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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Engineering Enzymes for Environmental Sustainability

The development and implementation of sustainable catalytic technologies is key to delivering our net-zero targets. Here we review how engineered enzymes, with a focus on those developed using directed evolution, can be deployed to improve the sustainability of numerous processes and help to conserve our environment. Efficient and robust biocatalysts have been engineered to capture carbon dioxide (CO2 ) and have been embedded into new efficient metabolic CO2 fixation pathways. Enzymes have been refined for bioremediation, enhancing their ability to degrade toxic and harmful pollutants. Biocatalytic recycling is gaining momentum, with engineered cutinases and PETases developed for the depolymerization of the abundant plastic, polyethylene terephthalate (PET). Finally, biocatalytic approaches for accessing petroleum-based feedstocks and chemicals are expanding, using optimized enzymes to convert plant biomass into biofuels or other high value products. Through these examples, we hope to illustrate how enzyme engineering and biocatalysis can contribute to the development of cleaner and more efficient chemical industry.

Ultrahigh-Throughput Directed Evolution of Polymer-Degrading Enzymes Using Yeast Display

Enzymes that degrade synthetic polymers have attracted intense interest for eco-friendly plastic recycling. However, because enzymes did not evolve for the cleavage of abiotic polymers, directed evolution strategies are needed to enhance activity for plastic degradation. Previous directed evolution efforts relied on polymer degradation assays that were limited to screening ∼104 mutants. Here, we report a high-throughput yeast surface display platform to rapidly evaluate >107 enzyme mutants for increased activity in cleaving synthetic polymers. In this platform, individual yeast cells display distinct mutants, and enzyme activity is detected by a change in fluorescence upon the cleavage of a synthetic probe resembling a polymer of interest. Highly active mutants are isolated by fluorescence activated cell sorting and identified through DNA sequencing. To demonstrate this platform, we performed directed evolution of a polyethylene terephthalate (PET)-depolymerizing enzyme, leaf and branch compost cutinase (LCC). We identified activity-boosting mutations that substantially increased the kinetics of degradation of solid PET films. Biochemical assays and molecular dynamics (MD) simulations of the most active variants suggest that the H218Y mutation improves the binding of the enzyme to PET. Overall, this evolution platform increases the screening throughput of polymer-degrading enzymes by 3 orders of magnitude and identifies mutations that enhance kinetics for depolymerizing solid substrates.

Regulatory mechanisms of one-carbon metabolism enzymes

One-carbon metabolism is a central metabolic pathway critical for the biosynthesis of several amino acids, methyl group donors, and nucleotides. The pathway mostly relies on the transfer of a carbon unit from the amino acid serine, through the cofactor folate (in its several forms), and to the ultimate carbon acceptors that include nucleotides and methyl groups used for methylation of proteins, RNA, and DNA. Nucleotides are required for DNA replication, DNA repair, gene expression, and protein translation, through ribosomal RNA. Therefore, the one-carbon metabolism pathway is essential for cell growth and function in all cells, but is specifically important for rapidly proliferating cells. The regulation of one-carbon metabolism is a critical aspect of the normal and pathological function of the pathway, such as in cancer, where hijacking these regulatory mechanisms feeds an increased need for nucleotides. One-carbon metabolism is regulated at several levels: via gene expression, posttranslational modification, subcellular compartmentalization, allosteric inhibition, and feedback regulation. In this review, we aim to inform the readers of relevant one-carbon metabolism regulation mechanisms and to bring forward the need to further study this aspect of one-carbon metabolism. The review aims to integrate two major aspects of cancer metabolism-signaling downstream of nutrient sensing and one-carbon metabolism, because while each of these is critical for the proliferation of cancerous cells, their integration is critical for comprehensive understating of cellular metabolism in transformed cells and can lead to clinically relevant insights.

Generalized Michaelis-Menten rate law with time-varying molecular concentrations

The Michaelis-Menten (MM) rate law has been the dominant paradigm of modeling biochemical rate processes for over a century with applications in biochemistry, biophysics, cell biology, systems biology, and chemical engineering. The MM rate law and its remedied form stand on the assumption that the concentration of the complex of interacting molecules, at each moment, approaches an equilibrium (quasi-steady state) much faster than the molecular concentrations change. Yet, this assumption is not always justified. Here, we relax this quasi-steady state requirement and propose the generalized MM rate law for the interactions of molecules with active concentration changes over time. Our approach for time-varying molecular concentrations, termed the effective time-delay scheme (ETS), is based on rigorously estimated time-delay effects in molecular complex formation. With particularly marked improvements in protein-protein and protein-DNA interaction modeling, the ETS provides an analytical framework to interpret and predict rich transient or rhythmic dynamics (such as autogenously-regulated cellular adaptation and circadian protein turnover), which goes beyond the quasi-steady state assumption.

B12-dependent radical SAM enzymes: Ever expanding structural and mechanistic diversity

In the last decade, B12-dependent radical SAM enzymes have emerged as central biocatalysts in the biosynthesis of a myriad of natural products. Notably, these enzymes have been shown to catalyze carbon-carbon bond formation on unactivated carbon atoms leading to unusual methylations. Recently, structural studies have revealed unprecedented insights into the complex chemistry catalyzed by these enzymes. In this review, we cover recent advances in our understanding of B12-dependent radical SAM enzymes from a mechanistic and structural perspective. We discuss the unanticipated diversity of these enzymes which suggests evolutionary links between various biosynthetic and metabolic pathways from antibiotic to RiPP and methane biosynthesis.

