From cactus to crop: genomic insights of a beneficial and non-pathogenic Curtobacterium flaccumfaciens strain and the evolution of its pathosystem.

With the advent of advanced sequencing technologies, new insights into the genomes of pathogens, including those in the genus Curtobacterium, have emerged. This research investigates a newly isolated C. flaccumfaciens strain 208 (Cf208) from Arthrocereus glaziovii, and endemic plant from Iron Quadrangle. Previous results show that Cf208 exhibits the potential to remediate soils, facilitating the growth of tomato plants. Furthermore, Cf208 showed no virulence towards bean plants, thus, confounding its phytopathogenic origins. Using a comprehensive comparative genomics approach, we analyzed the Cf208 genome against 34 other Curtobacterium strains, aiming to discern the genomic landmarks associated with its adaptation as an endophyte and its avirulence in bean crops. This revealed a predominant core genome comprising about 2426 genes (68%). Notably, Cf208 possesses a unique plasmid, pCF208-73, which contains 84 unique genes (2.5%). However, unlike the plasmids previously described for pathogenic strains, pCF208-73 does not feature genes associated with virulence induction. In contrast, while several genes traditionally linked to virulence, like pectate lyases and proteases were identified, but the T4P apparatus emerged as new crucial factor for understanding virulence in the Curtobacterium genus. The presence or absence of this apparatus, especially in strains from different clades, may determine their virulence towards leguminous plants. In conclusion, this work highlights the significance of comparative genomics in unraveling the complexities of pathogenicity within the Curtobacterium genus. Our findings suggest that, although the limited genetic variations, specific genes, particularly those linked to the T4P apparatus, play a fundamental role in their interactions with host plants.

Chiral nanoenzymes: synthesis and applications.

Chiral nanoenzymes are a new type of material that possesses both chiral nanostructures and enzymatic catalytic activity. These materials exhibit selectivity in their catalytic activity towards organisms due to the introduction of chiral features in nanomaterials and have inherent chiral discrimination in organisms. As synthetic enzymes, chiral nanoenzymes offer significant advantages over natural enzymes. Due to their unique chiral structure and distinctive physicochemical properties, chiral nanoenzymes play an important role in various fields, including biology, medicine, and environmental protection. Their strong stereospecificity and biocompatibility make them useful in disease therapy, biosensing, and chiral catalysis, setting them apart from conventional and natural enzymes. In recent years, the design of synthetic methods and biological applications of chiral nanoenzymes has received significant attention and extensive research among scientists. This paper provides a systematic review of the research progress in the discovery, development, and application of chiral nanoenzymes in the last decade. Additionally, it presents various applications of chiral nanoenzymes, such as disease therapy, biosensing, and chiral catalysis. Finally, the challenges and future prospects of chiral nanoenzymes are discussed.

Recent Progress in Regulating the Activity of Enzymes with Photoswitchable Inhibitors.

Photoregulation of biomolecules has become crucial tools in chemical biology, because light enables access under mild conditions and with delicate spatiotemporal control. The control of enzyme activity in a reversible way is a challenge. To achieve it, a facile approach is to use photoswitchable inhibitors. This review highlights recent progress in photoswitchable inhibitors based on azobenzenes units. The progress suggests that the incorporation of an azobenzene unit to a known inhibitor is an effective method for preparing a photoswitchable inhibitor, and with these photoswitchable inhibitors, the activity of enzymes can be regulated by optical control, which is valuable in both basic science and therapeutic applications.

Cellular location shapes quaternary structure of enzymes.

The main forces driving protein complex evolution are currently not well understood, especially in homomers, where quaternary structure might frequently evolve neutrally. Here we examine the factors determining oligomerisation by analysing the evolution of enzymes in circumstances where homomers rarely evolve. We show that 1) In extracellular environments, most enzymes with known structure are monomers, while in the cytoplasm homomers, indicating that the evolution of oligomers is cellular environment dependent; 2) The evolution of quaternary structure within protein orthogroups is more consistent with the predictions of constructive neutral evolution than an adaptive process: quaternary structure is gained easier than it is lost, and most extracellular monomers evolved from proteins that were monomers also in their ancestral state, without the loss of interfaces. Our results indicate that oligomerisation is context-dependent, and even when adaptive, in many cases it is probably not driven by the intrinsic properties of enzymes, like their biochemical function, but rather the properties of the environment where the enzyme is active. These factors might be macromolecular crowding and excluded volume effects facilitating the evolution of interfaces, and the maintenance of cellular homeostasis through shaping cytoplasm fluidity, protein degradation, or diffusion rates.

