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.

Cold-active microbial enzymes and their biotechnological applications.

Microorganisms known as psychrophiles/psychrotrophs, which survive in cold climates, constitute majority of the biosphere on Earth. Their capability to produce cold-active enzymes along with other distinguishing characteristics allows them to survive in the cold environments. Due to the relative ease of large-scale production compared to enzymes from plants and animals, commercial uses of microbial enzyme are alluring. The ocean depths, polar, and alpine regions, which make up over 85% of the planet, are inhabited to cold ecosystems. Microbes living in these regions are important for their metabolic contribution to the ecosphere as well as for their enzymes, which may have potential industrial applications. Cold-adapted microorganisms are a possible source of cold-active enzymes that have high catalytic efficacy at low and moderate temperatures at which homologous mesophilic enzymes are not active. Cold-active enzymes can be used in a variety of biotechnological processes, including food processing, additives in the detergent and food industries, textile industry, waste-water treatment, biopulping, environmental bioremediation in cold climates, biotransformation, and molecular biology applications with great potential for energy savings. Genetically manipulated strains that are suitable for producing a particular cold-active enzyme would be crucial in a variety of industrial and biotechnological applications. The potential advantage of cold-adapted enzymes will probably lead to a greater annual market than for thermo-stable enzymes in the near future. This review includes latest updates on various microbial source of cold-active enzymes and their biotechnological applications.

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.

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.

Design of Efficient Artificial Enzymes Using Crystallographically Enhanced Conformational Sampling.

The ability to create efficient artificial enzymes for any chemical reaction is of great interest. Here, we describe a computational design method for increasing the catalytic efficiency of de novo enzymes by several orders of magnitude without relying on directed evolution and high-throughput screening. Using structural ensembles generated from dynamics-based refinement against X-ray diffraction data collected from crystals of Kemp eliminases HG3 (kcat/KM 125 M-1 s-1) and KE70 (kcat/KM 57 M-1 s-1), we design from each enzyme ≤10 sequences predicted to catalyze this reaction more efficiently. The most active designs display kcat/KM values improved by 100-250-fold, comparable to mutants obtained after screening thousands of variants in multiple rounds of directed evolution. Crystal structures show excellent agreement with computational models, with catalytic contacts present as designed and transition-state root-mean-square deviations of ≤0.65 Å. Our work shows how ensemble-based design can generate efficient artificial enzymes by exploiting the true conformational ensemble to design improved active sites.

Frustration can Limit the Adaptation of Promiscuous Enzymes Through Gene Duplication and Specialisation.

Virtually all enzymes catalyse more than one reaction, a phenomenon known as enzyme promiscuity. It is unclear whether promiscuous enzymes are more often generalists that catalyse multiple reactions at similar rates or specialists that catalyse one reaction much more efficiently than other reactions. In addition, the factors that shape whether an enzyme evolves to be a generalist or a specialist are poorly understood. To address these questions, we follow a three-pronged approach. First, we examine the distribution of promiscuity in empirical enzymes reported in the BRENDA database. We find that the promiscuity distribution of empirical enzymes is bimodal. In other words, a large fraction of promiscuous enzymes are either generalists or specialists, with few intermediates. Second, we demonstrate that enzyme biophysics is not sufficient to explain this bimodal distribution. Third, we devise a constraint-based model of promiscuous enzymes undergoing duplication and facing selection pressures favouring subfunctionalization. The model posits the existence of constraints between the catalytic efficiencies of an enzyme for different reactions and is inspired by empirical case studies. The promiscuity distribution predicted by our constraint-based model is consistent with the empirical bimodal distribution. Our results suggest that subfunctionalization is possible and beneficial only in certain enzymes. Furthermore, the model predicts that conflicting constraints and selection pressures can cause promiscuous enzymes to enter a 'frustrated' state, in which competing interactions limit the specialisation of enzymes. We find that frustration can be both a driver and an inhibitor of enzyme evolution by duplication and subfunctionalization. In addition, our model predicts that frustration becomes more likely as enzymes catalyse more reactions, implying that natural selection may prefer catalytically simple enzymes. In sum, our results suggest that frustration may play an important role in enzyme evolution.

