Directed evolution: an evolving and enabling synthetic biology tool

Unnatural Amino Acids for Biological Spectroscopy and Microscopy

Due to advances in methods for site-specific incorporation of unnatural amino acids (UAAs) into proteins, a large number of UAAs with tailored chemical and/or physical properties have been developed and used in a wide array of biological applications. In particular, UAAs with specific spectroscopic characteristics can be used as external reporters to produce additional signals, hence increasing the information content obtainable in protein spectroscopic and/or imaging measurements. In this Review, we summarize the progress in the past two decades in the development of such UAAs and their applications in biological spectroscopy and microscopy, with a focus on UAAs that can be used as site-specific vibrational, fluorescence, electron paramagnetic resonance (EPR), or nuclear magnetic resonance (NMR) probes. Wherever applicable, we also discuss future directions.

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

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

A smart RBS library and its prediction model for robust and accurate fine-tuning of gene expression in Bacillus species

Bacillus species, such as Bacillus subtilis and Bacillus licheniformis, are important industrial bacteria. However, there is a lack of standardized and predictable genetic tools for convenient and reproducible assembly of genetic modules in Bacillus species to realize their full potential. In this study, we constructed a Ribosome Binding Site (RBS) library in B. licheniformis, which provides incremental regulation of expression levels over a 104-fold range. Additionally, we developed a model to quantify the resulting translation rates. We successfully demonstrated the robust expression of various target genes using the RBS library and showed that the model accurately predicts the translation rates of arbitrary coding genes. Importantly, we also extended the use of the RBS library and prediction model to B. subtilis, B. thuringiensis, and B. amyloliquefacie. The versatility of the RBS library and its prediction model enables quantification of biological behavior, facilitating reliable forward engineering of gene expression.

Engineering the Substrate Specificity of Toluene Degrading Enzyme XylM Using Biosensor XylS and Machine Learning

Enzyme engineering using machine learning has been developed in recent years. However, to obtain a large amount of data on enzyme activities for training data, it is necessary to develop a high-throughput and accurate method for evaluating enzyme activities. Here, we examined whether a biosensor-based enzyme engineering method can be applied to machine learning. As a model experiment, we aimed to modify the substrate specificity of XylM, a rate-determining enzyme in a multistep oxidation reaction catalyzed by XylMABC in Pseudomonas putida. XylMABC naturally converts toluene and xylene to benzoic acid and toluic acid, respectively. We aimed to engineer XylM to improve its conversion efficiency to a non-native substrate, 2,6-xylenol. Wild-type XylMABC slightly converted 2,6-xylenol to 3-methylsalicylic acid, which is the ligand of the transcriptional regulator XylS in P. putida. By locating a fluorescent protein gene under the control of the Pm promoter to which XylS binds, a XylS-producing Escherichia coli strain showed higher fluorescence intensity in a 3-methylsalicylic acid concentration-dependent manner. We evaluated the 3-methylsalicylic acid productivity of XylM variants using the fluorescence intensity of the sensor strain as an indicator. The obtained data provided the training data for machine learning for the directed evolution of XylM. Two cycles of machine learning-assisted directed evolution resulted in the acquisition of XylM-D140E-V144K-F243L-N244S with 15 times higher productivity than wild-type XylM. These results demonstrate that an indirect enzyme activity evaluation method using biosensors is sufficiently quantitative and high-throughput to be used as training data for machine learning. The findings expand the versatility of machine learning in enzyme engineering.

Research progress of pathway and genome evolution in microbes

Microbes can produce valuable natural products widely applied in medicine, food and other important fields. Nevertheless, it is usually challenging to achieve ideal industrial yields due to low production rate and poor toxicity tolerance. Evolution is a constant mutation and adaptation process used to improve strain performance. Generally speaking, the synthesis of natural products in microbes is often intricate, involving multiple enzymes or multiple pathways. Individual evolution of a certain enzyme often fails to achieve the desired results, and may lead to new rate-limiting nodes that affect the growth of microbes. Therefore, it is inevitable to evolve the biosynthetic pathways or the whole genome. Here, we reviewed the pathway-level evolution including multi-enzyme evolution, regulatory elements engineering, and computer-aided engineering, as well as the genome-level evolution based on several tools, such as genome shuffling and CRISPR/Cas systems. Finally, we also discussed the major challenges faced by in vivo evolution strategies and proposed some potential solutions.

