Construction and Application of Nanozyme Sensor Arrays.

Compared with traditional "lock-key mode" biosensors, a sensor array consists of a series of sensing elements based on intermolecular interactions (typically hydrogen bonds, van der Waals forces, and electrostatic interactions). At the same time, sensor arrays also have the advantages of fast response, high sensitivity, low energy consumption, low cost, rich output signals, and imageability, which have attracted widespread attention from researchers. Nanozymes are nanomaterials which own enzyme-like properties. Because of the adjustable activity, high stability, and cost effectiveness of nanozymes, they are potential candidates for construction of sensor arrays to output different signals from analytes through the chemoresponse of colorants, which solves the shortcomings of traditional sensors that they cannot support multiple detection and lack universality. Recently, a sensor array based on nanozymes as nonspecific recognition receptors has attracted much more attention from researchers and has been applied to precise recognition of proteins, bacteria, and heavy metals. In this perspective, attention is given to nanozymes and the regulation of their enzyme-like activity. Particularly, the building principles and methods for sensor arrays based on nanozymes are analyzed, and the applications are summarized. Finally, the approaches to overcome the challenges and perspectives are also presented and analyzed for facilitating further research and development of nanozyme sensor arrays. This perspective should be helpful for gaining insight into research ideas within the field of nanozyme sensor arrays.

A compound enzyme as an additive to a continuous anaerobic dynamic membrane bioreactor for enhanced lignocellulose removal from codigestion: Performance, membrane characteristics and microorganisms.

To explore the enzyme-enhanced strategy of a continuous anaerobic dynamic membrane reactor (AnDMBR), the anaerobic codigestion system of food waste and corn straw was first operated stably, and then the best combination of compound enzymes (laccase, endo-ß-1,4-glucanase, xylanase) was determined via a series of batch trials. The results showed that the methane yield (186.8 ± 19.9 mL/g VS) with enzyme addition was 12.2 % higher than that without enzyme addition. Furthermore, the removal rates of cellulose, hemicellulose and lignin increased by 31 %, 36 % and 78 %, respectively. In addition, dynamic membranes can form faster and more stably with enzyme addition. The addition of enzymes changed the structure of microbial communities while maintaining sufficient hydrolysis bacteria (Bacteroidetes), promoting the proliferation of Proteobacteria as a dominant strain and bringing stronger acetylation ability. In summary, the compound enzyme strengthening strategy successfully improved the methane production, dynamic membrane effect, and degradation rate of lignocellulose in AnDMBR.

Nanozyme as a rising star for metabolic disease management.

Nanozyme, characterized by outstanding and inherent enzyme-mimicking properties, have emerged as highly promising alternatives to natural enzymes owning to their exceptional attributes such as regulation of oxidative stress, convenient storage, adjustable catalytic activities, remarkable stability, and effortless scalability for large-scale production. Given the potent regulatory function of nanozymes on oxidative stress and coupled with the fact that reactive oxygen species (ROS) play a vital role in the occurrence and exacerbation of metabolic diseases, nanozyme offer a unique perspective for therapy through multifunctional activities, achieving essential results in the treatment of metabolic diseases by directly scavenging excess ROS or regulating pathologically related molecules. The rational design strategies, nanozyme-enabled therapeutic mechanisms at the cellular level, and the therapies of nanozyme for several typical metabolic diseases and underlying mechanisms are discussed, mainly including obesity, diabetes, cardiovascular disease, diabetic wound healing, and others. Finally, the pharmacokinetics, safety analysis, challenges, and outlooks for the application of nanozyme are also presented. This review will provide some instructive perspectives on nanozyme and promote the development of enzyme-mimicking strategies in metabolic disease therapy.

Single-Atom-Based Nanoenzyme in Tissue Repair.

Since the discovery of ferromagnetic nanoparticles Fe3O4 that exhibit enzyme-like activity in 2007, the research on nanoenzymes has made significant progress. With the in-depth study of various nanoenzymes and the rapid development of related nanotechnology, nanoenzymes have emerged as a promising alternative to natural enzymes. Within nanozymes, there is a category of metal-based single-atom nanozymes that has been rapidly developed due to low cast, convenient preparation, long storage, less immunogenicity, and especially higher efficiency. More importantly, single-atom nanozymes possess the capacity to scavenge reactive oxygen species through various mechanisms, which is beneficial in the tissue repair process. Herein, this paper systemically highlights the types of metal single-atom nanozymes, their catalytic mechanisms, and their recent applications in tissue repair. The existing challenges are identified and the prospects of future research on nanozymes composed of metallic nanomaterials are proposed. We hope this review will illuminate the potential of single-atom nanozymes in tissue repair, encouraging their sequential clinical translation.

