Functions and mechanisms of enzymes assembling lignans and norlignans.

Lignans and norlignans are distributed throughout the plant kingdom and exhibit diverse chemical structures and biological properties that offer potential for therapeutic use. Originating from the phenylpropanoid biosynthesis pathway, their characteristic carbon architectures are formed through unique enzyme catalysis, featuring regio- and stereoselective C-C bond forming processes. Despite extensive research on these plant natural products, their biosynthetic pathways, and enzyme mechanisms remain enigmatic. This review highlights recent advancements in elucidating the functions and mechanisms of the biosynthetic enzymes responsible for constructing the distinct carbon frameworks of lignans and norlignans.

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.

Leveraging conformal prediction to annotate enzyme function space with limited false positives.

Machine learning (ML) is increasingly being used to guide biological discovery in biomedicine such as prioritizing promising small molecules in drug discovery. In those applications, ML models are used to predict the properties of biological systems, and researchers use these predictions to prioritize candidates as new biological hypotheses for downstream experimental validations. However, when applied to unseen situations, these models can be overconfident and produce a large number of false positives. One solution to address this issue is to quantify the model's prediction uncertainty and provide a set of hypotheses with a controlled false discovery rate (FDR) pre-specified by researchers. We propose CPEC, an ML framework for FDR-controlled biological discovery. We demonstrate its effectiveness using enzyme function annotation as a case study, simulating the discovery process of identifying the functions of less-characterized enzymes. CPEC integrates a deep learning model with a statistical tool known as conformal prediction, providing accurate and FDR-controlled function predictions for a given protein enzyme. Conformal prediction provides rigorous statistical guarantees to the predictive model and ensures that the expected FDR will not exceed a user-specified level with high probability. Evaluation experiments show that CPEC achieves reliable FDR control, better or comparable prediction performance at a lower FDR than existing methods, and accurate predictions for enzymes under-represented in the training data. We expect CPEC to be a useful tool for biological discovery applications where a high yield rate in validation experiments is desired but the experimental budget is limited.

Insights into Error Control Mechanisms in Biological Processes: Copolymerization and Enzyme-Kinetics Revisited.

The high fidelity observed in biological information processing ranging from replication to translation has stimulated significant research efforts to clarify the underlying microscopic picture. Theoretically, several approaches to analyze the error rates have been proposed. The copolymerization theory describes the addition and removal of monomers at the growing tip of a copolymer, leading to a closed set of nonlinear equations. On the other hand, enzyme-kinetics approaches formulate linear equations of biochemical networks, describing transitions between discrete chemical states. However, it is still unclear whether the error values computed by the two approaches agree. Moreover, there are conflicting interpretations on whether the error is under thermodynamic or kinetic discrimination control. In this work, we examine the error rate in persistent copying biochemical processes by specifically analyzing both theoretical approaches. The initial disagreement of the results between the two theories motivated us to rederive the formula for the error rate in the kinetic model. The error computed with the new method resulted in excellent agreement between both theoretical approaches and with Monte Carlo simulations. Furthermore, our theoretical analysis shows that the kinetic discrimination controls the error, even when the energy difference between adding the right and wrong products is very small. Our theoretical investigation gives important insights into the physical-chemical properties of complex biological processes by providing the quantitative framework to evaluate them.

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.

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.

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.

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.

Novel types of RiPP-modifying enzymes.

Novel discoveries in natural product biosynthesis reveal hidden bioactive compounds and expand our knowledge in enzymology. Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a rapidly growing class of natural products featuring diverse non-canonical amino acids introduced by maturation enzymes as a class-defining characteristic. Underexplored RiPP sources, such as the human microbiome, the oceans, uncultured microorganisms, and plants are rich hunting grounds for novel enzymology. Unusual α- and ß-amino acids, peptide cleavages, lipidations, diverse macrocyclizations, and other features expand the range of chemical groups that are installed in RiPPs by often promiscuous enzymes. This review highlights the search for novelty in RiPP enzymology in the past two years, with respect to the discovery of new biochemical modifications but also towards novel applications.

Review on novel targeted enzyme drug delivery systems: enzymosomes.

