Biology subjects

Keasling, J. D.

Publications and source records attributed to Keasling, J. D..

At least 19 recordsLinked to original sources

bioRxiv2026

Expanding the scope of redox-balance growth coupling techniques with a carbon cofeeding strategy

Metabolic engineering to produce molecules not naturally synthesized by the host often requires directed evolution to improve pathway enzyme performance. Growth-coupled selection can dramatically increase directed-evolution throughput, and manipulation of redox balance has proven effective for tying reductase fitness to microbial growth. However, most redox-balance selections require feeding the reductase substrate because of stoichiometric constraints. This is impractical for many biosynthetic pathways either due to practical limitations on cost or complexity of bulk substrate synthesis, or the lack of an ability to transport substrate into cells, for example intracellular acyl-CoA/ACP intermediates. Here we define stoichiometric constraints that make substrate feeding necessary for many acetyl-CoA-derived reduction pathways in NADPH-imbalanced hosts. We overcome these constraints with a dual-feedstock strategy in which glucose provides reducing power while acetate supplies additional acetyl-CoA without directly perturbing redox balance. In an engineered E. coli selection strain, acetate co-feeding enabled growth coupling of acetaldehyde, 3-hydroxybutyrate, and mevalonate production and produced a linear correlation between product formation and growth. We then used this selection to evolve a class II HMG-CoA reductase (HMGR) from Delftia acidovorans toward NADPH utilization, enriching variants with improved NADPH-dependent activity. Finally, propionate co-feeding enabled growth coupling of propionyl-CoA reduction, supporting the generality of carbon co-feeding for selecting enzymes in pathways involving acyl-chain elongation and reduction. HighlightsO_LIStoichiometric limits of redox-balance growth coupling are defined C_LIO_LIAcetate co-feeding supplies acetyl-CoA without perturbing redox balance C_LIO_LICo-feeding enables growth coupling of acetaldehyde, 3-HB, and mevalonate C_LIO_LIGrowth coupling enables evolution of HMGR toward NADPH specificity C_LIO_LIPropionate co-feeding extends growth coupling to additional acyl-CoA substrates C_LI

bioengineering↗

bioRxiv2025

Active learning enables discovery of transcriptional activators across fungal evolutionary space

Biological discovery and design are increasingly being guided by predictive models in place of costly experimentation. However, existing datasets are often biased by overrepresentation from model organisms, leading to failures in evolutionary studies of non-model species. We present a hybrid framework that leverages high-throughput molecular assays and active learning to quantify biological properties across evolutionary space. We focus on transcriptional activators, which contain activation domains (ADs) that promote gene expression. ADs are intrinsically disordered and poorly conserved, which limits their study using comparative genomics. Here, we developed ADhunter, a high-capacity regression model that outperforms state-of-theart algorithms in identifying and quantifying the strength of transcriptional activators. Model uncertainty was used to guide evolutionary sampling across 7.8 million proteins from 2,400 fungal genomes. We functionally characterized 9,836 ADs from 1,071 fungal genomes, providing a 15.5-fold expansion in genome representation compared to existing datasets. Comprehensive sampling from non-model genomes improved model generalizability and provides the first functional annotation for 3,416 proteins from 670 non-model fungi. Model interpretability analysis aligns with the biophysical model of AD function and reveals novel, underrepresented protein codes, highlighting the importance of sampling from non-model organisms to build evolutionarily robust models for predicting biological properties.

genomics↗

bioRxiv2025

The mevalonate pathway of isoprenoid biosynthesis supports metabolic flexibility in Mycobacterium marinum

