ACS Synthetic Biology最新文献

Helical protein-protein interactions underpin transcriptional regulation, signal transduction, and self-assembly, yet their rational design remains challenging. Coiled coils (CCs) are particularly attractive as modular, programmable building blocks in synthetic biology, while also serving as therapeutic targets. Here we present InsiliCoil, a cross-platform software suite that unifies predictive modeling, selective peptide inhibitor discovery, and orthogonal interactome design into a single accessible framework. At its core, isCAN enables high-throughput identification of selective CC inhibitors, while CCIS systematically constructs orthogonal CC networks for synthetic biological circuits and biomaterials. Additional utilities support automatic heptad detection, heptad scanning, constraint analysis, charge block prediction, library generation, and large-scale visualization. Benchmarking against experimental data sets confirms that InsiliCoil reliably recovers validated inhibitors and interactomes, while offering orders-of-magnitude faster throughput than structure-based approaches. By providing a cohesive, user-friendly platform for controlling helix-mediated PPIs, InsiliCoil accelerates both therapeutic discovery and the rational engineering of programmable biological systems.

Pseudomonas putida is a metabolically versatile bacterium widely used in industrial biotechnology and synthetic biology. However, the lack of rapid, sensitive, and noninvasive tools for monitoring gene expression in P. putida limits the opportunities to study its gene regulation. We developed a plasmid-based dual-reporter system optimized for P. putida, which enables simultaneous monitoring of gene expression from promoter areas that contain divergently orientated promoters. Two fluorescent proteins (SYFP2 and Scarlet-I3) were selected for a reporter based on their compatibility with the intrinsic autofluorescence of P. putida and their detectability in LB medium. We engineered plasmid backbones containing the BBR1 and RK2 origins of replication and incorporated the toxin-antitoxin module hok-sok to ensure plasmid maintenance without antibiotic selection, making it possible to use this system to quantify gene expression in both planktonic and sessile (biofilm) states. Additionally, we created reporter systems with fused reporter genes with protein half-life decreasing tags, allowing dynamic assessment of transcriptional activity. Using confocal microscopy, we demonstrated spatially distinct expression patterns of biofilm-related genes (e.g., lapF) within mature biofilms. We also tested excludon-based transcriptional repression of a reporter gene in P. putida using this system, but observed limited efficiency under the tested conditions.

The health risks associated with exposure to arsenic (As)-contaminated water have spurred initiatives focused on As remediation through membrane filtration or chemical precipitation. Microbial approaches to sequestering As with biofilms present a promising alternative to these costly and chemical-intensive processes. In this study, we engineered the biofilm of Shewanella oneidensis to incorporate As-binding sites (ArsR) into the biofilm matrix through a matrix-associated protein, BpfA, for effective removal of As from water. Specifically, we constructed a chromosome-modified strain with constitutive expression of a genetically fused protein, BpfA-ArsR, along with two mutant strains harboring inducible plasmid constructs that link ArsR to truncated versions of BpfA for tunable expression. All three engineered strains produced biofilms comparable to that of the wild-type (WT). In comparison to the WT, the engineered strains demonstrated a significantly improved As sorption capability, achieving approximately 2.4-3.8 times the performance of the WT. Remarkably, the modified biofilm matrix continued to exhibit a strong preference for As sorption even in the presence of its chemical analog, phosphate. While bioremediation serves as an application example, the broader significance lies in establishing the biofilm matrix as a programmable and modular engineering space. The engineered biofilms developed here represent a generalizable platform for constructing matrix-integrated functional materials, enabling future applications in biosensing, resource recovery, extracellular catalysis, and adaptive living materials.

