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Results for “Synthetic Biology”

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bioRxiv2024

Global comparative structural analysis of responses to protein phosphorylation

Post-translational modifications (PTMs), particularly protein phosphorylation, are key regulators of cellular processes, impacting numerous aspects of protein activity. Despite widespread phosphorylation of eukaryotic proteomes, the function of most phosphosites remains unknown. Elucidating the structural mechanisms underlying phosphorylation is crucial for understanding its regulatory roles. Here, we conducted a comparative structural analysis of phosphorylated and non-phosphorylated proteins taken from the Protein Data Bank (PDB). Our study systematically evaluates how phosphorylation affects backbone conformation, protein dynamics, and mechanical strain. We found that phosphorylation commonly induces small, stabilizing conformational changes through conformational selection and frequently modulates local residue fluctuations, influencing overall protein motion. Notably, a small but significant subset of phosphosites shows mechanical coupling with functional sites, aligning with the domino model of allosteric signal transduction. This work provides a foundation for studying phosphorylation and other PTMs in their structural context, which will guide the rational design of synthetic phosphosites and enable the engineering of PTM-driven regulatory circuits in synthetic biology.

bioinformatics↗

bioRxiv2024

FoldMark: Protecting Protein Generative Models with Watermarking

Proteins are the principal architects of life, fueling advances in bioengineering, drug discovery, and synthetic biology. The integration of generative AI with computational protein science has revolutionized protein design while also posing dual-use risks, such as enabling the creation of pandemic-capable proteins that require strong biosecurity safeguards. Here, we introduce FoldMark, a first-of-its-kind watermarking strategy leveraging distributional and evolutionary principles tailored for protein generative models, balancing watermark capacity and structural quality. FoldMark achieves over 95% watermark bit accuracy at 32 bits with minimal impact on structural quality (>0.9 scTM scores) for leading models including AlphaFold3, ESMFold, RFDiffusion, and RFDiffusionAA. For user tracing, FoldMark can successfully trace up to 1 million users. To validate FoldMark in wet lab, we applied it to structure-based design of EGFP and CRISPR-Cas13, showing wildtype-level function (98% fluorescence, 95% editing efficiency) and >90% watermark detection, demonstrating its practical utility for safeguarding AI-driven protein research.

bioinformatics↗

bioRxiv2024

PXGS: a Poly-Transgene Expression System based on Mutually Exclusive Splicing of Dscam

Biologists often need to investigate multiple genes simultaneously in an organism. However, it is currently not possible to express more than a few transgenes in an animal under conditional control. Here, we developed a technique based on the mutually exclusive splicing of the Down Syndrome Cell Adhesion Molecule1 (Dscam1) gene in Drosophila melanogaster to achieve simultaneous transgene expression of 12 genes at a time. We show that the hypervariable Dscam1 exon 4 region maintains its alternative splicing when placed in a UAS expression vector. Each of the twelve exon 4 alternates can be replaced with an exogenous gene of at least 10 kilobases and will express properly in vivo all under conditional genetic control. We demonstrate the expression of four different fluorophores placed in different exon 4 alternate positions in neural and non-neural cells in vivo. We validated the technique by rewiring Drosophila sensory neuron axons in vivo by simultaneously expressing several cell surface receptors within the neuron. This technology will also enable Drosophila melanogaster as a model system for synthetic biology research.

cell biology↗

bioRxiv2024

All-atom simulations elucidate the molecular mechanismunderlying RNA-membrane interactions

