Scalable continuous evolution for the generation of diverse enzyme variants encompassing promiscuous activities

Directed evolution of aminoacyl-tRNA synthetases through in vivo hypermutation

Genetic code expansion (GCE) is a critical approach to the site-specific incorporation of non-canonical amino acids (ncAAs) into proteins. Central to GCE is the development of orthogonal aminoacyl-tRNA synthetase (aaRS)/tRNA pairs wherein engineered aaRSs recognize chosen ncAAs and charge them onto tRNAs that decode blank codons (e.g., the amber stop codon). However, evolving new aaRS/tRNA pairs traditionally relies on a labor-intensive process that often yields aaRSs with suboptimal ncAA incorporation efficiencies. Here, we present an OrthoRep-mediated strategy for aaRS evolution, which we demonstrate in 8 independent aaRS evolution campaigns, yielding multiple aaRSs that incorporate an overall range of 13 ncAAs tested. Some evolved systems enable ncAA-dependent translation at single amber codons with similar efficiency as natural translation at sense codons. Additionally, we discover an aaRS that regulated its own expression to enhance ncAA dependency. These findings demonstrate the potential of OrthoRep-driven aaRS evolution platforms to advance the field of GCE.

Pathway engineering for the biosynthesis of psychedelics

Naturally occurring psychoactive compounds have been used for cultural and ethnomedical purposes for centuries. Several more such molecules continue to be chemically synthesized, exhibiting a wide range of potency, therapeutic, and hallucinogenic effects. Promising clinical data and a renewed interest in understanding the cellular mechanisms of action have inspired synthetic biology efforts to develop alternative production routes for psychedelic compounds. Here, we highlight the latest biosynthetic accomplishments for indolamines (psilocybin, N,N-dimethyltryptamine, 5-methoxy-N,N-dimethyltryptamine, and bufotenine), ergolines (lysergic acid), and phenethylamines (mescaline) in both eukaryotic and prokaryotic production hosts. We further curate a list of relevant biosynthetic enzymes that have reports of successful in vivo heterologous activity.

Ultra-efficient Integration of Gene Libraries onto Yeast Cytosolic Plasmids

Efficient methods for diversifying genes of interest (GOIs) are essential in protein engineering. For example, OrthoRep, a yeast-based orthogonal DNA replication system that achieves the rapid in vivo diversification of GOIs encoded on a cytosolic plasmid (p1), has been successfully used to drive numerous protein engineering campaigns. However, OrthoRep-based GOI evolution has almost always started from single GOI sequences, limiting the number of locations on a fitness landscape from where evolutionary search begins. Here, we present a simple approach for the high-efficiency integration of GOI libraries onto OrthoRep. By leveraging integrases, we demonstrate recombination of donor DNA onto the cytosolic p1 plasmid at exceptionally high transformation efficiencies, even surpassing the transformation efficiency of standard circular plasmids and linearized plasmid fragments into yeast. We demonstrate our method's utility through the straightforward construction of mock nanobody libraries encoded on OrthoRep, from which rare binders were reliably enriched. Overall, integrase-assisted manipulation of yeast cytosolic plasmids should enhance the versatility of OrthoRep in continuous evolution experiments and support the routine construction of large GOI libraries in yeast, in general.

New insights into exploring new functional enzymes through the enzyme promiscuity

Enzyme promiscuity, defined as the ability of enzymes to catalyze reactions beyond their primary physiological functions, has emerged as a pivotal concept in modern enzyme engineering. This review provides a comprehensive exploration of enzyme promiscuity and its implications for the discovery and development of novel functional enzymes. Through targeted strategies such as (semi-)rational design, directed evolution, and de novo design, enzyme promiscuity has been harnessed to broaden substrate scopes, enhance catalytic efficiencies, and adapt enzymes to diverse reaction conditions. These modifications often involve subtle alterations to the active site, which impact catalytic mechanisms and open new pathways for the synthesis and degradation of complex organic compounds. Striking a balance between maintaining native activity and enhancing promiscuous functions remains a significant challenge in enzyme engineering. Nevertheless, advances in structural biology and computational modeling offer promising strategies to overcome these obstacles. By elucidating the mechanistic basis of enzyme promiscuity, this review aims to deepen our understanding of this phenomenon. It underscores the necessity of further investigating the mechanisms underlying promiscuous enzymatic activity and highlights the importance of leveraging promiscuous enzymes to address industrial application demands and drive the development of next-generation biocatalysts.

Recent advances in targeted mutagenesis to expedite the evolution of biological systems

Evolution has been systematically exploited to engineer biological systems to obtain improved or novel functionalities by selecting beneficial mutations. Recent innovations in continuous targeted mutagenesis within living cells have emerged to generate large sequence diversities without requiring multiple steps. This review comprehensively introduces recent advancements in this field, categorizing them into three approaches depending on methods to create mutations: orthogonal error-prone DNA polymerases, site-specific base editors, and homologous recombination of mutagenic DNA fragments. Combined with high-throughput screening methods, these advances expedited evolution processes with significant reduction of labor and time. These approaches promise broader industrial and research applications, including enzyme improvement, metabolic engineering, and drug resistance studies.

Accelerated enzyme engineering by machine-learning guided cell-free expression

Enzyme engineering is limited by the challenge of rapidly generating and using large datasets of sequence-function relationships for predictive design. To address this challenge, we develop a machine learning (ML)-guided platform that integrates cell-free DNA assembly, cell-free gene expression, and functional assays to rapidly map fitness landscapes across protein sequence space and optimize enzymes for multiple, distinct chemical reactions. We apply this platform to engineer amide synthetases by evaluating substrate preference for 1217 enzyme variants in 10,953 unique reactions. We use these data to build augmented ridge regression ML models for predicting amide synthetase variants capable of making 9 small molecule pharmaceuticals. Over these nine compounds, ML-predicted enzyme variants demonstrate 1.6- to 42-fold improved activity relative to the parent. Our ML-guided, cell-free framework promises to accelerate enzyme engineering by enabling iterative exploration of protein sequence space to build specialized biocatalysts in parallel.

