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Results for “Synthetic Biology”

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bioRxiv2022

Single-particle cryo-EM analysis of the shell architecture and internal organization of an intact α-carboxysome

Carboxysomes are proteaceous bacterial microcompartments (BMCs) that sequester the key enzymes for carbon fixation in cyanobacteria and some proteobacteria. They consist of a virus-like icosahedral shell, encapsulating carbonic anhydrase and ribulose 1,5-bisphosphate carboxylase/oxygenase (RuBisCO), which catalyses the dehydration of bicarbonate into CO2, the first step of the Calvin-Benson-Bassham cycle. Despite their significance in carbon fixation and great bioengineering potentials, the structural characterization of native carboxysomes, including the shell and the internal organization, is currently limited to low-resolution tomography studies. Notably, the degree of heterogeneity of the shell, and the internal arrangement of enzymes, remain poorly understood. Here, we report the structural characterization of a native -carboxysome from a marine cyanobacterium by single-particle cryo-EM. We determine the structure of RuBisCO enzyme at 2.9 [A] resolution. In addition, we obtain low-resolution maps of the icosahedral protein shell and the concentric interior organisation. In combination with artificial intelligence (AI)-driven modelling approaches, we exploited these maps to propose a complete atomic model of an intact carboxysome. This study provides insight into carboxysome structure and protein-protein interactions involved in carboxysome assembly. Advanced knowledge about carboxysome architecture and structural plasticity is critical for not only a better understanding of biological carbon fixation mechanism but also repurposing carboxysomes in synthetic biology for biotechnological applications.

biophysics↗

bioRxiv2023

Chemotactic interactions drive migration ofmembraneless active droplets

In nature, chemotactic interactions are ubiquitous and play a critical role in driving the collective behaviour of living organisms. Reproducing these interactions in vitrois still a paramount challenge due to the complexity of mimicking and controlling cellular features, such as metabolic density, cytosolic macromolecular crowding and cellular migration, on a microorganism size scale. Here we generate enzymatically-active cell-size droplets able to move freely and, by following a chemical gradient, able to interact with the surrounding droplets in a collective manner. The enzyme within the droplets generates a pH gradient that extends outside the edge of the droplets. We discovered that the external pH gradient triggers droplet migration and controls its directionality, which is selectively towards the neighbouring droplets. Hence, by changing the enzyme activity inside the droplet we tuned the droplet migration speed. Further, we showed that these cellular-like features can facilitate the reconstitution of a simple and linear protometabolic pathway with improved overall activity. Our work suggests that simple and stable membraneless droplets can be applied to reproduce complex biological phenomena opening new perspectives as bioinspired materials and synthetic biology tools.

biochemistry↗

bioRxiv2023

A hybrid nitrogenase with regulatory elasticity in Azotobacter vinelandii

Biological nitrogen fixation, the microbial reduction of atmospheric nitrogen to bioavailable ammonia, represents both a major limitation on biological productivity and a highly desirable engineering target for synthetic biology. However, engineering of nitrogen fixation requires an integrated understanding of how the gene regulatory dynamics of host diazotrophs restrict the available sequence-function space of its central catalytic metalloenzyme, nitrogenase. Here, we interrogate this relationship by analyzing the transcriptome of Azotobacter vinelandii engineered with a phylogenetically inferred, ancestral nitrogenase protein variant. The engineered strain exhibits reduced cellular nitrogenase activity but recovers wild-type growth rates following an extended lag period. We find that expression of genes within the immediate nitrogen fixation network is resilient to nitrogenase sequence-level perturbations. Rather, physiological compatibility with the ancestral nitrogenase variant is restored by reducing trace metal and electron resource allocation to nitrogenase. Our results spotlight cellular processes adjacent to nitrogen fixation as productive engineering targets to improve compatibility between remodeled nitrogenase proteins and engineered host diazotrophs. IMPORTANCEAzotobacter vinelandii is a key model bacterium for the study of biological nitrogen fixation, an important metabolic process catalyzed by nitrogenase enzymes. Here, we demonstrate that compatibilities between engineered A. vinelandii strains and remodeled nitrogenase variants can be modulated at the regulatory level. Engineered cells respond by adjusting expression of proteins involved in cellular processes adjacent to nitrogen fixation, rather than that of nitrogenase proteins themselves. These insights can inform future strategies to transfer nitrogenase variants to non-native hosts.

