Selection platforms for directed evolution in synthetic biology

Navigating directed evolution efficiently: optimizing selection conditions and selection output analysis

Directed evolution is a powerful tool that can bypass gaps in our understanding of the sequence-function relationship of proteins and still isolate variants with desired activities, properties, and substrate specificities. The rise of directed evolution platforms for polymerase engineering has accelerated the isolation of xenobiotic nucleic acid (XNA) synthetases and reverse transcriptases capable of processing a wide array of unnatural XNAs which have numerous therapeutic and biotechnological applications. Still, the current generation of XNA polymerases functions with significantly lower efficiency than the natural counterparts and retains a significant level of DNA polymerase activity which limits their in vivo applications. Although directed evolution approaches are continuously being developed and implemented to improve XNA polymerase engineering, the field lacks an in-depth analysis of the effect of selection parameters, library construction biases and sampling biases. Focusing on the directed evolution pipeline for DNA and XNA polymerase engineering, this work sets out a method for understanding the impact of selection conditions on selection success and efficiency. We also explore the influence of selection conditions on fidelity at the population and individual mutant level. Additionally, we explore the sequencing coverage requirements in directed evolution experiments, which differ from genome assembly and other -omics approaches. This analysis allowed us to identify the sequencing coverage threshold for the accurate and precise identification of significantly enriched mutants. Overall, this study introduces a robust methodology for optimizing selection protocols, which effectively streamlines selection processes by employing small libraries and cost-effective NGS sequencing. It provides valuable insights into critical considerations, thereby enhancing the overall effectiveness and efficiency of directed evolution strategies applicable to enzymes other than the ones considered here.

Growth-Coupled Evolutionary Pressure Improving Epimerases for D-Allulose Biosynthesis Using a Biosensor-Assisted In Vivo Selection Platform

Fast screening strategies that enable high-throughput evaluation and identification of desired variants from diversified enzyme libraries are crucial to tailoring biocatalysts for the synthesis of D-allulose, which is currently limited by the poor catalytic performance of ketose 3-epimerases (KEases). Here, the study designs a minimally equipment-dependent, high-throughput, and growth-coupled in vivo screening platform founded on a redesigned D-allulose-dependent biosensor system. The genetic elements modulating regulator PsiR expression levels undergo systematic optimization to improve the growth-responsive dynamic range of the biosensor, which presents ≈30-fold facilitated growth optical density with a high signal-to-noise ratio (1.52 to 0.05) toward D-allulose concentrations from 0 to 100 mm. Structural analysis and evolutionary conservation analysis of Agrobacterium sp. SUL3 D-allulose 3-epimerase (ADAE) reveal a highly conserved catalytic active site and variable hydrophobic pocket, which together regulate substrate recognition. Structure-guided rational design and directed evolution are implemented using the growth-coupled in vivo screening platform to reprogram ADAE, in which a mutant M42 (P38N/V102A/Y201L/S207N/I251R) is identified with a 6.28-fold enhancement of catalytic activity and significantly improved thermostability with a 2.5-fold increase of the half-life at 60 °C. The research demonstrates that biosensor-assisted growth-coupled evolutionary pressure combined with structure-guided rational design provides a universal route for engineering KEases.

Synthetic eco-evolutionary dynamics in simple molecular environment

The understanding of eco-evolutionary dynamics, and in particular the mechanism of coexistence of species, is still fragmentary and in need of test bench model systems. To this aim we developed a variant of SELEX in vitro selection to study the evolution of a population of ∼1015 single-strand DNA oligonucleotide 'individuals'. We begin with a seed of random sequences which we select via affinity capture from ∼1012 DNA oligomers of fixed sequence ('resources') over which they compete. At each cycle ('generation'), the ecosystem is replenished via PCR amplification of survivors. Massive parallel sequencing indicates that across generations the variety of sequences ('species') drastically decreases, while some of them become populous and dominate the ecosystem. The simplicity of our approach, in which survival is granted by hybridization, enables a quantitative investigation of fitness through a statistical analysis of binding energies. We find that the strength of individual resource binding dominates the selection in the first generations, while inter- and intra-individual interactions become important in later stages, in parallel with the emergence of prototypical forms of mutualism and parasitism.

An Update on Protection of 5'-Hydroxyl Functions of Nucleosides and Oligonucleotides

The synthesis of natural and chemically modified nucleosides and oligonucleotides is in great demand due to its increasing number of applications in diverse areas of research. These include tools for diagnostics and proteomics, research reagents for molecular biology, probes for functional genomics, and the design, discovery, development, and manufacture of new therapeutics. The likelihood of success in synthesizing these molecules is often dependent on the correct choice of a protection strategy to block the 5'-hydroxyl group of a carbohydrate moiety, nucleoside, or oligonucleotide. This topic was reviewed extensively in the year 2000. The purpose of this article is to complement and update the original review with recently published methodologies recommended for the protection and deprotection of the 5'-hydroxyl group. © 2024 Wiley Periodicals LLC.

