Biology subjects

Ellington, A. D.

Publications and source records attributed to Ellington, A. D..

At least 19 recordsLinked to original sources

bioRxiv2026

Design and characterization of peptide-responsive biosensors

Gram-positive bacteria use secreted peptides and peptide-responsive transcription factors for quorum sensing. Here, we demonstrate that intracellular expression of these peptide effectors with their cognate transcription factors leads to regulation of gene expression in both in vitro and in vivo systems. Because both effector and regulatory parts can be readily expressed, complex circuits, including auto-activation schemes, can be developed, with switch-like behaviors. Moreover, the peptide effectors can be fused to proteins, enabling a direct intracellular readout of translation and potentially allowing any protein to encode regulatory interactions in cells. Our results support a foundation for regulatory circuits that extend beyond canonical components such as LacI and TetR.

bioengineering↗

bioRxiv2026

Learning from human and chemical languages to predict biological function

Understanding how molecular structure encodes biological function remains a grand challenge in drug discovery. Here, we present PubCheF-1, a deep learning model that predicts literature-derived biological function directly from chemical structure. PubCheF-1 was trained on a dataset linking molecules to labels derived from the scientific articles in which they appear, a strategy that connects disparate compounds through the language used to describe their functionalities. When tasked with identifying inhibitors of {beta}-lactamases, including enzymes considered largely refractory to inhibition, PubCheF-1 predicted structurally distinct compounds that collectively have activity against all {beta}-lactamase classes. Furthermore, hit compounds directly bind the enzyme active site, restore antibiotic efficacy in multidrug-resistant high-priority pathogens, and demonstrate potent activity in animal infection models. Together, these findings establish that machine learning-based prediction of biological function derived from the language of scientific literature allows the identification of bioactive molecules at high hit rates, thereby accelerating therapeutic discovery.

bioinformatics↗

bioRxiv2026

Expression and Engineering of Conductive Cytochrome Nanowires

Electrically conductive protein nanowires produced by metal-reducing bacteria have attracted interest as sustainable electronic materials, but their study and engineering have been limited by difficulties in expression, purification, and genetic manipulation. Here we establish Shewanella oneidensis as a heterologous host for production of Geobacter sulfurreducens OmcZ nanowires and progress a complementary in vitro assembly strategy that yields highly pure nanowire preparations. This platform enabled systematic engineering of OmcZ, revealing extensive tolerance to sequence variation and faciliating the design of enhanced-conductivity variants. Guided by comparative analysis of environmental OmcZ homologs, we generated a chimeric nanowire, OmcZ+, that exhibited a [~]3.5-fold increase in bulk conductivity while retaining the overall structure of the parent nanowire. Cryo-electron microscopy revealed unexpected architectural plasticity in OmcZ+ wires, including non-linear dendritic and pentameric assemblies mediated by the solvent-exposed heme VII, suggesting previously unrecognized modes of cytochrome nanowire organization. Incorporation of engineered OmcZ variants into water evaporation-induced electricity generators produced power densities up to [~]25.3 W cm-{superscript 2} and enabled high-performance operation in saline environments, including seawater and human sweat. Together, these results establish a versatile platform for the production, structural analysis, and engineering of cytochrome nanowires, providing a foundation for the development of programmable biological electronic materials.

bioengineering↗

bioRxiv2026

Validation and analysis of 12,000 AI-driven CAR-T designs in the Bits to Binders competition

Artificial intelligence (AI) methods for proteins have advanced rapidly, improving structure prediction and design, particularly for de novo binders. However, most evaluations emphasize binding affinity rather than higher-order biological function. We present Bits to Binders, a global competition benchmarking de novo binder design in the context of chimeric antigen receptor (CAR) T cells. Teams from 42 countries submitted 12,000 designs of 80-amino acid binders targeting human CD20 as CAR binding domains. Designs were screened by pooled CAR-T proliferation, identifying 707 designs exhibiting significant CD20-specific enrichment, with team hit rates from 0.6% to 38.4%. Top-performing candidates were validated as individual constructs, measuring CD20-specific proliferation, expansion, cytokine production, and targeted cell lysis. We identified common design methodologies and factors correlated with DNA synthesis, expression, and target-specific T cell activation which nearly double the success rates when applied as a retrospective filter. We release this dataset as an open resource, with practical recommendations to support more effective AI-driven binder design.

