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.

Sorting drug conformers in enzyme active sites: the XTB way.

In the present study, we have used the MEI196 set of interaction energies to investigate low-cost computational chemistry approaches for the calculation of binding between a molecule and its environment. Density functional theory (DFT) methods, when used with the vDZP basis set, yield good agreement with the reference energies. On the other hand, semi-empirical methods are less accurate as expected. By examining different groups of systems within MEI196 that contain species of a similar nature, we find that chemical similarity leads to cancellation of errors in the calculation of relative binding energies. Importantly, the semi-empirical method GFN1-xTB (XTB1) yields reasonable results for this purpose. We have thus further assessed the performance of XTB1 for calculating relative energies of docking poses of substrates in enzyme active sites represented by cluster models or within the ONIOM protocol. The results support the observations on error cancellation. This paves the way for the use of XTB1 in parts of large-scale virtual screening workflows to accelerate the drug discovery process.

Enzymatic biosynthesis of D-galactose derivatives: Advances and perspectives.

D-Galactose derivatives, including galactosyl-conjugates and galactose-upgrading compounds, provide various physiological benefits and find applications in industries such as food, cosmetics, feed, pharmaceuticals. Many research on galactose derivatives focuses on identification, characterization, development, and mechanistic aspects of their physiological function, providing opportunities and challenges for the development of practical approaches for synthesizing galactose derivatives. This study focuses on recent advancements in enzymatic biosynthesis of galactose derivatives. Various strategies including isomerization, epimerization, transgalactosylation, and phosphorylation-dephosphorylation were extensively discussed under the perspectives of thermodynamic feasibility, theoretical yield, cost-effectiveness, and by-product elimination. Specifically, the enzymatic phosphorylation-dephosphorylation cascade is a promising enzymatic synthesis route for galactose derivatives because it can overcome the thermodynamic equilibrium of isomerization and utilize cost-effective raw materials. The study also elucidates the existing challenges and future trends in enzymatic biosynthesis of galactose derivatives. Collectively, this review provides a real-time summary aimed at promoting the practical biosynthesis of galactose derivatives through enzymatic catalysis.

Enzyme-mimic catalytic activities and biomedical applications of noble metal nanoclusters.

Noble metal (e.g., Au and Ag) nanoclusters (NCs), which exhibit structural complexity and hierarchy comparable to those of natural proteins, have been increasingly pursued in artificial enzyme research. The protein-like structure of metal NCs not only ensures enzyme-mimic catalytic activity, including peroxidase-, catalase-, and superoxide dismutase-mimic activities, but also affords an unprecedented opportunity to correlate the catalytic performance with the cluster structure at the molecular or atomic levels. In this review, we aim to summarize the recent progress in programming and demystify the enzyme-mimic catalytic activity of metal NCs, presenting the state-of-the-art understandings of the structure-property relationship of metal NC-based artificial enzymes. By leveraging on a concise anatomy of the hierarchical structure of noble metal NCs, we manage to unravel the structural origin of the catalytic performance of metal NCs. Noteworthily, it has been proven that the surface ligands and metal-ligand interface of metal NCs are instrumental in influencing enzyme-mimic catalytic activities. In addition to the structure-property correlation, we also discuss the synthetic methodologies feasible to tailoring the cluster structure at the atomic level. Prior to the closure of this review with our perspectives in noble metal NC-based artificial enzymes, we also exemplify the biomedical applications based on the enzyme-mimic catalysis of metal NCs with the theranostics of kidney injury, brain inflammation, and tumors. The fundamental and methodological advancements delineated in this review would be conducive to further development of metal NCs as an alternative family of artificial enzymes.

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.

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.

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.

Enzymatic Activity Profiling Using an Ultrasensitive Array of Chemiluminescent Probes for Bacterial Classification and Characterization.

