Machine Learning Guided Discovery of Superoxide Dismutase Nanozymes for Androgenetic Alopecia

Ultrahigh peroxidase-like catalytic performance of Cu-N4 and Cu-N4S active sites-containing reduced graphene oxide for sensitive electrochemical biosensing

Carbon-based nanozymes possessing peroxidase-like activity have attracted significant interest because of their potential to replace native peroxidases in biotechnology. Although various carbon-based nanozymes have been developed, their relatively low catalytic efficiency needs to be overcome to realize their practical utilization. Here, inspired by the elemental uniqueness of Cu and the doped elements N and S, as well as the active site structure of Cu-centered oxidoreductases, we developed a new carbon-based peroxidase-mimicking nanozyme, single-atom Cu-centered N- and S-codoped reduced graphene oxide (Cu-NS-rGO), which preserved many Cu-N4 and Cu-N4S active sites and showed dramatically high peroxidase-like activity without any oxidase-like activity, yielding up to 2500-fold higher catalytic efficiency (kcat/Km) than that of pristine rGO. The high catalytic activity of Cu-NS-rGO might be attributed to the acceleration of electron transfer from Cu single atom as well as synergistic effects from both Cu-N4 and Cu-N4S active sites, which was theoretically confirmed by Gibbs free energy calculations using density functional theory. The prepared Cu-NS-rGO was then used to construct an electrochemical bioassay system for detecting choline and acetylcholine by coupling with the corresponding oxidases. Using this system, both target molecules were selectively determined with high sensitivity that was sufficient to clinically determine their levels in physiological fluids. Overall, this study will facilitate the development of nanocarbon-based nanozymes and their electrochemical biosensing applications, which can be extended to the development of miniaturized devices in point-of-care testing environments.

Progress in the Computer-Aided Analysis in Multiple Aspects of Nanocatalysis Research

Making the utmost of the differences and advantages of multiple disciplines, interdisciplinary integration breaks the science boundaries and accelerates the progress in mutual quests. As an organic connection of material science, enzymology, and biomedicine, nanozyme-related research is further supported by computer technology, which injects in new vitality, and contributes to in-depth understanding, unprecedented insights, and broadened application possibilities. Utilizing computer-aided first-principles method, high-speed and high-throughput mathematic, physic, and chemic models are introduced to perform atomic-level kinetic analysis for nanocatalytic reaction process, and theoretically illustrate the underlying nanozymetic mechanism and structure-function relationship. On this basis, nanozymes with desirable properties can be designed and demand-oriented synthesized without repeated trial-and-error experiments. Besides that, computational analysis and device also play an indispensable role in nanozyme-based detecting methods to realize automatic readouts with improved accuracy and reproducibility. Here, this work focuses on the crossing of nanocatalysis research and computational technology, to inspire the research in computer-aided analysis in nanozyme field to a greater extent.

AI-Powered Knowledge Base Enables Transparent Prediction of Nanozyme Multiple Catalytic Activity

Nanozymes are unique materials with many valuable properties for applications in biomedicine, biosensing, environmental monitoring, and beyond. In this work, we developed a machine learning (ML) approach to search for new nanozymes and deployed a web platform, DiZyme, featuring a state-of-the-art database of nanozymes containing 1210 experimental samples, catalytic activity prediction, and DiZyme Assistant interface powered by a large language model (LLM). For the first time, we enable the prediction of multiple catalytic activities of nanozymes by training an ensemble learning algorithm achieving R2 = 0.75 for the Michaelis-Menten constant and R2 = 0.77 for the maximum velocity on unseen test data. We envision an accurate prediction of multiple catalytic activities (peroxidase, oxidase, and catalase) promoting novel applications for a wide range of surface-modified inorganic nanozymes. The DiZyme Assistant based on the ChatGPT model provides users with supporting information on experimental samples, such as synthesis procedures, measurement protocols, etc. DiZyme (dizyme.aicidlab.itmo.ru) is now openly available worldwide.

