Rosetta comparative modeling for library design: Engineering alternative inducer specificity in a transcription factor

Biological Switches: Past and Future Milestones of Transcription Factor-Based Biosensors

Since the description of the lac operon in 1961 by Jacob and Monod, transcriptional regulation in prokaryotes has been studied extensively and has led to the development of transcription factor-based biosensors. Due to the broad variety of detectable small molecules and their various applications across biotechnology, biosensor research and development have increased exponentially over the past decades. Throughout this period, key milestones in fundamental knowledge, synthetic biology, analytical tools, and computational learning have led to an immense expansion of the biosensor repertoire and its application portfolio. Over the years, biosensor engineering became a more multidisciplinary discipline, combining high-throughput analytical tools, DNA randomization strategies, forward engineering, and advanced protein engineering workflows. Despite these advances, many obstacles remain to fully unlock the potential of biosensor technology. This review analyzes the timeline of key milestones on fundamental research (1960s to 2000s) and engineering strategies (2000s onward), on both the DNA and protein level of biosensors. Moreover, insights into the future perspectives, remaining hurdles, and unexplored opportunities of this promising field are discussed.

Bacterial Metallostasis: Metal Sensing, Metalloproteome Remodeling, and Metal Trafficking

Transition metals function as structural and catalytic cofactors for a large diversity of proteins and enzymes that collectively comprise the metalloproteome. Metallostasis considers all cellular processes, notably metal sensing, metalloproteome remodeling, and trafficking (or allocation) of metals that collectively ensure the functional integrity and adaptability of the metalloproteome. Bacteria employ both protein and RNA-based mechanisms that sense intracellular transition metal bioavailability and orchestrate systems-level outputs that maintain metallostasis. In this review, we contextualize metallostasis by briefly discussing the metalloproteome and specialized roles that metals play in biology. We then offer a comprehensive perspective on the diversity of metalloregulatory proteins and metal-sensing riboswitches, defining general principles within each sensor superfamily that capture how specificity is encoded in the sequence, and how selectivity can be leveraged in downstream synthetic biology and biotechnology applications. This is followed by a discussion of recent work that highlights selected metalloregulatory outputs, including metalloproteome remodeling and metal allocation by metallochaperones to both client proteins and compartments. We close by briefly discussing places where more work is needed to fill in gaps in our understanding of metallostasis.

Assembly of functional microbial ecosystems: from molecular circuits to communities

Microbes compete and cooperate with each other via a variety of chemicals and circuits. Recently, to decipher, simulate, or reconstruct microbial communities, many researches have been engaged in engineering microbiomes with bottom-up synthetic biology approaches for diverse applications. However, they have been separately focused on individual perspectives including genetic circuits, communications tools, microbiome engineering, or promising applications. The strategies for coordinating microbial ecosystems based on different regulation circuits have not been systematically summarized, which calls for a more comprehensive framework for the assembly of microbial communities. In this review, we summarize diverse cross-talk and orthogonal regulation modules for de novo bottom-up assembling functional microbial ecosystems, thus promoting further consortia-based applications. First, we review the cross-talk communication-based regulations among various microbial communities from intra-species and inter-species aspects. Then, orthogonal regulations are summarized at metabolites, transcription, translation, and post-translation levels, respectively. Furthermore, to give more details for better design and optimize various microbial ecosystems, we propose a more comprehensive design-build-test-learn procedure including function specification, chassis selection, interaction design, system build, performance test, modeling analysis, and global optimization. Finally, current challenges and opportunities are discussed for the further development and application of microbial ecosystems.

Highly multiplexed design of an allosteric transcription factor to sense new ligands

Allosteric transcription factors (aTF) regulate gene expression through conformational changes induced by small molecule binding. Although widely used as biosensors, aTFs have proven challenging to design for detecting new molecules because mutation of ligand-binding residues often disrupts allostery. Here, we develop Sensor-seq, a high-throughput platform to design and identify aTF biosensors that bind to non-native ligands. We screen a library of 17,737 variants of the aTF TtgR, a regulator of a multidrug exporter, against six non-native ligands of diverse chemical structures - four derivatives of the cancer therapeutic tamoxifen, the antimalarial drug quinine, and the opiate analog naltrexone - as well as two native flavonoid ligands, naringenin and phloretin. Sensor-seq identifies biosensors for each of these ligands with high dynamic range and diverse specificity profiles. The structure of a naltrexone-bound design shows shape-complementary methionine-aromatic interactions driving ligand specificity. To demonstrate practical utility, we develop cell-free detection systems for naltrexone and quinine. Sensor-seq enables rapid and scalable design of new biosensors, overcoming constraints of natural biosensors.

