Directed Evolution: Past, Present and Future

The multifaceted role of proteases and modern analytical methods for investigation of their catalytic activity

We discuss the diverse functions of proteases in the context of their biotechnological and medical significance, as well as analytical approaches used to determine the functional activity of these enzymes. An insight into modern approaches to studying the kinetics and specificity of proteases, based on spectral (absorption, fluorescence), mass spectrometric, immunological, calorimetric, and electrochemical methods of analysis is given. We also examine in detail electrochemical systems for determining the activity and specificity of proteases. Particular attention is given to exploring innovative electrochemical systems based on the detection of the electrochemical oxidation signal of amino acid residues, thereby eliminating the need for extra redox labels in the process of peptide synthesis. In the review, we highlight the main prospects for the further development of electrochemical systems for the study of biotechnologically and medically significant proteases, which will enable the miniaturization of the analytical process for determining the catalytic activity of these enzymes.

Tuning tRNAs for improved translation

Transfer RNAs have been extensively explored as the molecules that translate the genetic code into proteins. At this interface of genetics and biochemistry, tRNAs direct the efficiency of every major step of translation by interacting with a multitude of binding partners. However, due to the variability of tRNA sequences and the abundance of diverse post-transcriptional modifications, a guidebook linking tRNA sequences to specific translational outcomes has yet to be elucidated. Here, we review substantial efforts that have collectively uncovered tRNA engineering principles that can be used as a guide for the tuning of translation fidelity. These principles have allowed for the development of basic research, expansion of the genetic code with non-canonical amino acids, and tRNA therapeutics.

Synthetic gene circuit evolution: Insights and opportunities at the mid-scale

Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.

The mechanistic insights into different aspects of promiscuity in metalloenzymes

Enzymes are nature's ultimate machinery to catalyze complex reactions. Though enzymes are evolved to catalyze specific reactions, they also show significant promiscuity in reactions and substrate selection. Metalloenzymes contain a metal ion or metal cofactor in their active site, which is crucial in their catalytic activity. Depending on the metal and its coordination environment, the metal ion or cofactor may function as a Lewis acid or base and a redox center and thus can catalyze a plethora of natural reactions. In fact, the versatility in the oxidation state of the metal ions provides metalloenzymes with a high level of catalytic adaptability and promiscuity. In this chapter, we discuss different aspects of promiscuity in metalloenzymes by using several recent experimental and theoretical works as case studies. We start our discussion by introducing the concept of promiscuity and then we delve into the mechanistic insight into promiscuity at the molecular level.

Pharmaceutical removal from wastewater by introducing cytochrome P450s into microalgae

Pharmaceuticals are of increasing environmental concern as they emerge and accumulate in surface- and groundwater systems around the world, endangering the overall health of aquatic ecosystems. Municipal wastewater discharge is a significant vector for pharmaceuticals and their metabolites to enter surface waters as humans incompletely absorb prescription drugs and excrete up to 50% into wastewater, which are subsequently incompletely removed during wastewater treatment. Microalgae present a promising target for improving wastewater treatment due to their ability to remove some pollutants efficiently. However, their inherent metabolic pathways limit their capacity to degrade more recalcitrant organic compounds such as pharmaceuticals. The human liver employs enzymes to break down and absorb drugs, and these enzymes are extensively researched during drug development, meaning the cytochrome P450 enzymes responsible for metabolizing each approved drug are well studied. Thus, unlocking or increasing cytochrome P450 expression in endogenous wastewater microalgae could be a cost-effective strategy to reduce pharmaceutical loads in effluents. Here, we discuss the challenges and opportunities associated with introducing cytochrome P450 enzymes into microalgae. We anticipate that cytochrome P450-engineered microalgae can serve as a new drug removal method and a sustainable solution that can upgrade wastewater treatment facilities to function as "mega livers".

State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems-biosensor-based controllers-for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.

Controlling Recombination to Evolve Bacteriophages

Recombination among different phages sometimes facilitates their ability to grow on new hosts. Protocols to direct the evolution of phage host range, as might be used in the application of phage therapy, would then benefit from including steps to enable recombination. Applying mathematical and computational models, in addition to experiments using phages T3 and T7, we consider ways that a protocol may influence recombination levels. We first address coinfection, which is the first step to enabling recombination. The multiplicity of infection (MOI, the ratio of phage to cell concentration) is insufficient for predicting (co)infection levels. The force of infection (the rate at which cells are infected) is also critical but is more challenging to measure. Using both a high force of infection and high MOI (>1) for the different phages ensures high levels of coinfection. We also apply a four-genetic-locus model to study protocol effects on recombinant levels. Recombinants accumulate over multiple generations of phage growth, less so if one phage outgrows the other. Supplementing the phage pool with the low-fitness phage recovers some of this 'lost' recombination. Overall, fine tuning of phage recombination rates will not be practical with wild phages, but qualitative enhancement can be attained with some basic procedures.

Empowering Protein Engineering through Recombination of Beneficial Substitutions

Directed evolution stands as a seminal technology for generating novel protein functionalities, a cornerstone in biocatalysis, metabolic engineering, and synthetic biology. Today, with the development of various mutagenesis methods and advanced analytical machines, the challenge of diversity generation and high-throughput screening platforms is largely solved, and one of the remaining challenges is: how to empower the potential of single beneficial substitutions with recombination to achieve the epistatic effect. This review overviews experimental and computer-assisted recombination methods in protein engineering campaigns. In addition, integrated and machine learning-guided strategies were highlighted to discuss how these recombination approaches contribute to generating the screening library with better diversity, coverage, and size. A decision tree was finally summarized to guide the further selection of proper recombination strategies in practice, which was beneficial for accelerating protein engineering.