Boron catalysis in a designer enzyme.

Enzymes play an increasingly important role in improving the benignity and efficiency of chemical production, yet the diversity of their applications lags heavily behind chemical catalysts as a result of the relatively narrow range of reaction mechanisms of enzymes. The creation of enzymes containing non-biological functionalities facilitates reaction mechanisms outside nature's canon and paves the way towards fully programmable biocatalysis1-3. Here we present a completely genetically encoded boronic-acid-containing designer enzyme with organocatalytic reactivity not achievable with natural or engineered biocatalysts4,5. This boron enzyme catalyses the kinetic resolution of hydroxyketones by oxime formation, in which crucial interactions with the protein scaffold assist in the catalysis. A directed evolution campaign led to a variant with natural-enzyme-like enantioselectivities for several different substrates. The unique activation mode of the boron enzyme was confirmed using X-ray crystallography, high-resolution mass spectrometry (HRMS) and 11B NMR spectroscopy. Our study demonstrates that genetic-code expansion can be used to create evolvable enantioselective enzymes that rely on xenobiotic catalytic moieties such as boronic acids and access reaction mechanisms not reachable through catalytic promiscuity of natural or engineered enzymes.

Improving plastic degrading enzymes via directed evolution.

Plastic degrading enzymes have immense potential for use in industrial applications. Protein engineering efforts over the last decade have resulted in considerable enhancement of many properties of these enzymes. Directed evolution, a protein engineering approach that mimics the natural process of evolution in a laboratory, has been particularly useful in overcoming some of the challenges of structure-based protein engineering. For example, directed evolution has been used to improve the catalytic activity and thermostability of polyethylene terephthalate (PET)-degrading enzymes, although its use for the improvement of other desirable properties, such as solvent tolerance, has been less studied. In this review, we aim to identify some of the knowledge gaps and current challenges, and highlight recent studies related to the directed evolution of plastic-degrading enzymes.

A review of nanotechnology in enzyme cascade to address challenges in pre-treating biomass.

Nanotechnology has become a revolutionary technique for improving the preliminary treatment of lignocellulosic biomass in the production of biofuels. Traditional methods of pre-treatment have encountered difficulties in effectively degrading the intricate lignocellulosic composition, thereby impeding the conversion of biomass into fermentable sugars. Nanotechnology has enabled the development of enzyme cascade processes that present a potential solution for addressing the limitations. The focus of this review article is to delve into the utilization of nanotechnology in the pretreatment of lignocellulosic biomass through enzyme cascade processes. The review commences with an analysis of the composition and structure of lignocellulosic biomass, followed by a discussion on the drawbacks associated with conventional pre-treatment techniques. The subsequent analysis explores the importance of efficient pre-treatment methods in the context of biofuel production. We thoroughly investigate the utilization of nanotechnology in the pre-treatment of enzyme cascades across three distinct sections. Nanomaterials for enzyme immobilization, enhanced enzyme stability and activity through nanotechnology, and nanocarriers for controlled enzyme delivery. Moreover, the techniques used to analyse nanomaterials and the interactions between enzymes and nanomaterials are introduced. This review emphasizes the significance of comprehending the mechanisms underlying the synergy between nanotechnology and enzymes establishing sustainable and environmentally friendly nanotechnology applications.

Unlocking the potential of enzyme engineering via rational computational design strategies.

Enzymes play a pivotal role in various industries by enabling efficient, eco-friendly, and sustainable chemical processes. However, the low turnover rates and poor substrate selectivity of enzymes limit their large-scale applications. Rational computational enzyme design, facilitated by computational algorithms, offers a more targeted and less labor-intensive approach. There has been notable advancement in employing rational computational protein engineering strategies to overcome these issues, it has not been comprehensively reviewed so far. This article reviews recent developments in rational computational enzyme design, categorizing them into three types: structure-based, sequence-based, and data-driven machine learning computational design. Case studies are presented to demonstrate successful enhancements in catalytic activity, stability, and substrate selectivity. Lastly, the article provides a thorough analysis of these approaches, highlights existing challenges and potential solutions, and offers insights into future development directions.

iCEED: Integrated customized extraction of enzyme data.