In Vitro Enzyme Self-Selection Using Molecular Programs

Directed evolution provides a powerful route for in vitro enzyme engineering. State-of-the-art techniques functionally screen up to millions of enzyme variants using high throughput microfluidic sorters, whose operation remains technically challenging. Alternatively, in vitro self-selection methods, analogous to in vivo complementation strategies, open the way to even higher throughputs, but have been demonstrated only for a few specific activities. Here, we leverage synthetic molecular networks to generalize in vitro compartmentalized self-selection processes. We introduce a programmable circuit architecture that can link an arbitrary target enzymatic activity to the replication of its encoding gene. Microencapsulation of a bacterial expression library with this autonomous selection circuit results in the single-step and screening-free enrichment of genetic sequences coding for programmed enzymatic phenotypes. We demonstrate the potential of this approach for the nicking enzyme Nt.BstNBI (NBI). We applied autonomous selection conditions to enrich for thermostability or catalytic efficiency, manipulating up to 107 microcompartments and 5 × 105 variants at once. Full gene reads of the libraries using nanopore sequencing revealed detailed mutational activity landscapes, suggesting a key role of electrostatic interactions with DNA in the enzyme's turnover. The most beneficial mutations, identified after a single round of self-selection, provided variants with, respectively, 20 times and 3 °C increased activity and thermostability. Based on a modular molecular programming architecture, this approach does not require complex instrumentation and can be repurposed for other enzymes, including those that are not related to DNA chemistry.

Analyzing Current Trends and Possible Strategies to Improve Sucrose Isomerases' Thermostability

Due to their ability to produce isomaltulose, sucrose isomerases are enzymes that have caught the attention of researchers and entrepreneurs since the 1950s. However, their low activity and stability at temperatures above 40 °C have been a bottleneck for their industrial application. Specifically, the instability of these enzymes has been a challenge when it comes to their use for the synthesis and manufacturing of chemicals on a practical scale. This is because industrial processes often require biocatalysts that can withstand harsh reaction conditions, like high temperatures. Since the 1980s, there have been significant advancements in the thermal stabilization engineering of enzymes. Based on the literature from the past few decades and the latest achievements in protein engineering, this article systematically describes the strategies used to enhance the thermal stability of sucrose isomerases. Additionally, from a theoretical perspective, we discuss other potential mechanisms that could be used for this purpose.

High-Throughput Screening of Streptavidin-Binding Proteins in Self-Assembled Solid Films for Directed Evolution of Materials

Evolution has shaped the development of proteins with an incredible diversity of properties. Incorporating proteins into materials is desirable for applications including biosensing; however, high-throughput selection techniques for screening protein libraries in materials contexts is lacking. In this work, a high-throughput platform to assess the binding affinity for ordered sensing proteins was established. A library of fusion proteins, consisting of an elastin-like polypeptide block, one of 22 variants of rcSso7d, and a coiled-coil order-directing sequence, was generated. All selected variants had high binding in films, likely due to the similarity of the assay to magnetic bead sorting used for initial selection, while solution binding was more variable. From these results, both the assembly of the fusion proteins in their operating state and the functionality of the binding protein are key factors in the biosensing performance. Thus, the integration of directed evolution with assembled systems is necessary to the design of better materials.

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

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

Deploying synthetic coevolution and machine learning to engineer protein-protein interactions

Fine-tuning of protein-protein interactions occurs naturally through coevolution, but this process is difficult to recapitulate in the laboratory. We describe a platform for synthetic protein-protein coevolution that can isolate matched pairs of interacting muteins from complex libraries. This large dataset of coevolved complexes drove a systems-level analysis of molecular recognition between Z domain-affibody pairs spanning a wide range of structures, affinities, cross-reactivities, and orthogonalities, and captured a broad spectrum of coevolutionary networks. Furthermore, we harnessed pretrained protein language models to expand, in silico, the amino acid diversity of our coevolution screen, predicting remodeled interfaces beyond the reach of the experimental library. The integration of these approaches provides a means of simulating protein coevolution and generating protein complexes with diverse molecular recognition properties for biotechnology and synthetic biology.

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.

Improving Both the Thermostability and Catalytic Efficiency of Phospholipase D from Moritella sp. JT01 through Disulfide Bond Engineering Strategy

Mining of Phospholipase D (PLD) with high activity and stability has attracted strong interest for investigation. A novel PLD from marine Moritella sp. JT01 (MsPLD) was biochemically and structurally characterized in our previous study; however, the short half-life time (t_1/2) under its optimum reaction temperature seriously hampered its further applications. Herein, the disulfide bond engineering strategy was applied to improve its thermostability. Compared with wild-type MsPLD, mutant S148C-T206C/D225C-A328C with the addition of two disulfide bonds exhibited a 3.1-fold t_1/2 at 35 °C and a 5.7 °C increase in melting temperature (T_m). Unexpectedly, its specific activity and catalytic efficiency (k_cat/K_m) also increased by 22.7% and 36.5%, respectively. The enhanced activity might be attributed to an increase in the activation entropy by displacing more water molecules by the transition state. The results of molecular dynamics simulations (MD) revealed that the introduction of double disulfide bonds rigidified the global structure of the mutant, which might cause the enhanced thermostability. Finally, the synthesis capacity of the mutant to synthesize phosphatidic acid (PA) was evaluated. The conversion rate of PA reached about 80% after 6 h reaction with wild-type MsPLD but reached 78% after 2 h with mutant S148C-T206C/D225C-A328C, which significantly reduced the time needed for the reaction to reach equilibrium. The present results pave the way for further application of MsPLD in the food and pharmaceutical industries.