Single time-point analysis of product and substrate inhibition.

When enzyme inhibition by either the product or excess substrate occurs, it is possible to determine the characteristic kinetic parameters based on [P]/t measurements, even when a large proportion of the substrate is converted. The advantages of various approaches are discussed. Most of them allow a good estimation of the V and Km values. Conversely, the determination of Kp (product inhibition) and Ki (inhibition by excess substrate) can be more challenging. In the first case, determination of the type of inhibition requires more complex experiments that are beyond the scope of the present contribution. In the second, the inhibition constant Ki can only be roughly estimated. In an experimental approach, we compared the results obtained either with initial rate measurements or with 50 to 60% conversion of the substrate. Similar values of V and Km were obtained. Measurements involving the conversion of a large proportion of substrate are particularly advantageous when the assay method is difficult or time-consuming, or when obtaining the substrate presents experimental difficulties or involves substantial costs.

Recent Advances in Nanozyme-Based Sensing Technology for Antioxidant Detection.

Antioxidants are substances that have the ability to resist or delay oxidative damage. Antioxidants can be used not only for the diagnosis and prevention of vascular diseases, but also for food preservation and industrial production. However, due to the excessive use of antioxidants, it can cause environmental pollution and endanger human health. It can be seen that the development of antioxidant detection technology is important for environment/health maintenance. It is found that traditional detection methods, including high performance liquid chromatography, gas chromatography, etc., have shortcomings such as cumbersome operation and high cost. In contrast, the nanozyme-based detection method features advantages of low cost, simple operation, and rapidity, which has been widely used in the detection of various substances such as glucose and antioxidants. This article focuses on the latest research progress of nanozymes for antioxidant detection. Nanozymes for antioxidant detection are classified according to enzyme-like types. Different types of nanozyme-based sensing strategies and detection devices are summarized. Based on the summary and analysis, one can find that the development of commercial nanozyme-based devices for the practical detection of antioxidants is still challenging. Some emerging technologies (such as artificial intelligence) should be fully utilized to improve the detection sensitivity and accuracy. This article aims to emphasize the application prospects of nanozymes in antioxidant detection and to provide new ideas and inspiration for the development of detection methods.

Emerging Roles of Nanozymes in Plant and Environmental Sectors.

The demand for food has increased dramatically as the global population increases, putting more strain on the sustainability of agriculture. To fulfill this requirement, it is imperative to develop brand-new technologies. The application potential of nanozymes in the plant and environmental sectors is progressively becoming apparent as a result of their effective enzymatic catalytic activity and the distinctive characteristics of nanomaterials, including size, specific surface area, optical properties, and thermal properties. Herein, we systematically analyze the catalytic mechanisms of nanozymes with different enzyme-mimetic activities and summarize their applications in improving crop yields by regulating ROS levels and enhancing stress resistance and detecting and removing hazardous pollutants. Finally, we thoroughly analyze the challenges faced by nanozymes regarding size, design, application, economy, and biosafety and look forward to their future development directions to better serve sustainable agriculture.

Plastic-Degrading Enzymes from Marine Microorganisms and Their Potential Value in Recycling Technologies.

Since the 2005 discovery of the first enzyme capable of depolymerizing polyethylene terephthalate (PET), an aromatic polyester once thought to be enzymatically inert, extensive research has been undertaken to identify and engineer new biocatalysts for plastic degradation. This effort was directed toward developing efficient enzymatic recycling technologies that could overcome the limitations of mechanical and chemical methods. These enzymes are versatile molecules obtained from microorganisms living in various environments, including soil, compost, surface seawater, and extreme habitats such as hot springs, hydrothermal vents, deep-sea regions, and Antarctic seawater. Among various plastics, PET and polylactic acid (PLA) have been the primary focus of enzymatic depolymerization research, greatly enhancing our knowledge of enzymes that degrade these specific polymers. They often display unique catalytic properties that reflect their particular ecological niches. This review explores recent advancements in marine-derived enzymes that can depolymerize synthetic plastic polymers, emphasizing their structural and functional features that influence the efficiency of these catalysts in biorecycling processes. Current status and future perspectives of enzymatic plastic depolymerization are also discussed, with a focus on the underexplored marine enzymatic resources.

