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Results for “Synthetic Biology”

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bioRxiv2021

Flux vs burden: biosensor optimization for dynamic pathway engineering

Recent progress in synthetic biology allows the construction of dynamic control circuits for metabolic engineering. This technology promises to overcome many challenges encountered in traditional pathway engineering, thanks to their ability to self-regulate gene expression in response to bioreactor perturbations. The central components in these control circuits are metabolite biosensors that read out pathway signals and actuate enzyme expression. However, the construction of metabolite biosensors is a major bottleneck for strain design, and a key challenge is to understand the relation between biosensor dose-response curves and pathway performance. Here we employ multiobjective optimization to quantify performance trade-offs that arise in the design and calibration of metabolite biosensors. Our approach reveals strategies for tuning dose-response curves along an optimal trade-off between production flux and the cost of an increased expression burden on the host. We explore properties of control architectures built in the literature, and identify their advantages and caveats in terms of performance and robustness to growth conditions and leaky promoters. We demonstrate the optimality of a control circuit for glucaric acid production in Escherichia coli, which has been shown to increase titer by 2.5-fold as compared to static designs. Our results lay the groundwork for the automated design of control circuits for pathway engineering, with applications in the food, energy and pharmaceutical sectors.

synthetic biology↗

bioRxiv2023

Integrated Constraint-Based Modeling of E. coli Cell-Free Protein Synthesis

Cell-free protein expression has become a widely used research tool in systems and synthetic biology and a promising technology for protein biomanufacturing. Cell-free protein synthesis relies on in-vitro transcription and translation processes to produce a protein of interest. However, transcription and translation depend upon the operation of complex metabolic pathways for precursor and energy regeneration. Toward understanding the role of metabolism in a cell-free system, we developed a dynamic constraint-based simulation of protein production in the myTXTL E. coli cell-free system with and without electron transport chain inhibitors. Time-resolved absolute metabolite measurements for [M] = 63 metabolites, along with absolute concentration measurements of the mRNA and protein abundance and measurements of enzyme activity, were integrated with kinetic and enzyme abundance information to simulate the time evolution of metabolic flux and protein production with and without inhibitors. The metabolic flux distribution estimated by the model, along with the experimental metabolite and enzyme activity data, suggested that the myTXTL cell-free system has an active central carbon metabolism with glutamate powering the TCA cycle. Further, the electron transport chain inhibitor studies suggested the presence of oxidative phosphorylation activity in the myTXTL cell-free system; the oxidative phosphorylation inhibitors provided biochemical evidence that myTXTL relied, at least partially, on oxidative phosphorylation to generate the energy required to sustain transcription and translation for a 16-hour batch reaction.

synthetic biology↗

bioRxiv2023

Deep learning-based codon optimization with large-scale synonymous variant datasets enables generalized tunable protein expression

Increasing recombinant protein expression is of broad interest in industrial biotechnology, synthetic biology, and basic research. Codon optimization is an important step in heterologous gene expression that can have dramatic effects on protein expression level. Several codon optimization strategies have been developed to enhance expression, but these are largely based on bulk usage of highly frequent codons in the host genome, and can produce unreliable results. Here, we develop deep contextual language models that learn the codon usage rules from natural protein coding sequences across members of the Enterobacterales order. We then fine-tune these models with over 150,000 functional expression measurements of synonymous coding sequences from three proteins to predict expression in E. coli. We find that our models recapitulate natural context-specific patterns of codon usage and can accurately predict expression levels across synonymous sequences. Finally, we show that expression predictions can generalize across proteins unseen during training, allowing for in silico design of gene sequences for optimal expression. Our approach provides a novel and reliable method for tuning gene expression with many potential applications in biotechnology and biomanufacturing.

