A general strategy to construct small molecule biosensors in eukaryotes

A versatile microbial platform as a tunable whole-cell chemical sensor

Biosensors are used to detect and quantify chemicals produced in industrial microbiology with high specificity, sensitivity, and portability. Most biosensors, however, are limited by the need for transcription factors engineered to recognize specific molecules. In this study, we overcome the limitations typically associated with traditional biosensors by engineering Pseudomonas putida for whole-cell sensing of a variety of chemicals. Our approach integrates fluorescent reporters with synthetic auxotrophies within central metabolism that can be complemented by target analytes in growth-coupled setups. This platform enables the detection of a wide array of structurally diverse chemicals under various conditions, including co-cultures of producer cell factories and sensor strains. We also demonstrate the applicability of this versatile biosensor platform for monitoring complex biochemical processes, including plastic degradation by either purified hydrolytic enzymes or engineered bacteria. This microbial system provides a rapid, sensitive, and readily adaptable tool for monitoring cell factory performance and for environmental analyzes.

Generative β-hairpin design using a residue-based physicochemical property landscape

De novo peptide design is a new frontier that has broad application potential in the biological and biomedical fields. Most existing models for de novo peptide design are largely based on sequence homology that can be restricted based on evolutionarily derived protein sequences and lack the physicochemical context essential in protein folding. Generative machine learning for de novo peptide design is a promising way to synthesize theoretical data that are based on, but unique from, the observable universe. In this study, we created and tested a custom peptide generative adversarial network intended to design peptide sequences that can fold into the β-hairpin secondary structure. This deep neural network model is designed to establish a preliminary foundation of the generative approach based on physicochemical and conformational properties of 20 canonical amino acids, for example, hydrophobicity and residue volume, using extant structure-specific sequence data from the PDB. The beta generative adversarial network model robustly distinguishes secondary structures of β hairpin from α helix and intrinsically disordered peptides with an accuracy of up to 96% and generates artificial β-hairpin peptide sequences with minimum sequence identities around 31% and 50% when compared against the current NCBI PDB and nonredundant databases, respectively. These results highlight the potential of generative models specifically anchored by physicochemical and conformational property features of amino acids to expand the sequence-to-structure landscape of proteins beyond evolutionary limits.

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.

Identifying LasR Quorum Sensors with Improved Signal Specificity by Mapping the Sequence-Function Landscape

Programmable intercellular signaling using components of naturally occurring quorum sensing can allow for coordinated functions to be engineered in microbial consortia. LuxR-type transcriptional regulators are widely used for this purpose and are activated by homoserine lactone (HSL) signals. However, they often suffer from imperfect molecular discrimination of structurally similar HSLs, causing misregulation within engineered consortia containing multiple HSL signals. Here, we studied one such example, the regulator LasR from Pseudomonas aeruginosa. We elucidated its sequence-function relationship for ligand specificity using targeted protein engineering and multiplexed high-throughput biosensor screening. A pooled combinatorial saturation mutagenesis library (9,486 LasR DNA sequences) was created by mutating six residues in LasR's β5 sheet with single, double, or triple amino acid substitutions. Sort-seq assays were performed in parallel using cognate and noncognate HSLs to quantify each corresponding sensor's response to each HSL signal, which identified hundreds of highly specific variants. Sensor variants identified were individually assayed and exhibited up to 60.6-fold (p = 0.0013) improved relative activation by the cognate signal compared to the wildtype. Interestingly, we uncovered prevalent mutational epistasis and previously unidentified residues contributing to signal specificity. The resulting sensors with negligible signal crosstalk could be broadly applied to engineer bacteria consortia.

Adaptable, turn-on maturation (ATOM) fluorescent biosensors for multiplexed detection in cells

A grand challenge in biosensor design is to develop a single-molecule, fluorescent protein-based platform that can be easily adapted to recognize targets of choice. Here, we created a family of adaptable, turn-on maturation (ATOM) biosensors consisting of a monobody (circularly permuted at one of two positions) or a nanobody (circularly permuted at one of three positions) inserted into a fluorescent protein at one of three surface loops. Multiplexed imaging of live human cells coexpressing cyan, yellow and red ATOM sensors detected biosensor targets that were specifically localized to various subcellular compartments. Fluorescence activation involved ligand-dependent chromophore maturation with turn-on ratios of up to 62-fold in cells and 100-fold in vitro. Endoplasmic reticulum- and mitochondria-localized ATOM sensors detected ligands that were targeted to those organelles. The ATOM design was validated with three monobodies and one nanobody inserted into distinct fluorescent proteins, suggesting that customized ATOM sensors can be generated quickly.

Engineering Ca2+-Dependent DNA Polymerase Activity

Advancements in synthetic biology have provided new opportunities in biosensing, with applications ranging from genetic programming to diagnostics. Next generation biosensors aim to expand the number of accessible environments for measurements, increase the number of measurable phenomena, and improve the quality of the measurement. To this end, an emerging area in the field has been the integration of DNA as an information storage medium within biosensor outputs, leveraging nucleic acids to record the biosensor state over time. However, slow signal transduction steps, due to the time scales of transcription and translation, bottleneck many sensing-DNA recording approaches. DNA polymerases (DNAPs) have been proposed as a solution to the signal transduction problem by operating as both the sensor and responder, but there is presently a lack of DNAPs with functional sensitivity to many desirable target ligands. Here, we engineer components of the Pol δ replicative polymerase complex of Saccharomyces cerevisiae to sense and respond to Ca2+, a metal cofactor relevant to numerous biological phenomena. Through domain insertion and binding site grafting to Pol δ subunits, we demonstrate functional allosteric sensitivity to Ca2+. Together, this work provides an important foundation for future efforts in the development of DNAP-based biosensors.