Engineering of the Ligand Specificity of Transcriptional Regulator XylS by Deep Mutational Scanning

Deep mutational scanning is a method for protein engineering. Here, we applied it to alter the ligand specificity of the transcriptional regulator XylS from Pseudomonas putida to recognize p-toluic acid instead of the native ligand m-toluic acid. For this purpose, we used an antibiotic resistance gene-based dual screening system, which was constructed for the directed evolution of XylS toward the above-mentioned ligand specificity. We constructed a xylS mutant library in which each codon for the amino acid residue of the putative ligand-binding domain (residues 1-213, except 7th residue) was randomized to generate all possible single amino acid-substituted XylS variants and introduced it into Escherichia coli harboring the selection plasmid for the screening system. The cells were cultured in the presence of appropriate antibiotics and m-toluic acid or p-toluic acid, and the frequency of each mutation present in the library was examined using a next-generation sequencer before and after cultivation. Heatmaps showing the enrichment score of each XylS variant were obtained. By searching for a p-toluic-acid-specific heatmap pattern, we focused on G71 and H77. Analysis of the ligand specificities of G71- or H77-substituted XylS variants revealed that several G71-substituted XylS variants responded specifically to p-toluic acid. Thus, the 71st residue was found to be an unprecedented residue that is important for switching ligand specificity. Our study demonstrated the usefulness of deep mutational scanning in engineering the ligand specificity of a transcriptional regulator without structural information. We also discussed the advantages and disadvantages of deep mutational scanning compared with directed evolution.

Systems biology–the transformative approach to integrate sciences across disciplines

Life science is the study of living organisms, including bacteria, plants, and animals. Given the importance of biology, chemistry, and bioinformatics, we anticipate that this chapter may contribute to a better understanding of the interdisciplinary connections in life science. Research in applied biological sciences has changed the paradigm of basic and applied research. Biology is the study of life and living organisms, whereas science is a dynamic subject that as a result of constant research, new fields are constantly emerging. Some fields come and go, whereas others develop into new, well-recognized entities. Chemistry is the study of composition of matter and its properties, how the substances merge or separate and also how substances interact with energy. Advances in biology and chemistry provide another means to understand the biological system using many interdisciplinary approaches. Bioinformatics is a multidisciplinary or rather transdisciplinary field that encourages the use of computer tools and methodologies for qualitative and quantitative analysis. There are many instances where two fields, biology and chemistry have intersection. In this chapter, we explain how current knowledge in biology, chemistry, and bioinformatics, as well as its various interdisciplinary domains are merged into life sciences and its applications in biological research.

Advantages of laboratory natural selection in the applied sciences

In the past three decades, laboratory natural selection has become a widely used technique in biological research. Most studies which have utilized this technique are in the realm of basic science, often testing hypotheses related to mechanisms of evolutionary change or ecological dynamics. While laboratory natural selection is currently utilized heavily in this setting, there is a significant gap with its usage in applied studies, especially when compared to the other selection experiment methodologies like artificial selection and directed evolution. This is despite avenues of research in the applied sciences which seem well suited to laboratory natural selection. In this review, we place laboratory natural selection in context with other selection experiments, identify the characteristics which make it well suited for particular kinds of applied research and briefly cover key examples of the usefulness of selection experiments within applied science. Finally, we identify three promising areas of inquiry for laboratory natural selection in the applied sciences: bioremediation technology, identifying mechanisms of drug resistance and optimizing biofuel production. Although laboratory natural selection is currently less utilized in applied science when compared to basic research, the method has immense promise in the field moving forward.

Looking Back: A Short History of the Discovery of Enzymes and How They Became Powerful Chemical Tools

Enzymatic approaches to challenges in chemical synthesis are increasingly popular and very attractive to industry given their green nature and high efficiency compared to traditional methods. In this historical review we highlight the developments across several fields that were necessary to create the modern field of biocatalysis, with enzyme engineering and directed evolution at its core. We exemplify the modular, incremental, and highly unpredictable nature of scientific discovery, driven by curiosity, and showcase the resulting examples of cutting-edge enzymatic applications in industry.