Machine learning to predict continuous protein properties from binary cell sorting data and map unseen sequence space

Proteins are a diverse class of biomolecules responsible for wide-ranging cellular functions, from catalyzing reactions to recognizing pathogens. The ability to evolve proteins rapidly and inexpensively toward improved properties is a common objective for protein engineers. Powerful high-throughput methods like fluorescent activated cell sorting and next-generation sequencing have dramatically improved directed evolution experiments. However, it is unclear how to best leverage these data to characterize protein fitness landscapes more completely and identify lead candidates. In this work, we develop a simple yet powerful framework to improve protein optimization by predicting continuous protein properties from simple directed evolution experiments using interpretable, linear machine learning models. Importantly, we find that these models, which use data from simple but imprecise experimental estimates of protein fitness, have predictive capabilities that approach more precise but expensive data. Evaluated across five diverse protein engineering tasks, continuous properties are consistently predicted from readily available deep sequencing data, demonstrating that protein fitness space can be reasonably well modeled by linear relationships among sequence mutations. To prospectively test the utility of this approach, we generated a library of stapled peptides and applied the framework to predict affinity and specificity from simple cell sorting data. We then coupled integer linear programming, a method to optimize protein fitness from linear weights, with mutation scores from machine learning to identify variants in unseen sequence space that have improved and co-optimal properties. This approach represents a versatile tool for improved analysis and identification of protein variants across many domains of protein engineering.

Design of a dual-responding genetic circuit for high-throughput identification of L-threonine-overproducing Escherichia coli

L-threonine is a crucial amino acid that is extensively employed in the realms of food, animal feed and pharmaceuticals. Unfortunately, the lack of an appropriate biosensor has hindered the establishment of a robust high-throughput screening (HTS) system for the identification of the desired strains from random mutants. In this study, a dual-responding genetic circuit that capitalizes on the L-threonine inducer-like effect, the L-threonine riboswitch, and a signal amplification system was designed for the purpose of screening L-threonine overproducers. This platform effectively enhanced the performance of the enzyme and facilitated the identification of high L-threonine-producing strains from a random mutant library. Consequently, pathway optimization and directed evolution of the key enzyme enhanced L-threonine production by 4 and 7-fold, respectively. These results demonstrate the potential of biosensor design for dynamic metabolite detection and offer a promising tool for HTS and metabolic regulation for the development of L-threonine-hyperproducing strains.

Improving Protein Expression, Stability, and Function with ProteinMPNN

Natural proteins are highly optimized for function but are often difficult to produce at a scale suitable for biotechnological applications due to poor expression in heterologous systems, limited solubility, and sensitivity to temperature. Thus, a general method that improves the physical properties of native proteins while maintaining function could have wide utility for protein-based technologies. Here, we show that the deep neural network ProteinMPNN, together with evolutionary and structural information, provides a route to increasing protein expression, stability, and function. For both myoglobin and tobacco etch virus (TEV) protease, we generated designs with improved expression, elevated melting temperatures, and improved function. For TEV protease, we identified multiple designs with improved catalytic activity as compared to the parent sequence and previously reported TEV variants. Our approach should be broadly useful for improving the expression, stability, and function of biotechnologically important proteins.

Computer-Aided Reconstruction and Application of Bacillus halodurans S7 Xylanase with Heat and Alkali Resistance

β-1,4-Endoxylanase is the most critical hydrolase for xylan degradation during lignocellulosic biomass utilization. However, its poor stability and activity in hot and alkaline environments hinder its widespread application. In this study, BhS7Xyl from Bacillus halodurans S7 was improved using a computer-aided design through isothermal compressibility (βT) perturbation engineering and by combining three thermostability prediction algorithms (ICPE-TPA). The best variant with remarkable improvement in specific activity, heat resistance (70 °C), and alkaline resistance (both pH 9.0 and 70 °C), R69F/E137M/E145L, exhibited a 4.9-fold increase by wild-type in specific activity (1368.6 U/mg), a 39.4-fold increase in temperature half-life (458.1 min), and a 57.6-fold increase in pH half-life (383.1 min). Furthermore, R69F/E137M/E145L was applied to the hydrolysis of agricultural waste (corncob and hardwood pulp) to efficiently obtain a higher yield of high-value xylooligosaccharides. Overall, the ICPE-TPA strategy has the potential to improve the functional performance of enzymes under extreme conditions for the high-value utilization of lignocellulosic biomass.

Multiple Rounds of In Vivo Random Mutagenesis and Selection in Vibrio natriegens Result in Substantial Increases in REE Binding Capacity

Rare earth elements (REE) are essential ingredients in many modern technologies, yet their purification remains either environmentally harmful or economically unviable. Adsorption, or biosorption, of REE onto bacterial cell membranes offers a sustainable alternative to traditional solvent extraction methods. But in order for biosorption-based REE purification to compete economically, the capacity and specificity of biosorption sites must be enhanced. Although there have been some recent advances in characterizing the genetics of REE-biosorption, the variety and complexity of bacterial membrane surface sites make targeted genetic engineering difficult. Here, we propose using multiple rounds of in vivo random mutagenesis induced by the MP6 plasmid combined with plate-throughput REE-biosorption screening to improve a microbe's capacity and selectivity for biosorbing REE. We engineered a strain of Vibrio natriegens capable of biosorbing 210% more dysprosium compared to the wild-type and produced selectivity improvements of up to 50% between the lightest (lanthanum) and heaviest (lutetium) REE. We believe that mutations we observed in ABC transporters as well as a nonessential protein in the BAM outer membrane β-barrel protein insertion complex likely contribute to some─but almost certainly not all─of the biosorption changes we observed. Given the ease of finding significant biosorption mutants, these results highlight just how many genes likely contribute to biosorption as well as the power of random mutagenesis in identifying genes of interest and optimizing a biological system for a task.