Enzymes catalyze diverse biochemical reactions and are building blocks of cellular and metabolic pathways. Data and metadata of enzymes are distributed across databases and are archived in various formats. The enzyme databases provide utilities for efficient searches and downloading enzyme records in batch mode but do not support organism-specific extraction of subsets of data. Users are required to write scripts for parsing entries for customized data extraction prior to downstream analysis. Integrated Customized Extraction of Enzyme Data (iCEED) has been developed to provide organism-specific customized data extraction utilities for seven commonly used enzyme databases and brings these resources under an integrated portal. iCEED provides dropdown menus and search boxes using typehead utility for submission of queries as well as enzyme class-based browsing utility. A utility to facilitate mapping and visualization of functionally important features on the three-dimensional (3D) structures of enzymes is integrated. The customized data extraction utilities provided in iCEED are expected to be useful for biochemists, biotechnologists, computational biologists, and life science researchers to build curated datasets of their choice through an easy to navigate web-based interface. The integrated feature visualization system is useful for a fine-grained understanding of the enzyme structure-function relationship. Desired subsets of data, extracted and curated using iCEED can be subsequently used for downstream processing, analyses, and knowledge discovery. iCEED can also be used for training and teaching purposes.

Complete biosynthesis of QS-21 in engineered yeast.

QS-21 is a potent vaccine adjuvant and remains the only saponin-based adjuvant that has been clinically approved for use in humans1,2. However, owing to the complex structure of QS-21, its availability is limited. Today, the supply depends on laborious extraction from the Chilean soapbark tree or on low-yielding total chemical synthesis3,4. Here we demonstrate the complete biosynthesis of QS-21 and its precursors, as well as structural derivatives, in engineered yeast strains. The successful biosynthesis in yeast requires fine-tuning of the host's native pathway fluxes, as well as the functional and balanced expression of 38 heterologous enzymes. The required biosynthetic pathway spans seven enzyme families-a terpene synthase, P450s, nucleotide sugar synthases, glycosyltransferases, a coenzyme A ligase, acyl transferases and polyketide synthases-from six organisms, and mimics in yeast the subcellular compartmentalization of plants from the endoplasmic reticulum membrane to the cytosol. Finally, by taking advantage of the promiscuity of certain pathway enzymes, we produced structural analogues of QS-21 using this biosynthetic platform. This microbial production scheme will allow for the future establishment of a structure-activity relationship, and will thus enable the rational design of potent vaccine adjuvants.

The Michaelis-Menten Reaction at Low Substrate Concentrations: Pseudo-First-Order Kinetics and Conditions for Timescale Separation.

We demonstrate that the Michaelis-Menten reaction mechanism can be accurately approximated by a linear system when the initial substrate concentration is low. This leads to pseudo-first-order kinetics, simplifying mathematical calculations and experimental analysis. Our proof utilizes a monotonicity property of the system and Kamke's comparison theorem. This linear approximation yields a closed-form solution, enabling accurate modeling and estimation of reaction rate constants even without timescale separation. Building on prior work, we establish that the sufficient condition for the validity of this approximation is s 0 ≪ K , where K = k 2 / k 1 is the Van Slyke-Cullen constant. This condition is independent of the initial enzyme concentration. Further, we investigate timescale separation within the linear system, identifying necessary and sufficient conditions and deriving the corresponding reduced one-dimensional equations.

Single-atom nanozymes shines diagnostics of gastrointestinal diseases.

Various clinical symptoms of digestive system, such as infectious, inflammatory, and malignant disorders, have a profound impact on the quality of life and overall health of patients. Therefore, the chase for more potent medicines is both highly significant and urgent. Nanozymes, a novel class of nanomaterials, amalgamate the biological properties of nanomaterials with the catalytic activity of enzymes, and have been engineered for various biomedical applications, including complex gastrointestinal diseases (GI). Particularly, because of their distinctive metal coordination structure and ability to maximize atom use efficiency, single-atom nanozymes (SAzymes) with atomically scattered metal centers are becoming a more viable substitute for natural enzymes. Traditional nanozyme design strategies are no longer able to meet the current requirements for efficient and diverse SAzymes design due to the diversification and complexity of preparation processes. As a result, this review emphasizes the design concept and the synthesis strategy of SAzymes, and corresponding bioenzyme-like activities, such as superoxide dismutase (SOD), peroxidase (POD), oxidase (OXD), catalase (CAT), and glutathione peroxidase (GPx). Then the various application of SAzymes in GI illnesses are summarized, which should encourage further research into nanozymes to achieve better application characteristics.