The goal of this review is to present enzymosomes as an innovative means for site-specific drug delivery. Enzymosomes make use of an enzyme's special characteristics, such as its capacity to accelerate the reaction rate and bind to a particular substrate at a regulated rate. Enzymosomes are created when an enzyme forms a covalent linkage with a liposome or lipid vesicle surface. To construct enzymosomes with specialized activities, enzymes are linked using acylation, direct conjugation, physical adsorption, and encapsulation techniques. By reducing the negative side effects of earlier treatment techniques and exhibiting efficient medication release, these cutting-edge drug delivery systems improve long-term sickness treatments. They could be a good substitute for antiplatelet medication, gout treatment, and other traditional medicines. Recently developed supramolecular vesicular delivery systems called enzymosomes have the potential to improve drug targeting, physicochemical characteristics, and ultimately bioavailability in the pharmaceutical industry. Enzymosomes have advantages over narrow-therapeutic index pharmaceuticals as focusing on their site of action enhances both their pharmacodynamic and pharmacokinetic profiles. Additionally, it reduces changes in normal enzymatic activity, which enhances the half-life of an enzyme and accomplishes enzyme activity on specific locations.

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.

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.

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.

Monitoring Enzyme Activity Using Near-Infrared Fluorescent Single-Walled Carbon Nanotubes.

Enzymes serve as pivotal biological catalysts that accelerate essential chemical reactions, thereby influencing a variety of physiological processes. Consequently, the monitoring of enzyme activity and inhibition not only yields crucial insights into health and disease conditions but also forms the basis of research in drug discovery, toxicology, and the understanding of disease mechanisms. In this context, near-infrared (NIR) fluorescent single-walled carbon nanotubes (SWCNTs) have emerged as effective tools for tracking enzyme activity and inhibition through diverse strategies. This perspective explores the physicochemical attributes of SWCNTs that render them well-suited for such monitoring. Additionally, we delve into the various strategies developed so far for successfully monitoring enzyme activity and inhibition, emphasizing the distinctive features of each principle. Furthermore, we contrast the benefits of SWCNT-based NIR probes with conventional gold standards in monitoring enzyme activity. Lastly, we highlight the current challenges faced in this field and suggest potential solutions to propel it forward. This perspective aims to contribute to the ongoing progress in biodiagnostics and seeks to engage the wider community in developing and applying enzymatic assays using SWCNTs.

Advances in the understanding and exploitation of carbohydrate-active enzymes.

Carbohydrate-active enzymes (CAZymes) are responsible for the biosynthesis, modification and degradation of all glycans in Nature. Advances in genomic and metagenomic methodologies, in conjunction with lower cost gene synthesis, have provided access to a steady stream of new CAZymes with both well-established and novel mechanisms. At the same time, increasing access to cryo-EM has resulted in exciting new structures, particularly of transmembrane glycosyltransferases of various sorts. This improved understanding has resulted in widespread progress in applications of CAZymes across diverse fields, including therapeutics, organ transplantation, foods, and biofuels. Herein, we highlight a few of the many important advances that have recently been made in the understanding and applications of CAZymes.

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.

Carbohydrate binding modules: Compact yet potent accessories in the specific substrate binding and performance evolution of carbohydrate-active enzymes.

Carbohydrate binding modules (CBMs) are independent non-catalytic domains widely found in carbohydrate-active enzymes (CAZymes), and they play an essential role in the substrate binding process of CAZymes by guiding the appended catalytic modules to the target substrates. Owing to their precise recognition and selective affinity for different substrates, CBMs have received increasing research attention over the past few decades. To date, CBMs from different origins have formed a large number of families that show a variety of substrate types, structural features, and ligand recognition mechanisms. Moreover, through the modification of specific sites of CBMs and the fusion of heterologous CBMs with catalytic domains, improved enzymatic properties and catalytic patterns of numerous CAZymes have been achieved. Based on cutting-edge technologies in computational biology, gene editing, and protein engineering, CBMs as auxiliary components have become portable and efficient tools for the evolution and application of CAZymes. With the aim to provide a theoretical reference for the functional research, rational design, and targeted utilization of novel CBMs in the future, we systematically reviewed the function-related characteristics and potentials of CAZyme-derived CBMs in this review, including substrate recognition and binding mechanisms, non-catalytic contributions to enzyme performances, module modifications, and innovative applications in various fields.

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.

Recent developments in the enzymatic modifications of steroid scaffolds.

Steroids are an important family of bioactive compounds. Steroid drugs are renowned for their multifaceted pharmacological activities and are the second-largest category in the global pharmaceutical market. Recent developments in biocatalysis and biosynthesis have led to the increased use of enzymes to enhance the selectivity, efficiency, and sustainability for diverse modifications of steroids. This review discusses the advancements achieved over the past five years in the enzymatic modifications of steroid scaffolds, focusing on enzymatic hydroxylation, reduction, dehydrogenation, cascade reactions, and other modifications for future research on the synthesis of novel steroid compounds and related drugs, and new therapeutic possibilities.

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.