RNA-membrane interactions are starting to emerge as an important organizing force in both natural and synthetic biological systems. Notably, RNA molecules were recently discovered to be present on the extracellular surface of living cells, where they mediate intercellular signalling. Furthermore, RNA-membrane interactions influence the efficacy of lipid-based RNA delivery systems. However, the molecular terms driving RNA localisation at the membrane remain poorly understood. In this work, we investigate how RNA-phospholipid membrane interactions occur, by means of all-atom simulations. We find that among the four RNA nucleobases guanine exhibits the strongest interaction with the membrane due to extensive hydrogen bond formation. Additionally, we show that intra-RNA base pairing present in organised RNA structures significantly hinders RNA binding to the membrane. Elucidating the molecular details of RNA-membrane association will importantly contribute to improving the design of RNA-based drugs as well as lipid-based RNA delivery systems and to parsing out RNA transport and localisation mechanisms.

biophysics↗

bioRxiv2024

AistSeq: An In-House Tn5-Based Plasmid Sequencing Platform Using A Compact Benchtop Sequencer

Sequence verification of plasmids is a fundamental process in synthetic biology. For plasmid sequence verification using next-generation sequencing (NGS) library preparation, Tn5 transposase is widely used. Streamlined sequencing workflow for laboratory-scale applications is important; however, recombinant Tn5 production in-house can be laborious. In this study, we demonstrated that the addition of a large soluble tag was not essential for purification and that the fusion of a His10 tag and protein A was sufficient to purify sufficient amounts of active Tn5 transposase. In addition, we evaluated exonuclease-based genomic DNA digestion for plasmid sequencing from an E. coli lysate and the data analysis pipeline of sequences derived from the Illumina iSeq100 platform for de novo assembly, reference mapping, and annotation. This study proposes a simple workflow of an in-house Tn5-based plasmid Sequencing platform using a compact benchtop sequencer (AistSeq). Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=37 SRC="FIGDIR/small/618112v1_ufig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@f1546corg.highwire.dtl.DTLVardef@1dd947aorg.highwire.dtl.DTLVardef@3efb78org.highwire.dtl.DTLVardef@efbde1_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

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

Sticky enzymes: increased metabolic efficiency via substrate-dependent enzyme clustering

Coclustering of subsequent enzymes in a pathway can accelerate the processing of metabolic intermediates, with benefits including increased pathway fluxes, reduced toxicity, and sensitive branch-point regulation. While the optimal organization of such clusters has been explored theoretically, little is known about how to achieve such organization inside cells. Here we propose that phase-separating enzymes can self-organize into nearly-optimally sized and spaced clusters, provided that their "stickiness" is regulated by local substrate availability. In a nutshell, enzyme clusters only form when and where they are needed to process substrate. We study a mathematical model that implements this scheme for simple metabolic pathways, including all thermodynamic constraints. We find that pathway fluxes can be increased by 50 to 1000-fold and toxic metabolites can be decreased by 10 to 100-fold, at realistic enzyme densities. Finally, we discuss how enzyme "stickiness" could be allosterically regulated. This study presents a self-organization strategy that goes beyond current paradigms for natural and engineered enzyme clusters, and thus represents a motivating challenge to the fields of synthetic biology and metabolic engineering.

biophysics↗

bioRxiv2024

Impact of different synonymous codon substitution strategies on SARS-CoV-2 nucleocapsid protein expression in Escherichia coli

Synonymous codon substitution, a gene engineering approach in synthetic biology, has been effective in improving the codon composition of recombinant genes of interest based on various criteria without altering the amino acid sequence. The SARS-CoV-2 virus nucleocapsid (N) protein is a stable, conserved and highly immunogenic that is less prone to mutation during infection, making it a key antigen in in vitro diagnosis, vaccine development, immunological and structural studies. While reports have focused on applying optimized N protein for different applications, the basic parameters used by different optimization tools for choosing the best approach for the N gene synonymous codon substitution are often neglected. Here, we analyzed the influence of different synonymous codon substitution strategies on SARS-CoV-2 N-protein expression in E. coli. Using different codon optimization (CO) and harmonization (CH) tools, we predicted and compared how parameters such as GC content, Codon Adaptation Index, codon quality and number of rare codons present in these sequences affect the N-protein expression. Our results also show that Minimum Free Energy (MFE) and RNA structure of N-term and C-tail of the N-protein coding sequence influence protein folding. We then predicted that the SR-rich region of the N-protein may contribute to slowing down the elongation rate during translation. This work presents a fundamental analysis of how different optimization tools affect SARS-CoV-2 N-protein expression and folding and suggests a basic approach to choosing the best strategy for optimal expression and folding of the protein for further studies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=60 SRC="FIGDIR/small/622014v3_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@1fbd7f8org.highwire.dtl.DTLVardef@11fe81borg.highwire.dtl.DTLVardef@1bf787borg.highwire.dtl.DTLVardef@17edc57_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsCodon substitution affects SARS-CoV-2 nucleocapsid (N) protein expression. SARS-CoV-2 N-protein expression varies with different codon optimization tools. RNA structures of N- and C-term impact RNA stability, protein expression and folding. SR-rich region of the N-protein may slow down elongation rate during translation.