microbiology↗

bioRxiv2019

Genetically encoded nanostructures enable acoustic manipulation of engineered cells

The ability to mechanically manipulate and control the spatial arrangement of biological materials is a critical capability in biomedicine and synthetic biology. Ultrasound has the ability to manipulate objects with high spatial and temporal precision via acoustic radiation force, but has not been used to directly control biomolecules or genetically defined cells. Here, we show that gas vesicles (GVs), a unique class of genetically encoded gas-filled protein nanostructures, can be directly manipulated and patterned by ultrasound and enable acoustic control of genetically engineered GV-expressing cells. Due to their differential density and compressibility relative to water, GVs experience sufficient acoustic radiation force to allow these biomolecules to be moved with acoustic standing waves, as demonstrated within microfluidic devices. Engineered variants of GVs differing in their mechanical properties enable multiplexed actuation and act as sensors of acoustic pressure. Furthermore, when expressed inside genetically engineered bacterial cells, GVs enable these cells to be selectively manipulated with sound waves, allowing patterning, focal trapping and translation with acoustic fields. This work establishes the first genetically encoded nanomaterial compatible with acoustic manipulation, enabling molecular and cellular control in a broad range of contexts.

bioengineering↗

bioRxiv2020

A synthetic mechanogenetic gene circuit for autonomous drug delivery in engineered tissues

Mechanobiologic signals regulate cellular responses under physiologic and pathologic conditions. Using synthetic biology and tissue engineering, we developed a mechanically-responsive bioartificial tissue that responds to mechanical loading to produce a pre-programmed therapeutic biologic drug. By deconstructing the signaling networks induced by activation of the mechanically-sensitive ion channel transient receptor potential vanilloid 4 (TRPV4), we created synthetic TRPV4-responsive genetic circuits in chondrocytes. We engineered these cells into living tissues that respond to mechanical loading by producing the anti-inflammatory biologic drug, interleukin-1 receptor antagonist. Chondrocyte TRPV4 is activated by osmotic loading and not direct cellular deformation, suggesting tissue loading is transduced into an osmotic signal that activates TRPV4. Either osmotic or mechanical loading of tissues transduced with TRPV4-responsive circuits protected constructs from inflammatory degradation by interleukin-1. This synthetic mechanobiology approach was used to develop a mechanogenetic system to enable long-term, autonomously regulated drug delivery driven by physiologically-relevant loading.

synthetic biology↗

bioRxiv2023

Cell-free quorum sensing to drive feedback and communication within populations of droplet interface bilayers

Building synthetic multicellular systems using non-living molecular components is a grand challenge in the field of bottom-up synthetic biology. Towards this goal, a diverse range of chemistries have been developed to provide mechanisms of intercellular communication and methods to assemble multicellular compartments. However, building bottom-up synthetic multicellular systems is still challenging because it requires the integration of intercellular reaction networks with compatible cellular compartment properties. In this study, we encapsulated cell-free expression systems (CFES) expressing two quorum sensing genetic circuits into droplet interface bilayer (DIB) synthetic cells to demonstrate intercellular communication and feedback. We further develop a method of generating custom DIB multicellular structures by acoustic liquid handling to automatically dispense the CFES droplets and show the potential for multiplexing compartmentalized gene circuits for generating heterogeneous populations of cells. Our work provides a step towards building more complex multicellular systems with feedback mechanisms from the bottom-up to study and experimentally model biological multiscalar processes.

synthetic biology↗

bioRxiv2020

Rational design and implementation of a chemically inducible hetero-trimerization system

Chemically inducible dimerization (CID) uses a small molecule to induce binding of two different proteins. CID tools exemplified by the FKBP/FRB/rapamycin system have been widely employed to probe molecular events inside and outside cells. While various CID tools are available, chemically inducible trimerization (CIT) has not been developed, due to inherent challenges in designing or identifying a chemical that simultaneously binds three proteins with high affinity and target specificity. Nevertheless, by introducing a third recruitable component, CIT could enable versatile applications. Here, we devised the CIT by rationally splitting FRB and FKBP. Based on cellular and structural datasets, select split pairs of FRB or FKBP underwent efficient trimerization with full length FKBP or FRB, respectively, upon addition of rapamycin. We also demonstrated its potential for cellular applications by rapidly inducing tri-organellar plasma membrane-ER-mitochondria junctions, and by perturbing intended membrane lipids exclusively at the plasma membrane-ER membrane contact sites. By conferring one additional condition to what is achievable with CID, CIT expands the types of manipulation in single live cells, to address cell biology questions otherwise intractable, and engineer cell functions for future synthetic biology applications.

