High-throughput directed evolution: a golden era for protein science

Fusion of enzymatic proteins: Enhancing biological activities and facilitating biological modifications

The fusion of enzymatic proteins represents a dynamic frontier in biotechnology and enzymatic engineering. This in-depth review looks at the many different ways that fusion proteins can be used, showing their importance in biosensing, gene therapy, targeted drug delivery, and biocatalysis. Fusion proteins have shown an astounding ability to improve and fine-tune biological functions by combining the most beneficial parts of different enzymes. Our first step is to explain how protein fusion increases biological functions. This will provide a broad picture of how this phenomenon has changed many fields. We dissect the intricate mechanisms through which fusion proteins orchestrate cellular processes, underscoring their potential to revolutionize the landscape of molecular biology. We also explore the complicated world of structural analysis and design strategies, stressing the importance of molecular insights for making the fusion protein approach work better. These insights broaden understanding of the underlying principles and illuminate the path toward unlocking untapped potential. The review also introduces cutting-edge techniques for constructing fusion protein libraries, such as DNA shuffling and phage display. These new methods allow scientists to build a molecular orchestra with an unprecedented level of accuracy, and thus use fusion proteins to their full potential in various situations. In conclusion, we provide a glimpse into the current challenges and prospects in fusion protein research, shedding light on recent advancements that promise to reshape the future of biotechnology. As we make this interesting journey through the field of enzymatic protein combination, it becomes clear that the fusion paradigm is about to start a new era of innovation that will push the limits of what is possible in biology and molecular engineering.

Improving d-Allulose Production via Alditol Oxidase-Directed Evolution Assisted by an Artificial Genetic Circuit

As a good sucrose substitute, d-allulose has been substantially used in the food industry. In a previous work, we developed a d-allulose synthetic pathway and revealed that the alditol oxidase (AldO) is the key rate-limiting node. The laboratory-directed evolution (LDE) has been largely used for improving enzyme performance but is commonly faced with time-consuming screening processes or expensive equipment demand. In this study, we tried to develop an easy approach to obtain higher-performing AldO mutants through continuous LDE. First, we designed and validated a d-allulose-responsive genetic circuit. Next, we tuned its sensitivity and responsive range to make it suitable for growth coupling. Subsequently, the chassis fitness was coupled to d-allulose production by controlling the expression of a tetracycline exporter. Finally, the chassis equipped with the genetic circuit was used to evolve the AldO mutant library. After 3 passages, a mutant M4-15 was picked out, which produced 19.97% more d-allulose. Structural analysis showed a widened entrance of the catalytic pocket. This study achieved the continuous LDE of AldO and provided an easy and simple approach to evolve the d-allulose synthetic pathway. This study also represented a proof of concept example of accelerating the LDE evolution using an artificial genetic circuit.

Deaminase-driven random mutation enables efficient DNA mutagenesis for protein evolution

Protein evolution has emerged as a crucial tool for generating proteins with novel characteristics. A key step in protein evolution is the mutagenesis of protein-coding DNA. Error-prone PCR (epPCR) is a frequently used technique, but its low mutation efficiency often requires multiple rounds of mutagenesis, which can be time-consuming. To address this, we developed a novel DNA mutagenesis strategy termed deaminase-driven random mutation (DRM). DRM utilizes the engineered cytidine deaminase A3A-RL and the engineered adenosine deaminase ABE8e to introduce a broad spectrum of mutations, including C-to-T, G-to-A, A-to-G, and T-to-C, in both the protein-coding strand and the complementary strand. This approach enables the generation of a multitude of DNA mutation types within a single round of mutagenesis, resulting in a higher DNA mutagenic capability than epPCR. The results show that the DRM strategy exhibits a 14.6-fold higher DNA mutation frequency and produces a 27.7-fold greater diversity of mutation types compared to epPCR, enabling a more comprehensive exploration of the genetic landscape. This enhanced mutagenic capability increases the chances of discovering novel and useful mutants. With its ability to produce high-quality DNA products and the superior protein mutant generation capacity, DRM is an attractive tool for researchers seeking to engineer new proteins or improve existing ones.

