Trends in Biotechnology

Cells are the fundamental unit of life. Yet there is no natural cell for which all its life-essential functions are understood. Here we demonstrate a complete cell cycle for a synthetic cell undergoing selection, with genome replication, growth, resource acquisition via feeding, and genetically encoded division. The cell is encoded via a 90kb genome that includes functions needed for resource uptake, transcription, translation, growth, genome replication, and division. The resulting synthetic cell is sufficiently encouraging to support routinization of synthetic cell engineering workflows, and will ultimately underlie diverse applications across all of biotechnology.

DNA assembly is a cornerstone of synthetic biology, enabling the construction of bespoke genetic systems for applications ranging from metabolic engineering to DNA nanotechnology. Conventional Gibson Assembly (GA), the most widely used method, relies on 5' exonucleolytic resection and elevated temperatures ([~]50 {degrees}C), which together prevent the retention of 5' modifications and restrict compatibility with temperature-sensitive functionalities. Here, we report a DNA assembly strategy, 3 exonuclease-mediated low-temperature DNA assembly (3LTDA), which generates complementary 5' overhangs while preserving 5' end integrity. This approach enables the efficient assembly of blunt-ended, 5'-functionalised DNA fragments into both linear and circular constructs at ambient temperature (21 {degrees}C), with some assembly observed at temperatures as low as 4{degrees}C. We systematically optimise reaction conditions and demonstrate that this method supports efficient plasmid re-circularisation and multi-fragment assembly, including the construction of a [~]12.5 kbp plasmid from multiple DNA components. Comparative analysis across several DNA substrates shows that, under their respective optimal conditions, this approach matches or exceeds GA performance, improving assembly efficiency by up to 12.8%. Sequence analysis confirms high fidelity with no detectable base-pairing errors across assembled junctions. Crucially, this method preserves chemically functionalised 5' termini, enabling downstream conjugation and biochemical functionality. Retention of azide and biotin modifications was verified through fluorescence imaging, bead-based co-localisation, and enzymatic activity in ELISA-based assays. This is in contrast to GA-assembled controls, which showed complete loss of functionality under comparable conditions. We further assembled 5 kbp dsDNA using 3LTDA from four independent segments, three with different fluorescence reporters, and the fourth containing a biotin group for microparticle conjugation, each on the 5 end. Under fluorescence illumination, bead-bound DNA with all three fluorescence markers were detected. Conventional GA assembled constructs, on the other hand, failed to retain the reporter groups and the fluorescent images did not show the presence of any fluorescent markers. In addition to enhanced performance, the method could also reduce reagent cost and eliminate the need for elevated temperatures, simplifying workflows and expanding the applicability of multi-functionalised DNA constructs. Collectively, this work establishes 3LTDA as a robust, low-temperature alternative to conventional GA, with advantages for applications requiring precise chemical modification, temperature-sensitive components, or deployment outside conventional laboratory environments.

The Modular Cloning (MoClo) and PhytoBrick standards have revolutionized plant synthetic biology by establishing a standardized, hierarchical assembly grammar. However, as the engineering of complex metabolic pathways, multi-trait stacks, and synthetic gene circuits expands, existing toolkits hit practical boundaries in assembly capacity and fixed grammars. To overcome these bottlenecks, we present MozClo, an expansion of the MoClo/PhytoBrick architecture. MozClo expands the standard Level 1 assembly framework to 10 positions using new L1 acceptors, end-linkers and dummy parts. We also identify and resolve a critical, sticky-end collision at L1 position 7 that has caused assembly failures during L2 cloning of large plasmids. To address commercial DNA synthesis length constraints and to lower cloning costs, we designed a universal 5-in-1 gene fragment multiplexing system. This architecture embeds up to five distinct parts flanked by orthogonal pairs of BpiI restriction sites into a single synthesized fragment, allowing them to sort independently into their respective L0 acceptor plasmids while maintaining complete modular flexibility of part types. Finally, we provide Level 2 cloning backbones with built in selection genes for common soybean transformation methods to facilitate downstream plant selection. Together, these advancements reduce DNA synthesis overhead and accelerate the construction of complex multigene payloads for plant biotechnology.

