combines the most recent Transformer and Graph Convolutional Network (GCN) to capture the information of both the sequence and 3D-structure of a protein. To improve the prediction accuracy, DeepEnzyme was trained by leveraging the integrated features from both sequences and 3D-structures. Consequently, DeepEnzyme exhibits remarkable robustness when processing enzymes with low sequence similarity compared to those in the training dataset by utilizing additional features from high-quality protein 3D-structures. DeepEnzyme also makes it possible to evaluate how point mutations affect the catalytic activity of the enzyme, which helps identify residue sites that are crucial for the catalytic function. In summary, DeepEnzyme represents a pioneering effort in predicting enzymes' kcat values with improved accuracy and robustness compared to previous algorithms. This advancement will significantly contribute to our comprehension of enzyme function and its evolutionary patterns across species.

MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction.

Enzymatic reaction kinetics are central in analyzing enzymatic reaction mechanisms and target-enzyme optimization, and thus in biomanufacturing and other industries. The enzyme turnover number (kcat) and Michaelis constant (Km), key kinetic parameters for measuring enzyme catalytic efficiency, are crucial for analyzing enzymatic reaction mechanisms and the directed evolution of target enzymes. Experimental determination of kcat and Km is costly in terms of time, labor, and cost. To consider the intrinsic connection between kcat and Km and further improve the prediction performance, we propose a universal pretrained multitask deep learning model, MPEK, to predict these parameters simultaneously while considering pH, temperature, and organismal information. Through testing on the same kcat and Km test datasets, MPEK demonstrated superior prediction performance over the previous models. Specifically, MPEK achieved the Pearson coefficient of 0.808 for predicting kcat, improving ca. 14.6% and 7.6% compared to the DLKcat and UniKP models, and it achieved the Pearson coefficient of 0.777 for predicting Km, improving ca. 34.9% and 53.3% compared to the Kroll_model and UniKP models. More importantly, MPEK was able to reveal enzyme promiscuity and was sensitive to slight changes in the mutant enzyme sequence. In addition, in three case studies, it was shown that MPEK has the potential for assisted enzyme mining and directed evolution. To facilitate in silico evaluation of enzyme catalytic efficiency, we have established a web server implementing this model, which can be accessed at http://mathtc.nscc-tj.cn/mpek.

Coupling Peptide-Based Encapsulation of Enzymes with Bacteria for Paraoxon Bioremediation.

The catalytic efficiency of enzymes can be harnessed as an environmentally friendly solution for decontaminating various xenobiotics and toxins. However, for some xenobiotics, several enzymatic steps are needed to obtain nontoxic products. Another challenge is the low durability and stability of many native enzymes in their purified form. Herein, we coupled peptide-based encapsulation of bacterial phosphotriesterase with soil-originated bacteria, Arthrobacter sp. 4Hβ as an efficient system capable of biodegradation of paraoxon, a neurotoxin pesticide. Specifically, recombinantly expressed and purified methyl parathion hydrolase (MPH), with high hydrolytic activity toward paraoxon, was encapsulated within peptide nanofibrils, resulting in increased shelf life and retaining ∼50% activity after 132 days since purification. Next, the addition of Arthrobacter sp. 4Hβ, capable of degrading para-nitrophenol (PNP), the hydrolysis product of paraoxon, which is still toxic, resulted in nondetectable levels of PNP. These results present an efficient one-pot system that can be further developed as an environmentally friendly solution, coupling purified enzymes and native bacteria, for pesticide bioremediation. We further suggest that this system can be tailored for different xenobiotics by encapsulating the rate-limiting key enzymes followed by their combination with environmental bacteria that can use the enzymatic step products for full degradation without the need to engineer synthetic bacteria.

