Directed evolution of enzyme catalysts

Rapid Assembly of Ultrafine Palladium Nanoparticle-Decorated HOF-101 Triggered by Guest Enzyme Encapsulation

Rapid enzyme immobilization is essential for enzyme catalysis and sensing applications, yet constructing effective immobilization systems is challenging due to the need to balance enzyme activity with the properties of the surrounding framework. Herein, taking glucose oxidase (GOx) as a model, a rapid and straightforward approach was presented for synthesizing palladium nanoparticles (PdNPs)-decorated GOx encapsulated in HOF-101 nanocomposite materials (designated as PdNPs/GOx@HOF-101) through an in situ photoreduction and enzyme-triggering HOF-101 encapsulation. The enzyme's surface residues trigger the nucleation of HOF-101 around it through the hydrogen-bonded bio interface, completing the self-assembly of HOF-101 in 0.5 h. Furthermore, the biocomposites loaded with ultrafine PdNPs show satisfactory photoelectrochemical (PEC) properties. As a proof-of-concept, a PEC biosensor was constructed by utilizing PdNPs/GOx@HOF-101 as a photoactive probe, which can quickly and sensitively detect glucose and simultaneously remain stable within the circumstance of 30-60 °C and pH 4-8. These attributes pave the way for diverse applications, including improved enzyme immobilization techniques, advanced biosensors, and more efficient biocatalytic processes.

ThermoLink: Bridging disulfide bonds and enzyme thermostability through database construction and machine learning prediction

Disulfide bonds, covalently formed by sulfur atoms in cysteine residues, play a crucial role in protein folding and structure stability. Considering their significance, artificial disulfide bonds are often introduced to enhance protein thermostability. Although an increasing number of tools can assist with this task, significant amounts of time and resources are often wasted owing to inadequate consideration. To enhance the accuracy and efficiency of designing disulfide bonds for protein thermostability improvement, we initially collected disulfide bond and protein thermostability data from extensive literature sources. Thereafter, we extracted various sequence- and structure-based features and constructed machine-learning models to predict whether disulfide bonds can improve protein thermostability. Among all models, the neighborhood context model based on the Adaboost-DT algorithm performed the best, yielding "area under the receiver operating characteristic curve" and accuracy scores of 0.773 and 0.714, respectively. Furthermore, we also found AlphaFold2 to exhibit high superiority in predicting disulfide bonds, and to some extent, the coevolutionary relationship between residue pairs potentially guided artificial disulfide bond design. Moreover, several mutants of imine reductase 89 (IR89) with artificially designed thermostable disulfide bonds were experimentally proven to be considerably efficient for substrate catalysis. The SS-bond data have been integrated into an online server, namely, ThermoLink, available at guolab.mpu.edu.mo/thermoLink.

Nanozyme-Enabled Biomedical Diagnosis: Advances, Trends, and Challenges

As nanoscale materials with the function of catalyzing substrates through enzymatic kinetics, nanozymes are regarded as potential alternatives to natural enzymes. Compared to protein-based enzymes, nanozymes exhibit attractive characteristics of low preparation cost, robust activity, flexible performance adjustment, and versatile functionalization. These advantages endow them with wide use from biochemical sensing and environmental remediation to medical theranostics. Especially in biomedical diagnosis, the feature of catalytic signal amplification provided by nanozymes makes them function as emerging labels for the detection of biomarkers and diseases, with rapid developments observed in recent years. To provide a comprehensive overview of recent progress made in this dynamic field, here an overview of biomedical diagnosis enabled by nanozymes is provided. This review first summarizes the synthesis of nanozyme materials and then discusses the main strategies applied to enhance their catalytic activity and specificity. Subsequently, representative utilization of nanozymes combined with biological elements in disease diagnosis is reviewed, including the detection of biomarkers related to metabolic, cardiovascular, nervous, and digestive diseases as well as cancers. Finally, some development trends in nanozyme-enabled biomedical diagnosis are highlighted, and corresponding challenges are also pointed out, aiming to inspire future efforts to further advance this promising field.

Engineering the next-generation synthetic cell factory driven by protein engineering

Synthetic cell factory offers substantial advantages in economically efficient production of biofuels, chemicals, and pharmaceutical compounds. However, to create a high-performance synthetic cell factory, precise regulation of cellular material and energy flux is essential. In this context, protein components including enzymes, transcription factor-based biosensors and transporters play pivotal roles. Protein engineering aims to create novel protein variants with desired properties by modifying or designing protein sequences. This review focuses on summarizing the latest advancements of protein engineering in optimizing various aspects of synthetic cell factory, including: enhancing enzyme activity to eliminate production bottlenecks, altering enzyme selectivity to steer metabolic pathways towards desired products, modifying enzyme promiscuity to explore innovative routes, and improving the efficiency of transporters. Furthermore, the utilization of protein engineering to modify protein-based biosensors accelerates evolutionary process and optimizes the regulation of metabolic pathways. The remaining challenges and future opportunities in this field are also discussed.

Enzyme engineering for functional lipids synthesis: recent advance and perspective

Functional lipids, primarily derived through the modification of natural lipids by various processes, are widely acknowledged for their potential to impart health benefits. In contrast to chemical methods for lipid modification, enzymatic catalysis offers distinct advantages, including high selectivity, mild operating conditions, and reduced byproduct formation. Nevertheless, enzymes face challenges in industrial applications, such as low activity, stability, and undesired selectivity. To address these challenges, protein engineering techniques have been implemented to enhance enzyme performance in functional lipid synthesis. This article aims to review recent advances in protein engineering, encompassing approaches from directed evolution to rational design, with the goal of improving the properties of lipid-modifying enzymes. Furthermore, the article explores the future prospects and challenges associated with enzyme-catalyzed functional lipid synthesis.