The development of enzymes for plastic recycling is reliant on the ability to identify and engineer novel biocatalysts. Nylon-6 is a plastic for which there is great importance for recycling and valorization due to its use in textiles, automotive components, and engineered materials. High-throughput screening is increasingly the preferred method for enzyme engineering, and while high-throughput assays exist for nylonase activity, they suffer from a variety of pitfalls including dependence on complex instrumentation, utilization of nonrepresentative model substrates, inconsistent product derivatization, and sensitivity to pH and protein concentrations. Limitations in high-throughput nylonase screening correspondingly limit the number of variants that can be tested and thus hamper efforts to improve the relatively small number of nylonases known. Here, we demonstrate the utilization of acid oligomerized nylon-6 (AON6) to assay the performance of nylon-6 hydrolyzing enzymes in a manner that is compatible with purified protein and cell lysate while also allowing for variation in pH, solid loading, and enzyme concentration.

Quorum sensing (QS) is a bacterial communication mechanism that regulates gene expression in a population density-dependent manner. Substantial progress has been made in the engineering of QS systems in Gram-negative bacteria, but development of engineered QS systems in Gram positive bacteria remains limited. In this study, the obligate anaerobic Gram-positive bacterium Clostridium sporogenes was engineered with the Staphylococcus aureus agr-QS system to enable density-dependent gene regulation. Using LC-MS/MS, we confirmed production of autoinducing peptides in the engineered C. sporogenes strain. A QS-regulated GFP reporter demonstrated activation of expression in response to both exogenous AIP addition and increasing cell density, confirming functional integration of the agr operon. A media refreshment experiment showed that replacing the culture supernatant delayed QS activation, highlighting the importance of signal accumulation. Moreover, we observed that a noncognate AIP from another agr specificity group acts as a competitive antagonist, inhibiting gene expression under the QS promoter. To our knowledge, this study presents the first successful engineering of the agr quorum sensing system in an obligate anaerobe, expanding the synthetic biology toolkit and offering new opportunities for bacterial therapies and metabolic engineering.

Medium-chain fatty acids (MCFAs) are valuable biochemicals, yet their inherent toxicity limits microbial productivity. Here, we developed a tunable hypermutation system in Escherichia coli by modulating translesion synthesis (TLS) pathways to accelerate adaptive laboratory evolution (ALE) for enhanced octanoic acid (C8) tolerance. Overexpression of dinB, encoding error-prone DNA polymerase IV, under T7 and BAD promoters yielded mutation rates 28.4-fold and 397-fold higher than the wild-type strain. ALE using these hypermutator strains yielded a robust variant capable of tolerating 50 mM C8. Whole-genome resequencing and reverse validation identified mutations related to membrane integrity and oxidative stress responses. Phenotypic analysis showed improved membrane integrity, reduced hydrophobicity, and lower reactive oxygen species (ROS) levels in the evolved strain under C8 stress. This study presents a hypermutation-assisted ALE strategy for improving microbial stress tolerance.

Protein language models (PLMs) have emerged as powerful tools for the generation of functional protein sequences. However, most efforts focus on enhancing protein stability for industrial applications, whereas there is untapped potential in designing proteins with reduced stability, which can be advantageous in specific contexts. For instance, in molecular biology workflows, enzymatic reagents such as DNase I are commonly used and subsequently inactivated to prevent residual activity from compromising downstream processes. Proteins that are easily inactivated offer a streamlined alternative to physical removal, simplifying protocols and reducing experimental complexity. In this study, we leverage RNase A, a paradigmatically stable enzyme, as a model for exploring the engineering of functional, yet less stable proteins. By sampling sequences from the PLM embedding space near the wild-type RNase A sequence, we engineered a variant, TempRNase, with reduced stability while retaining its RNA degradation activity. Using a fluorometric RNA degradation assay under varying conditions of heat and reducing treatment, we benchmark TempRNase against its wild-type counterparts and show that moderate heat and reducing treatment, with marginal effect on the wild-type, permanently inactivates TempRNase. Sequence and structural analyses of TempRNase reveal critical insights into the stability modulation and protein dynamics. Our findings establish the concept of engineering "worst of the best" enzymes that are functional but less stable. Furthermore, we highlight RNase A as a powerful model system for tuning protein stability with a quantitative assay.