Harnessing nature's catalysts: Advances in enzymatic alkene cleavage.

Double bonds are prevalent in various substrates and renewable feedstocks, and their cleavage typically necessitates harsh reaction conditions involving high temperatures, organic solvents, and hazardous catalysts such as heavy metals or ozone. This review explores the sustainable enzymatic alternatives developed by nature for alkene cleavage. It provides a comprehensive overview of alkene-cleaving enzymes, detailing their mechanisms, substrate specificities, and applications. The enzymes discussed include those acting on aliphatic, cyclic, and activated aromatic systems. Emphasizing the significance of these biocatalysts in green chemistry and biocatalysis, this review highlights their potential to replace traditional chemical oxidants with safer, cost-effective, and environmentally friendly options. Future research directions include expanding enzyme substrate scopes, enhancing their operational stability and activity, and integrating them into scalable processes for broader application in the pharmaceutical, flavor, and fragrance industries.

Noncanonical Amino Acids: Bringing New-to-Nature Functionalities to Biocatalysis.

Biocatalysis has become an important component of modern organic chemistry, presenting an efficient and environmentally friendly approach to synthetic transformations. Advances in molecular biology, computational modeling, and protein engineering have unlocked the full potential of enzymes in various industrial applications. However, the inherent limitations of the natural building blocks have sparked a revolutionary shift. In vivo genetic incorporation of noncanonical amino acids exceeds the conventional 20 amino acids, opening new avenues for innovation. This review provides a comprehensive overview of applications of noncanonical amino acids in biocatalysis. We aim to examine the field from multiple perspectives, ranging from their impact on enzymatic reactions to the creation of novel active sites, and subsequent catalysis of new-to-nature reactions. Finally, we discuss the challenges, limitations, and promising opportunities within this dynamic research domain.

Enzyme-responsive, multi-lock optical probes for molecular imaging and disease theranostics.

Optical imaging is an indispensable tool for non-invasive visualization of biomolecules in living organisms, thereby offering a sensitive approach for disease diagnosis and image-guided disease treatment. Single-lock activatable optical probes (SOPs) that specifically switch on optical signals in the presence of biomarkers-of-interest have shown both higher detection sensitivity and imaging quality as compared to conventional "always-on" optical probes. However, such SOPs can still show "false-positive" results in disease diagnosis due to non-specific biomarker expression in healthy tissues. By contrast, multi-lock activatable optical probes (MOPs) that simultaneously detect multiple biomarkers-of-interest could improve detection specificity towards certain biomolecular events or pathological conditions. In this Review, we discuss the recent advancements of enzyme-responsive MOPs, with a focus on their biomedical applications. The higher detection specificity of MOPs could in turn enhance disease diagnosis accuracy and improve treatment efficacy in image-guided disease therapy with minimal toxicity in the surrounding healthy tissues. Finally, we discuss the current challenges and suggest future applications of MOPs.

Model to rationalize and predict the formation of organic patterns originating from an enzyme-assisted self-assembly Liesegang-like process of peptides in a host hydrogel.

Recently, we have investigated the enzyme-assisted self-assembly of precursor peptides diffusing in an enzyme-containing host gel, leading to various self-assembly profiles within the gel. At high enzyme concentrations, the reaction-diffusion self-assembly processes result in the formation of a continuous non-monotonous peptide self-assembly profile. At low enzyme concentrations, they result in the formation of individual self-assembled peptide microglobules and at intermediate enzyme concentrations both kinds of self-assembled structures coexist. Herein, we develop a Liesegang-type model that considers four major points: (i) the diffusion of the precursor peptides within the host gel, (ii) the diffusion of the enzymes in the gel, (iii) the enzymatic transformation of the precursor peptides into the self-assembling ones and (iv) the nucleation of these building blocks as the starting point of the self-assembly process. This process is treated stochastically. Our model predicts most of the experimentally observed features and in particular (i) the transition from a continuous to a microglobular pattern of self-assembled peptides through five types of patterns by decreasing the enzyme concentration in the host hydrogel. (ii) It also predicts that when the precursor peptide concentration decreases, the enzyme concentration at which the continuous/microglobules transition appears increases. (iii) Finally, it predicts that for peptides whose critical self-assembly concentration in solution decreases, the peptide concentration at which the continuous-to-microglobular transition decreases too. All these predictions are observed experimentally.