Kinetic characterization and structure analysis of an altered polyol dehydrogenase with d-lactate dehydrogenase activity

During adaptive metabolic evolution a native glycerol dehydrogenase (GDH) acquired a d-lactate dehydrogenase (LDH) activity. Two active-site amino acid changes were detected in the altered protein. Biochemical studies along with comparative structure analysis using an X-ray crystallographic structure model of the protein with the two different amino acids allowed prediction of pyruvate binding into the active site. We propose that the F245S alteration increased the capacity of the glycerol binding site and facilitated hydrogen bonding between the S245 γ-O and the C1 carboxylate of pyruvate. To our knowledge, this is the first GDH to gain LDH activity due to an active site amino acid change, a desired result of in vivo enzyme evolution.

Manipulating regioselectivity of an epoxide hydrolase for single enzymatic synthesis of (R)-1,2-diols from racemic epoxides

Both the activity and regioselectivity of Phaseolus vulgaris epoxide hydrolase were remarkably improved via reshaping two substrate tunnels based on rational design.

Switching the Ligand Specificity of the Biosensor XylS from meta to para-Toluic Acid through Directed Evolution Exploiting a Dual Selection System

The Pseudomonas putida transcriptional activator XylS induces transcription from the Pm promoter in the presence of several benzoic acid effectors, with m-toluic acid being the most effective and p-toluic acid being much less effective. To alter the effector specificity of XylS, we developed a dual selection system in Escherichia coli, which consists of (i) an artificial operon of an ampicillin resistance gene and tetR under Pm promoter control and (ii) a chloramphenicol resistance gene under tetR promoter control. This system enabled both positive selection to concentrate XylS mutants recognizing a desired ligand and negative selection to exclude undesired XylS mutants such as those recognizing undesired ligands and those that are active without effectors. Application of a random mutagenesis library of xylS to directed evolution that exploited this selection system yielded two XylS mutants that recognize p-toluic acid more effectively. Analysis of each missense mutation indicated three amino acid residues (N7, T74, and I205) important for p-toluic acid recognition. Then, a codon-randomized xylS library at these three residues was similarly screened, resulting in three XylS mutants with increased p-toluic acid-recognition specificity. Analysis of each amino acid substitution revealed that T74P attributes to both m-toluic acid sensitivity loss and subtle p-toluic acid sensitivity acquisition, and that N7R increases the overall ligand-sensitivity. Finally, the combination of these two mutations generated a desirable XylS mutant, which has a high p-toluic acid sensitivity and scarcely responds to m-toluic acid. These results demonstrate the effectiveness of the dual selection system in the directed evolution of biosensors.

Enhanced degradation of perfluorooctanoic acid by a genome shuffling-modified Pseudomonas parafulva YAB-1

Perfluorooctanoic acid (PFOA) as an emerging persistent organic pollutant is hard to be degraded by conventional methods because of its stable physical and chemical properties. Microbial transformation is an attractive remediation approach to prevent and clean up PFOA contamination. To date, several strains of wild microbes have been reported to have limited capacity to degrade PFOA, selection of superior strains degrading PFOA become urgently necessary. Here, we report the application of genome shuffling to improve the PFOA-degrading bacterium Pseudomonas Parafulva YAB-1. The initial mutant populations of strain YAB1 were generated by nitrosoguanidine and ultraviolet irradiation mutagenesis respectively, resulting in mutants YM-9 and YM-19 with slightly improved PFOA-degrading ability. YM-9 and YM-19 were used as the starting strains for three rounds of recursive protoplast fusion. The positive mutants were screened on inorganic salt medium plates containing different concentrations of PFOA and selected based on their PFOA degradability in shake-flask fermentation test. The best performing recombinant F3-52 was isolated after three rounds of genome shuffling. In batch fermentation, the PFOA degradation rate of mutant F3-52 was up to 58.6%, which was 1.8-fold higher than that of the parent strain YAB1, and 1.6-fold higher than the initial mutants YM-9 and YM-19. Pass-generation test indicated that the heredity character of F3-52 was stable. The results demonstrated that genome shuffling was an efficient method for improving PFOA degradation of Pseudomonas Parafulva YAB1. The bred mutant F3-52 with 58.6% PFOA-degrading rate could be used for the environmental control of PFOA pollutant.