Ionic liquid functionalized magnetic polydopamine enhancing enzyme stability and catalytic efficiency for water pollutant treatment

Laccase has received widespread attention in pollutant degradation, but the current challenges in its poor stability, easy inactivation, and difficult recovery have seriously affected the application. The enzyme immobilization technology is a crucial approach to enhance the stability, recoverability, and other valuable properties to improve the effectiveness of biocatalysts. In this study, a polydopamine coating Fe3O4 nanoparticle was modified with an ionic liquid, which can be used for laccase immobilization. The incorporation of polydopamine provided a biocompatible and reactive coating for nanoparticles, while the presence of ionic liquids ensured that enzyme molecules were positioned away from the carrier surface, which facilitated the stabilization of the spatial structure of the enzyme and the retention of its activity. The immobilized laccase exhibited a broad pH and temperature and satisfactory stability. The activity of immobilized laccase remained at approximately 80 % of its activity even after incubation at 50 ℃, and over 65 % activity at storage for 30 days, which is 1.6 times and 2.9 times higher than the free laccase, respectively. The biocatalytic demonstrated excellent removal of phenolic contaminants, achieving removal efficiencies of approximately 97 % for 2,4-dichlorophenol. The remarkable magnetic response greatly facilitates the collection and subsequent reuse of the biocatalyst up to six times while still maintaining a relative removal efficiency of over 70 % for 2,4-dichlorophenol. Furthermore, the biocatalytic also exhibited excellent removal efficiency for Bisphenol A and phenol, which were 1.5 and 1.4 times more effective than the free laccase. The immobilization method provided in this work offers new ideas for the development and efficient biological enzyme preparation for industrial wastewater treatment.

Assay Replicability in β-Glucosidase Enzyme Kinetics Across Laboratories

Replicability is the foundation of research in any scientific discipline. Despite this fact, few studies address experimental variability within and across multiple institutions that operate under the same protocol. While consistency is usually well documented within the same lab, multi-institutional experiments may introduce new variables and, therefore, variability that may lead to inconsistent results. This study seeks to explore intra- and interinstitutional variability among enzyme catalytic efficiency values (KM and Kcat/KM) for the wild type of β-Glucosidase derived from Paenibacillus polymyxa. A standardized protocol for the assay was provided to all institutions that participated in the study. The analysis was conducted using data from 13 laboratories across the United States. The information was collected through the Design2Data CURE database. A total of 132 independent assays of β-Glucosidase were analyzed. Statistical analysis of Kcat/KM , KM, and T50 was conducted using SPSS, and Whisker Plots with a 90% confidence interval were generated for all parameters. Acceptance intervals for the parameters assayed of β-Glucosidase were determined; these could be used in all future experiments in the network as a reference by current and incoming laboratory members that incorporate into the network yearly. Ultimately, we aim to identify possible errors and misunderstandings in the protocol we use in the laboratories of the D2D network to improve assay replicability across institutions. High variability may be a concern when data is interpreted across institutions. This specific assay is used as a control for other, more complex assays in the CURE network and, therefore, the variability found can potentially lead to incorrect interpretation of results. It also highlights the high variability of a simple enzymatic assay when it is replicated in laboratories with different personnel and equipment, regardless of using the same standardized protocol.

Enhanced Catalytic Activity of a de novo Enzyme in a Coacervate Phase

Biomolecular condensates are membraneless organelles that orchestrate various metabolic pathways in living cells. Understanding how these crowded structures regulate enzyme reactions remains yet challenging due to their dynamic and intricate nature. Coacervate microdroplets formed by associative liquid‐liquid phase separation of oppositely charged polyions have emerged as relevant condensate models to study enzyme catalysis. Enzyme reactions within these droplets show altered kinetics, influenced by factors such as enzyme and substrate partitioning, crowding, and interactions with coacervate components; it is often challenging to disentangle the contributions of each. Here, we investigate the peroxidase activity of a de novo enzyme within polysaccharide‐based coacervates. By comparing the reaction kinetics in buffer, in a suspension of coacervates and in the bulk coacervate phase collected after centrifugation of the droplets, we show that the coacervate phase significantly increases the enzyme catalytic efficiency. We demonstrate that the main origin of this enhanced activity lies in macromolecular crowding coupled to changes in the conformational dynamics of the enzyme within the coacervate environment. Altogether, these findings underline the crucial role of the coacervate matrix in enzyme catalysis, beyond simple partitioning effects. The observed boost in enzyme activity within the coacervate phase provides insights for designing biocatalytically active synthetic organelles.

