Expanding chemistry through in vitro and in vivo biocatalysis.

Living systems contain a vast network of metabolic reactions, providing a wealth of enzymes and cells as potential biocatalysts for chemical processes. The properties of protein and cell biocatalysts-high selectivity, the ability to control reaction sequence and operation in environmentally benign conditions-offer approaches to produce molecules at high efficiency while lowering the cost and environmental impact of industrial chemistry. Furthermore, biocatalysis offers the opportunity to generate chemical structures and functions that may be inaccessible to chemical synthesis. Here we consider developments in enzymes, biosynthetic pathways and cellular engineering that enable their use in catalysis for new chemistry and beyond.

The mechanistic insights into different aspects of promiscuity in metalloenzymes.

Enzymes are nature's ultimate machinery to catalyze complex reactions. Though enzymes are evolved to catalyze specific reactions, they also show significant promiscuity in reactions and substrate selection. Metalloenzymes contain a metal ion or metal cofactor in their active site, which is crucial in their catalytic activity. Depending on the metal and its coordination environment, the metal ion or cofactor may function as a Lewis acid or base and a redox center and thus can catalyze a plethora of natural reactions. In fact, the versatility in the oxidation state of the metal ions provides metalloenzymes with a high level of catalytic adaptability and promiscuity. In this chapter, we discuss different aspects of promiscuity in metalloenzymes by using several recent experimental and theoretical works as case studies. We start our discussion by introducing the concept of promiscuity and then we delve into the mechanistic insight into promiscuity at the molecular level.

SACCHARIS v2: Streamlining Prediction of Carbohydrate-Active Enzyme Specificities Within Large Datasets.

Carbohydrates are chemically and structurally diverse, composed of a wide array of monosaccharides, stereochemical linkages, substituent groups, and intermolecular associations with other biological molecules. A large repertoire of carbohydrate-active enzymes (CAZymes) and enzymatic activities are required to form, dismantle, and metabolize these complex molecules. The software SACCHARIS (Sequence Analysis and Clustering of CarboHydrate Active enzymes for Rapid Informed prediction of Specificity) provides a rapid, easy-to-use pipeline for the prediction of potential CAZyme function in new datasets. We have updated SACCHARIS to (i) simplify its installation by re-writing in Python and packaging for Conda; (ii) enhance its usability through a new (optional) interactive GUI; and (iii) enable semi-automated annotation of phylogenetic tree output via a new R package or the commonly-used webserver iTOL. Significantly, SACCHARIS v2 has been developed with high-throughput omics in mind, with pipeline automation geared toward complex (meta)genome and (meta)transcriptome datasets to reveal the total CAZyme content ("CAZome") of an organism or community. Here, we outline the development and use of SACCHARIS v2 to discover and annotate CAZymes and provide insight into complex carbohydrate metabolisms in individual organisms and communities.

Overview on the Development of Electrochemical Immunosensors by the Signal Amplification of Enzyme- or Nanozyme-Based Catalysis Plus Redox Cycling.

Enzyme-linked electrochemical immunosensors have attracted considerable attention for the sensitive and selective detection of various targets in clinical diagnosis, food quality control, and environmental analysis. In order to improve the performances of conventional immunoassays, significant efforts have been made to couple enzyme-linked or nanozyme-based catalysis and redox cycling for signal amplification. The current review summarizes the recent advances in the development of enzyme- or nanozyme-based electrochemical immunosensors with redox cycling for signal amplification. The special features of redox cycling reactions and their synergistic functions in signal amplification are discussed. Additionally, the current challenges and future directions of enzyme- or nanozyme-based electrochemical immunosensors with redox cycling are addressed.

Extreme Biocatalysts: From Basic Research to Biotechnological Applications.

Biocatalysis, a cornerstone of modern biotechnology, is poised to revolutionize industrial processes across diverse sectors [...].

Nanoenzymes: A Radiant Hope for the Early Diagnosis and Effective Treatment of Breast and Ovarian Cancers.

