Biology subjects

d'Oelsnitz, S.

Publications and source records attributed to d'Oelsnitz, S..

13 recordsLinked to original sources

bioRxiv2026

Biocatalytic Production of Galantamine in Yeast through Cytochrome P450 Optimization

Galantamine is a pharmaceutically relevant Amaryllidaceae alkaloid used for the treatment of Alzheimer's disease. Its structural complexity and low abundance in plants motivate the development of alternative manufacturing routes. Here, we established the first engineered yeast platform for the biocatalytic production of galantamine from 4OMe-norbelladine. Heterologous expression of the downstream pathway enzymes NtCYP96T6, NtNMT1, and NtAKR1 from Narcissus cv. Tete-a-Tete in Saccharomyces cerevisiae was complemented by optimization of cultivation temperature, carbon source, and medium pH enabling the first demonstration of galantamine production in yeast. Systematic screening of cytochrome P450 reductase and cytochrome b partners to boost NtCYP96T6 activity led to the identification of a novel reductase mined from the Narcissus pseudonarcissus transcriptome, NpCPR, which supported the highest pathway flux and yielded 7.0 {+/-} 0.6 mg/L galantamine, corresponding to a 7.6 % molar yield from 250 M 4OMe-norbelladine and an approximately 173-fold improvement over the parental strain. We further exploited this yeast cell factory for the precursor-directed biosynthesis of 7F-galantamine, highlighting the potential of pathway enzyme promiscuity to access new-to-nature GAL analogues that may be challenging to produce through conventional chemical synthesis. Together, this work establishes a foundation for microbial galantamine production and biosynthetic diversification of its pharmaceutically relevant scaffold.

synthetic biology↗

bioRxiv2026

Engineering Biosensors to Enhance Monoterpene Indole Alkaloid Production in Yeast

Monoterpene Indole Alkaloids (MIAs) are a diverse family of plant natural products with various medicinal applications. Although MIAs, such as vinblastine and reserpine, are clinically validated, sourcing of MIAs for clinical use or drug discovery from natural resources or via chemical synthesis is hampered due to their scarcity and chemical complexity. Refactoring MIA biosynthesis pathways in microbial cell factories could offer an alternative, more stable and potentially sustainable manufacturing route for alkaloid medicines and novel therapies. However, reaching commercially attractive titers, rates and yields remains challenging owing to the length and complexity of these metabolic pathways. One critical bottleneck is the low screening throughput and very high cost of the analytical methods used to quantify MIA for optimizing production. In this study, we evolved RamR, a promiscuous bacterial transcription factor to respond to five different MIAs, resulting in highly sensitive and selective sensor variants (EC50<10 M). X-ray crystallography and computational modeling provided insight into the MIA binding of the evolved biosensor variants. The RamR biosensing platform was functionalized in yeast and subsequently applied in a cost-effective semi-throughput screening campaign of a 188-gene overexpression library to identify high-performing cell factory designs for strictosidine, the common precursor for all MIAs. The fluorescent biosensor signal correlated with HPLC quantification (r2 = 0.932) allowing identification of single metabolic engineering hits which when combined yielded a maximum titer of >220 mg/L strictosidine, 3-fold higher than the parental reference strain. This study demonstrates the development of selective biosensors for MIAs and the cost-effective identification of novel metabolic engineering hits for optimizing MIA production in microbial cell factories.

synthetic biology↗

bioRxiv2026

evedesign: accessible biosequence design with a unified framework

Machine learning methods for protein engineering are rarely interoperable, require bespoke workflows, and remain inaccessible to non-experts. Yet the design problems that matter most - conditional design subject to real-world constraints, multi-objective optimization, and iterative lab-in-the-loop workflows where experimental data continuously refines successive design rounds - demand exactly the kind of flexible, composable infrastructure that no single tool provides. We present evedesign, a unified open-source framework that formalizes conditional biosequence design in a method-agnostic way, enabling complex multiobjective workflows combining supervised and unsupervised models from standardized specifications, and built from the outset to support iterative experimental integration. An interactive web interface facilitates end-to-end design for a broad scientific audience at https://evedesign.bio. We demonstrate evedesigns utility in antibody engineering, enzyme design, and natural enzyme discovery, and invite open-source community contributions.