UniKP: a unified framework for the prediction of enzyme kinetic parameters

Prediction of enzyme kinetic parameters is essential for designing and optimizing enzymes for various biotechnological and industrial applications, but the limited performance of current prediction tools on diverse tasks hinders their practical applications. Here, we introduce UniKP, a unified framework based on pretrained language models for the prediction of enzyme kinetic parameters, including enzyme turnover number (kcat), Michaelis constant (Km), and catalytic efficiency (kcat / Km), from protein sequences and substrate structures. A two-layer framework derived from UniKP (EF-UniKP) has also been proposed to allow robust kcat prediction in considering environmental factors, including pH and temperature. In addition, four representative re-weighting methods are systematically explored to successfully reduce the prediction error in high-value prediction tasks. We have demonstrated the application of UniKP and EF-UniKP in several enzyme discovery and directed evolution tasks, leading to the identification of new enzymes and enzyme mutants with higher activity. UniKP is a valuable tool for deciphering the mechanisms of enzyme kinetics and enables novel insights into enzyme engineering and their industrial applications.

Characterization and designing of an SAM riboswitch to establish a high-throughput screening platform for SAM overproduction in Saccharomyces cerevisiae

S-adenosyl- l-methionine (SAM) is a high-value compound widely used in the treatment of various diseases. SAM can be produced through fermentation, but further enhancing the microbial production of SAM requires novel high-throughput screening methods for rapid detection and screening of mutant libraries. In this work, an SAM-OFF riboswitch capable of responding to the SAM concentration was obtained and a high-throughput platform for screening SAM overproducers was established. SAM synthase was engineered by semirational design and directed evolution, which resulted in the SAM2S203F,W164R,T251S,Y285F,S365R mutant with almost twice higher catalytic activity than the parental enzyme. The best mutant was then introduced into Saccharomyces cerevisiae BY4741, and the resulting strain BSM8 produced a sevenfold higher SAM titer in shake-flask fermentation, reaching 1.25 g L-1 . This work provides a reference for designing biosensors to dynamically detect metabolite concentrations for high-throughput screening and the construction of effective microbial cell factories.

Exploring the Protein Sequence Space with Global Generative Models

Recent advancements in specialized large-scale architectures for training images and language have profoundly impacted the field of computer vision and natural language processing (NLP). Language models, such as the recent ChatGPT and GPT-4, have demonstrated exceptional capabilities in processing, translating, and generating human language. These breakthroughs have also been reflected in protein research, leading to the rapid development of numerous new methods in a short time, with unprecedented performance. Several of these models have been developed with the goal of generating sequences in novel regions of the protein space. In this work, we provide an overview of the use of protein generative models, reviewing (1) language models for the design of novel artificial proteins, (2) works that use non-transformer architectures, and (3) applications in directed evolution approaches.

Enzyme Engineering Strategies for the Bioenhancement of L-Asparaginase Used as a Biopharmaceutical

Over the past few years, there has been a surge in the industrial production of recombinant enzymes from microorganisms due to their catalytic characteristics being highly efficient, selective, and biocompatible. L-asparaginase (L-ASNase) is an enzyme belonging to the class of amidohydrolases that catalyzes the hydrolysis of L-asparagine into L-aspartic acid and ammonia. It has been widely investigated as a biologic agent for its antineoplastic properties in treating acute lymphoblastic leukemia. The demand for L-ASNase is mainly met by the production of recombinant type II L-ASNase from Escherichia coli and Erwinia chrysanthemi. However, the presence of immunogenic proteins in L-ASNase sourced from prokaryotes has been known to result in adverse reactions in patients undergoing treatment. As a result, efforts are being made to explore strategies that can help mitigate the immunogenicity of the drug. This review gives an overview of recent biotechnological breakthroughs in enzyme engineering techniques and technologies used to improve anti-leukemic L-ASNase, taking into account the pharmacological importance of L-ASNase.

Modernized Machine Learning Approach to Illuminate Enzyme Immobilization for Biocatalysis

Biocatalysis is an established technology with significant application in the pharmaceutical industry. Immobilization of enzymes offers significant benefits for commercial and practical purposes to enhance the stability and recyclability of biocatalysts. Determination of the spatial and chemical distributions of immobilized enzymes on solid support materials is essential for an optimal catalytic performance. However, current analytical methodologies often fall short of rapidly identifying and characterizing immobilized enzyme systems. Herein, we present a new analytical methodology that combines non-negative matrix factorization (NMF)-an unsupervised machine learning tool-with Raman hyperspectral imaging to simultaneously resolve the spatial and spectral characteristics of all individual species involved in enzyme immobilization. Our novel approach facilitates the determination of the optimal NMF model using new data-driven, quantitative selection criteria that fully resolve all chemical species present, offering a robust methodology for analyzing immobilized enzymes. Specifically, we demonstrate the ability of NMF with Raman hyperspectral imaging to resolve the spatial and spectral profiles of an engineered pantothenate kinase immobilized on two different commercial microporous resins. Our results demonstrate that this approach can accurately identify and spatially resolve all species within this enzyme immobilization process. To the best of our knowledge, this is the first report of NMF within hyperspectral imaging for enzyme immobilization analysis, and as such, our methodology can now provide a new powerful tool to streamline biocatalytic process development within the pharmaceutical industry.

Bicyclo[3.2.0]carbocyclic Molecules and Redox Biotransformations: The Evolution of Closed-Loop Artificial Linear Biocatalytic Cascades and Related Redox-Neutral Systems

The role of cofactor recycling in determining the efficiency of artificial biocatalytic cascades has become paramount in recent years. Closed-loop cofactor recycling, which initially emerged in the 1990s, has made a valuable contribution to the development of this aspect of biotechnology. However, the evolution of redox-neutral closed-loop cofactor recycling has a longer history that has been integrally linked to the enzymology of oxy-functionalised bicyclo[3.2.0]carbocyclic molecule metabolism throughout. This review traces that relevant history from the mid-1960s to current times.