Computation-aided designs enable developing auxotrophic metabolic sensors for wide-range glyoxylate and glycolate detection

Auxotrophic metabolic sensors (AMS) are microbial strains modified so that biomass formation correlates with the availability of specific metabolites. These sensors are essential for bioengineering (e.g., in growth-coupled designs) but creating them is often a time-consuming and low-throughput process that can be streamlined by in silico analysis. Here, we present a systematic workflow for designing, implementing, and testing versatile AMS based on Escherichia coli. Glyoxylate, a key metabolite in (synthetic) CO2 fixation and carbon-conserving pathways, served as the test analyte. Through iterative screening of a compact metabolic model, we identify non-trivial growth-coupled designs that result in six AMS with a wide sensitivity range for glyoxylate, spanning three orders of magnitude in the detected analyte concentration. We further adapt these E. coli AMS for sensing glycolate and demonstrate their utility in both pathway engineering (testing a key metabolic module for carbon assimilation via glyoxylate) and environmental monitoring (quantifying glycolate produced by photosynthetic microalgae). Adapting this workflow to the sensing of different metabolites could facilitate the design and implementation of AMS for diverse biotechnological applications.

Illuminating anions in biology with genetically encoded fluorescent biosensors

Anions are critical to all life forms. Anions can be absorbed as nutrients or biosynthesized. Anions shape a spectrum of fundamental biological processes at the organismal, cellular, and subcellular scales. Genetically encoded fluorescent biosensors can capture anions in action across time and space dimensions with microscopy. The firsts of such technologies were reported more than 20 years for monoatomic chloride and polyatomic cAMP anions. However, the recent boom of anion biosensors illuminates the unknowns and opportunities that remain for toolmakers and end users to meet across the aisle to spur innovations in biosensor designs and applications for discovery anion biology. In this review, we will canvas progress made over the last three years for biologically relevant anions that are classified as halides, oxyanions, carboxylates, and nucleotides.

Engineering thermostability of industrial enzymes for enhanced application performance

Thermostability is a key factor for the industrial application of enzymes. This review categorizes enzymes by their applications and discusses the importance of engineering thermostability for practical use. It summarizes fundamental theories and recent advancements in enzyme thermostability modification, including directed evolution, semi-rational design, and rational design. Directed evolution uses high-throughput screening to generate random mutations, while semi-rational design combines hotspot identification with screening. Rational design focuses on key residues to enhance stability by improving rigidity, foldability, and reducing aggregation. The review also covers rational strategies like engineering folding energy, surface charge, machine learning methods, and consensus design, along with tools that support these approaches. Practical examples are critically assessed to highlight the benefits and limitations of these strategies. Finally, the challenges and potential contributions of artificial intelligence in enzyme thermostability engineering are discussed.

Development of robust constitutive synthetic promoter using genetic resources of plant pararetroviruses

With the advancement of plant synthetic biology, complex genetic engineering circuits are being developed, which require more diverse genetic regulatory elements (promoters) to operate. Constitutive promoters are widely used for such gene engineering projects, but the list of strong, constitutive plant promoters with strength surpassing the widely used promoter, the CaMV35S, is limited. In this work, we attempted to increase the constitutive promoter library by developing efficient synthetic promoters suitable for high-level gene expression. To do that, we selected three strong pararetroviral-based promoters from Mirabilis mosaic virus (MMV), Figwort mosaic virus (FMV), and Horseradish latent virus (HRLV) and rationally designed and combined their promoter elements. We then tested the newly developed promoters in Nicotiana benthamiana and found a highly active tri-hybrid promoter, MuasFuasH17 (MFH17). We further used these promoter elements in generating random mutant promoters by DNA shuffling techniques in an attempt to change/improve the MFH17 promoter. We further evaluated the activity of the MFH17 promoter in Oryza sativa seedlings and studied the effect of as-1 elements present in it. Finally, we tested the efficacy and tissue specificity of the MFH17 promoter in planta by developing transgenic Nicotiana tabacum and Arabidopsis thaliana plants and found it highly constitutive and efficient in driving the gene throughout the plant tissues. Overall, we conclude that this tripartite synthetic promoter MFH17 is a strong, highly constitutive, and dual-species (dicot and monocot) expressing promoter, which can be a valuable addition to the constitutive plant promoter library for plant synthetic biology.