Operant conditioning reflects the ability of organisms to adapt and learn. By implementing operant conditioning neural networks, complex brain-like behaviors can be simulated and learned at the molecular level. In this paper, an operant conditioning circuit with blocking effect and overshadowing effect is proposed by DNA strand displacement technique. The network can achieve blocking and overshadowing effects in the presence of multiple inputs. First, we construct DNA memristors using DNA strand displacement technology. The constructed DNA memristors are plastic and nonvolatile, which is very similar to biological synapses. The synaptic circuits constructed by DNA memristors have short-term plasticity, long-term plasticity, potentiation plasticity and depression plasticity. Second, the learning module, forgetting module, synaptic module, decision module and feedback module are constructed, and the operant conditioning circuit is realized. In addition, the operant conditioning circuit has relearning effect. Blocking and masking effects can also be achieved in the presence of multiple inputs. Finally, the reliability of the circuit is verified by the simulation of Visual DSD software. Our work demonstrates the potential of DNA molecules to build complex intelligent systems and provides an idea for using DNA molecules to achieve artificial intelligence.

Saccharomyces cerevisiae is a widely used chassis in metabolic engineering. Due to the Crabtree effect, it preferentially produces ethanol under high-glucose conditions, limiting the synthesis of other valuable metabolites. Conventional metabolic engineering approaches typically rely on irreversible genetic modifications, making it insufficient for dynamic metabolic control. In contrast, optogenetics offers a reversible and tunable method for regulating cellular metabolism with high temporal precision. In this study, we engineered the pyruvate decarboxylase isozyme 1 (Pdc1) by inserting the photosensory modules (AsLOV2 and cpLOV2 domains) into rationally selected positions within the enzyme. Through a growth phenotype-based screening system, we identified two blue light-responsive variants, OptoPdc1^D1 and OptoPdc1^D2, which enable light-dependent control of enzymatic activity. Leveraging these OptoPdc1 variants, we developed opto-S. cerevisiae strains, MLy-9 and MLy-10, which demonstrated high efficiency in modulating both cell growth and ethanol production. These strains allow reliable regulation of ethanol biosynthesis in response to blue light, achieving a dynamic control range of approximately 20- to 120-fold. The opto-S. cerevisiae strains exhibited dose-dependent production in response to blue light intensity and pulse patterns, confirming their potential for precise metabolic control. This work establishes a novel protein-level strategy for regulating metabolic pathways in S. cerevisiae and introduces an effective method for controlling ethanol metabolism via optogenetic regulation.

Engineered microorganisms in biotechnology present biosafety and environmental management challenges. As the synthetic biology market develops and deploys new technologies, these engineered organisms may escape into unintended environments. Improved predictive computational tools are necessary to assess the potential establishment risk and environmental location of these escaped engineered microorganisms, assisting their design and management. Here, we present EnCen, a risk assessment Python software package that predicts the environmental range of engineered microorganisms through annotated functional one-hot-encoded similarity between the engineered microorganism and resident microorganisms of a given environment. EnCen utilizes publicly available composite metagenomes as representatives of microbial environments that occur along an agriculture-water cycle and can be customized for any additional target environment. This tool was deployed against case studies reported in the literature and to reassess commercially available bacterial biopesticides, highlighting both the successful recapture of previously reported dynamics and the identification of select commercial products that pose a wider establishment risk in multiple environments. When further utilizing EnCen to investigate the receiving environments comprising the central database, key enzyme classes are mapped as characteristics to select environments, prioritizing certain modifications likely leading to a greater risk (or effectiveness) of establishment. The results demonstrate that EnCen meaningfully summarizes publicly available metagenomic data, prioritizes environments to monitor for adverse effects, and analyzes potential impacts on microbial community composition and functioning. Overall, this study demonstrates a computational approach to managing engineered microorganisms, aiding in the safe deployment and benefit of industrial synthetic biology.