Computationally guided bioengineering of the active site, substrate access pathway, and water channels of thermostable cytochrome P450, CYP175A1, for catalyzing the alkane hydroxylation reaction

Understanding structure-function relationships in proteins is pivotal in their development as industrial biocatalysts. In this regard, rational engineering of protein active site access pathways and various tunnels and channels plays a central role in designing competent enzymes with high stability and enhanced efficiency. Here, we report the rational evolution of a thermostable cytochrome P450, CYP175A1, to catalyze the C-H activation reaction of longer-chain alkanes. A strategy combining computational tools with experiments has shown that the substrate scope and enzymatic activity can be enhanced by rational engineering of certain important channels such as the substrate entry and water channels along with the active site of the enzyme. The evolved enzymes showed an improved catalytic rate for hexadecane hydroxylation with high regioselectivity. The Q67L/Y68F mutation showed binding of the substrate in the active site, water channel mutation L80F/V220T showed improved catalytic activity through the peroxide shunt pathway and substrate entry channel mutation W269F/I270A showed better substrate accessibility to the active pocket. All-atom MD simulations provided the rationale for the inactivity of the wild-type CYP175A1 for hexadecane hydroxylation and predicted the above hot-spot residues to enhance the activity. The reaction mechanism was studied by QM/MM calculations for enzyme-substrate complexes and reaction intermediates. Detailed thermal and thermodynamic stability of all the mutants were analyzed and the results showed that the evolved enzymes were thermally stable. The present strategy showed promising results, and insights gained from this work can be applied to the general enzymatic system to expand substrate scope and improve catalytic activity.

Bicyclo[3.2.0]carbocyclic Molecules and Redox Biotransformations: The Evolution of Closed-Loop Artificial Linear Biocatalytic Cascades and Related Redox-Neutral Systems

The role of cofactor recycling in determining the efficiency of artificial biocatalytic cascades has become paramount in recent years. Closed-loop cofactor recycling, which initially emerged in the 1990s, has made a valuable contribution to the development of this aspect of biotechnology. However, the evolution of redox-neutral closed-loop cofactor recycling has a longer history that has been integrally linked to the enzymology of oxy-functionalised bicyclo[3.2.0]carbocyclic molecule metabolism throughout. This review traces that relevant history from the mid-1960s to current times.

Crop adaptation to climate change: An evolutionary perspective

The disciplines of evolutionary biology and plant and animal breeding have been intertwined throughout their development, with responses to artificial selection yielding insights into the action of natural selection and evolutionary biology providing statistical and conceptual guidance for modern breeding. Here we offer an evolutionary perspective on a grand challenge of the 21st century: feeding humanity in the face of climate change. We first highlight promising strategies currently under way to adapt crops to current and future climate change. These include methods to match crop varieties with current and predicted environments and to optimize breeding goals, management practices, and crop microbiomes to enhance yield and sustainable production. We also describe the promise of crop wild relatives and recent technological innovations such as speed breeding, genomic selection, and genome editing for improving environmental resilience of existing crop varieties or for developing new crops. Next, we discuss how methods and theory from evolutionary biology can enhance these existing strategies and suggest novel approaches. We focus initially on methods for reconstructing the evolutionary history of crops and their pests and symbionts, because such historical information provides an overall framework for crop-improvement efforts. We then describe how evolutionary approaches can be used to detect and mitigate the accumulation of deleterious mutations in crop genomes, identify alleles and mutations that underlie adaptation (and maladaptation) to agricultural environments, mitigate evolutionary trade-offs, and improve critical proteins. Continuing feedback between the evolution and crop biology communities will ensure optimal design of strategies for adapting crops to climate change.

Fishing for Catalysis: Experimental Approaches to Narrowing Search Space in Directed Evolution of Enzymes

Directed evolution has transformed protein engineering offering a path to rapid improvement of protein properties. Yet, in practice it is limited by the hyper-astronomic protein sequence search space, and approaches to identify mutagenic hot spots, i.e., locations where mutations are most likely to have a productive impact, are needed. In this perspective, we categorize and discuss recent progress in the experimental approaches (broadly defined as structural, bioinformatic, and dynamic) to hot spot identification. Recent successes in harnessing protein dynamics and machine learning approaches provide new opportunities for the field and will undoubtedly help directed evolution reach its full potential.

Enabling Broader Adoption of Biocatalysis in Organic Chemistry

Biocatalysis is becoming an increasingly impactful method in contemporary synthetic chemistry for target molecule synthesis. The selectivity imparted by enzymes has been leveraged to complete previously intractable chemical transformations and improve synthetic routes toward complex molecules. However, the implementation of biocatalysis in mainstream organic chemistry has been gradual to this point. This is partly due to a set of historical and technological barriers that have prevented chemists from using biocatalysis as a synthetic tool with utility that parallels alternative modes of catalysis. In this Perspective, we discuss these barriers and how they have hindered the adoption of enzyme catalysts into synthetic strategies. We also summarize tools and resources that already enable organic chemists to use biocatalysts. Furthermore, we discuss ways to further lower the barriers for the adoption of biocatalysis by the broader synthetic organic chemistry community through the dissemination of resources, demystifying biocatalytic reactions, and increasing collaboration across the field.

Analysis of neutral mutational drift in an allosteric enzyme

Neutral mutational drift is an important source of biological diversity that remains underexploited in fundamental studies of protein biophysics. This study uses a synthetic transcriptional circuit to study neutral drift in protein tyrosine phosphatase 1B (PTP1B), a mammalian signaling enzyme for which conformational changes are rate limiting. Kinetic assays of purified mutants indicate that catalytic activity, rather than thermodynamic stability, guides enrichment under neutral drift, where neutral or mildly activating mutations can mitigate the effects of deleterious ones. In general, mutants show a moderate activity-stability tradeoff, an indication that minor improvements in the activity of PTP1B do not require concomitant losses in its stability. Multiplexed sequencing of large mutant pools suggests that substitutions at allosterically influential sites are purged under biological selection, which enriches for mutations located outside of the active site. Findings indicate that the positional dependence of neutral mutations within drifting populations can reveal the presence of allosteric networks and illustrate an approach for using synthetic transcriptional systems to explore these mutations in regulatory enzymes.