Compact tissue-specific promoters are highly desirable for gene therapy because viral vectors possess limited packaging capacity. However, existing promoter engineering strategies rely primarily on rational design or de novo sequence generation and lack efficient approaches for compressing long native promoters while preserving regulatory specificity. Although genome foundation models have substantially improved sequence-to-function prediction, they have not been effectively translated into computational platforms for promoter engineering. Here, we present VirEvo, a computational promoter engineering framework that integrates a virtual dual-luciferase assay (VirDLA), genome-foundation-model-guided genetic evolution, and an orthogonal Pan-Tissue Consistency Filter (PTCF). VirDLA introduces an internal-reference normalization strategy inspired by dual-luciferase reporter assays, enabling relative comparison of promoter activity across tissues without retraining AlphaGenome. Guided by these normalized activity scores, VirEvo iteratively optimizes promoter selectivity, off-target activity, and sequence length. Using the human p16INK4a promoter as a proof of concept, VirEvo evolved a compact synthetic promoter, SRP2M, of only 398 bp, representing an 85.9% reduction in sequence length. Experimental validation using dual-luciferase reporter assays in senescent IMR90 fibroblasts demonstrated that SRP2M retained 77% of wild-type senescence selectivity while reducing basal leakage to 52% of the wild-type level. Together, these results demonstrate the feasibility of genome-foundation-model-guided promoter engineering. VirEvo provides a generalizable framework for designing compact tissue-specific regulatory elements and extends the application of genome foundation models from functional prediction to synthetic regulatory engineering.

High-quality plasmid DNA purification at high throughput remains a significant bottleneck in molecular biology and bioengineering. Current methods frequently fail to deliver sufficient yields of pure, transfection-grade DNA required for genetic engineering applications in mammalian cells. Here, we present a Biofoundry-based automated pipeline using the CyBio FeliX robotic liquid handling platform to rapidly purify plasmid DNA with minimal manual intervention. The protocol leverages Solid Phase Reversible Immobilisation (SPRI)-based magnetic bead technology to ensure consistency, scalability, and DNA purity suitable for downstream viral particle production and mammalian cell transfection. The pipeline supports flexible processing of between 8 and 96 samples per run, making it adaptable across a wide range of experimental scales. The protocol is openly available via Earlham Institute GitHub repository, enabling broad adoption across the bioscientific community and contributing to the growing toolkit of reproducible, scalable engineering biology workflows. In this work, we employed an integrated robotic pipeline to process 528 pooled DNA plasmids and built a Lentiviral DNA plasmid library for lineage tracing, validated the library by sequencing, and demonstrated efficacy in downstream mammalian cell transfection experiments.

Carbonic anhydrases are among the fastest known biocatalysts, reversibly facilitating the hydration of CO2 to HCO3- at rates up to 107 s-1, which warrants their investigation for industrial carbon capture technologies. However, engineering carbonic anhydrases to maintain stability under harsh industrial process conditions remains a key challenge, and sequence-to-function datasets compatible with machine learning to inform forward engineering are lacking. Here, we developed a high-throughput platform that couples cell-free gene expression with a gaseous CO2 colorimetric assay to map the fitness landscapes of carbonic anhydrases. From 96 diverse natural homologs, we identified a robust variant from the Aquificota phylum and conducted an exhaustive mutational scan and functional assessment of this enzyme at 70C and 90C, covering >99% of all single-amino acid substitutions (totaling 4,365 mutations assayed in 39,285 reactions). This biochemical landscape was used to benchmark 22 zero-shot protein fitness models and identify critical mutations that improved enzyme stability at 90C by more than three-fold. We then used both zero-shot protein language models and supervised learning to filter 419 model-generated variants from a ProteinMPNN library of 100,000 sequences, leading to a best-in-class enzyme that retained activity after incubation at 95C. This work demonstrates that integrating cell-free enzyme engineering with machine learning enables opportunities for high-throughput experimental measurements to benchmark and improve protein language models, accelerate design loops, and expand functional exploration within protein families where experimental information is limited.