cell biology↗

bioRxiv2020

Computational study on ratio-sensing in yeast galactose utilization pathway

Metabolic networks undergo gene expression regulation in response to external nutrient signals. In microbes, the synthesis of enzymes that are used to transport and catabolize less preferred carbon sources is repressed in the presence of a preferred carbon source. For most microbes, glucose is a preferred carbon source, and it has long been believed that as long as glucose is present in the environment, the expression of genes related to the metabolism of alternative carbon sources is shut down, due to catabolite repression. However, recent studies have shown that the induction of the galactose (GAL) metabolic network does not solely depend on the exhaustion of glucose. Instead, the GAL genes respond to the external concentration ratio of galactose to glucose, a phenomenon of unknown mechanism that we termed ratio-sensing. Using mathematical modeling, we found that ratio-sensing is a general phenomenon that can arise from competition between two carbon sources for shared transporters, between transcription factors for binding to communal regulatory sequences of the target genes, or a combination of the aforementioned two levels of competition. We analyzed how the parameters describing the competitive interaction influenced ratio-sensing behaviors in each scenario and found that the concatenation of both layers of signal integration can expand the dynamical range of ratio-sensing. Finally, we investigated the influence of circuit topology on ratio-sensing and found that incorporating negative auto-regulation and/or coherent feedforward loop motifs to the basic signal integration unit can tune the sensitivity of the response to the external nutrient signals. Our study not only deepened our understanding of how ratio-sensing is achieved in yeast GAL metabolic regulation, but also elucidated design principles for ratio-sensing signal processing that can be used in other biological settings, such as being introduced into circuit designs for synthetic biology applications. Author summaryMicrobes make sophisticated choices about the uptake and metabolism of nutrients depending on the variety of nutrient choices available to them in their environment. In the well-studied yeast galactose utilization network, a recent study has shown that galactose metabolic genes respond to the external concentration ratio of galactose to glucose. Using computational models, we showed that this type of phenomenon could arise from a competition between galactose and glucose for transporters, a competition between transcription factors for promoters, or a combination of these two mechanisms. We further revealed the controlling parameters that determined the system sensitivity towards competing input signals and that determined the concentration ratio required to induce the metabolic network in each scenario. Combining competition inhibition at both the transporter level and the transcriptional level can enlarge the ratio-sensing regime, resulting a robust signal integration module. We suspect that modules of this kind may be common in many areas of biology.

systems biology↗

bioRxiv2026

Maximally Divergent Synonymous Gene Design with SIRIUS

The design of maximally divergent DNA sequences translating into the same protein is a critical problem in synthetic biology. Current design tools that rely on heuristics or machine learning often fail to effectively minimize the length of shared subsequences between the gene copies, compromising strain stability. Here, we introduce SIRIUS, a combinatorial optimization algorithm designed to generate maximally divergent coding sequences for a given protein of interest. Leveraging integer linear programming enforcing host-specific codon usage thresholds, SIRIUS stabilizes synthetic constructs and broadens the accessible design space for robust and scalable synethtic biology. Experimental results show that SIRIUS produces diverse sequences with fewer shared subsequences than existing methods. SIRIUS is freely available on GitHub at https://github.com/ucrbioinfo/sirius.