bioinformatics↗

bioRxiv2024

Systematic screen of RNA binding proteins that enhance circular RNA translation

Translatable circular RNAs (circRNAs) have emerged as a promising alternative to linear mRNA as new therapeutics due to its improved stability. The translation of circRNAs is mainly driving by internal ribosome entry site (IRES) or IRES-like elements, which is under regulation by various trans-acting RNA binding proteins (RBPs). Here we designed a cell-based system to systematically screen RBPs that enhance translation of circRNAs, and identified a total of 68 proteins as putative activators of noncanonical translation. These translation activators mainly involved in the functions of RNA processing, ribosomal biogenesis and translation initiation. Furthermore, we developed a machine learning algorithm to extract common sequence features of these activators, which predicted more potential RBPs with translation activator activities. The newly identified and predicted activators were subsequently demonstrated to promote the IRES-mediated circRNA translation in a context-dependent fashion. This investigation provides new insights to discover functions for IRES trans-acting factors and to expand the toolbox for engineered RBPs in RNA synthetic biology.

molecular biology↗

bioRxiv2024

Cross-species Comparison of Ultramafic Rock Bio-accelerated Weathering Performance

Carbon mineralization is a natural process that sequesters atmospheric CO2 by reacting it with cations (e.g., Mg2+, Ca2+) released by weathering of rocks to form solid carbonates. However, this process is too slow to capture the excess CO2 resulting from anthropogenic emissions in time to prevent significant warming of the atmosphere1-3. Additionally, accelerating carbon mineralization process by chemical or mechanical methods has proven prohibitively expensive to date4. Microbial rock-dissolution processes, including acidolysis, redoxolysis, and complexolysis, have the potential to accelerate weathering with low energy input5. However, there is no industrially useful microbe capable of dissolving ultramafic (rich in ferromagnesian minerals) rocks, suggesting that one will need to be discovered or built with synthetic biology. While microbes are known to dissolve ultramafic minerals6,7, the performance envelopes for these processes remain uncharacterized, and significant gaps exist in our knowledge of microbe-mineral interaction processes. Here, we make a normalized performance comparison of the dissolution of the ultramafic rock dunite (> 90% olivine ((Mg, Fe)2SiO4)) by three well-known mineral-dissolving microbes: Gluconobacter oxydans7-9, Sphingomonas desiccabilis10,11, and Penicillium simplicissimum12-14. We show that G. oxydans outperformed P. simplicissimum and S. desiccabilis, producing the most acidic biolixiviant (pH 2.15 when leaching 1% pulp density), and extracting the most magnesium (3,130 mg/L when leaching at 3% pulp density). Additionally, G. oxydans co-dissolves nine other metals, eight of which are critical for energy technologies (Cr, Mn, Co, Ni, Cu, and Zn)15,16 with a maximum dissolved concentration of 33 mg/L for Ni. While increasing the pulp density of the dunite (solid to liquid ratio) resulted in higher metal dissolution by G. oxydans and S. desiccabilis, notably pulp densities above 2% inhibited mineral dissolution by P. simplicissimum. Our results provide evidence that the gap in performance between G. oxydans and the other two microbes increases with pulp density, and thus, G. oxydans is best suited for process and genetic engineering to maximize performance and minimize costs and environmental impacts of bio-accelerated weathering. Finally, we propose that G. oxydans and P. simplicissimum can use cellulosic hydrolysate as a cost-effective substitute for glucose for biolixiviant production.