bioinformatics↗

bioRxiv2025

LigifyDB: An interactive platform for predicted small molecule biosensors

Prokaryotic transcription factors (TFs) are used as small molecule biosensors with broad applications in biotechnology, yet only a small fraction from microbial genomes have been characterized. To address this gap, we recently described the bioinformatic method Ligify, which leverages information from genome context and enzyme reaction databases to predict a TFs cognate effector molecule. Here we report Ligify 2.0, a modern web server for Ligify predictions. We systematically evaluate 10,965 small molecules within the Rhea enzyme reaction database for associations to TFs, ultimately generating 13,435 hypothetical interactions between 1,362 small molecules and 3,164 TFs. We then develop an interactive web server (https://ligify.groov.bio) to search and visualize prediction data. Each TF sensor page includes visualizations for chemical ligand structures, interactive TF protein structures, and genome context. Pages also include metadata links, predicted promoter sequences, prediction confidence metrics, and references to relevant literature. A plasmid builder tool enables users to generate custom biosensor circuit designs. Finally, we provide case studies using Ligify 2.0 to identify two TFs from the pathogens Escherichia coli O157:H7 and Mycobacterium abscessus responsive to 4-hydroxybenzoate and Pseudomonas Quinolone Signal, respectively. The Ligify web server aims to facilitate the systematic characterization of biosensors for chemical-control of biological systems. Key pointsO_LILigify 2.0 contains >13,000 predicted transcription factor-small molecule interactions C_LIO_LIA rich web interface provides interactive visualizations and a plasmid design tool C_LIO_LIPredicted ligands for regulators from pathogenic bacteria are experimentally validated C_LI Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=70 SRC="FIGDIR/small/683484v2_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1afa575org.highwire.dtl.DTLVardef@97c811org.highwire.dtl.DTLVardef@cfdb93org.highwire.dtl.DTLVardef@58977d_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2025

groovDB in 2026: A community-editable database of small molecule biosensors

The groovDB database (https://groov.bio) was launched in 2022 with the goal of organizing information on prokaryotic ligand-inducible transcription factors (TFs). This class of proteins is important in fundamental areas of microbiology research and for biotechnological applications that develop biosensors for diagnostics, enzyme screening, and real-time metabolite tracking. Uniquely, groovDB contains stringently curated, literature-referenced data on both TF:DNA and TF:ligand interactions. Here, we describe a major technical update to groovDB, making the database community-editable and adding several advanced features. Users can now add new TF entries and update existing entries using a simple online form. New user interface elements display interactive protein structures and DNA-binding motifs. Updated query methods enable database searches via text, chemical similarity, and attribute-filtering. A new data architecture reduces page load time by five-fold. Finally, the number of TF entries has more than doubled and all source code is now open-access. Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/670880v3_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@17842beorg.highwire.dtl.DTLVardef@65d608org.highwire.dtl.DTLVardef@1c6e2eborg.highwire.dtl.DTLVardef@5bd824_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

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↗

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

Directed evolution of generalist biosensors for single ring aromatics

Biosensors can accelerate the engineering of new biosynthetic pathways. Phloroglucinol is a platform chemical of wide utility that can be produced at limited titers in Escherichia coli. Starting from the TetR family repressor RolR that is responsive to the related compound resorcinol, we were able to employ a combined selection and screen to identify variants that had greatly improved activities with phloroglucinol (EC50 for phloroglucinol of 131 uM, relative to an estimated 42 mM for wild-type RolR). The variants obtained were further screened with a panel of similar single ring aromatics, and several were found to be generalists, consistent with the hypothesis that both natural and directed evolution tend to first create semi-specific pockets prior to further optimization for new function

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↗

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↗

bioRxiv2021

Using structurally fungible biosensors to evolve improved alkaloid biosyntheses

A key bottleneck in the microbial production of therapeutic plant metabolites is identifying enzymes that can greatly improve yield. The facile identification of genetically encoded biosensors can overcome this limitation and become part of a general method for engineering scaled production. We have developed a unique combined screening and selection approach that quickly refines the affinities and specificities of generalist transcription factors, and using RamR as a starting point we evolve highly specific (>100-fold preference) and sensitive (EC50 <30 M) biosensors for the alkaloids tetrahydropapaverine, papaverine, glaucine, rotundine, and noscapine. High resolution structures reveal multiple evolutionary avenues for the fungible effector binding site, and the creation of new pockets for different chemical moieties. These sensors further enabled the evolution of a streamlined pathway for tetrahydropapaverine, an immediate precursor to four modern pharmaceuticals, collapsing multiple methylation steps into a single evolved enzyme. Our methods for evolving biosensors now enable the rapid engineering of pathways for therapeutic alkaloids.

synthetic biology↗