bioinformatics↗

bioRxiv2025

Directed evolution of L-DOPA sensing and production enables efficient incorporation of an expanded genetic code into proteins

3,4-dihydroxyphenylalanine (L-DOPA) is a catechol-containing amino acid derived from L-tyrosine that can be introduced into proteins primarily as a post-translational modification. It can also be introduced in a site-specific manner into proteins via orthogonal tRNA synthetase:tRNA pairs. However, despite the interest in expanded genetic codes containing L-DOPA, efforts to improve metabolism relating to its production have been limited. Here, we report and engineer a LysR-family transcription factor, PP_2251, that can serve as a biosensor for L-DOPA, thereby allowing the optimization of genetic circuits for vastly improved L-DOPA production via compartmentalized partnered replication (CPR), an emulsion PCR-based directed evolution scheme. Using this biosensor, a promiscuous E. coli flavin-dependent monooxygenase (HpaB) was optimized via CPR for enhanced L-DOPA biosynthesis. Engineered HpaB variants could support efficient translational incorporation of L-DOPA into proteins with 80% incorporation efficiency and yields in excess of 250 mg.L-1. Improvements in L-DOPA production are not only of great interest for protein engineering and genetic code expansion but should impact the production of DOPA-derived natural products including catecholamine neurotransmitters and benzylisoquinoline alkaloids (BIAs), molecules of intense pharmaceutical interest.

synthetic biology↗

bioRxiv2025

Fieldable isothermal nucleic acid test for rapid semi-quantitative visual readout of Enterococci in recreational waters

Enterococci are fecal indicator bacteria whose presence in water suggests the potential for gastrointestinal pathogens. Culture-based detection is slow and may require expensive apparatus and proprietary media. Molecular tests based on qPCR can be technically difficult and require complex devices. Loop-mediated isothermal amplification (LAMP) tests that need minimal instrumentation have been described but they only qualitatively indicate presence or absence of Enterococci, which is difficult to compare with quantitative contamination standards for assessing water quality. Here, we demonstrate semi-quantitative thresholded LAMP that uses internal competition with pre-defined false targets and an oligonucleotide strand displacement (OSD) probe to measure the degree of Enterococcus contamination in water. The assay needs only one hour of incubation at a single temperature following which a simple visual examination of endpoint OSD fluorescence allows order of magnitude scale estimation of Enterococcus amounts. Environmental water samples with low, medium, and high Enterococcus contamination could be readily distinguished by thresholded LAMP-OSD without interference from non-specific signals. LAMP-OSD performance correlated with the outcomes of both qPCR and plate cultures of the samples. The simplicity of implementing thresholded LAMP-OSD makes it well suited for point-of-need and low-cost water quality monitoring.

molecular biology↗

bioRxiv2025

Design Principles for Polymerase Strand Recycling Circuits

Cell-free biosensing systems are being engineered as versatile and programmable diagnostic technologies. A core component of cell-free biosensors are programmable molecular circuits that improve biosensor speed, sensitivity and specificity by performing molecular computations such as logic evaluation and signal amplification. In previous work, we developed one such circuit system called Polymerase Strand Recycling (PSR) which amplifies cell-free molecular circuits by using T7 RNA polymerase off-target transcription to recycle nucleic acid inputs. We showed that PSR circuits can be configured to detect RNA target inputs as well as be interfaced with allosteric transcription factor-based biosensors to amplify signal and enhance sensitivity. Here we expand the development of PSR circuit design principles to generalize the platform for detecting a diverse set of model microRNA inputs. We show that PSR circuit function can be enhanced through engineering T7 RNAP, and present troubleshooting strategies to optimize PSR circuit performance.

synthetic biology↗

bioRxiv2025

Directed evolution of a SelB variant that does not require a SECIS element for function

In bacteria the incorporation of selenocysteine is achieved through the interaction of the selenocysteine specific elongation factor (SelB) with selenocysteine-charged tRNASec and a selenocysteine insertion sequence (SECIS) element adjacent to an opal stop codon in a mRNA. The more generalized, SECIS-independent incorporation of selenocysteine is of interest because of the high nucleophilicity of selenium and the greater durability of diselenide bonds. It is likely that during the course of evolution selenocysteine insertion originally arose without the presence of a SECIS element, relying only on SelB. Herein we undertake experiments to evolve an ancestral version of SelB that is SECIS-independent and show that not only can this protein (SelB-v2) generally incorporate selenocysteine across from stop codons, but also that the new, orthogonal translation factor can be repurposed to other amino acids, such as serine. Given the delicate energetic balancing act already performed by EF-Tu, this achievement raises the possibility that greatly expanded genetic codes that relied in part on SelB-based loading can now be contrived.