Enzyme function and evolution through the lens of bioinformatics

Enzymes have been shaped by evolution over billions of years to catalyse the chemical reactions that support life on earth. Dispersed in the literature, or organised in online databases, knowledge about enzymes can be structured in distinct dimensions, either related to their quality as biological macromolecules, such as their sequence and structure, or related to their chemical functions, such as the catalytic site, kinetics, mechanism, and overall reaction. The evolution of enzymes can only be understood when each of these dimensions is considered. In addition, many of the properties of enzymes only make sense in the light of evolution. We start this review by outlining the main paradigms of enzyme evolution, including gene duplication and divergence, convergent evolution, and evolution by recombination of domains. In the second part, we overview the current collective knowledge about enzymes, as organised by different types of data and collected in several databases. We also highlight some increasingly powerful computational tools that can be used to close gaps in understanding, in particular for types of data that require laborious experimental protocols. We believe that recent advances in protein structure prediction will be a powerful catalyst for the prediction of binding, mechanism, and ultimately, chemical reactions. A comprehensive mapping of enzyme function and evolution may be attainable in the near future.

Engineering of Therapeutic and Detoxifying Enzymes

Therapeutic enzymes present excellent opportunities for the treatment of human disease, modulation of metabolic pathways and system detoxification. However, current use of enzyme therapy in the clinic is limited as naturally occurring enzymes are seldom optimal for such applications and require substantial improvement by protein engineering. Engineering strategies such as design and directed evolution that have been successfully implemented for industrial biocatalysis can significantly advance the field of therapeutic enzymes, leading to biocatalysts with new-to-nature therapeutic activities, high selectivity, and suitability for medical applications. This minireview highlights case studies of how state-of-the-art and emerging methods in protein engineering are explored for the generation of therapeutic enzymes and discusses gaps and future opportunities in the field of enzyme therapy.

Acid-resistant enzymes: the acquisition strategies and applications

Enzymes have promising applications in chemicals, food, pharmaceuticals, and other variety products because of their high efficiency, specificity, and environmentally friendly properties. However, due to the complexity of raw materials, pH, temperature, solvents, etc., the application range of enzymes is greatly limited in the industry. Protein engineering and enzyme immobilization are classical strategies to overcome the limitations of industrial applications. Although the pH tendency of enzymes has been extensively researched, the mechanism underlying enzyme acid resistance is unclear, and a less practical strategy for altering the pH propensity of enzymes has been suggested. This review proposes that the optimum pH of enzyme is determined by the pKa values of active center ionizable amino acid residues. Three levels of acquiring acid-resistant enzymes are summarized: mining from extreme environments and enzyme databases, modification with protein engineering and enzyme microenvironment engineering, and de novo synthesis. The industrial applications of acid-resistant enzymes in chemicals, food, and pharmaceuticals are also summarized. KEY POINTS: • The mechanism of enzyme acid resistance is fundamentally determined. • The three aspects of the method for acquiring acid-resistant enzymes are summarized. • Computer-aided strategies and artificial intelligence are used to obtain acid-resistant enzymes.

Control analysis in the identification of key enzymes driving metabolic adaptations: Towards drug target discovery

Metabolic Control Analysis (MCA) marked a turning point in understanding the design principles of metabolic network control by establishing control coefficients as a means to quantify the degree of control that an enzyme exerts on flux or metabolite concentrations. MCA has demonstrated that control of metabolic pathways is distributed among many enzymes rather than depending on a single rate-limiting step. MCA also proved that this distribution depends not only on the stoichiometric structure of the network but also on other kinetic determinants, such as the degree of saturation of the enzyme active site, the distance to thermodynamic equilibrium, and metabolite feedback regulatory loops. Consequently, predicting the alterations that occur during metabolic adaptation in response to strong changes involving a redistribution in such control distribution can be challenging. Here, using the framework provided by MCA, we illustrate how control distribution in a metabolic pathway/network depends on enzyme kinetic determinants and to what extent the redistribution of control affects our predictions on candidate enzymes suitable as targets for small molecule inhibition in the drug discovery process. Our results uncover that kinetic determinants can lead to unexpected control distribution and outcomes that cannot be predicted solely from stoichiometric determinants. We also unveil that the inference of key enzyme-drivers of an observed metabolic adaptation can be dramatically improved using mean control coefficients and ruling out those enzyme activities that are associated with low control coefficients. As the use of constraint-based stoichiometric genome-scale metabolic models (GSMMs) becomes increasingly prevalent for identifying genes/enzymes that could be potential drug targets, we anticipate that incorporating kinetic determinants and ruling out enzymes with low control coefficients into GSMM workflows will facilitate more accurate predictions and reveal novel therapeutic targets.

Endophytic bacteria isolated from Urtica dioica L.- preliminary screening for enzyme and polyphenols production

Endophytes, especially those isolated from herbal plants, may act as a reservoir of a variety of secondary metabolites exhibiting biological activity. Some endophytes express the ability to produce the same bioactive compounds as their plant hosts, making them a more sustainable industrial supply of these substances. Urtica dioica L. (common stinging nettle) is a synanthropic plant that is widely used in herbal medicine due to the diversity of bioactive chemicals it contains, e.g., polyphenols, which demonstrate anti-inflammatory, antioxidant, and anti-cancerous capabilities. This study aimed at isolating endophytic bacteria from stinging nettles for their bioactive compounds. The endophytic isolates were identified by both biochemical and molecular methods (16S rRNA) and investigated for enzymes, biosurfactants, and polyphenols production. Each of the isolated bacterial strains was capable of producing biosurfactants and polyphenols. However, three of the isolated endophytes, identified as two strains of Bacillus cereus and one strain of Bacillus mycoides, possessed the greatest capacity to produce biosurfactants and polyphenols. The derivatized extracts from culture liquid showed the 1.633 mol l-1 (9.691 mg l-1) concentration of polyphenol compounds. Therefore, the present study signifies that endophytic B. cereus and B. mycoides isolated from Urtica dioica L. could be a potential source of biosurfactants and polyphenols. However, further study is required to understand the mechanism of the process and achieve efficient polyphenol production by endophytic bacteria.