synthetic biology↗

bioRxiv2014

Synthesis and patterning of tunable multiscale materials with engineered cells

A major challenge in materials science is to create self-assembling, functional, and environmentally responsive materials which can be patterned across multiple length scales. Natural biological systems, such as biofilms, shells, and skeletal tissues, implement dynamic regulatory programs to assemble complex multiscale materials comprised of living and non-living components1-9. Such systems can provide inspiration for the design of heterogeneous functional systems which integrate biotic and abiotic materials via hierarchical self-assembly. Here, we present a synthetic-biology platform for synthesizing and patterning self-assembled functional amyloid materials across multiple length scales with bacterial biofilms. We engineered Escherichia coli curli amyloid production under the tight control of synthetic regulatory circuits and interfaced amyloids with inorganic materials to create a biofilm-based electrical switch whose conductance can be selectively toggled by specific environmental signals. Furthermore, we externally tuned synthetic biofilms to build nanoscale amyloid biomaterials with different structure and composition through the controlled expression of their constituent subunits with artificial gene circuits. By using synthetic cell-cell communication, our engineered biofilms can also autonomously manufacture dynamic materials whose structure and composition change with time. In addition, we show that by combining subunit-level protein engineering, controlled genetic expression of self-assembling subunit proteins, and macroscale spatial gradients, synthetic biofilms can pattern protein biomaterials across multiple length scales. This work lays a foundation for synthesizing, patterning, and controlling composite materials with engineered biological systems. We envision that this approach can be expanded to other cellular and biomaterials contexts for the construction of self-organizing, environmentally responsive, and tunable multiscale composite materials with heterogeneous functionalities.

Synthetic Biology↗

bioRxiv2025

Mixed positive and negative feedback loops drive diverse single-cell gene expression dynamics

Genetic circuits with only a few components can generate complex gene regulatory dynamics. Here, we combine stochastic modelling and single-cell time-lapse microscopy to reveal the possible dynamics generated by a key gene circuit motif: the mixed positive/negative feedback loop. Our minimal stochastic model of this motif reveals ten distinct classes of dynamic output, including stochastic pulsing, oscillations, and bistability. We systematically map how the circuits core parameters can be tuned to generate each of the behaviours. Experimental validation in two different mixed feedback circuits in the bacterium Bacillus subtilis, {sigma}B and {sigma}V, confirms our models predictive power. Guided by our simulations, we are able to transition between dynamic behaviours by modulating in vivo parameters. Together, these results demonstrate how mixed feedback loops generate diverse single-cell dynamics, improving our understanding of this common biological network motif and informing our efforts to engineer them for synthetic biology applications.

systems biology↗

bioRxiv2024

Engineering cell fate with adaptive feedback control

Engineering cell fate is fundamental for optimizing stem cell-based therapies aimed at replacing cells in patients suffering from trauma or disease. By timely administering molecular regulators--such as transcription factors, RNAs, or small molecules--in a process that mimics in vivo embryonic development, stem cell differentiation can be guided toward a specific cell fate. A significant challenge in scaling up these therapies is that such differentiation strategies often result in mixed cellular populations. While synthetic biology approaches have been proposed to increase the yield of desired cell types, designing gene circuits that effectively redirect cell fate decisions requires mechanistic insight into the dynamics of endogenous regulatory networks that govern decision-making. In this work, we present a biomolecular adaptive controller based on an Incoherent Feedforward Loop (IFFL)-like topology designed to favor a specific cell fate. This controller requires minimal knowledge of the endogenous network as it exhibits adaptive, non-reference-based behavior. The synthetic circuit operates through a sequestration mechanism and a delay introduced by an intermediate species, producing an output that asymptotically approximates a discrete temporal derivative of its input, provided there is a sufficiently fast sequestration rate. By allowing the controller to actuate over a target species involved in the decision-making process, a tunable, synthetic bias is created that favors the production of the desired species with minimal alteration to the overall equilibrium landscape of the endogenous network. Through theoretical and computational analysis, we provide design guidelines for the controllers optimal operation, evaluate its performance under parametric perturbations, and extend its applicability to various examples of common multistable systems in biology.

synthetic biology↗

bioRxiv2015

Engineering Modular Viral Scaffolds for Targeted Bacterial Population Editing

Bacteria are central to human health and disease, but the tools available for modulating and editing bacterial communities are limited. New technologies for tuning microbial populations would facilitate the targeted manipulation of the human microbiome and treatment of bacterial infections. For example, antibiotics are often broad spectrum in nature and cannot be used to accurately manipulate bacterial communities. Bacteriophages can provide highly specific targeting of bacteria, but relying solely on natural phage isolation strategies to assemble well-defined and uniform phage cocktails that are amenable to engineering can be a time-consuming and labor-intensive process. Here, we present a synthetic-biology strategy to modulate phage host ranges by manipulating phage genomes in Saccharomyces cerevisiae. We used this technology to swap multiple modular phage tail components and demonstrated that Escherichia coli phage scaffolds can be redirected to target pathogenic Yersinia and Klebsiella bacteria, and conversely, Klebsiella phage scaffolds can be redirected to target E. coli. The synthetic phages achieved multiple orders-of-magnitude killing of their new target bacteria and were used to selectively remove specific bacteria from multi-species bacterial communities. We envision that this approach will accelerate the study of phage biology, facilitate the tuning of phage host ranges, and enable new tools for microbiome engineering and the treatment of infectious diseases.