GRACE: Generative Redesign in Artificial Computational Enzymology

Designing de novo enzymes is complex and challenging, especially to maintain the activity. This research focused on motif design to identify the crucial domain in the enzyme and uncovered the protein structure by molecular docking. Therefore, we developed a Generative Redesign in Artificial Computational Enzymology (GRACE), which is an automated workflow for reformation and creation of the de novo enzymes for the first time. GRACE integrated RFdiffusion for structure generation, ProteinMPNN for sequence interpretation, CLEAN for enzyme classification, and followed by solubility analysis and molecular dynamic simulation. As a result, we selected two gene sequences associated with carbonic anhydrase from among 10,000 protein candidates. Experimental validation confirmed that these two novel enzymes, i.e., dCA12_2 and dCA23_1, exhibited favorable solubility, promising substrate-active site interactions, and achieved activity of 400 WAU/mL. This workflow has the potential to greatly streamline experimental efforts in enzyme engineering and unlock new avenues for rational protein design.

The clinical potential of l-oligonucleotides: challenges and opportunities

Chemically modified nucleotides are central to the development of biostable research tools and oligonucleotide therapeutics. In this context, l-oligonucleotides, the synthetic enantiomer of native d-nucleic acids, hold great promise. As enantiomers, l-oligonucleotides share the same physical and chemical properties as their native counterparts, yet their inverted l-(deoxy)ribose sugars afford them orthogonality towards the stereospecific environment of biology. Notably, l-oligonucleotides are highly resistant to degradation by cellular nucleases, providing them with superior biostability. As a result, l-oligonucleotides are being increasingly utilized for the development of diverse biomedical technologies, including molecular imaging tools, diagnostic biosensors, and aptamer-based therapeutics. Herein, we present recent such examples that highlight the clinical potential of l-oligonucleotides. Additionally, we provide our perspective on the remaining challenges and practical considerations currently associated with the use of l-oligonucleotides and explore potential solutions that will lead to the broader adoption of l-oligonucleotides in clinical applications.

Optimizing a CRISPR-Cas13d Gene Circuit for Tunable Target RNA Downregulation with Minimal Collateral RNA Cutting

The invention of RNA-guided DNA cutting systems has revolutionized biotechnology. More recently, RNA-guided RNA cutting by Cas13d entered the scene as a highly promising alternative to RNA interference to engineer cellular transcriptomes for biotechnological and therapeutic purposes. Unfortunately, "collateral damage" by indiscriminate off-target cutting tampered enthusiasm for these systems. Yet, how collateral activity, or even RNA target reduction depends on Cas13d and guide RNA abundance has remained unclear due to the lack of expression-tuning studies to address this question. Here we use precise expression-tuning gene circuits to show that both nonspecific and specific, on-target RNA reduction depend on Cas13d and guide RNA levels, and that nonspecific RNA cutting from trans cleavage might contribute to on-target RNA reduction. Using RNA-level control techniques, we develop new Multi-Level Optimized Negative-Autoregulated Cas13d and crRNA Hybrid (MONARCH) gene circuits that achieve a high dynamic range with low basal on-target RNA reduction while minimizing collateral activity in human kidney cells and green monkey cells most frequently used in human virology. MONARCH should bring RNA-guided RNA cutting systems to the forefront, as easily applicable, programmable tools for transcriptome engineering in biotechnological and medical applications.