Biosynthesis of Vanillin by Rational Design of Enoyl-CoA Hydratase/Lyase

Vanillin holds significant importance as a flavoring agent in various industries, including food, pharmaceuticals, and cosmetics. The CoA-dependent pathway for the biosynthesis of vanillin from ferulic acid involved feruloyl-CoA synthase (Fcs) and enoyl-CoA hydratase/lyase (Ech). In this research, the Fcs and Ech were derived from Streptomyces sp. strain V-1. The sequence conservation and structural features of Ech were analyzed by computational techniques including sequence alignment and molecular dynamics simulation. After detailed study for the major binding modes and key amino acid residues between Ech and substrates, a series of mutations (F74W, A130G, A130G/T132S, R147Q, Q255R, ΔT90, ΔTGPEIL, ΔN1-11, ΔC260-287) were obtained by rational design. Finally, the yield of vanillin produced by these mutants was verified by whole-cell catalysis. The results indicated that three mutants, F74W, Q147R, and ΔN1-11, showed higher yields than wild-type Ech. Molecular dynamics simulations and residue energy decomposition identified the basic residues K37, R38, K561, and R564 as the key residues affecting the free energy of binding between Ech and feruloyl-coenzyme A (FCA). The large changes in electrostatic interacting and polar solvating energies caused by the mutations may lead to decreased enzyme activity. This study provides important theoretical guidance as well as experimental data for the biosynthetic pathway of vanillin.

Mechanistic constraints on the trade-off between photosynthesis and respiration in response to warming

Phytoplankton are responsible for half of all oxygen production and drive the ocean carbon cycle. Metabolic theory predicts that increasing global temperatures will cause phytoplankton to become more heterotrophic and smaller. Here, we uncover the metabolic trade-offs between cellular space, energy, and stress management driving phytoplankton thermal acclimation and how these might be overcome through evolutionary adaptation. We show that the observed relationships between traits such as chlorophyll, lipid content, C:N, and size can be predicted on the basis of the metabolic demands of the cell, the thermal dependency of transporters, and changes in membrane lipids. We suggest that many of the observed relationships are not fixed physiological constraints but rather can be altered through adaptation. For example, the evolution of lipid metabolism can favor larger cells with higher lipid content to mitigate oxidative stress. These results have implications for rates of carbon sequestration and export in a warmer ocean.

Targeted In Vivo Mutagenesis in Yeast Using CRISPR/Cas9 and Hyperactive Cytidine and Adenine Deaminases

Directed evolution is a preferred strategy to improve the function of proteins such as enzymes that act as bottlenecks in metabolic pathways. Common directed evolution approaches rely on error-prone PCR-based libraries where the number of possible variants is usually limited by cellular transformation efficiencies. Targeted in vivo mutagenesis can advance directed evolution approaches and help to overcome limitations in library generation. In the current study, we aimed to develop a high-efficiency time-controllable targeted mutagenesis toolkit in the yeast Saccharomyces cerevisiae by employing the CRISPR/Cas9 technology. To that end, we fused the dCas9 protein with hyperactive variants of adenine and cytidine deaminases aiming to create an inducible CRISPR-based mutagenesis tool targeting a specific DNA sequence in vivo with extended editing windows and high mutagenesis efficiency. We also investigated the effect of guide RNA multiplexing on the mutagenesis efficiency both phenotypically and on the DNA level.

Recent advances in enzymatic biofuel cells enabled by innovative materials and techniques

The past few decades have seen increasingly rapid advances in the field of sustainable energy technologies. As a new bio- and eco-friendly energy source, enzymatic biofuel cells (EBFCs) have garnered significant research interest due to their capacity to power implantable bioelectronics, portable devices, and biosensors by utilizing biomass as fuel under mild circumstances. Nonetheless, numerous obstacles impeded the commercialization of EBFCs, including their relatively modest power output and poor long-term stability of enzymes. To depict the current progress of EBFC and address the challenges it faces, this review traces back the evolution of EBFC and focuses on contemporary advances such as newly emerged multi or single enzyme systems, various porous framework-enzyme composites techniques, and innovative applications. Besides emphasizing current achievements in this field, from our perspective part we also introduced novel electrode and cell design for highly effective EBFC fabrication. We believe this review will assist readers in comprehending the basic research and applications of EBFCs as well as potentially spark interdisciplinary collaboration for addressing the pressing issues in this field.

Mutational Paths with Sequence-Based Models of Proteins: From Sampling to Mean-Field Characterization

Identifying and characterizing mutational paths is an important issue in evolutionary biology, with potential applications to bioengineering. We here propose an algorithm to sample mutational paths, which we benchmark on exactly solvable models of proteins in silico, and apply to data-driven models of natural proteins learned from sequence data with restricted Boltzmann machines. We then use mean-field theory to characterize paths for different mutational dynamics of interest, and to extend Kimura's estimate of evolutionary distances to sequence-based epistatic models of selection.

Microbial β-glucanases: production, properties, and engineering

Lignocellulosic biomass, which mainly consists of cellulose and hemicellulose, is the most abundant renewable biopolymer on earth. β-Glucanases are glycoside hydrolases (GHs) that hydrolyze β-glucan, one of the dominant components of the plant cell wall, into cello-oligosaccharides and glucose. Among them, endo-β-1,4-glucanase (EC 3.2.1.4), exo-glucanase/cellobiohydrolase (EC 3.2.1.91), and β-glucosidase (EC 3.2.1.21) play critical roles in the digestion of glucan-like substrates. β-Glucanases have attracted considerable interest within the scientific community due to their applications in the feed, food, and textile industries. In the past decade, there has been considerable progress in the discovery, production, and characterization of novel β-glucanases. Advances in the development of next-generation sequencing techniques, including metagenomics and metatranscriptomics, have unveiled novel β-glucanases isolated from the gastrointestinal microbiota. The study of β-glucanases is beneficial for research and development of commercial products. In this study, we review the classification, properties, and engineering of β-glucanases.

