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.

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.

Physical limits to acceleration of enzymatic reactions inside phase-separated compartments.

We present a theoretical analysis of phase-separated compartments to facilitate enzymatic chemical reactions. While phase separation can facilitate reactions by increasing local concentration, it can also hinder the mobility of reactants. In particular, we find that the attractive interactions that concentrate reactants within the dense phase can inhibit reactions by lowering the mobility of the reactants. This mobility loss severely limits the potential to enhance reaction rates. Phase separation provides greater benefit in situations where multiple sequential reactions occur and/or high order reactions, provided the enzymes are unsaturated, transport to the condensate is not limiting, and the reactants are mobile. We show that mobility can be maintained if recruitment to the condensed phase is driven by multiple attractive moieties that can bind and release independently. However, the spacers necessary to ensure independence between stickers are prone to entangle with the dense phase scaffold. We find an optimal sticker affinity that balances the need for rapid binding/unbinding kinetics and minimal entanglement. Reaction rates can be accelerated by shrinking the size of the dense phase with a corresponding increase in the number of stickers. Our results showcase the potential capabilities of phase-separated compartments to act as biochemical reaction crucibles within living cells.

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.

Theory on the rate equations of Michaelis-Menten type enzyme kinetics with competitive inhibition.

We derive approximate expressions for pre- and post-steady state regimes of the velocity-substrate-inhibitor spaces of the Michaelis-Menten enzyme kinetic scheme with fully and partial competitive inhibition. Our refinement over the currently available standard quasi steady state approximation (sQSSA) seems to be valid over wide range of enzyme to substrate and enzyme to inhibitor concentration ratios. Further, we show that the enzyme-inhibitor-substrate system can exhibit temporally well-separated two different steady states with respect to both enzyme-substrate and enzyme-inhibitor complexes under certain conditions. We define the ratios fS = vmax/(KMS + e0) and fI = umax/(KMI + e0) as the acceleration factors with respect to the catalytic conversion of substrate and inhibitor into their respective products. Here KMS and KMI are the Michaelis-Menten parameters associated respectively with the binding of substrate and inhibitor with the enzyme, vmax and umax are the respective maximum reaction velocities and e0, s0, and i0 are total enzyme, substrate and inhibitor levels. When (fS/fI) < 1, then enzyme-substrate complex will show multiple steady states and it reaches the full-fledged steady state only after the depletion of enzyme-inhibitor complex. When (fS/fI) > 1, then the enzyme-inhibitor complex will show multiple steady states and it reaches the full-fledged steady state only after the depletion of enzyme-substrate complex. This multi steady-state behavior especially when (fS/fI) ≠ 1 is the root cause of large amount of error in the estimation of various kinetic parameters of fully and partial competitive inhibition schemes using sQSSA. Remarkably, we show that our refined expressions for the reaction velocities over enzyme-substrate-inhibitor space can control this error more significantly than the currently available sQSSA expressions.

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/.

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.

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.

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.

[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.

Extreme Biocatalysts: From Basic Research to Biotechnological Applications.

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

Enzyme-responsive liposomes for controlled drug release.

Compared to other nanovectors, liposomes exhibit unique advantages, such as good biosafety and high drug-loading capacity. However, slow drug release from conventional liposomes makes most payloads unavailable, restricting the therapeutic efficacy. Therefore, in the last ∼20 years, enzyme-responsive liposomes have been extensively investigated, which liberate drugs under the stimulation of enzymes overexpressed at disease sites. In this review, we elaborate on the research progress on enzyme-responsive liposomes. The involved enzymes mainly include phospholipases, particularly phospholipase A2, matrix metalloproteinases, cathepsins, and esterases. These enzymes can cleave ester bonds or specific peptide sequences incorporated in the liposomes for controlled drug release by disrupting the primary structure of liposomes, detaching protective polyethylene glycol shells, or activating liposome-associated prodrugs. Despite decades of efforts, there are still a lack marketed products of enzyme-responsive liposomes. Therefore, more efforts should be made to improve the safety and effectiveness of enzyme-responsive liposomes and address the issues associated with production scale-up.

REME: an integrated platform for reaction enzyme mining and evaluation.

A key challenge in pathway design is finding proper enzymes that can be engineered to catalyze a non-natural reaction. Although existing tools can identify potential enzymes based on similar reactions, these tools encounter several issues. Firstly, the calculated similar reactions may not even have the same reaction type. Secondly, the associated enzymes are often numerous and identifying the most promising candidate enzymes is difficult due to the lack of data for evaluation. Thirdly, existing web tools do not provide interactive functions that enable users to fine-tune results based on their expertise. Here, we present REME (https://reme.biodesign.ac.cn/), the first integrated web platform for reaction enzyme mining and evaluation. Combining atom-to-atom mapping, atom type change identification, and reaction similarity calculation enables quick ranking and visualization of reactions similar to an objective non-natural reaction. Additional functionality enables users to filter similar reactions by their specified functional groups and candidate enzymes can be further filtered (e.g. by organisms) or expanded by Enzyme Commission number (EC) or sequence homology. Afterward, enzyme attributes (such as kcat, Km, optimal temperature and pH) can be assessed with deep learning-based methods, facilitating the swift identification of potential enzymes that can catalyze the non-natural reaction.