Precision cellular agriculture: The future role of recombinantly expressed protein as food

Cellular agriculture is a rapidly emerging field, within which cultured meat has attracted the majority of media attention in recent years. An equally promising area of cellular agriculture, and one that has produced far more actual food ingredients that have been incorporated into commercially available products, is the use of cellular hosts to produce soluble proteins, herein referred to as precision cellular agriculture (PCAg). In PCAg, specific animal- or plant-sourced proteins are expressed recombinantly in unicellular hosts-the majority of which are yeast-and harvested for food use. The numerous advantages of PCAg over traditional agriculture, including a smaller carbon footprint and more consistent products, have led to extensive research on its utility. This review is the first to survey proteins currently being expressed using PCAg for food purposes. A growing number of viable expression hosts and recent advances for increased protein yields and process optimization have led to its application for producing milk, egg, and muscle proteins; plant hemoglobin; sweet-tasting plant proteins; and ice-binding proteins. Current knowledge gaps present research opportunities for optimizing expression hosts, tailoring posttranslational modifications, and expanding the scope of proteins produced. Considerations for the expansion of PCAg and its implications on food regulation, society, ethics, and the environment are also discussed. Considering the current trajectory of PCAg, food proteins from any biological source can likely be expressed recombinantly and used as purified food ingredients to create novel and tailored food products.

Using machine learning to predict the effects and consequences of mutations in proteins

Machine and deep learning approaches can leverage the increasingly available massive datasets of protein sequences, structures, and mutational effects to predict variants with improved fitness. Many different approaches are being developed, but systematic benchmarking studies indicate that even though the specifics of the machine learning algorithms matter, the more important constraint comes from the data availability and quality utilized during training. In cases where little experimental data are available, unsupervised and self-supervised pre-training with generic protein datasets can still perform well after subsequent refinement via hybrid or transfer learning approaches. Overall, recent progress in this field has been staggering, and machine learning approaches will likely play a major role in future breakthroughs in protein biochemistry and engineering.

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

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

Rational design of enzyme activity and enantioselectivity

The strategy of rational design to engineer enzymes is to predict the potential mutants based on the understanding of the relationships between protein structure and function, and subsequently introduce the mutations using the site-directed mutagenesis. Rational design methods are universal, relatively fast and have the potential to be developed into algorithms that can quantitatively predict the performance of the designed sequences. Compared to the protein stability, it was more challenging to design an enzyme with improved activity or selectivity, due to the complexity of enzyme molecular structure and inadequate understanding of the relationships between enzyme structures and functions. However, with the development of computational force, advanced algorithm and a deeper understanding of enzyme catalytic mechanisms, rational design could significantly simplify the process of engineering enzyme functions and the number of studies applying rational design strategy has been increasing. Here, we reviewed the recent advances of applying the rational design strategy to engineer enzyme functions including activity and enantioselectivity. Five strategies including multiple sequence alignment, strategy based on steric hindrance, strategy based on remodeling interaction network, strategy based on dynamics modification and computational protein design are discussed and the successful cases using these strategies are introduced.

Structural and functional analyses of GH51 alpha-L-arabinofuranosidase of Geobacillus vulcani GS90 reveal crucial residues for catalytic activity and thermostability

Alpha-L-arabinofuranosidase (Abf) is of big interest in various industrial areas. Directed evolution is a powerful strategy to identify significant residues underlying Abf properties. Here, six active variants from GH51 Abf of Geobacillus vulcani GS90 (GvAbf) by directed evolution were overproduced, extracted, and analyzed at biochemical and structural levels. According to the activity and thermostability results, the most-active and the least-active variants were found as GvAbf51 and GvAbf52, respectively. GvAbf63 variant was more active than parent GvAbf by 20% and less active than GvAbf51. Also, the highest thermostability belonged to GvAbf52 with 80% residual activity after 1 h. Comparative sequence and structure analyses revealed that GvAbf51 possessed L307S displacement. Thus, this study suggested that L307 residue may be critical for GvAbf activity. GvAbf63 had H30D, Q90H, and L307S displacements, and H30 was covalently bound to E29 catalytic residue. Thus, H30D may decrease the positive effect of L307S on GvAbf63 activity, preventing E29 action. Besides, GvAbf52 possessed S215N, L307S, H473P, and G476C substitutions and S215 was close to E175 (acid-base residue). S215N may partially disrupt E175 action. Overall effect of all substitutions in GvAbf52 may result in the formation of the C-C bond between C171 and C213 by becoming closer to each other.

Characterization of the Binding Behavior of Specific Cobalt and Nickel Ion-Binding Peptides Identified by Phage Surface Display

In recent years, the application focus of phage surface display (PSD) technology has been extended to the identification of metal ion-selective peptides. In previous studies, two phage clones—a nickel-binding one with the peptide motif CNAKHHPRCGGG and a cobalt-binding one with the peptide motif CTQMLGQLCGGG—were isolated, and their binding ability to metal-loaded NTA agarose beads was investigated. Here, the free cyclic peptides are characterized by UV/VIS spectroscopy with respect to their binding capacity for the respective target ion and in crossover experiments for the other ion by isothermal titration calorimetry (ITC) in different buffer systems. This revealed differences in selectivity and affinity. The cobalt-specific peptide is very sensitive to different buffers; it has a 20-fold higher affinity for cobalt and nickel under suitable conditions. The nickel-specific peptide binds more moderately and robustly in different buffers but only selectively to nickel.

Artificial selection methods from evolutionary computing show promise for directed evolution of microbes

Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a general-purpose search engine for solutions to challenging computational problems. Despite their overlapping approaches, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. In this work, we ask whether parent selection algorithms—procedures for choosing promising progenitors—from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we introduce an agent-based model of directed microbial evolution, which we used to evaluate how well three selection algorithms from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to methods commonly used in the laboratory (elite and top 10% selection). We found that multiobjective selection techniques from evolutionary computing (lexicase and non-dominated elite) generally outperformed the commonly used directed evolution approaches when selecting for multiple traits of interest. Our results motivate ongoing work transferring these multiobjective selection procedures into the laboratory and a continued evaluation of more sophisticated artificial selection methods.