Application of microbial enzymes in medicine and industry: current status and future perspectives.

Microbes are a major source of enzymes due to their ability to be mass-cultivated and genetically modified. Compared with plant and animal enzymes, microbial enzymes are more stable and active. Enzymes are generally classified into six classes based on their reaction, substrate specificity and mechanism of action. In addition to their application in medicine for treating diseases, these compounds are used as anti-inflammatory, thrombolytic and digestive agents. However, challenges such as immunogenicity, tissue specificity and short in vivo half-life make clinical trials complex. Enzymes are metabolic catalysts in industry and their production and extraction must be optimized to preserve profitability due to rising demand. The present review highlights the increasing importance of bacterial enzymes in industry and medicine and explores methods for their production, extraction and purification.

Enzymes are important substances made by the cells of plants and animals. They are catalysts, or substances that control how quickly chemical reactions occur. These reactions are the processes that keep all plants and animals functioning. They are present in almost every natural organism, from microorganisms to plants and mammals. But plants and animals produce small amounts of enzymes unsuitable for industrial applications. The use of microbial enzymes in industry offers many advantages over plant and animal enzymes. People use enzymes in industry and medicine. Enzymes help to heal cuts and to diagnose certain diseases. They are also an important part of the process called fermentation. In industries, they are applied in the textile, starch, bakery, and detergent industries. This helps turn milk into cheese and juice into wine, and it makes bread rise before it is baked.

Modeling Enzyme Kinetics: Current Challenges and Future Perspectives for Biocatalysis.

Biocatalysis is becoming a data science. High-throughput experimentation generates a rapidly increasing stream of biocatalytic data, which is the raw material for mechanistic and novel data-driven modeling approaches for the predictive design of improved biocatalysts and novel bioprocesses. The holistic and molecular understanding of enzymatic reaction systems will enable us to identify and overcome kinetic bottlenecks and shift the thermodynamics of a reaction. The full characterization and modeling of reaction systems is a community effort; therefore, published methods and results should be findable, accessible, interoperable, and reusable (FAIR), which is achieved by developing standardized data exchange formats, by a complete and reproducible documentation of experimentation, by collaborative platforms for developing sustainable software and for analyzing data, and by repositories for publishing results together with raw data. The FAIRification of biocatalysis is a prerequisite to developing highly automated laboratory infrastructures that improve the reproducibility of scientific results and reduce the time and costs required to develop novel synthesis routes.

Strangers in a foreign land: 'Yeastizing' plant enzymes.

Expressing plant metabolic pathways in microbial platforms is an efficient, cost-effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour-intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost-effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data-driven machine learning framework designed to extract 'yeastizing' rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design-build-test cycle.

Enzymes from Fishery and Aquaculture Waste: Research Trends in the Era of Artificial Intelligence and Circular Bio-Economy.

In the era of the blue bio-economy, which promotes the sustainable utilization and exploitation of marine resources for economic growth and development, the fisheries and aquaculture industries still face huge sustainability issues. One of the major challenges of these industries is associated with the generation and management of wastes, which pose a serious threat to human health and the environment if not properly treated. In the best-case scenario, fishery and aquaculture waste is processed into low-value commodities such as fishmeal and fish oil. However, this renewable organic biomass contains a number of highly valuable bioproducts, including enzymes, bioactive peptides, as well as functional proteins and polysaccharides. Marine-derived enzymes are known to have unique physical, chemical and catalytic characteristics and are reported to be superior to those from plant and animal origins. Moreover, it has been established that enzymes from marine species possess cold-adapted properties, which makes them interesting from technological, economic and sustainability points of view. Therefore, this review centers around enzymes from fishery and aquaculture waste, with a special focus on proteases, lipases, carbohydrases, chitinases and transglutaminases. Additionally, the use of fishery and aquaculture waste as a substrate for the production of industrially relevant microbial enzymes is discussed. The application of emerging technologies (i.e., artificial intelligence and machine learning) in microbial enzyme production is also presented.