Isoprenoids are a diverse class of natural products that are essential in all domains of life. Most bacteria synthesize isoprenoids through either the methylerythritol phosphate (MEP) pathway or the mevalonate (MEV) pathway, while a small subset encodes both pathways, including the pathogen Mycobacterium marinum (Mm). It is unclear whether the MEV pathway is functional in Mm, or why Mm encodes seemingly redundant metabolic pathways. Here we show that the MEP pathway is essential in Mm while the MEV pathway is dispensable in culture, with the {Delta}MEV mutant having no growth defect in axenic culture but a competitive growth defect compared to WT Mm. We found that the MEV pathway does not play a role in ex vivo or in vivo infection but does play a role in survival of peroxide stress. Metabolite profiling revealed that modulation of the MEV pathway causes compensatory changes in the concentration of MEP intermediates DOXP and CDP-ME, suggesting that the MEV pathway is functional and that the pathways interact at the metabolic level. Finally, the MEV pathway is upregulated early in the shift down to hypoxia, suggesting that it may provide metabolic flexibility to this bacterium. Interestingly, we found that our complemented strains, which vary in copy number of the polyprenyl synthetase idsB2, responded differently to peroxide and UV stresses, suggesting a role for this gene as a determinant of downstream prenyl phosphate metabolism. Together, these findings suggest that MEV may serve as an anaplerotic pathway to make isoprenoids under stress conditions. ImportanceOrganisms from all domains of life utilize isoprenoids to carry out thousands of critical and auxiliary cellular processes, including signaling, membrane integrity, stress response, and host-pathogen interactions. The common precursor of all isoprenoids is synthesized via one of two biosynthetic pathways and importantly, some bacteria encode both pathways, including M. marinum. We found that only one pathway is essential in M. marinum, while the nonessential pathway may confer metabolic flexibility to help the bacterium better adapt to various environmental conditions. We also found that the polyprenyl synthetase IdsB2 plays an important role in driving such phenotypes. Further, we demonstrate metabolic interplay between both functional pathways. These insights represent the first characterization of isoprenoid biosynthesis in dual pathway-encoding mycobacteria.

microbiology↗

bioRxiv2025

Carbon-Efficient Conversion through Hydrogen and Formate-Driven Metabolism in Escherichia coli

Product yields for biomanufacturing processes are often constrained by the tight coupling of cellular energy generation and carbon metabolism in sugar-based fermentation systems. To overcome this limitation, we engineered Escherichia coli to utilize hydrogen gas (H2) and formate (HCOO-) as alternative sources of energy and reducing equivalents, thereby decoupling energy generation from carbon metabolism. This approach enabled precise suppression of decarboxylative oxidation during acetate growth, with 86.6 {+/-} 1.6% of electrons from hydrogen gas (via soluble hydrogenase from Cupriavidus necator H16) and 98.4 {+/-} 3.6% of electrons from formate (via formate dehydrogenase from Pseudomonas sp. 101) offsetting acetate oxidation. Hydrogen gas supplementation led to a titratable and stoichiometric reduction in CO2 evolution in acetate-fed cultures. Metabolomic analysis suggests that this metabolic decoupling redirects carbon flux through the glyoxylate shunt, partially bypassing two decarboxylative steps in the TCA cycle. We demonstrated the utility of this strategy by applying it to mevalonate biosynthesis, where formate supplementation during glucose fermentation increased titers by 57.6% in our best-performing strain. Flux balance analysis further estimated that 99.0 {+/-} 2.8% of electrons from formate were used to enhance mevalonate production. These findings highlight a broadly applicable strategy for enhancing biomanufacturing efficiency by leveraging external reducing power to optimize feedstock and energy use. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=65 SRC="FIGDIR/small/657096v2_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@12f77dborg.highwire.dtl.DTLVardef@165d6c1org.highwire.dtl.DTLVardef@f6302forg.highwire.dtl.DTLVardef@d59bb3_HPS_FORMAT_FIGEXP M_FIG C_FIG Created in BioRender. Panich, J. (2025) https://BioRender.com/0tmtp94

synthetic biology↗

bioRxiv2025

Leveraging a synthetic biology approach to enhance BCG-mediated expansion of Vγ9Vδ2 T cells