Identification and characterization of bacterial species in clinical and industrial settings necessitate the use of diverse, labor-intensive, and time-consuming protocols as well as the utilization of expensive and high-maintenance equipment. Furthermore, while cutting-edge identification technologies such as mass spectrometry and PCR are highly effective in identifying bacterial pathogens, they fall short in providing additional information for identifying bacteria not present in the databases upon which these methods rely. In response to these challenges, we present a robust and general approach to bacterial identification based on their unique enzymatic activity profiles. This method delivers results within 90 min, utilizing an array of highly sensitive and enzyme-selective chemiluminescent probes. Leveraging our recently developed technology of chemiluminescent luminophores, which emit light under physiological conditions, we have crafted an array of probes designed to rapidly detect various bacterial enzymatic activities. The array includes probes for detecting resistance to the important and large class of ß-lactam antibiotics. The analysis of chemiluminescent fingerprints from a diverse range of prominent bacterial pathogens unveiled distinct enzymatic activity profiles for each strain. The reported universally applicable identification procedure offers a highly sensitive and expeditious means to delineate bacterial enzymatic activity fingerprints. This opens new avenues for characterizing and identifying pathogens in research, clinical, and industrial applications.

Self-Propelling Macroscale Sheets Powered by Enzyme Pumps.

Nanoscale enzymes anchored to surfaces act as chemical pumps by converting chemical energy released from enzymatic reactions into spontaneous fluid flow that propels entrained nano- and microparticles. Enzymatic pumps are biocompatible, highly selective, and display unique substrate specificity. Utilizing these pumps to trigger self-propelled motion on the macroscale has, however, constituted a significant challenge and thus prevented their adaptation in macroscopic fluidic devices and soft robotics. Using experiments and simulations, we herein show that enzymatic pumps can drive centimeter-scale polymer sheets along directed linear paths and rotational trajectories. In these studies, the sheets are confined to the air/water interface. With the addition of appropriate substrate, the asymmetric enzymatic coating on the sheets induces chemically driven, buoyancy flows that controllably propel the sheet's motion on the air/water interface. The directionality and speed of the motion can be tailored by changing the pattern of the enzymatic coating, type of enzyme, and nature and concentration of the substrate. This work highlights the utility of biocompatible enzymes for generating motion in macroscale fluidic devices and robotics and indicates their potential utility for in vivo applications.

From nature to industry: Harnessing enzymes for biocatalysis.

Biocatalysis harnesses enzymes to make valuable products. This green technology is used in countless applications from bench scale to industrial production and allows practitioners to access complex organic molecules, often with fewer synthetic steps and reduced waste. The last decade has seen an explosion in the development of experimental and computational tools to tailor enzymatic properties, equipping enzyme engineers with the ability to create biocatalysts that perform reactions not present in nature. By using (chemo)-enzymatic synthesis routes or orchestrating intricate enzyme cascades, scientists can synthesize elaborate targets ranging from DNA and complex pharmaceuticals to starch made in vitro from CO2-derived methanol. In addition, new chemistries have emerged through the combination of biocatalysis with transition metal catalysis, photocatalysis, and electrocatalysis. This review highlights recent key developments, identifies current limitations, and provides a future prospect for this rapidly developing technology.

Molecular noise-induced activator-inhibitor duality in enzyme inhibition kinetics.

Classical theories of enzyme inhibition kinetics predict a monotonic decrease in the mean catalytic activity with the increase in inhibitor concentration. The steady-state result, derived from deterministic mass action kinetics, ignores molecular noise in enzyme-inhibition mechanisms. Here, we present a stochastic generalization of enzyme inhibition kinetics to mesoscopic enzyme concentrations by systematically accounting for molecular noise in competitive and uncompetitive mechanisms of enzyme inhibition. Our work reveals an activator-inhibitor duality as a non-classical effect in the transient regime in which inhibitors tend to enhance enzymatic activity. We introduce statistical measures that quantify this counterintuitive response through the stochastic analog of the Lineweaver-Burk plot that shows a merging of the inhibitor-dependent velocity with the Michaelis-Menten velocity. The statistical measures of mean and temporal fluctuations - fractional enzyme activity and waiting time correlations - show a non-monotonic rise with the increase in inhibitors before subsiding to their baseline value. The inhibitor and substrate dependence of the fractional enzyme activity yields kinetic phase diagrams for non-classical activator-inhibitor duality. Our work links this duality to a molecular memory effect in the transient regime, arising from positive correlations between consecutive product turnover times. The vanishing of memory in the steady state recovers all the classical results.