Advancements in microneedle fabrication techniques: artificial intelligence assisted 3D-printing technology

Microneedles (MNs) are micron-scale needles that are a painless alternative to injections for delivering drugs through the skin. MNs find applications as biosensing devices and could serve as real-time diagnosis tools. There have been numerous fabrication techniques employed for producing quality MN-based systems, prominent among them is the three-dimensional (3D) printing. 3D printing enables the production of quality MNs of tuneable characteristics using a variety of materials. Further, the possible integration of artificial intelligence (AI) tools such as machine learning (ML) and deep learning (DL) with 3D printing makes it an indispensable tool for fabricating microneedles. Provided that these AI tools can be trained and act with minimal human intervention to control the quality of products produced, there is also a possibility of mass production of MNs using these tools in the future. This work reviews the specific role of AI in the 3D printing of MN-based devices discussing the use of AI in predicting drug release patterns, its role as a quality control tool, and in predicting the biomarker levels. Additionally, the autonomous 3D printing of microneedles using an integrated system of the internet of things (IoT) and machine learning (ML) is discussed in brief. Different categories of machine learning including supervised learning, semi-supervised learning, unsupervised learning, and reinforced learning have been discussed in brief. Lastly, a brief section is dedicated to the biosensing applications of MN-based devices.

Polydopamine Synergizes with Quercetin Nanosystem to Reshape the Perifollicular Microenvironment for Accelerating Hair Regrowth in Androgenetic Alopecia

Accumulated reactive oxygen species (ROS) and their resultant vascular dysfunction in androgenic alopecia (AGA) hinder hair follicle survival and cause permanent hair loss. However, safe and effective strategies to rescue hair follicle viability to enhance AGA therapeutic efficiency remain challenging. Herein, we fabricated a quercetin-encapsulated (Que) and polydopamine-integrated (PDA@QLipo) nanosystem that can reshape the perifollicular microenvironment to initial hair follicle regeneration for AGA treatment. Both the ROS scavenging and angiogenesis promotion abilities of PDA@QLipo were demonstrated. In vivo assays revealed that PDA@QLipo administrated with roller-microneedles successfully rejuvenated the "poor" perifollicular microenvironment, thereby promoting cell proliferation, accelerating hair follicle renewal, and facilitating hair follicle recovery. Moreover, PDA@QLipo achieved a higher hair regeneration coverage of 92.5% in the AGA mouse model than minoxidil (87.8%), even when dosed less frequently. The nanosystem creates a regenerative microenvironment by scavenging ROS and augmenting neovascularity for hair regrowth, presenting a promising approach for AGA clinical treatment.

Machine Learning-Accelerated High-Throughput Computational Screening: Unveiling Bimetallic Nanoparticles with Peroxidase-Like Activity

Bimetallic nanoparticles (NPs) with peroxidase-like (POD-like) activity play a crucial role in biosensing, disease treatment, environmental management, and other fields. However, their development is impeded by a vast range of tunable properties in components and structures, making the establishment of structure-effect relationships and the discovery of active materials challenging. Addressing this, we established robust scaling relationships by meticulously analyzing the catalytic reaction networks of pure metal NPs, which laid the volcano-shaped correlation between the activity and O* adsorption energy. Utilizing these relationships, we introduced an innovative and versatile descriptor of the NPs, which was then integrated into a machine learning-accelerated high-throughput computational workflow, significantly boosting the predictive accuracy for the POD-like activity of bimetallic NPs. Our methodological approach enabled the successful prediction of activities for 1260 bimetallic NPs, leading to the identification of several highly effective catalysts. Furthermore, we distilled several strategies for designing efficient bimetallic NPs based on our screening results.

Machine learning in nanozymes: from design to application

Nanozymes, a distinctive class of nanomaterials endowed with enzyme-like activity and kinetics akin to enzyme-catalysed reactions, present several advantages over natural enzymes, including cost-effectiveness, heightened stability, and adjustable activity. However, the conventional trial-and-error methodology for developing novel nanozymes encounters growing challenges as research progresses. The advent of artificial intelligence (AI), particularly machine learning (ML), has ushered in innovative design approaches for researchers in this domain. This review delves into the burgeoning role of ML in nanozyme research, elucidating the advancements achieved through ML applications. The review explores successful instances of ML in nanozyme design and implementation, providing a comprehensive overview of the evolving landscape. A roadmap for ML-assisted nanozyme research is outlined, offering a universal guideline for research in this field. In the end, the review concludes with an analysis of challenges encountered and anticipates future directions for ML in nanozyme research. The synthesis of knowledge in this review aims to foster a cross-disciplinary study, propelling the revolutionary field forward.