Synthetic biology for plant genetic engineering and molecular farming

Many efforts have been put into engineering plants to improve crop yields and stress tolerance and boost the bioproduction of valuable molecules. Yet, our capabilities are still limited due to the lack of well-characterized genetic building blocks and resources for precise manipulation and given the inherently challenging properties of plant tissues. Advancements in plant synthetic biology can overcome these bottlenecks and release the full potential of engineered plants. In this review, we first discuss the recently developed plant synthetic elements from single parts to advanced circuits, software, and hardware tools expediting the engineering cycle. Next, we survey the advancements in plant biotechnology enabled by these recent resources. We conclude the review with outstanding challenges and future directions of plant synthetic biology.

A general method for the development of multicolor biosensors with large dynamic ranges

Fluorescent biosensors enable the study of cell physiology with spatiotemporal resolution; yet, most biosensors suffer from relatively low dynamic ranges. Here, we introduce a family of designed Förster resonance energy transfer (FRET) pairs with near-quantitative FRET efficiencies based on the reversible interaction of fluorescent proteins with a fluorescently labeled HaloTag. These FRET pairs enabled the straightforward design of biosensors for calcium, ATP and NAD+ with unprecedented dynamic ranges. The color of each of these biosensors can be readily tuned by changing either the fluorescent protein or the synthetic fluorophore, which enables simultaneous monitoring of free NAD+ in different subcellular compartments following genotoxic stress. Minimal modifications of these biosensors furthermore allow their readout to be switched to fluorescence intensity, fluorescence lifetime or bioluminescence. These FRET pairs thus establish a new concept for the development of highly sensitive and tunable biosensors.

Divergent directed evolution of a TetR-type repressor towards aromatic molecules

Reprogramming cellular behaviour is one of the hallmarks of synthetic biology. To this end, prokaryotic allosteric transcription factors (aTF) have been repurposed as versatile tools for processing small molecule signals into cellular responses. Expanding the toolbox of aTFs that recognize new inducer molecules is of considerable interest in many applications. Here, we first establish a resorcinol responsive aTF-based biosensor in Escherichia coli using the TetR-family repressor RolR from Corynebacterium glutamicum. We then perform an iterative walk along the fitness landscape of RolR to identify new inducer specificities, namely catechol, methyl catechol, caffeic acid, protocatechuate, L-DOPA, and the tumour biomarker homovanillic acid. Finally, we demonstrate the versatility of these engineered aTFs by transplanting them into the model eukaryote Saccharomyces cerevisiae. This work provides a framework for efficient aTF engineering to expand ligand specificity towards novel molecules on laboratory timescales, which, more broadly, is invaluable across a wide range of applications such as protein and metabolic engineering, as well as point-of-care diagnostics.

Applications and Tuning Strategies for Transcription Factor-Based Metabolite Biosensors

Transcription factor (TF)-based biosensors are widely used for the detection of metabolites and the regulation of cellular pathways in response to metabolites. Several challenges hinder the direct application of TF-based sensors to new hosts or metabolic pathways, which often requires extensive tuning to achieve the optimal performance. These tuning strategies can involve transcriptional or translational control depending on the parameter of interest. In this review, we highlight recent strategies for engineering TF-based biosensors to obtain the desired performance and discuss additional design considerations that may influence a biosensor's performance. We also examine applications of these sensors and suggest important areas for further work to continue the advancement of small-molecule biosensors.

Design of Four Small-Molecule-Inducible Systems in the Yeast Chromosome, Applied to Optimize Terpene Biosynthesis

The optimization of cellular functions often requires the balancing of gene expression, but the physical construction and screening of alternative designs are costly and time-consuming. Here, we construct a strain of Saccharomyces cerevisiae that contains a "sensor array" containing bacterial regulators that respond to four small-molecule inducers (vanillic acid, xylose, aTc, IPTG). Four promoters can be independently controlled with low background and a 40- to 5000-fold dynamic range. These systems can be used to study the impact of changing the level and timing of gene expression without requiring the construction of multiple strains. We apply this approach to the optimization of a four-gene heterologous pathway to the terpene linalool, which is a flavor and precursor to energetic materials. Using this approach, we identify bottlenecks in the metabolic pathway. This work can aid the rapid automated strain development of yeasts for the bio-manufacturing of diverse products, including chemicals, materials, fuels, and food ingredients.