bioengineering↗

bioRxiv2024

Fusions of catalytically inactive RusA to FokI nuclease coupled with PNA enable programable site-specific double-stranded DNA breaks

Programmable site-specific nucleases have revolutionized the genome editing. However, these systems still face challenges such as guide dependency, delivery issues, and off-target effects. Harnessing the natural functions of structure-guided nucleases offer promising alternatives for generating site-specific double-strand DNA breaks. Yet, structure-guided nucleases require precise reaction conditions and validation for in-vivo applicability. To address these limitations, we developed the PNA-Coupled FokI-(d)RusA (PC-FIRA) system. PC-FIRA combines the sequence-specific binding ability of peptide nucleic acids (PNAs) with the catalytic efficiency of FokI nuclease fused to a structurally-guided inactive RusA resolvase (FokI-(d)RusA). This system allows for precise double-strand DNA breaks without the constraints of existing site-specific nuclease and structure-guided nucleases. Through in vitro optimizations, we achieved high target specificity and cleavage efficiency. This included adjusting incubation temperature, buffer composition, ion concentration, and cleavage timing. Diverse DNA structures, such as Holliday Junctions, linear, and circular DNA, were tested demonstrating the potential activity on different target forms. Further investigation has revealed the PC-FIRA system capacity for facilitating the precise deletion of large DNA fragments. This can be useful in cloning, large-fragment DNA assembly, and genome engineering, with promising applications in biotechnology, medicine, agriculture, and synthetic biology.

bioengineering↗

bioRxiv2024

Pre-trained protein language model for codon optimization

Messenger ribonucleic acid (mRNA) vaccines represent a major advancement in synthetic biology, yet their efficacy remains limited by how efficiently the encoded protein is translated within the host. Since multiple codons can code for the same amino acid, the search space of possible coding sequences (CDS) within mRNA grows exponentially with protein length, making the problem highly underdetermined. Finding CDSs that yield efficient translation hinges on codon optimization--the process of choosing among synonymous codons, while encoding the same protein but differ in their effects on translation speed, tRNA availability, and mRNA secondary structure. Recent deep learning approaches have framed codon optimization as a sequence learning problem, where the goal is to model context-dependent codon usage patterns across the amino acid in protein sequence. However, these methods rely on large sequence models that learn amino-acid embeddings from scratch, leading to computationally intensive training. We propose ppLM-CO, a lightweight codon optimization framework that integrates pretrained protein language models (ppLMs) to directly provide contextual amino-acid embeddings, thereby eliminating the need for embedding learning. This design reduces trainable parameters by over 92% - 99% compared with prior deep models while maintaining complete biological fidelity. In-silico evaluations across three species and two vaccine targets--SARS-CoV-2 spike and Varicella-Zoster Virus (VZV) gE viral proteins--demonstrate that ppLM-CO consistently achieves higher expression and competitive stability, establishing a scalable and biologically consistent approach for codon optimization.

bioinformatics↗

bioRxiv2024

Antibiotics that Kill Gram-negative Bacteria by Restructuring the Outer Membrane Protein BamA

