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

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.

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.

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.

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.

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'.

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.

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.

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.

evSeq: Cost-Effective Amplicon Sequencing of Every Variant in a Protein Library

Widespread availability of protein sequence-fitness data would revolutionize both our biochemical understanding of proteins and our ability to engineer them. Unfortunately, even though thousands of protein variants are generated and evaluated for fitness during a typical protein engineering campaign, most are never sequenced, leaving a wealth of potential sequence-fitness information untapped. Primarily, this is because sequencing is unnecessary for many protein engineering strategies; the added cost and effort of sequencing are thus unjustified. It also results from the fact that, even though many lower-cost sequencing strategies have been developed, they often require at least some access to and experience with sequencing or computational resources, both of which can be barriers to access. Here, we present every variant sequencing (evSeq), a method and collection of tools/standardized components for sequencing a variable region within every variant gene produced during a protein engineering campaign at a cost of cents per variant. evSeq was designed to democratize low-cost sequencing for protein engineers and, indeed, anyone interested in engineering biological systems. Execution of its wet-lab component is simple, requires no sequencing experience to perform, relies only on resources and services typically available to biology labs, and slots neatly into existing protein engineering workflows. Analysis of evSeq data is likewise made simple by its accompanying software (found at github.com/fhalab/evSeq, documentation at fhalab.github.io/evSeq), which can be run on a personal laptop and was designed to be accessible to users with no computational experience. Low-cost and easy-to-use, evSeq makes the collection of extensive protein variant sequence-fitness data practical.

In silico evolution of nucleic acid-binding proteins from a nonfunctional scaffold

Directed evolution emulates the process of natural selection to produce proteins with improved or altered functions. These approaches have proven to be very powerful but are technically challenging and particularly time and resource intensive. To bypass these limitations, we constructed a system to perform the entire process of directed evolution in silico. We employed iterative computational cycles of mutation and evaluation to predict mutations that confer high-affinity binding activities for DNA and RNA to an initial de novo designed protein with no inherent function. Beneficial mutations revealed modes of nucleic acid recognition not previously observed in natural proteins, highlighting the ability of computational directed evolution to access new molecular functions. Furthermore, the process by which new functions were obtained closely resembles natural evolution and can provide insights into the contributions of mutation rate, population size and selective pressure on functionalization of macromolecules in nature.

Machine learning modeling of family wide enzyme-substrate specificity screens

Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates. While machine learning and in silico directed evolution are well-posed for this predictive modeling challenge, efforts to date have primarily aimed to increase activity against a single known substrate, rather than to identify enzymes capable of acting on new substrates of interest. To address this need, we curate 6 different high-quality enzyme family screens from the literature that each measure multiple enzymes against multiple substrates. We compare machine learning-based compound-protein interaction (CPI) modeling approaches from the literature used for predicting drug-target interactions. Surprisingly, comparing these interaction-based models against collections of independent (single task) enzyme-only or substrate-only models reveals that current CPI approaches are incapable of learning interactions between compounds and proteins in the current family level data regime. We further validate this observation by demonstrating that our no-interaction baseline can outperform CPI-based models from the literature used to guide the discovery of kinase inhibitors. Given the high performance of non-interaction based models, we introduce a new structure-based strategy for pooling residue representations across a protein sequence. Altogether, this work motivates a principled path forward in order to build and evaluate meaningful predictive models for biocatalysis and other drug discovery applications.

Predicting interactions between compounds and proteins represents a long-standing dream of drug discovery and protein engineering. Robust models of enzyme-substrate scope would dramatically advance our ability to design synthetic routes involving enzymatic catalysis. However, the lack of standardization between compound-protein interaction studies makes it difficult to evaluate the generalizability of such models. In this work we take a critical step forward by standardizing high-quality datasets measuring enzyme-substrate interactions, outlining rigorous evaluations, and proposing a new way to integrate structural information into protein representations. In testing previous modeling approaches, we highlight a surprising inability of existing models to effectively leverage compound-protein interactions to improve generalization, which challenges a perception in the literature. This establishes future opportunities for model development and integration of enzyme-substrate scope models into computer-aided synthesis planning software.