synthetic biology↗

bioRxiv2024

Directed evolution of an orthogonal transcription engine for programmable gene expression in eukaryotes

T7 RNA polymerase has enabled orthogonal control of gene expression and recombinant protein production across diverse prokaryotic host chassis organisms for decades. However, the absence of 5 methyl guanosine caps on T7 RNAP derived transcripts has severely limited its utility and widespread adoption in eukaryotic systems. To address this shortcoming, we evolved a fusion enzyme combining T7 RNAP with the single subunit capping enzyme from African swine fever virus using Saccharomyces cerevisiae. We isolated highly active variants of this fusion enzyme, which exhibited roughly two orders of magnitude higher protein expression compared to the wild-type enzyme. We demonstrate the programmable control of gene expression using T7 RNAP-based genetic circuits in yeast and validate enhanced performance of these engineered variants in mammalian cells. This study presents a robust, orthogonal gene regulatory system applicable across diverse eukaryotic hosts, enhancing the versatility and efficiency of synthetic biology applications.

synthetic biology↗

bioRxiv2024

Ligify: Automated genome mining for ligand-inducible transcription factors

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/581298v1_ufig1.gif" ALT="Figure 1"> View larger version (8K): org.highwire.dtl.DTLVardef@15eef04org.highwire.dtl.DTLVardef@19e1851org.highwire.dtl.DTLVardef@4e9fe8org.highwire.dtl.DTLVardef@1e4628f_HPS_FORMAT_FIGEXP M_FIG C_FIG Prokaryotic transcription factors can be repurposed into biosensors for the ligand-inducible control of gene expression, but the landscape of chemical ligands for which biosensors exist is extremely limited. To expand this landscape, we developed Ligify, a web application that leverages information in enzyme reaction databases to predict transcription factors that may be responsive to user-defined chemicals. Candidate transcription factors are then incorporated into automatically generated plasmid sequences that are designed to express GFP in response to the target chemical. Our benchmarking analyses demonstrated that Ligify correctly predicted 31/100 previously validated biosensors, and highlighted strategies for further improvement. We then used Ligify to build a panel of genetic circuits that could induce a 47-fold, 5-fold, 9-fold, and 27-fold change in fluorescence in response to D-ribose, L-sorbose, isoeugenol, and 4-vinylphenol, respectively. Ligify should enhance the ability of researchers to quickly develop biosensors for an expanded range of chemicals, and is publicly available at https://ligify.streamlit.app.

synthetic biology↗

bioRxiv2023

Snowprint: a predictive tool for genetic biosensor discovery

Bioengineers increasingly rely on ligand-inducible transcription regulators for chemical-responsive control of gene expression, yet the number of regulators available is limited. Novel regulators can be mined from genomes, but an inadequate understanding of their DNA specificity complicates genetic design. Here we present Snowprint, a simple yet powerful bioinformatic tool for predicting regulator:DNA interactions. Benchmarking results demonstrate that Snowprint predictions are significantly similar for >45% of experimentally validated regulator:operator pairs from organisms across nine phyla and for regulators that span five distinct structural families. We then use Snowprint to design promoters for 33 previously uncharacterized regulators sourced from diverse phylogenies, of which 28 were shown to influence gene expression and 24 produced a >20-fold signal-to-noise ratio. A panel of the newly domesticated regulators were then screened for response to biomanufacturing-relevant compounds, yielding new sensors for a polyketide (olivetolic acid), terpene (geraniol), steroid (ursodiol), and alkaloid (tetrahydropapaverine) with induction ranges up to 10.7-fold. Snowprint represents a unique, generalizable tool that greatly facilitates the discovery of ligand-inducible transcriptional regulators for bioengineering applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=119 SRC="FIGDIR/small/538814v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@14f4939org.highwire.dtl.DTLVardef@5066e3org.highwire.dtl.DTLVardef@92d7cforg.highwire.dtl.DTLVardef@968163_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2023