Psychrophilic enzymes: strategies for cold-adaptation

Psychrophilic organisms thriving at near-zero temperatures synthesize cold-adapted enzymes to sustain cell metabolism. These enzymes have overcome the reduced molecular kinetic energy and increased viscosity inherent to their environment and maintained high catalytic rates by development of a diverse range of structural solutions. Most commonly, they are characterized by a high flexibility coupled with an intrinsic structural instability and reduced substrate affinity. However, this paradigm for cold-adaptation is not universal as some cold-active enzymes with high stability and/or high substrate affinity and/or even an unaltered flexibility have been reported, pointing to alternative adaptation strategies. Indeed, cold-adaptation can involve any of a number of a diverse range of structural modifications, or combinations of modifications, depending on the enzyme involved, its function, structure, stability, and evolutionary history. This paper presents the challenges, properties, and adaptation strategies of these enzymes.

Rank-ordering of known enzymes as starting points for re-engineering novel substrate activity using a convolutional neural network

Retro-biosynthetic approaches have made significant advances in predicting synthesis routes of target biofuel, bio-renewable or bio-active molecules. The use of only cataloged enzymatic activities limits the discovery of new production routes. Recent retro-biosynthetic algorithms increasingly use novel conversions that require altering the substrate or cofactor specificities of existing enzymes while connecting pathways leading to a target metabolite. However, identifying and re-engineering enzymes for desired novel conversions are currently the bottlenecks in implementing such designed pathways. Herein, we present EnzRank, a convolutional neural network (CNN) based approach, to rank-order existing enzymes in terms of their suitability to undergo successful protein engineering through directed evolution or de novo design towards a desired specific substrate activity. We train the CNN model on 11,800 known active enzyme-substrate pairs from the BRENDA database as positive samples and data generated by scrambling these pairs as negative samples using substrate dissimilarity between an enzyme's native substrate and all other molecules present in the dataset using Tanimoto similarity score. EnzRank achieves an average recovery rate of 80.72% and 73.08% for positive and negative pairs on test data after using a 10-fold holdout method for training and cross-validation. We further developed a web-based user interface (available at https://huggingface.co/spaces/vuu10/EnzRank) to predict enzyme-substrate activity using SMILES strings of substrates and enzyme sequence as input to allow convenient and easy-to-use access to EnzRank. In summary, this effort can aid de novo pathway design tools to prioritize starting enzyme re-engineering candidates for novel reactions as well as in predicting the potential secondary activity of enzymes in cell metabolism.

Research Progress in Construction and Application of Enzyme-Based DNA Logic Gates

As a research hotspot in the field of information processing, DNA computing exhibits several important underlying characteristics-from parallel computing and low energy consumption to high-performance storage capabilities-thereby enabling its wide application in nanomachines, molecular encryption, biological detection, medical diagnosis, etc. Based on DNA computing, the most rapidly developed field focuses on DNA molecular logic-gates computing. In particular, the recent advances in enzyme-based DNA logic gates has emerged as ideal materials for constructing DNA logic gates. In this review, we explore protein enzymes that can manipulate DNA, especially, nicking enzymes and polymerases with high efficiency and specificity, which are widely used in constructing DNA logic gates, as well as ribozyme that can construct DNA logic gates following various mechanism with distinct biomaterials. Accordingly, the review highlights the characteristics and applications of various types of DNAzyme-based logic gates models, considering their future developments in information, biomedicine, chemistry, and computers.

Protein Dynamics and Enzymatic Catalysis

This Perspective presents a review of our work and that of others in the highly controversial topic of the coupling of protein dynamics to reaction in enzymes. We have been involved in studying this topic for many years. Thus, this perspective will naturally present our own views, but it also is designed to present an overview of the variety of viewpoints of this topic, both experimental and theoretical. This is obviously a large and contentious topic.

Metabolite-induced in vivo fabrication of substrate-free organic bioelectronics

Interfacing electronics with neural tissue is crucial for understanding complex biological functions, but conventional bioelectronics consist of rigid electrodes fundamentally incompatible with living systems. The difference between static solid-state electronics and dynamic biological matter makes seamless integration of the two challenging. To address this incompatibility, we developed a method to dynamically create soft substrate-free conducting materials within the biological environment. We demonstrate in vivo electrode formation in zebrafish and leech models, using endogenous metabolites to trigger enzymatic polymerization of organic precursors within an injectable gel, thereby forming conducting polymer gels with long-range conductivity. This approach can be used to target specific biological substructures and is suitable for nerve stimulation, paving the way for fully integrated, in vivo-fabricated electronics within the nervous system.