Humans have long known how to co-opt evolutionary processes for their own benefit. Carefully choosing which individuals to breed so that beneficial traits would take hold, they have domesticated dogs, wheat, cows and many other species to fulfil their needs.

Biologists have recently refined these ‘artificial selection’ approaches to focus on microorganisms. The hope is to obtain microbes equipped with desirable features, such as the ability to degrade plastic or to produce valuable molecules. However, existing ways of using artificial selection on microbes are limited and sometimes not effective.

Computer scientists have also harnessed evolutionary principles for their own purposes, developing highly effective artificial selection protocols that are used to find solutions to challenging computational problems. Yet because of limited communication between the two fields, sophisticated selection protocols honed over decades in evolutionary computing have yet to be evaluated for use in biological populations.

In their work, Lalejini et al. compared popular artificial selection protocols developed for either evolutionary computing or work with microorganisms. Two computing selection methods showed promise for improving directed evolution in the laboratory. Crucially, these selection protocols differed from conventionally used methods by selecting for both diversity and performance, rather than performance alone. These promising approaches are now being tested in the laboratory, with potentially far-reaching benefits for medical, biotech, and agricultural applications.

While evolutionary computing owes its origins to our understanding of biological processes, it has much to offer in return to help us harness those same mechanisms. The results by Lalejini et al. help to bridge the gap between computational and biological communities who could both benefit from increased collaboration.

Directed Evolution Enables Simultaneous Controlled Release of Multiple Therapeutic Proteins from Biopolymer-Based Hydrogels

With the advent of increasingly complex combination strategies of biologics, independent control over their delivery is the key to their efficacy; however, current approaches are hindered by the limited independent tunability of their release rates. To overcome these limitations, directed evolution is used to engineer highly specific, low affinity affibody binding partners to multiple therapeutic proteins to independently control protein release rates. As a proof-of-concept, specific affibody binding partners for two proteins with broad therapeutic utility: insulin-like growth factor-1 (IGF-1) and pigment epithelium-derived factor (PEDF) are identified. Protein-affibody binding interactions specific to these target proteins with equilibrium dissociation constants (K_D ) between 10^-7 and 10^-8 m are discovered. The affibodies are covalently bound to the backbone of crosslinked hydrogels using click chemistry, enabling sustained, independent, and simultaneous release of bioactive IGF-1 and PEDF over 7 days. The system is tested with C57BL/6J mice in vivo, and the affibody-controlled release of IGF-1 results in sustained activity when compared to bolus IGF-1 delivery. This work demonstrates a new, broadly applicable approach to tune the release of therapeutic proteins simultaneously and independently and thus the way for precise control over the delivery of multicomponent therapies is paved.

Designing Artificial Metalloenzymes by Tuning of the Environment beyond the Primary Coordination Sphere

Metalloenzymes catalyze a variety of reactions using a limited number of natural amino acids and metallocofactors. Therefore, the environment beyond the primary coordination sphere must play an important role in both conferring and tuning their phenomenal catalytic properties, enabling active sites with otherwise similar primary coordination environments to perform a diverse array of biological functions. However, since the interactions beyond the primary coordination sphere are numerous and weak, it has been difficult to pinpoint structural features responsible for the tuning of activities of native enzymes. Designing artificial metalloenzymes (ArMs) offers an excellent basis to elucidate the roles of these interactions and to further develop practical biological catalysts. In this review, we highlight how the secondary coordination spheres of ArMs influence metal binding and catalysis, with particular focus on the use of native protein scaffolds as templates for the design of ArMs by either rational design aided by computational modeling, directed evolution, or a combination of both approaches. In describing successes in designing heme, nonheme Fe, and Cu metalloenzymes, heteronuclear metalloenzymes containing heme, and those ArMs containing other metal centers (including those with non-native metal ions and metallocofactors), we have summarized insights gained on how careful controls of the interactions in the secondary coordination sphere, including hydrophobic and hydrogen bonding interactions, allow the generation and tuning of these respective systems to approach, rival, and, in a few cases, exceed those of native enzymes. We have also provided an outlook on the remaining challenges in the field and future directions that will allow for a deeper understanding of the secondary coordination sphere a deeper understanding of the secondary coordintion sphere to be gained, and in turn to guide the design of a broader and more efficient variety of ArMs.

Molecular advances in microbial α-galactosidases: challenges and prospects

α-Galactosidase (α-D-galactosidase galactohydrolase; EC 3.2.1.22), is an industrially important enzyme that hydrolyzes the galactose residues in galactooligosaccharides and polysaccharides. The industrial production of α-galactosidase is currently insufficient owing to the high production cost, low production efficiency and low enzyme activity. Recent years have witnessed an increase in the worldwide research on molecular techniques to improve the production efficiency of microbial α-galactosidases. Cloning and overexpression of the gene sequences coding for α-galactosidases can not only increase the enzyme yield but can confer industrially beneficial characteristics to the enzyme protein. This review focuses on the molecular advances in the overexpression of α-galactosidases in bacterial and yeast/fungal expression systems. Recombinant α-galactosidases have improved biochemical and hydrolytic properties compared to their native counterparts. Metabolic engineering of microorganisms to produce high yields of α-galactosidase can also assist in the production of value-added products. Developing new variants of α-galactosidases through directed evolution can yield enzymes with increased catalytic activity and altered regioselectivity. The bottlenecks in the recombinant production of α-galactosidases are also discussed. The knowledge about the hurdles in the overexpression of recombinant proteins illuminates the emerging possibilities of developing a successful microbial cell factory and widens the opportunities for the production of industrially beneficial α-galactosidases.