Neutral drift upon threshold-like selection promotes variation in antibiotic resistance phenotype

Heritable phenotypic variation plays a central role in evolution by conferring rapid adaptive capacity to populations. Mechanisms that can explain genetic diversity by describing connections between genotype and organismal fitness have been described. However, the difficulty of acquiring comprehensive data on genotype-phenotype-environment relationships has hindered the efforts to explain how the ubiquitously observed phenotypic variation in populations emerges and is maintained. To address this challenge, we establish an experimental system where we can examine the genotype-phenotype relationships in a controlled environment. We perform long-term experimental evolution on VIM-2 β-lactamase, an antibiotic-resistance enzyme, to explore the conditions that promote the emergence and maintenance of phenotypic variation. We found that evolution in a static environment with low antibiotic concentrations can promote and maintain significant phenotypic variation within populations. Notably, evolution of VIM-2 under selection with a low antibiotic concentration led to variants that conferred resistance to over 100-fold higher antibiotic concentrations than used in selection. A model based on the previously described threshold-like relationship between enzyme phenotype and fitness generated using VIM-2's all single amino acid variants, sufficiently explains the emergence of standing phenotypic variation under static environmental conditions. Overall, our approach provides a tractable model for studying phenotypic variation and evolvability at the population level.

Enhancing Cellular and Enzymatic Properties Through In Vivo Continuous Evolution

Directed evolution seeks to evolve target genes at a rate far exceeding the natural mutation rate, thereby endowing cellular and enzymatic properties with desired traits. In vivo continuous directed evolution achieves these purposes by generating libraries within living cells, enabling a continuous cycle of mutant generation and selection, enhancing the exploration of gene variants. Continuous evolution has become powerful tools for unraveling evolution mechanism and improving cellular and enzymatic properties. This review categorizes current continuous evolution into three distinct classes: non-targeted chromosomal, targeted chromosomal, and extra-chromosomal hypermutation approaches. It also compares various continuous evolution strategies based on different principles, providing a reference for selecting suitable methods for specific evolutionary goals. Furthermore, this review discusses the two primary limitations for further widespread application of in vivo continuous evolution, which are lack of general applicability and insufficient mutagenic capability. We envision that developing generally applicable mutagenic components and methods to enhance mutation rates for in vivo continuous evolution are promising future directions for wide range applications of continuous evolution.

Ultra-Efficient Integration of Gene Libraries onto Yeast Cytosolic Plasmids

Efficient methods for diversifying genes of interest (GOIs) are essential in protein engineering. For example, OrthoRep, a yeast-based orthogonal DNA replication system that achieves the rapid in vivo diversification of GOIs encoded on a cytosolic plasmid (p1), has been successfully used to drive numerous protein engineering campaigns. However, OrthoRep-based GOI evolution has almost always started from single GOI sequences, limiting the number of locations on a fitness landscape from where evolutionary search begins. Here, we present a simple approach for the high-efficiency integration of GOI libraries onto OrthoRep. By leveraging integrases, we demonstrate recombination of donor DNA onto the cytosolic p1 plasmid at exceptionally high transformation efficiencies, even surpassing the transformation efficiency of standard circular plasmids into yeast. We demonstrate our method's utility through the straightforward construction of mock nanobody libraries encoded on OrthoRep, from which rare binders were reliably enriched. Overall, integrase-assisted manipulation of yeast cytosolic plasmids should enhance the versatility of OrthoRep in continuous evolution experiments and support the routine construction of large GOI libraries in yeast in general.

Systematic in vitro evolution in Plasmodium falciparum reveals key determinants of drug resistance

Surveillance of drug resistance and the discovery of novel targets-key objectives in the fight against malaria-rely on identifying resistance-conferring mutations in Plasmodium parasites. Current approaches, while successful, require laborious experimentation or large sample sizes. To elucidate shared determinants of antimalarial resistance that can empower in silico inference, we examined the genomes of 724 Plasmodium falciparum clones, each selected in vitro for resistance to one of 118 compounds. We identified 1448 variants in 128 recurrently mutated genes, including drivers of antimalarial multidrug resistance. In contrast to naturally occurring variants, those selected in vitro are more likely to be missense or frameshift, involve bulky substitutions, and occur in conserved, ordered protein domains. Collectively, our dataset reveals mutation features that predict drug resistance in eukaryotic pathogens.

Continuous evolution of user-defined genes at 1 million times the genomic mutation rate

When nature evolves a gene over eons at scale, it produces a diversity of homologous sequences with patterns of conservation and change that contain rich structural, functional, and historical information about the gene. However, natural gene diversity accumulates slowly and likely excludes large regions of functional sequence space, limiting the information that is encoded and extractable. We introduce upgraded orthogonal DNA replication (OrthoRep) systems that radically accelerate the evolution of chosen genes under selection in yeast. When applied to a maladapted biosynthetic enzyme, we obtained collections of extensively diverged sequences with patterns that revealed structural and environmental constraints shaping the enzyme's activity. Our upgraded OrthoRep systems should support the discovery of factors influencing gene evolution, uncover previously unknown regions of fitness landscapes, and find broad applications in biomolecular engineering.

Directed evolution of aminoacyl-tRNA synthetases through in vivo hypermutation

Genetic code expansion (GCE) has become a critical tool in biology by enabling the site-specific incorporation of non-canonical amino acids (ncAAs) into proteins. Central to GCE is the development of orthogonal aminoacyl-tRNA synthetase (aaRS)/tRNA pairs wherein engineered aaRSs recognize chosen ncAAs and charge them onto tRNAs that decode blank codons ( e.g ., the amber stop codon). Many orthogonal aaRS/tRNA pairs covering a wide range of ncAAs have been generated by directed evolution, yet the evolution of new aaRS/tRNA pairs by standard strategies remains a labor-intensive process that often produces aaRS/tRNA pairs with suboptimal ncAA incorporation efficiencies. In this study, we present a strategy for evolving aaRSs that leverages OrthoRep to drive their continuous hypermutation in yeast. We demonstrate our strategy in 8 independent aaRS evolution campaigns starting from 4 different aaRS/tRNA parents targeting 7 distinct ncAAs. We observed the rapid evolution of multiple novel aaRSs capable of incorporating an overall range of 13 ncAAs tested into proteins in response to the amber codon. Some evolved systems reached efficiencies for amber codon-specified ncAA-dependent translation comparable to translation with natural amino acids specified by sense codons in yeast. Additionally, we discovered a surprising aaRS that evolved to self-regulate its own expression for greater dependency on ncAAs for translation. These findings demonstrate the potential of OrthoRep-driven aaRS evolution platforms in supporting the continued growth of GCE technologies.