In vitro demetalation of central magnesium in various chlorophyll derivatives using Mg-dechelatase homolog from the chloroflexi Anaerolineae.

In the metabolic pathway of chlorophylls (Chls), an enzyme called STAY-GREEN or SGR catalyzes the removal of the central magnesium ion of Chls and their derivatives to their corresponding free bases, including pheophytins. The substrate specificity of SGR has been investigated through in vitro reactions using Chl-related molecules. However, information about the biochemical properties and reaction mechanisms of SGR and its substrate specificity remains elusive. In this study, we synthesized various Chl derivatives and investigated their in vitro dechelations using an SGR enzyme. Chl-a derivatives with the C3-vinyl group on the A-ring, which is commonly found as a substituent in natural substrates, and their analogs with ethyl, hydroxymethyl, formyl, and styryl groups at the C3-position were prepared as substrates. In vitro dechelatase reactions of these substrates were performed using an SGR enzyme derived from an Anaerolineae bacterium, allowing us to investigate their specificity. Reactivity was reduced for substrates with an electron-withdrawing formyl or sterically demanding styryl group at the C3-position. Furthermore, the Chl derivative with the C8-styryl group on the B-ring was less reactive for SGR dechelation than the C3-styryl substrate. These results indicate that the SGR enzyme recognizes substituents on the B-ring of substrates more than those on the A-ring.

Multidomain chimeric enzymes as a promising alternative for biocatalysts improvement: a minireview.

Searching for new and better biocatalysts is an area of study in constant development. In nature, mechanisms generally occurring in evolution, such as genetic duplication, recombination, and natural selection processes, produce various enzymes with different architectures and properties. The recombination of genes that code proteins produces multidomain chimeric enzymes that contain two or more domains that sometimes enhance their catalytic properties. Protein engineering has mimicked this process to enhance catalytic activity and the global stability of enzymes, searching for new and better biocatalysts. Here, we present and discuss examples from both natural and synthetic multidomain chimeric enzymes and how additional domains heighten their stability and catalytic activity. Moreover, we also describe progress in developing new biocatalysts using synthetic fusion enzymes and revise some methodological strategies to improve their biological fitness.

The unreasonable effectiveness of the total quasi-steady state approximation, and its limitations.

In this note, we discuss the range of parameters for which the total quasi-steady-state approximation of the Michaelis-Menten reaction mechanism holds validity. We challenge the prevalent notion that total quasi-steady-state approximation is "roughly valid" across all parameters, showing that its validity cannot be assumed, even roughly, across the entire parameter space - when the initial substrate concentration is high. On the positive side, we show that the linearized one-dimensional equation for total substrate is a valid approximation when the initial reduced substrate concentration s0/KM is small. Moreover, we obtain a precise picture of the long-term time course of both substrate and complex.

Strategies for designing biocatalysts with new functions.

The engineering of natural enzymes has led to the availability of a broad range of biocatalysts that can be used for the sustainable manufacturing of a variety of chemicals and pharmaceuticals. However, for many important chemical transformations there are no known enzymes that can serve as starting templates for biocatalyst development. These limitations have fuelled efforts to build entirely new catalytic sites into proteins in order to generate enzymes with functions beyond those found in Nature. This bottom-up approach to enzyme development can also reveal new fundamental insights into the molecular origins of efficient protein catalysis. In this tutorial review, we will survey the different strategies that have been explored for designing new protein catalysts. These methods will be illustrated through key selected examples, which demonstrate how highly proficient and selective biocatalysts can be developed through experimental protein engineering and/or computational design. Given the rapid pace of development in the field, we are optimistic that designer enzymes will begin to play an increasingly prominent role as industrial biocatalysts in the coming years.

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.

High-Throughput Screening Techniques for the Selection of Thermostable Enzymes.

