SEARCH · bioRxiv Science

Results for “Synthetic Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,297 records · Page 72Linked to original sources

bioRxiv2025

Efficient Cell Factory Design by Combining Meta-Heuristic Algorithm with Enzyme Constrained Metabolic Models

The rational design of high-performance microbial cell factories remains a central challenge in sustainable biomanufacturing due to the complexity of metabolic networks and the difficulty of predicting synergistic genetic interventions. Despite recent advances in strain design algorithms, predicting combinatorial targets remains computationally prohibitive due to the combinatorial explosion. Here, we present MetaStrain, a unified computational framework that integrates enzyme-constrained models (ecModels) with meta-heuristic algorithms to identify non-intuitive combinatorial gene targets for improving product yields. MetaStrain first performs pre-screening through a modified enforced objective flux scanning algorithm to reduce the dimensionality of candidate genes and annotate editing strategies. The subsequent search module employs flexible individual encodings compatible with diverse meta-heuristic algorithms and adopts a bottom-up phenotype evaluation strategy enabling efficient exploration of the combinatorial design space. Integrated redundancy analysis tools further identify single and fixed-size combinatorial strategies, facilitating direct experimental implementation. Computational simulation in Saccharomyces cerevisiae reveals significant enhancements in 2-phenylethanol and spermidine biosynthesis, while controlling target count and covering experimentally validated targets. Experimental validation in Escherichia coli further confirmed the algorithms predictive power, achieving up to a 61.25% increase in L-tryptophan titer of the five-target combination strain. Overall, MetaStrain achieves high computational efficiency, stable convergence, and broad adaptability across diverse metabolic targets. This study provides a powerful tool for metabolic engineering, bridging computational prediction and experimental realization, and highlighting the potential of meta-heuristic optimization in synthetic biology.

bioinformatics↗

bioRxiv2025

Engineering Escherichia coli FAD synthetase for the biosynthesis of FAD nucleotide analogues results in aminoglycoside antibiotic tolerance

Cofactors such as flavin adenine dinucleotide (FAD), nicotinamide adenine dinucleotide, coenzyme A, and S-adenosylmethionine cofactors share a common adenosine moiety that is generally not considered to contribute directly to catalysis. In FAD biosynthesis, the adenosine group is installed by the flavin mononucleotide adenylyltransferase (FMNAT) domain of the bifunctional FAD synthetase using FMN and ATP. This study explores the nucleotide selectivity of the Escherichia coli FMNAT domain and physiological effects of FAD nucleotide analogues synthesized with cellular nucleotides other than ATP. First, we engineer the FAD synthetase enzyme at an active site loop to produce FAD nucleotide analogues. Chromosomal substitution of the wild-type E. coli FAD synthetase with the mutated version results in the intracellular synthesis of these noncanonical FAD nucleotide analogues and confers tolerance to aminoglycoside antibiotics. Upon investigating their potential downstream metabolic roles, we find that their biosynthesis perturbs cellular metabolism and they could bind to a range of E. coli enzymes. These findings validate the FMNAT domain loop as a viable site for cofactor engineering, with potential for synthetic biology applications. Finally, the observed correlation between FAD analogue production and increased tolerance to aminoglycosides reveals novel metabolic mechanisms underlying antibiotic sensitivity.

microbiology↗

bioRxiv2025

Context-aware geometric deep learning for RNA sequence design

RNA design has emerged to play a crucial role in synthetic biology and therapeutics. Although tertiary structure-based RNA design methods have been developed recently, they still overlook the broader molecular context, such as interactions with proteins, ligands, DNA, or ions, limiting the accuracy and functionality of designed sequences. To address this challenge, we present RISoTTo (RIbonucleic acid Sequence design from TerTiary structure), a parameter-free geometric deep learning approach that generates RNA sequences conditioned on both their backbone scaffolds and the surrounding molecular context. We evaluate the designed sequences based on their native sequence recovery rate and further validate them by predicting their secondary structures in silico and comparing them to the corresponding native structures. RISoTTo performs well on both metrics, demonstrating its ability to generate accurate and structurally consistent RNA sequences. Additionally, we present an in silico design study of domain 1 of the NAD+ riboswitch, where RISoTTo-generated sequences are predicted to exhibit enhanced binding affinity for both the U1A protein and the NAD+ ligand.