Genetically Engineered Proteins to Improve Biomass Conversion: New Advances and Challenges for Tailoring Biocatalysts

Protein engineering emerged as a powerful approach to generate more robust and efficient biocatalysts for bio-based economy applications, an alternative to ecologically toxic chemistries that rely on petroleum. On the quest for environmentally friendly technologies, sustainable and low-cost resources such as lignocellulosic plant-derived biomass are being used for the production of biofuels and fine chemicals. Since most of the enzymes used in the biorefinery industry act in suboptimal conditions, modification of their catalytic properties through protein rational design and in vitro evolution techniques allows the improvement of enzymatic parameters such as specificity, activity, efficiency, secretability, and stability, leading to better yields in the production lines. This review focuses on the current application of protein engineering techniques for improving the catalytic performance of enzymes used to break down lignocellulosic polymers. We discuss the use of both classical and modern methods reported in the literature in the last five years that allowed the boosting of biocatalysts for biomass degradation.

GeneORator: An Effective Strategy for Navigating Protein Sequence Space More Efficiently through Boolean OR-Type DNA Libraries

Directed evolution requires the creation of genetic diversity and subsequent screening or selection for improved variants. For DNA mutagenesis, conventional site-directed methods implicitly utilize the Boolean AND operator (creating all mutations simultaneously), producing a combinatorial explosion in the number of genetic variants as the number of mutations increases. We introduce GeneORator, a novel strategy for creating DNA libraries based on the Boolean logical OR operator. Here, a single library is divided into many subsets, each containing different combinations of the desired mutations. Consequently, the effect of adding more mutations on the number of genetic combinations is additive (Boolean OR logic) and not exponential (AND logic). We demonstrate this strategy with large-scale mutagenesis studies, using monoamine oxidase-N ( Aspergillus niger) as the exemplar target. First, we mutated every residue in the secondary structure-containing regions (276 out of a total 495 amino acids) to screen for improvements in k_cat. Second, combinatorial OR-type libraries permitted screening of diverse mutation combinations in the enzyme active site to detect activity toward novel substrates. In both examples, OR-type libraries effectively reduced the number of variants searched up to 10¹⁰-fold, dramatically reducing the screening effort required to discover variants with improved and/or novel activity. Importantly, this approach enables the screening of a greater diversity of mutation combinations, accessing a larger area of a protein's sequence space. OR-type libraries can be applied to any biological engineering objective requiring DNA mutagenesis, and the approach has wide ranging applications in, for example, enzyme engineering, antibody engineering, and synthetic biology.

Redesigning thiamin synthesis: Prospects and potential payoffs

Thiamin is essential for plant growth but is short-lived in vivo and energetically very costly to produce - a combination that makes thiamin biosynthesis a prime target for improvement by redesign. Thiamin consists of thiazole and pyrimidine moieties. Its high biosynthetic cost stems from use of the suicide enzyme THI4 to form the thiazole and the near-suicide enzyme THIC to form the pyrimidine. These energetic costs lower biomass yield potential and are likely compounded by environmental stresses that destroy thiamin and hence increase the rate at which it must be made. The energy costs could be slashed by refactoring the thiamin biosynthesis pathway to eliminate the suicidal THI4 and THIC reactions. To substantiate this design concept, we first document the energetic costs of the THI4 and THIC steps in the pathway and explain how cutting these costs could substantially increase crop biomass and grain yields. We then show that a refactored pathway must produce thiamin itself rather than a stripped-down analog because the thiamin molecule cannot be simplified without losing biological activity. Lastly, we consider possible energy-efficient alternatives to the inefficient natural THI4- and THIC-mediated steps.

Functional Site Discovery in a Sulfur Metabolism Enzyme by Using Directed Evolution

In human pathogens, the sulfate assimilation pathway provides reduced sulfur for biosynthesis of essential metabolites, including cysteine and low-molecular-weight thiol compounds. Sulfonucleotide reductases (SRs) catalyze the first committed step of sulfate reduction. In this reaction, activated sulfate in the form of adenosine-5'-phosphosulfate (APS) or 3'-phosphoadenosine 5'-phosphosulfate (PAPS) is reduced to sulfite. Gene knockout, transcriptomic and proteomic data have established the importance of SRs in oxidative stress-inducible antimicrobial resistance mechanisms. In previous work, we focused on rational and high-throughput design of small-molecule inhibitors that target the active site of SRs. However, another critical goal is to discover functionally important regions in SRs beyond the traditional active site. As an alternative to conservation analysis, we used directed evolution to rapidly identify functional sites in PAPS reductase (PAPR). Four new regions were discovered that are essential to PAPR function and lie outside the substrate binding pocket. Our results highlight the use of directed evolution as a tool to rapidly discover functionally important sites in proteins.