Understanding the Interactions of Ubiquitin-Specific Protease 7 with Its Substrates through Molecular Dynamics Simulations: Insights into the Role of Its C-Terminal Domains in Substrate Recognition.

Ubiquitin-specific protease 7 (USP7) is a deubiquitinase enzyme that plays a critical role in regulating various cellular processes by cleaving ubiquitin molecules from target proteins. The C-terminal loop (CTL) motif is a specific region at the C-terminal end of the USP7 enzyme. Recent experiments suggest that the CTL motif plays a role in USP7's catalytic activity by contributing to the enzyme's structural stability, substrate recognition, and catalytic efficiency. The objective of this work is to elucidate these roles through the utilization of computational methods for molecular simulations. For this, we conducted extensive molecular dynamics (MD) simulations to investigate the conformational dynamics and protein-protein interactions within the USP7 enzyme-substrate complex with the substrate consisting of the ubiquitin tagged with the fluorescent label rhodamine 110-gly (Ub-Rho). Our results demonstrate that the CTL motif plays a crucial role in stabilizing the Ubl domains' conformation and augmenting the stability of active conformations within the enzyme-substrate complex. Conversely, the absence of the CTL motif results in increased flexibility and variability in Ubl domains' motion, leading to a reduced percentage of active conformations. Furthermore, our analysis of protein-protein interactions highlights the significance of the CTL motif in anchoring the Ubl45 domains to the catalytic domain (CD), thereby facilitating stable interactions with the substrate. Overall, our findings provide valuable insights into the conformational dynamics and protein-protein interactions inherent in the USP7 enzyme-substrate complex. These insights shed light on some mechanistic details of USP7 concerning the substrate's recognition before its catalytic action.

Balancing the pretreatment effect of deep eutectic solvents on biomass and the activity of β-glucosidase originating from Paenibacillus sp. LLZ1

The two major impediments to biomass resourcing are the catalytic efficiency of enzymes and the effectiveness of biomass pretreatment. In this study, six genes encoding β-Glucosidases (BGLs) derived from Paenibacillus sp. LLZ1 were cloned and expressed. The activity of BGL-F, which had the highest enzyme activity, was 24.1 U/mg, which was 39.8–83.7% higher than that of the other BGLs. Molecular docking and surface charge analyses showed that the binding energy of BGL-F to cellobiose and its electronegative ligand-binding pocket were greater than those of other BGLs. On the other hand, by calculating and comparing the Kamlet–Taft (KT) values of deep eutectic solvent (DES) composed of different hydrogen bond donors (HBD) and hydrogen bond acceptors (HDA), it was found that DES composed of betaine and glycerol had the highest α value of 0.97, meaning that it could provide the highest hydrogen bonding capacity, and thus it had the best pretreatment effect on biomass. Analysis by circular dichroism (CD) spectroscopy revealed a decrease in α-helix and an increase in β-strand in the secondary structure of the DES-treated BGL-F, implying an increase in enzyme activity and stability. This result was also confirmed experimentally by the fact that the Km and Vmax of the enzyme in the DES system increased by 13.2% and 57.9%, respectively, and the inactivation rate (kd) decreased by 21.4%. The results of this study showed that DES consisting of glycerol-betaine achieved a better balance between the promotion of enzyme activity and biomass pretreatment.