Probing nanomotion of single bacteria with graphene drums

Motion is a key characteristic of every form of life¹. Even at the microscale, it has been reported that colonies of bacteria can generate nanomotion on mechanical cantilevers², but the origin of these nanoscale vibrations has remained unresolved^3,4. Here, we present a new technique using drums made of ultrathin bilayer graphene, where the nanomotion of single bacteria can be measured in its aqueous growth environment. A single Escherichia coli cell is found to generate random oscillations with amplitudes of up to 60 nm, exerting forces of up to 6 nN to its environment. Using mutant strains that differ by single gene deletions that affect motility, we are able to pinpoint the bacterial flagella as the main source of nanomotion. By real-time tracing of changes in nanomotion on administering antibiotics, we demonstrate that graphene drums can perform antibiotic susceptibility testing with single-cell sensitivity. These findings deepen our understanding of processes underlying cellular dynamics, and pave the way towards high-throughput and parallelized rapid screening of the effectiveness of antibiotics in bacterial infections with graphene devices.

"Multiagent" Screening Improves Directed Enzyme Evolution by Identifying Epistatic Mutations

Enzyme evolution has enabled numerous advances in biotechnology and synthetic biology, yet still requires many iterative rounds of screening to identify optimal mutant sequences. This is due to the sparsity of the fitness landscape, which is caused by epistatic mutations that only offer improvements when combined with other mutations. We report an approach that incorporates diverse substrate analogues in the screening process, where multiple substrates act like multiple agents navigating the fitness landscape, identifying epistatic mutant residues without a need for testing the entire combinatorial search space. We initially validate this approach by engineering a malonyl-CoA synthetase and identify numerous epistatic mutations improving activity for several diverse substrates. The majority of these mutations would have been missed upon screening for a single substrate alone. We expect that this approach can accelerate a wide array of enzyme engineering programs.

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

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

Directed Evolution of Replication-Competent Double-Stranded DNA Bacteriophage toward New Host Specificity

In the fight against antimicrobial resistance, bacteriophages are a promising alternative to antibiotics. However, due to their narrow spectra, phage therapy requires the careful matching between the host and bacteriophage to be effective. Despite our best efforts, nature remains as the only source of novel phage specificity. Directed evolution can potentially open an avenue for engineering phage specificity and improving qualities of phages that are not strongly selected for in their natural environments but are important for therapeutic applications. In this work, we present a strategy that generates large libraries of replication-competent phage variants directly from synthetic DNA fragments, with no restriction on their host specificity. Using the T7 bacteriophage as a proof-of-concept, we created a large library of tail fiber mutants with at least 10⁷ unique variants. From this library, we identified mutants that have broadened specificity as evidenced by their novel lytic activity against Yersinia enterocolitica, a strain that the wild-type T7 was unable to lyse. Using the same concept, mutants with improved lytic efficiency and characteristics, such as lytic condition tolerance and resistance suppression, were also identified. However, the observed limitations in altering host specificity by tail fiber mutagenesis suggest that other bottlenecks could be of equal or even greater importance.

Switching promotor recognition of phage RNA polymerase in silico along lab-directed evolution path

In this work, we computationally investigated how a viral RNA polymerase (RNAP) from bacteriophage T7 evolves into RNAP variants under lab-directed evolution to switch recognition from T7 promoter to T3 promoter in transcription initiation. We first constructed a closed initiation complex for the wild-type T7 RNAP and then for six mutant RNAPs discovered from phage-assisted continuous evolution experiments. All-atom molecular dynamics simulations up to 1 μs each were conducted on these RNAPs in a complex with the T7 and T3 promoters. Our simulations show notably that protein-DNA electrostatic interactions or stabilities at the RNAP-DNA promoter interface well dictate the promoter recognition preference of the RNAP and variants. Key residues and structural elements that contribute significantly to switching the promoter recognition were identified. Followed by a first point mutation N748D on the specificity loop to slightly disengage the RNAP from the promoter to hinder the original recognition, we found an auxiliary helix (206-225) that takes over switching the promoter recognition upon further mutations (E222K and E207K) by forming additional charge interactions with the promoter DNA and reorientating differently on the T7 and T3 promoters. Further mutations on the AT-rich loop and the specificity loop can fully switch the RNAP-promoter recognition to the T3 promoter. Overall, our studies reveal energetics and structural dynamics details along an exemplary directed evolutionary path of the phage RNAP variants for a rewired promoter recognition function. The findings demonstrate underlying physical mechanisms and are expected to assist knowledge and data learning or rational redesign of the protein enzyme structure function.

Engineering the 2-Oxoglutarate Dehydrogenase Complex to Understand Catalysis and Alter Substrate Recognition

The E. coli 2-oxoglutarate dehydrogenase complex (OGDHc) is a multienzyme complex in the tricarboxylic acid cycle, consisting of multiple copies of three components, 2-oxoglutarate dehydrogenase (E1o), dihydrolipoamide succinyltransferase (E2o) and dihydrolipoamide dehydrogenase (E3), which catalyze the formation of succinyl-CoA and NADH (+H+) from 2-oxoglutarate. This review summarizes applications of the site saturation mutagenesis (SSM) to engineer E. coli OGDHc with mechanistic and chemoenzymatic synthetic goals. First, E1o was engineered by creating SSM libraries at positions His260 and His298.Variants were identified that: (a) lead to acceptance of substrate analogues lacking the 5-carboxyl group and (b) performed carboligation reactions producing acetoin-like compounds with good enantioselectivity. Engineering the E2o catalytic (core) domain enabled (a) assignment of roles for pivotal residues involved in catalysis, (b) re-construction of the substrate-binding pocket to accept substrates other than succinyllysyldihydrolipoamide and (c) elucidation of the mechanism of trans-thioesterification to involve stabilization of a tetrahedral oxyanionic intermediate with hydrogen bonds by His375 and Asp374, rather than general acid–base catalysis which has been misunderstood for decades. The E. coli OGDHc is the first example of a 2-oxo acid dehydrogenase complex which was evolved to a 2-oxo aliphatic acid dehydrogenase complex by engineering two consecutive E1o and E2o components.