Modular Coculture to Reduce Substrate Competition and Off-Target Intermediates in Androstenedione Biosynthesis

Substrate competition within a metabolic network constitutes a common challenge in microbial biosynthesis system engineering, especially if indispensable enzymes can produce multiple intermediates from a single substrate. Androstenedione (4AD) is a central intermediate in the production of a series of steroidal pharmaceuticals; however, its yield via the coexpression of 3β-hydroxysteroid dehydrogenase (3β-HSD) and 17α-hydroxylase/17,20-lyase (CYP17A1) in a microbial chassis affords a nonlinear pathway in which these enzymes compete for substrates and produce structurally similar unwanted intermediates, thereby reducing 4AD yields. To avoid substrate competition, we split the competing 3β-HSD and CYP17A1 pathway components into two separate Yarrowia lipolytica strains to linearize the pathway. This spatial segregation increased substrate availability for 3β-HSD in the upstream strain, consequently decreasing the accumulation of the unwanted intermediate 17-hydroxypregnenolone (17OHP5) from 94.8 ± 4.4% in single-chassis monocultures to 24.8 ± 12.6% in cocultures of strains expressing 3β-HSD and CYP17A1 separately. Orthologue screening to increase CYP17A1 catalytic efficiency and the preferential production of desired intermediates increased the biotransformation capacity in the downstream pathway, further decreasing 17OHP5 accumulation to 3.9%. Furthermore, nitrogen limitation induced early 4AD accumulation (final titer, 7.71 mg/L). This study provides a framework for reducing intrapathway competition between essential enzymes during natural product biosynthesis as well as a proof-of-concept platform for linear steroid production.

An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties

The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol^-1 to 2.45 kcal mol^-1, showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.

Reshaping de Novo Protein Switches into Bioresponsive Materials for Biomarker, Toxin, and Viral Detection

De novo designed protein switches are powerful tools to specifically and sensitively detect diverse targets with simple chemiluminescent readouts. Finding an appropriate material host for de novo designed protein switches without altering their thermodynamics while preserving their intrinsic stability over time would enable the development of a variety of sensing formats to monitor exposure to pathogens, toxins, and for disease diagnosis. Here, a de novo protein-biopolymer hybrid that maintains the detection capabilities induced by the conformational change of the incorporated proteins in response to analytes of interest is generated in multiple, shelf-stable material formats without the need of refrigerated storage conditions. A set of functional demonstrator devices including personal protective equipment such as masks and laboratory gloves, free-standing films, air quality monitors, and wearable devices is presented to illustrate the versatility of the approach. Such formats are designed to be responsive to human epidermal growth factor receptor (HER2), anti-hepatitis B (HBV) antibodies, Botulinum neurotoxin B (BoNT/B), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This combination of form and function offers wide opportunities for ubiquitous sensing in multiple environments by enabling a large class of bio-responsive interfaces of broad utility.

Chemically inducible split protein regulators for mammalian cells

Chemically inducible systems represent valuable synthetic biology tools that enable the external control of biological processes. However, their translation to therapeutic applications has been limited because of unfavorable ligand characteristics or the immunogenicity of xenogeneic protein domains. To address these issues, we present a strategy for engineering inducible split protein regulators (INSPIRE) in which ligand-binding proteins of human origin are split into two fragments that reassemble in the presence of a cognate physiological ligand or clinically approved drug. We show that the INSPIRE platform can be used for dynamic, orthogonal and multiplex control of gene expression in mammalian cells. Furthermore, we demonstrate the functionality of a glucocorticoid-responsive INSPIRE platform in vivo and apply it for perturbing an endogenous regulatory network. INSPIRE presents a generalizable approach toward designing small-molecule responsive systems that can be implemented for the construction of new sensors, regulatory networks and therapeutic applications.

CRISPRi-based programmable logic inverter cascade for antibiotic-free selection and maintenance of multiple plasmids

Antibiotics have been widely used for plasmid-mediated cell engineering. However, continued use of antibiotics increases the metabolic burden, horizontal gene transfer risks, and biomanufacturing costs. There are limited approaches to maintaining multiple plasmids without antibiotics. Herein, we developed an inverter cascade using CRISPRi by building a plasmid containing a single guide RNA (sgRNA) landing pad (pSLiP); this inhibited host cell growth by repressing an essential cellular gene. Anti-sgRNAs on separate plasmids restored cell growth by blocking the expression of growth-inhibitory sgRNAs in pSLiP. We maintained three plasmids in Escherichia coli with a single antibiotic selective marker. To completely avoid antibiotic use and maintain the CRISPRi-based logic inverter cascade, we created a novel d-glutamate auxotrophic E. coli. This enabled the stable maintenance of the plasmid without antibiotics, enhanced the production of the terpenoid, (-)-α-bisabolol, and generation of an antibiotic-resistance gene-free plasmid. CRISPRi is therefore widely applicable in genetic circuits and may allow for antibiotic-free biomanufacturing.

A Modular Scaffold for Controlling Transcriptional Activation via Homomeric Protein-Protein Interactions

Protein-protein interactions (PPIs) have been extensively utilized in synthetic biology to construct artificial gene networks. However, synthetic regulation of gene expression by PPIs in E. coli has largely relied upon repressors, and existing PPI-controlled transcriptional activators have generally been employed with heterodimeric interactions. Here we report a highly modular, PPI-dependent transcriptional activator, cCadC, that was designed to be compatible with homomeric interactions. We describe the process of engineering cCadC from a transmembrane protein into a soluble cytosolic regulator. We then show that gene transcription by cCadC can be controlled by homomeric PPIs and furthermore discriminates between dimeric and higher-order interactions. Finally, we demonstrate that cCadC activity can be placed under small molecule regulation using chemically induced dimerization or ligand dependent stabilization. This work should greatly expand the scope of PPIs that can be employed in artificial gene circuits in E. coli and complements the existing repertoire of tools for transcriptional regulation in synthetic biology.