High performance computational approach to study model describing reversible two-step enzymatic reaction with time fractional derivative.

Enzyme reactions have numerous applications in diverse disciplines of science like chemistry, biology and biomechanics. In this study, we examine the role and act of enzymes in chemical reactions which is considered in the frame of fractional order model. The proposed model includes system of four equations which are studied via Caputo fractional operator. The systems of non-linear equations are evaluated by a semi-analytical approach called q -homotopy analysis transform method. The uniqueness and existence of the solutions has been investigated through fixed point theorem. The solutions of the proposed model are achieved through the considered method and the obtained outcomes are in the form of series which shows rapid convergence. The solutions are computed and graphs are plotted for the obtained results using mathematica software. The achieved results by the proposed method are unique and illustrate the significant dynamics of the considered model via 3D plots and graphs. The results of this study demonstrate the importance and effectiveness of projected derivative and technique in the analysis of time dependent fractional mathematical models. This study also gives an idea to extend the applications of enzymatic reactions in drug development, bio mechanics, and chemical reactions in various cellular metabolisms. Also, enzymatic reactions have a vital role in the fields of the food industry for processing food, in biotechnology for the manufacture of biofuels, and in metabolic engineering to design metabolic pathways.

BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks.

BACKGROUND: Chemical bioproduction has attracted attention as a key technology in a decarbonized society. In computational design for chemical bioproduction, it is necessary to predict changes in metabolic fluxes when up-/down-regulating enzymatic reactions, that is, responses of the system to enzyme perturbations. Structural sensitivity analysis (SSA) was previously developed as a method to predict qualitative responses to enzyme perturbations on the basis of the structural information of the reaction network. However, the network structural information can sometimes be insufficient to predict qualitative responses unambiguously, which is a practical issue in bioproduction applications. To address this, in this study, we propose BayesianSSA, a Bayesian statistical model based on SSA. BayesianSSA extracts environmental information from perturbation datasets collected in environments of interest and integrates it into SSA predictions. RESULTS: We applied BayesianSSA to synthetic and real datasets of the central metabolic pathway of Escherichia coli. Our result demonstrates that BayesianSSA can successfully integrate environmental information extracted from perturbation data into SSA predictions. In addition, the posterior distribution estimated by BayesianSSA can be associated with the known pathway reported to enhance succinate export flux in previous studies. CONCLUSIONS: We believe that BayesianSSA will accelerate the chemical bioproduction process and contribute to advancements in the field.

Field theory of enzyme-substrate systems with restricted long-range interactions.

Enzyme-substrate kinetics form the basis of many biomolecular processes. The interplay between substrate binding and substrate geometry can give rise to long-range interactions between enzyme binding events. Here we study a general model of enzyme-substrate kinetics with restricted long-range interactions described by an exponent -γ. We employ a coherent-state path integral and renormalization group approach to calculate the first moment and two-point correlation function of the enzyme-binding profile. We show that starting from an empty substrate the average occupancy follows a power law with an exponent 1/(1-γ) over time. The correlation function decays algebraically with two distinct spatial regimes characterized by exponents -γ on short distances and -(2/3)(2-γ) on long distances. The crossover between both regimes scales inversely with the average substrate occupancy. Our work allows associating experimental measurements of bound enzyme locations with their binding kinetics and the spatial conformation of the substrate.

Coenzymes in a pre-enzymatic metabolism.

Experiments now support theoretical suggestions that coenzymes mediated key metabolic reactions before the emergence of enzymes. Three coenzymes believed essential to the core metabolism of the last universal common ancestor to extant life (pyridoxal phosphate, adenosine diphosphate, and nicotinamide adenine dinucleotide) were recently found to be active in their corresponding metabolic reactions in the absence of enzymes. These findings suggest an earlier contribution of coenzymes to abiogenesis, ultimately yielding insights into the prebiotic origins of metabolism.