A combinatorially complete epistatic fitness landscape in an enzyme active site

Protein engineering often targets binding pockets or active sites which are enriched in epistasis-nonadditive interactions between amino acid substitutions-and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable β-subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences-a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling.

The β-subunit of tryptophan synthase is a latent tyrosine synthase

Aromatic amino acids and their derivatives are diverse primary and secondary metabolites with critical roles in protein synthesis, cell structure and integrity, defense and signaling. All de novo aromatic amino acid production relies on a set of ancient and highly conserved chemistries. Here we introduce a new enzymatic transformation for L-tyrosine synthesis by demonstrating that the β-subunit of tryptophan synthase-which natively couples indole and L-serine to form L-tryptophan-can act as a latent 'tyrosine synthase'. A single substitution of a near-universally conserved catalytic residue unlocks activity toward simple phenol analogs and yields exclusive para carbon-carbon bond formation to furnish L-tyrosines. Structural and mechanistic studies show how a new active-site water molecule orients phenols for a nonnative mechanism of alkylation, with additional directed evolution resulting in a net >30,000-fold rate enhancement. This new biocatalyst can be used to efficiently prepare valuable L-tyrosine analogs at gram scales and provides the missing chemistry for a conceptually different pathway to L-tyrosine.

Systems and Methods for Continuous Evolution of Enzymes

Directed evolution generates novel biomolecules with desired functions by iteratively diversifying the genetic sequence of wildtype biomolecules, relaying the genetic information to the molecule with function, and selecting the variants that progresses towards the properties of interest. While traditional directed evolution consumes significant labor and time for each step, continuous evolution seeks to automate all steps so directed evolution can proceed with minimum human intervention and dramatically shortened time. A major application of continuous evolution is the generation of novel enzymes, which catalyze reactions under conditions that are not favorable to their wildtype counterparts, or on altered substrates. The challenge to continuously evolve enzymes lies in automating sufficient, unbiased gene diversification, providing selection for a wide array of reaction types, and linking the genetic information to the phenotypic function. Over years of development, continuous evolution has accumulated versatile strategies to address these challenges, enabling its use as a general tool for enzyme engineering. As the capability of continuous evolution continues to expand, its impact will increase across various industries. In this review, we summarize the working mechanisms of recently developed continuous evolution strategies, discuss examples of their applications focusing on enzyme evolution, and point out their limitations and future directions.

An Orthogonal T7 Replisome for Continuous Hypermutation and Accelerated Evolution in E. coli

Systems that perform continuous hypermutation of designated genes without compromising the integrity of the host genome can dramatically accelerate the evolution of new or enhanced protein functions. We describe an orthogonal DNA replication system in E. coli based on the controlled expression of the replisome of bacteriophage T7. The system replicates circular plasmids that enable high transformation efficiencies and seamless integration into standard molecular biology workflows. Engineering of T7 DNA polymerase yielded variant proteins with mutation rates of 1.7 × 10 -5 substitutions per base in vivo - 100,000-fold above the genomic mutation rate. Continuous evolution using the mutagenic T7 replisome was demonstrated by expanding the substrate scope of TEM-1 β-lactamase and increase activity 1,000-fold against clinically relevant monobactam and cephalosporin antibiotics in less than one week.

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.

Accelerating Genetic Sensor Development, Scale-up, and Deployment Using Synthetic Biology

Living cells are exquisitely tuned to sense and respond to changes in their environment. Repurposing these systems to create engineered biosensors has seen growing interest in the field of synthetic biology and provides a foundation for many innovative applications spanning environmental monitoring to improved biobased production. In this review, we present a detailed overview of currently available biosensors and the methods that have supported their development, scale-up, and deployment. We focus on genetic sensors in living cells whose outputs affect gene expression. We find that emerging high-throughput experimental assays and evolutionary approaches combined with advanced bioinformatics and machine learning are establishing pipelines to produce genetic sensors for virtually any small molecule, protein, or nucleic acid. However, more complex sensing tasks based on classifying compositions of many stimuli and the reliable deployment of these systems into real-world settings remain challenges. We suggest that recent advances in our ability to precisely modify nonmodel organisms and the integration of proven control engineering principles (e.g., feedback) into the broader design of genetic sensing systems will be necessary to overcome these hurdles and realize the immense potential of the field.

6Pgdh polymorphism in wild bulb mite populations: prevalence, environmental correlates and life history trade-offs

Genetic polymorphism in key metabolic genes plays a pivotal role in shaping phenotypes and adapting to varying environments. Polymorphism in the metabolic gene 6-phosphogluconate dehydrogenase (6Pgdh) in bulb mites, Rhizoglyphus robini is characterized by two alleles, S and F, that differ by a single amino acid substitution and correlate with male reproductive fitness. The S-bearing males demonstrate a reproductive advantage. Although the S allele rapidly fixes in laboratory settings, the persistence of polymorphic populations in the wild is noteworthy. This study examines the prevalence and stability of 6Pgdh polymorphism in natural populations across Poland, investigating potential environmental influences and seasonal variations. We found widespread 6Pgdh polymorphism in natural populations, with allele frequencies varying across locations and sampling dates but without clear geographical or seasonal clines. This widespread polymorphism and spatio-temporal variability may be attributed to population demography and gene flow between local populations. We found some correlation between soil properties, particularly cation content (Na, K, Ca, and Mg) and 6Pgdh allele frequencies, showcasing the connection between mite physiology and soil characteristics and highlighting the presence of environment-dependent balancing selection. We conducted experimental fitness assays to determine whether the allele providing the advantage in male-male competition has antagonistic effects on life-history traits and if these effects are temperature-dependent. We found that temperature does not differentially influence development time or juvenile survival in different 6Pgdh genotypes. This study reveals the relationship between genetic variation, environmental factors, and reproductive fitness in natural bulb mite populations, shedding light on the dynamic mechanisms governing 6Pgdh polymorphism.