Directed evolution of colE1 plasmid replication compatibility: a fast tractable tunable model for investigating biological orthogonality

Plasmids of the ColE1 family are among the most frequently used in molecular biology. They were adopted early for many biotechnology applications, and as models to study plasmid biology. Their mechanism of replication is well understood, involving specific interactions between a plasmid encoded sense-antisense gene pair (RNAI and RNAII). Due to such mechanism, two plasmids with the same origin cannot be stably maintained in cells-a process known as incompatibility. While mutations in RNAI and RNAII can make colE1 more compatible, there has been no systematic effort to engineer new compatible colE1 origins, which could bypass technical design constraints for multi-plasmid applications. Here, we show that by diversifying loop regions in RNAI (and RNAII), it is possible to select new viable colE1 origins compatible with the wild-type one. We demonstrate that sequence divergence is not sufficient to enable compatibility and pairwise interactions are not an accurate guide for higher order interactions. We identify potential principles to engineer plasmid copy number independently from other regulatory strategies and we propose plasmid compatibility as a tractable model to study biological orthogonality.

Sustainable isomaltulose production in Corynebacterium glutamicum by engineering the thermostability of sucrose isomerase coupled with one-step simplified cell immobilization

Sucrose isomerase (SI), catalyzing sucrose to isomaltulose, has been widely used in isomaltulose production, but its poor thermostability is still resisted in sustainable batches production. Here, protein engineering and one-step immobilized cell strategy were simultaneously coupled to maintain steady state for long-term operational stabilities. First, rational design of Pantoea dispersa SI (PdSI) for improving its thermostability by predicting and substituting the unstable amino acid residues was investigated using computational analysis. After screening mutagenesis library, two single mutants (PdSIV280L and PdSIS499F) displayed favorable characteristics on thermostability, and further study found that the double mutant PdSIV280L/S499F could stabilize PdSIWT better. Compared with PdSIWT, PdSIV280L/S499F displayed a 3.2°C-higher T m , and showed a ninefold prolonged half-life at 45°C. Subsequently, a one-step simplified immobilization method was developed for encapsulation of PdSIV280L/S499F in food-grade Corynebacterium glutamicum cells to further enhance the recyclability of isomaltulose production. Recombinant cells expressing combinatorial mutant (RCSI2) were successfully immobilized in 2.5% sodium alginate without prior permeabilization. The immobilized RCSI2 showed that the maximum yield of isomaltulose by batch conversion reached to 453.0 g/L isomaltulose with a productivity of 41.2 g/l/h from 500.0 g/L sucrose solution, and the conversion rate remained 83.2% after 26 repeated batches.

Learning Strategies in Protein Directed Evolution

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.

Versatile selective evolutionary pressure using synthetic defect in universal metabolism

The non-natural needs of industrial applications often require new or improved enzymes. The structures and properties of enzymes are difficult to predict or design de novo. Instead, semi-rational approaches mimicking evolution entail diversification of parent enzymes followed by evaluation of isolated variants. Artificial selection pressures coupling desired enzyme properties to cell growth could overcome this key bottleneck, but are usually narrow in scope. Here we show diverse enzymes using the ubiquitous cofactors nicotinamide adenine dinucleotide (NAD) or nicotinamide adenine dinucleotide phosphate (NADP) can substitute for defective NAD regeneration, representing a very broadly-applicable artificial selection. Inactivation of Escherichia coli genes required for anaerobic NAD regeneration causes a conditional growth defect. Cells are rescued by foreign enzymes connected to the metabolic network only via NAD or NADP, but only when their substrates are supplied. Using this principle, alcohol dehydrogenase, imine reductase and nitroreductase variants with desired selectivity modifications, and a high-performing isopropanol metabolic pathway, are isolated from libraries of millions of variants in single-round experiments with typical limited information to guide design.

A synthetic RNA-mediated evolution system in yeast

Laboratory evolution is a powerful approach to search for genetic adaptations to new or improved phenotypes, yet either relies on labour-intensive human-guided iterative rounds of mutagenesis and selection, or prolonged adaptation regimes based on naturally evolving cell populations. Here we present CRISPR- and RNA-assisted in vivo directed evolution (CRAIDE) of genomic loci using evolving chimeric donor gRNAs continuously delivered from an error-prone T7 RNA polymerase, and directly introduced as RNA repair donors into genomic targets under either Cas9 or dCas9 guidance. We validate CRAIDE by evolving novel functional variants of an auxotrophic marker gene, and by conferring resistance to a toxic amino acid analogue in baker's yeast Saccharomyces cerevisiae with a mutation rate >3,000-fold higher compared to spontaneous native rate, thus enabling the first demonstrations of in vivo delivery and information transfer from long evolving RNA donor templates into genomic context without the use of in vitro supplied and pre-programmed repair donors.

Directing evolution of novel ligands by mRNA display

mRNA display is a powerful biological display platform for the directed evolution of proteins and peptides. mRNA display libraries covalently link the displayed peptide or protein (phenotype) with the encoding genetic information (genotype) through the biochemical activity of the small molecule puromycin. Selection for peptide/protein function is followed by amplification of the linked genetic material and generation of a library enriched in functional sequences. Iterative selection cycles are then performed until the desired level of function is achieved, at which time the identity of candidate peptides can be obtained by sequencing the genetic material. The purpose of this review is to discuss the development of mRNA display technology since its inception in 1997 and to comprehensively review its use in the selection of novel peptides and proteins. We begin with an overview of the biochemical mechanism of mRNA display and its variants with a particular focus on its advantages and disadvantages relative to other biological display technologies. We then discuss the importance of scaffold choice in mRNA display selections and review the results of selection experiments with biological (e.g., fibronectin) and linear peptide library architectures. We then explore recent progress in the development of "drug-like" peptides by mRNA display through the post-translational covalent macrocyclization and incorporation of non-proteogenic functionalities. We conclude with an examination of enabling technologies that increase the speed of selection experiments, enhance the information obtained in post-selection sequence analysis, and facilitate high-throughput characterization of lead compounds. We hope to provide the reader with a comprehensive view of current state and future trajectory of mRNA display and its broad utility as a peptide and protein design tool.