The community-function landscape of microbial consortia

Quantitatively linking the composition and function of microbial communities is a major aspiration of microbial ecology. Microbial community functions emerge from a complex web of molecular interactions between cells, which give rise to population-level interactions among strains and species. Incorporating this complexity into predictive models is highly challenging. Inspired by a similar problem in genetics of predicting quantitative phenotypes from genotypes, an ecological community-function (or structure-function) landscape could be defined that maps community composition and function. In this piece, we present an overview of our current understanding of these community landscapes, their uses, limitations, and open questions. We argue that exploiting the parallels between both landscapes could bring powerful predictive methodologies from evolution and genetics into ecology, providing a boost to our ability to engineer and optimize microbial consortia.

Identification of a S-(2-succino)cysteine breakdown pathway that uses a novel S-(2-succino) lyase

Succination is the spontaneous reaction between the respiratory intermediate fumarate and cellular thiols that forms stable S-(2-succino)-adducts such as S-(2-succino)cysteine (2SC). 2SC is a biomarker for conditions associated with elevated fumarate levels, including diabetes, obesity, and certain cancers, and succination likely contributes to disease progression. Bacillus subtilis has a yxe operon-encoded breakdown pathway for 2SC that involves three distinct enzymatic conversions. The first step is N-acetylation of 2SC by YxeL to form N-acetyl-2SC (2SNAC). YxeK catalyzes the oxygenation of 2SNAC, resulting in its breakdown to oxaloacetate and N-acetylcysteine, which is deacetylated by YxeP to give cysteine. The monooxygenase YxeK is key to the pathway but is rare, with close homologs occurring infrequently in prokaryote and fungal genomes. The existence of additional 2SC breakdown pathways was not known prior to this study. Here, we used comparative genomics to identify a S-(2-succino) lyase (2SL) that replaces yxeK in some yxe gene clusters. 2SL genes from Enterococcus italicus and Dickeya dadantii complement B. subtilis yxeK mutants. We also determined that recombinant 2SL enzymes efficiently break down 2SNAC into fumarate and N-acetylcysteine, can perform the reverse reaction, and have minor activity against 2SC and other small molecule thiols. The strong preferences both YxeK and 2SL enzymes have for 2SNAC indicate that 2SC acetylation is a conserved breakdown step. The identification of a second naturally occurring 2SC breakdown pathway underscores the importance of 2SC catabolism and defines a general strategy for 2SC breakdown involving acetylation, breakdown, and deacetylation.

Engineering functional thermostable proteins using ancestral sequence reconstruction

Natural proteins are often only slightly more stable in the native state than the denatured state, and an increase in environmental temperature can easily shift the balance toward unfolding. Therefore, the engineering of proteins to improve protein stability is an area of intensive research. Thermostable proteins are required to withstand industrial process conditions, for increased shelf-life of protein therapeutics, for developing robust 'biobricks' for synthetic biology applications, and for research purposes (e.g., structure determination). In addition, thermostability buffers the often destabilizing effects of mutations introduced to improve other properties. Rational design approaches to engineering thermostability require structural information, but even with advanced computational methods, it is challenging to predict or parameterize all the relevant structural factors with sufficient precision to anticipate the results of a given mutation. Directed evolution is an alternative when structures are unavailable but requires extensive screening of mutant libraries. Recently, however, bioinspired approaches based on phylogenetic analyses have shown great promise. Leveraging the rapid expansion in sequence data and bioinformatic tools, ancestral sequence reconstruction can generate highly stable folds for novel applications in industrial chemistry, medicine, and synthetic biology. This review provides an overview of the factors important for successful inference of thermostable proteins by ancestral sequence reconstruction and what it can reveal about the determinants of stability in proteins.

Synthetic Biology: Bottom-Up Assembly of Molecular Systems

The bottom-up assembly of biological and chemical components opens exciting opportunities to engineer artificial vesicular systems for applications with previously unmet requirements. The modular combination of scaffolds and functional building blocks enables the engineering of complex systems with biomimetic or new-to-nature functionalities. Inspired by the compartmentalized organization of cells and organelles, lipid or polymer vesicles are widely used as model membrane systems to investigate the translocation of solutes and the transduction of signals by membrane proteins. The bottom-up assembly and functionalization of such artificial compartments enables full control over their composition and can thus provide specifically optimized environments for synthetic biological processes. This review aims to inspire future endeavors by providing a diverse toolbox of molecular modules, engineering methodologies, and different approaches to assemble artificial vesicular systems. Important technical and practical aspects are addressed and selected applications are presented, highlighting particular achievements and limitations of the bottom-up approach. Complementing the cutting-edge technological achievements, fundamental aspects are also discussed to cater to the inherently diverse background of the target audience, which results from the interdisciplinary nature of synthetic biology. The engineering of proteins as functional modules and the use of lipids and block copolymers as scaffold modules for the assembly of functionalized vesicular systems are explored in detail. Particular emphasis is placed on ensuring the controlled assembly of these components into increasingly complex vesicular systems. Finally, all descriptions are presented in the greater context of engineering valuable synthetic biological systems for applications in biocatalysis, biosensing, bioremediation, or targeted drug delivery.