CRMAGE: CRISPR Optimized MAGE Recombineering

A bottleneck in metabolic engineering and systems biology approaches is the lack of efficient genome engineering technologies. Here, we combine CRISPR/Cas9 and λ Red recombineering based MAGE technology (CRMAGE) to create a highly efficient and fast method for genome engineering of Escherichia coli. Using CRMAGE, the recombineering efficiency was between 96.5% and 99.7% for gene recoding of three genomic targets, compared to between 0.68% and 5.4% using traditional recombineering. For modulation of protein synthesis (small insertion/RBS substitution) the efficiency was increased from 6% to 70%. CRMAGE can be multiplexed and enables introduction of at least two mutations in a single round of recombineering with similar efficiencies. PAM-independent loci were targeted using degenerate codons, thereby making it possible to modify any site in the genome. CRMAGE is based on two plasmids that are assembled by a USER-cloning approach enabling quick and cost efficient gRNA replacement. CRMAGE furthermore utilizes CRISPR/Cas9 for efficient plasmid curing, thereby enabling multiple engineering rounds per day. To facilitate the design process, a web-based tool was developed to predict both the λ Red oligos and the gRNAs. The CRMAGE platform enables highly efficient and fast genome editing and may open up promising prospective for automation of genome-scale engineering.

Nature versus design: synthetic biology or how to build a biological non-machine

We suggest that progress in synthetic biology will be achieved by abandoning the bio-machine paradigm and by using an alliance between engineering and evolution as a guiding tool.

Combinatorial Screening for Transgenic Yeasts with High Cellulase Activities in Combination with a Tunable Expression System

Combinatorial screening used together with a broad library of gene expression cassettes is expected to produce a powerful tool for the optimization of the simultaneous expression of multiple enzymes. Recently, we proposed a highly tunable protein expression system that utilized multiple genome-integrated target genes to fine-tune enzyme expression in yeast cells. This tunable system included a library of expression cassettes each composed of three gene-expression control elements that in different combinations produced a wide range of protein expression levels. In this study, four gene expression cassettes with graded protein expression levels were applied to the expression of three cellulases: cellobiohydrolase 1, cellobiohydrolase 2, and endoglucanase 2. After combinatorial screening for transgenic yeasts simultaneously secreting these three cellulases, we obtained strains with higher cellulase expressions than a strain harboring three cellulase-expression constructs within one high-performance gene expression cassette. These results show that our method will be of broad use throughout the field of metabolic engineering.

Economical analysis of saturation mutagenesis experiments

Saturation mutagenesis is a powerful technique for engineering proteins, metabolic pathways and genomes. In spite of its numerous applications, creating high-quality saturation mutagenesis libraries remains a challenge, as various experimental parameters influence in a complex manner the resulting diversity. We explore from the economical perspective various aspects of saturation mutagenesis library preparation: We introduce a cheaper and faster control for assessing library quality based on liquid media; analyze the role of primer purity and supplier in libraries with and without redundancy; compare library quality, yield, randomization efficiency, and annealing bias using traditional and emergent randomization schemes based on mixtures of mutagenic primers; and establish a methodology for choosing the most cost-effective randomization scheme given the screening costs and other experimental parameters. We show that by carefully considering these parameters, laboratory expenses can be significantly reduced.