Breast and ovarian cancers, despite having chemotherapy and surgical treatment, still have the lowest survival rate. Experimental stages using nanoenzymes/nanozymes for ovarian cancer diagnosis and treatment are being carried out, and correspondingly the current treatment approaches to treat breast cancer have a lot of adverse side effects, which is the reason why researchers and scientists are looking for new strategies with less side effects. Nanoenzymes have intrinsic enzyme-like activities and can reduce the shortcomings of naturally occurring enzymes due to the ease of storage, high stability, less expensive, and enhanced efficiency. In this review, we have discussed various ways in which nanoenzymes are being used to diagnose and treat breast and ovarian cancer. For breast cancer, nanoenzymes and their multi-enzymatic properties can control the level of reactive oxygen species (ROS) in cells or tissues, for example, oxidase (OXD) and peroxidase (POD) activity can be used to generate ROS, while catalase (CAT) or superoxide dismutase (SOD) activity can scavenge ROS. In the case of ovarian cancer, most commonly nanoceria is being investigated, and also when folic acid is combined with nanoceria there are additional advantages like inhibition of beta galactosidase. Nanocarriers are also used to deliver small interfering RNA that are effective in cancer treatment. Studies have shown that iron oxide nanoparticles are actively being used for drug delivery, similarly ferritin carriers are used for the delivery of nanozymes. Hypoxia is a major factor in ovarian cancer, therefore MnO2-based nanozymes are being used as a therapy. For cancer diagnosis and screening, nanozymes are being used in sonodynamic cancer therapy for cancer diagnosis and screening, whereas biomedical imaging and folic acid gold particles are also being used for image guided treatments. Nanozyme biosensors have been developed to detect ovarian cancer. This review article summarizes a detailed insight into breast and ovarian cancers in light of nanozymes-based diagnostic and therapeutic approaches.

ifDEEPre: large protein language-based deep learning enables interpretable and fast predictions of enzyme commission numbers.

Accurate understanding of the biological functions of enzymes is vital for various tasks in both pathologies and industrial biotechnology. However, the existing methods are usually not fast enough and lack explanations on the prediction results, which severely limits their real-world applications. Following our previous work, DEEPre, we propose a new interpretable and fast version (ifDEEPre) by designing novel self-guided attention and incorporating biological knowledge learned via large protein language models to accurately predict the commission numbers of enzymes and confirm their functions. Novel self-guided attention is designed to optimize the unique contributions of representations, automatically detecting key protein motifs to provide meaningful interpretations. Representations learned from raw protein sequences are strictly screened to improve the running speed of the framework, 50 times faster than DEEPre while requiring 12.89 times smaller storage space. Large language modules are incorporated to learn physical properties from hundreds of millions of proteins, extending biological knowledge of the whole network. Extensive experiments indicate that ifDEEPre outperforms all the current methods, achieving more than 14.22% larger F1-score on the NEW dataset. Furthermore, the trained ifDEEPre models accurately capture multi-level protein biological patterns and infer evolutionary trends of enzymes by taking only raw sequences without label information. Meanwhile, ifDEEPre predicts the evolutionary relationships between different yeast sub-species, which are highly consistent with the ground truth. Case studies indicate that ifDEEPre can detect key amino acid motifs, which have important implications for designing novel enzymes. A web server running ifDEEPre is available at https://proj.cse.cuhk.edu.hk/aihlab/ifdeepre/ to provide convenient services to the public. Meanwhile, ifDEEPre is freely available on GitHub at https://github.com/ml4bio/ifDEEPre/.

A Theoretical Approach to Understand the Nonlinear Processes in Enzymatic Electrochemical Biosensors.

The enzymatic biosensors' response can be monitored based on the results of nonlinear differential equations. The nonlinear reaction-diffusion equations proposed for this enzyme-based electrochemical biosensor include a nonlinear term associated with Michaelis-Menten kinetics. Herein, the system of nonlinear reaction-diffusion equations is solved using a modified homotopy perturbation method. For all values of the rate constants, the approximate analytical expressions for the concentration profiles, current, sensitivity, and gradient of biosensor have been determined. Performance factors of an enzymatic electrochemical biosensor, such as response time, sensitivity, accuracy, and resistance, are discussed. The analytical results and numerically simulated outcomes using Matlab software have been compared.