synthetic biology↗

bioRxiv2023

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 characterize the PcaR TF belonging to the IclR family. We present in vitro functional analysis of PcaRs ligand profile and 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 TF. This work provides key structural and functional insights enabling the engineering of PcaR for dicarboxylic acid biosensors. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=50 SRC="FIGDIR/small/550818v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@16bcdd2org.highwire.dtl.DTLVardef@95ba9forg.highwire.dtl.DTLVardef@f598borg.highwire.dtl.DTLVardef@1bfe5f2_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIPcaR is an IclR family transcription regulator responsive to dicarboxylic acids C_LIO_LIPcaR was established as an in vivo biosensor in yeast C_LIO_LICrystal structure of PcaR in the apo form was solved C_LIO_LICrystal structure with PcaR in complex with succinate was solved C_LIO_LISequence alignments unveil ligand-binding positions in the IclR family C_LI

synthetic biology↗

bioRxiv2021

ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds

Synthetic biology and metabolic engineering rely on computational search tools for predictions of novel biosynthetic pathways to industrially important compounds, many of which are derived from aromatic amino acids. Pathway search tools vary in their scope of covered reactions and compounds, as well as in metrics for ranking and evaluation. In this work, we present a new computational resource called ARBRE: Aromatic compounds RetroBiosynthesis Repository and Explorer. It consists of a comprehensive biochemical reaction network centered around aromatic amino acid biosynthesis and a computational toolbox for navigating this network. ARBRE encompasses over 28000 known and 100000 novel reactions predicted with generalized enzymatic reactions rules and over 70000 compounds, of which 22000 are known to biochemical databases and 48000 only to PubChem. Over 1,000 molecules that were solely part of the PubChem database before and were previously impossible to integrate into a biochemical network are included into the ARBRE reaction network by assigning enzymatic reactions. ARBRE can be applied for pathway search, enzyme annotation, pathway ranking, visualization, and network expansion around known biochemical pathways to predict valuable compound derivations. In line with the standards of open science, we have made the toolbox freely available to the scientific community at http://lcsb-databases.epfl.ch/arbre/. We envision that ARBRE will provide the community with a new computational toolbox and comprehensive search tool to predict and rank pathways towards industrially important aromatic compounds.

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bioRxiv2024

Direct high-throughput deconvolution of unnatural bases via nanopore sequencing and bootstrapped learning

The discovery of non-canonical bases (NCBs) in viruses and the development of synthetic xeno-nucleic acids (XNAs) to expand the genetic alphabet has spawned interest in many applications, from viral genomics, to synthetic biology and DNA storage. However, the inability to read non-canonical bases in a direct, high-throughput manner has been a significant limitation to its study and applicability. Here we demonstrate that XNA templates containing non-canonical bases can be directly and robustly sequenced (>2.3 million reads/flowcell, similar to DNA controls) on a MinION sequencer from Oxford Nanopore Technologies to obtain signal data that is significantly distinct from DNA controls (median fold-change >6x). To enable training of machine learning models that deconvolve these signals and basecall non-canonical and canonical bases, we developed a framework to synthesize a complex pool of 1,024 NCB-containing oligonucleotides with diverse 6-mer sequence contexts and high purity (>90% NCB-insertion on average). Bootstrapped models to assist in data preparation, and data augmentation with spliced reads to provide high context diversity, enabled learning of a generalizable model to call canonical as well as non-canonical bases with high accuracy (>80%) and specificity (99%). These results highlight the versatility of nanopore sequencing as a platform for interrogating nucleic acids for viral genomic and xenobiology applications, and the potential to transform the study of genetic material beyond those that use canonical bases.

synthetic biology↗

bioRxiv2026

A Cofactor-Activated Molecular Switch for Condensate Biogenesis and Catalysis in Escherichia coli