The acquisition of a thermostable enzyme is an indispensable prerequisite for its successful implementation in industrial applications and the development of novel functionalities. Various protein engineering approaches, including rational design, semirational design, and directed evolution, have been employed to enhance thermostability. However, all of these approaches require sensitive and reliable high-throughput screening (HTS) technologies to efficiently and rapidly identify variants with improved properties. While numerous reviews focus on modification strategies for enhancing enzyme thermostability, there is a dearth of literature reviewing HTS methods specifically aimed at this objective. Herein, we present a comprehensive overview of various HTS methods utilized for modifying enzyme thermostability across different screening platforms. Additionally, we highlight significant recent examples that demonstrate the successful application of these methods. Furthermore, we address the technical challenges associated with HTS technologies used for screening thermostable enzyme variants and discuss valuable perspectives to promote further advancements in this field. This review serves as an authoritative reference source offering theoretical support for selecting appropriate screening strategies tailored to specific enzymes with the aim of improving their thermostability.

PredictEFC: a fast and efficient multi-label classifier for predicting enzyme family classes.

BACKGROUND: Enzymes play an irreplaceable and important role in maintaining the lives of living organisms. The Enzyme Commission (EC) number of an enzyme indicates its essential functions. Correct identification of the first digit (family class) of the EC number for a given enzyme is a hot topic in the past twenty years. Several previous methods adopted functional domain composition to represent enzymes. However, it would lead to dimension disaster, thereby reducing the efficiency of the methods. On the other hand, most previous methods can only deal with enzymes belonging to one family class. In fact, several enzymes belong to two or more family classes. RESULTS: In this study, a fast and efficient multi-label classifier, named PredictEFC, was designed. To construct this classifier, a novel feature extraction scheme was designed for processing functional domain information of enzymes, which counting the distribution of each functional domain entry across seven family classes in the training dataset. Based on this scheme, each training or test enzyme was encoded into a 7-dimenion vector by fusing its functional domain information and above statistical results. Random k-labelsets (RAKEL) was adopted to build the classifier, where random forest was selected as the base classification algorithm. The two tenfold cross-validation results on the training dataset shown that the accuracy of PredictEFC can reach 0.8493 and 0.8370. The independent test on two datasets indicated the accuracy values of 0.9118 and 0.8777. CONCLUSION: The performance of PredictEFC was slightly lower than the classifier directly using functional domain composition. However, its efficiency was sharply improved. The running time was less than one-tenth of the time of the classifier directly using functional domain composition. In additional, the utility of PredictEFC was superior to the classifiers using traditional dimensionality reduction methods and some previous methods, and this classifier can be transplanted for predicting enzyme family classes of other species. Finally, a web-server available at http://124.221.158.221/ was set up for easy usage.

Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity.

Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.

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.

Novel Biocatalysts from Specialized Metabolism.

Enzymes are increasingly recognized as valuable (bio)catalysts that complement existing synthetic methods. However, the range of biotransformations used in the laboratory is limited. Here we give an overview on the biosynthesis-inspired discovery of novel biocatalysts that address various synthetic challenges. Prominent examples from this dynamic field highlight remarkable enzymes for protecting-group-free amide formation and modification, control of pericyclic reactions, stereoselective hetero- and polycyclizations, atroposelective aryl couplings, site-selective C-H activations, introduction of ring strain, and N-N bond formation. We also explore unusual functions of cytochrome P450 monooxygenases, radical SAM-dependent enzymes, flavoproteins, and enzymes recruited from primary metabolism, which offer opportunities for synthetic biology, enzyme engineering, directed evolution, and catalyst design.

Polyoxometalates as Potential Artificial Enzymes toward Biological Applications.

Artificial enzymes, as alternatives to natural enzymes, have attracted enormous attention in the fields of catalysis, biosensing, diagnostics, and therapeutics because of their high stability and low cost. Polyoxometalates (POMs), a class of inorganic metal oxides, have recently shown great potential in mimicking enzyme activity due to their well-defined structure, tunable composition, high catalytic efficiency, and easy storage properties. This review focuses on the recent advances in POM-based artificial enzymes. Different types of POMs and their derivatives-based mimetic enzyme functions are covered, as well as the corresponding catalytic mechanisms (where available). An overview of the broad applications of representative POM-based artificial enzymes from biosensing to theragnostic is provided. Insight into the current challenges and the future directions for POMs-based artificial enzymes is discussed.