Possibilities of Using De Novo Design for Generating Diverse Functional Food Enzymes

Food enzymes have an important role in the improvement of certain food characteristics, such as texture improvement, elimination of toxins and allergens, production of carbohydrates, enhancing flavor/appearance characteristics. Recently, along with the development of artificial meats, food enzymes have been employed to achieve more diverse functions, especially in converting non-edible biomass to delicious foods. Reported food enzyme modifications for specific applications have highlighted the significance of enzyme engineering. However, using direct evolution or rational design showed inherent limitations due to the mutation rates, which made it difficult to satisfy the stability or specific activity needs for certain applications. Generating functional enzymes using de novo design, which highly assembles naturally existing enzymes, provides potential solutions for screening desired enzymes. Here, we describe the functions and applications of food enzymes to introduce the need for food enzymes engineering. To illustrate the possibilities of using de novo design for generating diverse functional proteins, we reviewed protein modelling and de novo design methods and their implementations. The future directions for adding structural data for de novo design model training, acquiring diversified training data, and investigating the relationship between enzyme-substrate binding and activity were highlighted as challenges to overcome for the de novo design of food enzymes.

Expanding the viewpoint: Leveraging sequence information in enzymology

The use of protein sequence to inform enzymology in terms of structure, mechanism, and function has burgeoned over the past two decades. Referred to as genomic enzymology, the utilization of bioinformatic tools such as sequence similarity networks and phylogenetic analyses has allowed the identification of new substrates and metabolites, novel pathways, and unexpected reaction mechanisms. The holistic examination of superfamilies can yield insight into the origins and paths of evolution of enzymes and the range of their substrates and mechanisms. Herein, we highlight advances in the use of genomic enzymology to address problems which the in-depth analyses of a single enzyme alone could not enable.

Data-driven enzyme engineering to identify function-enhancing enzymes

Identifying function-enhancing enzyme variants is a 'holy grail' challenge in protein science because it will allow researchers to expand the biocatalytic toolbox for late-stage functionalization of drug-like molecules, environmental degradation of plastics and other pollutants, and medical treatment of food allergies. Data-driven strategies, including statistical modeling, machine learning, and deep learning, have largely advanced the understanding of the sequence-structure-function relationships for enzymes. They have also enhanced the capability of predicting and designing new enzymes and enzyme variants for catalyzing the transformation of new-to-nature reactions. Here, we reviewed the recent progresses of data-driven models that were applied in identifying efficiency-enhancing mutants for catalytic reactions. We also discussed existing challenges and obstacles faced by the community. Although the review is by no means comprehensive, we hope that the discussion can inform the readers about the state-of-the-art in data-driven enzyme engineering, inspiring more joint experimental-computational efforts to develop and apply data-driven modeling to innovate biocatalysts for synthetic and pharmaceutical applications.

Radical SAM enzymes: Nature's choice for radical reactions

Enzymes that use a [4Fe-4S]1+ cluster plus S-adenosyl-l-methionine (SAM) to initiate radical reactions (radical SAM) form the largest enzyme superfamily, with over half a million members across the tree of life. This review summarizes recent work revealing the radical SAM reaction pathway, which ultimately liberates the 5'-deoxyadenosyl (5'-dAdo•) radical to perform extremely diverse, highly regio- and stereo-specific, transformations. Most surprising was the discovery of an organometallic intermediate Ω exhibiting an Fe-C5'-adenosyl bond. Ω liberates 5'-dAdo• through homolysis of the Fe-C5' bond, in analogy to Co-C5' bond homolysis in B12 , previously viewed as biology's paradigmatic radical generator. The 5'-dAdo• has been trapped and characterized in radical SAM enzymes via a recently discovered photoreactivity of the [4Fe-4S]+ /SAM complex, and has been confirmed as a catalytically active intermediate in enzyme catalysis. The regioselective SAM S-C bond cleavage to produce 5'-dAdo• originates in the Jahn-Teller effect. The simplicity of SAM as a radical precursor, and the exquisite control of 5'-dAdo• reactivity in radical SAM enzymes, may be why radical SAM enzymes pervade the tree of life, while B12 enzymes are only a few.

A recommendation on the teaching of Michaelis-Menten kinetics in biochemistry courses

Most textbooks and lecturers present Michaelis-Menten kinetics using the equation v = V_max [S]/(K_m  + [S]). There are advantages to presenting this relationship in a slightly different form, namely v = V_max /{1 + (K_m /[S])}. We articulate advantages for single-substrate reactions and extend the formalism to include the three classes of bi-substrate reactions.

A designed photoenzyme for enantioselective [2+2] cycloadditions

The ability to program new modes of catalysis into proteins would allow the development of enzyme families with functions beyond those found in nature. To this end, genetic code expansion methodology holds particular promise, as it allows the site-selective introduction of new functional elements into proteins as noncanonical amino acid side chains1-4. Here we exploit an expanded genetic code to develop a photoenzyme that operates by means of triplet energy transfer (EnT) catalysis, a versatile mode of reactivity in organic synthesis that is not accessible to biocatalysis at present5-12. Installation of a genetically encoded photosensitizer into the beta-propeller scaffold of DA_20_00 (ref. 13) converts a de novo Diels-Alderase into a photoenzyme for [2+2] cycloadditions (EnT1.0). Subsequent development and implementation of a platform for photoenzyme evolution afforded an efficient and enantioselective enzyme (EnT1.3, up to 99% enantiomeric excess (e.e.)) that can promote intramolecular and bimolecular cycloadditions, including transformations that have proved challenging to achieve selectively with small-molecule catalysts. EnT1.3 performs >300 turnovers and, in contrast to small-molecule photocatalysts, can operate effectively under aerobic conditions and at ambient temperatures. An X-ray crystal structure of an EnT1.3-product complex shows how multiple functional components work in synergy to promote efficient and selective photocatalysis. This study opens up a wealth of new excited-state chemistry in protein active sites and establishes the framework for developing a new generation of enantioselective photocatalysts.