Functional and structural characterization of an IclR family transcription factor for the development of dicarboxylic acid biosensors

Prokaryotic transcription factors (TFs) regulate gene expression in response to small molecules, thus representing promising candidates as versatile small molecule-detecting biosensors valuable for synthetic biology applications. The engineering of such biosensors requires thorough in vitro and in vivo characterization of TF ligand response as well as detailed molecular structure information. In this work, we functionally and structurally characterize the Pca regulon regulatory protein (PcaR) transcription factor belonging to the IclR transcription factor family. Here, we present in vitro functional analysis of the ligand profile of PcaR and the construction of genetic circuits for the characterization of PcaR as an in vivo biosensor in the model eukaryote Saccharomyces cerevisiae. We report the crystal structures of PcaR in the apo state and in complex with one of its ligands, succinate, which suggests the mechanism of dicarboxylic acid recognition by this transcription factor. This work contributes key structural and functional insights enabling the engineering of PcaR for dicarboxylic acid biosensors, in addition to providing more insights into the IclR family of regulators.

Highly multiplexed design of an allosteric transcription factor to sense novel ligands

Allosteric transcription factors (aTF), widely used as biosensors, have proven challenging to design for detecting novel molecules because mutation of ligand-binding residues often disrupts allostery. We developed Sensor-seq, a high-throughput platform to design and identify aTF biosensors that bind to non-native ligands. We screened a library of 17,737 variants of the aTF TtgR, a regulator of a multidrug exporter, against six non-native ligands of diverse chemical structures - four derivatives of the cancer therapeutic tamoxifen, the antimalarial drug quinine, and the opiate analog naltrexone - as well as two native flavonoid ligands, naringenin and phloretin. Sensor-seq identified novel biosensors for each of these ligands with high dynamic range and diverse specificity profiles. The structure of a naltrexone-bound design showed shape-complementary methionine-aromatic interactions driving ligand specificity. To demonstrate practical utility, we developed cell-free detection systems for naltrexone and quinine. Sensor-seq enables rapid, scalable design of new biosensors, overcoming constraints of natural biosensors.

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.

Advances in ligand-specific biosensing for structurally similar molecules

The specificity of biological systems makes it possible to develop biosensors targeting specific metabolites, toxins, and pollutants in complex medical or environmental samples without interference from structurally similar compounds. For the last two decades, great efforts have been devoted to creating proteins or nucleic acids with novel properties through synthetic biology strategies. Beyond augmenting biocatalytic activity, expanding target substrate scopes, and enhancing enzymes' enantioselectivity and stability, an increasing research area is the enhancement of molecular specificity for genetically encoded biosensors. Here, we summarize recent advances in the development of highly specific biosensor systems and their essential applications. First, we describe the rational design principles required to create libraries containing potential mutants with less promiscuity or better specificity. Next, we review the emerging high-throughput screening techniques to engineer biosensing specificity for the desired target. Finally, we examine the computer-aided evaluation and prediction methods to facilitate the construction of ligand-specific biosensors.

Targeted mutagenesis and high-throughput screening of diversified gene and promoter libraries for isolating gain-of-function mutations

Targeted mutagenesis of a promoter or gene is essential for attaining new functions in microbial and protein engineering efforts. In the burgeoning field of synthetic biology, heterologous genes are expressed in new host organisms. Similarly, natural or designed proteins are mutagenized at targeted positions and screened for gain-of-function mutations. Here, we describe methods to attain complete randomization or controlled mutations in promoters or genes. Combinatorial libraries of one hundred thousands to tens of millions of variants can be created using commercially synthesized oligonucleotides, simply by performing two rounds of polymerase chain reactions. With a suitably engineered reporter in a whole cell, these libraries can be screened rapidly by performing fluorescence-activated cell sorting (FACS). Within a few rounds of positive and negative sorting based on the response from the reporter, the library can rapidly converge to a few optimal or extremely rare variants with desired phenotypes. Library construction, transformation and sequence verification takes 6-9 days and requires only basic molecular biology lab experience. Screening the library by FACS takes 3-5 days and requires training for the specific cytometer used. Further steps after sorting, including colony picking, sequencing, verification, and characterization of individual clones may take longer, depending on number of clones and required experiments.

Tackling the Catch-22 Situation of Optimizing a Sensor and a Transporter System in a Whole-Cell Microbial Biosensor Design for an Anthropogenic Small Molecule