A cAMP Sensor Based on Ligand-Dependent Protein Stabilization

cAMP is a ubiquitous second messenger with many functions in diverse organisms. Current cAMP sensors, including Föster resonance energy transfer (FRET)-based and single-wavelength-based sensors, allow for real time visualization of this small molecule in cultured cells and in some cases in vivo. Nonetheless the observation of cAMP in living animals is still difficult, typically requiring specialized microscopes and ex vivo tissue processing. Here we used ligand-dependent protein stabilization to create a new cAMP sensor. This sensor allows specific and sensitive detection of cAMP in living zebrafish embryos, which may enable new understanding of the functions of cAMP in living vertebrates.

Protein design via deep learning

Proteins with desired functions and properties are important in fields like nanotechnology and biomedicine. De novo protein design enables the production of previously unseen proteins from the ground up and is believed as a key point for handling real social challenges. Recent introduction of deep learning into design methods exhibits a transformative influence and is expected to represent a promising and exciting future direction. In this review, we retrospect the major aspects of current advances in deep-learning-based design procedures and illustrate their novelty in comparison with conventional knowledge-based approaches through noticeable cases. We not only describe deep learning developments in structure-based protein design and direct sequence design, but also highlight recent applications of deep reinforcement learning in protein design. The future perspectives on design goals, challenges and opportunities are also comprehensively discussed.

Advances in the Computational Design of Small-Molecule-Controlled Protein-Based Circuits for Synthetic Biology

Synthetic biology approaches living systems with an engineering perspective and promises to deliver solutions to global challenges in healthcare and sustainability. A critical component is the design of biomolecular circuits with programmable input-output behaviors. Such circuits typically rely on a sensor module that recognizes molecular inputs, which is coupled to a functional output via protein-level circuits or regulating the expression of a target gene. While gene expression outputs can be customized relatively easily by exchanging the target genes, sensing new inputs is a major limitation. There is a limited repertoire of sensors found in nature, and there are often difficulties with interfacing them with engineered circuits. Computational protein design could be a key enabling technology to address these challenges, as it allows for the engineering of modular and tunable sensors that can be tailored to the circuit's application. In this article, we review recent computational approaches to design protein-based sensors for small-molecule inputs with particular focus on those based on the widely used Rosetta software suite. Furthermore, we review mechanisms that have been harnessed to couple ligand inputs to functional outputs. Based on recent literature, we illustrate how the combination of protein design and synthetic biology enables new sensors for diverse applications ranging from biomedicine to metabolic engineering. We conclude with a perspective on how strategies to address frontiers in protein design and cellular circuit design may enable the next generation of sense-response networks, which may increasingly be assembled from de novo components to display diverse and engineerable input-output behaviors.

Engineering of Synthetic Transcriptional Switches in Yeast

Transcriptional switches can be utilized for many purposes in synthetic biology, including the assembly of complex genetic circuits to achieve sophisticated cellular systems and the construction of biosensors for real-time monitoring of intracellular metabolite concentrations. Although to date such switches have mainly been developed in prokaryotes, those for eukaryotes are increasingly being reported as both rational and random engineering technologies mature. In this review, we describe yeast transcriptional switches with different modes of action and how to alter their properties. We also discuss directed evolution technologies for the rapid and robust construction of yeast transcriptional switches.

Strategies for Improving Small-Molecule Biosensors in Bacteria

In recent years, small-molecule biosensors have become increasingly important in synthetic biology and biochemistry, with numerous new applications continuing to be developed throughout the field. For many biosensors, however, their utility is hindered by poor functionality. Here, we review the known types of mechanisms of biosensors within bacterial cells, and the types of approaches for optimizing different biosensor functional parameters. Discussed approaches for improving biosensor functionality include methods of directly engineering biosensor genes, considerations for choosing genetic reporters, approaches for tuning gene expression, and strategies for incorporating additional genetic modules.

Episome-Based Gene Expression Modulation Platform in the Model Diatom Phaeodactylum tricornutum

Chemically inducible gene expression systems have been an integral part of the advanced synthetic genetic circuit design and are employed for precise dynamic control over genetically engineered traits. However, the current systems for controlling transgene expression in most algae are limited to endogenous promoters that respond to different environmental factors. We developed a highly efficient, tunable, and reversible episome-based transcriptional control system in the model diatom alga, Phaeodactylum tricornutum. We assessed the time- and dose-response dynamics of each expression system using a reporter protein (eYFP) as a readout. Using our circuit configuration, we found two inducible expression systems with a high dynamic range and confirmed the suitability of an episome expression platform for synthetic biological applications in diatoms. These systems are controlled by the presence of β-estradiol and digoxin. Addition of either chemical to transgenic strains activates transcription with a dynamic range of up to ∼180-fold and ∼90-fold, respectively. We demonstrated that our episome-based transcriptional control systems are tunable and reversible in a dose- and time-dependent manner. Using droplet digital polymerase chain reaction (PCR), we also confirmed that inducer-dependent transcriptional activation starts within minutes of inducer application without any detectable transcript in the uninduced controls. The system described here expands the molecular and synthetic biology toolkits in algae and will facilitate future gene discovery and metabolic engineering efforts.