Uniform binding and negative catalysis at the origin of enzymes

Enzymes are well known for their catalytic abilities, some even reaching "catalytic perfection" in the sense that the reaction they catalyze has reached the physical bound of the diffusion rate. However, our growing understanding of enzyme superfamilies has revealed that only some share a catalytic chemistry while others share a substrate-handle binding motif, for example, for a particular phosphate group. This suggests that some families emerged through a "substrate-handle-binding-first" mechanism ("binding-first" for brevity) instead of "chemistry-first" and we are, therefore, left to wonder what the role of non-catalytic binders might have been during enzyme evolution. In the last of their eight seminal, back-to-back articles from 1976, John Albery and Jeremy Knowles addressed the question of enzyme evolution by arguing that the simplest mode of enzyme evolution is what they defined as "uniform binding" (parallel stabilization of all enzyme-bound states to the same degree). Indeed, we show that a uniform-binding proto-catalyst can accelerate a reaction, but only when catalysis is already present, that is, when the transition state is already stabilized to some degree. Thus, we sought an alternative explanation for the cases where substrate-handle-binding preceded any involvement of a catalyst. We find that evolutionary starting points that exhibit negative catalysis can redirect the reaction's course to a preferred product without need for rate acceleration or product release; that is, if they do not stabilize, or even destabilize, the transition state corresponding to an undesired product. Such a mechanism might explain the emergence of "binding-first" enzyme families like the aldolase superfamily.

Microscale Colocalization of Cascade Enzymes Yields Activity Enhancement

Colocalization of cascade enzymes is broadly discussed as a phenomenon that can boost the cascade reaction throughput, although a direct experimental verification is often challenging. This is mainly due to difficulties in establishing proper size regimes and in the analytical quantification of colocalization effect with adequate experimental systems and simulations. In this study, by taking advantage of reversible DNA-directed colocalization of enzymes on microspheres, we established a cascade system that can be used to directly evaluate the colocalization effect with exactly the same experimental settings except for the state of enzyme dispersion. In the regime of highly dilute microspheres of particular sizes, the colocalized cascade shows enhanced activity compared with the freely diffusing cascade, as evidenced by a shortened lag phase in the time-course production. Reaction-diffusion modeling reveals that the enhancement can be ascribed to the initial accumulation of intermediate substrate around the colocalized enzymes and is found to be carrier-size-dependent. This work demonstrates the dependence of the colocalization effect of enzyme cascades on an interplay of nano- and microscales, lending theoretical support to the rational design of highly efficient multienzyme catalysts.

Oviposition behaviour and biochemical response of an insect pest, Zeugodacus cucurbitae (Coquillett) (Diptera: Tephritidae) to plant phenolic compound phloroglucinol

Phenolic compounds are the secondary metabolites (SMs) present in plants carrying different bioactivities. In the present study, we explored the influence of a phenolic compound namely phloroglucinol on oviposition behaviour and different biochemical entities of an insect pest Zeugodacus cucurbitae (Coquillett) (Diptera: Tephritidae) using artificial diet. Phloroglucinol (IUPAC name: benzene-1,3,5-triol) affected the activity of antioxidant and detoxifying enzymes viz. superoxide dismutases (SOD), catalase (CAT), ascorbate peroxidases (APOX). dehydroascorbate reductase (DHAR), peroxidases (POX), phenol oxidase (PO), glutathione peroxidase (GPOX), glutathione S-transferase peroxidase (GSTpox), glutathione reductase (GR), glutathione S-transferase (GST) and esterases (EST) as well as the biological antioxidants viz. ascorbate content and glutathione. The lipid peroxide content (LP) and hydrogen peroxide content (H₂O₂) were significantly enhanced in the treated larvae indicating oxidative stress in the insect. Significant inhibition in oviposition was observed and effective repellency percentage increased with phloroglucinol treatment as compared to control. The oviposition deterrent activity and toxic effects of phloroglucinol on various biochemical parameters of Z. cucurbitae larvae revealed in the present study clearly confirms its suitability for use in pest management.

The potential protective effects of estradiol and 2-methoxyestradiol in ischemia reperfusion-induced kidney injury in ovariectomized female rats

AIMS

Investigating the impact of 17β estradiol (E2) and its endogenous non-hormonal metabolite 2-methoxyestradiol (2ME) on renal ischemia-reperfusion (RIR) induced kidney injury in ovariectomized (OVX) rats and the role of catechol-O-methyltransferase (COMT) in their effects.

MAIN METHODS

Eighty female rats were allocated into eight groups. Control group, Sham group, OVX group, OVX and RIR group, OVX + RIR + E2 group, OVX + RIR + 2ME group, OVX + RIR + E2 + Entacapone group and OVX + RIR + 2ME + Entacapone group, respectively. Twenty-four hours post RIR, creatinine (Cr) and blood urea nitrogen (BUN) were determined in serum, while malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), Glutathione (GSH), myeloperoxidase (MPO), as well as the expressions of COMT, hypoxia inducible factor-1α (HIF-1α) and tyrosine hydroxylase (TH) were assessed in the kidney tissues.

KEY FINDINGS

Serum Cr, BUN, MPO, as well as HIF-1α and TH expressions were significantly higher with concomitant decrease in COMT expression, SOD and CAT activities and GSH content observed in OVX and RIR group compared to sham group. E2 and 2ME treatment significantly ameliorated all parameters measured in OVX and RIR rats. On the other hand, Entacapone significantly decreased the effect of E2, with no effect on 2ME treatment.