Synthetic Biology↗

bioRxiv2020

Analysis of Metabolic Network Disruption in Engineered Microbial Hosts due to Enzyme Promiscuity

BackgroundIncreasing understanding of metabolic and regulatory networks underlying microbial physiology has enabled creation of progressively more complex synthetic biological systems for biochemical, biomedical, agricultural, and environmental applications. However, despite best efforts, confounding phenotypes still emerge from unforeseen interplay between biological parts, and the design of robust and modular biological systems remains elusive. Such interactions are difficult to predict when designing synthetic systems and may manifest during experimental testing as inefficiencies that need to be overcome. Despite advances in tools and methodologies for strain engineering, there remains a lack of tools that can systematically identify incompatibilities between the native metabolism of the host and its engineered modifications. ResultsTransforming organisms such as Escherichia coli into microbial factories is achieved via a number of engineering strategies, used individually or in combination, with the goal of maximizing the production of chosen target compounds. One technique relies on suppressing or overexpressing selected genes; another involves on introducing heterologous enzymes into a microbial host. These modifications steer mass flux towards the set of desired metabolites but may create unexpected interactions. In this work, we develop a computational method, termed Metabolic Disruption Workflow (MDFlow), for discovering interactions and network disruption arising from enzyme promiscuity - the ability of enzymes to act on a wide range of molecules that are structurally similar to their native substrates. We apply MDFlow to two experimentally verified cases where strains with essential genes knocked out are rescued by interactions resulting from overexpression of one or more other genes. We then apply MDFlow to predict and evaluate a number of putative promiscuous reactions that can interfere with two heterologous pathways designed for 3-hydroxypropic acid (3-HP) production. ConclusionsUsing MDFlow, we can identify putative enzyme promiscuity and the subsequent formation of unintended and undesirable byproducts that are not only disruptive to the host metabolism but also to the intended end-objective of high biosynthetic productivity and yield. In addition, we show how enzyme promiscuity can potentially be responsible for the adaptability of cells to the disruption of essential pathways in terms of biomass growth.

systems biology↗

bioRxiv2022

Overproduction of Native and Click-able Colanic Acid Slime from Engineered Escherichia coli

The fundamental biology and application of bacterial exopolysaccharides is gaining increasing attention. However, current synthetic biology efforts to produce the major component of Escherichia sp. slime, colanic acid, and functional derivatives thereof have been limited. Herein, we report the overproduction of colanic acid (up to 1.32 g/L) from D-glucose in an engineered strain of E. coli JM109. Furthermore, we report that chemically-synthesized L-fucose analogues containing an azide motif can be metabolically incorporated into the slime layer via a heterologous fucose salvage pathway from Bacteroides sp. and used in a click reaction to attach an organic cargo to the cell surface. This molecular engineered bio-polymer possesses enormous potential as a new tool for use in chemical, biological and materials research.

bioengineering↗

bioRxiv2023

Localgini: A method for harnessing inequality in geneexpression to improve the quality of context-specific models