Rational Engineering of 3α-Hydroxysteroid Dehydrogenase/Carbonyl Reductase for a Biomimetic Nicotinamide Mononucleotide Cofactor

Enzymes are powerful biological catalysts for natural substrates but they have low catalytic efficiency for non-natural substrates. Protein engineering can be used to optimize enzymes for catalysis and stability. 3α-Hydroxysteroid dehydrogenase/carbonyl reductase (3α-HSD/CR) catalyzes the oxidoreduction reaction of NAD+ with androsterone. Based on the structure and catalytic mechanism, we mutated the residues of T11, I13, D41, A70, and I112 and they interacted with different portions of NAD+ to switch cofactor specificity to biomimetic cofactor nicotinamide mononucleotide (NMN+). Compared to wild-type 3α-HSD/CR, the catalytic efficiency of these mutants for NAD+ decreased significantly except for the T11 mutants but changed slightly for NMN+ except for the A70K mutant. The A70K mutant increased the catalytic efficiency for NMN+ by 8.7-fold, concomitant with a significant decrease in NAD+ by 1.4 × 104-fold, resulting in 9.6 × 104-fold cofactor specificity switch toward NMN+ over NAD+. Meanwhile, the I112K variant increased the thermal stability and changed to a three-state transition from a two-state transition of thermal unfolding of wild-type 3α-HSD/CR by differential scanning fluorimetry. Molecular docking analysis indicated that mutations on these residues affect the position and conformation of the docked NAD+ and NMN+, thereby affecting their activity. A70K variant sterically blocks the binding with NAD+, restores the H-bonding interactions of catalytic residues of Y155 and K159 with NMN+, and enhances the catalytic efficiency for NMN+.

Computational design of an amidase by combining the best electrostatic features of two promiscuous hydrolases

While there has been emerging interest in designing new enzymes to solve practical challenges, computer-based options to redesign catalytically active proteins are rather limited. Here, a rational QM/MM molecular dynamics strategy based on combining the best electrostatic properties of enzymes with activity in a common reaction is presented. The computational protocol has been applied to the re-design of the protein scaffold of an existing promiscuous esterase from Bacillus subtilis Bs2 to enhance its secondary amidase activity. After the alignment of Bs2 with a non-homologous amidase Candida antarctica lipase B (CALB) within rotation quaternions, a relevant spatial aspartate residue of the latter was transferred to the former as a means to favor the electrostatics of transition state formation, where a clear separation of charges takes place. Deep computational insights, however, revealed a significant conformational change caused by the amino acid replacement, provoking a shift in the pK a of the inserted aspartate and counteracting the anticipated catalytic effect. This prediction was experimentally confirmed with a 1.3-fold increase in activity. The good agreement between theoretical and experimental results, as well as the linear correlation between the electrostatic properties and the activation energy barriers, suggest that the presented computational-based investigation can transform in an enzyme engineering approach.

Combining chemistry and protein engineering for new-to-nature biocatalysis

Biocatalysis, the application of enzymes to solve synthetic problems of human import, has blossomed into a powerful technology for chemical innovation. In the past decade, a threefold partnership, where nature provides blueprints for enzymatic catalysis, chemists introduce innovative activity modes with abiological substrates, and protein engineers develop new tools and algorithms to tune and improve enzymatic function, has unveiled the frontier of new-to-nature enzyme catalysis. In this perspective, we highlight examples of interdisciplinary studies which have helped to expand the scope of biocatalysis, including concepts of enzymatic versatility explored through the lens of biomimicry, to achieve both activities and selectivities that are not currently possible with chemocatalysis. We indicate how modern tools, such as directed evolution, computational protein design and machine learning-based protein engineering methods, have already impacted and will continue to influence enzyme engineering for new abiological transformations. A sustained collaborative effort across disciplines is anticipated to spur further advances in biocatalysis in the coming years.

Droplet Microfluidics and Directed Evolution of Enzymes: An Intertwined Journey

Evolution is essential to the generation of complexity and ultimately life. It relies on the propagation of the properties, traits, and characteristics that allow an organism to survive in a challenging environment. It is evolution that shaped our world over about four billion years by slow and iterative adaptation. While natural evolution based on selection is slow and gradual, directed evolution allows the fast and streamlined optimization of a phenotype under selective conditions. The potential of directed evolution for the discovery and optimization of enzymes is mostly limited by the throughput of the tools and methods available for screening. Over the past twenty years, versatile tools based on droplet microfluidics have been developed to address the need for higher throughput. In this Review, we provide a chronological overview of the intertwined development of microfluidics droplet-based compartmentalization methods and in vivo directed evolution of enzymes.

Structure-Functional Analysis of Human Cytochrome P450 2C8 Using Directed Evolution

The human genome includes four cytochrome P450 2C subfamily enzymes, and CYP2C8 has generated research interest because it is subject to drug-drug interactions and various polymorphic outcomes. To address the structure-functional complexity of CYP2C8, its catalytic activity was studied using a directed evolution analysis. Consecutive rounds of random mutagenesis and screening using 6-methoxy-luciferin produced two mutants, which displayed highly increased luciferase activity. Wild-type and selected mutants were expressed on a large scale and purified. The expression levels of the D349Y and D349Y/V237A mutants were ~310 and 460 nmol per liter of culture, respectively. The steady-state kinetic analysis of paclitaxel 6α-hydroxylation showed that the mutants exhibited a 5-7-fold increase in k_cat values and a 3-5-fold increase in catalytic efficiencies (k_cat/K_M). In arachidonic acid epoxidation, two mutants exhibited a 30-150-fold increase in k_cat values and a 40-110-fold increase in catalytic efficiencies. The binding titration analyses of paclitaxel and arachidonic acid showed that the V237A mutation had a lower K_d value, indicating a tighter substrate-binding affinity. The structural analysis of CYP2C8 indicated that the D349Y mutation was close enough to the putative binding domain of the redox partner; the increase in catalytic activity could be partially attributed to the enhancement of the P450 coupling efficiency or electron transfer.