ROS Balance Autoregulating Core-Shell CeO2@ZIF-8/Au Nanoplatform for Wound Repair

Reactive oxygen species (ROS) plays important roles in living organisms. While ROS is a double-edged sword, which can eliminate drug-resistant bacteria, but excessive levels can cause oxidative damage to cells. A core-shell nanozyme, CeO2@ZIF-8/Au, has been crafted, spontaneously activating both ROS generating and scavenging functions, achieving the multi-faceted functions of eliminating bacteria, reducing inflammation, and promoting wound healing. The Au Nanoparticles (NPs) on the shell exhibit high-efficiency peroxidase-like activity, producing ROS to kill bacteria. Meanwhile, the encapsulation of CeO2 core within ZIF-8 provides a seal for temporarily limiting the superoxide dismutase and catalase-like activities of CeO2 nanoparticles. Subsequently, as the ZIF-8 structure decomposes in the acidic microenvironment, the CeO2 core is gradually released, exerting its ROS scavenging activity to eliminate excess ROS produced by the Au NPs. These two functions automatically and continuously regulate the balance of ROS levels, ultimately achieving the function of killing bacteria, reducing inflammation, and promoting wound healing. Such innovative ROS spontaneous regulators hold immense potential for revolutionizing the field of antibacterial agents and therapies.

Traditional Scraping (Gua Sha) Combined with Copper-Curcumin Nanoparticle Oleogel for Accurate and Multi-Effective Therapy of Androgenic Alopecia

Androgenetic alopecia (AGA) is a prevalent systemic disease caused by diverse factors, for which effective treatments are currently limited. Herein, the oleogel (OG) containing copper-curcumin (CuR) nanoparticles is developed, designated as CuRG, which is also combined with traditional naturopathic scraping (Gua Sha, SCR) as a multifunctional therapy for AGA. With the assistance of lipophilic OG and SCR, CuR can efficaciously penetrate the epidermal and dermal regions where most hair follicles (HFs) reside, thereby releasing curcumin (CR) and copper ions (Cu2+) subcutaneously to facilitate hair regeneration. Concomitantly, the mechanical stimulation induced by SCR promotes the formation of new blood vessels, which is conducive to reshaping the microenvironment of HFs. This study validates that the combination of CuRG and SCR is capable of systematically interfering with different pathological processes, ranging from improvement of perifollicular microenvironment (oxidative stress and insufficient vascularization), regulation of inflammatory responses to degradation of androgen receptor, thus potentiating hair growth. Compared with minoxidil, a widely used clinical drug for AGA therapy, the designed synergistic system displays augmented hair regeneration in the AGA mouse model.

Reaction Mechanisms and Kinetics of Nanozymes: Insights from Theory and Computation

"Nanozymes" usually refers to inorganic nanomaterials with enzyme-like catalytic activities. The research into nanozymes is one of the hot topics on the horizon of interdisciplinary science involving materials, chemistry, and biology. Although great progress has been made in the design, synthesis, characterization, and application of nanozymes, the study of the underlying microscopic mechanisms and kinetics is still not straightforward. Density functional theory (DFT) calculations compute the potential energy surfaces along the reaction coordinates for chemical reactions, which can give atomistic-level insights into the micro-mechanisms and kinetics for nanozymes. Therefore, DFT calculations have been playing an increasingly important role in exploring the mechanisms and kinetics for nanozymes in the past years. The calculations either predict the microscopic details for the catalytic processes to complement the experiments or further develop theoretical models to depict the physicochemical rules. In this review, the corresponding research progress is summarized. Particularly, the review focuses on the computational studies that closely interplay with the experiments. The relevant experimental results without DFT calculations will be also briefly discussed to offer a historic overview of how the computations promote the understanding of the microscopic mechanisms and kinetics of nanozymes.

Machine-Learning-Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes

Nanozymes are nanomaterials that exhibit enzyme-like biomimicry. In combination with intrinsic characteristics of nanomaterials, nanozymes have broad applicability in materials science, chemical engineering, bioengineering, biochemistry, and disease theranostics. Recently, the heterogeneity of published results has highlighted the complexity and diversity of nanozymes in terms of consistency of catalytic capacity. Machine learning (ML) shows promising potential for discovering new materials, yet it remains challenging for the design of new nanozymes based on ML approaches. Alternatively, ML is employed to promote optimization of intelligent design and application of catalytic materials and engineered enzymes. Incorporation of the successful ML algorithms used in the intelligent design of catalytic materials and engineered enzymes can concomitantly facilitate the guided development of next-generation nanozymes with desirable properties. Here, recent progress in ML, its utilization in the design of catalytic materials and enzymes, and how emergent ML applications serve as promising strategies to circumvent challenges associated with time-expensive and laborious testing in nanozyme research and development are summarized. The potential applications of successful examples of ML-aided catalytic materials and engineered enzymes in nanozyme design are also highlighted, with special focus on the unified aims in enhancing design and recapitulation of substrate selectivity and catalytic activity.

