Drying-rewetting alters arsenic ecotoxicity: From the perspective of enzyme-based functional diversity.

Drying-rewetting (DW) cycles can significantly influence soil properties and microbial community composition, leading to direct or indirect changes in arsenic (As) toxicity, which inturn affects soil ecological functions. Despite this, there has been insufficient focus on accurately evaluating As ecotoxicity and its impact on soil ecological function under DW conditions. This study seeks to address this gap by examining the effects of DW on As toxicity and the characteristics of soil ecological function, specifically from the perspective of enzyme-based functional diversity. Our results reveal that compared to constant moisture conditions, DW treatment significantly increased the toxicity of As on alkaline phosphatase and ß-glucosidase, with maximum inhibition rates observed at 46.29% and 21.54%, respectively. Conversely, for other tested enzymes including invertase, fluorescein diacetate hydrolase, and dehydrogenase, DW treatment decreased As toxicity, possibly be due to the different stability of these enzymes under varying soil moisture conditions. From an enzyme functional diversity perspective, DW treatment reduced the As toxicity, as evidenced by the reduced inhibition rates and a lower coefficient of variation. In conclusion, DW appears to enhance soil functional resilience against arsenic pollution. These findings contribute to a better understanding of changes in ecological functions in heavy metal-contaminated soils under dynamic environmental conditions, offering insights for improved monitoring and mitigation strategies for metalloids toxicity in natural environments.

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.

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.

Accurately predicting enzyme functions through geometric graph learning on ESMFold-predicted structures.

Enzymes are crucial in numerous biological processes, with the Enzyme Commission (EC) number being a commonly used method for defining enzyme function. However, current EC number prediction technologies have not fully recognized the importance of enzyme active sites and structural characteristics. Here, we propose GraphEC, a geometric graph learning-based EC number predictor using the ESMFold-predicted structures and a pre-trained protein language model. Specifically, we first construct a model to predict the enzyme active sites, which is utilized to predict the EC number. The prediction is further improved through a label diffusion algorithm by incorporating homology information. In parallel, the optimum pH of enzymes is predicted to reflect the enzyme-catalyzed reactions. Experiments demonstrate the superior performance of our model in predicting active sites, EC numbers, and optimum pH compared to other state-of-the-art methods. Additional analysis reveals that GraphEC is capable of extracting functional information from protein structures, emphasizing the effectiveness of geometric graph learning. This technology can be used to identify unannotated enzyme functions, as well as to predict their active sites and optimum pH, with the potential to advance research in synthetic biology, genomics, and other fields.

EnzChemRED, a rich enzyme chemistry relation extraction dataset.

Expert curation is essential to capture knowledge of enzyme functions from the scientific literature in FAIR open knowledgebases but cannot keep pace with the rate of new discoveries and new publications. In this work we present EnzChemRED, for Enzyme Chemistry Relation Extraction Dataset, a new training and benchmarking dataset to support the development of Natural Language Processing (NLP) methods such as (large) language models that can assist enzyme curation. EnzChemRED consists of 1,210 expert curated PubMed abstracts where enzymes and the chemical reactions they catalyze are annotated using identifiers from the protein knowledgebase UniProtKB and the chemical ontology ChEBI. We show that fine-tuning language models with EnzChemRED significantly boosts their ability to identify proteins and chemicals in text (86.30% F1 score) and to extract the chemical conversions (86.66% F1 score) and the enzymes that catalyze those conversions (83.79% F1 score). We apply our methods to abstracts at PubMed scale to create a draft map of enzyme functions in literature to guide curation efforts in UniProtKB and the reaction knowledgebase Rhea.

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.

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.

Combining effects of submerged macrophytes and lanthanum-modified bentonite on sediment enzyme activity: Evidence from mesocosm study.

Lanthanum-modified bentonite (LMB) combined with submerged macrophytes (SM) has been a conventional means of eutrophication management in lakes in recent years, and is one of the most important methods for P removal. However, trends in nutrients and sediment enzymes at the water-sediment interface during this process have not been systematically assessed, and there are still some gaps in how abiotic properties drive changes in enzyme activity. Here, we show changes in aquatic environmental conditions under the action of different ratios of modified bentonite (0, 10%, 20%, and 30%) in combination with SM (Vallisneria natans, Potamogeton lucens, and Hydrilla verticillate) and quantify their effects on sediment enzyme activities. The results showed that the nutrient cycling at the water-sediment interface was facilitated by the combined effect of SM and LMB, which effectively reduced the overlying water nutrient concentration, increased the sediment enzyme activity and enhanced the N cycling process. Partial least squares structural equation model (PLS-SEM) showed that sediment parameters strongly influenced changes in enzyme activity, with NO3-N as the main controlling factors. Our study fills in the process of changing environmental conditions in lake water under geoengineered materials combined with macrophyte measures, especially the changes in biological properties enzyme activities, which contributes to a clearer understanding of nutrient fluxes during the management of eutrophication in lakes.