bioinformatics↗

bioRxiv2025

Light-orchestrated multi-step solid-phase picodroplet reactors

Picoliter droplet reactors, enabled by droplet microfluidics, are revolutionary tools for biochemical reactions with high efficiency, precision, and minimal reagent use. They excel in single-step reactions and reagent addition through droplet merging. Integrating reagent exchange and washing capabilities directly into these platforms may enable complex multi-step processes, such as de novo oligonucleotide synthesis and multiplex immunoassays. However, current picodroplet microfluidics, lacking such capabilities, remain critically deficient in executing multi-step processes. Here we introduce light-orchestrated solid-phase picodroplet reactors to overcome these limitations. Our platform employs optoelectronic tweezers to manipulate individual picodroplets and microbeads. Using this platform, we demonstrate an eight-step click chemistry-based DNA ligation synthesis cycle, with real-time in situ fluorescence detection of reaction products. This platform overcomes single-step limitations by achieving precise sequential encapsulation and decapsulation of beads with different reagent droplets, ensuring uniform reagent exposure and effective washing. Hence it mitigates reaction errors caused by nonuniform reagent exposure and trapped impurities in conventional bulk processing of microbeads. With minimal reagent consumption, real-time analysis, and programmable light- based control, the platform can potentially be scaled and fully automated into a universal and versatile picoliter-scale reagent handling robot for miniaturizing and streamlining workflows in synthetic biology, drug discovery, and beyond.

bioengineering↗

bioRxiv2025

Pepper RNA variants reveal decoupling of HBC530 binding thermodynamics and fluorescence activation

Fluorogenic RNA aptamers have emerged as powerful tools for live-cell imaging and synthetic biology applications due to their ability to activate fluorescence upon ligand binding. However, the sequence-structure-function relationships governing ligand recognition and fluorescence activation remain poorly understood, limiting rational aptamer design. The Pepper aptamer binds HBC530 with nanomolar affinity in a magnesium-dependent manner, producing bright fluorescence suitable for cellular applications. Here, we generated a library of 53 Pepper variants containing substitutions, insertions, and/or deletions to quantitatively evaluate the contributions of individual nucleotides to HBC530 binding affinity, magnesium binding affinity, and fluorescence intensity. Our results reveal that the correlation between HBC530 binding affinity and fluorescence intensity is only modest. We identify several variants with binding affinities similar to wild-type Pepper but dramatically reduced fluorescence, indicating that ligand recognition and fluorescence intensity are decoupled. Further, we find that distal structural elements significantly influence both binding thermodynamics and fluorescence intensity. Confirming the critical role of divalent cations in Pepper-HBC530 recognition, we observe a strong correlation between HBC530 and magnesium binding thermodynamics. These findings provide quantitative insights into the molecular mechanisms underlying fluorescence activation, toward developing a framework for the rational design of the next generation of fluorogenic RNA aptamers with enhanced performance. KEY POINTSO_LIThe HBC530 binding affinities of 53 Pepper RNA variants are only moderately correlated with fluorescence intensity C_LIO_LIStructural elements distal to the HBC530 binding pocket influence both binding affinity and fluorescence C_LIO_LIThe binding affinities of Pepper RNA to HBC530 and Mg2+ are highly correlated, and Mg2+ stoichiometry is correlated with binding thermodynamics and fluorescence intensity C_LI

biophysics↗

bioRxiv2025

Genome Mining and Characterization of a Heme-Dependent Enzyme Catalyzing Intermolecular Nitrogen-Nitrogen Bond Formation in Hydrazinosuccinic Acid Biosynthesis