RNAi-assisted genome evolution in Saccharomyces cerevisiae for complex phenotype engineering

A fundamental challenge in basic and applied biology is to reprogram cells with improved or novel traits on a genomic scale. However, the current ability to reprogram a cell on the genome scale is limited to bacterial cells. Here, we report RNA interference (RNAi)-assisted genome evolution (RAGE) as a generally applicable method for genome-scale engineering in the yeast Saccharomyces cerevisiae. Through iterative cycles of creating a library of RNAi induced reduction-of-function mutants coupled with high throughput screening or selection, RAGE can continuously improve target trait(s) by accumulating multiplex beneficial genetic modifications in an evolving yeast genome. To validate the RNAi library constructed with yeast genomic DNA and convergent-promoter expression cassette, we demonstrated RNAi screening in Saccharomyces cerevisiae for the first time by identifying two known and three novel suppressors of a telomerase-deficient mutation yku70Δ. We then showed the application of RAGE for improved acetic acid tolerance, a key trait for microbial production of chemicals and fuels. Three rounds of iterative RNAi screening led to the identification of three gene knockdown targets that acted synergistically to confer an engineered yeast strain with substantially improved acetic acid tolerance. RAGE should greatly accelerate the design and evolution of organisms with desired traits and provide new insights on genome structure, function, and evolution.

Fluorescent monitoring of RNA assembly and processing using the split-spinach aptamer

As insights into RNA's many diverse cellular roles continue to be gained, interest and applications in RNA self-assembly and dynamics remain at the forefront of structural biology. The bifurcation of functional molecules into nonfunctional fragments provides a useful strategy for controlling and monitoring cellular RNA processes and functionalities. Herein we present the bifurcation of the preexisting Spinach aptamer and demonstrate its utility as a novel split aptamer system for monitoring RNA self-assembly as well as the processing of pre-short interfering substrates. We show for the first time that the Spinach aptamer can be divided into two nonfunctional halves that, once assembled, restore the original fluorescent signal characteristic of the unabridged aptamer. In this regard, the split-Spinach aptamer is represented as a potential tool for monitoring the self-assembly of artificial and/or natural RNAs.

Directed evolution combined with synthetic biology strategies expedite semi-rational engineering of genes and genomes

Owing to our limited understanding of the relationship between sequence and function and the interaction between intracellular pathways and regulatory systems, the rational design of enzyme-coding genes and de novo assembly of a brand-new artificial genome for a desired functionality or phenotype are difficult to achieve. As an alternative approach, directed evolution has been widely used to engineer genomes and enzyme-coding genes. In particular, significant developments toward DNA synthesis, DNA assembly (in vitro or in vivo), recombination-mediated genetic engineering, and high-throughput screening techniques in the field of synthetic biology have been matured and widely adopted, enabling rapid semi-rational genome engineering to generate variants with desired properties. In this commentary, these novel tools and their corresponding applications in the directed evolution of genomes and enzymes are discussed. Moreover, the strategies for genome engineering and rapid in vitro enzyme evolution are also proposed.

An autonomous in vivo dual selection protocol for boolean genetic circuits

Success in synthetic biology depends on the efficient construction of robust genetic circuitry. However, even the direct engineering of the simplest genetic elements (switches, logic gates) is a challenge and involves intense lab work. As the complexity of biological circuits grows, it becomes more complicated and less fruitful to rely on the rational design paradigm, because it demands many time-consuming trial-and-error cycles. One of the reasons is the context-dependent behavior of small assembly parts (like BioBricks), which in a complex environment often interact in an unpredictable way. Therefore, the idea of evolutionary engineering (artificial directed in vivo evolution) based on screening and selection of randomized combinatorial genetic circuit libraries became popular. In this article we build on the so-called dual selection technique. We propose a plasmid-based framework using toxin-antitoxin pairs together with the relaxase conjugative protein, enabling an efficient autonomous in vivo evolutionary selection of simple Boolean circuits in bacteria (E. coli was chosen for demonstration). Unlike previously reported protocols, both on and off selection steps can run simultaneously in various cells in the same environment without human intervention; and good circuits not only survive the selection process but are also horizontally transferred by conjugation to the neighbor cells to accelerate the convergence rate of the selection process. Our directed evolution strategy combines a new dual selection method with fluorescence-based screening to increase the robustness of the technique against mutations. As there are more orthogonal toxin-antitoxin pairs in E. coli, the approach is likely to be scalable to more complex functions. In silico experiments based on empirical data confirm the high search and selection capability of the protocol.