Nondegenerate Saturation Mutagenesis: Library Construction and Analysis via MAX and ProxiMAX Randomization

Protein engineering can enhance desirable features and improve performance outside of the natural context. Several strategies have been adopted over the years for gene diversification, and engineering of modular proteins in particular is most effective when a high-throughput, library-based approach is employed. Nondegenerate saturation mutagenesis plays a dynamic role in engineering proteins by targeting multiple codons to generate massively diverse gene libraries. Herein, we describe the nondegenerate saturation mutagenesis techniques that we have developed for contiguous (ProxiMAX) and noncontiguous (MAX) randomized codon generation to create precisely defined, diverse gene libraries, in the context of other fully nondegenerate strategies. ProxiMAX randomization comprises saturation cycling with repeated cycles of blunt-ended ligation, type IIS restriction, and PCR amplification, and is now a commercially automated process predominantly used for antibody library generation. MAX randomization encompasses a manual process of selective hybridisation between individual custom oligonucleotide mixes and a conventionally randomized template and is principally employed in the research laboratory setting, to engineer alpha helical proteins and active sites of enzymes. DNA libraries generated using either technology create high-throughput amino acid substitutions via codon randomization, to generate genetically diverse clones.

Forty Years of Directed Evolution and its Continuously Evolving Technology Toolbox - A Review of the Patent Landscape

Generating functional protein variants with novel or improved characteristics has been a goal of the biotechnology industry and life sciences, for decades. Rational design and directed evolution are two major pathways to achieve the desired ends. Whilst rational protein design approach has made substantial progress, the idea of using a method based on cycles of mutagenesis and natural selection to develop novel binding proteins, enzymes and structures has attracted great attention. Laboratory evolution of proteins/enzymes requires new tools and analytical approaches to create genetic diversity and identifying variants with desired traits. In this pursuit, construction of sufficiently large libraries of target molecules to search for improved variants and the need for new protocols to alter the properties of target molecules has been a continuing challenge in the directed evolution experiments. This review will discuss the in vivo and in vitro gene diversification tools, library screening or selection approaches, and artificial intelligence/machine-learning-based strategies to mutagenesis developed in the last forty years to accelerate the natural process of evolution in creating new functional protein variants, optimization of microbial strains and transformation of enzymes into industrial machines. Analyzing patent position over these techniques and mechanisms also constitutes an integral and distinctive part of this review. The aim is to provide an up-to-date resource/technology toolbox for research-based and pharmaceutical companies to discover the boundaries of competitor's intellectual property (IP) portfolio, their freedom-to-operate in the relevant IP landscape, and the need for patent due diligence analysis to rule out whether use of a particular patented mutagenesis method, library screening/selection technique falls outside the safe harbor of experimental use exemption. While so doing, we have referred to some recent cases that emphasize the significance of selecting a suitable gene diversification strategy in directed evolution experiments. This article is protected by copyright. All rights reserved.

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.

Directed genome evolution driven by structural rearrangement techniques

Directed genome evolution simulates the process of natural evolution at the genomic level in the laboratory to generate desired phenotypes. Here we review the applications of recent technological advances in genome writing and editing to directed genome evolution, with a focus on structural rearrangement techniques. We highlight how these techniques can be used to generate diverse genotypes, and to accelerate the evolution of phenotypic traits. We also discuss the perspectives of directed genome evolution.

PROFICS: A bacterial selection system for directed evolution of proteases

Proteases serve as important tools in biotechnology and as valuable drugs or drug targets. Efficient protein engineering methods to study and modulate protease properties are thus of great interest for a plethora of applications. We established PROFICS (PRotease Optimization via Fusion-Inhibited Carbamoyltransferase-based Selection), a bacterial selection system, which enables the optimization of proteases for biotechnology, therapeutics or diagnosis in a simple overnight process. During the PROFICS process, proteases are selected for their ability to specifically cut a tag from a reporter enzyme and leave a native N-terminus. Precise and efficient cleavage after the recognition sequence reverses the phenotype of an Escherichia coli knockout strain deficient in an essential enzyme of pyrimidine synthesis. A toolbox was generated to select for proteases with different preferences for P1' residues (the residue immediately following the cleavage site). The functionality of PROFICS is demonstrated with viral proteases and human caspase-2. PROFICS improved caspase-2 activity up to 25-fold after only one round of mutation and selection. Additionally, we found a significantly improved tolerance for all P1' residues caused by a mutation in a substrate interaction site. We showed that this improved activity enables cells containing the new variant to outgrow cells containing all other mutants, facilitating its straightforward selection. Apart from optimizing enzymatic activity and P1' tolerance, PROFICS can be used to reprogram specificities, erase off-target activity, optimize expression via tags/codon usage, or even to screen for potential drug-resistance-conferring mutations in therapeutic targets such as viral proteases in an unbiased manner.

Step-by-step design of proteins for small molecule interaction: A review on recent milestones

Protein design is the field of synthetic biology that aims at developing de novo custom-made proteins and peptides for specific applications. Despite exploring an ambitious goal, recent computational advances in both hardware and software technologies have paved the way to high-throughput screening and detailed design of novel folds and improved functionalities. Modern advances in the field of protein design for small molecule targeting are described in this review, organized in a step-by-step fashion: from the conception of a new or upgraded active binding site, to scaffold design, sequence optimization, and experimental expression of the custom protein. In each step, contemporary examples are described, and state-of-the-art software is briefly explored.