Descriptor-augmented machine learning for enzyme-chemical interaction predictions

Descriptors play a pivotal role in enzyme design for the greener synthesis of biochemicals, as they could characterize enzymes and chemicals from the physicochemical and evolutionary perspective. This study examined the effects of various descriptors on the performance of Random Forest model used for enzyme-chemical relationships prediction. We curated activity data of seven specific enzyme families from the literature and developed the pipeline for evaluation the machine learning model performance using 10-fold cross-validation. The influence of protein and chemical descriptors was assessed in three scenarios, which were predicting the activity of unknown relations between known enzymes and known chemicals (new relationship evaluation), predicting the activity of novel enzymes on known chemicals (new enzyme evaluation), and predicting the activity of new chemicals on known enzymes (new chemical evaluation). The results showed that protein descriptors significantly enhanced the classification performance of model on new enzyme evaluation in three out of the seven datasets with the greatest number of enzymes, whereas chemical descriptors appear no effect. A variety of sequence-based and structure-based protein descriptors were constructed, among which the esm-2 descriptor achieved the best results. Using enzyme families as labels showed that descriptors could cluster proteins well, which could explain the contributions of descriptors to the machine learning model. As a counterpart, in the new chemical evaluation, chemical descriptors made significant improvement in four out of the seven datasets, while protein descriptors appear no effect. We attempted to evaluate the generalization ability of the model by correlating the statistics of the datasets with the performance of the models. The results showed that datasets with higher sequence similarity were more likely to get better results in the new enzyme evaluation and datasets with more enzymes were more likely beneficial from the protein descriptor strategy. This work provides guidance for the development of machine learning models for specific enzyme families.

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.

Ultrahigh Throughput Evolution of Tryptophan Synthase in Droplets via an Aptamer Sensor

Tryptophan synthase catalyzes the synthesis of a wide array of noncanonical amino acids and is an attractive target for directed evolution. Droplet microfluidics offers an ultrahigh throughput approach to directed evolution (up to 107 experiments per day), enabling the search for biocatalysts in wider regions of sequence space with reagent consumption minimized to the picoliter volume (per library member). While the majority of screening campaigns in this format on record relied on an optically active reaction product, a new assay is needed for tryptophan synthase. Tryptophan is not fluorogenic in the visible light spectrum and thus falls outside the scope of conventional droplet microfluidic readouts, which are incompatible with UV light detection at high throughput. Here, we engineer a tryptophan DNA aptamer into a sensor to quantitatively report on tryptophan production in droplets. The utility of the sensor was validated by identifying five-fold improved tryptophan synthases from ∼100,000 protein variants. More generally, this work establishes the use of DNA-aptamer sensors with a fluorogenic read-out in widening the scope of droplet microfluidic evolution.

Understanding activity-stability tradeoffs in biocatalysts by enzyme proximity sequencing

Understanding the complex relationships between enzyme sequence, folding stability and catalytic activity is crucial for applications in industry and biomedicine. However, current enzyme assay technologies are limited by an inability to simultaneously resolve both stability and activity phenotypes and to couple these to gene sequences at large scale. Here we present the development of enzyme proximity sequencing, a deep mutational scanning method that leverages peroxidase-mediated radical labeling with single cell fidelity to dissect the effects of thousands of mutations on stability and catalytic activity of oxidoreductase enzymes in a single experiment. We use enzyme proximity sequencing to analyze how 6399 missense mutations influence folding stability and catalytic activity in a D-amino acid oxidase from Rhodotorula gracilis. The resulting datasets demonstrate activity-based constraints that limit folding stability during natural evolution, and identify hotspots distant from the active site as candidates for mutations that improve catalytic activity without sacrificing stability. Enzyme proximity sequencing can be extended to other enzyme classes and provides valuable insights into biophysical principles governing enzyme structure and function.

Ultrahigh-throughput screening-assisted in vivo directed evolution for enzyme engineering

BACKGROUND

Classical directed evolution is a powerful approach for engineering biomolecules with improved or novel functions. However, it traditionally relies on labour- and time-intensive iterative cycles, due in part to the need for multiple molecular biology steps, including DNA transformation, and limited screening throughput.

RESULTS

In this study, we present an ultrahigh throughput in vivo continuous directed evolution system with thermosensitive inducible tunability, which is based on error-prone DNA polymerase expression modulated by engineered thermal-responsive repressor cI857, and genomic MutS mutant with temperature-sensitive defect for fixation of mutations in Escherichia coli. We demonstrated the success of the in vivo evolution platform with β-lactamase as a model, with an approximately 600-fold increase in the targeted mutation rate. Furthermore, the platform was combined with ultrahigh-throughput screening methods and employed to evolve α-amylase and the resveratrol biosynthetic pathway. After iterative rounds of enrichment, a mutant with a 48.3% improvement in α-amylase activity was identified via microfluidic droplet screening. In addition, when coupled with an in vivo biosensor in the resveratrol biosynthetic pathway, a variant with 1.7-fold higher resveratrol production was selected by fluorescence-activated cell sorting.

CONCLUSIONS

In this study, thermal-responsive targeted mutagenesis coupled with ultrahigh-throughput screening was developed for the rapid evolution of enzymes and biosynthetic pathways.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

Engineered bacterial orthogonal DNA replication system for continuous evolution

Continuous evolution can generate biomolecules for synthetic biology and enable evolutionary investigation. The orthogonal DNA replication system (OrthoRep) in yeast can efficiently mutate long DNA fragments in an easy-to-operate manner. However, such a system is lacking in bacteria. Therefore, we developed a bacterial orthogonal DNA replication system (BacORep) for continuous evolution. We achieved this by harnessing the temperate phage GIL16 DNA replication machinery in Bacillus thuringiensis with an engineered error-prone orthogonal DNA polymerase. BacORep introduces all 12 types of nucleotide substitution in 15-kilobase genes on orthogonally replicating linear plasmids with a 6,700-fold higher mutation rate than that of the host genome, the mutation rate of which is unchanged. Here we demonstrate the utility of BacORep-based continuous evolution by generating strong promoters applicable to model bacteria, Bacillus subtilis and Escherichia coli, and achieving a 7.4-fold methanol assimilation increase in B. thuringiensis. BacORep is a powerful tool for continuous evolution in prokaryotic cells.