Enzyme-mediated green synthesis of glycosaminoglycans and catalytic process intensification.

Glycosaminoglycans (GAGs) are a family of structurally complex heteropolysaccharides that play pivotal roles in biological functions, including the regulation of cell proliferation, enzyme inhibition, and activation of growth factor receptors. Therefore, the synthesis of GAGs is a hot research topic in drug development. The enzymatic synthesis of GAGs has received widespread attention due to their eco-friendly nature, high regioselectivity, and stereoselectivity. The enhancement of the enzymatic synthesis process is the key to its industrial applications. In this review, we overviewed the construction of more efficient in vitro biomimetic synthesis systems of glycosaminoglycans and presented the different strategies to improve enzyme catalysis, including the combination of chemical and enzymatic methods, solid-phase synthesis, and protein engineering to solve the problems of enzyme stability, separation and purification of the product, preparation of structurally defined sugar chains, etc., and discussed the challenges and opportunities in large-scale green synthesis of GAGs.

[Progress in the application of artificial intelligence-assisted molecular modification of enzymes].

Natural enzymes are often difficult to meet the needs of application and research in terms of activity, enantiomer selectivity or thermal stability. Therefore, it is an important task of enzyme engineering to explore efficient molecular modification technologies to improve the properties of such enzymes. The molecular modification technologies of enzymes mainly include rational design, directed evolution, and artificial intelligence-assisted design. Directed evolution and rational design are experiment-driven molecular modification approaches of enzymes and have been successfully applied to enzyme engineering. However, due to the huge space sizes of protein sequences and the lack of experimental data, the current modification methods still face major challenges. With the development of next-generation sequencing, high-throughput screening, protein databases, and artificial intelligence (AI), data-driven enzyme engineering is emerging as a promising solution to these challenges. The AI-assisted statistical learning method has been used to establish a model for predicting the sequence/structure-properties of enzymes in a data-driven manner. Excellent mutant enzymes can be selected according to the prediction results, which greatly improve the efficiency of molecular modification. Considering the application requirements of molecular modification of enzymes, this paper reviews the data acquisition methods and application examples of AI-assisted molecular modification of enzymes, with focuses on the convolutional neural network method for predicting protein thermostability, aiming to provide reference for researchers in this field.

Beyond homogeneity: Assessing the validity of the Michaelis-Menten rate law in spatially heterogeneous environments.

The Michaelis-Menten (MM) rate law has been a fundamental tool in describing enzyme-catalyzed reactions for over a century. When substrates and enzymes are homogeneously distributed, the validity of the MM rate law can be easily assessed based on relative concentrations: the substrate is in large excess over the enzyme-substrate complex. However, the applicability of this conventional criterion remains unclear when species exhibit spatial heterogeneity, a prevailing scenario in biological systems. Here, we explore the MM rate law's applicability under spatial heterogeneity by using partial differential equations. In this study, molecules diffuse very slowly, allowing them to locally reach quasi-steady states. We find that the conventional criterion for the validity of the MM rate law cannot be readily extended to heterogeneous environments solely through spatial averages of molecular concentrations. That is, even when the conventional criterion for the spatial averages is satisfied, the MM rate law fails to capture the enzyme catalytic rate under spatial heterogeneity. In contrast, a slightly modified form of the MM rate law, based on the total quasi-steady state approximation (tQSSA), is accurate. Specifically, the tQSSA-based modified form, but not the original MM rate law, accurately predicts the drug clearance via cytochrome P450 enzymes and the ultrasensitive phosphorylation in heterogeneous environments. Our findings shed light on how to simplify spatiotemporal models for enzyme-catalyzed reactions in the right context, ensuring accurate conclusions and avoiding misinterpretations in in silico simulations.

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.

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.

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-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 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.

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.