Bridging the gap: a roadmap to breaking the biological design barrier

This paper presents an analysis of an emerging bottleneck in organism engineering, and paths by which it may be overcome. Recent years have seen the development of a profusion of synthetic biology tools, largely falling into two categories: high-level “design” tools aimed at mapping from organism specifications to nucleic acid sequences implementing those specifications, and low-level “build and test” tools aimed at faster, cheaper, and more reliable fabrication of those sequences and assays of their behavior in engineered biological organisms. Between the two families, however, there is a major gap: we still largely lack the predictive models and component characterization data required to effectively determine which of the many possible candidate sequences considered in the design phase are the most likely to produce useful results when built and tested. As low-level tools continue to mature, the bottleneck in biological systems engineering is shifting to be dominated by design, making this gap a critical barrier to progress. Considering how to address this gap, we find that widespread adoption of readily available analytic and assay methods is likely to lead to rapid improvement in available predictive models and component characterization models, as evidenced by a number of recent results. Such an enabling development is, in turn, likely to allow high-level tools to break the design barrier and support rapid development of transformative biological applications.

A highly tunable system for the simultaneous expression of multiple enzymes in Saccharomyces cerevisiae

Control of the expression levels of multiple enzymes in transgenic yeasts is essential for the effective production of complex molecules through fermentation. Here, we propose a tunable strategy for the control of expression levels based on the design of terminator regions and other gene-expression control elements in Saccharomyces cerevisiae. Our genome-integrated system, which is capable of producing high expression levels over a wide dynamic range, will broadly enable metabolic engineering and synthetic biology. We demonstrated that the activities of multiple cellulases and the production of ethanol were doubled in a transgenic yeast constructed with our system compared with those achieved with a standard expression system.

Construction of Escherichia Coli Cell Factories for Production of Organic Acids and Alcohols

Production of bulk chemicals from renewable biomass has been proved to be sustainable and environmentally friendly. Escherichia coli is the most commonly used host strain for constructing cell factories for production of bulk chemicals since it has clear physiological and genetic characteristics, grows fast in minimal salts medium, uses a wide range of substrates, and can be genetically modified easily. With the development of metabolic engineering, systems biology, and synthetic biology, a technology platform has been established to construct E. coli cell factories for bulk chemicals production. In this chapter, we will introduce this technology platform, as well as E. coli cell factories successfully constructed for production of organic acids and alcohols.

Dynamic combinatorial/covalent chemistry: a tool to read, generate and modulate the bioactivity of compounds and compound mixtures

Reversible covalent bond formation under thermodynamic control adds reactivity to self-assembled supramolecular systems, and is therefore an ideal tool to assess complexity of chemical and biological systems. Dynamic combinatorial/covalent chemistry (DCC) has been used to read structural information by selectively assembling receptors with the optimum molecular fit around a given template from a mixture of reversibly reacting building blocks. This technique allows access to efficient sensing devices and the generation of new biomolecules, such as small molecule receptor binders for drug discovery, but also larger biomimetic polymers and macromolecules with particular three-dimensional structural architectures. Adding a kinetic factor to a thermodynamically controlled equilibrium results in dynamic resolution and in self-sorting and self-replicating systems, all of which are of major importance in biological systems. Furthermore, the temporary modification of bioactive compounds by reversible combinatorial/covalent derivatisation allows control of their release and facilitates their transport across amphiphilic self-assembled systems such as artificial membranes or cell walls. The goal of this review is to give a conceptual overview of how the impact of DCC on supramolecular assemblies at different levels can allow us to understand, predict and modulate the complexity of biological systems.

High Throughput Screening and Selection Methods for Directed Enzyme Evolution

Successful evolutionary enzyme engineering requires a high throughput screening or selection method, which considerably increases the chance of obtaining desired properties and reduces the time and cost. In this review, a series of high throughput screening and selection methods are illustrated with significant and recent examples. These high throughput strategies are also discussed with an emphasis on compatibility with phenotypic analysis during directed enzyme evolution. Lastly, certain limitations of current methods, as well as future developments, are briefly summarized.

Integration of microfluidics into the synthetic biology design flow

One goal of synthetic biology is to design and build genetic circuits in living cells for a range of applications. Major challenges in these efforts include increasing the scalability and robustness of engineered biological systems and streamlining and automating the synthetic biology workflow of specification-design-assembly-verification. We present here a summary of the advances in microfluidic technology, particularly microfluidic large scale integration, that can be used to address the challenges facing each step of the synthetic biology workflow. Microfluidic technologies allow precise control over the flow of biological content within microscale devices, and thus may provide more reliable and scalable construction of synthetic biological systems. The integration of microfluidics and synthetic biology has the capability to produce rapid prototyping platforms for characterization of genetic devices, testing of biotherapeutics, and development of biosensors.