Directed evolution consisting of iterative rounds of diversification, selection, and counter-selection, underlies modern protein and antibody engineering, yet small-molecule drug design still advances largely through high-throughput screening and medicinal-chemistry intuition. Transformer softmax attention is mathematically identical to the Boltzmann distribution that governs molecular binding at thermal equilibrium1, an isomorphism that prescribes a sequence-native Specificity Foundation Model (SFM)2. This framework was recently applied across seven molecular recognition domains3,4 and scaled into the drug-target SFM (dtSFM), the first to pair a full-scale encoder with a generative decoder5. Whether such a model can be driven, iteratively and under selection, to optimize leads rather than sample them once has not been shown. Here we present GenLoop, a closed generative drug design loop that turns single-pass generation into directed evolution of chemistry. dtSFM generates target-conditioned molecules and reranks them by their thermodynamic compatibility score. An orthogonal structural verifier, AlphaFold 3, is used that shares no architecture or training data with dtSFM. Cheminformatics filters enforce developability, and generative evolution is performed on the structurally verified candidates, selecting for predicted binders and counter-selecting against off-target chemistry. Applied across twelve drug targets spanning pharmacologically distinct mechanism classes, GenLoop produced AlphaFold 3-verified designs that reached the structural confidence of the approved drug for five of the twelve targets, with the best designs at interface iPTM 0.93-0.98 and PAE 0.8-2.0 [A], as well as resolving paralog selectivity across nine targets. Two full disease campaigns followed. For the cystic-fibrosis transmembrane conductance regulator, GenLoop designed nine developability-filtered and structurally novel lead candidates (iPTM up to 0.93, interface PAE 2.3 [A]) targeting all three orthogonal sites of the approved drug Trikafta. For the GLP-1 receptor family, dtSFM engineered tunable single-, dual-, and triple-receptor incretin designs, yielding 23 central-pocket candidates that are structurally novel at median iPTM 0.89 and interface PAE 1.95 [A]. GenLoop with dtSFM brings directed evolution to small molecules through computational-thermodynamic selection; wet-lab validation is the immediate next step.

Here we introduce OpenEvo, a fully open-source, low-cost turbidostat platform for automated continuous culture and directed evolution experiments. Existing tools are expensive, complex, or lack open-source hardware; OpenEvo addresses this gap. OpenEvo is a complete, fully automated evolution platform with detailed, illustrated construction instructions for beginners, open-source software and firmware, and a single device priced around $300. An optional PC-based version offers enhanced functionality, including remote access, programmable evolution cycles, programmable LED stimulation, and a data visualization tool. OpenEvo can cycle through three types of media for positive, negative, and neutral selection conditions, supporting a wide range of experimental designs. We validate the use of OpenEvo by evolving H. volcanii to grow from 15% to 12% salt over ~150 cycles, ~1,000 hours. Evolved cells grew 36% faster than wild-type at 12% salt. Whole-genome sequencing of adapted cells found SNPs and large deletions. We also demonstrate positive and negative selection using the OpenEvo LEDs to drive optogenetics via a Phytochrome B-based optogenetic tool, with light as the selection stimulus during over 4000 hours of growth. OpenEvo lowers the technical and cost barriers for continuous evolution experiments, serves as a teaching tool, and is designed to grow an open community of users who share modifications.

Multidrug-resistant pathogens pose a major threat to One Health. Within the past decade, CRISPR-Cas systems have been explored as sequence-specific antimicrobials. While chromosomal injury has been considered the primary mechanism underlying pathogen killing by CRISPR-Cas antimicrobials, the synergistic role of gene disruption together with chromosomal injuries remains poorly understood. In this study, we characterized a new class of CRISPR-Cas antimicrobials that simultaneously cotarget essential and defensive genes to enhance potency against the clinically relevant pathogen Staphylococcus aureus. High-throughput CRISPR screening identified top-performing guide RNAs for twenty functionally diverse essential and defensive genes across the S. aureus genome. CRISPR-Cas antimicrobials were modularly formulated to target single or multiple gene loci and packaged in phage-like particles for specific delivery. By engineering an S. aureus production host with a chromosomally integrated anti-CRISPR protein, we demonstrated efficient production of CRISPR-Cas antimicrobials targeting any S. aureus chromosomal locus without self-targeting. Characterization of CRISPR-Cas antimicrobials with single guide RNA designs revealed that potency varied according to targeted gene function, achieving up to a 4-log10 reduction in viability and outperforming traditional antibiotics. Multiplexed configurations were consistently more effective than single-targeting designs, with the top-performing design demonstrating a 4.7-log10 reduction in viability. Cotargeting essential and defensive genes revealed synergies that led to improved lethality and attenuated resistance, with enhanced activity in biofilms compared to traditional antibiotics. Genes involved in signaling and stress responses were important defensive targets for developing cotargeting CRISPR-Cas antimicrobials. Overall, this study establishes design principles for synergistic CRISPR-Cas antimicrobials applicable to next-generation precision antimicrobial development. SIGNIFICANCEThe ability to effectively combat multidrug-resistant pathogens is of primary importance to One Health. This study develops a generalizable design principle for formulating potent CRISPR-Cas antimicrobials that exploit synergistic cotargeting strategies for enhanced pathogen killing. In addition to chromosomal injuries, we found that disruption of gene function plays a crucial role in determining the lethality of CRISPR-Cas antimicrobials, providing a generalizable framework for effective CRISPR-Cas antimicrobial design. The development of a CRISPR-Cas antimicrobial production host with stable, chromosomally integrated anti-CRISPR genes greatly expands the modularity, adaptability, and efficiency of formulating CRISPR-Cas antimicrobials and enables deeper insights into the molecular mechanisms involved in eliminating multidrug-resistant pathogens.