Towards the understanding of molecular motors and its relationship with local unfolding.

Molecular motors are machines essential for life since they convert chemical energy into mechanical work. However, the precise mechanism by which nucleotide binding, catalysis, or release of products is coupled to the work performed by the molecular motor is still not entirely clear. This is due, in part, to a lack of understanding of the role of force in the mechanical-structural processes involved in enzyme catalysis. From a mechanical perspective, one promising hypothesis is the Haldane-Pauling hypothesis which considers the idea that part of the enzymatic catalysis is strain-induced. It suggests that enzymes cannot be efficient catalysts if they are fully complementary to the substrates. Instead, they must exert strain on the substrate upon binding, using enzyme-substrate energy interaction (binding energy) to accelerate the reaction rate. A novel idea suggests that during catalysis, significant strain energy is built up, which is then released by a local unfolding/refolding event known as 'cracking'. Recent evidence has also shown that in catalytic reactions involving conformational changes, part of the heat released results in a center-of-mass acceleration of the enzyme, raising the possibility that the heat released by the reaction itself could affect the enzyme's integrity. Thus, it has been suggested that this released heat could promote or be linked to the cracking seen in proteins such as adenylate kinase (AK). We propose that the energy released as a consequence of ligand binding/catalysis is associated with the local unfolding/refolding events (cracking), and that this energy is capable of driving the mechanical work.

A role for conformational changes in enzyme catalysis.

The role played by conformational changes in enzyme catalysis is controversial. In addition to examining specific enzymes, studying formal models can help identify the conditions under which conformational changes promote catalysis. Here, we present a model demonstrating how conformational changes can break a generic trade-off due to the conflicting requirements of successive steps in catalytic cycles, namely high specificity for the transition state to accelerate the chemical transformation and low affinity for the products to favor their release. The mechanism by which the trade-off is broken is a transition between conformations with different affinities for the substrate. The role of the effector that induces the transition is played by a substrate "handle," a part of the substrate that is not chemically transformed but whose interaction with the enzyme is nevertheless essential to rapidly complete the catalytic cycle. A key element of the model is the formalization of the constraints causing the trade-off that the presence of multiple states breaks, which we attribute to the strong chemical similarity between successive reaction states-substrates, transition states, and products. For the sake of clarity, we present our model for irreversible one-step unimolecular reactions. In this context, we demonstrate how the different forms that chemical similarities between reaction states can take impose limits on the overall catalytic turnover. We first analyze catalysts without internal degrees of freedom and then show how two-state catalysts can overcome their limitations. Our results recapitulate previous proposals concerning the role of conformational changes and substrate handles in a formalism that makes explicit the constraints that elicit these features. In addition, our approach establishes links with studies in the field of heterogeneous catalysis, where the same trade-offs are observed and where overcoming them is a well-recognized challenge.

Nanozymes for the Therapeutic Treatment of Diabetic Foot Ulcers.

Diabetic foot ulcers (DFU) are chronic, refractory wounds caused by diabetic neuropathy, vascular disease, and bacterial infection, and have become one of the most serious and persistent complications of diabetes mellitus because of their high incidence and difficulty in healing. Its malignancy results from a complex microenvironment that includes a series of unfriendly physiological states secondary to hyperglycemia, such as recurrent infections, excessive oxidative stress, persistent inflammation, and ischemia and hypoxia. However, current common clinical treatments, such as antibiotic therapy, insulin therapy, surgical debridement, and conventional wound dressings all have drawbacks, and suboptimal outcomes exacerbate the financial and physical burdens of diabetic patients. Therefore, development of new, effective and affordable treatments for DFU represents a top priority to improve the quality of life of diabetic patients. In recent years, nanozymes-based diabetic wound therapy systems have been attracting extensive interest by integrating the unique advantages of nanomaterials and natural enzymes. Compared with natural enzymes, nanozymes possess more stable catalytic activity, lower production cost and greater maneuverability. Remarkably, many nanozymes possess multienzyme activities that can cascade multiple enzyme-catalyzed reactions simultaneously throughout the recovery process of DFU. Additionally, their favorable photothermal-acoustic properties can be exploited for further enhancement of the therapeutic effects. In this review we first describe the characteristic pathological microenvironment of DFU, then discuss the therapeutic mechanisms and applications of nanozymes in DFU healing, and finally, highlight the challenges and perspectives of nanozyme development for DFU treatment.

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.

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.