Biocatalytic oxidation of fatty alcohols into aldehydes for the flavors and fragrances industry

From Egyptian mummies to the Chanel n°5 perfume, fatty aldehydes have long been used and keep impacting our senses in a wide range of foods, beverages and perfumes. Natural sources of fatty aldehydes are threatened by qualitative and quantitative variability while traditional chemical routes are insufficient to answer the society shift toward more sustainable and natural products. The production of fatty aldehydes using biotechnologies is therefore the most promising alternative for the flavors and fragrances industry. In this review, after drawing the portrait of the origin and characteristics of fragrant fatty aldehydes, we present the three main classes of enzymes that catalyze the reaction of fatty alcohols oxidation into aldehydes, namely alcohol dehydrogenases, flavin-dependent alcohol oxidases and copper radical alcohol oxidases. The constraints, challenges and opportunities to implement these oxidative enzymes in the flavors and fragrances industry are then discussed. By setting the scene on the biocatalytic production of fatty aldehydes, and providing a critical assessment of its potential, we expect this review to contribute to the development of biotechnology-based solutions in the flavors and fragrances industry.

Improving the Thermostability of Xylanase A from Bacillus subtilis by Combining Bioinformatics and Electrostatic Interactions Optimization

The rational improvement of the enzyme catalytic activity is one of the most significant challenges in biotechnology. Most conventional strategies used to engineer enzymes involve selecting mutations to increase their thermostability. Determining good criteria for choosing these substitutions continues to be a challenge. In this work, we combine bioinformatics, electrostatic analysis, and molecular dynamics to predict beneficial mutations that may improve the thermostability of XynA from Bacillus subtilis. First, the Tanford-Kirkwood surface accessibility method is used to characterize each ionizable residue contribution to the protein native state stability. Residues identified to be destabilizing were mutated with the corresponding residues determined by the consensus or ancestral sequences at the same locations. Five mutants (K99T/N151D, K99T, S31R, N151D, and K154A) were investigated and compared with 12 control mutants derived from experimental approaches from the literature. Molecular dynamics results show that the mutants exhibited folding temperatures in the order K99T > K99T/N151D > S31R > N151D > WT > K154A. The combined approaches employed provide an effective strategy for low-cost enzyme optimization needed for large-scale biotechnological and medical applications.

Protein engineering for natural product biosynthesis and synthetic biology applications

As protein engineering grows more salient, many strategies have emerged to alter protein structure and function, with the goal of redesigning and optimizing natural product biosynthesis. Computational tools, including machine learning and molecular dynamics simulations, have enabled the rational mutagenesis of key catalytic residues for enhanced or altered biocatalysis. Semi-rational, directed evolution and microenvironment engineering strategies have optimized catalysis for native substrates and increased enzyme promiscuity beyond the scope of traditional rational approaches. These advances are made possible using novel high-throughput screens, including designer protein-based biosensors with engineered ligand specificity. Herein, we detail the most recent of these advances, focusing on polyketides, non-ribosomal peptides and isoprenoids, including their native biosynthetic logic to provide clarity for future applications of these technologies for natural product synthetic biology.

Multiplexed characterization of rationally designed promoter architectures deconstructs combinatorial logic for IPTG-inducible systems

A crucial step towards engineering biological systems is the ability to precisely tune the genetic response to environmental stimuli. In the case of Escherichia coli inducible promoters, our incomplete understanding of the relationship between sequence composition and gene expression hinders our ability to predictably control transcriptional responses. Here, we profile the expression dynamics of 8269 rationally designed, IPTG-inducible promoters that collectively explore the individual and combinatorial effects of RNA polymerase and LacI repressor binding site strengths. We then fit a statistical mechanics model to measured expression that accurately models gene expression and reveals properties of theoretically optimal inducible promoters. Furthermore, we characterize three alternative promoter architectures and show that repositioning binding sites within promoters influences the types of combinatorial effects observed between promoter elements. In total, this approach enables us to deconstruct relationships between inducible promoter elements and discover practical insights for engineering inducible promoters with desirable characteristics.

KnowVolution of prodigiosin ligase PigC towards condensation of short-chain prodiginines

One round of KnowVolution enhanced the catalytic activity of prodigiosin ligase PigC with short-chain monopyrroles, opening access to anticancer prodiginines.

Inducible plasmid display system for high-throughput selection of proteins with improved solubility

Soluble expression of enzymes inside the cell is a prerequisite for the successful biotransformation of valuable products. Some key enzymes involved in biotransformation processes, however, are hardly expressed in their soluble forms. Here, we propose an inducible plasmid display, which is a molecular evolution strategy coupled with a high-throughput screening and/or selection method, as a simple and powerful tool for improving the solubility of target enzymes. Specifically, the Oct-1 DNA-binding domain and intein (i.e., auto-processing domain) were employed as anchoring and protein trans-splicing motifs to develop the system, in which the probability of protein trans-splicing is dependent on the soluble property of target proteins. The applicability of inducible plasmid display was investigated using an α-1,2-fucosyltransferase (FucT2) from Helicobacter pylori, a highly insoluble and unstable enzyme in the cytoplasmic space of Escherichia coli, as a model protein. One round of the overall inducible plasmid display process, which consists of in vivo production of FucT2 mutants and in vitro screening, enabled soluble expression of FucT2 and selection of plasmids containing the corresponding genetic information. The inducible plasmid display developed in this study will contribute to the rapid and efficient screening and/or selection of soluble proteins.

Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning

Serotonin plays a central role in cognition and is the target of most pharmaceuticals for psychiatric disorders. Existing drugs have limited efficacy; creation of improved versions will require better understanding of serotonergic circuitry, which has been hampered by our inability to monitor serotonin release and transport with high spatial and temporal resolution. We developed and applied a binding-pocket redesign strategy, guided by machine learning, to create a high-performance, soluble, fluorescent serotonin sensor (iSeroSnFR), enabling optical detection of millisecond-scale serotonin transients. We demonstrate that iSeroSnFR can be used to detect serotonin release in freely behaving mice during fear conditioning, social interaction, and sleep/wake transitions. We also developed a robust assay of serotonin transporter function and modulation by drugs. We expect that both machine-learning-guided binding-pocket redesign and iSeroSnFR will have broad utility for the development of other sensors and in vitro and in vivo serotonin detection, respectively.