Whole-cell biosensors provide a convenient detection tool for the high-throughput screening of genetically engineered biocatalytic activity. But establishing a biosensor for an anthropogenic molecule requires both a custom transporter and a transcription factor. This results in an unavoidable "Catch-22" situation in which transporter activity cannot be easily confirmed without a biosensor and a biosensor cannot be established without a functional transporter in a host organism. We overcame this type of circular problem while developing an adipic acid (ADA) sensor. First, leveraging an established cis,cis-muconic acid (ccMA) sensor, an annotated ccMA transporter MucK, which is expected to be broadly responsive to dicarboxylates, was stably expressed in the genome of Pseudomonas putida to function as a transporter for ADA, and then a PcaR transcription factor (endogenous to the strain and naturally induced by β-ketoadipic acid, BKA) was diversified and selected to detect the ADA molecule. While MucK expression is otherwise very unstable in P. putida under strong promoter expression, our optimized mucK expression was functional for over 70 generations without loss of function, and we selected an ADA sensor that showed a specificity switch of over 35-fold from BKA at low concentrations (typically <0.1 mM of inducers). Our ADA and BKA biosensors show high sensitivity (low detection concentration <10 μM) and dynamic range (∼50-fold) in an industrially relevant organism and will open new avenues for high throughput discovery and optimization of enzymes and metabolic pathways for the biomanufacture of these molecules. In particular, the novel ADA sensor will aid the discovery and evolution of efficient biocatalysts for the biological recycling of ADA from the degradation of nylon-6,6 waste.

Advances in engineering and optimization of transcription factor-based biosensors for plug-and-play small molecule detection

Transcription factor (TF)-based biosensors have been applied in biotechnology for a variety of functions, including protein engineering, dynamic control, environmental detection, and point-of-care diagnostics. Such biosensors are promising analytical tools due to their wide range of detectable ligands and modular nature. However, designing biosensors tailored for applications of interest with the desired performance parameters, including ligand specificity, remains challenging. Biosensors often require significant engineering and tuning to meet desired specificity, sensitivity, dynamic range, and operating range parameters. Another limitation is the orthogonality of biosensors across hosts, given the role of the cellular context. Here, we describe recent advances and examples in the engineering and optimization of TF-based biosensors for plug-and-play small molecule detection. We highlight novel developments in TF discovery and biosensor design, TF specificity engineering, and biosensor tuning, with emphasis on emerging computational methods.

Directed evolution of the PobR allosteric transcription factor to generate a biosensor for 4-hydroxymandelic acid

Hydroxy-mandelic acid (HMA) is widely applied in pharmaceuticals, food and cosmetics. In this study, we aimed to develop an allosteric transcription factors (aTFs) based biosensor for HMA. PobR, an aTF for HMA analog 4-hydroxybenzoic acid, was used to alter its selectivity and create novel aTFs responsive to HMA by directed evolution. We established a PobR mutant library with a capacity of 550,000 mutants using error-prone PCR and Megawhop PCR. Through our screening, two mutants were obtained with responsiveness to HMA. Analysis of each missense mutation indicating residues 122-126 were involved in its PobR ligand specificity. These results showed the effectiveness of directed evolution in switching the ligand specificity of a biosensor and improving HMA production.

Intracellular biosensor-based dynamic regulation to manipulate gene expression at the spatiotemporal level

The use of intracellular, biosensor-based dynamic regulation strategies to regulate and improve the production of useful compounds have progressed significantly over previous decades. By employing such an approach, it is possible to simultaneously realize high productivity and optimum growth states. However, industrial fermentation conditions contain a mixture of high- and low-performance non-genetic variants, as well as young and aged cells at all growth phases. Such significant individual variations would hinder the precise controlling of metabolic flux at the single-cell level to achieve high productivity at the macroscopic population level. Intracellular biosensors, as the regulatory centers of metabolic networks, can real-time sense intra- and extracellular conditions and, thus, could be synthetically adapted to balance the biomass formation and overproduction of compounds by individual cells. Herein, we highlight advances in the designing and engineering approaches to intracellular biosensors. Then, the spatiotemporal properties of biosensors associated with the distribution of inducers are compared. Also discussed is the use of such biosensors to dynamically control the cellular metabolic flux. Such biosensors could achieve single-cell regulation or collective regulation goals, depending on whether or not the inducer distribution is only intracellular.

Directed Evolution of Transcription Factor-Based Biosensors for Altered Effector Specificity

Transcription factor-based biosensors are important tools in Synthetic Biology for the sensing of industrially valuable molecules and clinically important metabolites, therefore presenting applications in the bioremediation, industrial biotechnology, and biomedical fields. The directed evolution of allosteric transcription factors (aTFs) with the aim of altering effector specificity has the potential for the development of new biosensors to detect natural and nonnatural molecules, expanding the scope of available aTF-based biosensors. In this chapter, we delineate a general method for the directed evolution of aTFs. The theory of library design is discussed, along with the detailed methodology for an improved transformation of combined libraries, and the experimental search space by counterselection using fluorescence-activated cell sorting (FACS) is presented.

Development of Genetically Encoded Biosensors for Reporting the Methyltransferase-Dependent Biosynthesis of Semisynthetic Macrolide Antibiotics

Clarithromycin is an improved semisynthetic analogue of the naturally occurring macrolide, erythromycin. The subtle modification of a methyl group on the C-6 hydroxyl group endows the molecule with improved acid stability and results in a clinically useful antibiotic. Here, we show that the effector specificity of the biosensor protein, MphR, can be evolved to selectively recognize clarithromycin and therefore report on the production of this molecule in vivo. In addition, a crystal structure of the evolved variant reveals the molecular basis for selectivity and provides a guide for the evolution of a new metabolic function using this biosensor.