Genome-scale metabolic models (GEMs) are valuable tools for investigating normal and disease phenotypes of biological systems through the prediction of fluxes in biochemical reactions. However, in specific contexts such as different cell lines, tissues, or diseases, only a subset of reactions is active. To address this, several model extraction methods (MeMs) have been developed to filter the reactions in GEMs and extract context-specific models. These methods utilize gene expression data as a source of context-specific information. To construct context-specific models, MeMs require core reactions specific to the given context as input. Typically, core reactions are derived using a single threshold applied to gene expression data. Reactions associated with genes whose expression values exceed the threshold are considered as core reactions. However, it is important to note that enzyme activity is not solely determined by gene expression levels. This approach based on a single threshold may inadvertently exclude reactions that require enzymes in smaller quantities. In this study, we propose a novel thresholding algorithm called Localgini, which leverages the Gini coefficient and transcriptomics data to derive gene-specific thresholds. Localgini is implemented as a pre-processing step to obtain core reactions for MeMs. To demonstrate the effectiveness of Localgini, we construct context-specific models for NCI-60 cancer cell lines and human tissues using different MeMs. We compare the performance of Localgini with existing thresholding methods, namely LocalT2 and StanDep. The results show that the models derived using Localgini recover a greater number of housekeeping functionalities compared to the other thresholding methods. Moreover, the Localgini-derived core reactions exhibit increased self-consistency and display enhanced consensus among models built using different MeMs. By incorporating transcriptomic support, Localgini includes low-expression reactions in the core reaction list, enhancing the comprehensiveness of the resulting models. Codes used in this study, compatible with COBRA toolbox are available at https://github.com/NiravBhattLab/Localgini Author summaryGenome-scale models are becoming a desirable tool to understand the metabolism of a biological system and hence find applications in the fields of systems and synthetic biology. These models are often integrated with transcriptomics data to improve prediction accuracy. Algorithms developed to integrate transcriptomics data with genome-scale models require core reactions to be derived from omics data using a threshold. In this work, we propose a thresholding method that uses an inequality-based metric to derive thresholds. We implied the proposed method and other existing methods to datasets of cancer cell lines and human tissue. We showed that our method improves the inclusion of reactions required for basic cellular maintenance. Furthermore, we validated the built models for the reduction in variance owing to the model-extraction algorithms. Overall, the proposed method improves the quality of metabolic models by inferring inequality in the distribution of gene expression levels across samples/contexts.

systems biology↗

bioRxiv2025

GenomeOcean: An Efficient Genome Foundation Model Trained on Large-Scale Metagenomic Assemblies

Functional genomic sequences occupy an infinitesimal fraction of the astronomically large DNA sequence space, implying that evolution explores a low-dimensional manifold shaped by universal biochemical and evolutionary constraints. To investigate this underexplored Genomic Manifold Hypothesis using scalable, data-driven evidence, we developed GenomeOcean, a 4-billion-parameter generative model trained on 219 TB of diverse, co-assembled global environmental metagenomic data using an optimized transformer. The model captures both universal phylogenetic syntax and protein-coding fidelity. Furthermore, we validate the embeddings intrinsic low dimensionality. Crucially, comparisons with the independently trained Evo 2 model demonstrate a strong linear correspondence between their embedding spaces and convergent generative behaviors. These results suggest that the genomic manifold represents a fundamental biological principle, offering a unified framework for understanding evolutionary constraints and guiding synthetic biology.

bioinformatics↗

bioRxiv2026

Systematic mapping of insertion-tolerant regions enables capsid engineering of the RNA phage PP7

RNA phages are attractive platforms for the design of programmable bioparticles, but their development has been constrained by limited knowledge of genomic sites that can tolerate sequence insertion. Here, we combined MuA transposase-mediated in vitro insertion mutagenesis with our established reverse genetics systems to systematically identify insertion-tolerant regions (ITRs) in the RNA phages MS2 and PP7. Screening of 4,555 MS2 and 2,228 PP7 random insertion clones identified 29 and 26 nonredundant ITRs, respectively. We further analyzed and compared these ITRs in the context of RNA genome organization and virion architecture. Both phages contained ITRs within the maturation protein (MP), whereas only PP7 tolerated insertions within the coat protein (CP). On the basis of structural location and plaque-forming capacity, an ITR situated between Gly74 and Glu75 (GGC^GAG) in the PP7 CP was selected for further study. Infectious phage particles generated from cDNA clones retained the 15-bp insertion at both the RNA and protein levels. Engineered PP7 phages carrying an RGD motif inserted into the CP at this ITR displayed enhanced in vivo clearance in a Drosophila model, despite having in vitro stability comparable to that of the wild type. These findings provide the first example of CP engineering in an RNA phage and establish a framework for engineering RNA phages for biological and biotechnological applications. IMPORTANCEA major obstacle to developing RNA phages as synthetic biology platforms is the lack of design principles for genomic insertion. Here, we address this limitation by establishing a mutagenesis-and-recovery workflow that systematically identifies insertion-tolerant regions (ITRs) in the RNA phages MS2 and PP7. The resulting maps reveal distinct structural constraints in the two phages and enable rational engineering of a peptide-display site in the PP7 capsid. Using this approach, we generated an engineered infectious phage with a modified capsid, thereby providing the first demonstration of capsid engineering in an RNA phage. This study lays the groundwork for the rational design of RNA phage virions as tractable and engineerable scaffolds for future biological and biotechnological applications.

microbiology↗