There is an urgent need to develop a more efficacious anti-tuberculosis vaccine as the current live-attenuated vaccine strain BCG fails to prevent pulmonary infection in adults. Our long-term goal is to test whether increasing the immunogenicity of BCG will improve vaccine effectiveness while maintaining its proven safety profile. In this study, we leverage a synthetic biology approach to engineer BCG to produce more (E)-4-hydroxy-3-methyl-but-2-enyl pyrophosphate (HMBPP), a phosphoantigen produced as an intermediate of bacterial--but not host--isoprenoid biosynthesis via the methylerythritol phosphate (MEP) pathway. Importantly, HMBPP strongly activates and expands V{gamma}9V{delta}2 T cells, which are unique to higher-order primates and protect against Mycobacterium tuberculosis infection. Prior work to engineer BCG to produce specific ligands and antigens has been attempted to some success; however, our strategy exploits a self-nonself recognition mechanism in the host via HMBPP sensing, which has not been attempted before in this way. To inform the design of our recombinant strains, we performed synteny analyses of >63 mycobacterial species, which revealed that isoprenoid biosynthetic genes are not found in gene clusters or operons across all the 356 surveyed genomes. This analysis also revealed pair biases of isoprenoid biosynthesis genes frequently found in close proximity. In our engineering attempts, we found that simply overexpressing the rate-limiting gene in the pathway was toxic to the bacterium. Thus, we generated synthetic loci with the goal of specifically overproducing HMBPP, and tested the ability of these engineered strains to induce human V{gamma}9V{delta}2 expansion in an in vitro stimulation assay. We found that BCG expressing a rationally-designed, synthetic MEP locus did not enhance V{gamma}9V{delta}2 T cell expansion over the wild-type vaccine strain, suggesting that ectopic expression of multiple MEP genes may result in feedback inhibition of the pathway. However we found that overexpression of the HMBPP synthase GcpE alone potently induced V{gamma}9V{delta}2 T cell expansion and did not result in downregulation of other pathway genes, presenting a successful strategy to accumulate HMBPP and overcome feedback inhibition in this pathway. While much remains to be done to ultimately develop a more efficacious vaccine, our data present a promising system to improve upon the BCG platform. To our knowledge, this is the first work to attempt reengineering of the MEP pathway in BCG to improve vaccine efficacy.

bioengineering↗

bioRxiv2025

RB-TnSeq elucidates dicarboxylic acid specific catabolism in β-proteobacteria for improved plastic monomer upcycling

Dicarboxylic acids are key components of many polymers and plastics, making them a target for both engineered microbial degradation and sustainable bioproduction. In this study, we generated a comprehensive dataset of functional evidence for the genetic basis of dicarboxylic and fatty acid metabolism using randomly-barcoded transposon sequencing (RB-TnSeq). We identified four {beta}-proteobacteria that displayed robust growth with dicarboxylic acid sole carbon sources and cultured their mutant libraries with dicarboxylic and fatty acids with carbon chain lengths from C3 to C12. The resulting fitness data suggested that dicarboxylic and fatty acid metabolisms are largely distinct, and different sets of {beta}-oxidation genes are required for catabolizing dicarboxylic versus fatty acids of the same carbon chain lengths. Additionally, we identified transcriptional regulators and transporters with strong fitness phenotypes related to dicarboxylic acid utilization. In Ralstonia sp. UNC404CL21Col (R. CL21), we deleted two transcriptional repressors to improve its utilization of short chain dicarboxylic acids. We exploited the diacid-utilizing catabolism of R. CL21 to upcycle a mock mixture of the dicarboxylic acids produced when polyethylene is oxidized. After introducing a heterologous indigoidine production pathway, this engineered Rastonia produced 0.56 {+/-} 0.02 g/L indigoidine from a mixture of dicarboxylic acids as a carbon source, demonstrating the potential of R. CL21 to upcycle plastics waste to products derived from tricarboxylic acid (TCA) cycle intermediates. ImportanceUpcycling the carbon in plastic wastes to value-added products is a promising approach to address the plastics waste and climate crises, and dicarboxylic acid metabolism is an important facet of several approaches. Improving our understanding of the genetic basis of this metabolism has the potential to uncover new enzymes and genetic parts for engineered pathways involving dicarboxylic acids. Our dataset is the most comprehensive interrogation of dicarboxylic acid catabolism to date, and this work will be of utility to researchers interested in both plastics bioproduction and upcycling applications.