The essential outer membrane protein insertase BamA has recently emerged as a valid target for killing Gram-negative bacteria. Bamabactins, competitive inhibitors targeting the lateral gate of BamA, disrupt the substrate folding process, compromise the outer membrane integrity, and lead to bacterial cell death. Despite their promise, the full pharmacological potential of bamabactins remains underexploited. We applied phylogenetic genome mining and synthetic biology to identify xenorceptides which selectively kill Enterobacteriaceae. Mode of action studies show that xenorceptide A2 integrates itself into BamA as an additional {beta}-strand between {beta}1 and {beta}16 at the lateral gate, inducing a conformation of BamA that has not been observed before. Biological evaluation of xenorceptide A2 shows promising activity in vitro and in vivo, and limited resistance which differentiates it from other bamabactin antibiotics. Our data show that the chemical diversity of bamabactins is far greater than previously recognized and thus an attractive source for antibiotic discovery.

microbiology↗

bioRxiv2025

Discovery of Expression-Governing Residues in Proteins

Understanding how amino acids influence protein expression is crucial for advancements in biotechnology and synthetic biology. In this study, we introduce Venus-TIGER, a deep learning model designed to accurately identify amino acids critical for expression. By constructing a two-dimensional matrix that links model representations to experimental fitness, Venus-TIGER achieves improved predictive accuracy and enhanced extrapolation capability. We validated our approach on both public deep mutational scanning datasets and low-throughput experimental datasets, demonstrating notable performance compared to traditional methods. Venus-TIGER exhibits robust trans-ferability in zero-shot predicting scenarios and enhanced predictive performance in few-shot learning, even with limited experimental data. This capability is particularly valuable for protein design aimed at enhancing expression, where generating large datasets can be costly and time-consuming. Additionally, we conducted a statistical analysis to identify expression-associated features, such as sequence and structural preferences, distinguishing between those linked to high and low expression. Our investigation also revealed a correlation among stability, activity and expression, providing insight into their interconnected roles and underlying mechanisms.

bioengineering↗

bioRxiv2025

A tunable affinity fusion tag for protein self-assembly

The concentrations of individual proteins vary between cells, both developmentally and stochastically. The functional consequences of this variation remain largely unexplored due to limited experimental tools to manipulate the relationship of protein concentration to activity. Here, we introduce a genetically encoded tool based on a tunable amyloid that enables precise control of protein concentration thresholds in cells. By systematically screening dipeptide repeats, we identified poly-threonine alanine (poly-TA) as an ideal candidate due to its unique ability to form amyloid-like assemblies with a negligible nucleation barrier at arbitrarily chosen concentration thresholds. We demonstrate that the saturating concentration (Csat) of poly-TA can be finely tuned by adjusting the length of uninterrupted TA repeats, even while maintaining length and composition, providing a modular system for manipulating protein solubility. This tool offers a powerful approach to investigate the relationship between protein concentration, phase separation, and cellular function, with potential applications in cell-, developmental-, and synthetic biology.

cell biology↗

bioRxiv2025

High-yield bioproduction of virus-free P4-EKORhE multi-lysin transducing particles as an antimicrobial gene therapeutic

A description of the construction of the bioengineered P4-EKORhE and a comprehensive method for producing very high yields (up to 1012 particles per millilitre) enable the use of virus-like particles to transduce genetically encoded antimicrobials through a combination of synthetic biology and optimised upstream and downstream processing. The final product, a gene-delivered antimicrobial in the form of the multi-lysins cassette, is fully functional before and after packaging within P4-EKORhE particles. The antimicrobial activity of the multilysins cassette, characterized by its lysis proteins, was tested in vivo in both pure bacterial Escherichia coli (E. coli) cultures and in a model of infection using A549 immortalised human epithelial tissue cell cultures. This work exemplifies several bioproduction methods and demonstrates how the virology of the P4 and P2 phages can be harnessed to establish a bioprocess for producing transducing particles at very high yields, avoiding contamination by the natural virus while maintaining the antimicrobial effectiveness of the final product.

bioengineering↗

bioRxiv2025

Engineering microalgal cell wall-anchored proteins using GP1 PPSPX motifs and releasing with intein-mediated fusion