Sorting drug conformers in enzyme active sites: the XTB way.

In the present study, we have used the MEI196 set of interaction energies to investigate low-cost computational chemistry approaches for the calculation of binding between a molecule and its environment. Density functional theory (DFT) methods, when used with the vDZP basis set, yield good agreement with the reference energies. On the other hand, semi-empirical methods are less accurate as expected. By examining different groups of systems within MEI196 that contain species of a similar nature, we find that chemical similarity leads to cancellation of errors in the calculation of relative binding energies. Importantly, the semi-empirical method GFN1-xTB (XTB1) yields reasonable results for this purpose. We have thus further assessed the performance of XTB1 for calculating relative energies of docking poses of substrates in enzyme active sites represented by cluster models or within the ONIOM protocol. The results support the observations on error cancellation. This paves the way for the use of XTB1 in parts of large-scale virtual screening workflows to accelerate the drug discovery process.

ALDELE: All-Purpose Deep Learning Toolkits for Predicting the Biocatalytic Activities of Enzymes.

Rapidly predicting enzyme properties for catalyzing specific substrates is essential for identifying potential enzymes for industrial transformations. The demand for sustainable production of valuable industry chemicals utilizing biological resources raised a pressing need to speed up biocatalyst screening using machine learning techniques. In this research, we developed an all-purpose deep-learning-based multiple-toolkit (ALDELE) workflow for screening enzyme catalysts. ALDELE incorporates both structural and sequence representations of proteins, alongside representations of ligands by subgraphs and overall physicochemical properties. Comprehensive evaluation demonstrated that ALDELE can predict the catalytic activities of enzymes, and particularly, it identifies residue-based hotspots to guide enzyme engineering and generates substrate heat maps to explore the substrate scope for a given biocatalyst. Moreover, our models notably match empirical data, reinforcing the practicality and reliability of our approach through the alignment with confirmed mutation sites. ALDELE offers a facile and comprehensive solution by integrating different toolkits tailored for different purposes at affordable computational cost and therefore would be valuable to speed up the discovery of new functional enzymes for their exploitation by the industry.

A life cycle assessment of early-stage enzyme manufacturing simulations from sustainable feedstocks.

Enzyme-catalyzed reactions have relatively small environmental footprints. However, enzyme manufacturing significantly impacts the environment through dependence on traditional feedstocks. With the objective of determining the environmental impacts of enzyme production, the sustainability potential of six cradle-to-gate enzyme manufacturing systems focusing on glucose, sea lettuce, acetate, straw, and phototrophic growth, was thoroughly evaluated. Human and ecosystem toxicity categories dominated the overall impacts. Sea lettuce, straw, or phototrophic growth reduces fermentation-based emissions by 51.0, 63.7, and 79.7%, respectively. Substituting glucose-rich media demonstrated great potential to reduce marine eutrophication, land use, and ozone depletion. Replacing organic nitrogen sources with inorganic ones could further lower these impacts. Location-specific differences in electricity result in a 14% and a 27% reduction in the carbon footprint for operation in Denmark compared to the US and China. Low-impact feedstocks can be competitive if they manage to achieve substrate utilization rates and productivity levels of conventional enzyme production processes.

Automated in vivo enzyme engineering accelerates biocatalyst optimization.

Achieving cost-competitive bio-based processes requires development of stable and selective biocatalysts. Their realization through in vitro enzyme characterization and engineering is mostly low throughput and labor-intensive. Therefore, strategies for increasing throughput while diminishing manual labor are gaining momentum, such as in vivo screening and evolution campaigns. Computational tools like machine learning further support enzyme engineering efforts by widening the explorable design space. Here, we propose an integrated solution to enzyme engineering challenges whereby ML-guided, automated workflows (including library generation, implementation of hypermutation systems, adapted laboratory evolution, and in vivo growth-coupled selection) could be realized to accelerate pipelines towards superior biocatalysts.

Cold-active microbial enzymes and their biotechnological applications.