microbiology↗

bioRxiv2024

Giving it a fair shake: A simple, fast, & efficient method to extract amatoxins from the death cap mushroom, Amanita phalloides

1.1.The death cap mushroom, Amanita phalloides, is well known for containing amatoxins such as alpha- and beta-amanitin, which inhibit eukaryotic RNA polymerase II. While these toxins have been used in research for almost a century, they have recently garnered attention for their role in drug-antibody conjugates. The amatoxins are still largely extracted from wild mushrooms, which cannot be made to fruit in the lab. We propose simplified extraction methods that could reduce hazardous exposures to dust and expedite sample analysis without sacrificing accuracy. We recently developed a Lateral Flow Immunoassay (LFIA), for which we identified that sample maceration was not needed to extract the amatoxins and that the incubation time for extraction could be accomplished in 1 minute. In this current work, we hypothesized that these same extraction adjustments-minimal tissue maceration and reduced incubation time-could be transferable to instrumental detection methods. To test the need for sample maceration, we utilized three different techniques: 1) traditional mortar and pestle, 2) a similarly disruptive method of bead beating, and 3) no grinding, but rather hand shaking dried mushroom tissue in extraction buffer. In addition, we performed the solvent extraction step at varying times to observe if more time allows for more toxin to be removed from the tissue. Lastly, we utilized two comparable solvent evaporation methods (rotovap or speedvac) to establish if multiple samples could be processed simultaneously, thus improving sample throughput. We adjusted aspects of the typical extraction protocol, which resulted in a rapid (1 min) incubation step, along with minimal sample handling (no grinding) of the dried mushroom tissue. We present an extraction protocol that saves time, reduces equipment contamination, and minimizes risk to the researcher. The impact of this faster, safer method may help produce these important toxins faster, for both research and medical use.

microbiology↗

bioRxiv2024

Biosynth Pipeline: An integrated platform for merging the computational design of chimeric type I polyketide synthases with enzymatic pathways for chemical biosynthesis

Synthetic biology offers the promise of manufacturing chemicals more sustainably than petrochemistry. Yet, both the rate at which biomanufacturing can synthesize these molecules and the net chemical accessible space are limited by existing pathway discovery methods which can often rely on arduous literature searches. Here, we present an automated retrobiosynthesis tool, BioPKS Pipeline, that simultaneously tackles both problems by integrating multifunctional type I polyketide synthases (PKSs) with monofunctional enzymes to propose the synthesis of desired target chemicals via two new tools: DORAnet and RetroTide. While monofunctional enzymes are valuable for carefully decorating a substrates carbon backbone, they typically cannot expand the backbone itself. PKSs can, instead, predictably do this through their unique ability to catalyze carbon-carbon bond formation reactions iteratively. We have evaluated the performance of BioPKS Pipeline against a previously published set of 155 molecules of interest for biomanufacturing, and report that BioPKS Pipeline could produce exact designs for 93 of them, as well as pipelines to a chemically similar product for most of the remaining molecules. Furthermore, BioPKS Pipeline successfully proposes biosynthetic routes for complex therapeutic natural products (cryptofolione and basidalin) for which no known biosynthetic pathway currently exists.

bioinformatics↗

bioRxiv2024

A polyketide-based biosynthetic platform for diols, amino alcohols and hydroxyacids