Rational design approach to improve the solubility of the β-sandwich domain 1 of a thermophilic protein

The β-sandwich domain 1 (SD1) of islandisin is a stable thermophilic protein with surface loops that can be redesigned for specific target binding, architecturally comparable to the variable domain of immunoglobulin (IgG). SD1's propensity to aggregate due to incorrect folding and subsequent accumulation in Escherichia coli inclusion bodies limits its use in biotechnological applications. We rationally designed SD1 for improved variants that were expressed in soluble forms in E. coli while maintaining the intrinsic thermal stability of the protein (melting temperature (Tm) = 73). We used FoldX's ΔΔG predictions to find beneficial mutations and aggregation-prone regions (APRs) using Tango. The S26K substitution within protein core residues did not affect protein stability. Among the soluble mutants studied, the S26K/Q91P combination significantly improved the expression and solubility of SD1. We also examined the effects of the surface residue, pH, and concentration on the solubility of SD1. We showed that the surface polarity of proteins had little or no effect on solubility, whereas surface charges played a substantial role. The storage stability of several SD1 variants was impaired at pH values near their isoelectric point, and pH levels resulting in highly charged groups. We observed that mutations that create an uneven distribution of charged groups on the SD1 surface could enhance protein solubility by eliminating favorable protein-protein surface charge interactions. Our findings suggest that SD1 is mutationally tolerant to new functionalities, thus providing a novel perspective for the application of rational design to improve the solubility of targeted proteins.

Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology.

Strangers in a foreign land: 'Yeastizing' plant enzymes

Expressing plant metabolic pathways in microbial platforms is an efficient, cost-effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour-intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost-effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data-driven machine learning framework designed to extract 'yeastizing' rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design-build-test cycle.

Current status and emerging frontiers in enzyme engineering: An industrial perspective

Protein engineering mechanisms can be an efficient approach to enhance the biochemical properties of various biocatalysts. Immobilization of biocatalysts and the introduction of new-to-nature chemical reactivities are also possible through the same mechanism. Discovering new protocols that enhance the catalytic active protein that possesses novelty in terms of being stable, active, and, stereoselectivity with functions could be identified as essential areas in terms of concurrent bioorganic chemistry (synergistic relationship between organic chemistry and biochemistry in the context of enzyme engineering). However, with our current level of knowledge about protein folding and its correlation with protein conformation and activities, it is almost impossible to design proteins with specific biological and physical properties. Hence, contemporary protein engineering typically involves reprogramming existing enzymes by mutagenesis to generate new phenotypes with desired properties. These processes ensure that limitations of naturally occurring enzymes are not encountered. For example, researchers have engineered cellulases and hemicellulases to withstand harsh conditions encountered during biomass pretreatment, such as high temperatures and acidic environments. By enhancing the activity and robustness of these enzymes, biofuel production becomes more economically viable and environmentally sustainable. Recent trends in enzyme engineering have enabled the development of tailored biocatalysts for pharmaceutical applications. For instance, researchers have engineered enzymes such as cytochrome P450s and amine oxidases to catalyze challenging reactions involved in drug synthesis. In addition to conventional methods, there has been an increasing application of machine learning techniques to identify patterns in data. These patterns are then used to predict protein structures, enhance enzyme solubility, stability, and function, forecast substrate specificity, and assist in rational protein design. In this review, we discussed recent trends in enzyme engineering to optimize the biochemical properties of various biocatalysts. Using examples relevant to biotechnology in engineering enzymes, we try to expatiate the significance of enzyme engineering with how these methods could be applied to optimize the biochemical properties of a naturally occurring enzyme.

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.

A Robust Growth-Based Selection Platform to Evolve an Enzyme via Dependency on Noncanonical Tyrosine Analogues

Growth-based selections evaluate the fitness of individual organisms at a population level. In enzyme engineering, such growth selections allow for the rapid and straightforward identification of highly efficient biocatalysts from extensive libraries. However, selection-based improvement of (synthetically useful) biocatalysts is challenging, as they require highly dependable strategies that artificially link their activities to host survival. Here, we showcase a robust and scalable growth-based selection platform centered around the complementation of noncanonical amino acid-dependent bacteria. Specifically, we demonstrate how serial passaging of populations featuring millions of carbamoylase variants autonomously selects biocatalysts with up to 90,000-fold higher initial rates. Notably, selection of replicate populations enriched diverse biocatalysts, which feature distinct amino acid motifs that drastically boost carbamoylase activity. As beneficial substitutions also originated from unintended copying errors during library preparation or cell division, we anticipate that our growth-based selection platform will be applicable to the continuous, autonomous evolution of diverse biocatalysts in the future.