Biomolecular condensates formed through liquid-liquid phase separation (LLPS) compartmentalize biochemical reactions without enclosing membranes, enabling spatiotemporal control over diverse cellular processes. Engineering genetically encoded proteins that phase separate in response to defined chemical inputs remains a central challenge for synthetic biology. Here, we report a coiled-coil peptide polymer, M1, that undergoes cofactor-dependent condensation both in vitro and in Escherichia coli. M1 is an ABA triblock construct comprising two terminal helical domains connected by a flexible, intrinsically disordered linker. The terminal domains are derived from a heme-responsive coiled-coil motif that is destabilized in the apo state but assembles into a four-helix bundle upon metalloporphyrin coordination. We demonstrate that M1 forms condensates exclusively in its cofactor-bound state, both in vitro and in cells. In E. coli, these intracellular condensates accumulate at the cell poles in a concentration-dependent manner. Depletion of cellular heme biosynthetic capacity suppressed condensate formation, which was rescued by supplementation with the heme precursor {delta}-aminolevulinic acid ({delta}-ALA) and iron, consistent with metalloporphyrin coordination triggering assembly. The condensates retain peroxidase activity characteristic of heme-containing proteins and catalyze the oxidation of Amplex Red to resorufin both in vitro and in living cells. These results establish metalloporphyrin binding as a molecular switch for condensate biogenesis in a structured peptide polymer, directly coupling cofactor coordination, mesoscale assembly, and catalytic function within a single designed system. SYNOPSIS O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/734084v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1bc678eorg.highwire.dtl.DTLVardef@32bb4corg.highwire.dtl.DTLVardef@a6890aorg.highwire.dtl.DTLVardef@16f13c2_HPS_FORMAT_FIGEXP M_FIG C_FIG

Synthetic Biology↗

bioRxiv2021

Experimental and Biophysical Modeling of Transcription and Translation Dynamics in Bacterial- and Mammalian-based Cell-Free Expression Systems

Cell-free expression (CFE) systems have been used extensively in system and synthetic biology as a promising platform for manufacturing proteins and chemicals. Currently, the most widely used CFE system is in vitro protein transcription and translation platform. As the rapidly increased applications and uses, it is crucial to have a standard biophysical model for quantitative studies of gene circuits, which will provide a fundamental understanding of basic working mechanisms of CFE systems. Current modeling approaches mainly focus on the characterization of E. coli-based CFE systems, a computational model that can be utilized to both bacterial- and mammalianbased CFE has not been investigated. Here, we developed a simple ODE (ordinary differential equation)-based biophysical model to simulate transcription and translation dynamics for both bacterial- and mammalian-based CFE systems. The key parameters were estimated and adjusted based on experimental results. We next tested four gene circuits to characterize kinetic dynamics of transcription and translation in E. coli- and HeLa-based CFE systems. The real-time transcription and translation were monitored using Broccoli aptamer, double stranded locked nucleic acid (dsLNA) probe and fluorescent protein. We demonstrated the difference of kinetic dynamics for transcription and translation in both systems, which will provide valuable information for quantitative genomic and proteomic studies. This simple biophysical model and the experimental data for both E. coli- and HeLa-based CFE will be useful for researchers that are interested in genetic engineering and CFE bio-manufacturing.

synthetic biology↗

bioRxiv2024

MMINT: a Metabolic Model Interactive Network Tool for the exploration and comparative visualisation of metabolic networks

Genome-scale metabolic models (GEMs) are essential tools in systems and synthetic biology, enabling the mathematical simulation of metabolic pathways encoded in genomes to predict phenotypes. The complexity of GEMs, however, can often limit the interpretation and comparison of their outputs. Here, we present MMINT (Metabolic Modelling Interactive Network Tool), designed to facilitate the exploration and comparison of metabolic networks. MMINT employs GEM networks and flux solutions derived from Constraint Based Analysis (e.g. Flux Balance Analysis) to create interactive visualizations. This tool allows for seamless toggling of source and target metabolites, network decluttering, enabling exploration and comparison of flux solutions by highlighting similarities and differences between metabolic states, which enhances the identification of mechanistic drivers of phenotypes. We demonstrate MMINTs capabilities using the Pyrococcus furiosus GEM, showcasing its application in distinguishing the metabolic drivers of acetate- and ethanol-producing phenotypes. By providing an intuitive and responsive model-exploration experience, MMINT addresses the need for a tool that simplifies the interpretation of GEM outputs and supports the discovery of novel metabolic engineering strategies. MMINT is available at https://doi.org/10.6084/m9.figshare.26409328 Graphical abstractMMINT functionalities provide an intuitive and responsive model-exploration experience, enabling flux solution comparison and the identification of metabolic drivers of phenotypes O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/606923v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@163d42eorg.highwire.dtl.DTLVardef@ff2922org.highwire.dtl.DTLVardef@1e5881aorg.highwire.dtl.DTLVardef@4a8bf0_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2017

Toward a Genome Scale Sequence Specific Dynamic Model of Cell-Free Protein Synthesis in Escherichia coli