Messenger RNA (mRNA) is canonically composed of ribonucleotides, with sporadic incorporation of deoxyribonucleotides into natural RNA transcripts being traditionally regarded as a rare, deleterious error arising from transcriptional infidelity. Here, we challenge this paradigm by demonstrating controlled partial substitution of ribonucleotides with deoxyribonucleotides during in vitro transcription (IVT) generates intact, stable and fully translationally competent IVT-mRNA. Unexpectedly, chimeric DNA-RNA backbone modification exhibits markedly enhanced IVT-mRNA translation several fold across multiple cell types and in vivo via diverse dosing routes relative to their ribonucleotide-based counterparts. 25% substitution of cytidine triphosphate with deoxycytidine triphosphate achieved best-performing translational output, surpassing the current gold-standard N1-methylpseudouridine (m1{Psi})-modified IVT-mRNA in a B16-OVA tumor vaccination model. These findings identify nucleotide class composition as a previously unrecognized parameter governing IVT-mRNA function and establish hybrid ribonucleotide-deoxyribonucleotide backbone engineering as a versatile strategy to expand the chemical space for next-generation mRNA therapeutics.

Methanogenic archaea, such as Methanothermobacter marburgensis, represent a powerful biological platform for carbon capture and valorization, directly converting carbon dioxide (CO2) and molecular hydrogen (H2) into proteinogenic amino acids (AAs). In this study, we present a controlled and scalable strategy for tailoring AA production (biosynthesis and secretion) in continuous gas fermentation. By applying various Design of Experiments (DOE) techniques, we systematically identified and optimized key process parameters governing AA biosynthesis and shaping a targeted AA secretion profile. A hybrid modeling framework combining experimental data with scale-independent parameters derived from computational fluid dynamics (CFD) enabled robust performance prediction across bioreactor scales. This model-driven approach successfully translated the process from 120 mL glass bottles via 2 L to 150 L reactors, corresponding to a reaction-volume scale-up factor of 2000. These findings set the foundation for a robust and predictive platform for sustainable AA production, positioning archaea as a high-potential alternative in industrial biotechnology.

RNA-based sensors offer powerful and programmable control of gene expression, yet our understanding of the structural principles that govern their potential design space remains incomplete. Here, we challenged a community of designers to generate novel riboregulators capable of activating translation in response to specific RNA targets. Participants submitted diverse sequence architectures, which were synthesized and evaluated in a cell-free transcription-translation system. Across 100 designs, community-generated riboregulators displayed wide variability in activation dynamics, fold change, and structural features, outperforming some canonical toehold-switch designs and achieving up to 80-fold activation. Structural ensemble analyses identified accessibility patterns near the ribosome binding site that distinguish high- from low-performing regulators, highlighting the central role of RBS sequestration and release in modulating expression. Together, we demonstrate community-driven design can expand the accessible structural space of riboregulators and uncover mechanistic features governing translational activation. Our findings establish quantitative links between RNA folding energetics and gene expression output, providing design principles for next-generation programmable RNA sensors.