Have you tried turning it off and on again? Oscillating selection to enhance fitness-landscape traversal in adaptive laboratory evolution experiments

Adaptive Laboratory Evolution (ALE) is a powerful tool for engineering and understanding microbial physiology. ALE relies on the selection and enrichment of mutations that enable survival or faster growth under a selective condition imposed by the experimental setup. Phenotypic fitness landscapes are often underpinned by complex genotypes involving multiple genes, with combinatorial positive and negative effects on fitness. Such genotype relationships result in mutational fitness landscapes with multiple local fitness maxima and valleys. Traversing local maxima to find a global maximum often requires an individual or sub-population of cells to traverse fitness valleys. Traversing involves gaining mutations that are not adaptive for a given local maximum but are necessary to 'peak shift' to another local maximum, or eventually a global maximum. Despite these relatively well understood evolutionary principles, and the combinatorial genotypes that underlie most metabolic phenotypes, the majority of applied ALE experiments are conducted using constant selection pressures. The use of constant pressure can result in populations becoming trapped within local maxima, and often precludes the attainment of optimum phenotypes associated with global maxima. Here, we argue that oscillating selection pressures is an easily accessible mechanism for traversing fitness landscapes in ALE experiments, and provide theoretical and practical frameworks for implementation.

Evolution and synthetic biology

Evolutionary observations have often served as an inspiration for biological design. Decoding of the central dogma of life at a molecular level and understanding of the cellular biochemistry have been elegantly used to engineer various synthetic biology applications, including building genetic circuits in vitro and in cells, building synthetic translational systems, and metabolic engineering in cells to biosynthesize and even bioproduce complex high-value molecules. Here, we review three broad areas of synthetic biology that are inspired by evolutionary observations: (i) combinatorial approaches toward cell-based biomolecular evolution, (ii) engineering interdependencies to establish microbial consortia, and (iii) synthetic immunology. In each of the areas, we will highlight the evolutionary premise that was central toward designing these platforms. These are only a subset of the examples where evolution and natural phenomena directly or indirectly serve as a powerful source of inspiration in shaping synthetic biology and biotechnology.

Protein Engineering and High-Throughput Screening by Yeast Surface Display: Survey of Current Methods

Yeast surface display (YSD) is a powerful tool in biotechnology that links genotype to phenotype. In this review, the latest advancements in protein engineering and high-throughput screening based on YSD are covered. The focus is on innovative methods for overcoming challenges in YSD in the context of biotherapeutic drug discovery and diagnostics. Topics ranging from titrating avidity in YSD using transcriptional control to the development of serological diagnostic assays relying on serum biopanning and mitigation of unspecific binding are covered. Screening techniques against nontraditional cellular antigens, such as cell lysates, membrane proteins, and extracellular matrices are summarized and techniques are further delved into for expansion of the chemical repertoire, considering protein-small molecule hybrids and noncanonical amino acid incorporation. Additionally, in vivo gene diversification and continuous evolution in yeast is discussed. Collectively, these techniques enhance the diversity and functionality of engineered proteins isolated via YSD, broadening the scope of applications that can be addressed. The review concludes with future perspectives and potential impact of these advancements on protein engineering. The goal is to provide a focused summary of recent progress in the field.

Growth-coupled high throughput selection for directed enzyme evolution

Directed enzyme evolution has revolutionized the rapid development of enzymes with desired properties. However, the lack of a high-throughput method to identify the most suitable variants from a large pool of genetic diversity poses a major bottleneck. To overcome this challenge, growth-coupled in vivo high-throughput selection approaches (GCHTS) have emerged as a novel selection system for enzyme evolution. GCHTS links the survival of the host cell with the properties of the target protein, resulting in a screening system that is easily measurable and has a high throughput-scale limited only by transformation efficiency. This allows for the rapid identification of desired variants from a pool of >109 variants in each experiment. In recent years, GCHTS approaches have been extensively utilized in the directed evolution of multiple enzymes, demonstrating success in catalyzing non-native substrates, enhancing catalytic activity, and acquiring novel functions. This review introduces three main strategies employed to achieve GCHTS: the elimination of toxic compounds via desired variants, enabling host cells to thrive in hazardous conditions; the complementation of an auxotroph with desired variants, where essential genes for cell growth have been eliminated; and the control of the transcription or expression of a reporter gene related to host cell growth, regulated by the desired variants. Additionally, we highlighted the recent developments in the in vivo continuous evolution of enzyme technology, including phage-assisted continuous evolution (PACE) and orthogonal DNA Replication (OrthoRep). Furthermore, this review discusses the challenges and future prospects in the field of growth-coupled selection for protein engineering.

Adapting enzymes to improve their functionality in plants: why and how

Synthetic biology creates new metabolic processes and improves existing ones using engineered or natural enzymes. These enzymes are often sourced from cells that differ from those in the target plant organ with respect to, e.g. redox potential, effector levels, or proteostasis machinery. Non-native enzymes may thus need to be adapted to work well in their new plant context ('plantized') even if their specificity and kinetics in vitro are adequate. Hence there are two distinct ways in which an enzyme destined for use in plants can require improvement: In catalytic properties such as substrate and product specificity, kcat, and KM; and in general compatibility with the milieu of cells that express the enzyme. Continuous directed evolution systems can deliver both types of improvement and are so far the most broadly effective way to deliver the second type. Accordingly, in this review we provide a short account of continuous evolution methods, emphasizing the yeast OrthoRep system because of its suitability for plant applications. We then cover the down-to-earth and increasingly urgent issues of which enzymes and enzyme properties can - or cannot - be improved in theory, and which in practice are the best to target for crop improvement, i.e. those that are realistically improvable and important enough to warrant deploying continuous directed evolution. We take horticultural crops as examples because of the opportunities they present and to sharpen the focus.

