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[学术文献 ] Multilevel Systematic Optimization To Achieve Efficient Integrated Expression of Escherichia coli 进入全文
ACS Synthetic Biology
Genomic integration of heterologous genes is the preferred approach in industrial fermentation-related strains due to the drawbacks associated with plasmid-mediated microbial fermentation, including additional growth burden, genetic instability, and antibiotic contamination. Synthetic biology and genome editing advancements have made gene integration convenient. Integrated expression is extensively used in the field of biomanufacturing and is anticipated to become the prevailing method for expressing recombinant proteins. Therefore, it is pivotal to strengthen the expression of exogenous genes at the genome level. Here, we systematically optimized the integrated expression system of Escherichia coli from 3 aspects. First, the integration site slmA with the highest expression activity was screened out of 18 sites in the ORI region of the E. coli BL21 (DE3) genome. Second, we characterized 16 endogenous promoters in E. coli and combined them with the T7 promoter. A constitutive promoter, Plpp-T7, exhibited significantly higher expression strength than the T7 promoter, achieving a 3.3-fold increase in expression levels. Finally, to further enhance the T7 expression system, we proceeded with overexpression of T7 RNA polymerase at the chassis cell level. The resulting constitutive efficient integrated expression system (CEIES_Ecoli) showed a 2-fold increase in GFP expression compared to the pET3b recombinant plasmid. Therefore, CEIES_Ecoli was applied to the integrated expression of nitrilase and hyaluronidase, achieving stable and efficient enzyme expression, with enzyme activities of 22.87 and 12,195 U·mL–1, respectively, comparable to plasmid levels. Overall, CEIES_Ecoli provides a stable and efficient method of gene expression without the need for antibiotics or inducers, making it a robust tool for synthetic biology, enzyme engineering, and related applications.
[学术文献 ] Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences 进入全文
ACS Synthetic Biology
N-terminal coding sequence (NCS) influences gene expression by impacting the translation initiation rate. The NCS optimization problem is to find an NCS that maximizes gene expression. The problem is important in genetic engineering. However, current methods for NCS optimization such as rational design and statistics-guided approaches are labor-intensive yield only relatively small improvements. This paper introduces a deep learning/synthetic biology codesigned few-shot training workflow for NCS optimization. Our method utilizes k-nearest encoding followed by word2vec to encode the NCS, then performs feature extraction using attention mechanisms, before constructing a time-series network for predicting gene expression intensity, and finally a direct search algorithm identifies the optimal NCS with limited training data. We took green fluorescent protein (GFP) expressed by Bacillus subtilis as a reporting protein of NCSs, and employed the fluorescence enhancement factor as the metric of NCS optimization. Within just six iterative experiments, our model generated an NCS (MLD62) that increased average GFP expression by 5.41-fold, outperforming the state-of-the-art NCS designs. Extending our findings beyond GFP, we showed that our engineered NCS (MLD62) can effectively boost the production of N-acetylneuraminic acid by enhancing the expression of the crucial rate-limiting GNA1 gene, demonstrating its practical utility. We have open-sourced our NCS expression database and experimental procedures for public use.
[学术文献 ] Characteristics of a Novel Zearalenone Lactone Hydrolase ZHRnZ and Its Thermostability Modification 进入全文
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Zearalenone (ZEN) is a toxic secondary metabolite produced by the Fusarium fungi, which widely contaminates grains, food, and feed, causing health hazards for humans and animals. Therefore, it is essential to find effective ZEN detoxification methods. Enzymatic degradation of ZEN is believed to be an eco-friendly detoxification strategy, specifically thermostable ZEN degradation enzymes are needed in the food and feed industry. In this study, a novel ZEN lactone hydrolase ZHRnZ from Rosellinia necatrix was discovered using bioinformatic and molecular docking technology. The recombinant ZHRnZ showed the best activity at pH 9.0 and 45 °C with more than 90% degradation for ZEN, α-zearalenol (α-ZOL), β-zearalenol (β-ZOL) and α-zearalanol (α-ZAL) after incubation for 15 min. We obtained 10 mutants with improved thermostability by single point mutation technology. Among them, mutants E122Q and E122R showed the best performance, which retained more than 30% of their initial activity at 50 °C for 2 min, and approximately 10% of their initial activity at 60 °C for 1 min. The enzymatic kinetic study showed that the catalytic efficiency of E122R was 1.3 times higher than that of the wild-type (WT). Comprehensive consideration suggests that mutant E122R is a promising hydrolase to detoxify ZEN in food and feed.