Using continuous directed evolution to improve enzymes for plant applications

Continuous directed evolution of enzymes and other proteins in microbial hosts is capable of outperforming classical directed evolution by executing hypermutation and selection concurrently in vivo, at scale, with minimal manual input. Provided that a target enzyme's activity can be coupled to growth of the host cells, the activity can be improved simply by selecting for growth. Like all directed evolution, the continuous version requires no prior mechanistic knowledge of the target. Continuous directed evolution is thus a powerful way to modify plant or non-plant enzymes for use in plant metabolic research and engineering. Here, we first describe the basic features of the yeast (Saccharomyces cerevisiae) OrthoRep system for continuous directed evolution and compare it briefly with other systems. We then give a step-by-step account of three ways in which OrthoRep can be deployed to evolve primary metabolic enzymes, using a THI4 thiazole synthase as an example and illustrating the mutational outcomes obtained. We close by outlining applications of OrthoRep that serve growing demands (i) to change the characteristics of plant enzymes destined for return to plants, and (ii) to adapt ("plantize") enzymes from prokaryotes-especially exotic prokaryotes-to function well in mild, plant-like conditions.

EASINESS: E. coli Assisted Speedy affINity-maturation Evolution SyStem

Antibodies are one of the most important groups of biomolecules for both clinical and basic research and have been developed as potential therapeutics. Affinity is the key feature for biological activity and clinical efficacy of an antibody, especially of therapeutic antibodies, and thus antibody affinity improvement is indispensable and still remains challenging. To address this issue, we developed the E. coli Assisted Speed affINity-maturation Evolution SyStem (EASINESS) for continuous directed evolution of Ag-Ab interactions. Two key components of EASINESS include a mutation system modified from error-prone DNA polymerase I (Pol I) that selectively mutates ColE1 plasmids in E. coli and a protein-protein interaction selection system from mDHFR split fragments. We designed a GCN4 variant which barely forms a homodimer, and during a single round of evolution, we reversed the homodimer formation activity from the GCN4 variant to verify the feasibility of EASINESS. We then selected a potential therapeutic antibody 18A4Hu and improved the affinity of the antibody (18A4Hu) to its target (ARG2) 12-fold in 7 days while requiring very limited hands-on time. Remarkably, these variants of 18A4Hu revealed a significant improved ability to inhibit melanoma pulmonary metastasis in a mouse model. These results indicate EASINESS could be as an attractive choice for antibody affinity maturation.

Virus-templated magnetic composite hydrogels for surface immobilization of mimic-free-lipase

Surface immobilization of enzymes on magnetic-recoverable carriers is of great interest and importance for the biocatalysis of relatively large molecules. In this work, the nanosized amino-rich filamentous M13 virus, a versatile biological scaffold, was applied as the unique soft backbone for lipase immobilization. Based on the structure and capsid proteins of M13 phages, the magnetic-recoverable mimic-free-lipases (MFLs) composed of the M13 hydrogels and magnetic particles were developed in two designs. In the first design, nanosized wild M13 phages were crosslinked into a phage hydrogel through the N-terminals of pVIII peptides while NH₂-Fe₃O₄ magnetic nanoparticles (MNPs) were attached to the M13 virus through glutaraldehyde, forming the M13-(NH₂-Fe₃O₄) magnetic phage hydrogel. In the second design, special M13 with Fe₃O₄ affinity pIII-peptide (FAP-M13) was biopanned for strongly binding towards bare Fe₃O₄ with the "hook"-like pIII-peptide (N-LPLSTQH-C). TEM observation confirmed the direct grasp of FAP-M13 on bare Fe₃O₄, forming the magnetic (FAP-M13)-Fe₃O₄ virus hydrogel. Lipases were uniformly anchored on the phage surface of nanoscale by crosslinking with the N-terminals of pVIII peptides, and then lipase@M13-(NH₂-Fe₃O₄) and lipase@(FAP-M13)-Fe₃O₄ MFLs were constructed. For both MFLs, high activity recovery yield (>95%) and efficient magnetic separation were characterized. Significantly reduced MNP-usage-amount and enhanced lipase-loading-amount both by about 40 folds were obtained, compared with the conventional NH₂-Fe₃O₄ carriers. The quantified K_m and V_max/K_m values were almost equal to those of the free lipases, verifying free-enzyme-mimicking features of the MFLs. High pH-tolerance, wide temperature adaptability, enhanced thermal stability and stable magnetic separation capability of both MFLs were also observed. In particular, the (FAP-M13)-Fe₃O₄ magnetic virus hydrogel simply using bare Fe₃O₄ MNPs would be more convenient and economical in the scaled-up biocatalysis.