Artificial enzymes bringing together computational design and directed evolution

Enzymes are proteins that catalyse chemical reactions and, as such, have been widely used to facilitate a variety of natural and industrial processes, dating back to ancient times. In fact, the global enzymes market is projected to reach $10.5 billion in 2024. The development of computational and DNA editing tools boosted the creation of artificial enzymes (de novo enzymes) - synthetic or organic molecules created to present abiological catalytic functions. These novel catalysts seek to expand the catalytic power offered by nature through new functions and properties. In this manuscript, we discuss the advantages of combining computational design with directed evolution for the development of artificial enzymes and how this strategy allows to fill in the gaps that these methods present individually by providing key insights about the sequence-function relationship. We also review examples, and respective strategies, where this approach has enabled the creation of artificial enzymes with promising catalytic activity. Such key enabling technologies are opening new windows of opportunity in a variety of industries, including pharmaceutical, chemical, biofuels, and food, contributing towards a more sustainable development.

Computational and synthetic biology approaches for the biosynthesis of antiviral and anticancer terpenoids from Bacillus subtilis

Recent advancements in medicinal research have identified several antiviral and anticancer terpenoids that are usually deployed as a source of flavor, fragrances and pharmaceuticals. Under the current COVID-19 pandemic conditions, natural therapeutics with least side effects are the need of the hour to save the patients, especially, which are pre-affected with other medical complications. Although, plants are the major sources of terpenoids; however, for the environmental concerns, the global interest has shifted to the biocatalytic production of molecules from microbial sources. The gram-positive bacterium Bacillus subtilis is a suitable host in this regard due to its GRAS (generally regarded as safe) status, ease in genetic manipulations and wide industrial acceptability. The B. subtilis synthesizes its terpenoid molecules from 1-deoxy-d-xylulose-5-phosphate (DXP) pathway, a common route in almost all microbial strains. Here, we summarize the computational and synthetic biology approaches to improve the production of terpenoid-based therapeutics from B. subtilis by utilizing DXP pathway. We focus on the in-silico approaches for screening the functionally improved enzyme-variants of the two crucial enzymes namely, the DXP synthase (DXS) and farnesyl pyrophosphate synthase (FPPS). The approaches for engineering the active sites are subsequently explained. It will be helpful to construct the functionally improved enzymes for the high-yield production of terpenoid-based anticancer and antiviral metabolites, which would help to reduce the cost and improve the availability of such therapeutics for the humankind.

Unsupervised Inference of Protein Fitness Landscape from Deep Mutational Scan

The recent technological advances underlying the screening of large combinatorial libraries in high-throughput mutational scans deepen our understanding of adaptive protein evolution and boost its applications in protein design. Nevertheless, the large number of possible genotypes requires suitable computational methods for data analysis, the prediction of mutational effects, and the generation of optimized sequences. We describe a computational method that, trained on sequencing samples from multiple rounds of a screening experiment, provides a model of the genotype-fitness relationship. We tested the method on five large-scale mutational scans, yielding accurate predictions of the mutational effects on fitness. The inferred fitness landscape is robust to experimental and sampling noise and exhibits high generalization power in terms of broader sequence space exploration and higher fitness variant predictions. We investigate the role of epistasis and show that the inferred model provides structural information about the 3D contacts in the molecular fold.

Mammalian Expression and In Situ Biotinylation of Extracellular Protein Targets for Directed Evolution

Directed evolution is a powerful tool for the selection of functional ligands from molecular libraries. Extracellular domains (ECDs) of cell surface receptors are common selection targets for therapeutic and imaging agent development. Unfortunately, these proteins are often post-translationally modified and are therefore unsuitable for expression in bacterial systems. Directional immobilization of these targets is further hampered by the absence of biorthogonal groups for site-specific chemical conjugation. We have developed a nonadherent mammalian expression system for rapid, high-yield expression of biotinylated ECDs. ECDs from EGFR, HER2, and HER3 were site-specifically biotinylated in situ and recovered from the cell culture supernatant with yields of up to 10 mg/L at >90% purity. Biotinylated ECDs also contained a protease cleavage site for rapid and selective release of the ECD after immobilization on avidin/streptavidin resins and library binding. A model mRNA display selection round was carried out against the HER2 ECD with the HER2 affibody expressed as an mRNA-protein fusion. HER2 affibody-mRNA fusions were selectively released by thrombin and quantitative PCR revealed substantial improvements in the enrichment of functional affibody-mRNA fusions relative to direct PCR amplification of the resin-bound target. This methodology allows rapid purification of high-quality targets for directed evolution and selective elution of functional sequences at the conclusion of each selection round.

Bacterial Cell Display as a Robust and Versatile Platform for Engineering Low-Affinity Ligands and Enzymes

Directed evolution has been remarkably successful at expanding the chemical and functional boundaries of biology. That progress is heavily dependent on the robustness and flexibility of the available selection platforms, given the significant cost to (re)develop a given platform to target a new desired function. Bacterial cell display has a significant track record as a viable strategy for the engineering of mesophilic enzymes, as enzyme activity can be probed directly and free from interference from the cellular milieu, but its adoption has lagged behind other display-based methods. Herein, we report the development of SNAP as a quantitative reporter for bacterial cell display, which enables fast troubleshooting and the systematic development of the display-based selection platform, thus improving its robustness. In addition, we demonstrate that even weak interactions between displayed proteins and nucleic acids can be harnessed for the specific labelling of bacterial cells, allowing functional characterisation of DNA binding proteins and enzymes, thus making it a highly flexible platform for these biochemical functions. Together, this establishes bacterial display as a robust and flexible platform, ideally suited for the systematic engineering of ligands and enzymes needed for XNA molecular biology.