How Flexibility Can Enhance Catalysis.

Conformational changes are observed in many enzymes, but their role in catalysis is highly controversial. Here we present a theoretical model that illustrates how rigid catalysts can be fundamentally limited and how a conformational change induced by substrate binding can overcome this limitation, ultimately enabling barrier-free catalysis. The model is deliberately minimal, but the principle it illustrates is general and consistent with unique features of proteins as well as with previous informal proposals to explain the superiority of enzymes over other classes of catalysts. Implementing the discriminative switch suggested by the model could help overcome limitations currently encountered in the design of artificial catalysts.

Recent advances in the synthesis and applications of single-atom nanozymes in food safety monitoring.

Nanozymes are synthetic compounds with enzyme-like tunable catalytic properties. The success of nanozymes for catalytic applications can be attributed to their small dimensions, cost-effective synthesis, appreciable stability, and scalability to molecular dimensions. The emergence of single atom nanozymes (SANzymes) has opened up new possibilities in bioanalytical applications. In this regard, this review outlines enzyme-mimicking features of SANzymes for food safety applications in relation to the key variables controlling their catalytic performance. The discussion is extended further to cover the applications of SANzymes for the monitoring of various compounds/biomaterials of significance with respect to food safety (e.g., pesticides, veterinary drug residues, foodborne pathogenic bacteria, mycotoxins/bacterial endotoxin, antioxidant residues, hydrogen peroxide residues, and heavy metal ions). Furthermore, the performance of SANzymes is evaluated in terms of various performance metrics such as limit of detection (LOD), linear dynamic range, and figure of merit (FoM). The challenges and future road map for the applications of SANzymes are also addressed along with their upscaling in the area of food safety.

Cold-adapted enzymes: mechanisms, engineering and biotechnological application.

Most cold-adapted enzymes display high catalytic activity at low temperatures (20-25 °C) and can still maintain more than 40-50% of their maximum activity at lower temperatures (0-10 °C) but are inactivated after a moderate increase in temperature. The activity of some cold-adapted enzymes increases significantly in the presence of high salt concentrations and metal ions. Interestingly, we also observed that some cold-adapted enzymes have a wide range of optimum temperatures, exhibiting not only maximum activity under low-temperature conditions but also the ability to maintain high enzyme activity under high-temperature conditions, which is a novel feature of cold-adapted enzymes. This unique property of cold-adapted enzymes is generally attractive for biotechnological and industrial applications because these enzymes can reduce energy consumption and the chance of microbial contamination, thereby lowering the production costs and maintaining the flavor, taste and quality of foods. How high catalytic activity is maintained at low temperatures remains unknown. The relationship between the structure of cold-adapted enzymes and their activity, flexibility and stability is complex, and thus far, a unified explanation has not been provided. Herein, we systematically review the sources, catalytic characteristics and cold adaptation of enzymes from four enzymes categories systematically and discuss how these properties may be exploited in biotechnology. A thorough understanding of the properties, catalytic mechanisms, and engineering of cold-adapted enzymes is critical for future biotechnological applications in the detergent industry and food and beverage industries.

Kinetics of the enzyme titration process by reversible modifiers.