SIGNIFICANCE

E2 ameliorates RIR-induced kidney injury and this effect is mediated, at least in part, via its COMT-mediated conversion to 2ME. Thus, 2ME by the virtue of its pleiotropic pharmacological effects can be used as a safe and effective treatment of RIR injury.

Henri-Michaelis-Menten kinetics of reversible enzymic reactions, and the determination of rate constants from kinetic constants

The Michaelis constants derived for two reversible uni-reactant - uni-product reaction models, given originally by Haldane, are corrected. In the direction starting with the reactant having the lower binding constant, the steady state is one in which the enzyme-reactant intermediate has a concentration approximating the final equilibrium concentration., Consequently, the Haldane relationship is generally invalid, and kinetic analyses to validate the use of the kinetic constant ratio (kcat/Km) as a measure of specificity are also generally invalid. This correction of Michaelis constants is pertinent to attempts to back calculate rate constants from experimental values: the Michaelis constant used must be correct.

The Transformative Power of Biocatalysis in Convergent Synthesis

Achieving convergent synthetic strategies has long been a gold standard in constructing complex molecular skeletons, allowing for the rapid generation of complexity in comparatively streamlined synthetic routes. Traditionally, biocatalysis has not played a prominent role in convergent laboratory synthesis, with the application of biocatalysts in convergent strategies primarily limited to the synthesis of chiral fragments. Although the use of enzymes to enable convergent synthetic approaches is relatively new and emerging, combining the efficiency of convergent transformations with the selectivity achievable through biocatalysis creates new opportunities for efficient synthetic strategies. This Perspective provides an overview of recent developments in biocatalytic strategies for convergent transformations and offers insights into the advantages of these methods compared to their small molecule-based counterparts.

Ecotoxicity of herbicide carfentrazone-ethyl towards earthworm Eisenia fetida in soil

Herbicides pose a potential threat to the soil biodiversity and health. Carfentrazone-ethyl (CE), a triazolinones herbicide, is increasingly used in agricultural production. Its non-target toxic effects on soil microorganisms and soil enzymes are reported recently. However, the sublethal toxicity of CE on soil invertebrates like earthworms is not yet known. Therefore, in this work, the sublethal toxic effects of CE (0.05, 0.5, and 5.0 μg/g in soil) on the soil earthworm (Eisenia fetida) were evaluated using a battery of biomarkers including reactive oxygen species (ROS), enzyme (superoxide dismutase-SOD, catalase-CAT, peroxidase-POD, and glutathione S-transferase-GST) activities, malondialdehyde (MDA) contents, histopathological and DNA damage. Results indicated that CE increased ROS contents, enzyme activities, and MDA contents in the short-time (14 d), thus, causing a slight oxidative stress to E. fetida. However, the toxic effects of CE on earthworms gradually disappeared after 14 days. The CE did not cause histopathological and DNA damage in earthworms. Integrated Biological Response index (IBR) indicated that both concentration and exposure time of CE regulated its sublethal toxicity on earthworms. In conclusion, herbicide CE is safe to soil invertebrate earthworms when applied at the recommended doses. Our results contribute to the current understanding of CE effects on soil earthworms, and can be useful in developing soil health strategies under agrochemical use.

Acute exposure to environmentally relevant concentrations of phenytoin damages early development and induces oxidative stress in zebrafish embryos

Phenytoin (PHE) is an antiepileptic drug that has been widely used in clinical practice for about 80 years. It is mainly used in the treatment of tonic-clonic and partial seizures. The widespread consumption of this drug around the world has led to PHE being introduced into water bodies through municipal, hospital, and industrial effluent discharges. Since the toxic effects of this drug on aquatic species has been scarcely explored, the aim of this work was to investigate the influence of low (25-400 ngL^-1) and high (500-1500 ngL^-1) environmentally relevant concentrations of PHE on the development and oxidative status of zebrafish (Danio rerio) embryos. The toxicity of PHE was evaluated from 12 to 96 h after fertilization in D. rerio at concentrations between 25 and 1500 ngL^-1. In both the control group and the 0.05% DMSO system, no malformations were observed, all embryos developed normally after 96 h. The severity and frequency of malformations increased with increasing PHE concentration compared to embryos in the control group. Malformations observed included developmental delay, hypopigmentation, miscellaneous (more than one malformation in the same embryo), modified chorda structure, tail malformation, and yolk deformation. Concerning the biomarkers of oxidative stress, an increase in the degree of lipid peroxidation, protein carbonylation, and hydroperoxide content was observed (p < 0.05) concerning the control. In addition, a significant increase (p < 0.05) in antioxidant enzymes (SOD, CAT, and GPx) was observed at low exposure concentrations (25-400 ngL^-1), with a decrease in enzyme activity at high concentrations (500-1500 ngL^-1). Our IBR analysis demonstrated that oxidative damage biomarkers got more influence at 500ngL^-1 of PHE. The results demonstrated that PHE may affect the embryonic development of zebrafish and that oxidative stress may be involved in the generation of this embryotoxic process.