Nanomaterials for co‐immobilization of multiple enzymes

AbstractIn order to co‐immobilize multiple enzymes, a wide range of nanomaterials has been designed to achieve synergistic enzyme activity and enhance catalytic efficiency. Nanomaterials, as carriers for enzyme co‐immobilization, possess various advantages such as tunable morphology and size, high specific surface area, and abundant chemically active sites. They can significantly enhance enzyme stability, activity, and catalytic efficiency. We overview the commonly used methods and strategies of enzyme co‐immobilization. This review further summarizes the latest research advances in nanomaterials for enzyme co‐immobilization applications over the past 5 years. Meanwhile, the advantages and challenges of these nanomaterials used for enzyme co‐immobilization as well as some potential future directions are also discussed.

Revving up a Designed Copper Nitrite Reductase Using Non-Coded Active Site Ligands.

Herein, we report a three stranded coiled-coil (3SCC) de novo protein containing a type II copper center (CuT2) composed of 6-membered ring N-heterocycles. This design yields the most active homogenous copper nitrite reductase (CuNiR) mimic in water. We achieved this result by controlling three factors. First, previous studies with Nδ and Nε -Methyl Histidine had indicated that a ligand providing pyridine-like electronic character to the copper site was superior to the more donating Nδ for nitrite reduction. By substitution of the parent histidine with the non-coded amino acids pyridyl alanine (3'-Pyridine [3'Py] vs 4'-Pyridine [4'Py]), an authentic pyridine donor was employed without the complications of the coupling of both electronic and tautomeric effects of histidine or methylated histidine. Second, by changing the position of the nitrogen atom within the active site (4'-Pyridine vs. 3'Pyridine) a doubling of the enzyme's catalytic efficiency resulted. This effect was driven exclusivity by substrate binding to the copper site. Third, we replaced the leucine layer adjacent to the active site with an alanine, and the disparity between the 3'Py and 4'Py became more apparent. The decreased steric bulk minimally impacted the 3'Py derivative; however, the 4'Py K m decreased by an order of magnitude (600 mM to 50 mM), resulting in a 40-fold enhancement in the k cat/K m compared to the analogues histidine site and a 1500-fold improvement compared with the initially reported CuNiR catalyst of this family, TRIW-H. When combined with XANES/EXAFS data, the relaxing of the Cu(I) site to a more 2-coordinate Cu(I) like structure in the resting state increases the overall catalytic efficiency of nitrite reduction via the lowering of K m. This study illustrates how by combining advanced spectroscopic methods, detailed kinetic analysis, and a broad toolbox of amino acid side chain functionality, one can rationally design systems that optimize biomimetic catalysis.

Protein-directed hydrogen-bonded organic framework with enhanced enzyme stability and catalytic efficiency for sensitive colorimetric/fluorometric immunoassays

The fragility inherent in enzymes can be overcome by immobilizing them within nano-carriers, which offer designability and controllability and serve as a promising strategy for overcoming the inherent fragility of enzymes. While enzyme immobilization enhances enzyme stability and reusability, it can potentially hinder their bioactivity and catalytic efficiency. In this study, we present an enzyme-directed biomimetic mineralization approach for the in situ encapsulation of horseradish peroxidase (HRP) within hydrogen-bonded organic frameworks (HOFs) to obtain HOF@HRP nanocomposites. Remarkably, the HOF@HRP nanocomposites exhibit an ultrahigh protein content and demonstrate comparable catalytic activity to that of the free enzyme. Moreover, the HOFs act as protective shields, safeguarding the internal enzymes against various environmental disturbances. Leveraging the unique properties of HOF@HRP, we propose a dual-modal enzyme-linked immunosorbent assay for the sensitive detection of prostate-specific antigen in human serum. The resultant biosensor exhibits a good linear range, excellent sensitivity, and selectivity, showcasing its promising potential and clinical application in the field of diagnostic medicine.

Application of Synthetic Biology in Directed Evolution to Enhance Enzyme Catalytic Efficiency

Application of Synthetic Biology in Directed Evolution to Enhance Enzyme Catalytic Efficiency

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.