Testing for the ability to modify antibiotics of Panus tigrinus 8/18 Lentinus strigosus 1566 laccase / Teste de capacidade para modificar antibióticos a partir da lacase de Panus tigrinus 8/18 e Lentinus strigosus 1566

In advanced biotechnology, the utilization of enzymes to achieve new or modified compounds with antibacterial, fungicidal, and anti-cancer specifications is crucial. Mushroom lactases are a hopeful biocatalyst for the synthesis and modification of different compounds. They are an accessible and inexpensive enzyme for the preparation of reaction objects and have recently received attention. Laccase purification was performed from basidiomycete Lentinus strigosus (LS) in several stages: Stage 1. On ion-exchange chromatography on TEAE Servacell 23 (400 ml), two distinctly separated laccase activity peaks were observed, eluted from the carrier at 0.21 and 0.27 M NaCl. In order to reduce the loss of enzymes, all fractions with laccase activity were collected, concentrated, and desalted using an ultrafiltration cell (Amicon, United States) with a UM-10 membrane. Stage 2. The resulting preparation with laccase activity was applied to a Q-Sepharose column (60 ml). Two well-separated peaks with laccase activity were obtained during the elution: laccase I (0.12 M NaCl) and laccase II (0.2 M NaCl). Stage 3. In the course of further purification of both enzymes, carried out on anion-exchange carrier Resource Q (6 ml), a broken gradient was used: 0 - 10%, 10 - 20%, and 20 - 100% with 1M NaCl. Stage 4. Both laccase I and laccase II, obtained after Resource Q, were desalted, concentrated to 1 ml each, and applied to a Superdex 75 gel filtration column. As a result, two laccases were obtained in a homogeneous form.

Na biotecnologia moderna, o uso de enzimas para obter compostos novos ou modificados com propriedades antibacterianas, antifúngicas e anticancerígenas é crucial. Lactases de cogumelos são biocatalisadores promissores para síntese e modificação de diferentes compostos, por serem enzimas baratas e disponíveis para a preparação de componentes de reação, e vem recebendo a devida atenção recentemente. A purificação da lacase foi realizada a partir do basidiomiceto Lentinus strigosus em vários estágios: Etapa 1 - na cromatografia de troca iônica em TEAE Servacell 23 (400 ml), foram observados dois picos de atividade da lacase distintamente separados, com eluição do transportador a 0,21 e 0,27 M de NaCl. Para reduzir a perda de enzimas, todas as frações com atividade de lacase foram coletadas, concentradas e dessalinizadas em uma célula de ultrafiltração (Amicon, Estados Unidos) com membrana UM-10; Etapa 2 - a preparação resultante com atividade de lacase foi aplicada a uma coluna Q-Sepharose (60 ml). Durante a eluição, foram obtidos dois picos bem separados com atividade de lacase: lacase I (NaCl 0,12 M) e lacase II (NaCl 0,2 M); Etapa 3 - no decurso da purificação adicional de ambas as enzimas, realizada no Recurso Q de transportador de troca aniônica (6 ml), um gradiente quebrado foi usado: 0-10%, 10-20% e 20-100% com NaCl 1M; Etapa 4 - tanto a lacase I como a lacase II, obtidas após o Recurso Q, foram dessalinizadas e concentradas para 1 ml cada e aplicadas a uma coluna de filtração em gel Superdex 75. Como resultado, duas lacases foram obtidas de forma homogênea.

Macro-AST as a cause of hypertransaminasemia: an uncommon entity but it exists

Macroenzymes are complexes formed by the binding of enzyme molecules to each other or to other plasma components. We report the clinical case of a woman with abnormal liver enzymes secondary to macro-AST. Macro-AST should be considered in the differential diagnosis as a cause of isolated AST elevation, thus avoiding unnecessary additional tests. (AU)