New Trends in Bioremediation Technologies Toward Environment-Friendly Society: A Mini-Review

Today's environmental balance has been compromised by the unreasonable and sometimes dangerous actions committed by humans to maintain their dominance over the Earth's natural resources. As a result, oceans are contaminated by the different types of plastic trash, crude oil coming from mismanagement of transporting ships spilling it in the water, and air pollution due to increasing production of greenhouse gases, such as CO₂ and CH₄ etc., into the atmosphere. The lands, agricultural fields, and groundwater are also contaminated by the infamous chemicals viz., polycyclic aromatic hydrocarbons, pyrethroids pesticides, bisphenol-A, and dioxanes. Therefore, bioremediation might function as a convenient alternative to restore a clean environment. However, at present, the majority of bioremediation reports are limited to the natural capabilities of microbial enzymes. Synthetic biology with uncompromised supervision of ethical standards could help to outsmart nature's engineering, such as the CETCH cycle for improved CO₂ fixation. Additionally, a blend of synthetic biology with machine learning algorithms could expand the possibilities of bioengineering. This review summarized current state-of-the-art knowledge of the data-assisted enzyme redesigning to actively promote new research on important enzymes to ameliorate the environment.

A System for the Evolution of Protein-Protein Interaction Inducers

Molecules that induce interactions between proteins, often referred to as "molecular glues", are increasingly recognized as important therapeutic modalities and as entry points for rewiring cellular signaling networks. Here, we report a new PACE-based method to rapidly select and evolve molecules that mediate interactions between otherwise noninteracting proteins: rapid evolution of protein-protein interaction glues (rePPI-G). By leveraging proximity-dependent split RNA polymerase-based biosensors, we developed E. coli-based detection and selection systems that drive gene expression outputs only when interactions between target proteins are induced. We then validated the system using engineered bivalent molecular glues, showing that rePPI-G robustly selects for molecules that induce the target interaction. Proof-of-concept evolutions demonstrated that rePPI-G reduces the "hook effect" of the engineered molecular glues, due at least in part to tuning the interaction affinities of each individual component of the bifunctional molecule. Altogether, this work validates rePPI-G as a continuous, phage-based evolutionary technology for optimizing molecular glues, providing a strategy for developing molecules that reprogram protein-protein interactions.

Metal-Organic Frameworks as a Versatile Materials Platform for Unlocking New Potentials in Biocatalysis

Biocatalysts immobilization with nanomaterials has promoted the development of biocatalysis significantly and made it an indispensable part of catalysis industries nowadays. Metal-organic frameworks (MOFs), constructed from organic linkers and metal ions or clusters, have raised significant interests for biocatalysts immobilization in recent years. The diversity of building units, molecular-scale tunability, and modular synthetic routes of MOFs greatly expand its ability as the host to integrate with biocatalysts. In this review, the general synthetic strategies of MOFs with biocatalysts are first summarized. Then, the recent progress of MOFs as a versatile host for a series of biocatalysts, including natural enzymes, nanozymes, and organism-based biocatalysts, followed by the introduction of MOFs themselves as biocatalysts, is discussed. Furthermore, the stimuli-responsive properties of MOFs themselves or the additional functionalization of protein, polymer, and peptide within/on MOF that enable the biocatalysts with the controllable and tunable behavior are also summarized, which could unlock new potentials in biocatalysis. Finally, a perspective of the upcoming challenges, potential impacts, and future directions of biocatalytic MOFs is provided.

New insights into the degradation of synthetic pollutants in contaminated environments

The environment is contaminated by synthetic contaminants owing to their extensive applications globally. Hence, the removal of synthetic pollutants (SPs) from the environment has received widespread attention. Different remediation technologies have been investigated for their abilities to eliminate SPs from the ecosystem; these include photocatalysis, sonochemical techniques, nanoremediation, and bioremediation. SPs, which can be organic or inorganic, can be degraded by microbial metabolism at contaminated sites. Owing to their diverse metabolisms, microbes can adapt to a wide variety of environments. Several microbial strains have been reported for their bioremediation potential concerning synthetic chemical compounds. The selection of potential strains for large-scale removal of organic pollutants is an important research priority. Additionally, novel microbial consortia have been found to be capable of efficient degradation owing to their combined and co-metabolic activities. Microbial engineering is one of the most prominent and promising techniques for providing new opportunities to develop proficient microorganisms for various biological processes; here, we have targeted the SP-degrading mechanisms of microorganisms. This review provides an in-depth discussion of microbial engineering techniques that are used to enhance the removal of both organic and inorganic pollutants from different contaminated environments and under different conditions. The degradation of these pollutants is investigated using abiotic and biotic approaches; interestingly, biotic approaches based on microbial methods are preferable owing to their high potential for pollutant removal and cost-effectiveness.

Enhancing the stability of Geobacillus zalihae T1 lipase in organic solvents and insights into the structural stability of its variants

Critical to the applications of proteins in non-aqueous enzymatic processes is their structural dynamics in relation to solvent polarity. A pool of mutants derived from Geobacillus zalihae T1 lipase was screened in organic solvents (methanol, ethanol, propanol, butanol and pentanol) resulting in the selection of six mutants at initial screening (A83D/K251E, R21C, G35D/S195 N, K84R/R103C/M121I/T272 M and R106H/G327S). Site-directed mutagenesis further yielded quadruple mutants A83D/M121I/K251E/G327S and A83D/M121I/S195 N/T272 M, both of which had improved activity after incubation in methanol. The k_m and k_cat values of these mutants vary marginally with the wild-type enzyme in the methanol/substrate mixture. Thermally induced unfolding of mutants was accompanied with some loss of secondary structure content. The root mean square deviations (RMSD) and B-factors revealed that changes in the structural organization are intertwined with an interplay of the protein backbone with organic solvents. Spatially exposed charged residues showed correlations between the solvation dynamics of the methanol solvent and the hydrophobicity of the residues. The short distances of the radial distribution function provided the required distances for hydrogen bond formation and hydrophobic interactions. These dynamic changes demonstrate newly formed structural interactions could be targeted and incorporated experimentally on the basis of solvent mobility and mutant residues.