Nitrogen-nitrogen (N-N) bond-forming enzymes are rare but play vital roles in both primary and secondary metabolism. Guided by a nitric oxide synthase (NOS)-based genome mining strategy, we report the discovery and characterization of a new heme-dependent enzyme system that catalyzes intermolecular N-N bond formation. Using both in vivo and in vitro reconstitution approaches, we demonstrated that a protein complex, comprising a heme enzyme and a 2[4Fe-4S] ferredoxin partner, mediates the coupling of the -amine group of L-aspartate with inorganic nitrogen oxide species, such as nitrite or nitric oxide, to generate hydrazinosuccinic acid, a key biosynthetic precursor in several natural product pathways. Structural modeling and site-directed mutagenesis suggest a plausible catalytic mechanism involving the formation of a reactive nitrogen intermediate, potentially a heme-bound nitrene species. These findings reveal a new family of N-N bond-forming biocatalysts that leverage inorganic nitrogen sources, offering valuable tools for genome mining and the synthetic biology.

biochemistry↗

bioRxiv2025

Lipid composition effects on the number and size of liposomes formed by the inverted emulsion method

Liposomes have been widely employed as membrane scaffolds in the construction of minimal cell models. In 2002, the inverted emulsion method was introduced as a novel technique for generating giant liposomes by transferring water-in-oil droplets across an oil/water interface. This technique enables the encapsulation of purified proteins or cytoplasmic extracts into cell-sized liposomes under physiological buffer conditions, and has since become a cornerstone in bottom-up synthetic biology. Despite its broad application over the past two decades, the effects of lipid composition on the production yield and size distribution of liposomes generated by the inverted emulsion method remain largely unknown. In this study, we systematically investigated the effects of phospholipid composition on the production yield and size distribution of liposomes generated using the inverted emulsion method. We used a natural phosphatidylcholine purified from chicken egg yolk (egg PC) as the base membrane component, and examined the extent to which substituting a fraction of egg PC with other phospholipids--differing in net electric charge and/or the molecular weight of their hydrophilic head groups--affects the number and size distribution of liposomes. We found that a 10% replacement of egg PC with charged phospholipids significantly enhances the production yield of liposomes by approximately tenfold, transformed by both the natural gravitational force and the greater force applied by centrifugation. In addition, lipids with smaller head groups slightly enhance the production yield in both methods. Lipids with larger head groups also slightly enhance the production yield and promote the formation of larger liposomes in the natural sedimentation method, but these effects are diminished in the centrifugal sedimentation method, presumably due to the strong centrifugal force. These findings provide valuable guidelines for optimizing preparation protocols for minimal cell models.

biophysics↗

bioRxiv2025

Deciphering the altered conformational states of bifunctional thaumarchaeal crotonyl-CoA hydratase and 3-hydroxypropionyl-CoA dehydratase from Nitrosopumilus maritimus

The thaumarchaeal 3-hydroxypropionate/4-hydroxybutyrate (3HP/4HB) cycle represents one of the most efficient mechanisms for CO2 fixation discovered to date. Within this cycle, the enzyme encoded by Nmar_1308 from Nitrosopumilus maritimus SCM1 plays a crucial role due to its dual functionality as both a crotonyl-CoA hydratase (CCAH) and a 3-hydroxypropionyl-CoA dehydratase (3HPD). Although the importance of a bifunctional enzyme for lowering the cost of biosynthesis, the details of structural dynamics are still missing. Here, in addition to our cryogenic temperature structures, we determined the first ambient temperature structures of the Nmar_1308 protein by Serial Femtosecond X-ray Crystallography (SFX). The determined structures capture previously unobserved conformational dynamics of the Nmar_1308 protein, providing invaluable information for future synthetic biology applications.

biochemistry↗

bioRxiv2025

Engineering site-specific nucleic acid-protein conjugates by utilizing a natural RNAylation reaction

Nucleic acid-protein conjugates are valuable for synthetic biology, therapeutics, and nanotechnology, but current methods often lack site specificity and rely on non-natural linkages. RNAylation, a one-step enzymatic reaction catalyzed by the bacteriophage T4 enzyme ModB where first discovered in vivo during phage infection, enables site-specific conjugation of nucleic acids to proteins via a natural N-glycosidic bond. Here, we establish RNAylation as a novel and robust in vitro platform for generating nucleic acid-protein conjugates, overcoming key limitations of existing strategies. We define design principles for this approach, demonstrate enhanced nucleic acid stability in human cell lysates, and develop an efficient purification workflow. Furthermore, we achieve successful delivery of purified conjugates into human cells, highlighting the potential for functional in vivo applications. Our work expands RNAylation from a phage-specific phenomenon to a versatile, biologically relevant strategy with broad biotechnological potential.