Saccharomyces cerevisiae as a Heterologous Host for Natural Products

Cell factories can provide a sustainable supply of natural products with applications as pharmaceuticals, food-additives or biofuels. Besides being an important model organism for eukaryotic systems, Saccharomyces cerevisiae is used as a chassis for the heterologous production of natural products. Its success as a cell factory can be attributed to the vast knowledge accumulated over decades of research, its overall ease of engineering and its robustness. Many methods and toolkits have been developed by the yeast metabolic engineering community with the aim of simplifying and accelerating the engineering process.In this chapter, a range of methodologies are highlighted, which can be used to develop novel natural product cell factories or to improve titer, rate and yields of an existing cell factory with the goal of developing an industrially relevant strain. The addressed topics are applicable for different stages of a cell factory engineering project and include the choice of a natural product platform strain, expression cassette design for heterologous or native genes, basic and advanced genetic engineering strategies, and library screening methods using biosensors. The many engineering methods available and the examples of yeast cell factories underline the importance and future potential of this host for industrial production of natural products.

Bacterial two-component systems as sensors for synthetic biology applications

Two-component systems (TCSs) are a ubiquitous family of signal transduction pathways that enable bacteria to sense and respond to diverse physical, chemical, and biological stimuli outside and inside the cell. Synthetic biologists have begun to repurpose TCSs for applications in optogenetics, materials science, gut microbiome engineering, and soil nutrient biosensing, among others. New engineering methods including genetic refactoring, DNA-binding domain swapping, detection threshold tuning, and phosphorylation cross-talk insulation are being used to increase the reliability of TCS sensor performance and tailor TCS signaling properties to the requirements of specific applications. There is now potential to combine these methods with large-scale gene synthesis and laboratory screening to discover the inputs sensed by many uncharacterized TCSs and develop a large new family of genetically-encoded sensors that respond to an unrivaled breadth of stimuli.

Design and development of engineered receptors for cell and tissue engineering

Advances in synthetic biology have provided genetic tools to reprogram cells to obtain desired cellular functions that include tools to enable the customization of cells to sense an extracellular signal and respond with a desired output. These include a variety of engineered receptors capable of transmembrane signaling that transmit information from outside of the cell to inside when specific ligands bind to them. Recent advances in synthetic receptor engineering have enabled the reprogramming of cell and tissue behavior, controlling cell fate decisions, and providing new vehicles for therapeutic delivery.

De novo protein fold families expand the designable ligand binding site space

A major challenge in designing proteins de novo to bind user-defined ligands with high affinity is finding backbones structures into which a new binding site geometry can be engineered with high precision. Recent advances in methods to generate protein fold families de novo have expanded the space of accessible protein structures, but it is not clear to what extend de novo proteins with diverse geometries also expand the space of designable ligand binding functions. We constructed a library of 25,806 high-quality ligand binding sites and developed a fast protocol to place (“match”) these binding sites into both naturally occurring and de novo protein families with two fold topologies: Rossman and NTF2. Each matching step involves engineering new binding site residues into each protein “scaffold”, which is distinct from the problem of comparing already existing binding pockets. 5,896 and 7,475 binding sites could be matched to the Rossmann and NTF2 fold families, respectively. De novo designed Rossman and NTF2 protein families can support 1,791 and 678 binding sites that cannot be matched to naturally existing structures with the same topologies, respectively. While the number of protein residues in ligand binding sites is the major determinant of matching success, ligand size and primary sequence separation of binding site residues also play important roles. The number of matched binding sites are power law functions of the number of members in a fold family. Our results suggest that de novo sampling of geometric variations on diverse fold topologies can significantly expand the space of designable ligand binding sites for a wealth of possible new protein functions.

De novo design of proteins that can bind to novel and highly diverse user-defined small molecule ligands could have broad biomedical and synthetic biology applications. Because ligand binding site geometries need to be accommodated by protein backbone scaffolds at high accuracy, the diversity of scaffolds is a major limitation for designing new ligand binding functions. Advances in computational protein structure design methods have significantly increased the number of accessible stable scaffold structures. Understanding how many new ligand binding sites can be designed into the de novo scaffolds is important for engineering novel ligand binding proteins. To answer this question, we constructed a large library of ligand binding sites from the Protein Data Bank (PDB). We tested the number of ligand binding sites that can be designed into de novo scaffolds and naturally existing scaffolds with the same fold topologies. The results showed that de novo scaffolds significantly expanded the potential ligand binding space of their respective fold topologies. We also identified factors that affect difficulties of binding site accommodation, as well as the relationship between the number of scaffolds and the accessible ligand binding site space. We believe our findings will benefit future method development and applications of ligand binding protein design.