Multivariate modular metabolic engineering for pathway and strain optimization

Despite the potential in utilizing microbial fermentation for chemical production, the field of industrial biotechnology still lacks a standard, universally applicable principle for strain optimization. A key challenge has been in finding and applying effective ways to address metabolic flux imbalances. Strategies based on rational design require significant a priori knowledge and often fail to take a holistic view of cellular metabolism. Combinatorial approaches enable more global searches but require a high-throughput screen. Here, we present the recent advances and promises of a novel approach to metabolic pathway and strain optimization called multivariate modular metabolic engineering (MMME). In this technique, key enzymes are organized into distinct modules and simultaneously varied based on expression to balance flux through a pathway. Because of its simplicity and broad applicability, MMME has the potential to systematize and revolutionize the field of metabolic engineering and industrial biotechnology.

Mutagenic Organized Recombination Process by Homologous IN vivo Grouping (MORPHING) for directed enzyme evolution

Approaches that depend on directed evolution require reliable methods to generate DNA diversity so that mutant libraries can focus on specific target regions. We took advantage of the high frequency of homologous DNA recombination in Saccharomyces cerevisiae to develop a strategy for domain mutagenesis aimed at introducing and in vivo recombining random mutations in defined segments of DNA. Mutagenic Organized Recombination Process by Homologous IN vivo Grouping (MORPHING) is a one-pot random mutagenic method for short protein regions that harnesses the in vivo recombination apparatus of yeast. Using this approach, libraries can be prepared with different mutational loads in DNA segments of less than 30 amino acids so that they can be assembled into the remaining unaltered DNA regions in vivo with high fidelity. As a proof of concept, we present two eukaryotic-ligninolytic enzyme case studies: i) the enhancement of the oxidative stability of a H₂O₂-sensitive versatile peroxidase by independent evolution of three distinct protein segments (Leu28-Gly57, Leu149-Ala174 and Ile199-Leu268); and ii) the heterologous functional expression of an unspecific peroxygenase by exclusive evolution of its native 43-residue signal sequence.

Generating targeted libraries by the combinatorial incorporation of synthetic oligonucleotides during gene shuffling (ISOR)

Protein engineering by directed evolution relies on the use of libraries enriched with beneficial variants. Such libraries should explore large mutational diversities while avoiding high loads of deleterious mutations. Here we describe a simple protocol for incorporating synthetic oligonucleotides that encode designed, site-specific mutations by assembly PCR. This protocol enables a researcher to "hedge the bets," namely, to explore a large number of potentially beneficial mutations in a combinatorial manner such that individual library variants carry a limited number of mutations.

Transposon-based approaches for generating novel molecular diversity during directed evolution

This chapter introduces a set of transposon-based methods that were developed to sample trinucleotide deletion, trinucleotide replacement, and domain insertion. Each approach has a common initial step that utilizes an engineered version of the Mu transposon called MuDel. The inherent low sequence specificity of MuDel results in its random insertion into target DNA during in vitro transposition. Removal of the transposon using a type IIS restriction endonuclease generates blunt-end random breaks at a frequency of one per target gene and the concomitant loss of 3 bp. Self-ligation or insertion of another DNA cassette results in the sampling of trinucleotide deletion or trinucleotide substitution/domain insertion, respectively.

Bioprospecting in the genomic age

The genomic revolution promises great advances in the search for useful biocatalysts. Function-based metagenomic approaches have identified several enzymes with properties that make them useful candidates for a variety of bioprocesses. As DNA sequencing costs continue to decline, the volume of genomic data, along with their corresponding predicted protein sequences, will continue to increase dramatically, necessitating new approaches to leverage this information for gene-based bioprospecting efforts. Additionally, as new functions are discovered and correlated with this sequence information, the knowledge of the often complex relationship between a protein's sequence and function will improve. This in turn will lead to better gene-based bioprospecting approaches and facilitate the tailoring of desired properties through protein engineering projects. In this chapter, we discuss a number of recent advances in bioprospecting within the context of the genomic age.