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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Vocabulary Matters: An Annotation Pipeline and Four Deep Learning Algorithms for Enzyme Named Entity Recognition.

Enzymes are indispensable in many biological processes, and with biomedical literature growing exponentially, effective literature review becomes increasingly challenging. Natural language processing methods offer solutions to streamline this process. This study aims to develop an annotated enzyme corpus for training and evaluating enzyme named entity recognition (NER) models. A novel pipeline, combining dictionary matching and rule-based keyword searching, automatically annotated enzyme entities in >4800 full-text publications. Four deep learning NER models were created with different vocabularies (BioBERT/SciBERT) and architectures (BiLSTM/transformer) and evaluated on 526 manually annotated full-text publications. The annotation pipeline achieved an F1-score of 0.86 (precision = 1.00, recall = 0.76), surpassed by fine-tuned transformers for F1-score (BioBERT: 0.89, SciBERT: 0.88) and recall (0.86) with BiLSTM models having higher precision (0.94) than transformers (0.92). The annotation pipeline runs in seconds on standard laptops with almost perfect precision, but was outperformed by fine-tuned transformers in terms of F1-score and recall, demonstrating generalizability beyond the training data. In comparison, SciBERT-based models exhibited higher precision, and BioBERT-based models exhibited higher recall, highlighting the importance of vocabulary and architecture. These models, representing the first enzyme NER algorithms, enable more effective enzyme text mining and information extraction. Codes for automated annotation and model generation are available from https://github.com/omicsNLP/enzymeNER and https://zenodo.org/doi/10.5281/zenodo.10581586.

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.

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.

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.

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.

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.

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.