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@e26126org.highwire.dtl.DTLVardef@580c65org.highwire.dtl.DTLVardef@95ed54org.highwire.dtl.DTLVardef@110271f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Edible adipose tissue can enhance the sensory and nutritional qualities of cultivated and plant-based meats, yet efficient adipogenic differentiation remains a major bottleneck and synthetic PPAR{gamma} agonists are not approved for use in food production. Here, we report a natural compound screen in 3T3-L1 adipocytes that identifies magnolol and dicoumarol as enhancers of adipogenesis; this combination also robustly promotes lipid accumulation in primary porcine dedifferentiated fat cells and ovine preadipocytes. Transcriptomic analyses show that magnolol and dicoumarol induce adipogenesis in murine and porcine cell systems through canonical adipogenic pathways with a narrower transcriptional footprint than the potent PPAR{gamma} agonist rosiglitazone. These findings support the potential of naturally occurring compounds magnolol and dicoumarol as enhancers of adipogenesis for both mechanistic studies and food-relevant applications. More broadly, our findings establish a generalizable screening framework and identify small-molecule combinations that accelerate adipose tissue engineering across murine, porcine, and ovine culture systems.

De novo protein design is advancing rapidly1,2. This is being driven by AI to generate protein backbones, sequences, and structural models3-7. As a result, de novo designed proteins are becoming larger and more complex8-10, and increasingly explore new protein structures11,12. By contrast, natural proteins have evolved structural and functional complexity by modular combination of recurring protein domains13. Approximately 25% of these natural domains are mostly -helical structures14. Here we show how these can be expanded using rational computational design. Following the domain classification scheme CATH15, we build complex all- de novo proteins hierarchically using sequence-to-structure relationships for helix-helix interactions, systematic rules to connect helices, computational tools to design loops, and in silico evaluation. The pipeline starts with a target architecture of free-standing helices. These are connected into a topology by considering local arrangements of helical bundles using understood sequence-to-structure relationships for helix packing. Single-chain sequences are completed using template- and AI-based methods. Finally, AlphaFold models are assessed to give small numbers of designs for experimental validation. We test 31 designs for 14 different architectures and 25 topologies. 75% of these express as stable, monomeric, water-soluble proteins; and >30% yield X-ray crystal structures matching the designs to atomic accuracy and with new-to-nature structures. Finally, several of the scaffolds are functionalised through one-shot designs to deliver ion, small-molecule and protein binders.

Genetic manipulation of bacteriophages is essential for interrogating phage biology and advancing antimicrobial therapies. However, current genome editing approaches can be inefficient, require multiple steps, or drastically reduce phage titers. Here, we show that targeted DNA nicking enables template-mediated editing of phage genomes in one step without reducing phage titers. Using T7 phage, we show that Cas9-mediated nicking achieved up to 100% recombination across multiple loci, including substitutions and deletions of up to 200 bp and insertions of up to 500 bp, all while preserving phage titers. Editing in T7 was RecA-independent and extended to other phages. Leveraging high titers, we engineered a T7 library of over 440,000 tail-fiber mutants, with isolated mutants restoring infection of two LPS-deficient Escherichia coli hosts by shifting recognition to core LPS components. Overall, DNA nicking is a simple and distinct editing strategy that can advance phage genome engineering, genetic interrogation, and antimicrobial development.

Triculamin is a potent antibiotic lasso peptide first isolated in 1967. Previous studies have demonstrated that its biosynthesis follows a non-canonical logic unlike any other lasso peptide. In this study, we investigate the role of the unusual follower peptide and demonstrate that it is essential for efficient biosynthesis. Using structural prediction and targeted mutations of key conserved residues, we hypothesize that the interactions between the follower peptide and the macrocyclase create an enzyme-substrate complex that ensures delivery of the core peptide to the enzyme active site. Moreover, we demonstrate that analogs of the lasso peptide can be produced by modifying the core peptide, highlighting the substrate promiscuity of the lasso macrocyclase and identifying lysine-3 in the lasso peptide ring as the site of acetylation. Lastly, we achieve successful heterologous expression in Burkholderia sp. FERM 3421, which proves to be a superior heterologous host.