Fluorescence-Based Screens for Engineering Enzymes Linked to Halogenated Tryptophan

Directed evolution is often limited by the throughput of accurate screening methods. Here we demonstrate the feasibility of utilizing a singular transcription factor (TF)-system that can be refactored in two ways (both as an activator and repressor). Specifically, we showcase the use of previously evolved 5-halo- or 6-halo-tryptophan-specific TF biosensors suitable for the detection of a halogenated tryptophan molecule in vivo. We subsequently validate the biosensor's utility for two halogenase-specific halo-tryptophan accumulation screens. First, we isolated 5-tryptophan-halogenase, XsHal, from a mixed pool of halogenases with 100% efficiency. Thereafter, we generated a targeted library of the catalytic residue of 6-tryptophan halogenase, Th-Hal, and isolated functioning halogenases with 100% efficiency. Lastly, we refactor the TF circuit to respond to the depletion of halogenated tryptophan and prototype a high-throughput biosensor-directed evolution scheme to screen for downstream enzyme variants capable of promiscuously converting halogenated tryptophan. Altogether, this work takes a significant step toward the rapid and higher throughput screening of halogenases and halo-tryptophan converting enzymes to further reinforce efforts to enable high-level bioproduction of halogenated chemicals.

Shifting the pH profiles of Staphylococcus epidermidis lipase (SEL) and Staphylococcus hyicus lipase (SHL) through generating chimeric lipases by DNA shuffling strategy

Enzymes are often required to function in a particular reaction condition by the industrial procedure. In order to identify critical residues affecting the optimum pH of Staphylococcal lipases, chimeric lipases from homologous lipases were generated via a DNA shuffling strategy. Chimeric 1 included mutations of G166S, K212E, T243A, H271Y. Chimeric 2 consisted of substitutions of K212E, T243A, H271Y. Chimeric 3 contained substitutions of K212E, R359L. From the screening results, the pH profiles for chimeric 1 and 2 lipases were shifted from pH 7 to 6. While the pH of chimeric 3 was shifted to 8. It seems the mutation of K212E in chimeric 1 and 2 decreased the pH to 6 by changing the electrostatic potential surface. Furthermore, chimeric 3 showed 10 ˚C improvement in the optimum temperature due to the rigidification of the catalytic loop through the hydrophobic interaction network. Moreover, the substrate specificity of chimeric 1 and 2 was increased towards the longer carbon length chains due to the mutation of T243A adjacent to the lid region through increasing the flexibility of the lid. Current study illustrated that directed evolution successfully modified lipase properties including optimum pH, temperature and substrate specificity through mutations, especially near catalytic and lid regions.

Improving plastic degrading enzymes via directed evolution

Plastic degrading enzymes have immense potential for use in industrial applications. Protein engineering efforts over the last decade have resulted in considerable enhancement of many properties of these enzymes. Directed evolution, a protein engineering approach that mimics the natural process of evolution in a laboratory, has been particularly useful in overcoming some of the challenges of structure-based protein engineering. For example, directed evolution has been used to improve the catalytic activity and thermostability of polyethylene terephthalate (PET)-degrading enzymes, although its use for the improvement of other desirable properties, such as solvent tolerance, has been less studied. In this review, we aim to identify some of the knowledge gaps and current challenges, and highlight recent studies related to the directed evolution of plastic-degrading enzymes.