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.

Conformational control via sequence for a heteropeptoid in water: coupled NMR and Rosetta modelling

We report a critical advance in the generation and characterization of peptoid hetero-oligomers. A library of sub-monomers with amine and carboxylate side-chains are combined in different sequences using microwave-assisted synthesis. Their sequence-structure propensity is confirmed by circular dichroism, and conformer subtypes are enumerated by NMR. Biasing the ψ-angle backbone to trans (180°) in Monte Carlo modelling favors i to i + 3 naphthyl-naphthyl stacking, and matches experimental ensemble distributions. Taken together, high-yield synthesis of heterooligomers and NMR with structure prediction enables rapid determination of sequences that induce secondary structural propensities for predictive design of hydrophilic peptidomimetic foldamers and their future libraries.

Computational Redesign of an ω-Transaminase from Pseudomonas jessenii for Asymmetric Synthesis of Enantiopure Bulky Amines

ω-Transaminases (ω-TA) are attractive biocatalysts for the production of chiral amines from prochiral ketones via asymmetric synthesis. However, the substrate scope of ω-TAs is usually limited due to steric hindrance at the active site pockets. We explored a protein engineering strategy using computational design to expand the substrate scope of an (S)-selective ω-TA from Pseudomonas jessenii (PjTA-R6) toward the production of bulky amines. PjTA-R6 is attractive for use in applied biocatalysis due to its thermostability, tolerance to organic solvents, and acceptance of high concentrations of isopropylamine as amino donor. PjTA-R6 showed no detectable activity for the synthesis of six bicyclic or bulky amines targeted in this study. Six small libraries composed of 7–18 variants each were separately designed via computational methods and tested in the laboratory for ketone to amine conversion. In each library, the vast majority of the variants displayed the desired activity, and of the 40 different designs, 38 produced the target amine in good yield with >99% enantiomeric excess. This shows that the substrate scope and enantioselectivity of PjTA mutants could be predicted in silico with high accuracy. The single mutant W58G showed the best performance in the synthesis of five structurally similar bulky amines containing the indan and tetralin moieties. The best variant for the other bulky amine, 1-phenylbutylamine, was the triple mutant W58M + F86L + R417L, indicating that Trp58 is a key residue in the large binding pocket for PjTA-R6 redesign. Crystal structures of the two best variants confirmed the computationally predicted structures. The results show that computational design can be an efficient approach to rapidly expand the substrate scope of ω-TAs to produce enantiopure bulky amines.

Development of a Biosensor Platform for Phenolic Compounds Using a Transition Ligand Strategy

The time-consuming and laborious characterization of protein or microbial strain designs limits the development of high-performance biocatalysts for biotechnological applications. Here, transcriptional biosensors emerged as valuable tools as they allow for rapid characterization of several thousand variants within a very short time. However, for many molecules of interest, no specific transcriptional regulator determining a biosensor's specificity is available. We present an approach for rapidly engineering biosensor specificities using a semirational transition ligand approach combined with fluorescence-activated cell sorting. In this two-step approach, a biosensor is first evolved toward a more relaxed-ligand specificity before using the resulting variant as the starting point in a second round of directed evolution toward high specificity for several chemically different ligands. By following this strategy, highly specific biosensors for 4-hydroxybenzoic acid, p-coumaric acid, 5-bromoferulic acid, and 6-methyl salicylic acid were developed, starting from a biosensor for the intracellular detection of trans-cinnamic acid.

Computational Enzyme Engineering Pipelines for Optimized Production of Renewable Chemicals

To enable a sustainable supply of chemicals, novel biotechnological solutions are required that replace the reliance on fossil resources. One potential solution is to utilize tailored biosynthetic modules for the metabolic conversion of CO₂ or organic waste to chemicals and fuel by microorganisms. Currently, it is challenging to commercialize biotechnological processes for renewable chemical biomanufacturing because of a lack of highly active and specific biocatalysts. As experimental methods to engineer biocatalysts are time- and cost-intensive, it is important to establish efficient and reliable computational tools that can speed up the identification or optimization of selective, highly active, and stable enzyme variants for utilization in the biotechnological industry. Here, we review and suggest combinations of effective state-of-the-art software and online tools available for computational enzyme engineering pipelines to optimize metabolic pathways for the biosynthesis of renewable chemicals. Using examples relevant for biotechnology, we explain the underlying principles of enzyme engineering and design and illuminate future directions for automated optimization of biocatalysts for the assembly of synthetic metabolic pathways.