Synthetic microbial sensing and biosynthesis of amaryllidaceae alkaloids

A major challenge to achieving industry-scale biomanufacturing of therapeutic alkaloids is the slow process of biocatalyst engineering. Amaryllidaceae alkaloids, such as the Alzheimers medication galantamine, are complex plant secondary metabolites with recognized therapeutic value. Due to their difficult synthesis they are regularly sourced by extraction and purification from low-yielding plants, including the wild daffodil Narcissus pseudonarcissus. Engineered biocatalytic methods have the potential to stabilize the supply chain of amaryllidaceae alkaloids. Here, we propose a highly efficient biosensor-AI technology stack for biocatalyst development, which we apply to engineer amaryllidaceae alkaloid production in Escherichia coli. Directed evolution is used to develop a highly sensitive (EC50= 20 uM) and specific biosensor for the key amaryllidaceae alkaloid branchpoint 4-OMethylnorbelladine. A machine learning model (MutComputeX) was subsequently developed and used to generate activity-enriched variants of a plant methyltransferase, which were rapidly screened with the biosensor. Functional enzyme variants were identified that yielded a 60% improvement in product titer, 17-fold reduced remnant substrate, and 3-fold lower off-product regioisomer formation.

synthetic biology↗

bioRxiv2022

Humanized CB1R and CB2R yeast biosensors enable facile screening of cannabinoid compounds

Yeast expression of human G Protein Coupled Receptors (GPCRs) can be used as a biosensor platform for the detection of pharmaceuticals. The Cannabinoid receptors type 1 and 2 (CB1/2R) are of particular interest, given the cornucopia of natural and synthetic cannabinoids being explored as therapeutics. We show for the first time that engineering the N-terminus of CB1R allows for efficient signal transduction in yeast, and that engineering the sterol composition of the yeast membrane optimizes CB2R performance. Using the dual cannabinoid biosensors, large libraries of synthetic cannabinoids and terpenes could be quickly screened to elucidate known and novel structure-activity relationships, including compounds and trends that more selectively target each of the two receptors. The biosensor strains offer a ready platform for evaluating the activity of new synthetic cannabinoids, monitoring drugs of abuse, and developing molecules that target the therapeutically important CB2R receptor while minimizing psychoactive effects.

synthetic biology↗

bioRxiv2022

GroovDB: A database of ligand-inducible transcription factors

Genetic biosensors are integral to synthetic biology. In particular, ligand-inducible prokaryotic transcription factors are frequently used in high-throughput screening, for dynamic feedback regulation, as multi-layer logic gates, and in diagnostic applications. In order to provide a curated source that users can rely on for engineering applications, we have developed GroovDB (available at https://groov.bio), a Web-accessible database of ligand-inducible transcription factors that contains all information necessary to build chemically-responsive genetic circuits, including biosensor sequence, ligand, and operator data. Ligand and DNA interaction data has been verified against the literature, while an automated data curation pipeline is used to programmatically fetch metadata, structural information, and references for every entry. A custom tool to visualize the natural genetic context of biosensor entries provides additional information that provides potential insights into alternative ligands and systems biology.

synthetic biology↗

bioRxiv2022

Antibody escape and cryptic cross-domain stabilization in the SARS CoV-2 Omicron spike protein

The worldwide spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the repeated emergence of variants of concern. The Omicron variant has two dominant sub-lineages, BA.1 and BA.2, each with unprecedented numbers of nonsynonymous and indel spike protein mutations: 33 and 29, respectively. Some of these mutations individually increase transmissibility and enhance immune evasion, but their interactions within the Omicron mutational background is unknown. We characterize the molecular effects of all Omicron spike mutations on expression, human ACE2 receptor affinity, and neutralizing antibody recognition. We show that key mutations enable escape from neutralizing antibodies at a variety of epitopes. Stabilizing mutations in the N-terminal and S2 domains of the spike protein compensate for destabilizing mutations in the receptor binding domain, thereby enabling the record number of mutations in Omicron sub-lineages. Taken together, our results provide a comprehensive account of the mutational effects in the Omicron spike protein and illuminate previously unknown mechanisms of how the N-terminal domain can compensate for destabilizing mutations within the more evolutionarily constrained RBD.

immunology↗

bioRxiv2021

Orthogonal control of gene expression in plants using synthetic promoters and CRISPR-based transcription factors