Cell-free protein expression systems have become widely used in systems and synthetic biology. In this study, we developed an ensemble of dynamic E. coli cell-free protein synthesis (CFPS) models. Model parameters were estimated from a training dataset for the cell-free production of a protein product, chloramphenicol acetyltransferase (CAT). The dataset consisted of measurements of glucose, organic acids, energy species, amino acids, and CAT. The ensemble accurately predicted these measurements, especially those of the central carbon metabolism. We then used the trained model to evaluate the optimality of protein production. CAT was produced with an energy efficiency of 12%, suggesting that the process could be further optimized. Reaction group knockouts showed that protein productivity and the metabolism as a whole depend most on oxidative phosphorylation and glycolysis and gluco-neogenesis. Amino acid biosynthesis is also important for productivity, while the overflow metabolism and TCA cycle affect the overall system state. In addition, the translation rate is shown to be more important to productivity than the transcription rate. Finally, CAT production was robust to allosteric control, as was most of the network, with the exception of the organic acids in central carbon metabolism. This study is the first to use kinetic modeling to predict dynamic protein production in a cell-free E. coli system, and should provide a foundation for genome scale, dynamic modeling of cell-free E. coli protein synthesis.

synthetic biology↗

bioRxiv2026

Impact of coding region accessibility and uridine chemistry on translation in mRNA-DNA hybrid origami

Messenger RNA (mRNA) is a prerequisite for programmable protein expression, but its therapeutic and synthetic-biology applications are limited by instability and susceptibility to degradation. Hybridizing mRNA to short DNA strands can fold it into a compact origami nanostructure, protecting it from degradation but impeding ribosome access. However, how such a folded mRNA is translated, and which parts must be left unpaired, remain unclear. Here we fold an EGFP-encoding mRNA into a six-helix bundle and leave defined regions of the coding sequence unpaired to examine what the ribosome requires. We find that the start of the coding sequence must be accessible for translation, whereas leaving the far end unpaired makes no difference. Counterintuitively, leaving more of the coding sequence unpaired does not help: translation first falls and then partially recovers as the unpaired region lengthens, a reproducible pattern set by how that region folds rather than by its length. Modified mRNAs carrying 5-methoxyuridine or N1-methylpseudouridine still fold correctly into the six-helix bundle and show the non-monotonic translation pattern; the modification only shifts the overall level of protein produced, with N1-methylpseudouridine giving the most. Together these results begin to define how a folded mRNA can be made both stable and efficiently translated. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/734245v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@a54ac6org.highwire.dtl.DTLVardef@1d02202org.highwire.dtl.DTLVardef@168f0cforg.highwire.dtl.DTLVardef@99b51e_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2017

Scalable composition frameworks for multicellular logic

A major goal of synthetic biology is to reprogram living organisms to solve pressing challenges in manufacturing, environmental remediation, or healthcare. While many types of genetic logic gates have been engineered, their scalability remains limited. Previous work demonstrated that Distributed Multicellular Computation (DMC) enables the implementation of complex logic within cellular consortia. However, current DMC systems require spatial separation of cellular subpopulation to be scalable to N-inputs, and need cell-cell communication channels to operate. Here we present scalable composition frameworks for the systematic design of multicellular consortia performing recombinase-based Boolean or history-dependent logic, and integrating an arbitrary number of inputs. The theoretical designs for both Boolean and history-dependent logic are based on reduced sets of computational modules implemented into specific cellular subpopulations which can then be combined in various manners to implement all logic functions. Our systems mark a departure from previous DMC architectures as they do not require either cell-cell communication nor spatial separation, greatly facilitating their implementation and making them fully autonomous. Due to their scalability and composability, we anticipate that the design strategies presented here will help researchers and engineers to reprogram cellular behavior for various applications in a streamlined manner. We provide an online tool for automated design of DNA architectures allowing the implementation of multicellular N-inputs logic functions at: http://synbio.cbs.cnrs.fr/calin.