Modular tuning engineering and versatile applications of genetically encoded biosensors

Genetically encoded biosensors have a diverse range of detectable signals and potential applications in many fields, including metabolism control and high-throughput screening. Their ability to be used in situ with minimal interference to the bioprocess of interest could revolutionize synthetic biology and microbial cell factories. The performance and functions of these biosensors have been extensively studied and have been rapidly improved. We review here current biosensor tuning strategies and attempt to unravel how to obtain ideal biosensor functions through experimental adjustments. Strategies for expanding the biosensor input signals that increases the number of detectable compounds have also been summarized. Finally, different output signals and their practical requirements for biotechnology and biomedical applications and environmental safety concerns have been analyzed. This in-depth review of the responses and regulation mechanisms of genetically encoded biosensors will assist to improve their design and optimization in various application scenarios.

Development of an Haa1-based biosensor for acetic acid sensing in Saccharomyces cerevisiae

Acetic acid is one of the main inhibitors of lignocellulosic hydrolysates and acetic acid tolerance is crucial for the development of robust cell factories for conversion of biomass. As a precursor of acetyl-coenzyme A, it also plays an important role in central carbon metabolism. Thus, monitoring acetic acid levels is a crucial aspect when cultivating yeast. Transcription factor-based biosensors represent useful tools to follow metabolite concentrations. Here, we present the development of an acetic acid biosensor based on the Saccharomyces cerevisiae transcription factor Haa1 that upon binding to acetic acid relocates to the nucleus. In the biosensor, a synthetic transcription factor consisting of Haa1 and BM3R1 from Bacillus megaterium was used to control expression of a reporter gene under a promoter containing BM3R1 binding sites. The biosensor did not drive expression under a promoter containing Haa1 binding sites and responded to acetic acid over a linear range spanning from 10 to 60 mM. To validate its applicability, the biosensor was integrated into acetic acid-producing strains. A direct correlation between biosensor output and acetic acid production was detected. The developed biosensor enables high-throughput screening of strains producing acetic acid and could also be used to investigate acetic acid-tolerant strain libraries.

Ten future challenges for synthetic biology

After 2 decades of growth and success, synthetic biology has now become a mature field that is driving significant innovation in the bioeconomy and pushing the boundaries of the biomedical sciences and biotechnology. So what comes next? In this article, 10 technological advances are discussed that are expected and hoped to come from the next generation of research and investment in synthetic biology; from ambitious projects to make synthetic life, cell simulators and custom genomes, through to new methods of engineering biology that use automation, deep learning and control of evolution. The non-exhaustive list is meant to inspire those joining the field and looks forward to how synthetic biology may evolve over the coming decades.

Recent trends in biocatalysis

Biocatalysis has undergone revolutionary progress in the past century. Benefited by the integration of multidisciplinary technologies, natural enzymatic reactions are constantly being explored. Protein engineering gives birth to robust biocatalysts that are widely used in industrial production. These research achievements have gradually constructed a network containing natural enzymatic synthesis pathways and artificially designed enzymatic cascades. Nowadays, the development of artificial intelligence, automation, and ultra-high-throughput technology provides infinite possibilities for the discovery of novel enzymes, enzymatic mechanisms and enzymatic cascades, and gradually complements the lack of remaining key steps in the pathway design of enzymatic total synthesis. Therefore, the research of biocatalysis is gradually moving towards the era of novel technology integration, intelligent manufacturing and enzymatic total synthesis.

Multilayer Genetic Circuits for Dynamic Regulation of Metabolic Pathways

The dynamic regulation of metabolic pathways is based on changes in external signals and endogenous changes in gene expression levels and has extensive applications in the field of synthetic biology and metabolic engineering. However, achieving dynamic control is not trivial, and dynamic control is difficult to obtain using simple, single-level, control strategies because they are often affected by native regulatory networks. Therefore, synthetic biologists usually apply the concept of logic gates to build more complex and multilayer genetic circuits that can process various signals and direct the metabolic flux toward the synthesis of the molecules of interest. In this review, we first summarize the applications of dynamic regulatory systems and genetic circuits and then discuss how to design multilayer genetic circuits to achieve the optimal control of metabolic fluxes in living cells.

A suppressor tRNA-mediated feedforward loop eliminates leaky gene expression in bacteria

Ligand-inducible genetic systems are the mainstay of synthetic biology, allowing gene expression to be controlled by the presence of a small molecule. However, 'leaky' gene expression in the absence of inducer remains a persistent problem. We developed a leak dampener tool that drastically reduces the leak of inducible genetic systems while retaining signal in Escherichia coli. Our system relies on a coherent feedforward loop featuring a suppressor tRNA that enables conditional readthrough of silent non-sense mutations in a regulated gene, and this approach can be applied to any ligand-inducible transcription factor. We demonstrate proof-of-principle of our system with the lactate biosensor LldR and the arabinose biosensor AraC, which displayed a 70-fold and 630-fold change in output after induction of a fluorescence reporter, respectively, without any background subtraction. Application of the tool to an arabinose-inducible mutagenesis plasmid led to a 540-fold change in its output after induction, with leak decreasing to the level of background mutagenesis. This study provides a modular tool for reducing leak and improving the fold-induction within genetic circuits, demonstrated here using two types of biosensors relevant to cancer detection and genetic engineering.