Developing a cell-bound detection system for the screening of oxidase activity using the fluorescent peroxide sensor roGFP2-Orp1

Accurate yet efficient high-throughput screenings have emerged as essential technology for enzyme engineering via directed evolution. Modern high-throughput screening platforms for oxidoreductases are commonly assisted by technologies such as surface display and rely on emulsification techniques to facilitate single-cell analysis via fluorescence-activated cell sorting. Empowered by the dramatically increased throughput, the screening of significantly larger sequence spaces in acceptable time frames is achieved but usually comes at the cost of restricted applicability. In this work, we tackle this problem by utilizing roGFP2-Orp1 as a fluorescent one-component detection system for enzymatic H2O2 formation. We determined the kinetic parameters of the roGFP2-Orp1 reaction with H2O2 and established an efficient immobilization technique for the sensor on Saccharomyces cerevisiae cells employing the lectin Concanavalin A. This allowed to realize a peroxide-sensing shell on enzyme-displaying cells, a system that was successfully employed to screen for H2O2 formation of enzyme variants in a whole-cell setting.

High-Throughput Screening for Substrate Specificity-Adapted Mutants of the Nisin Dehydratase NisB

Microbial lanthipeptides are formed by a two-step enzymatic introduction of (methyl)lanthionine rings. A dehydratase catalyzes the dehydration of serine and threonine residues, yielding dehydroalanine and dehydrobutyrine, respectively. Cyclase-catalyzed coupling of the formed dehydroresidues to cysteines forms (methyl)lanthionine rings in a peptide. Lanthipeptide biosynthetic systems allow discovery of target-specific, lanthionine-stabilized therapeutic peptides. However, the substrate specificity of existing modification enzymes impose limitations on installing lanthionines in non-natural substrates. The goal of the present study was to obtain a lanthipeptide dehydratase with the capacity to dehydrate substrates that are unsuitable for the nisin dehydratase NisB. We report high-throughput screening for tailored specificity of intracellular, genetically encoded NisB dehydratases. The principle is based on the screening of bacterially displayed lanthionine-constrained streptavidin ligands, which have a much higher affinity for streptavidin than linear ligands. The designed NisC-cyclizable high-affinity ligands can be formed via mutant NisB-catalyzed dehydration but less effectively via wild-type NisB activity. In Lactococcus lactis, a cell surface display precursor was designed comprising DSHPQFC. The Asp residue preceding the serine in this sequence disfavors its dehydration by wild-type NisB. The cell surface display vector was coexpressed with a mutant NisB library and NisTC. Subsequently, mutant NisB-containing bacteria that display cyclized strep ligands on the cell surface were selected via panning rounds with streptavidin-coupled magnetic beads. In this way, a NisB variant with a tailored capacity of dehydration was obtained, which was further evaluated with respect to its capacity to dehydrate nisin mutants. These results demonstrate a powerful method for selecting lanthipeptide modification enzymes with adapted substrate specificity.

How Do Cells Adapt? Stories Told in Landscapes

Cells adapt to changing environments. Perturb a cell and it returns to a point of homeostasis. Perturb a population and it evolves toward a fitness peak. We review quantitative models of the forces of adaptation and their visualizations on landscapes. While some adaptations result from single mutations or few-gene effects, others are more cooperative, more delocalized in the genome, and more universal and physical. For example, homeostasis and evolution depend on protein folding and aggregation, energy and protein production, protein diffusion, molecular motor speeds and efficiencies, and protein expression levels. Models provide a way to learn about the fitness of cells and cell populations by making and testing hypotheses.

Microfluidic Pneumatic Printed Sandwiched Microdroplet Array for High-Throughput Enzymatic Reaction and Screening

High-throughput enzyme screening for desired functionality is highly demanded. This paper utilizes a newly developed microfluidic pneumatic printing platform for high-throughput enzyme screening applications. The novel printing platform can achieve distinct features including a disposable cartridge, which avoids crosstalk; a flexible cartridge design, allowing for integration of multiple channels; and fast printing speed with submicroliter spot size. Moreover, a polydimethylsiloxane (PDMS)-based sandwich structure has been proposed and used during the printing and imaging, which can lead to better results, including reduced evaporation as well as a uniform light path during imaging. Using this microfluidic pneumatic printed PDMS sandwiched microdroplet array platform, we have demonstrated the capability of high-throughput generation of a combinatorial droplet array with concentration and volume gradients. Furthermore, the potential for enzymatic study has been validated by quantified cellulose reaction implemented with the printing platform.

Structure-based directed evolution improves S. cerevisiae growth on xylose by influencing in vivo enzyme performance

BACKGROUND

Efficient bioethanol production from hemicellulose feedstocks by Saccharomyces cerevisiae requires xylose utilization. Whereas S. cerevisiae does not metabolize xylose, engineered strains that express xylose isomerase can metabolize xylose by converting it to xylulose. For this, the type II xylose isomerase from Piromyces (PirXI) is used but the in vivo activity is rather low and very high levels of the enzyme are needed for xylose metabolism. In this study, we explore the use of protein engineering and in vivo selection to improve the performance of PirXI. Recently solved crystal structures were used to focus mutagenesis efforts.

RESULTS

We constructed focused mutant libraries of Piromyces xylose isomerase by substitution of second shell residues around the substrate- and metal-binding sites. Following library transfer to S. cerevisiae and selection for enhanced xylose-supported growth under aerobic and anaerobic conditions, two novel xylose isomerase mutants were obtained, which were purified and subjected to biochemical and structural analysis. Apart from a small difference in response to metal availability, neither the new mutants nor mutants described earlier showed significant changes in catalytic performance under various in vitro assay conditions. Yet, in vivo performance was clearly improved. The enzymes appeared to function suboptimally in vivo due to enzyme loading with calcium, which gives poor xylose conversion kinetics. The results show that better in vivo enzyme performance is poorly reflected in kinetic parameters for xylose isomerization determined in vitro with a single type of added metal.