Recent advances in nanoenzymes for Alzheimer's disease treatment.

Alzheimer's disease (AD) remains one of the most challenging neurodegenerative disorders to treat, with oxidative stress playing a significant role in its pathology. Recent advancements in nanoenzymes technology offer a promising approach to mitigate this oxidative damage. Nanoenzymes, with their unique enzyme-mimicking activities, effectively scavenge reactive oxygen species and reduce oxidative stress, thereby providing neuroprotective effects. This review delves into the underlying mechanisms of AD, focusing on oxidative stress and its impact on disease progression. We explore the latest developments in nanoenzymes applications for AD treatment, highlighting their multifunctional capabilities and potential for targeted delivery to amyloid-beta plaques. Despite the exciting prospects, the clinical translation of nanoenzymes faces several challenges, including difficulties in brain targeting, consistent quality production, and ensuring safety and biocompatibility. We discuss these limitations in detail, emphasizing the need for rigorous evaluation and standardized protocols. This paper aims to provide a comprehensive overview of the current state of nanoenzymes research in AD, shedding light on both the opportunities and obstacles in the path towards effective clinical applications.

Emerging Nanomaterials as Versatile Nanozymes: A New Dimension in Biomedical Research.

The enzyme-mimicking nature of versatile nanomaterials proposes a new class of materials categorized as nano-enzymes, ornanozymes. They are artificial enzymes fabricated by functionalizing nanomaterials to generate active sites that can mimic enzyme-like functions. Materials extend from metals and oxides to inorganic nanoparticles possessing intrinsic enzyme-like properties. High cost, low stability, difficulty in separation, reusability, and storage issues of natural enzymes can be well addressed by nanozymes. Since 2007, more than 100 nanozymes have been reported that mimic enzymes like peroxidase, oxidase, catalase, protease, nuclease, hydrolase, superoxide dismutase, etc. In addition, several nanozymes can also exhibit multi-enzyme properties. Vast applications have been reported by exploiting the chemical, optical, and physiochemical properties offered by nanozymes. This review focuses on the reported nanozymes fabricated from a variety of materials along with their enzyme-mimicking activity involving tuning of materials such as metal nanoparticles (NPs), metal-oxide NPs, metal-organic framework (MOF), covalent organic framework (COF), and carbon-based NPs. Furthermore, diverse applications of nanozymes in biomedical research are discussed in detail.

Rational enzyme design by reducing the number of hotspots and library size.

Biocatalysts that are eco-friendly, sustainable, and highly specific have great potential for applications in the production of fine chemicals, food, detergents, biofuels, pharmaceuticals, and more. However, due to factors such as low activity, narrow substrate scope, poor thermostability, or incorrect selectivity, most natural enzymes cannot be directly used for large-scale production of the desired products. To overcome these obstacles, protein engineering methods have been developed over decades and have become powerful and versatile tools for adapting enzymes with improved catalytic properties or new functions. The vastness of the protein sequence space makes screening a bottleneck in obtaining advantageous mutated enzymes in traditional directed evolution. In the realm of mathematics, there are two major constraints in the protein sequence space: (1) the number of residue substitutions (M); and (2) the number of codons encoding amino acids as building blocks (N). This feature review highlights protein engineering strategies to reduce screening efforts from two dimensions by reducing the numbers M and N, and also discusses representative seminal studies of rationally engineered natural enzymes to deliver new catalytic functions.

Approaching Optimal pH Enzyme Prediction with Large Language Models.

Enzymes are widely used in biotechnology due to their ability to catalyze chemical reactions: food making, laundry, pharmaceutics, textile, brewing─all these areas benefit from utilizing various enzymes. Proton concentration (pH) is one of the key factors that define the enzyme functioning and efficiency. Usually there is only a narrow range of pH values where the enzyme is active. This is a common problem in biotechnology to design an enzyme with optimal activity in a given pH range. A large part of this task can be completed in silico, by predicting the optimal pH of designed candidates. The success of such computational methods critically depends on the available data. In this study, we developed a language-model-based approach to predict the optimal pH range from the enzyme sequence. We used different splitting strategies based on sequence similarity, protein family annotation, and enzyme classification to validate the robustness of the proposed approach. The derived machine-learning models demonstrated high accuracy across proteins from different protein families and proteins with lower sequence similarities compared with the training set. The proposed method is fast enough for the high-throughput virtual exploration of protein space for the search for sequences with desired optimal pH levels.