Advances and Prospects of Phenolic Acids Production, Biorefinery and Analysis

Biotechnological production of phenolic acids is attracting increased interest due to their superior antioxidant activity, as well as other antimicrobial, dietary, and health benefits. As secondary metabolites, primarily found in plants and fungi, they are effective free radical scavengers due to the phenolic group available in their structure. Therefore, phenolic acids are widely utilised by pharmaceutical, food, cosmetic, and chemical industries. A demand for phenolic acids is mostly satisfied by utilising chemically synthesised compounds, with only a low quantity obtained from natural sources. As an alternative to chemical synthesis, environmentally friendly bio-based technologies are necessary for development in large-scale production. One of the most promising sustainable technologies is the utilisation of microbial cell factories for biosynthesis of phenolic acids. In this paper, we perform a systematic comparison of the best known natural sources of phenolic acids. The advances and prospects in the development of microbial cell factories for biosynthesis of these bioactive compounds are discussed in more detail. A special consideration is given to the modern production methods and analytics of phenolic acids.

A genome-wide approach for identification and characterisation of metabolite-inducible systems

Inducible gene expression systems are vital tools for the advancement of synthetic biology. Their application as genetically encoded biosensors has the potential to contribute to diagnostics and to revolutionise the field of microbial cell factory development. Currently, the number of compounds of biological interest by far exceeds the number of available biosensors. Here, we address this limitation by developing a generic genome-wide approach to identify transcription factor-based inducible gene expression systems. We construct and validate 15 functional biosensors, provide a characterisation workflow to facilitate forward engineering efforts, exemplify their broad-host-range applicability, and demonstrate their utility in enzyme screening. Previously uncharacterised interactions between sensors and compounds of biological relevance are identified by employing the largest reported library of metabolite-responsive biosensors in an automated high-throughput screen. With the rapidly growing genomic data these innovative capabilities offer a platform to vastly increase the number of biologically detectable molecules.

Inducible gene expression tools have important applications as genetically encoded biosensors. Here the authors conduct a genome-wide approach to identify and utilise functional sensors.

Directed evolution of the PcaV allosteric transcription factor to generate a biosensor for aromatic aldehydes

Background

Transcription factor-based biosensors are useful tools for the detection of metabolites and industrially valuable molecules, and present many potential applications in biotechnology and biomedicine. However, the most common approach to develop biosensors relies on employing a limited set of naturally occurring allosteric transcription factors (aTFs). Therefore, altering the ligand specificity of aTFs towards the detection of new effectors is an important goal.

Results

Here, the PcaV repressor, a member of the MarR aTF family, was used to develop a biosensor for the detection of hydroxyl-substituted benzoic acids, including protocatechuic acid (PCA). The PCA biosensor was further subjected to directed evolution to alter its ligand specificity towards vanillin and other closely related aromatic aldehydes, to generate the Van2 biosensor. Ligand recognition of Van2 was explored in vitro using a range of biochemical and biophysical analyses, and extensive in vivo genetic-phenotypic analysis was performed to determine the role of each amino acid change upon biosensor performance.

Conclusions

This is the first study to report directed evolution of a member of the MarR aTF family, and demonstrates the plasticity of the PCA biosensor by altering its ligand specificity to generate a biosensor for aromatic aldehydes.

Genetically encoded biosensors for lignocellulose valorization

Modern society is hugely dependent on finite oil reserves for the supply of fuels and chemicals. Moving our dependence away from these unsustainable oil-based feedstocks to renewable ones is, therefore, a critical factor towards the development of a low carbon bioeconomy. Lignin derived from biomass feedstocks offers great potential as a renewable source of aromatic compounds if methods for its effective valorization can be developed. Synthetic biology and metabolic engineering offer the potential to synergistically enable the development of cell factories with novel biosynthetic routes to valuable chemicals from these sustainable sources. Pathway design and optimization is, however, a major bottleneck due to the lack of high-throughput methods capable of screening large libraries of genetic variants and the metabolic burden associated with bioproduction. Genetically encoded biosensors can provide a solution by transducing the target metabolite concentration into detectable signals to provide high-throughput phenotypic read-outs and allow dynamic pathway regulation. The development and application of biosensors in the discovery and engineering of efficient biocatalytic processes for the degradation, conversion, and valorization of lignin are paving the way towards a sustainable and economically viable biorefinery.

Artificial Caprolactam-Specific Riboswitch as an Intracellular Metabolite Sensor

Caprolactam is a monomer used for the synthesis of nylon-6, and a recombinant microbial strain for biobased production of nylon-6 was recently developed. An intracellular biosensor for caprolactam can facilitate high-throughput metabolic engineering of recombinant microbial strains. Because of the mixed production of caprolactam and valerolactam in the recombinant strain, a caprolactam biosensor should be highly specific for caprolactam. However, a highly specific caprolactam sensor has not been reported. Here, we developed an artificial riboswitch that specifically responds to caprolactam. This riboswitch was prepared using a coupled in vitro- in vivo selection strategy with a heterogeneous pool of RNA aptamers obtained from in vitro selection to construct a riboswitch library used in in vivo selection. The caprolactam riboswitch successfully discriminated caprolactam from valerolactam. Moreover, the riboswitch was activated by 3.36-fold in the presence of 50 mM caprolactam. This riboswitch enabled caprolactam-dependent control of cell growth, which will be useful for improving caprolactam production and is a valuable tool for metabolic engineering.