Transcription factor-based biosensors: a molecular-guided approach for natural product engineering

Natural products and their derivatives offer a rich source of chemical and biological diversity; however, traditional engineering of their biosynthetic pathways to improve yields and access to unnatural derivatives requires a precise understanding of their enzymatic processes. High-throughput screening platforms based on allosteric transcription-factor based biosensors can be leveraged to overcome the screening bottleneck to enable searching through large libraries of pathway/strain variants. Herein, the development and application of engineered allosteric transcription factor-based biosensors is described that enable optimization of precursor availability, product titers, and downstream product tailoring for advancing the natural product bioeconomy. We discuss recent successes for tailoring biosensor design, including computationally-based approaches, and present our future outlook with the integration of cell-free technologies and de novo protein design for rapidly generating biosensor tools.

Recent advances in de novo protein design: Principles, methods, and applications

The computational de novo protein design is increasingly applied to address a number of key challenges in biomedicine and biological engineering. Successes in expanding applications are driven by advances in design principles and methods over several decades. Here, we review recent innovations in major aspects of the de novo protein design and include how these advances were informed by principles of protein architecture and interactions derived from the wealth of structures in the Protein Data Bank. We describe developments in de novo generation of designable backbone structures, optimization of sequences, design scoring functions, and the design of the function. The advances not only highlight design goals reachable now but also point to the challenges and opportunities for the future of the field.

Quantitative and Predictive Genetic Parts for Plant Synthetic Biology

Plant synthetic biology aims to harness the natural abilities of plants and to turn them to new purposes. A primary goal of plant synthetic biology is to produce predictable and programmable genetic circuits from simple regulatory elements and well-characterized genetic components. The number of available DNA parts for plants is increasing, and the methods for rapid quantitative characterization are being developed, but the field of plant synthetic biology is still in its early stages. We here describe methods used to describe the quantitative properties of genetic components needed for plant synthetic biology. Once the quantitative properties and transfer function of a variety of genetic parts are known, computers can select the optimal components to assemble into functional devices, such as toggle switches and positive feedback circuits. However, while the variety of circuits and traits that can be put into plants are limitless, doing synthetic biology in plants poses unique challenges. Plants are composed of differentiated cells and tissues, each representing potentially unique regulatory or developmental contexts to introduced synthetic genetic circuits. Further, plants have evolved to be highly sensitive to environmental influences, such as light or temperature, any of which can affect the quantitative function of individual parts or whole circuits. Measuring the function of plant components within the context of a plant cell and, ideally, in a living plant, will be essential to using these components in gene circuits with predictable function. Mathematical modeling will be needed to account for the variety of contexts a genetic part will experience in different plant tissues or environments. With such understanding in hand, it may be possible to redesign plant traits to serve human and environmental needs.

Rosa26 docking sites for investigating genetic circuit silencing in stem cells

Approaches in mammalian synthetic biology have transformed how cells can be programmed to have reliable and predictable behavior, however, the majority of mammalian synthetic biology has been accomplished using immortalized cell lines that are easy to grow and easy to transfect. Genetic circuits that integrate into the genome of these immortalized cell lines remain functional for many generations, often for the lifetime of the cells, yet when genetic circuits are integrated into the genome of stem cells gene silencing is observed within a few generations. To investigate the reactivation of silenced genetic circuits in stem cells, the Rosa26 locus of mouse pluripotent stem cells was modified to contain docking sites for site-specific integration of genetic circuits. We show that the silencing of genetic circuits can be reversed with the addition of sodium butyrate, a histone deacetylase inhibitor. These findings demonstrate an approach to reactivate the function of genetic circuits in pluripotent stem cells to ensure robust function over many generations. Altogether, this work introduces an approach to overcome the silencing of genetic circuits in pluripotent stem cells that may enable the use of genetic circuits in pluripotent stem cells for long-term function.

Beyond natural: synthetic expansions of botanical form and function

Powered by developments that enabled genome-scale investigations, systems biology emerged as a field aiming to understand how phenotypes emerge from network functions. These advances fuelled a new engineering discipline focussed on synthetic reconstructions of complex biological systems with the goal of predictable rational design and control. Initially, progress in the nascent field of synthetic biology was slow due to the ad hoc nature of molecular biology methods such as cloning. The application of engineering principles such as standardisation, together with several key technical advances, enabled a revolution in the speed and accuracy of genetic manipulation. Combined with mathematical and statistical modelling, this has improved the predictability of engineering biological systems of which nonlinearity and stochasticity are intrinsic features leading to remarkable achievements in biotechnology as well as novel insights into biological function. In the past decade, there has been slow but steady progress in establishing foundations for synthetic biology in plant systems. Recently, this has enabled model-informed rational design to be successfully applied to the engineering of plant gene regulation and metabolism. Synthetic biology is now poised to transform the potential of plant biotechnology. However, reaching full potential will require conscious adjustments to the skillsets and mind sets of plant scientists.

Genetically-encoded biosensors for analyzing and controlling cellular process in yeast

Yeast has been a robust platform to manufacture a broad range of biofuels, commodity chemicals, natural products and pharmaceuticals. The membrane-bound organelles in yeast provide us the means to access the specialized metabolism for various biosynthetic applications. The separation and compartmentalization of genetic and metabolic events presents us the opportunity to precisely control and program gene expression for higher order biological functions. To further advance yeast synthetic biology platform, genetically encoded biosensors and actuators haven been engineered for in vivo monitoring and controlling cellular processes with spatiotemporal resolutions. The dynamic response, sensitivity and operational range of these genetically encoded sensors are determined by the regulatory architecture, dynamic assemly and interactions of the related proteins and genetic elements. This review provides an update of the basic design principles underlying the allosteric transcription factors, GPCR and optogenetics-based sensors, aiming to precisely analyze and control yeast cellular processes for various biotechnological applications.