Versatile phenotype-activated cell sorting

Unraveling the genetic and epigenetic determinants of phenotypes is critical for understanding and re-engineering biology and would benefit from improved methods to separate cells based on phenotypes. Here, we report SPOTlight, a versatile high-throughput technique to isolate individual yeast or human cells with unique spatiotemporal profiles from heterogeneous populations. SPOTlight relies on imaging visual phenotypes by microscopy, precise optical tagging of single target cells, and retrieval of tagged cells by fluorescence-activated cell sorting. To illustrate SPOTlight's ability to screen cells based on temporal properties, we chose to develop a photostable yellow fluorescent protein for extended imaging experiments. We screened 3 million cells expressing mutagenesis libraries and identified a bright new variant, mGold, that is the most photostable yellow fluorescent protein reported to date. We anticipate that the versatility of SPOTlight will facilitate its deployment to decipher the rules of life, understand diseases, and engineer new molecules and cells.

Modified nucleic acids: replication, evolution, and next-generation therapeutics

Modified nucleic acids, also called xeno nucleic acids (XNAs), offer a variety of advantages for biotechnological applications and address some of the limitations of first-generation nucleic acid therapeutics. Indeed, several therapeutics based on modified nucleic acids have recently been approved and many more are under clinical evaluation. XNAs can provide increased biostability and furthermore are now increasingly amenable to in vitro evolution, accelerating lead discovery. Here, we review the most recent discoveries in this dynamic field with a focus on progress in the enzymatic replication and functional exploration of XNAs.

Enhancing the thermostability of Rhizopus chinensis lipase by rational design and MD simulations

To improve the thermostability of r27RCL from Rhizopus chinensis and broaden its industrial applications, we used rational design (FoldX) according to ΔΔG calculation to predict mutations. Four thermostable variants S142A, D217V, Q239F, and S250Y were screened out and then combined together to generate a quadruple-mutation (S142A/D217V/Q239F/S250Y) variant, called m31. m31 exhibited enhanced thermostability with a 41.7-fold longer half-life at 60 °C, a 5 °C higher of t_opt, and 15.8 °C higher of T₅₀³⁰ compared to that of r27RCL expressed in Pichiapastoris. Molecular dynamics simulations were conducted to analyze the mechanism of the thermostable mutant. The results indicated that the rigidity of m31 was improved due to the decreased solvent accessible surface area, a newly formed salt bridge of Glu292:His171, and the increased ΔΔG of m31. According to the root-mean-square-fluctuation analysis, three positive mutations S142A, D217V, and Q239F located in the thermal weak regions and greatly decreased the distribution of thermal-fluctuated regions of m31, compared to that of r27RCL. These results suggested that to simultaneously implement MD simulations and ΔΔG-based rational approaches will be more accurate and efficient for the improvement of enzyme thermostability.

Hydrogel Microparticles Functionalized with Engineered Escherichia coli as Living Lactam Biosensors

Recently there has been an increasing need for synthesizing valued chemicals through biorefineries. Lactams are an essential family of commodity chemicals widely used in the nylon industry with annual production of millions of tons. The bio-production of lactams can substantially benefit from high-throughput lactam sensing strategies for lactam producer screening. We present here a robust and living lactam biosensor that is directly compatible with high-throughput analytical means. The biosensor is a hydrogel microparticle encapsulating living microcolonies of engineered lactam-responsive Escherichia coli. The microparticles feature facile and ultra-high throughput manufacturing of up to 10,000,000 per hour through droplet microfluidics. We show that the biosensors can specifically detect major lactam species in a dose-dependent manner, which can be quantified using flow cytometry. The biosensor could potentially be used for high-throughput metabolic engineering of lactam biosynthesis.

Directed Evolution of Plant Processes: Towards a Green (r)Evolution?

Directed evolution (DE) is a powerful approach for generating proteins with new chemical and physical properties. It mimics the principles of Darwinian evolution by imposing selective pressure on a large population of molecules harboring random genetic variation in DNA, such that sequences with specific desirable properties are generated and selected. We propose that combining DE and genome-editing (DE-GE) technologies represents a powerful tool to discover and integrate new traits into plants for agronomic and biotechnological gain. DE-GE has the potential to deliver a new green (r)evolution research platform that can provide novel solutions to major trait delivery aspirations for sustainable agriculture, climate-resilient crops, and improved food security and nutritional quality.

Magnetic bead-based semi-automated phage display panning strategy for the directed evolution of antibodies

Directed evolution is a proven approach to fine tune or modify biomolecules for various applications ranging from research to industry. The process of evolution requires methods that are capable of not only generating genetic diversity but also to distinguish the variants of desired characteristics. One method that is synonymous with directed evolution of proteins is phage display. Here, we present a protocol describing the application of magnetic nanoparticles coupled with a processor to carry out the identification of monoclonal antibodies (mAbs) from a diverse antibody library via phage display. Target antigens are coupled to magnetic nanoparticles as the solid phase for the isolation of the binding mAbs via affinity. A gradual enrichment in clones would result in increasing ELISA readouts with increasing rounds of panning. During monoclonal level analysis, positivity can be deduced with comparison to background and controls. The biopanning process can also be adopted for the directed evolution of enzymes, scaffold proteins or even peptides.