Biosystem-Inspired Engineering of Nanozymes for Biomedical Applications

Nanozymes with intrinsic enzyme-mimicking activities have shown great potential to become surrogates of natural enzymes in many fields by virtue of their advantages of high catalytic stability, ease of functionalization, and low cost. However, due to the lack of predictable descriptors, most of the nanozymes reported in the past have been obtained mainly through trial-and-error strategies, and the catalytic efficacy, substrate specificity, as well as practical application effect under physiological conditions, are far inferior to that of natural enzymes. To optimize the catalytic efficacies and functions of nanozymes in biomedical settings, recent studies have introduced biosystem-inspired strategies into nanozyme design. In this review, recent advances in the engineering of biosystem-inspired nanozymes by leveraging the refined catalytic structure of natural enzymes, simulating the behavior changes of natural enzymes in the catalytic process, and mimicking the specific biological processes or living organisms, are introduced. Furthermore, the currently involved biomedical applications of biosystem-inspired nanozymes are summarized. More importantly, the current opportunities and challenges of the design and application of biosystem-inspired nanozymes are discussed. It is hoped that the studies of nanozymes based on bioinspired strategies will be beneficial for constructing the new generation of nanozymes and broadening their biomedical applications.

Nanozymes: Definition, Activity, and Mechanisms

"Nanozyme" is used to describe various catalysts from immobilized inorganic metal complexes, immobilized enzymes to inorganic nanoparticles. Here, the history of nanozymes is dvescribed in detail, and they can be largely separated into two types. Type 1 nanozymes refer to immobilized catalysts or enzymes on nanomaterials, which were dominant in the first decade since 2004. Type 2 nanozymes, which rely on the surface catalytic properties of inorganic nanomaterials, are the dominating type in the past decade. The definition of nanozymes is evolving, and a definition based on the same substrates and products as enzymes are able to cover most currently claimed nanozymes, although they may have different mechanisms compared to their enzyme counterparts. A broader definition can inspire application-based research to replace enzymes with nanomaterials for analytical, environmental, and biomedical applications. Comparison with enzymes also requires a clear definition of a nanozyme unit. Four ways of defining a nanozyme unit are described, with iron oxide and horseradish peroxidase activity comparison as examples in each definition. Growing work is devoted to understanding the catalytic mechanism of nanozymes, which provides a basis for further rational engineering of active sites. Finally, future perspective of the nanozyme field is discussed.

Nanozyme-Engineered Hydrogels for Anti-Inflammation and Skin Regeneration

Inflammatory skin disorders can cause chronic scarring and functional impairments, posing a significant burden on patients and the healthcare system. Conventional therapies, such as corticosteroids and nonsteroidal anti-inflammatory drugs, are limited in efficacy and associated with adverse effects. Recently, nanozyme (NZ)-based hydrogels have shown great promise in addressing these challenges. NZ-based hydrogels possess unique therapeutic abilities by combining the therapeutic benefits of redox nanomaterials with enzymatic activity and the water-retaining capacity of hydrogels. The multifaceted therapeutic effects of these hydrogels include scavenging reactive oxygen species and other inflammatory mediators modulating immune responses toward a pro-regenerative environment and enhancing regenerative potential by triggering cell migration and differentiation. This review highlights the current state of the art in NZ-engineered hydrogels (NZ@hydrogels) for anti-inflammatory and skin regeneration applications. It also discusses the underlying chemo-mechano-biological mechanisms behind their effectiveness. Additionally, the challenges and future directions in this ground, particularly their clinical translation, are addressed. The insights provided in this review can aid in the design and engineering of novel NZ-based hydrogels, offering new possibilities for targeted and personalized skin-care therapies.