bioengineering↗

bioRxiv2025

OptoChaperone - A biohybrid tool for regulating protein condensates in cells and in vitro

Protein condensates formed via liquid-liquid phase separation (LLPS) are increasingly recognized as key players in diverse cellular processes, including those associated with disease. Despite extensive efforts to characterize their formation and function, tools that enable precise, reversible, and spatiotemporal control of LLPS remain limited. Here, we report OptoChaperone, a light-activatable molecular system designed to manipulate protein condensates both in vitro and in living cells. This biohybrid system leverages photoresponsive switching to control chaperone activity: blue light triggers the suppressive function, leading to the dissolution of protein condensates, whereas UV light deactivates the system, allowing condensate formation. We demonstrate the efficacy of OptoChaperone in regulating several disease-related protein condensates, such as fused in sarcoma, TAR DNA-binding protein 43, and heat shock factor 1. Importantly, the system exhibits reversible and robust control over droplet dynamics without requiring chemical additives or genetic modifications of the client proteins. Given the reversibility and efficiency of OptoChaperone in the manipulation of protein condensates, this tool offers a powerful platform for dissecting the roles of protein condensation in cellular physiology and pathology. This strategy also holds potential for broader applications in synthetic biology, biomolecular engineering, and therapeutic modulation of aberrant phase separation.

biochemistry↗

bioRxiv2025

Learning the native-like codons with a 5'UTR and secondary RNA structure aided species-informed transformer model

Efficient protein expression across heterologous hosts remains a major challenge in synthetic biology, largely due to species-specific differences in codon usage and regulatory sequence context. A key difficulty lies in reconstructing the codon landscape of the target expression system within a foreign host with a native-like codon preference. To address this, we present TransCodon, a Transformer-based deep learning model that leverages both 5 untranslated regions (5UTRs) and coding sequences (CDS), along with explicit species identifiers and RNA secondary structure information, to learn nuanced codon usage patterns across diverse organisms. By incorporating multisource genomic data and modeling sequence dependencies in a masked language modeling paradigm, TransCodon effectively captures both local and global determinants of codon preference. Our experiments demonstrate that integrating species-level information during training significantly improves the models ability to predict optimal synonymous codons when considering different evaluation metrics. More importantly it identifies native-like codons with less divergence from natural sequences compared to other methods. Besides, TransCodon could capture more low-frequency codons which are often omitted by other deep learning-based methods. The results thus indicate that TransCodon as a robust codon language model has the potential for generating native-like CDS with high translational efficiency in target hosts.

bioinformatics↗

bioRxiv2025

Estimating flux dynamics from metabolite concentration time courses with dynafluxr

MotivationQuantifying metabolic fluxes and their dynamics is crucial for addressing fundamental and applied questions in systems and synthetic biology, health, and biotechnology. Since metabolic fluxes are not directly observable, they must be estimated using model-based approaches. While several methods are available to quantify steady-state fluxes, approaches for quantifying flux dynamics remain limited. ResultsWe present a method to quantify flux dynamics from metabolite concentration time courses. Our approach employs constrained B-splines to solve mass balance equations without requiring prior knowledge of catalytic or regulatory mechanisms, or enzymatic parameters. We provide a comprehensive mathematical framework for dynamic flux calculation and demonstrate that our method enables accurate and robust quantification of flux dynamics for both simulated and experimental datasets. Availability and implementationdynafluxr is implemented in R and available on Windows, Unix, and MacOS platforms. The source code and the documentation are freely distributed under GPL-3 license at https://github.com/MetaSys-LISBP/dynafluxr/. A companion notebook producing figures similar to those of this article is available at https://github.com/MetaSys-LISBP/DynafluxR_notebook.