The effect of reversible modifiers on the initial rate of enzyme catalysed reactions has been investigated in a quasi-equilibrium approximation using the general modifier mechanism of Botts and Morales. It has been shown that, when investigating the dependence of the initial rate on the modifier concentration at a fixed substrate concentration, the kinetics of enzyme titration by reversible modifiers can generally be described using two kinetic constants. Just as the dependence of the initial rate on the substrate concentration (at a fixed modifier concentration) is described using two kinetic constants: the Michaelis constant Km and the limiting rate Vm. Only one constant M50 is needed to describe the kinetics of linear inhibition, and in the case of nonlinear inhibition and activation, along with M50 the constant QM is also needed. Knowing the values of the constants M50 and QM, it is possible to unambiguously determine the modification efficiency, that is, to calculate how many times the initial rate of the enzyme catalysed reaction will change when a certain modifier concentration is added to the incubation medium. The properties of these fundamental constants have been analysed in detail and the dependence of these constants on other parameters of the Botts-Morales model have been shown. Equations describing the dependence of relative reaction rates on the modifier concentration using these kinetic constants are presented. Various ways of linearising these equations for calculating the kinetic constants M50 and QM from experimental data are also presented.

EFI-EST, EFI-GNT, and EFI-CGFP: Enzyme Function Initiative (EFI) Web Resource for Genomic Enzymology Tools.

The Enzyme Function Initiative (EFI) provides a web resource with "genomic enzymology" web tools to leverage the protein (UniProt) and genome (European Nucleotide Archive; ENA; https://www.ebi.ac.uk/ena/) databases to assist the assignment of in vitro enzymatic activities and in vivo metabolic functions to uncharacterized enzymes (https://efi.igb.illinois.edu/). The tools enable (1) exploration of sequence-function space in enzyme families using sequence similarity networks (SSNs; EFI-EST), (2) easy access to genome context for bacterial, archaeal, and fungal proteins in the SSN clusters so that isofunctional families can be identified and their functions inferred from genome context (EFI-GNT); and (3) determination of the abundance of SSN clusters in NIH Human Metagenome Project metagenomes using chemically guided functional profiling (EFI-CGFP). We describe enhancements that enable SSNs to be generated from taxonomy categories, allowing higher resolution analyses of sequence-function space; we provide examples of the generation of taxonomy category-specific SSNs.

Unravelling the complexity of enzyme catalysis.

The study of enzymes never disappoints. Despite its long history-almost 150 years following the first documented use of the word enzyme in 1878-the field of enzymology advances apace. This long journey has witnessed landmark developments that have defined modern enzymology as a broad discipline, leading to improved understanding at the molecular level, as we aspire to discover the complex relationships between enzyme structures, catalytic mechanisms and biological function. How enzymes are regulated at the gene and post-translational levels and how catalytic activity is modulated by interactions with small ligands and macromolecules, or the broader enzyme environment, are topical areas of study. Insights from such studies guide the exploitation of natural and engineered enzymes in biomedical or industrial processes; for example, in diagnostics, pharmaceuticals manufacture and processing technologies that use immobilised enzymes and enzyme reactor-based systems. In this Focus Issue, The FEBS Journal seeks to highlight breaking science and informative reviews, as well as personal reflections, to illustrate the breadth and importance of contemporary molecular enzymology research.

Enzyme function prediction using contrastive learning.

Enzyme function annotation is a fundamental challenge, and numerous computational tools have been developed. However, most of these tools cannot accurately predict functional annotations, such as enzyme commission (EC) number, for less-studied proteins or those with previously uncharacterized functions or multiple activities. We present a machine learning algorithm named CLEAN (contrastive learning-enabled enzyme annotation) to assign EC numbers to enzymes with better accuracy, reliability, and sensitivity compared with the state-of-the-art tool BLASTp. The contrastive learning framework empowers CLEAN to confidently (i) annotate understudied enzymes, (ii) correct mislabeled enzymes, and (iii) identify promiscuous enzymes with two or more EC numbers-functions that we demonstrate by systematic in silico and in vitro experiments. We anticipate that this tool will be widely used for predicting the functions of uncharacterized enzymes, thereby advancing many fields, such as genomics, synthetic biology, and biocatalysis.