Enzymes-Assisted Extraction of Plants for Sustainable and Functional Applications

The scientific community and industrial companies have discovered significant enzyme applications to plant material. This rise imparts to changing consumers’ demands while searching for ‘clean label’ food products, boosting the immune system, uprising resistance to bacterial and fungal diseases, and climate change challenges. First, enzymes were used for enhancing production yield with mild and not hazardous applications. However, enzyme specificity, activity, plant origin and characteristics, ratio, and extraction conditions differ depending on the goal. As a result, researchers have gained interest in enzymes’ ability to cleave specific bonds of macroelements and release bioactive compounds by enhancing value and creating novel derivatives in plant extracts. The extract is enriched with reducing sugars, phenolic content, and peptides by disrupting lignocellulose and releasing compounds from the cell wall and cytosolic. Nonetheless, depolymerizing carbohydrates and using specific enzymes form and release various saccharides lengths. The latest studies show that oligosaccharides released and formed by enzymes have a high potential to be slowly digestible starches (SDS) and possibly be labeled as prebiotics. Additionally, they excel in new technological, organoleptic, and physicochemical properties. Released novel derivatives and phenolic compounds have a significant role in human and animal health and gut-microbiota interactions, affecting many metabolic pathways. The latest studies have contributed to enzyme-modified extracts and products used for functional, fermented products development and sustainable processes: in particular, nanocellulose, nanocrystals, nanoparticles green synthesis with drug delivery, wound healing, and antimicrobial properties. Even so, enzymes’ incorporation into processes has limitations and is regulated by national and international levels.

An insight to rhizosphere bacterial community composition and structure of consecutive winter-initiated sugarcane ratoon crop in Southern China

BACKGROUND

Ratooning in sugarcane is a crucial strategy for ensuring the long-term sustainability of the sugarcane industry. Knowledge gap relating to the interaction between rhizosphere microbiome and ratooning crop, particularly the impact of different sugarcane cultivars on the rhizosphere microbiome in consecutive ratooning, requires additional research. The response of two different sugarcane cultivars, viz ZZ-1 and ZZ-13, were evaluated in consecutive ratooning towards the rhizosphere microbial community and cane morphological characters.

RESULTS

Significant changes in the rhizosphere microbiome were observed in the second ratooning over the years. Several important genera were observed in high abundance during the second ratooning, including Burkholderia, Sphingomonas, Bradyzhizobium, and Acidothermus. Cultivar ZZ-13 caused more alterations in the rhizosphere microbiome than ZZ-1, resulting in a more favorable rhizosphere environment for sugarcane growth. The genotypes also varied in terms of nutrients and enzyme activity over the years. There were significant differences between the genotypes and year for number of stalks and yield was significant for genotypes, years and genotype × year.

CONCLUSION

This finding will help to understand thorough interactions between rhizosphere microorganisms and ratoon sugarcane and lay the foundation for promoting and maximizing yield as far as possible. In the future, this work can serve as guidance in sugarcane husbandry, mainly in Guangxi, China.

Advances in enzyme-mediated proximity labeling and its potential for plant research

Cellular processes rely on the intimate interplay of different molecules, including DNA, RNA, proteins, and metabolites. Obtaining and integrating data on their abundance and dynamics at high temporal and spatial resolution are essential for our understanding of plant growth and development. In the past decade, enzymatic proximity labeling (PL) has emerged as a powerful tool to study local protein and nucleotide ensembles, discover protein-protein and protein-nucleotide interactions, and resolve questions about protein localization and membrane topology. An ever-growing number and continuous improvement of enzymes and methods keep broadening the spectrum of possible applications for PL and make it more accessible to different organisms, including plants. While initial PL experiments in plants required high expression levels and long labeling times, recently developed faster enzymes now enable PL of proteins on a cell type-specific level, even with low-abundant baits, and in different plant species. Moreover, expanding the use of PL for additional purposes, such as identification of locus-specific gene regulators or high-resolution electron microscopy may now be in reach. In this review, we give an overview of currently available PL enzymes and their applications in mammalian cell culture and plants. We discuss the challenges and limitations of PL methods and highlight open questions and possible future directions for PL in plants.

Compartmentalized Aqueous-in-Aqueous Droplets for Flow Biocatalysis

Compartmentalized bioreactions are vital for living cells to regulate biological events since they facilitate isolated yet orchestrated reactions and releases of biological molecules. Engineering bioreactions in compartmentalized droplet bioreactors not only promotes understanding of biological cells but also enhances control in synthetic biology systems. A typical droplet bioreactor is enclosed by impermeable water-in-oil interfaces, which inhibit the reaction rate with the accumulation of aqueous products. This work constructs aqueous two-phase system (ATPS) droplet bioreactors featuring selectively permeable interfaces, which are capable of sequestering reagents in aqueous droplets while constantly releasing products into the aqueous surroundings. Benefiting from this selective permeability, the proposed droplet bioreactor achieves a conversion rate up to 63.2% compared to the 17.9% from the impermeable aqueous-in-oil droplet reactor via coupled reaction-separation. More importantly, it is revealed that uniform aqueous-in-aqueous droplet clusters by microfluidics exhibit an up to 6-fold reaction rate enhancement compared to non-microfluidic ATPS reactors, indicating a unique flow interface effect in droplet clusters. This work offers a new route to allow enzymatic reactions to benefit from efficient flow chemistry via optimized aqueous-aqueous interfaces.