Integrating continuous hypermutation with high-throughput screening for optimization of cis,cis-muconic acid production in yeast

Directed evolution is a powerful method to optimize proteins and metabolic reactions towards user-defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection and amplification. While powerful, systematic searches through large sequence-spaces is a labour-intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here, we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generate enzyme variants using the recently developed orthogonal replication system, OrthoRep and screens for optimal performance in high-throughput using a transcription factor-based biosensor. We demonstrate the strengths of this workflow by evolving a rate-limiting enzymatic reaction of the biosynthetic pathway for cis,cis-muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high-throughput sorting of best-performing variants using a transcription factor-based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers > 13-fold compared with reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.

EHreact: Extended Hasse Diagrams for the Extraction and Scoring of Enzymatic Reaction Templates

Data-driven computer-aided synthesis planning utilizing organic or biocatalyzed reactions from large databases has gained increasing interest in the last decade, sparking the development of numerous tools to extract, apply, and score general reaction templates. The generation of reaction rules for enzymatic reactions is especially challenging since substrate promiscuity varies between enzymes, causing the optimal levels of rule specificity and optimal number of included atoms to differ between enzymes. This complicates an automated extraction from databases and has promoted the creation of manually curated reaction rule sets. Here, we present EHreact, a purely data-driven open-source software tool, to extract and score reaction rules from sets of reactions known to be catalyzed by an enzyme at appropriate levels of specificity without expert knowledge. EHreact extracts and groups reaction rules into tree-like structures, Hasse diagrams, based on common substructures in the imaginary transition structures. Each diagram can be utilized to output a single or a set of reaction rules, as well as calculate the probability of a new substrate to be processed by the given enzyme by inferring information about the reactive site of the enzyme from the known reactions and their grouping in the template tree. EHreact heuristically predicts the activity of a given enzyme on a new substrate, outperforming current approaches in accuracy and functionality.

Phage-Assisted Continuous Evolution and Selection of Enzymes for Chemical Synthesis

Ligand-dependent biosensors are valuable tools for coupling the intracellular concentrations of small molecules to easily detectable readouts such as absorbance, fluorescence, or cell growth. While ligand-dependent biosensors are widely used for monitoring the production of small molecules in engineered cells and for controlling or optimizing biosynthetic pathways, their application to directed evolution for biocatalysts remains underexplored. As a consequence, emerging continuous evolution technologies are rarely applied to biocatalyst evolution. Here, we develop a panel of ligand-dependent biosensors that can detect a range of small molecules. We demonstrate that these biosensors can link enzymatic activity to the production of an essential phage protein to enable biocatalyst-dependent phage-assisted continuous evolution (PACE) and phage-assisted continuous selection (PACS). By combining these phage-based evolution and library selection technologies, we demonstrate that we can evolve enzyme variants with improved and expanded catalytic properties. Finally, we show that the genetic diversity resulting from a highly mutated PACS library is enriched for active enzyme variants with altered substrate scope. These results lay the foundation for using phage-based continuous evolution and selection technologies to engineer biocatalysts with novel substrate scope and reactivity.