A novel ultrasound-assisted enzyme extraction method of total flavonoids from Viticis Fructus and processed Viticis Fructus: Comparison of in vitro antioxidant activity.

In this study, it is the first that the Viticis Fructus (VF) was used as the raw material for extracting total flavonoids using the ultrasound-assisted enzyme extraction (UAE) method. Response surface methodology was employed to determine the optimal extraction parameters. The optimal conditions were as follows: 60 % ethanol solution as the extract solvent, material-liquid ratio of 1:25, pH value of 4, enzyme addition amount of 1.5 %, enzymatic hydrolysis time of 30 min, enzymatic hydrolysis temperature of 40 ℃, and ultrasonic time of 50 min. Comparing the total flavonoid yield of VF and processed VF (PVF) extracted using different methods, it was observed that UAE resulted in a higher total flavonoid yield compared to traditional ultrasound extraction and enzyme extraction. Additionally, the total flavonoid yield of PVF extracted by all three methods was generally higher than that of VF. The PVF solution extracted by UAE also demonstrated better in vitro antioxidant activity compared to VF. These results suggest that UAE is an effective method to enhance the activity of natural total flavonoids. The study of the physicochemical properties and in vitro antioxidant activity of VF and PVF showed that the total flavonoid yield and antioxidant activity significantly increased after VF stir-frying, indicating that their efficacy can also be enhanced.

Pen and palm model to envision the coexistence of induced-fit and substrate-strain theories of enzyme action.

Competency-based physiology and biochemistry education can benefit from the creative integration of imaginative narratives into traditional teaching methods. This paper proposes an innovative model using a pen and palm analogy to visualize enzyme function theories. The pen (substrate) must fit snugly into the palm (enzyme's active site) for catalysis to occur, akin to induced-fit theory. Pressing the pen's top button with the thumb represents the strain needed to convert substrate (pen with nib inside) into product (pen with nub out, ready to write). By leveraging everyday objects creatively, students can enhance their understanding and engagement with enzymatic reactions.NEW & NOTEWORTHY Understanding how enzymes work can be tricky, but a new teaching method using everyday objects like pens and palms helps make it easier. Two main theories explain this: the induced-fit model and the substrate-strain model. To visualize this, imagine a pen as the substrate and your palm as the enzyme. When you hold the pen with your fingers (induced-fit), it's like the enzyme changing shape to hold the substrate. Pressing the pen's button with your thumb (substrate-strain) is like the enzyme applying pressure to make the pen ready to write. This simple analogy helps students better understand these complex processes, making learning more engaging and accessible.

[Enzyme engineering in microbial biosynthesis of terpenoids: progress and perspectives].

Terpenoids, known for their structural and functional diversity, are highly valued, especially in food, cosmetics, and cleaning products. Microbial biosynthesis has emerged as a sustainable and environmentally friendly approach for the production of terpenoids. However, the natural enzymes involved in the synthesis of terpenoids have problems such as low activity, poor specificity, and insufficient stability, which limit the biosynthesis efficiency. Enzyme engineering plays a pivotal role in the microbial synthesis of terpenoids. By modifying the structures and functions of key enzymes, researchers have significantly improved the catalytic activity, specificity, and stability of enzymes related to terpenoid synthesis, providing strong support for the sustainable production of terpenoids. This article reviews the strategies for the modification of key enzymes in microbial synthesis of terpenoids, including improving enzyme activity and stability, changing specificity, and promoting mass transfer through multi-enzyme collaboration. Additionally, this article looks forward to the challenges and development directions of enzyme engineering in the microbial synthesis of terpenoids.

[Enzyme metabolism and functions in vitamin biosynthesis pathways].

Vitamins, as indispensable organic compounds in life activities, demonstrate a complex and refined metabolic network in organisms. This network involves the coordination of multiple enzymes and the integration of various metabolic pathways. Despite the achievements in metabolic engineering and catalytic mechanism research, the lack of studies regarding detailed enzymatic properties for a large number of key enzymes limits the enhancement of vitamin production efficiency and hinders the in-depth understanding and optimization of vitamin synthesis mechanisms. Such limitations not only restrict the industrial application of vitamins but also impede the development of related bio-technologies. This study comprehensively reviews the research progress in the enzymes involved in vitamin biosynthesis and details the current status of research on the enzymes of 13 vitamin synthesis pathways, including their catalytic mechanisms, kinetic properties, and applications in biology. In addition, this study compares the properties of enzymes involved in vitamin metabolic pathways and the glycolysis pathway, and reveals the characteristics of catalytic efficiency and substrate affinity of enzymes in vitamin synthesis pathways. Furthermore, this study discusses the potential and prospects of applying deep learning methods to the research on properties of enzymes associated with vitamin biosynthesis, giving new insights into the production and optimization of vitamins.