Deep Insight of Design, Mechanism, and Cancer Theranostic Strategy of Nanozymes

Since the discovery of enzyme-like activity of Fe3O4 nanoparticles in 2007, nanozymes are becoming the promising substitutes for natural enzymes due to their advantages of high catalytic activity, low cost, mild reaction conditions, good stability, and suitable for large-scale production. Recently, with the cross fusion of nanomedicine and nanocatalysis, nanozyme-based theranostic strategies attract great attention, since the enzymatic reactions can be triggered in the tumor microenvironment to achieve good curative effect with substrate specificity and low side effects. Thus, various nanozymes have been developed and used for tumor therapy. In this review, more than 270 research articles are discussed systematically to present progress in the past five years. First, the discovery and development of nanozymes are summarized. Second, classification and catalytic mechanism of nanozymes are discussed. Third, activity prediction and rational design of nanozymes are focused by highlighting the methods of density functional theory, machine learning, biomimetic and chemical design. Then, synergistic theranostic strategy of nanozymes are introduced. Finally, current challenges and future prospects of nanozymes used for tumor theranostic are outlined, including selectivity, biosafety, repeatability and stability, in-depth catalytic mechanism, predicting and evaluating activities.

Nanodrug Delivery Strategies to Signaling Pathways in Alopecia

Over 50% of the global population suffers from hair loss. The mixed results in the treatment of hair loss reveal the limitations of conventional commercial topical drugs. One the one hand, the definite pathogenesis of hair loss is still an enigma. On the other hand, targeted drug carriers ensure the drug therapeutic effect and low side effects. This review highlights the organization and overview of nine crucial signaling pathways associated with hair loss, as well as the development of nanobased topical delivery systems loading the clinical drugs, which will fuel emerging hair loss treatment strategies.

Accessing Properties of Molecular Compounds Involved in Cellular Metabolic Processes with Electron Paramagnetic Resonance, Raman Spectroscopy, and Differential Scanning Calorimetry

In this work, we review some physical methods of macroscopic experiments, which have been recently argued to be promising for the acquisition of valuable characteristics of biomolecular structures and interactions. The methods we focused on are electron paramagnetic resonance spectroscopy, Raman spectroscopy, and differential scanning calorimetry. They were chosen since it can be shown that they are able to provide a mutually complementary picture of the composition of cellular envelopes (with special attention paid to mycobacteria), transitions between their molecular patterning, and the response to biologically active substances (reactive oxygen species and their antagonists-antioxidants-as considered in our case study).

Rational Design Strategies for Nanozymes

Nanozymes constitute an emerging class of nanomaterials with enzyme-like characteristics. Over the past 15 years, more than 1200 nanozymes have been developed, and they have demonstrated promising potentials in broad applications. With the diversification and complexity of its applications, traditional empirical and trial-and-error design strategies no longer meet the requirements for efficient nanozyme design. Thanks to the rapid development of computational chemistry and artificial intelligence technologies, first-principles methods and machine-learning algorithms are gradually being adopted as a more efficient and easier means to assist nanozyme design. This review focuses on the potential elementary reaction mechanisms in the rational design of nanozymes, including peroxidase (POD)-, oxidase (OXD)-, catalase (CAT)-, superoxide dismutase (SOD)-, and hydrolase (HYL)-like nanozymes. The activity descriptors are introduced, with the aim of providing further guidelines for nanozyme active material screening. The computing- and data-driven approaches are thoroughly reviewed to give a proposal on how to proceed with the next-generation paradigm rational design. At the end of this review, personal perspectives on the prospects and challenges of the rational design of nanozymes are put forward, hoping to promote the further development of nanozymes toward superior application performance in the future.

Machine learning facilitating the rational design of nanozymes

As a component substitute for natural enzymes, nanozymes have the advantages of easy synthesis, convenient modification, low cost, and high stability, and are widely used in many fields. However, their application is seriously restricted by the difficulty of rapidly creating high-performance nanozymes. The use of machine learning techniques to guide the rational design of nanozymes holds great promise to overcome this difficulty. In this review, we introduce the recent progress of machine learning in assisting the design of nanozymes. Particular attention is given to the successful strategies of machine learning in predicting the activity, selectivity, catalytic mechanisms, optimal structures and other features of nanozymes. The typical procedures and approaches for conducting machine learning in the study of nanozymes are also highlighted. Moreover, we discuss in detail the difficulties of machine learning methods in dealing with the redundant and chaotic nanozyme data and provide an outlook on the future application of machine learning in the nanozyme field. We hope that this review will serve as a useful handbook for researchers in related fields and promote the utilization of machine learning in nanozyme rational design and related topics.