systems biology↗

bioRxiv2025

De novo design of light-regulated dynamic proteins using deep learning

Recent advances in deep learning have enabled accurate design of static protein structures, but the de novo design of protein functions controlled by programmable, intramolecular conformational changes remains an unsolved challenge. Here, we present a general deep learning-guided framework for designing dynamic, multi-domain proteins allosterically regulated by light. By integrating photoresponsive domains into de novo scaffolds, we engineered conformational switches that exhibit precise, reversible structural transitions upon illumination. Structural, spectroscopic, and functional analyses validated our designs and demonstrated precise spatio-temporal optogenetic control of diverse cellular processes, including subcellular localization, intercellular signaling, and population-level behaviors. This work establishes a broadly applicable strategy for encoding long-range allosteric control through designed intramolecular motions, and opens new avenues for programming dynamic protein functions from first principles, with implications for basic research, synthetic biology, and therapeutic development.

biophysics↗

bioRxiv2025

An Expanded Repertoire of tRNA Sources for Cell-Free Protein Synthesis

Cell-free expression systems (CFE) are flexible protein translation platforms that simplify the central dogma into an accessible reaction space. Within these systems, bulk transfer RNAs (tRNAs) are critical substrates which deliver amino acids to the elongating ribosome. For years, CFE systems were completed with commercially available tRNA isolated from E. coli MRE600. All commercial sources of tRNA have since been discontinued, jeopardizing future work in all applications of cell-free translation. Here, we address this need by repurposing previously described tRNA isolation methods to produce tRNAs suitable for CFE applications. We isolated the tRNA pools of E. coli strains A19, BL21(DE3), and Rosetta2 BL21(DE3), finding A19 tRNAs but not BL21(DE3) or Rosetta2 BL21(DE3) capable of robust in vitro translation. We determined the abundances of individual tRNAs using tRNA-seq, finding BL21(DE3) and Rosetta2 BL21(DE3) contained outsized abundances of several tRNAs, compromising translation activity. Using codon optimization strategies which align codon usage to tRNA abundance, we were able to mitigate the impact of misaligned tRNA abundances. We extended these studies to V. natriegens, a promising platform for synthetic biology and CFE. We find that neither exogenous V. natriegens tRNAs nor codon optimization are viable options to improve translation yields. Our work here highlights the importance of tRNA abundance within the context of CFE, and simultaneously addresses a critical challenge within cell-free translation.

biochemistry↗

bioRxiv2025

EnzymeTuning: a Deep-learning-based Toolbox for Optimizing Enzyme-constrained Metabolic Modeling with Enhanced Proteome Abundance Prediction

AbstractThe accuracy of enzyme kinetic parameters, particularly the turnover number (kcat), is critical for the predictive power of enzyme-constrained genome-scale metabolic models (ecGEMs). However, current kinetic datasets remain sparse and often fail to capture in vivo enzyme behavior, compromising model predictive capacity. To address these challenges, we developed EnzymeTuning----a generative adversarial network (GAN)-based framework for the global kcat optimization. This approach significantly enhances both the accuracy and proteome-level coverage of ecGEM predictions. Moreover, by incorporating literature-derived protein degradation constants (kdeg), we inferred the protein synthesis rate and systematically evaluated their impact on model performance. The framework was validated across distinct yeast species, including Saccharomyces cerevisiae, Kluyveromyces lactis, Kluyveromyces marxianus, and Yarrowia lipolytica, demonstrating its generalizability. Further, we found that EnzymeTuning facilitates the identification of context-specific enzyme usage patterns and adaptive catalytic resource allocation under varying carbon-to-nitrogen (C/N) ratios, showcasing the substantial potential of our toolbox for integrative omics analysis. Overall, EnzymeTuning provides a robust and scalable solution for refining kinetic parameters in ecGEMs, thereby promoting the wide applications of these computational models in systems and synthetic biological studies.