Sensor-Enabled Alleviation of Product Inhibition in Chorismate Pyruvate-Lyase

Product inhibition is a frequent bottleneck in industrial enzymes, and testing mutations to alleviate product inhibition via traditional methods remains challenging as many variants need to be tested against multiple substrate and product concentrations. Further, traditional screening methods are conducted in vitro, and resulting enzyme variants may perform differently in vivo in the context of whole-cell metabolism and regulation. In this study, we address these two problems by establishing a high-throughput screening method to alleviate product inhibition in an industrially relevant enzyme, chorismate pyruvate-lyase (UbiC). First, we engineered a highly specific, genetically encoded biosensor for 4-hydroxybenzoate (4HB) in an industrially relevant host, Pseudomonas putida KT2440. We subsequently applied the biosensor to detect the activity of a heterologously expressed UbiC that converts chorismate into 4HB and pyruvate. By using benzoate as a product surrogate that inhibits UbiC without activating the biosensor, we were able to efficiently create and screen a diversified library for UbiC variants with reduced product inhibition. Introduction of the improved UbiC enzyme variant into an experimental production strain for the industrial precursor cis,cis-muconic acid (muconate), enabled a >2-fold yield improvement for glucose to muconate conversion when the new UbiC variant was expressed from a plasmid and a 60% yield increase when the same UbiC variant was genomically integrated into the strain. Overall, this work demonstrates that by coupling a library of enzyme variants to whole-cell catalysis and biosensing, variants with reduced product inhibition can be identified, and that this improved enzyme can result in increased titers of a downstream molecule of interest.

A new benchmark illustrates that integration of geometric constraints inferred from enzyme reaction chemistry can increase enzyme active site modeling accuracy

Enzymes play a critical role in a wide array of industrial, medical, and research applications and with the recent explosion of genomic sequencing, we now have sequences for millions of enzymes for which there is no known structure. In order to utilize modern computational design tools for constructing inhibitors or engineering novel catalysts, the ability to accurately model enzymes is critical. A popular approach for modeling enzymes are comparative modeling techniques which can often accurately predict the global structural features. However, achieving atomic accuracy of an active site remains a challenge and is an issue when trying to utilize the molecular details for designing inhibitors or enhanced catalysts. Here we explore integrating knowledge about the required geometric orientation of conserved catalytic residues into the comparative modeling process in order to improve modeling accuracy. In order to investigate the utility of adding this information, we first carefully construct a benchmark set of reference structures to use. Consistent with previous findings, our benchmark demonstrates that the geometry between catalytic residues across an enzyme family is conserved and does not tend to deviate by more than 0.5Å. We then find that by integrating these geometric constraints during modeling, we can double the number of atomic level accuracy models (<1Å RMSD to the crystal structure ligand) within our benchmarking dataset, even for targets with templates as low as 20-30% sequence identity. Catalytic residues within an enzyme family are highly conserved and can often be readily identified through comparative sequence analysis to a known structure within the enzyme family. Therefore utilizing this readily available information has the potential to significantly improve drug design and enzyme engineering efforts for which there is no known structure for the enzyme of interest.

Chimeric LysR-Type Transcriptional Biosensors for Customizing Ligand Specificity Profiles toward Flavonoids

Transcriptional biosensors enable key applications in both metabolic engineering and synthetic biology. Due to nature's immense variety of metabolites, these applications require biosensors with a ligand specificity profile customized to the researcher's needs. In this work, chimeric biosensors were created by introducing parts of a donor regulatory circuit from Sinorhizobium meliloti, delivering the desired luteolin-specific response, into a nonspecific biosensor chassis from Herbaspirillum seropedicae. Two strategies were evaluated for the development of chimeric LysR-type biosensors with customized ligand specificity profiles toward three closely related flavonoids, naringenin, apigenin, and luteolin. In the first strategy, chimeric promoter regions were constructed at the biosensor effector module, while in the second strategy, chimeric transcription factors were created at the biosensor detector module. Via both strategies, the biosensor repertoire was expanded with luteolin-specific chimeric biosensors demonstrating a variety of response curves and ligand specificity profiles. Starting from the nonspecific biosensor chassis, a shift from 27.5% to 95.3% luteolin specificity was achieved with the created chimeric biosensors. Both strategies provide a compelling, faster, and more accessible route for the customization of biosensor ligand specificity, compared to de novo design and construction of each biosensor circuit for every desired ligand specificity.