Open-endedness in synthetic biology: A route to continual innovation for biological design

Design in synthetic biology is typically goal oriented, aiming to repurpose or optimize existing biological functions, augmenting biology with new-to-nature capabilities, or creating life-like systems from scratch. While the field has seen many advances, bottlenecks in the complexity of the systems built are emerging and designs that function in the lab often fail when used in real-world contexts. Here, we propose an open-ended approach to biological design, with the novelty of designed biology being at least as important as how well it fulfils its goal. Rather than solely focusing on optimization toward a single best design, designing with novelty in mind may allow us to move beyond the diminishing returns we see in performance for most engineered biology. Research from the artificial life community has demonstrated that embracing novelty can automatically generate innovative and unexpected solutions to challenging problems beyond local optima. Synthetic biology offers the ideal playground to explore more creative approaches to biological design.

CRISPR base editor-based targeted random mutagenesis (BE-TRM) toolbox for directed evolution

Directed evolution (DE) of desired locus by targeted random mutagenesis (TRM) tools is a powerful approach for generating genetic variations with novel or improved functions, particularly in complex genomes. TRM-based DE involves developing a mutant library of targeted DNA sequences and screening the variants for the desired properties. However, DE methods have for a long time been confined to bacteria and yeasts. Lately, CRISPR/Cas and DNA deaminase-based tools that circumvent enduring barriers such as longer life cycle, small library sizes, and low mutation rates have been developed to facilitate DE in native genetic environments of multicellular organisms. Notably, deaminase-based base editing-TRM (BE-TRM) tools have greatly expanded the scope and efficiency of DE schemes by enabling base substitutions and randomization of targeted DNA sequences. BE-TRM tools provide a robust platform for the continuous molecular evolution of desired proteins, metabolic pathway engineering, creation of a mutant library of desired locus to evolve novel functions, and other applications, such as predicting mutants conferring antibiotic resistance. This review provides timely updates on the recent advances in BE-TRM tools for DE, their applications in biology, and future directions for further improvements. [BMB Reports 2024; 57(1): 30-39].

Genetically Encoded Biosensor Engineering for Application in Directed Evolution

Although rational genetic engineering is nowadays the favored method for microbial strain improvement, building up mutant libraries based on directed evolution for improvement is still in many cases the better option. In this regard, the demand for precise and efficient screening methods for mutants with high performance has stimulated the development of biosensor-based high-throughput screening strategies. Genetically encoded biosensors provide powerful tools to couple the desired phenotype to a detectable signal, such as fluorescence and growth rate. Herein, we review recent advances in engineering several classes of biosensors and their applications in directed evolution. Furthermore, we compare and discuss the screening advantages and limitations of two-component biosensors, transcription-factor-based biosensors, and RNA-based biosensors. Engineering these biosensors has focused mainly on modifying the expression level or structure of the biosensor components to optimize the dynamic range, specificity, and detection range. Finally, the applications of biosensors in the evolution of proteins, metabolic pathways, and genome-scale metabolic networks are described. This review provides potential guidance in the design of biosensors and their applications in improving the bioproduction of microbial cell factories through directed evolution.

Improving the soluble expression of difficult-to-express proteins in prokaryotic expression system via protein engineering and synthetic biology strategies

Solubility and folding stability are key concerns for difficult-to-express proteins (DEPs) restricted by amino acid sequences and superarchitecture, resolved by the precise distribution of amino acids and molecular interactions as well as the assistance of the expression system. Therefore, an increasing number of tools are available to achieve efficient expression of DEPs, including directed evolution, solubilization partners, chaperones, and affluent expression hosts, among others. Furthermore, genome editing tools, such as transposons and CRISPR Cas9/dCas9, have been developed and expanded to construct engineered expression hosts capable of efficient expression ability of soluble proteins. Accounting for the accumulated knowledge of the pivotal factors in the solubility and folding stability of proteins, this review focuses on advanced technologies and tools of protein engineering, protein quality control systems, and the redesign of expression platforms in prokaryotic expression systems, as well as advances of the cell-free expression technologies for membrane proteins production.