Droplet Microfluidics-Enabled High-Throughput Screening for Protein Engineering

Protein engineering—the process of developing useful or valuable proteins—has successfully created a wide range of proteins tailored to specific agricultural, industrial, and biomedical applications. Protein engineering may rely on rational techniques informed by structural models, phylogenic information, or computational methods or it may rely upon random techniques such as chemical mutation, DNA shuffling, error prone polymerase chain reaction (PCR), etc. The increasing capabilities of rational protein design coupled to the rapid production of large variant libraries have seriously challenged the capacity of traditional screening and selection techniques. Similarly, random approaches based on directed evolution, which relies on the Darwinian principles of mutation and selection to steer proteins toward desired traits, also requires the screening of very large libraries of mutants to be truly effective. For either rational or random approaches, the highest possible screening throughput facilitates efficient protein engineering strategies. In the last decade, high-throughput screening (HTS) for protein engineering has been leveraging the emerging technologies of droplet microfluidics. Droplet microfluidics, featuring controlled formation and manipulation of nano- to femtoliter droplets of one fluid phase in another, has presented a new paradigm for screening, providing increased throughput, reduced reagent volume, and scalability. We review here the recent droplet microfluidics-based HTS systems developed for protein engineering, particularly directed evolution. The current review can also serve as a tutorial guide for protein engineers and molecular biologists who need a droplet microfluidics-based HTS system for their specific applications but may not have prior knowledge about microfluidics. In the end, several challenges and opportunities are identified to motivate the continued innovation of microfluidics with implications for protein engineering.

Tools and systems for evolutionary engineering of biomolecules and microorganisms

Evolutionary approaches have been providing solutions to various bioengineering challenges in an efficient manner. In addition to traditional adaptive laboratory evolution and directed evolution, recent advances in synthetic biology and fluidic systems have opened a new era of evolutionary engineering. Synthetic genetic circuits have been created to control mutagenesis and enable screening of various phenotypes, particularly metabolite production. Fluidic systems can be used for high-throughput screening and multiplexed continuous cultivation of microorganisms. Moreover, continuous directed evolution has been achieved by combining all the steps of evolutionary engineering. Overall, modern tools and systems for evolutionary engineering can be used to establish the artificial equivalent to natural evolution for various research applications.

Molecular evolution of peptides by yeast surface display technology

Genetically encoded peptides possess unique properties, such as a small molecular weight and ease of synthesis and modification, that make them suitable to a large variety of applications. However, despite these favorable qualities, naturally occurring peptides are often limited by intrinsic weak binding affinities, poor selectivity and low stability that ultimately restrain their final use. To overcome these limitations, a large variety of in vitro display methodologies have been developed over the past few decades to evolve genetically encoded peptide molecules with superior properties. Phage display, mRNA display, ribosome display, bacteria display, and yeast display are among the most commonly used methods to engineer peptides. While most of these in vitro methodologies have already been described in detail elsewhere, this review describes solely the yeast surface display technology and its valuable use for the evolution of a wide range of peptide formats.

Towards the directed evolution of protein materials

Protein-based materials have emerged as a powerful instrument for a new generation of biological materials, with many chemical and mechanical capabilities. Through the manipulation of DNA, researchers can design proteins at the molecular level, engineering a vast array of structural building blocks. However, our capability to rationally design and predict the properties of such materials is limited by the vastness of possible sequence space. Directed evolution has emerged as a powerful tool to improve biological systems through mutation and selection, presenting another avenue to produce novel protein materials. In this prospective review, we discuss the application of directed evolution for protein materials, reviewing current examples and developments that could facilitate the evolution of protein for material applications.

Plant Synthetic Biology: Quantifying the "Known Unknowns" and Discovering the "Unknown Unknowns"

Biosensors, advanced microscopy, and single- cell transcriptomics are advancing plant synthetic biology.

Rapid micro-assays for amylolytic activities determination: customization and validation of the tests

High-throughput function-based screening techniques remain the major bottleneck in the novel biocatalysts development pipeline. In the present study, we customized protocols for amylolytic activity determination (Somogyi-Nelson and starch-iodine tests) to micro-volume thermalcycler-based assays (linearity range 60-600 μM of reducing sugar, R2 = 0.9855; 0-2 mg/mL of starch, R2 = 0.9921, respectively). Exploitation of a thermalcycler enabled rapid and accurate temperature control, further reduction of reagents and samples volumes, and limited evaporation of the reaction mixtures, meeting several crucial requirements of an adequate enzymatic assay. In the optimized micro-volume Somogyi-Nelson protocol, we were able to reduce the time required for high-temperature heating sixfold (down to 5 min) and further increase sensitivity of the assay (tenfold), when compared to the previous MTP-based protocol. The optimized microassays have complementary scope of specificities: micro-starch-iodine test for endoglucanases, micro-Somogyi-Nelson test for exoglucanases. Due to rapid, micro-volume and high-throughput character, the methods can complement toolbox assisting development of novel biocatalysts and analysis of saccharides-containing samples.