Medium- and branched-chain diols and amino alcohols are important industrial solvents, polymer building blocks, cosmetics and pharmaceutical ingredients, yet biosynthetically challenging to produce. Here, we present a novel approach utilising a modular polyketide synthase (PKS) platform for the efficient production of these compounds. This platform takes advantage of a versatile loading module from the rimocidin PKS and NADPH-dependent terminal thioreductases (TRs), previously untapped in engineered PKSs. Reduction of the terminal aldehyde with specific alcohol dehydrogenases enables production of diols, oxidation enables production of hydroxy acids, and transamination with specific transaminases enables production of various amino alcohols. Furthermore, replacement of the malonyl-coenzyme A (CoA)-specific acyltransferase (AT) in the extension module with methyl- or ethylmalonyl- CoA-specific ATs enables production of branched-chain diols and amino alcohols. In total, we demonstrated production of nine 1,3-diols (including the difficult-to-produce insect repellent and cosmetic ingredient 2-ethyl-1,3-hexanediol), six amino alcohols, and two carboxylic acids using our PKS platform in Streptomyces albus. Finally, tuning production of the PKS acyl-CoA substrates enabled production of high titers of specific diols and amino alcohols (1 g/L diol titer in shake flasks), demonstrating high tunability and efficiency of the platform.

bioengineering↗

bioRxiv2024

Multi-omics analysis of a traditional fermented food reveals a byproduct-associated subpopulation of Neurospora intermedia for waste-to-food upcycling

Fungal solid-state fermentation (SSF) of byproducts has promise for increasing food sustainability and security, but fungal waste-to-food upcycling remains poorly understood at the molecular level. Here we use a multi-omics approach to characterize oncom - a fermented food traditionally produced from byproducts in Java, Indonesia - as a model system for understanding fungal waste conversion. Metagenomic sequencing of two oncom types (red and black) indicated that Neurospora intermedia is the fungus dominating red oncom. Further transcriptomic, metabolomic, and phylogenomic analysis revealed that oncom-derived N. intermedia utilizes pectin and cellulose degradation for substrate conversion and belongs to a distinct byproduct-associated subpopulation that differs from wild strains at the genetic and biochemical level. Finally, we found that N. intermedia grew on a range of industrially relevant byproducts, did not encode for any known mycotoxins, and could be used to create foods that were positively perceived by consumers outside Indonesia. This study uncovers the microbial and genetic basis of a traditional upcycled food, sheds light on human domestication of microbes for sustainability challenges, and establishes the edible N. intermedia as a promising fungus for byproduct upcycling in SSF and beyond.

microbiology↗

bioRxiv2024

Metabolic engineering of yeast for de novo production of kratom monoterpene indole alkaloids

Monoterpene indole alkaloids (MIAs) from Mitragyna speciosa ("kratom"), such as mitragynine and speciogynine, are promising novel scaffolds for opioid receptor ligands for treatment of pain, addiction, and depression. While kratom leaves have been used for centuries in South-East Asia as stimulant and pain management substance, the biosynthetic pathway of these psychoactives have only recently been partially elucidated. Here, we demonstrate the de novo production of mitragynine and speciogynine in Saccharomyces cerevisiae through the reconstruction of a five-step synthetic pathway from common MIA precursor strictosidine comprising fungal tryptamine 4-monooxygenase to bypass an unknown kratom hydroxylase. Upon optimizing cultivation conditions, a titer of [~]290 {micro}g/L kratom MIAs from glucose was achieved. Untargeted metabolomics analysis of lead production strains led to the identification of numerous shunt products derived from the activity of strictosidine synthase (STR) and dihydrocorynantheine synthase (DCS), highlighting them as candidates for enzyme engineering to further improve kratom MIAs production in yeast. Finally, by feeding fluorinated tryptamine and expressing a human tailoring enzyme, we further demonstrate production of fluorinated and hydroxylated mitragynine derivatives with potential applications in drug discovery campaigns. Altogether, this study introduces a yeast cell factory platform for the biomanufacturing of complex natural and new-to-nature kratom MIAs derivatives with therapeutic potential.

synthetic biology↗

bioRxiv2024

Harnessing Chemical Space Neural Networks to Systematically Annotate GPCR ligands