Harnessing and controlling the localization of recombinant proteins is essential for advancing synthetic biology, industrial biotechnology, and drug delivery. This study presents a dual system for protein anchoring and controlled release in Chlamydomonas reinhardtii. Using truncated variants of the GP1 glycoprotein fused to the plastic-degrading enzyme PHL7, we identified the PPSPX motif as critical for anchoring proteins to the cell wall. Constructs with increased PPSPX content exhibited reduced secretion but enhanced anchoring, revealing key anchor-signal sites within GP1. To enable controlled release, we incorporated a pH-sensitive intein from derived from Mycobacterium tuberculosis RecA. Under acidic conditions (pH 5.5), this intein efficiently cleaved and released mCherry and PHL7 from the GP1 anchor. Fluorescence kinetics demonstrated significant mCherry release from the mCherry-intein-GP1 construct within 4 hours at pH 5.5, while release was minimal at pH 8.0 and negligible for the mCherry-GP1 control. Western blot analysis confirmed efficient cleavage, showing a lower band corresponding to free mCherry at pH 5.5 and no release at pH 8.0. This anchor-release strategy integrates glycomodules with pH-sensitive inteins to enable precise spatial and temporal protein control. The system offers broad utility for targeted drug delivery, environmental biosensing, and biocatalyst deployment. Overall, we establish a versatile framework for optimizing protein localization and environmentally triggered release in C. reinhardtii, with broad implications for proteomics, biofilm engineering, and scalable therapeutic delivery systems. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/634604v2_ufig1.gif" ALT="Figure 1"> View larger version (56K): org.highwire.dtl.DTLVardef@8bdc2org.highwire.dtl.DTLVardef@1e213c7org.highwire.dtl.DTLVardef@1808559org.highwire.dtl.DTLVardef@b2c1e9_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2025

Survival and Spread of Engineered Mycobacterium smegmatis and Associated Mycobacteriophage in Soil Microcosms

The inoculation of microbes into soil environments has numerous applications for improving soil quality and crop health; however, the ability of exogenous and engineered microbes to survive and spread in soil remains uncertain. To address this challenge, we assayed the survival and spread of Mycobacterium smegmatis, engineered with either plasmid transformation or genome integration, as well as its mycobacteriophage Kampy, in both sterilized and non-sterilized soil microcosms over a period of 49 days. While engineered M. smegmatis and Kampy persisted in all soil microcosms, there was minimal evidence of spread to 5 cm away from the inoculation site. There was a higher prevalence of Kampy observed in sterilized soil than non-sterilized soil, suggesting a detrimental effect of the native soil biotic and viral community on the ability of this phage to proliferate in the soil microcosm. Additionally, higher abundance of the genome-integrated bacteria relative to the plasmid-carrying bacteria as well as evidence for loss of plasmid over the duration of the experiment suggest a burden associated with bacteria harboring plasmids, although plasmids were still retained across 49 days. To our knowledge, this is the first study to simultaneously measure the persistence and spread of bacteria and their associated phage in both sterilized and non-sterilized soil microcosms, employing bacteria with plasmid-based and genome-integrated engineered circuits. As such, this study provides a novel understanding of challenges associated with the deployment of bioengineered microbes into soil environments. ImportanceHealthy soil is essential to sustain life, as it provides habitable land, enables food production, promotes biodiversity, sequesters and cycles nutrients, and filters water. Given the prevalence of soil degradation, treatment of soil with microbes that promote soil and crop health could improve global soil sustainability; furthermore, the application of bioengineering and synthetic biology to these microbes allows fine-tunable and robust control of gene-of-interest expression. These solutions require the successful deployment of bacteria into the soil, an environment in which abundant competition and often limited nutrients can result in bacterial death or dormancy. This study employs Mycobacterium smegmatis as a chassis alongside its bacteriophage Kampy in soil microcosms to assess the ability of non-native microbes to survive and spread in soil. Insights from this experiment highlight important challenges which must be overcome for successful deployment of engineered microbes in the field.

microbiology↗