Microorganisms known as psychrophiles/psychrotrophs, which survive in cold climates, constitute majority of the biosphere on Earth. Their capability to produce cold-active enzymes along with other distinguishing characteristics allows them to survive in the cold environments. Due to the relative ease of large-scale production compared to enzymes from plants and animals, commercial uses of microbial enzyme are alluring. The ocean depths, polar, and alpine regions, which make up over 85% of the planet, are inhabited to cold ecosystems. Microbes living in these regions are important for their metabolic contribution to the ecosphere as well as for their enzymes, which may have potential industrial applications. Cold-adapted microorganisms are a possible source of cold-active enzymes that have high catalytic efficacy at low and moderate temperatures at which homologous mesophilic enzymes are not active. Cold-active enzymes can be used in a variety of biotechnological processes, including food processing, additives in the detergent and food industries, textile industry, waste-water treatment, biopulping, environmental bioremediation in cold climates, biotransformation, and molecular biology applications with great potential for energy savings. Genetically manipulated strains that are suitable for producing a particular cold-active enzyme would be crucial in a variety of industrial and biotechnological applications. The potential advantage of cold-adapted enzymes will probably lead to a greater annual market than for thermo-stable enzymes in the near future. This review includes latest updates on various microbial source of cold-active enzymes and their biotechnological applications.

Enzymatic biosynthesis of D-galactose derivatives: Advances and perspectives.

D-Galactose derivatives, including galactosyl-conjugates and galactose-upgrading compounds, provide various physiological benefits and find applications in industries such as food, cosmetics, feed, pharmaceuticals. Many research on galactose derivatives focuses on identification, characterization, development, and mechanistic aspects of their physiological function, providing opportunities and challenges for the development of practical approaches for synthesizing galactose derivatives. This study focuses on recent advancements in enzymatic biosynthesis of galactose derivatives. Various strategies including isomerization, epimerization, transgalactosylation, and phosphorylation-dephosphorylation were extensively discussed under the perspectives of thermodynamic feasibility, theoretical yield, cost-effectiveness, and by-product elimination. Specifically, the enzymatic phosphorylation-dephosphorylation cascade is a promising enzymatic synthesis route for galactose derivatives because it can overcome the thermodynamic equilibrium of isomerization and utilize cost-effective raw materials. The study also elucidates the existing challenges and future trends in enzymatic biosynthesis of galactose derivatives. Collectively, this review provides a real-time summary aimed at promoting the practical biosynthesis of galactose derivatives through enzymatic catalysis.

Enzyme-mimic catalytic activities and biomedical applications of noble metal nanoclusters.

Noble metal (e.g., Au and Ag) nanoclusters (NCs), which exhibit structural complexity and hierarchy comparable to those of natural proteins, have been increasingly pursued in artificial enzyme research. The protein-like structure of metal NCs not only ensures enzyme-mimic catalytic activity, including peroxidase-, catalase-, and superoxide dismutase-mimic activities, but also affords an unprecedented opportunity to correlate the catalytic performance with the cluster structure at the molecular or atomic levels. In this review, we aim to summarize the recent progress in programming and demystify the enzyme-mimic catalytic activity of metal NCs, presenting the state-of-the-art understandings of the structure-property relationship of metal NC-based artificial enzymes. By leveraging on a concise anatomy of the hierarchical structure of noble metal NCs, we manage to unravel the structural origin of the catalytic performance of metal NCs. Noteworthily, it has been proven that the surface ligands and metal-ligand interface of metal NCs are instrumental in influencing enzyme-mimic catalytic activities. In addition to the structure-property correlation, we also discuss the synthetic methodologies feasible to tailoring the cluster structure at the atomic level. Prior to the closure of this review with our perspectives in noble metal NC-based artificial enzymes, we also exemplify the biomedical applications based on the enzyme-mimic catalysis of metal NCs with the theranostics of kidney injury, brain inflammation, and tumors. The fundamental and methodological advancements delineated in this review would be conducive to further development of metal NCs as an alternative family of artificial enzymes.

Kinetic View of Enzyme Catalysis from Enhanced Sampling QM/MM Simulations.

The rate constants of enzyme-catalyzed reactions (kcat) are often approximated from the barrier height of the reactive step. We introduce an enhanced sampling QM/MM approach that directly calculates the kinetics of enzymatic reactions, without introducing the transition-state theory assumptions, and takes into account the dynamical equilibrium between the reactive and non-reactive conformations of the enzyme/substrate complex. Our computed kcat values are in order-of-magnitude agreement with the experimental data for two representative enzymatic reactions.