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bioRxiv2019

Cell-free prototyping of AND-logic gates based on heterogeneous RNA activators

RNA-based devices controlling gene expression bear great promise for synthetic biology, as they offer many advantages like short response times and light metabolic burden compared to protein-circuits. However, little work has been done regarding their integration to multi-level regulated circuits. In this work, we combined a variety of small transcriptional activator RNAs (STARs) and toehold switches to build highly effective AND-gates. To characterise the components and their dynamic range, we used an Escherichia coli (E. coli) cell-free transcription-translation (TX-TL) system dispensed via nanoliter droplets. We analysed a prototype gate in vitro as well as in silico, employing parameterised ordinary differential equations (ODEs), where parameters were inferred via parallel tempering, a Markov chain Monte Carlo (MCMC) method. Based on this analysis, we created nine additional AND-gates and tested them in vitro. The functionality of the gates was found to be highly dependent on the concentration of the activating RNA for either the STAR or the toehold switch. All gates were successfully implemented in vivo, offering a dynamic range comparable to the level of protein circuits. This study shows the potential of a rapid prototyping approach for RNA circuit design, using cell-free systems in combination with a model prediction.\n\nAbbreviationsTX-TL (transcription-translation), ODEs (ordinary differential equations), STARs (small transcriptional activator RNAs), MCMC (Markov chain Monte Carlo).

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bioRxiv2026

Selective Recognition, Covalent Linkage and Chemical Exchange of Artificial Cells through Engineered α-Hemolysin

Bottom-up synthetic biology aims not only to build individual artificial cells but also to connect them into higher-order systems that communicate with each other. Reported artificial cells communicate through partner recognition, physical connection, and chemical signaling that are typically implemented by separate molecular species. Here, we engineer the pore-forming protein -hemolysin (AH) with the SpyTag-SpyCatcher (ST-SC) pair to integrate partner recognition, covalent linkage between cells, and nanopore-mediated connection of their lumens into a single protein system. Fusion of ST or SC to AH preserved heptamer assembly and pore formation, and allowed the two heptamers (AH-ST and AH-SC) to be covalently joined. Cryo-electron microscopy resolved the resulting head-to-head 14-mer assembly at 2.20 [A]. Calcein leakage and single-channel recordings confirmed that the engineered pores remain functional. Mixing ST- and SC-displaying giant unilamellar vesicles (GUVs) resulted in partner-specific association between the two vesicle populations. Finally, photo-triggered release of a newly synthesized caged ethidium bromide (EtBr) in sender GUVs produced EtBr accumulation in receiver GUVs that required direct physical linkage between the two GUVs. This platform provides a modular route toward artificial-cell assemblies in which both connectivity and cargo exchange are programmable.

synthetic biology↗

bioRxiv2021

Transcriptional interference in toehold switch-based RNA circuits

Gene regulation based on regulatory RNA is an important mechanism in cells and is increasingly used for regulatory circuits in synthetic biology. Toehold switches are rationally designed post-transcriptional riboregulators placed in the 5 untranslated region of mRNA molecules. In the inactive state of a toehold switch, the ribosome-binding site is inaccessible for the ribosome. In the presence of a trigger RNA molecule protein production is turned on. Using antisense RNA against trigger molecules (anti-trigger RNA), gene expression can also be switched off again. We here study the utility and regulatory effect of antisense transcription in this context, which enables a particularly compact circuit design. Our circuits utilize two inducible promoters that separately regulate trigger and anti-trigger transcription, whereas their cognate toehold switch, regulating expression of a reporter protein, is transcribed from a constitutive promoter. We explore various design options for the arrangement of the promoters and demonstrate that the resulting dynamic behavior is strongly influenced by transcriptional interference (TI) effects, leading to more than four-fold differences in expression levels. Our experimental results are consistent with previous findings that enhanced local RNA polymerase concentrations due to active promoters in close proximity lead to an increase in transcriptional activity of the strongest promoter in the circuits. Based on this insight, we selected optimum promoter designs and arrangements for the realization of a genetic circuit comprised of two toehold switches, two triggers and two anti-triggers that function as a post-transcriptional RNA regulatory exclusive OR (XOR) gate.

synthetic biology↗

bioRxiv2018

Optimization of Golden Gate assembly through application of ligation sequence-dependent fidelity and bias profiling