CONCLUSION

This study shows that in vivo selection can identify xylose isomerase mutants with only minor changes in catalytic properties measured under standard conditions. Metal loading of xylose isomerase expressed in yeast is suboptimal and strongly influences kinetic properties. Metal uptake, distribution and binding to xylose isomerase are highly relevant for rapid xylose conversion and may be an important target for optimizing yeast xylose metabolism.

Development of an engineered thermostable amine dehydrogenase for the synthesis of structurally diverse chiral amines

Structurally diverse chiral amines and amino alcohols were synthesized using an engineered thermostable amine dehydrogenase, demonstrating its extensive synthesis potential.

[Directed evolution in drug and antibody development : From the Nobel Prize to broad clinical application]

Combinatorial procedures have become established in recent years as alternatives to rational design in drug research, particularly when no structural information is available. This article presents the principle that was originally developed by three scientists and was honored with the Nobel Prize for Chemistry in 2018. Furthermore, the application in the field of monclonal antibodies is discussed.

Sorting by interfacial tension (SIFT): Label-free enzyme sorting using droplet microfluidics

Droplet microfluidics has the ability to greatly increase the throughput of screening and sorting of enzymes by carrying reagents in picoliter droplets flowing in inert oils. It was found with the use of a specific surfactant, the interfacial tension of droplets can be very sensitive to droplet pH. This enables the sorting of droplets of different pH when confined droplets encounter a microfabricated trench. The device can be extended to sort enzymes, as a large number of enzymatic reactions lead to the production of an acidic or basic product and a concurrent change in solution pH. The progress of an enzymatic reaction is tracked from the position of a flowing train of droplets. We demonstrate the sorting of esterase isoenzymes based on their enzymatic activity. This label-free technology, that we dub droplet sorting by interfacial tension (SIFT), requires no active components and would have applications for enzyme sorting in high-throughput applications that include enzyme screening and directed evolution of enzymes.

VEGAS as a Platform for Facile Directed Evolution in Mammalian Cells

Directed evolution, artificial selection toward designed objectives, is routinely used to develop new molecular tools and therapeutics. Successful directed molecular evolution campaigns repeatedly test diverse sequences with a designed selective pressure. Unicellular organisms and their viral pathogens are exceptional for this purpose and have been used for decades. However, many desirable targets of directed evolution perform poorly or unnaturally in unicellular backgrounds. Here, we present a system for facile directed evolution in mammalian cells. Using the RNA alphavirus Sindbis as a vector for heredity and diversity, we achieved 24-h selection cycles surpassing 10^-3 mutations per base. Selection is achieved through genetically actuated sequences internal to the host cell, thus the system's name: viral evolution of genetically actuating sequences, or "VEGAS." Using VEGAS, we evolve transcription factors, GPCRs, and allosteric nanobodies toward functional signaling endpoints each in less than 1 weeks' time.

Structural and catalytic insights into HoLaMa, a derivative of Klenow DNA polymerase lacking the proofreading domain

We report here on the stability and catalytic properties of the HoLaMa DNA polymerase, a Klenow sub-fragment lacking the 3’-5’ exonuclease domain. HoLaMa was overexpressed in Escherichia coli, and the enzyme was purified by means of standard chromatographic techniques. High-resolution NMR experiments revealed that HoLaMa is properly folded at pH 8.0 and 20°C. In addition, urea induced a cooperative folding to unfolding transition of HoLaMa, possessing an overall thermodynamic stability and a transition midpoint featuring ΔG and C_M equal to (15.7 ± 1.9) kJ/mol and (3.5 ± 0.6) M, respectively. When the catalytic performances of HoLaMa were compared to those featured by the Klenow enzyme, we did observe a 10-fold lower catalytic efficiency by the HoLaMa enzyme. Surprisingly, HoLaMa and Klenow DNA polymerases possess markedly different sensitivities in competitive inhibition assays performed to test the effect of single dNTPs.

Ionic liquids and protein folding-old tricks for new solvents

One important aspect of the green chemistry revolution has been the use of ionic liquids as the solvent in liquid-phase enzymatic catalysis. An essential requirement for protein enzyme function is the correct folding of the polypeptide chain into its functional "native" state. Quantitative assessment of protein structure may be carried out either empirically, or by using model-based characterization procedures, in which the parameters are defined in terms of a standard reference state. In this short note, we briefly outline the nature of the parameters associated with different empirical and model-based characterization procedures and point out factors which affect their interpretation when using a base solvent different from water. This review principally describes arguments developed by Wakayama et al., Protein Solubility and Amorphous Aggregation: From Academic Research to Applications in Drug Discovery and Bioindustry, 2019, edited by Y. Kuroda and F. Arisaka; CMC Publishing House. Sections of that work are translated from the original Japanese and republished here with the full permission of CMC Publishing Corporation.

Engineering enzyme catalysis: an inverse approach

Enzymes' inherent chirality confers their exquisite enantiomeric specificity and makes their use as green alternatives to chiral metal complexes or chiral organocatalysts invaluable to the fine chemical industry. The most prevalent way to alter enzyme activity in terms of regioselectivity and stereoselectivity for both industry and fundamental research is to engineer the enzyme. In a recent article by Keinänen et al., published in Bioscience Reports 2018, 'Controlling the regioselectivity and stereoselectivity of FAD-dependent polyamine oxidases with the use of amine-attached guide molecules as conformational modulators', an inverse approach was presented that focuses on the manipulation of the enzyme substrate rather than the enzyme. This approach not only uncovered dormant enantioselectivity in related enzymes but allowed for its control by the use of guide molecules simply added to the reaction solution or covalently linked to an achiral scaffold molecule.