Enzymatic Conversion of CO2: From Natural to Artificial Utilization

Enzymatic carbon dioxide fixation is one of the most important metabolic reactions as it allows the capture of inorganic carbon from the atmosphere and its conversion into organic biomass. However, due to the often unfavorable thermodynamics and the difficulties associated with the utilization of CO2, a gaseous substrate that is found in comparatively low concentrations in the atmosphere, such reactions remain challenging for biotechnological applications. Nature has tackled these problems by evolution of dedicated CO2-fixing enzymes, i.e., carboxylases, and embedding them in complex metabolic pathways. Biotechnology employs such carboxylating and decarboxylating enzymes for the carboxylation of aromatic and aliphatic substrates either by embedding them into more complex reaction cascades or by shifting the reaction equilibrium via reaction engineering. This review aims to provide an overview of natural CO2-fixing enzymes and their mechanistic similarities. We also discuss biocatalytic applications of carboxylases and decarboxylases for the synthesis of valuable products and provide a separate summary of strategies to improve the efficiency of such processes. We briefly summarize natural CO2 fixation pathways, provide a roadmap for the design and implementation of artificial carbon fixation pathways, and highlight examples of biocatalytic cascades involving carboxylases. Additionally, we suggest that biochemical utilization of reduced CO2 derivates, such as formate or methanol, represents a suitable alternative to direct use of CO2 and provide several examples. Our discussion closes with a techno-economic perspective on enzymatic CO2 fixation and its potential to reduce CO2 emissions.

Recent developments in the engineering of Rubisco activase for enhanced crop yield

Rubisco activase (RCA) catalyzes the release of inhibitory sugar phosphates from ribulose-1,6-biphosphate carboxylase/oxygenase (Rubisco) and can play an important role in biochemical limitations of photosynthesis under dynamic light and elevated temperatures. There is interest in increasing RCA activity to improve crop productivity, but a lack of understanding about the regulation of photosynthesis complicates engineering strategies. In this review, we discuss work relevant to improving RCA with a focus on advances in understanding the structural cause of RCA instability under heat stress and the regulatory interactions between RCA and components of photosynthesis. This reveals substantial variation in RCA thermostability that can be influenced by single amino acid substitutions, and that engineered variants can perform better in vitro and in vivo under heat stress. In addition, there are indications RCA activity is controlled by transcriptional, post-transcriptional, post-translational, and spatial regulation, which may be important for balancing between carbon fixation and light capture. Finally, we provide an overview of findings from recent field experiments and consider the requirements for commercial validation as part of efforts to increase crop yields in the face of global climate change.

A PCR-free rapid protocol for one-pot construction of highly diverse genetic libraries

In vitro protein display methods can access extensive libraries (e.g., 1012-1014) and play an increasingly important role in protein engineering. However, the preparation of large libraries remains a laborious and time-consuming process. Here we report an efficient one-pot ligation & elongation (L&E) method for sizeable synthetic library preparation free of PCR amplification or any purification steps. As a proof of concept, we constructed an ankyrin repeat protein templated synthetic library with 1011 variants in 150 μL volume. The entire process from the oligos to DNA template ready for transcription is linearly scalable and took merely 90 minutes. We believe this L&E method can significantly simplify the preparation of synthetic libraries and accelerate in vitro protein display experiments.

High-throughput approaches to functional characterization of genetic variation in yeast

Expansion of sequencing efforts to include thousands of genomes is providing a fundamental resource for determining the genetic diversity that exists in a population. Now, high-throughput approaches are necessary to begin to understand the role these genotypic changes play in affecting phenotypic variation. Saccharomyces cerevisiae maintains its position as an excellent model system to determine the function of unknown variants with its exceptional genetic diversity, phenotypic diversity, and reliable genetic manipulation tools. Here, we review strategies and techniques developed in yeast that scale classic approaches of assessing variant function. These approaches improve our ability to better map quantitative trait loci at a higher resolution, even for rare variants, and are already providing greater insight into the role that different types of mutations play in phenotypic variation and evolution not just in yeast but across taxa.