Engineered Biocatalytic Synthesis of β-N-Substituted-α-Amino Acids

Non-canonical amino acids (ncAAs) are useful synthons for the development of new medicines, materials, and probes for bioactivity. Recently, enzyme engineering has been leveraged to produce a suite of highly active enzymes for the synthesis of β-substituted amino acids. However, there are few examples of biocatalytic N-substitution reactions to make α,β-diamino acids. In this study, we used directed evolution to engineer the β-subunit of tryptophan synthase, TrpB, for improved activity with diverse amine nucleophiles. Mechanistic analysis shows that high yields are hindered by product re-entry into the catalytic cycle and subsequent decomposition. Additional equivalents of l-serine can inhibit product reentry through kinetic competition, facilitating preparative scale synthesis. We show β-substitution with a dozen aryl amine nucleophiles, including demonstration on a gram scale. These transformations yield an underexplored class of amino acids that can serve as unique building blocks for chemical biology and medicinal chemistry.

Engineering chitin deacetylase AsCDA for improving the catalytic efficiency towards crystalline chitin

Chitin deacetylase (CDA) catalyzing the deacetylation of crystal chitin is a crucial step in the biosynthesis of chitosan, and also a scientific problem to be solved, which restricts the high-value utilization of chitin resources. This study aims to improve the catalytic efficiency of AsCDA from Acinetobacter schindleri MCDA01 by a semi-rational design using alanine scanning mutagenesis and saturation mutagenesis. The quadruple mutant M11 displayed a 2.31 and 1.73-fold improvement in kcat/Km and specific activity over AsCDA, which can remove 68 % of the acetyl groups from α-chitin. Furthermore, structural analysis suggested that additional hydrogen bonds, contributing the flexibility of amino acids and increasing the negative charge in M11 increased the catalytic efficiency. The microstructure changes of α-chitin pretreated by the mutant M11 were observed and evaluated using ¹³C CP/MAS NMR spectroscopy, FT-IR spectroscopy, XRD and SEM, and the results showed that M11 more efficiently catalyzed the release of acetyl groups from α-chitin. This study would provide a theoretical basis for the molecular modification of CDAs and accelerate the process of industrial production of chitosan by CDAs.

Multiplexed Assessment of Promiscuous Non-Canonical Amino Acid Synthase Activity in a Pyridoxal Phosphate-Dependent Protein Family

Pyridoxal phosphate (PLP)-dependent enzymes afford access to a variety of non-canonical amino acids (ncAAs), which are premier buildings blocks for the construction of complex bioactive molecules. The vinylglycine ketimine (VGK) subfamily of PLP-dependent enzymes plays a critical role in sulfur metabolism and is home to a growing set of secondary metabolic enzymes that synthesize γ-substituted ncAAs. Identification of VGK enzymes for biocatalysis faces a distinct challenge because the subfamily contains both desirable synthases as well as lyases that break down ncAAs. Some enzymes have both activities, which may contribute to pervasive mis-annotation. To navigate this complex functional landscape, we used a substrate multiplexed screening approach to rapidly measure the substrate promiscuity of 40 homologs in the VGK subfamily. We found that enzymes involved in transsulfuration are less likely to have promiscuous activities and often possess undesirable lyase activity. Enzymes from direct sulfuration and secondary metabolism generally had a high degree of substrate promiscuity. From this cohort, we identified an exemplary γ-synthase from Caldicellulosiruptor hydrothermalis (CahyGS). This enzyme is thermostable and has high expression (~400 mg protein per L culture), enabling preparative scale synthesis of thioether containing ncAAs. When assayed with l-allylglycine, CahyGS catalyzes a stereoselective γ-addition reaction to afford access to a unique set of γ-methyl branched ncAAs. We determined high-resolution crystal structures of this enzyme that define an open-close transition associated with ligand binding and set the stage for future engineering within this enzyme subfamily.

Advances in ultrahigh-throughput screening technologies for protein evolution

Inspired by natural evolution, directed evolution randomly mutates the gene of interest through artificial evolution conditions with variants being screened for the required properties. Directed evolution is vital to the enhancement of protein properties and comprises the construction of libraries with considerable diversity as well as screening methods with sufficient efficiency as key steps. Owing to the various characteristics of proteins, specific methods are urgently needed for library screening, which is one of the main limiting factors in accelerating evolution. This review initially organizes the principles of ultrahigh-throughput screening from the perspective of protein properties. It then provides a comprehensive introduction to the latest progress and future trends in ultrahigh-throughput screening technologies for directed evolution.

Molecular determinants of protein evolvability

The plethora of biological functions that sustain life is rooted in the remarkable evolvability of proteins. An emerging view highlights the importance of a protein's initial state in dictating evolutionary success. A deeper comprehension of the mechanisms that govern the evolvability of these initial states can provide invaluable insights into protein evolution. In this review, we describe several molecular determinants of protein evolvability, unveiled by experimental evolution and ancestral sequence reconstruction studies. We further discuss how genetic variation and epistasis can promote or constrain functional innovation and suggest putative underlying mechanisms. By establishing a clear framework for these determinants, we provide potential indicators enabling the forecast of suitable evolutionary starting points and delineate molecular mechanisms in need of deeper exploration.

Second-Shell Residues Contribute to Catalysis by Predominately Preorganizing the Apo State in PafA

Residues beyond the first coordination shell are often observed to make considerable cumulative contributions in enzymes. Due to typically indirect perturbations of multiple physicochemical properties of the active site, however, their individual and specific roles in enzyme catalysis and disease-causing mutations remain difficult to predict and understand at the molecular level. Here we analyze the contributions of several second-shell residues in phosphate-irrepressible alkaline phosphatase of flavobacterium (PafA), a representative system as one of the most efficient enzymes. By adopting a multifaceted approach that integrates quantum-mechanical/molecular-mechanical free energy computations, molecular-mechanical molecular dynamics simulations, and density functional theory cluster model calculations, we probe the rate-limiting phosphoryl transfer step and structural properties of all relevant enzyme states. In combination with available experimental data, our computational results show that mutations of the studied second-shell residues impact catalytic efficiency mainly by perturbation of the apo state and therefore substrate binding, while they do not affect the ground state or alter the nature of phosphoryl transfer transition state significantly. Several second-shell mutations also modulate the active site hydration level, which in turn influences the energetics of phosphoryl transfer. These mechanistic insights also help inform strategies that may improve the efficiency of enzyme design and engineering by going beyond the current focus on the first coordination shell.