Inhibition of PD1:PD-L1 interaction by an E. coli-derived optimized PD1 variant

Immune-checkpoint receptors are a set of signal transduction proteins that can stimulate or inhibit specific anti-tumor responses. It is well established that cancer cells interact with different immune checkpoints to shut down T-cell response, thereby enabling cancer proliferation. Given the importance of immune checkpoint receptors, a structure-function analysis of these systems is imperative. However, recombinant expression and purification of these membrane originated proteins is still a challenge. Therefore, many attempts are being made to improve their expression and solubility while preserving their biological relevance. For this purpose, we designed an E. coli-based optimization system that enables the acquisition of mutations that increases protein solubility and affinity towards its native ligand, while maintaining biological activity. Here we focused on the well-characterized extracellular domain of the 'programmed cell death protein 1' (PD1), an immune checkpoint receptor known to inhibit T-cell proliferation by interacting with its ligands PD-L1 and PD-L2. The simple ELISA-based screening system shown here enabled the identification of high-affinity, highly soluble, functional variants derived from the extracellular domain of human PD1. The system was based on the expression of a GST-tagged variants library in E. coli, which enabled the selection of improved PD1 variants after a single optimization round. Within only two screening rounds, the most active variant showed a 5-fold higher affinity and 2.4-fold enhanced cellular activity as compared to the wild type protein. This scheme can be translated toward other types of challenging receptors toward development of research tools or alternative therapeutics.

Engineering-driven biological insights into DNA polymerase mechanism

DNA-dependent DNA polymerases have been extensively studied for over 60 years and lie at the core of multiple biotechnological and diagnostic applications. Nevertheless, these complex molecular machines remain only partially understood. Here we present some evidence on how polymerase engineering for the synthesis and replication of xenobiotic nucleic acids (XNAs) have improved our understanding of these enzymes and how that can be used to gain further insight into their mechanism. Better understanding of the mechanisms of DNA polymerases can accelerate their engineering and we highlight how it is now feasible to use structure-based and function-based approaches to systematically and iteratively develop XNA polymerases for increasingly divergent chemistries.

A Processive Protein Chimera Introduces Mutations across Defined DNA Regions In Vivo

Laboratory time scale evolution in vivo relies on the generation of large, mutationally diverse gene libraries to rapidly explore biomolecule sequence landscapes. Traditional global mutagenesis methods are problematic because they introduce many off-target mutations that are often lethal and can engender false positives. We report the development and application of the MutaT7 chimera, a potent and highly targeted in vivo mutagenesis agent. MutaT7 utilizes a DNA-damaging cytidine deaminase fused to a processive RNA polymerase to continuously direct mutations to specific, well-defined DNA regions of any relevant length. MutaT7 thus provides a mechanism for in vivo targeted mutagenesis across multi-kb DNA sequences. MutaT7 should prove useful in diverse organisms, opening the door to new types of in vivo evolution experiments.

Fast and Flexible Synthesis of Combinatorial Libraries for Directed Evolution

Directed evolution (DE) is a powerful tool for optimizing an enzyme's properties toward a particular objective, such as broader substrate scope, greater thermostability, or increased k_cat. A successful DE project requires the generation of genetic diversity and subsequent screening or selection to identify variants with improved fitness. In contrast to random methods (error-prone PCR or DNA shuffling), site-directed mutagenesis enables the rational design of variant libraries and provides control over the nature and frequency of the encoded mutations. Knowledge of protein structure, dynamics, enzyme mechanisms, and natural evolution demonstrates that multiple (combinatorial) mutations are required to discover the most improved variants. To this end, we describe an experimentally straightforward and low-cost method for the preparation of combinatorial variant libraries. Our approach employs a two-step PCR protocol, first producing mutagenic megaprimers, which can then be combined in a "mix-and-match" fashion to generate diverse sets of combinatorial variant libraries both quickly and accurately.

Directed evolution of the bacterial endo-β-1,4-glucanase from Streptomyces sp. G12 towards improved catalysts for lignocellulose conversion

With the aim to develop biocatalysts for enhanced hydrolysis of (hemi)cellulose into monosaccharides, random diversity by directed evolution was introduced in the gene coding for the endo-β-1,4-glucanase from Streptomyces sp. G12 which had been recombinantly expressed in Escherichia coli and named rCelStrep. The main objectives were therefore to set up a complete strategy for creation and automated screening of rCelStrep evolved direct mutants and to apply it to generate and screen a library of 10,000 random mutants to select the most active variants. The diversity was introduced in the gene by error-prone polymerase chain reaction. A primary qualitative screening on solid plates containing carboxymethylcellulose as the substrate allowed selecting 2200 active clones that were then subjected to a secondary quantitative screening towards AZO-CMC for the selection of 76 improved variants that were cultured in flasks and characterized. Five rCelStrep mutants exhibiting the highest hydrolytic activities than the wild-type enzyme were further characterized and applied to the bioconversion of the pretreated Arundo donax lignocellulosic biomass. It is worth of noting that one of the five tested mutants exhibited a 30% improvement in bioconversion yields compared to the wild-type enzyme, despite the absence of the carbohydrate binding module domain in this variant. Homology models of the three-dimensional structures of the catalytic and binding modules of rCelStrep were obtained and localization of mutations on these models allowed us to speculate on the structure-function relationships of the mutants.