OptoGPCRs are light-activatable G protein-coupled receptors (GPCRs) used for optogenetic control of physiological processes. Most existing optoGPCRs are based on monostable opsins, which are limited by photobleaching and irreversibility. The bistable jumping spider rhodopsin 1 (JSR1) carrying the single point mutant S199F introduces a [~]150 nm spectral separation between the active and inactive states, enabling bidirectional control with distinct wavelengths of light. Here, we show that JSR1-S199F demonstrates robust, light-reversible arrestin recruitment and Gq/i protein activity. We then engineered JSR1-S199F-based optoGPCRs with Gs protein activity, expanding the limited repertoire of bistable Gs-coupled opsins. Specifically, we present optoDRD1, a chimeric optoGPCR that redirects the native Gq/i protein activity of JSR1 towards the Gs pathway of the dopamine D1 receptor (DRD1). Through systematic screening of intracellular domain combinations, we identified an optimal chimeric configuration comprising ICL2, ICL3, helix 8, and the C-terminus from DRD1. The resulting optoGPCR is activated by violet light ({lambda}max = 397 nm) and deactivated by green light ({lambda}max = 531 nm) at physiologically relevant light intensities. A single violet light pulse drives sustained Gs signalling for several hours, while green light deactivation enables precise signal termination at any timepoint. OptoDRD1 closely mimics wild-type DRD1 signalling kinetics and G protein selectivity. Compared to JellyOp, the only previously characterised natively Gs-coupled opsin, optoDRD1 shows higher signal amplitude and reversibility over multiple light cycles. We further demonstrate optoDRD1s utility for optogenetic control of Gs-regulated processes in vitro, including insulin secretion in human {beta}-cells and signalling modulation in a neuronal cell line, supporting its potential for in vivo applications. The biochemical stability and known structure of JSR1 make it a robust scaffold for this rational engineering and for future biophysical characterization. Together, optoDRD1 and JSR1-S199F expand the optoGPCR toolkit and open new opportunities for dissecting dopaminergic signalling, Gs-mediated physiology, and GPCR signalling pharmacology.

Cell-free biosensors leverage in vitro gene expression reactions to detect chemicals. While inexpensive, modular, and distributable, these platforms are constrained by slow readouts at ambient temperatures, precluding practical field operation. In cells, phase separation accelerates biochemical reactions; however, recapitulating these gains in vitro has remained challenging for complex biochemistries. Here, we report the first self-assembling coacervate system that accelerates in vitro transcription. Prepared by simple mixing, coacervation with spermine and polyacrylic acid occurs dynamically in response to NTP consumption and co-localizes DNA templates and RNA polymerase to accelerate transcription, mimicking intracellular phenomena. We exploit this discovery to accelerate the cell-free biosensing of six ligands, demonstrating that coacervation can preserve platform modularity, improve sensitivity, retain lyophilization compatibility, function in field matrices, and reduce ambient-temperature time-to-signal by hours. This work contributes to a growing understanding of phase separation in biology and advances the use of membrane-less organization for real-world applications.

Programmable molecular biology increasingly requires strategies for converting engineered recognition or proximity modules into measurable outputs, particularly within transcriptional regulation, RNA imaging, and CRISPR-associated systems. Synthetic chemically induced dimerization (CID) systems provide a class of programmable recognition modules for such applications, yet generalized strategies for coupling structurally diverse CIDs to functional readouts remain limited. Here, we introduce a CID-to-output conversion strategy based on engineering of the linker-mediated coupling interface. Using single-fluorescent-protein sensors as an experimentally tractable optical model readout, we systematically varied paired N- and C-terminal linkers flanking circularly permuted green fluorescent protein (cpGFP) to map coupling landscapes across synthetic CID systems derived from combinatorial selection and computational protein design. The results revealed strong non-additive interactions across paired linkers and suggest that linker length is a first-order determinant of CID-to-output coupling. Across nanobody-, monobody-, and de novo-designed CID architectures, this framework yielded functional sensors with dynamic ranges up to 1270% and robust responses in mammalian cells. Together, this work demonstrates that effective CID-to-output conversion can be achieved by empirically mapping the linker-mediated coupling interface, providing a practical route for adapting synthetic CID to diverse programmable molecular readouts and nucleic-acid-associated synthetic biology systems O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/735888v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1111094org.highwire.dtl.DTLVardef@1579e8aorg.highwire.dtl.DTLVardef@16981feorg.highwire.dtl.DTLVardef@1d588f7_HPS_FORMAT_FIGEXP M_FIG C_FIG