BackgroundThe construction and application of synthetic genetic circuits is frequently improved if gene expression can be orthogonally controlled, relative to the host. In plants, orthogonality can be achieved via the use of CRISPR-based transcription factors that are programmed to act on natural or synthetic promoters. The construction of complex gene circuits can require multiple, orthogonal regulatory interactions, and this in turn requires that the full programmability of CRISPR elements be adapted to non-natural and non-standard promoters that have few constraints on their design. Therefore, we have developed synthetic promoter elements in which regions upstream of the minimal 35S CaMV promoter are designed from scratch to interact via programmed gRNAs with dCas9 fusions that allow activation of gene expression. ResultsA panel of three, mutually orthogonal promoters that can be acted on by artificial gRNAs bound by CRISPR regulators were designed. Guide RNA expression targeting these promoters was in turn controlled by either Pol III (U6) or ethylene-inducible Pol II promoters, implementing for the first time a fully artificial Orthogonal Control System (OCS). Following demonstration of the complete orthogonality of the designs, the OCS was tied to cellular metabolism by putting gRNA expression under the control of an endogenous plant signaling molecule, ethylene. The ability to form complex circuitry was demonstrated via the ethylene-driven, ratiometric expression of fluorescent proteins in single plants. ConclusionsThe design of synthetic promoters is highly generalizable to large tracts of sequence space, allowing Orthogonal Control Systems of increasing complexity to potentially be generated at will. The ability to tie in several different basal features of plant molecular biology (Pol II and Pol III promoters, ethylene regulation) to the OCS demonstrates multiple opportunities for engineering at the system level. Moreover, given the fungibility of the core 35S CaMV promoter elements, the derived synthetic promoters can potentially be utilized across a variety of plant species.

synthetic biology↗

bioRxiv2021

Deep learning redesign of PETase for practical PET degrading applications

Plastic waste poses an ecological challenge1. While current plastic waste management largely relies on unsustainable, energy-intensive, or even hazardous physicochemical and mechanical processes, enzymatic degradation offers a green and sustainable route for plastic waste recycling2. Poly(ethylene terephthalate) (PET) has been extensively used in packaging and for the manufacture of fabrics and single-used containers, accounting for 12% of global solid waste3. The practical application of PET hydrolases has been hampered by their lack of robustness and the requirement for high processing temperatures. Here, we use a structure-based, deep learning algorithm to engineer an extremely robust and highly active PET hydrolase. Our best resulting mutant (FAST-PETase: Functional, Active, Stable, and Tolerant PETase) exhibits superior PET-hydrolytic activity relative to both wild-type and engineered alternatives, (including a leaf-branch compost cutinase and its mutant4) and possesses enhanced thermostability and pH tolerance. We demonstrate that whole, untreated, post-consumer PET from 51 different plastic products can all be completely degraded by FAST-PETase within one week, and in as little as 24 hours at 50 {degrees}C. Finally, we demonstrate two paths for closed-loop PET recycling and valorization. First, we re-synthesize virgin PET from the monomers recovered after enzymatic depolymerization. Second, we enable in situ microbially-enabled valorization using a Pseudomonas strain together with FAST-PETase to degrade PET and utilize the evolved monomers as a carbon source for growth and polyhydroxyalkanoate production. Collectively, our results demonstrate the substantial improvements enabled by deep learning and a viable route for enzymatic plastic recycling at the industrial scale.

bioengineering↗

bioRxiv2021

Evolving a generalist biosensor for bicyclic monoterpenes

Prokaryotic transcription factors can be repurposed as analytical and synthetic tools for precise chemical measurement and regulation. Monoterpenes encompass a broad chemical family that are commercially valuable as flavors, cosmetics, and fragrances, but have proven difficult to measure, especially in cells. Herein, we develop genetically-encoded, generalist monoterpene biosensors by using directed evolution to expand the effector specificity of the camphor-responsive TetR-family regulator CamR from Pseudomonas putida. Using a novel negative selection coupled with a high-throughput positive screen (Seamless Enrichment of Ligand-Inducible Sensors, SELIS), we evolve CamR biosensors that can recognize four distinct monoterpenes: borneol, fenchol, eucalyptol, and camphene. Different evolutionary trajectories surprisingly yielded common mutations, emphasizing the utility of CamR as a platform for creating generalist biosensors. Systematic promoter optimization driving the reporter increased the systems signal-to-noise ratio to 150-fold. These sensors can serve as a starting point for the high-throughput screening and dynamic regulation of bicyclic monoterpene production strains.

synthetic biology↗