Directed Evolution of Proteins Based on Mutational Scanning

Directed evolution has emerged as one of the most effective protein engineering methods in basic research as well as in applications in synthetic organic chemistry and biotechnology. The successful engineering of protein activity, allostery, binding affinity, expression, folding, fluorescence, solubility, substrate scope, selectivity (enantio-, stereo-, and regioselectivity), and/or stability (temperature, organic solvents, pH) is just limited by the throughput of the genetic selection, display, or screening system that is available for a given protein. Sometimes it is possible to analyze millions of protein variants from combinatorial libraries per day. In other cases, however, only a few hundred variants can be screened in a single day, and thus the creation of smaller yet smarter libraries is needed. Different strategies have been developed to create these libraries. One approach is to perform mutational scanning or to construct "mutability landscapes" in order to understand sequence-function relationships that can guide the actual directed evolution process. Herein we provide a protocol for economically constructing scanning mutagenesis libraries using a cytochrome P450 enzyme in a high-throughput manner. The goal is to engineer activity, regioselectivity, and stereoselectivity in the oxidative hydroxylation of a steroid, a challenging reaction in synthetic organic chemistry. Libraries based on mutability landscapes can be used to engineer any fitness trait of interest. The protocol is also useful for constructing gene libraries for deep mutational scanning experiments.

Bacterial Microcolonies in Gel Beads for High-Throughput Screening of Libraries in Synthetic Biology

Synthetic biologists increasingly rely on directed evolution to optimize engineered biological systems. Applying an appropriate screening or selection method for identifying the potentially rare library members with the desired properties is a crucial step for success in these experiments. Special challenges include substantial cell-to-cell variability and the requirement to check multiple states (e.g., being ON or OFF depending on the input). Here, we present a high-throughput screening method that addresses these challenges. First, we encapsulate single bacteria into microfluidic agarose gel beads. After incubation, they harbor monoclonal bacterial microcolonies (e.g., expressing a synthetic construct) and can be sorted according their fluorescence by fluorescence activated cell sorting (FACS). We determine enrichment rates and demonstrate that we can measure the average fluorescent signals of microcolonies containing phenotypically heterogeneous cells, obviating the problem of cell-to-cell variability. Finally, we apply this method to sort a pBAD promoter library at ON and OFF states.

Understanding enzyme function evolution from a computational perspective

In this review, we will explore recent computational approaches to understand enzyme evolution from the perspective of protein structure, dynamics and promiscuity. We will present quantitative methods to measure the size of evolutionary steps within a structural domain, allowing the correlation between change in substrate and domain structure to be assessed, and giving insights into the evolvability of different domains in terms of the number, types and sizes of evolutionary steps observed. These approaches will help to understand the evolution of new catalytic and non-catalytic functionality in response to environmental demands, showing potential to guide de novoenzyme design and directed evolution experiments.

Rewriting the Genetic Code

The genetic code-the language used by cells to translate their genomes into proteins that perform many cellular functions-is highly conserved throughout natural life. Rewriting the genetic code could lead to new biological functions such as expanding protein chemistries with noncanonical amino acids (ncAAs) and genetically isolating synthetic organisms from natural organisms and viruses. It has long been possible to transiently produce proteins bearing ncAAs, but stabilizing an expanded genetic code for sustained function in vivo requires an integrated approach: creating recoded genomes and introducing new translation machinery that function together without compromising viability or clashing with endogenous pathways. In this review, we discuss design considerations and technologies for expanding the genetic code. The knowledge obtained by rewriting the genetic code will deepen our understanding of how genomes are designed and how the canonical genetic code evolved.

Synthetic alienation of microbial organisms by using genetic code engineering: Why and how?

The main goal of synthetic biology (SB) is the creation of biodiversity applicable for biotechnological needs, while xenobiology (XB) aims to expand the framework of natural chemistries with the non-natural building blocks in living cells to accomplish artificial biodiversity. Protein and proteome engineering, which overcome limitation of the canonical amino acid repertoire of 20 (+2) prescribed by the genetic code by using non-canonic amino acids (ncAAs), is one of the main focuses of XB research. Ideally, estranging the genetic code from its current form via systematic introduction of ncAAs should enable the development of bio-containment mechanisms in synthetic cells potentially endowing them with a "genetic firewall" i.e. orthogonality which prevents genetic information transfer to natural systems. Despite rapid progress over the past two decades, it is not yet possible to completely alienate an organism that would use and maintain different genetic code associations permanently. In order to engineer robust bio-contained life forms, the chemical logic behind the amino acid repertoire establishment should be considered. Starting from recent proposal of Hartman and Smith about the genetic code establishment in the RNA world, here the authors mapped possible biotechnological invasion points for engineering of bio-contained synthetic cells equipped with non-canonical functionalities.

Synthetic Biology-The Synthesis of Biology

Synthetic biology concerns the engineering of man-made living biomachines from standardized components that can perform predefined functions in a (self-)controlled manner. Different research strategies and interdisciplinary efforts are pursued to implement engineering principles to biology. The "top-down" strategy exploits nature's incredible diversity of existing, natural parts to construct synthetic compositions of genetic, metabolic, or signaling networks with predictable and controllable properties. This mainly application-driven approach results in living factories that produce drugs, biofuels, biomaterials, and fine chemicals, and results in living pills that are based on engineered cells with the capacity to autonomously detect and treat disease states in vivo. In contrast, the "bottom-up" strategy seeks to be independent of existing living systems by designing biological systems from scratch and synthesizing artificial biological entities not found in nature. This more knowledge-driven approach investigates the reconstruction of minimal biological systems that are capable of performing basic biological phenomena, such as self-organization, self-replication, and self-sustainability. Moreover, the syntheses of artificial biological units, such as synthetic nucleotides or amino acids, and their implementation into polymers inside living cells currently set the boundaries between natural and artificial biological systems. In particular, the in vitro design, synthesis, and transfer of complete genomes into host cells point to the future of synthetic biology: the creation of designer cells with tailored desirable properties for biomedicine and biotechnology.