Cu-GA-coordination polymer nanozymes with triple enzymatic activity for wound disinfection and accelerated wound healing

During the past few years, bacterial infection and oxidative stress have become important issues for wound healing. However, the emergence of numerous drug-resistant superbugs has had a serious impact on the treatment of infected wounds. Presently, the development of new nanomaterials has become one of the most important approaches to the treatment of drug-resistant bacterial infections. Herein, coordination polymer copper-gallic acid (Cu-GA) nanorods with multi-enzyme activity is successfully prepared for efficient wound treatment of bacterial infection, which can effectively promote wound healing. Cu-GA can be efficiently prepared by a simple solution method and had good physiological stability. Interestingly, Cu-GA shows enhanced multienzyme activity (peroxidase, glutathione peroxidase, and superoxide dismutase), which can produce a large number of reactive oxygen species (ROS) under acidic conditions while scavenging ROS under neutral conditions. In acidic environment, Cu-GA possesses POD (peroxidase)-like and glutathione peroxidase (GSH-Px)-like catalytic activities that is capable of killing bacteria; but in neutral environment, Cu-GA exhibits superoxide dismutase (SOD)-like catalytic activity that can scavenge ROS and promote wound healing. In vivo studies show that Cu-GA can promote wound infection healing and have good biosafety. Cu-GA contributes to the healing of infected wounds by inhibiting bacterial growth, scavenging reactive oxygen species, and promoting angiogenesis. STATEMENT OF SIGNIFICANCE: Cu-GA-coordinated polymer nanozymes with multienzyme activity were successfully prepared for efficient wound treatment of bacterial infection, which could effectively promote wound healing. Interestingly, Cu-GA exhibited enhanced multienzyme activity (peroxidase, glutathione peroxidase, and superoxide dismutase), which could produce a large number of reactive oxygen species (ROS) under acidic conditions and scavenge ROS under neutral conditions. In vitro and in vivo studies demonstrated that Cu-GA was capable of killing bacteria, controlling inflammation, and promoting angiogenesis.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Enhancing Catalytic Activity of a Nickel Single Atom Enzyme by Polynary Heteroatom Doping for Ferroptosis-Based Tumor Therapy

As a rising generation of nanozymes, single atom enzymes show significant promise for cancer therapy, due to their maximum atom utilization efficiency and well-defined electronic structures. However, it remains a tremendous challenge to precisely produce a heteroatom-doped single atom enzyme with an expected coordination environment. Herein, we develop an anion exchange strategy for precisely controlled production of an edge-rich sulfur (S)- and nitrogen (N)-decorated nickel single atom enzyme (S-N/Ni PSAE). In particular, sulfurized S-N/Ni PSAE exhibits stronger peroxidase-like and glutathione oxidase-like activities than the nitrogen-monodoped nickel single atom enzyme, which is attributed to the vacancies and defective sites of sulfurized nitrogen atoms. Moreover, both in vitro and in vivo results demonstrate that, compared with nitrogen-monodoped N/Ni PSAE, sulfurized S-N/Ni PSAE more effectively triggers ferroptosis of tumor cells via inactivating glutathione peroxidase 4 and inducing lipid peroxidation. This study highlights the enhanced catalytic efficacy of a polynary heteroatom-doped single atom enzyme for ferroptosis-based cancer therapy.

Molecular insights of nanozymes from design to catalytic mechanism

Emerging as cost-effective potential alternatives to natural enzymes, nanozymes have attracted increasing interest in broad fields. To exploit the in-depth potential of nanozymes, rational structural engineering and explicit catalytic mechanisms at the molecular scale are required. Recently, impressive progress has been made in mimicking the characteristics of natural enzymes by constructing metal active sites, binding pockets, scaffolds, and delicate allosteric regulation. Ingenious in-depth studies have been conducted with advances in structural characterization and theoretical calculations, unveiling the "black box" of nanozyme-catalytic mechanisms. This review introduces the state-of-art synthesis strategies by learning from the natural enzyme counterparts and summarizes the general overview of the nanozyme mechanism with a particular emphasis on the adsorbed intermediates and descriptors that predict the nanozyme activity The emerging activity assessment methodology that illustrates the relationship between electrochemical oxygen reduction and enzymatic oxygen reduction is discussed with up-to-date advances Future opportunities and challenges are presented in the end to spark more profound work and attract more researchers from various backgrounds to the flourishing field of nanozymes.