Modern synthetic biology depends on the manufacture of large DNA constructs from libraries of genes, regulatory elements or other genetic parts. Type IIS restriction enzyme-dependent DNA assembly methods (e.g., Golden Gate) enable rapid one-pot, ordered, multi-fragment DNA assembly, facilitating the generation of high-complexity constructs. The order of assembly of genetic parts is determined by the ligation of flanking Watson-Crick base-paired overhangs. The ligation of mismatched overhangs leads to erroneous assembly, and the need to avoid such pairings has typically been accomplished by using small sets of empirically vetted junction pairs, limiting the number of parts that can be joined in a single reaction. Here, we report the use of a comprehensive method for profiling end-joining ligation fidelity and bias to predict highly accurate sets of connections for ligation-based DNA assembly methods. This data set allows quantification of sequence-dependent ligation efficiency and identification of mismatch-prone pairings. The ligation profile accurately predicted junction fidelity in ten-fragment Golden Gate assembly reactions, and enabled efficient assembly of a lac cassette from up to 24-fragments in a single reaction. Application of the ligation fidelity profile to inform choice of junctions thus enables highly flexible assembly design, with >20 fragments in a single reaction.

synthetic biology↗

bioRxiv2018

Coiled Coil-Mediated Assembly of an Icosahedral Protein Cage with Extremely High Thermal and Chemical Stability

The organization of protein molecules into higher-order nanoscale architectures is ubiquitous in Nature and represents an important goal in synthetic biology. Here we describe the symmetry-directed design of a hollow protein cage with dimensions similar to those of many icosahedral viruses. The cage was constructed based on icosahedral symmetry by genetically fusing a trimeric protein (TriEst) to a small pentameric de novo-designed coiled coil domain, separated by a flexible oligo-glycine linker sequence. Screening a small library of designs in which the linker length varied from 2 to 12 residues identified a construct containing 8 glycine residues (Ico8) that formed well-defined cages. Characterization by dynamic light scattering, negative stain and cryo EM, and by atomic force and IR-photo-induced force microscopy established that Ico8 assembles into a flexible hollow cage with comprising 60-subunits with overall icosahedral geometry. Unexpectedly, the cages were found to encapsulate DNA, even though neither protein component binds nucleic acids on its own. Notably, the cages formed by Ico8 proved to be extremely stable towards thermal and chemical denaturation: whereas TriEst was unfolded by heating (Tm ~75 {degrees}C) or denatured by 1.5 M guanidine hydrochloride, the Ico8 cages remained folded even at 120 {degrees}C or in 8 M guanidine hydrochloride. The encapsulation of DNA and increased stability of the cages are new properties that emerge from the higher order structure of the protein cage, rather than being intrinsic to the components from which it is constructed.

synthetic biology↗

bioRxiv2022

Enhancing the performance of Magnets photosensors through directed evolution

Photosensory protein domains are the basis of optogenetic protein engineering. These domains originate from natural sources where they fulfill specific functions ranging from the protection against photooxidative damage to circadian rhythms. When used in synthetic biology, the features of these photosensory domains can be specifically tailored towards the application of interest, enabling their full exploitation for optogenetic regulation in basic research and applied bioengineering. In this work, we develop and apply a simple, yet powerful, directed evolution and high-throughput screening strategy that allows us to alter the most fundamental property of the widely used nMag/pMag photodimerization system: its light sensitivity. We identify a set of mutations located within the photosensory domains, which either increase or decrease the light sensitivity at sub-saturating light intensities, while also improving the dark-to-light fold change in certain variants. For some of these variants, photosensitivity and expression levels could be changed independently, showing that the shape of the light-activity dose-response curve can be tuned and adjusted. We functionally characterize the variants in vivo in bacteria on the single-cell and the population levels. We further show that a subset of these variants can be transferred into the mOptoT7 for gene expression regulation in mammalian cells. We demonstrate increased gene expression levels for low light intensities, resulting in reduced potential phototoxicity in long-term experiments. Our findings expand the applicability of the widely used Magnets photosensors by enabling a tuning towards the needs of specific optogenetic regulation strategies. More generally, our approach will aid optogenetic approaches by making the adaptation of photosensor properties possible to better suit specific experimental or bioprocess needs.

synthetic biology↗