systems biology↗

bioRxiv2025

One-pot cloning and protein expression platform for genetic engineering

In this work, we present a streamlined one-pot cloning and protein expression platform that integrates mutagenesis, plasmid assembly, and functional protein testing in a single reaction. By combining Golden Gate cloning with cell-free transcription-translation, we demonstrate efficient generation and screening of genetic variants without the need for intermediate purification or bacterial amplification. Using fluorescent proteins, luciferase enzymes, antibiotic-converting enzymes, and the violacein biosynthetic pathway, we validate the versatility of this approach for single-and multi-site mutagenesis, combinatorial variant libraries, metabolic pathway programming, and whole-plasmid assembly. By demonstrating compatibility with multiplexed reactions and multi-cistronic constructs, we establish this approach as a generalizable and automatable method for high-throughput cloning and protein engineering in synthetic biology.

bioengineering↗

bioRxiv2025

Engineering Escherichia coli Nissle as safe chassis for delivery of therapeutic peptides

Synthetic biology enables the integration of sophisticated genetic programs into microorganisms, transforming them into potent vehicles for therapeutic applications. Engineering strategies for microorganisms are rapidly evolving, offering promising solutions for cancer therapy, microbiome modulation, digestive health support, and beyond. Developing novel tools to engineer safe, nonpathogenic microbial platforms is essential for advancing clinical therapies. In this work, we present an innovative engineering approach for the probiotic Escherichia coli Nissle (EcN), aimed at creating a safe and efficient chassis for the bioproduction of therapeutics. The EcN endogenous pM1 and pM2 plasmids were cured and re-engineered to introduce a CRISPR-Cas12 chromosome shredding device and a therapeutic-producing genetic circuit, thereby generating a nonproliferative therapeutic-delivery system. Next, we build an AI-based bioinformatic pipeline to predict Anticancer-Cell-Penetrating Peptides (ACCPP) candidates. As a proof-of-concept, a selected ACCPP was produced in the engineered EcN chromosome-shredded (CS) chassis. This strategy yields a robust and controllable platform for the safe production and delivery of therapeutics, paving the way for the future development of microbial therapies and their clinical applications. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=57 SRC="FIGDIR/small/673081v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@2430d4org.highwire.dtl.DTLVardef@1ce2borg.highwire.dtl.DTLVardef@8686cdorg.highwire.dtl.DTLVardef@1fc23fb_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2025

Universal principles of protein resource allocation among cellular organelles revealed by yeast large-scale absolute quantitative proteomics

The presence of organelles is a hallmark distinguishing eukarya from bacteria and archaea, and this culminates in compartmentalization of cellular metabolism and subsequent metabolic specialization. Here we established a dataset encompassing over 300 absolute quantitative proteomes, the largest to date, across two yeast species under diverse experimental conditions. Leveraging big data analysis, formula fitting, and machine learning models, quantitative correlations among protein abundance, organelle-level resource distribution, and cellular phenotypes were elucidated at a system level. We found that protein resources always exhibit robust and precise distribution at the organelle level across distinct conditions. Specifically, at high specific growth rates, the protein mass fraction from some main organelles, i.e., peroxisome and nucleus, is consistently reduced to offset the increasing protein resource demand from the ribosome. Meanwhile, we found that the nutrition limitation could induce resource recycling by upregulating protein resources within the vacuole and lipid droplets to sustain stress adaptation. Importantly, our integrative analysis demonstrates that protein mass fraction from less than 4 organelles (e.g., nucleus and ribosomes) can accurately predict diverse yeast physiological parameters (e.g., specific growth rate, oxygen uptake rate), and a core set of 37 proteins could predict resource allocation among 24 main organelles and sub-organelles with high accuracy (average R^2 > 0.9). Finally, we found organelle resource allocation reflects the divergence of yeast species. For example, anaerobic conditions and respiratory suppression have less influence on Crabtree-positive yeast, i.e., Saccharomyces cerevisiae, with respect to organelle resource allocation but have a larger effect on the Crabtree-negative yeast Issatchenkia orientalis, thus suggesting that cellular resources have facilitated adaptive evolution. In summary, the high-quality, genome-scale quantitative proteomic dataset for yeast species offers an unprecedented opportunity for understanding the basic principles underlying resource allocation at the organelle level, laying theoretical foundations for precision engineering of cell factories in synthetic biology. The resource used in this study is available at https://yeast-proteome-database.streamlit.app/.

systems biology↗