[学术文献 ] Mutagenesis techniques for evolutionary engineering of microbes – exploiting CRISPR-Cas, oligonucleotides, recombinases, and polymerases 进入全文
Trends in Microbiology
The natural process of evolutionary adaptation is often exploited as a powerful tool to obtain microbes with desirable traits. For industrial microbes, evolutionary engineering is often used to generate variants that show increased yields or resistance to stressful industrial environments, thus obtaining superior microbial cell factories. However, even in large populations, the natural supply of beneficial mutations is typically low, which implies that obtaining improved microbes is often time-consuming and inefficient. To overcome this limitation, different techniques have been developed that boost mutation rates. While some of these methods simply increase the overall mutation rate across a genome, others use recent developments in DNA synthesis, synthetic biology, and CRISPR-Cas techniques to control the type and location of mutations. This review summarizes the most important recent developments and methods in the field of evolutionary engineering in model microorganisms. It discusses how both in vitro and in vivo approaches can increase the genetic diversity of the host, with a special emphasis on in vivo techniques for the optimization of metabolic pathways for precision fermentation.
[学术文献 ] Approaching Optimal pH Enzyme Prediction with Large Language Models 进入全文
ACS Synthetic Biology
Enzymes are widely used in biotechnology due to their ability to catalyze chemical reactions: food making, laundry, pharmaceutics, textile, brewing─all these areas benefit from utilizing various enzymes. Proton concentration (pH) is one of the key factors that define the enzyme functioning and efficiency. Usually there is only a narrow range of pH values where the enzyme is active. This is a common problem in biotechnology to design an enzyme with optimal activity in a given pH range. A large part of this task can be completed in silico, by predicting the optimal pH of designed candidates. The success of such computational methods critically depends on the available data. In this study, we developed a language-model-based approach to predict the optimal pH range from the enzyme sequence. We used different splitting strategies based on sequence similarity, protein family annotation, and enzyme classification to validate the robustness of the proposed approach. The derived machine-learning models demonstrated high accuracy across proteins from different protein families and proteins with lower sequence similarities compared with the training set. The proposed method is fast enough for the high-throughput virtual exploration of protein space for the search for sequences with desired optimal pH levels.
[学术文献 ] Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites 进入全文
Nature Communications
Annotating active sites in enzymes is crucial for advancing multiple fields including drug discovery, disease research, enzyme engineering, and synthetic biology. Despite the development of numerous automated annotation algorithms, a significant trade-off between speed and accuracy limits their large-scale practical applications. We introduce EasIFA, an enzyme active site annotation algorithm that fuses latent enzyme representations from the Protein Language Model and 3D structural encoder, and then aligns protein-level information with the knowledge of enzymatic reactions using a multi-modal cross-attention framework. EasIFA outperforms BLASTp with a 10-fold speed increase and improved recall, precision, f1 score, and MCC by 7.57%, 13.08%, 9.68%, and 0.1012, respectively. It also surpasses empirical-rule-based algorithm and other state-of-the-art deep learning annotation method based on PSSM features, achieving a speed increase ranging from 650 to 1400 times while enhancing annotation quality. This makes EasIFA a suitable replacement for conventional tools in both industrial and academic settings. EasIFA can also effectively transfer knowledge gained from coarsely annotated enzyme databases to smaller, high-precision datasets, highlighting its ability to model sparse and high-quality databases. Additionally, EasIFA shows potential as a catalytic site monitoring tool for designing enzymes with desired functions beyond their natural distribution.