Mutation bias and the predictability of evolution

Predicting evolutionary outcomes is an important research goal in a diversity of contexts. The focus of evolutionary forecasting is usually on adaptive processes, and efforts to improve prediction typically focus on selection. However, adaptive processes often rely on new mutations, which can be strongly influenced by predictable biases in mutation. Here, we provide an overview of existing theory and evidence for such mutation-biased adaptation and consider the implications of these results for the problem of prediction, in regard to topics such as the evolution of infectious diseases, resistance to biochemical agents, as well as cancer and other kinds of somatic evolution. We argue that empirical knowledge of mutational biases is likely to improve in the near future, and that this knowledge is readily applicable to the challenges of short-term prediction. This article is part of the theme issue 'Interdisciplinary approaches to predicting evolutionary biology'.

Expanded MutaT7 toolkit efficiently and simultaneously accesses all possible transition mutations in bacteria

Targeted mutagenesis mediated by nucleotide base deaminase-T7 RNA polymerase fusions has recently emerged as a novel and broadly useful strategy to power genetic diversification in the context of in vivo directed evolution campaigns. Here, we expand the utility of this approach by introducing a highly active adenosine deaminase-T7 RNA polymerase fusion protein (eMutaT7A→G), resulting in higher mutation frequencies to enable more rapid directed evolution. We also assess the benefits and potential downsides of using this more active mutator. We go on to show in Escherichia coli that adenosine deaminase-bearing mutators (MutaT7A→G or eMutaT7A→G) can be employed in tandem with a cytidine deaminase-bearing mutator (MutaT7C→T) to introduce all possible transition mutations simultaneously. We illustrate the efficacy of this in vivo mutagenesis approach by exploring mutational routes to antibacterial drug resistance. This work sets the stage for general application of optimized MutaT7 tools able to induce all types of transition mutations during in vivo directed evolution campaigns across diverse organisms.

Peptide Variant Detection by a Living Yeast Biosensor via an Epitope-Selective Protease

We previously demonstrated that we could hijack the fungal pheromone signaling pathway to provide a living yeast biosensor where peptide biomarkers were recognized by G-protein-coupled receptors and engineered to transcribe a readout. Here, we demonstrated that the protease could be reintroduced to the biosensor to provide a simple mechanism for distinguishing single-amino-acid changes in peptide ligands that, otherwise, would likely be difficult to detect using binding-based assays. We characterized the dose-response curves for five fungal pheromone G-protein-coupled receptors, peptides, and proteases-Saccharomyces cerevisiae, Candida albicans, Schizosaccharomyces pombe, Schizosaccharomyces octosporus, and Schizosaccharomyces japonicus. Alanine scanning was carried out for the most selective of these-S. cerevisiae and C. albicans-with and without the protease. Two peptide variants were discovered, which showed diminished cleavage by the protease (CaPep2A and CaPep2A13A). Those peptides were then distinguished by utilizing the biosensor strains with and without the protease, which selectively cleaved and altered the apparent concentration of peptide required for half-maximal activation for 2 peptides-CaPep and CaPep13A, respectively-by more than one order of magnitude. These results support the hypothesis that the living yeast biosensor with a sequence-specific protease can translate single-amino-acid changes into more than one order of magnitude apparent shift in the concentration of peptide required for half-maximal activation. With further engineering by computational modeling and directed evolution, the biosensor could likely distinguish a wide variety of peptide sequences beyond the alanine scanning carried out here. In the future, we envision incorporating proteases into our living yeast biosensor for use as a point of care diagnostic, a scalable communication language, and other applications.

Titrating Avidity of Yeast-Displayed Proteins Using a Transcriptional Regulator

Yeast surface display is a valuable tool for protein engineering and directed evolution; however, significant variability in the copy number (i.e., avidity) of displayed variants on the yeast cell wall complicates screening and selection campaigns. Here, we report an engineered titratable display platform that modulates the avidity of Aga2-fusion proteins on the yeast cell wall dependent on the concentration of the anhydrotetracycline (aTc) inducer. Our design is based on a genomic Aga1 gene copy and an episomal Aga2-fusion construct both under the control of an aTc-dependent transcriptional regulator that enables stoichiometric and titratable expression, secretion, and display of Aga2-fusion proteins. We demonstrate tunable display levels over 2-3 orders of magnitude for various model proteins, including glucose oxidase enzyme variants, mechanostable dockerin-binding domains, and anti-PDL1 affibody domains. By regulating the copy number of displayed proteins, we demonstrate the effects of titratable avidity levels on several specific phenotypic activities, including enzyme activity and cell adhesion to surfaces under shear flow. Finally, we show that titrating down the display level allows yeast-based binding affinity measurements to be performed in a regime that avoids ligand depletion effects while maintaining small sample volumes, avoiding a well-known artifact in yeast-based binding assays. The ability to titrate the multivalency of proteins on the yeast cell wall through simple inducer control will benefit protein engineering and directed evolution methodology relying on yeast display for broad classes of therapeutic and diagnostic proteins of interest.

Selecting Better Biocatalysts by Complementing Recoded Bacteria

In vivo selections are powerful tools for the directed evolution of enzymes. However, the need to link enzymatic activity to cellular survival makes selections for enzymes that do not fulfill a metabolic function challenging. Here, we present an in vivo selection strategy that leverages recoded organisms addicted to non-canonical amino acids (ncAAs) to evolve biocatalysts that can provide these building blocks from synthetic precursors. We exemplify our platform by engineering carbamoylases that display catalytic efficiencies more than five orders of magnitude higher than those observed for the wild-type enzyme for ncAA-precursors. As growth rates of bacteria under selective conditions correlate with enzymatic activities, we were able to elicit improved variants from populations by performing serial passaging. By requiring minimal human intervention and no specialized equipment, we surmise that our strategy will become a versatile tool for the in vivo directed evolution of diverse biocatalysts.