Machine learning (ML) has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors (hGPCRs) in FDA-approved drugs, exhaustive in-distribution drug-target interaction (DTI) testing across all pairs of hGPCRs and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution (OOD) exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks (CSNN) that leverages network homophily and training-free graph neural networks (GNNs) with Labels as Features (LaF). We show that CSNNs ability to make accurate predictions strongly correlates with network homophily. Thus, LaFs strongly increase a ML models capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 DTIs, 539 compounds, 7 hGPCRs) to discover novel DTIs for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

bioinformatics↗

bioRxiv2023

Development of Corynebacterium glutamicum as a monoterpene production platform

Monoterpenes are commonly known for their role in the flavors and fragrances industry and are also gaining attention for other uses like insect repellant and as potential renewable fuels for aviation. Corynebacterium glutamicum, a Generally Recognized as Safe microbe, has been a choice organism in industry for the annual million ton-scale bioproduction of amino acids for more than 50 years; however, efforts to produce monoterpenes in C. glutamicum have remained relatively limited. In this study, we report a further expansion of the C. glutamicum biosynthetic repertoire through the development and optimization of a mevalonate-based monoterpene platform. In the course of our plasmid design iterations, we increased flux through the mevalonate-based bypass pathway, measuring isoprenol production as a proxy for monoterpene precursor abundance and demonstrating the highest reported titers in C. glutamicum to date at nearly 1500 mg/L. Our designs also evaluated the effects of backbone, promoter, and GPP synthase homolog origin on monoterpene product titers. Monoterpene production was further improved by disrupting competing pathways for isoprenoid precursor supply and by implementing a biphasic production system to prevent volatilization. With this platform, we achieved 321.1 mg/L of geranoids, 723.6 mg/L of 1,8-cineole, and 227.8 mg/L of linalool. Furthermore, we determined that C. glutamicum first oxidizes geraniol through an aldehyde intermediate before it is asymmetrically reduced to citronellol. Additionally, we demonstrate that the aldehyde reductase, AdhC, possesses additional substrate promiscuity for acyclic monoterpene aldehydes. HighlightsO_LIDesign of a mevalonate-based monoterpene production platform in C. glutamicum C_LIO_LIHighest production titers of geranoids, eucalyptol, and linalool reported in C. glutamicum to date C_LIO_LIIdentification of citronellal as an intermediate in the reduction of geraniol to citronellol by C. glutamicum C_LI

synthetic biology↗

bioRxiv2023

Genetically refactored Agrobacterium-mediated transformation

Members of Agrobacterium are costly plant pathogens while also essential tools for plant transformation. Though Agrobacterium-mediated transformation (AMT) has been heavily studied, its polygenic nature and its complex transcriptional regulation make identifying the genetic basis of transformational efficiency difficult through traditional genetic and bioinformatic approaches. Here we use a bottom-up synthetic approach to systematically refactor the tumor-inducing plasmid, wherein the majority of AMT machine components are encoded, into a minimal set of genes capable of plant and fungal transformation that is both controllable and orthogonal to its environment. We demonstrate that engineered vectors can be transferred to new heterologous bacteria, enabling them to transform plants. Our reductionist approach demonstrates how bottom-up engineering can be used to dissect and elucidate the genetic underpinnings of complex biological traits, and may lead to the development of strains of bacteria more capable of transforming recalcitrant plant species of societal importance.

microbiology↗

bioRxiv2023

Characterization and diversification of AraC/XylS family regulators guided by transposon sequencing

In this study, we explored the development of engineered inducible systems. Publicly available data from previous transposon sequencing assays were used to identify regulators of metabolism in Pseudomonas putida KT2440. For the AraC-family regulators (AFRs) represented in this data, we posited AFR/promoter/inducer groupings. Eleven promoters were characterized for a response to their proposed inducers in P. putida, and the resultant data were used to create and test nine two-plasmid sensor systems in E. coli. Several of these were further developed into a palette of single-plasmid inducible systems. From these experiments, we observed an unreported inducer response from a previously characterized AFR, demonstrated that the addition of a P. putida transporter improved the sensor dynamics of an AFR in E. coli, and identified an uncharacterized AFR with a novel potential inducer specificity. Finally, targeted mutations in an AFR, informed by structural predictions, enabled further diversification of these inducible plasmids. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=105 SRC="FIGDIR/small/550116v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@fb1ee2org.highwire.dtl.DTLVardef@a88f1dorg.highwire.dtl.DTLVardef@4bbd5borg.highwire.dtl.DTLVardef@7cdbfc_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2023