Biosensors design in yeast and applications in metabolic engineering

Engineering microbial cell factories is a potential approach of sustainable production of chemicals, fuels and pharmaceuticals. However, testing the production of molecules in high throughput is still a time-consuming and laborious process since product synthesis usually does not confer a clear phenotype. Therefore, it is necessary to develop new techniques for fast high-producer screening. Genetically encoded biosensors are considered to be promising devices for high-throughput analysis owing to their ability to sense metabolites and couple detection to an actuator, thereby facilitating the rapid detection of small molecules at single-cell level. Here, we review recent advances in the design and engineering of biosensors in Saccharomyces cerevisiae, and their applications in metabolic engineering. Three types of biosensor are introduced in this review: transcription factor based, RNA-based and enzyme-coupled biosensors. The studies to improve the features of biosensors are also described. Moreover, we summarized their metabolic engineering applications in dynamic regulation and high producer selection. Current challenges in biosensor design and future perspectives on sensor applications are also discussed.

Computational design of a modular protein sense-response system

Sensing and responding to signals is a fundamental ability of living systems, but despite substantial progress in the computational design of new protein structures, there is no general approach for engineering arbitrary new protein sensors. Here, we describe a generalizable computational strategy for designing sensor-actuator proteins by building binding sites de novo into heterodimeric protein-protein interfaces and coupling ligand sensing to modular actuation through split reporters. Using this approach, we designed protein sensors that respond to farnesyl pyrophosphate, a metabolic intermediate in the production of valuable compounds. The sensors are functional in vitro and in cells, and the crystal structure of the engineered binding site closely matches the design model. Our computational design strategy opens broad avenues to link biological outputs to new signals.

Engineering Synthetic Signaling in Plants

Synthetic signaling is a branch of synthetic biology that aims to understand native genetic regulatory mechanisms and to use these insights to engineer interventions and devices that achieve specified design parameters. Applying synthetic signaling approaches to plants offers the promise of mitigating the worst effects of climate change and providing a means to engineer crops for entirely novel environments, such as those in space travel. The ability to engineer new traits using synthetic signaling methods will require standardized libraries of biological parts and methods to assemble them; the decoupling of complex processes into simpler subsystems; and mathematical models that can accelerate the design-build-test-learn cycle. The field of plant synthetic signaling is relatively new, but it is poised for rapid advancement. Translation from the laboratory to the field is likely to be slowed, however, by the lack of constructive dialogue between researchers and other stakeholders.

Directed Evolution of the Stringency of the LuxR Vibrio fischeri Quorum Sensor without OFF-State Selection

Stringency (low leak) is one of the most important specifications required for genetic circuits and induction systems, but it is challenging to evolve without sacrificing the maximum output level. This problem also comes from the absence of truly tunable negative selection methods. This paper reports that stringently switching variants can sometimes emerge with surprising frequency upon mutations. We randomly mutated the previously generated leaky variants of LuxR, the quorum-sensing transcription activator from Vibrio fischeri, to restore the stringency. We found as much as 10-20% of the entire population exhibited significantly improved signal-to-noise ratios compared with their parents. This indicated that these mutants arose by the loss of folding capability by accumulating destabilizing mutations, not by introducing rare adaptive mutations, thereby becoming AHL-dependent folders. Only four rounds of mutagenesis and ON-state selection resulted in the domination of the entire population by the improved variants with low leak, without direct selection pressure for stringency. With this surprising frequency, conversion into the "ligand-addicted folders" should be one of the prevailing modes of evolving stringency both in the laboratory and in nature, and the workflow described here provides a rapid and versatile method of improving the signal-to-noise ratio of various genetic switches.

Evolution-guided engineering of small-molecule biosensors

Allosteric transcription factors (aTFs) have proven widely applicable for biotechnology and synthetic biology as ligand-specific biosensors enabling real-time monitoring, selection and regulation of cellular metabolism. However, both the biosensor specificity and the correlation between ligand concentration and biosensor output signal, also known as the transfer function, often needs to be optimized before meeting application needs. Here, we present a versatile and high-throughput method to evolve prokaryotic aTF specificity and transfer functions in a eukaryote chassis, namely baker's yeast Saccharomyces cerevisiae. From a single round of mutagenesis of the effector-binding domain (EBD) coupled with various toggled selection regimes, we robustly select aTF variants of the cis,cis-muconic acid-inducible transcription factor BenM evolved for change in ligand specificity, increased dynamic output range, shifts in operational range, and a complete inversion-of-function from activation to repression. Importantly, by targeting only the EBD, the evolved biosensors display DNA-binding affinities similar to BenM, and are functional when ported back into a prokaryotic chassis. The developed platform technology thus leverages aTF evolvability for the development of new host-agnostic biosensors with user-defined small-molecule specificities and transfer functions.

Designer sense-response systems

Artificial chemically induced protein dimerization yields biological sensor-actuator devices