High throughput mutagenesis and screening for yeast engineering

The eukaryotic yeast Saccharomyces cerevisiae is a model host utilized for whole cell biocatalytic conversions, protein evolution, and scientific inquiries into the pathogenesis of human disease. Over the past decade, the scale and pace of such studies has drastically increased alongside the advent of novel tools for both genome-wide studies and targeted genetic mutagenesis. In this review, we will detail past and present (e.g., CRISPR/Cas) genome-scale screening platforms, typically employed in the context of growth-based selections for improved whole cell phenotype or for mechanistic interrogations. We will further highlight recent advances that enable the rapid and often continuous evolution of biomolecules with improved function. Additionally, we will detail the corresponding advances in high throughput selection and screening strategies that are essential for assessing or isolating cellular and protein improvements. Finally, we will describe how future developments can continue to advance yeast high throughput engineering.

Biocatalytic Friedel-Crafts Reactions

Friedel-Crafts alkylation and acylation reactions are important methodologies in synthetic and industrial chemistry for the construction of aryl-alkyl and aryl-acyl linkages that are ubiquitous in bioactive molecules. Nature also exploits these reactions in many biosynthetic processes. Much work has been done to expand the synthetic application of these enzymes to unnatural substrates through directed evolution. The promise of such biocatalysts is their potential to supersede inefficient and toxic chemical approaches to these reactions, with mild operating conditions - the hallmark of enzymes. Complementary work has created many bio-hybrid Friedel-Crafts catalysts consisting of chemical catalysts anchored into biomolecular scaffolds, which display many of the same desirable characteristics. In this Review, we summarise these efforts, focussing on both mechanistic aspects and synthetic considerations, concluding with an overview of the frontiers of this field and routes towards more efficient and benign Friedel-Crafts reactions for the future of humankind.

Kinetic compartmentalization by unnatural reaction for itaconate production

Physical compartmentalization of metabolism using membranous organelles in eukaryotes is helpful for chemical biosynthesis to ensure the availability of substrates from competitive metabolic reactions. Bacterial hosts lack such a membranous system, which is one of the major limitations for efficient metabolic engineering. Here, we employ kinetic compartmentalization with the introduction of an unnatural enzymatic reaction by an engineered enzyme as an alternative strategy to enable substrate availability from competitive reactions through kinetic isolation of metabolic pathways. As a proof of concept, we kinetically isolate the itaconate synthetic pathway from the tricarboxylic acid cycle in Escherichia coli, which is natively separated by mitochondrial membranes in Aspergillus terreus. Specifically, 2-methylcitrate dehydratase is engineered to alternatively catalyze citrate and kinetically secure cis-aconitate for efficient production using a high-throughput screening system. Itaconate production can be significantly improved with kinetic compartmentalization and its strategy has the potential to be widely applicable.

Directed evolution of phosphite dehydrogenase to cycle noncanonical redox cofactors via universal growth selection platform

Noncanonical redox cofactors are attractive low-cost alternatives to nicotinamide adenine dinucleotide (phosphate) (NAD(P)+) in biotransformation. However, engineering enzymes to utilize them is challenging. Here, we present a high-throughput directed evolution platform which couples cell growth to the in vivo cycling of a noncanonical cofactor, nicotinamide mononucleotide (NMN+). We achieve this by engineering the life-essential glutathione reductase in Escherichia coli to exclusively rely on the reduced NMN+ (NMNH). Using this system, we develop a phosphite dehydrogenase (PTDH) to cycle NMN+ with ~147-fold improved catalytic efficiency, which translates to an industrially viable total turnover number of ~45,000 in cell-free biotransformation without requiring high cofactor concentrations. Moreover, the PTDH variants also exhibit improved activity with another structurally deviant noncanonical cofactor, 1-benzylnicotinamide (BNA+), showcasing their broad applications. Structural modeling prediction reveals a general design principle where the mutations and the smaller, noncanonical cofactors together mimic the steric interactions of the larger, natural cofactors NAD(P)+.

Dual cytoplasmic-peroxisomal engineering for high-yield production of sesquiterpene α-humulene in Yarrowia lipolytica

The sesquiterpene α-humulene is an important plant natural product, which has been used in pharmaceutical industry due to the anti-inflammatory and anticancer activities. Although phytoextraction and chemical synthesis have previously been applied into α-humulene production, the low efficiency and high costs limit the development. In this study, Y. lipolytica was engineered as the robust cell factory for sustainable α-humulene production. First, a chassis with high α-humulene output in the cytoplasm was constructed by integrating α-humulene synthases with high catalytic activity, optimizing the flux of MVA and acetyl-CoA pathways. Subsequently, the strategy of dual cytoplasmic-peroxisomal engineering was adopted in Y. lipolytica, the best strain GQ3006 generated by introducing 31 copies of 12 different genes could produce 2280.3 ± 38.2 mg/L (98.7 ± 4.2 mg/g DCW) α-humulene, a 100-fold improvement relative to the baseline strain. In order to further improve the titer, a novel strategy for downregulation of squalene biosynthesis based on Cu²⁺ -repressible promoters was firstly established, which significantly improved the α-humulene titer by 54.2 % to 3516.6 ± 34.3 mg/L. Finally, the engineered strain could produce 21.7 g/L α-humulene in 5-L bioreactor, 6.8-fold higher than the highest α-humulene titer reported prior to this study. Overall, system metabolic engineering strategies used in this study provide a valuable reference for highly sustainable production of terpenoids in Y. lipolytica. This article is protected by copyright. All rights reserved.

Microbiome engineering for sustainable agriculture: using synthetic biology to enhance nitrogen metabolism in plant-associated microbes

Plants benefit from symbiotic relationships with their microbiomes. Modifying these microbiomes to further promote plant growth and improve stress tolerance in crops is a promising strategy. However, such efforts have had limited success, perhaps because the original microbiomes quickly re-establish. Since the complex biological networks involved are little understood, progress through conventional means is time-consuming. Synthetic biology, with its practical successes in multiple industries, could speed up this research considerably. Some fascinating candidates for production by synthetic microbiomes are organic nitrogen metabolites and related pyridoxal-5'-phosphate-dependent enzymes, which have pivotal roles in microbe-microbe and plant-microbe interactions. This review summarizes recent studies of these metabolites and enzymes and discusses prospective synthetic biology platforms for sustainable agriculture.