Literate programming for iterative design-build-test-learn cycles in bioengineering

Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of complex biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of a metabolic pathway designs for production of a key precursor to medicinal alkaloids. The teemi platform is publicly available at PyPi and GitHub.

bioinformatics↗

bioRxiv2023

Maximizing Heterologous Expression of Engineered Type I Polyketide Synthases: Investigating Codon Optimization Strategies

Type I polyketide synthases (T1PKSs) hold an enormous potential as a rational production platform for the biosynthesis of specialty chemicals. However, despite the great progress in this field, the heterologous expression of PKSs remains a major challenge. One of the first measures to improve heterologous gene expression can be codon optimization. Although controversial, choosing the wrong codon optimization strategy can have detrimental effects on protein and product levels. In this study, we analyzed 11 different codon variants of an engineered T1PKS and investigated in a systematic approach their influence on heterologous expression in Corynebacterium glutamicum, Escherichia coli, and Pseudomonas putida. Our best performing codon variants exhibited a minimum 50-fold increase in PKS protein levels, which also enables the production of an unnatural polyketide in each of the hosts. Furthermore, we developed a free online tool (https://basebuddy.lbl.gov) that offers transparent and highly customizable codon optimization with up-to-date codon usage tables. Here, we not only highlight the significance of codon optimization but also establish the groundwork for high-throughput assembly and characterization of PKS pathways in alternative hosts.

synthetic biology↗

bioRxiv2023

Foldy: a web application for interactive protein structure analysis

Foldy is a cloud-based application that allows non-expert scientists to easily access and utilize advanced AI-based structural biology tools, including AlphaFold and DiffDock. Built on Kubernetes, it can be deployed by universities, departments, and labs without requiring hardware resources, but can also be configured to utilize available computers. Foldy enables scientists to predict the structure of proteins and complexes up to 3000 amino acids, visualize Pfam annotations, and dock ligands with AutoDock Vina and DiffDock. Our manuscript describes the user interface and deployment considerations of Foldy, as well as some of our applications. By democratizing access to sophisticated AI-based tools, Foldy can facilitate life science research and promote the wider adoption of structural bioinformatics tools. Our work demonstrates that even the most advanced tools can be made accessible to a broad audience through user-friendly platforms like Foldy, and we believe it will be a valuable resource for researchers across scientific disciplines. The public structures available on the Lawrence Berkeley Labs Foldy deployment can be viewed at https://foldy.lbl.gov. Author SummaryFoldy is a cloud-based application that enables scientists to use AI-based structural biology tools such as AlphaFold and DiffDock without software expertise. Built on Kubernetes, it can be set up by universities, departments, and labs with no need for hardware resources. Foldy can predict the structure of proteins and complexes up to 3000 amino acids, visualize Pfam annotations, and dock ligands with AutoDock Vina and DiffDock. Our public structures can be viewed at https://foldy.lbl.gov. Our manuscript highlights the user interface, deployment considerations, and product applications of Foldy. Its an accessible solution for researchers who are not software experts and can handle the traffic of thousands of users and hundreds of thousands of protein structures and docked ligands. This makes advanced AI-based tools more widely available, paving the way for accelerating life science research. By developing an easy-to-use platform, our work demonstrates that even the most sophisticated AI-based tools can be made accessible to a wide audience. Foldy enables more scientists to draw from the rapidly growing field of structural biology, making it a valuable tool for researchers across scientific disciplines. We look forward to its adoption by the scientific community.

bioinformatics↗