A System for the Evolution of Protein-Protein Interaction Inducers

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

Computational Studies Suggest Promiscuous Candida antarctica Lipase B as an Environmentally Friendly Alternative for the Production of Epoxides

Environmentally friendly processes are nowadays a trending topic to get highly desired chemical compounds and, in this sense, the use of enzyme-catalyzed routes is becoming a promising alternative to traditional synthetic methods. In the present paper, a hybrid quantum mechanics/molecular mechanics (QM/MM) computational study on the epoxidation of alkenes catalyzed by the Ser105Ala variant of the promiscuous Candida antarctica lipase B (CALB) is presented in an attempt to search for alternative paths to get useful intermediates in industries. The catalyzed reaction, described at the atomistic level with a model of the full solvated in a box of water molecules, is compared with the alternative epoxidation of alkenes by peroxy acids in chloroform. Free-energy profiles obtained at the density functional theory (DFT)/MM level show how Ser105Ala CALB is capable of epoxide short alkenes in a two-step process with free-energy barriers, in agreement with available experimental data, that are significantly lower than those of the single-step reaction in solution. The possible (R)-enantioselectivity dictated by the binding step, explored by means of alchemical QM/MM free-energy perturbation (FEP) methods, and the preference for the (S)-enantiomer derived from the free-energy landscape of the chemical steps would cancel out, thus predicting the lack of enantioselectivity experimentally observed. In general, our results provide general information on the molecular mechanism employed by a highly promiscuous enzyme, with potential applications in biotechnology.

Rapid generation of potent antibodies by autonomous hypermutation in yeast

The predominant approach for antibody generation remains animal immunization, which can yield exceptionally selective and potent antibody clones owing to the powerful evolutionary process of somatic hypermutation. However, animal immunization is inherently slow, not always accessible and poorly compatible with many antigens. Here, we describe 'autonomous hypermutation yeast surface display' (AHEAD), a synthetic recombinant antibody generation technology that imitates somatic hypermutation inside engineered yeast. By encoding antibody fragments on an error-prone orthogonal DNA replication system, surface-displayed antibody repertoires continuously mutate through simple cycles of yeast culturing and enrichment for antigen binding to produce high-affinity clones in as little as two weeks. We applied AHEAD to generate potent nanobodies against the SARS-CoV-2 S glycoprotein, a G-protein-coupled receptor and other targets, offering a template for streamlined antibody generation at large.

Systems for in vivo hypermutation: a quest for scale and depth in directed evolution

Traditional approaches to the directed evolution of genes of interest (GOIs) place constraints on the scale of experimentation and depth of evolutionary search reasonably achieved. Engineered genetic systems that dramatically elevate the mutation of target GOIs in vivo relieve these constraints by enabling continuous evolution, affording new strategies in the exploration of sequence space and fitness landscapes for GOIs. We describe various in vivo hypermutation systems for continuous evolution, discuss how different architectures for in vivo hypermutation facilitate evolutionary search scale and depth in their application to problems in protein evolution and engineering, and outline future opportunities for the field.

Directed Evolution of a Glutathione Transferase for the Development of a Biosensor for Alachlor Determination

In the present work, DNA recombination of three homologous tau class glutathione transferases (GSTUs) allowed the creation of a library of tau class GmGSTUs. The library was activity screened for the identification of glutathione transferase (GST) variants with enhanced catalytic activity towards the herbicide alachlor (2-chloro-2′,6′-diethyl-N-(methoxymethyl)acetanilide). One enzyme variant (GmGSTsf) with improved catalytic activity and binding affinity for alachlor was identified and explored for the development of an optical biosensor for alachlor determination. Kinetics analysis and molecular modeling studies revealed a key mutation (Ile69Val) at the subunit interface (helix α3) that appeared to be responsible for the altered catalytic properties. The enzyme was immobilized directly on polyvinylidenefluoride membrane by crosslinking with glutaraldehyde and was placed on the inner surface of a plastic cuvette. The rate of pH changes observed as a result of the enzyme reaction was followed optometrically using a pH indicator. A calibration curve indicated that the linear concentration range for alachlor was 30–300 μM. The approach used in the present study can provide tools for the generation of novel enzymes for eco-efficient and environment-friendly analytical technologies. In addition, the outcome of this study gives an example for harnessing protein symmetry for enzyme design.