In vivo biosensors: mechanisms, development, and applications

In vivo biosensors can recognize and respond to specific cellular stimuli. In recent years, biosensors have been increasingly used in metabolic engineering and synthetic biology, because they can be implemented in synthetic circuits to control the expression of reporter genes in response to specific cellular stimuli, such as a certain metabolite or a change in pH. There are many types of natural sensing devices, which can be generally divided into two main categories: protein-based and nucleic acid-based. Both can be obtained either by directly mining from natural genetic components or by engineering the existing genetic components for novel specificity or improved characteristics. A wide range of new technologies have enabled rapid engineering and discovery of new biosensors, which are paving the way for a new era of biotechnological progress. Here, we review recent advances in the design, optimization, and applications of in vivo biosensors in the field of metabolic engineering and synthetic biology.

A protocatechuate biosensor for Pseudomonas putida KT2440 via promoter and protein evolution

Robust fluorescence-based biosensors are emerging as critical tools for high-throughput strain improvement in synthetic biology. Many biosensors are developed in model organisms where sophisticated synthetic biology tools are also well established. However, industrial biochemical production often employs microbes with phenotypes that are advantageous for a target process, and biosensors may fail to directly transition outside the host in which they are developed. In particular, losses in sensitivity and dynamic range of sensing often occur, limiting the application of a biosensor across hosts. Here we demonstrate the optimization of an Escherichia coli-based biosensor in a robust microbial strain for the catabolism of aromatic compounds, Pseudomonas putida KT2440, through a generalizable approach of modulating interactions at the protein-DNA interface in the promoter and the protein-protein dimer interface. The high-throughput biosensor optimization approach demonstrated here is readily applicable towards other allosteric regulators.

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A biosensor optimized for a robust, industrially useful P. putida strain.

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Modulation of protein-DNA and protein-protein interactions pursued.

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Offers a generalized optimization protocol for transcription factor-based sensors.

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Intracellular metabolite production and detection made possible in P. putida.

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Functional biosensor in P. putida will allow high throughput strain evolution.

Enabling tools for high-throughput detection of metabolites: Metabolic engineering and directed evolution applications

Within the Design-Build-Test Cycle for strain engineering, rapid product detection and selection strategies remain challenging and limit overall throughput. Here we summarize a wide variety of modalities that transduce chemical concentrations into easily measured absorbance, luminescence, and fluorescence signals. Specifically, we cover protein-based biosensors (including transcription factors), nucleic acid-based biosensors, coupled enzyme reactions, bioorthogonal chemistry, and fluorescent and chromogenic dyes and substrates as modalities for detection. We focus on the use of these methods for strain engineering and enzyme discovery and conclude with remarks on the current and future state of biosensor development for application in the metabolic engineering field.

Computational design of ligand-binding proteins

Custom-designed ligand-binding proteins with novel functions hold the potential for numerous applications. In recent years, the developments of computational methods together with high-throughput experimental screening techniques have led to the generation of novel, high-affinity ligand-binding proteins for given ligands. In addition, naturally occurring ligand-binding proteins have been computationally designed to recognize new ligands while keeping their original biological functions at the same time. Furthermore, metalloproteins have been successfully designed for novel functions and applications. Though much has been learned in these successful design cases, advances in our understanding of protein dynamics and functions related to ligand binding and development of novel computational strategies are necessary to further increase the success rate of computational protein-ligand binding design.

Tailor-made transcriptional biosensors for optimizing microbial cell factories

Monitoring cellular behavior and eventually properly adapting cellular processes is key to handle the enormous complexity of today's metabolic engineering questions. Hence, transcriptional biosensors bear the potential to augment and accelerate current metabolic engineering strategies, catalyzing vital advances in industrial biotechnology. The development of such transcriptional biosensors typically starts with exploring nature's richness. Hence, in a first part, the transcriptional biosensor architecture and the various modi operandi are briefly discussed, as well as experimental and computational methods and relevant ontologies to search for natural transcription factors and their corresponding binding sites. In the second part of this review, various engineering approaches are reviewed to tune the main characteristics of these (natural) transcriptional biosensors, i.e., the response curve and ligand specificity, in view of specific industrial biotechnology applications, which is illustrated using success stories of transcriptional biosensor engineering.

A microbial sensor for organophosphate hydrolysis exploiting an engineered specificity switch in a transcription factor

A whole-cell biosensor utilizing a transcription factor (TF) is an effective tool for sensitive and selective detection of specialty chemicals or anthropogenic molecules, but requires access to an expanded repertoire of TFs. Using homology modeling and ligand docking for binding pocket identification, assisted by conservative mutations in the pocket, we engineered a novel specificity in an Acinetobacter TF, PobR, to ‘sense’ a chemical p-nitrophenol (pNP) and measured the response via a fluorescent protein reporter expressed from a PobR promoter. Out of 10⁷ variants of PobR, four were active when dosed with pNP, with two mutants showing a specificity switch from the native effector 4-hydroxybenzoate (4HB). One of the mutants, pNPmut1 was then used to create a smart microbial cell responding to pNP production from hydrolysis of an insecticide, paraoxon, in a coupled assay involving phosphotriesterase (PTE) enzyme expressed from a separate promoter. We show the fluorescence of the cells correlated with the catalytic efficiency of the PTE variant expressed in each cell. High selectivity between similar molecules (4HB versus pNP), high sensitivity for pNP detection (∼2 μM) and agreement of apo- and holo-structures of PobR scaffold with predetermined computational models are other significant results presented in this work.