Identification and characterization of unique 5-hydroxyisoflavonoid biosynthetic key enzyme genes in Lupinus albus

5-Hydroxyisoflavonoids, no 5-deoxyisoflavonoids, in Lupinus species, are due to lack of CHRs and Type II CHIs, and the key enzymes of isoflavonoid biosynthetic pathway in white lupin were identified. White lupin (Lupinus albus) is used as food ingredients owing to rich protein, low starch, and rich bioactive compounds such as isoflavonoids. The isoflavonoids biosynthetic pathway in white lupin still remains unclear. In this study, only 5-hydroxyisoflavonoids, but no 5-deoxyisoflavonoids, were detected in white lupin and other Lupinus species. No 5-deoxyisoflavonoids in Lupinus species are due to lack of CHRs and Type II CHIs. We further found that the CHI gene cluster containing both Type I and Type II CHIs possibly arose after the divergence of Lupinus with other legume clade. LaCHI1 and LaCHI2 identified from white lupin metabolized naringenin chalcone to naringenin in yeast and tobacco (Nicotiana benthamiana), and were bona fide Type I CHIs. We further identified two isoflavone synthases (LaIFS1 and LaIFS2), catalyzing flavanone naringenin into isoflavone genistein and also catalyzing liquiritigenin into daidzein in yeast and tobacco. In addition, LaG6DT1 and LaG6DT2 prenylated genistein at the C-6 position into wighteone. Two glucosyltransferases LaUGT1 and LaUGT2 metabolized genistein and wighteone into its 7-O-glucosides. Taken together, our study not only revealed that exclusive 5-hydroxyisoflavonoids do exist in Lupinus species, but also identified key enzymes in the isoflavonoid biosynthetic pathway in white lupin.

H2S aids osmotic stress resistance by S-sulfhydration of melatonin production-related enzymes in Arabidopsis thaliana

Hydrogen sulfide closed Arabidopsis thaliana stomata by increasing the transcription of melatonin-producing enzymes and the post-translational modification levels to combat osmotic stress. Hydrogen sulfide (H₂S) and melatonin (MEL) reportedly have similar functions in many aspects of plant growth, development and stress response. They regulate stomatal movement and enhance drought resistance. However, their physiological relationship is not well understood. Here, their crosstalk involved in osmotic stress resistance in Arabidopsis thaliana was studied. Exogenous H₂S and MEL closed stomata under normal or osmotic stress conditions and increased the relative water contents of plants under osmotic stress conditions. At the same time, exogenous H₂S and MEL responded to osmotic stress by increasing the content of proline and soluble sugar, and reducing malondialdehyde (MDA) content and relative conductivity. Using mutants in the MEL-associated production of serotonin N-acetyltransferase (snat), caffeic acid O-methyltransferase (comt1) and N-acetylserotonin methyltransferase (asmt), we determined that H₂S was partially dependent on MEL to close stomata. Additionally, the overexpression of ASMT promoted stomatal closure. Exogenous H₂S increased the transcription levels of SNAT, ASMT and COMT1. Furthermore, exogenous H₂S treatments increased the endogenous MEL content significantly. At the post-translational level, H₂S sulfhydrated the SNAT and ASMT, but not COMT1, enzymes associated with MEL production. Thus, H₂S appeared to promote stomatal closure in response to osmotic stress by increasing the transcription levels of MEL synthesis-related genes and the sulfhydryl modification of the encoded enzymes. These results increased our understanding of H₂S and MEL functions and interactions under osmotic stress conditions.

A Review of Advanced Molecular Engineering Approaches to Enhance the Thermostability of Enzyme Breakers: From Prospective of Upstream Oil and Gas Industry

During the fracture stimulation of oil and gas wells, fracturing fluids are used to create fractures and transport the proppant into the fractured reservoirs. The fracturing fluid viscosity is responsible for proppant suspension, the viscosity can be increased through the incorporation of guar polymer and cross-linkers. After the fracturing operation, the fluid viscosity is decreased by breakers for efficient oil and gas recovery. Different types of enzyme breakers have been engineered and employed to reduce the fracturing fluid′s viscosity, but thermal stability remains the major constraint for the use of enzymes. The latest enzyme engineering approaches such as direct evolution and rational design, have great potential to increase the enzyme breakers’ thermostability against high temperatures of reservoirs. In this review article, we have reviewed recently advanced enzyme molecular engineering technologies and how these strategies could be used to enhance the thermostability of enzyme breakers in the upstream oil and gas industry.

Transcriptional and physiological data revealed cold tolerance in a photo-thermo sensitive genic male sterile line Yu17S

BACKGROUND

Rice is highly sensitive to chilling stress during the seedling stage. However, the adaptable photo-thermo sensitive genic male sterile (PTGMS) rice line, Yu17S, exhibits tolerance to low temperatures. Currently, the molecular characteristics of Yu17S are unclear.

RESULTS

To evaluate the molecular mechanisms behind cold responses in rice seedlings, a comparative transcriptome analysis was performed in Yu17S during seedling development under normal temperature and low temperature conditions. In total, 9317 differentially expressed genes were detected. Gene ontology and pathway analyses revealed that these genes were involved mostly in photosynthesis, carotenoid biosynthesis, carbohydrate metabolism and plant hormone signal transduction. An integrated analysis of specific pathways combined with physiological data indicated that rice seedlings improved the performance of photosystem II when exposed to cold conditions. Genes involved in starch degradation and sucrose metabolism were activated in rice plants exposed to cold stress treatments, which was accompanied by the accumulation of soluble sugar, trehalose, raffinose and galactinol. Furthermore, chilling stress induced the expression of phytoene desaturase, 15-cis-ζ-carotene isomerase, ζ-carotene desaturase, carotenoid isomerase and β-carotene hydroxylase; this was coupled with the activation of carotenoid synthase activity and increases in abscisic acid (ABA) levels in rice seedlings.

CONCLUSIONS

Our results suggest that Yu17S exhibited better tolerance to cold stress with the activation of carotenoid synthase activity and increasing of ABA levels, and as well as the expression of photosynthesis-related genes under cold condition in rice seedlings.