Sensing new chemicals with bacterial transcription factors

Bacteria rely on allosteric transcription factors (aTFs) to sense a wide range of chemicals. The variety of effectors has contributed in making aTFs the most used input system in synthetic biological circuits. Considering their enabling role in biotechnology, an important question concerns the size of the chemical space that can potentially be detected by these biosensors. From digging into the ever changing repertoire of natural regulatory circuits, to advances in aTF engineering, we review here different strategies that are pushing the boundaries of this chemical space. We also review natural and synthetic cases of indirect sensing, where aTFs work in combination with metabolism to enable detection of new molecules.

SensiPath: computer-aided design of sensing-enabling metabolic pathways

Genetically-encoded biosensors offer a wide range of opportunities to develop advanced synthetic biology applications. Circuits with the ability of detecting and quantifying intracellular amounts of a compound of interest are central to whole-cell biosensors design for medical and environmental applications, and they also constitute essential parts for the selection and regulation of high-producer strains in metabolic engineering. However, the number of compounds that can be detected through natural mechanisms, like allosteric transcription factors, is limited; expanding the set of detectable compounds is therefore highly desirable. Here, we present the SensiPath web server, accessible at http://sensipath.micalis.fr SensiPath implements a strategy to enlarge the set of detectable compounds by screening for multi-step enzymatic transformations converting non-detectable compounds into detectable ones. The SensiPath approach is based on the encoding of reactions through signature descriptors to explore sensing-enabling metabolic pathways, which are putative biochemical transformations of the target compound leading to known effectors of transcription factors. In that way, SensiPath enlarges the design space by broadening the potential use of biosensors in synthetic biology applications.

Engineering Transcriptional Regulator Effector Specificity Using Computational Design and In Vitro Rapid Prototyping: Developing a Vanillin Sensor

The pursuit of circuits and metabolic pathways of increasing complexity and robustness in synthetic biology will require engineering new regulatory tools. Feedback control based on relevant molecules, including toxic intermediates and environmental signals, would enable genetic circuits to react appropriately to changing conditions. In this work, variants of qacR, a tetR family repressor, were generated by computational protein design and screened in a cell-free transcription-translation (TX-TL) system for responsiveness to a new targeted effector. The modified repressors target vanillin, a growth-inhibiting small molecule found in lignocellulosic hydrolysates and other industrial processes. Promising candidates from the in vitro screen were further characterized in vitro and in vivo in a gene circuit. The screen yielded two qacR mutants that respond to vanillin both in vitro and in vivo. While the mutants exhibit some toxicity to cells, presumably due to off-target effects, they are prime starting points for directed evolution toward vanillin sensors with the specifications required for use in a dynamic control loop. We believe this process, a combination of the generation of variants coupled with in vitro screening, can serve as a framework for designing new sensors for other target compounds.

Biosensor-based engineering of biosynthetic pathways

Biosynthetic pathways provide an enzymatic route from inexpensive renewable resources to valuable metabolic products such as pharmaceuticals and plastics. Designing these pathways is challenging due to the complexities of biology. Advances in the design and construction of genetic variants has enabled billions of cells, each possessing a slightly different metabolic design, to be rapidly generated. However, our ability to measure the quality of these designs lags by several orders of magnitude. Recent research has enabled cells to report their own success in chemical production through the use of genetically encoded biosensors. A new engineering discipline is emerging around the creation and application of biosensors. Biosensors, implemented in selections and screens to identify productive cells, are paving the way for a new era of biotechnological progress.

Engineering an allosteric transcription factor to respond to new ligands

Genetic regulatory proteins inducible by small molecules are useful synthetic biology tools as sensors and switches. Bacterial allosteric transcription factors (aTFs) are a major class of regulatory proteins, but few aTFs have been redesigned to respond to new effectors beyond natural aTF-inducer pairs. Altering inducer specificity in these proteins is difficult because substitutions that affect inducer binding may also disrupt allostery. We engineered an aTF, the Escherichia coli lac repressor, LacI, to respond to one of four new inducer molecules: fucose, gentiobiose, lactitol and sucralose. Using computational protein design, single-residue saturation mutagenesis or random mutagenesis, along with multiplex assembly, we identified new variants comparable in specificity and induction to wild-type LacI with its inducer, isopropyl β-D-1-thiogalactopyranoside (IPTG). The ability to create designer aTFs will enable applications including dynamic control of cell metabolism, cell biology and synthetic gene circuits.