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[学术文献 ] Metabolic Engineering of Corynebacterium glutamicum for High-Level Production of 1,5-Pentanediol, a C5 Diol Platform Chemical 进入全文
Advanced Science
The biobased production of chemicals is essential for advancing a sustainable chemical industry. 1,5-Pentanediol (1,5-PDO), a five-carbon diol with considerable industrial relevance, has shown limited microbial production efficiency until now. This study presents the development and optimization of a microbial system to produce 1,5-PDO from glucose in Corynebacterium glutamicum via the l-lysine-derived pathway. Engineering began with creating a strain capable of producing 5-hydroxyvaleric acid (5-HV), a key precursor to 1,5-PDO, by incorporating enzymes from Pseudomonas putida (DavB, DavA, and DavT) and Escherichia coli (YahK). Two conversion pathways for further converting 5-HV to 1,5-PDO are evaluated, with the CoA-independent pathway—utilizing Mycobacterium marinum carboxylic acid reductase (CAR) and E. coli YqhD—proving greater efficiency. Further optimization continues with chromosomal integration of the 5-HV module, increasing 1,5-PDO production to 5.48 g L−1. An additional screening of 13 CARs identifies Mycobacterium avium K-10 (MAP1040) as the most effective, and its engineered M296E mutant further increases production to 23.5 g L−1. A deep-learning analysis reveals that Gluconobacter oxydans GOX1801 resolves the limitations of NADPH, allowing the final strain to produce 43.4 g L−1 1,5-PDO without 5-HV accumulation in fed-batch fermentation. This study demonstrates systematic approaches to optimizing microbial biosynthesis, positioning C. glutamicum as a promising platform for sustainable 1,5-PDO production.
[学术文献 ] Chemoenzymatic Synthesis Planning Guided by Reaction Type Score 进入全文
Journal of Chemical Information and Modeling
Thanks to the growing interest in computer-aided synthesis planning (CASP), a wide variety of retrosynthesis and retrobiosynthesis tools have been developed in the past decades. However, synthesis planning tools for multistep chemoenzymatic reactions are still rare despite the widespread use of enzymatic reactions in chemical synthesis. Herein, we report a reaction type score (RTscore)-guided chemoenzymatic synthesis planning (RTS-CESP) strategy. Briefly, the RTscore is trained using a text-based convolutional neural network (TextCNN) to distinguish synthesis reactions from decomposition reactions and evaluate synthesis efficiency. Once multiple chemical synthesis routes are generated by a retrosynthesis tool for a target molecule, RTscore is used to rank them and find the step(s) that can be replaced by enzymatic reactions to improve synthesis efficiency. As proof of concept, RTS-CESP was applied to 10 molecules with known chemoenzymatic synthesis routes in the literature and was able to predict all of them with six being the top-ranked routes. Moreover, RTS-CESP was employed for 1000 molecules in the boutique database and was able to predict the chemoenzymatic synthesis routes for 554 molecules, outperforming ASKCOS, a state-of-the-art chemoenzymatic synthesis planning tool. Finally, RTS-CESP was used to design a new chemoenzymatic synthesis route for the FDA-approved drug Alclofenac, which was shorter than the literature-reported route and has been experimentally validated.
[学术文献 ] Targeting protein–ligand neosurfaces with a generalizable deep learning tool 进入全文
Nature
Molecular recognition events between proteins drive biological processes in living systems1. However, higher levels of mechanistic regulation have emerged, in which protein-protein interactions are conditioned to small molecules2, 3, 4-5. Despite recent advances, computational tools for the design of new chemically induced protein interactions have remained a challenging task for the field6,7. Here we present a computational strategy for the design of proteins that target neosurfaces, that is, surfaces arising from protein-ligand complexes. To develop this strategy, we leveraged a geometric deep learning approach based on learned molecular surface representations8,9 and experimentally validated binders against three drug-bound protein complexes: Bcl2-venetoclax, DB3-progesterone and PDF1-actinonin. All binders demonstrated high affinities and accurate specificities, as assessed by mutational and structural characterization. Remarkably, surface fingerprints previously trained only on proteins could be applied to neosurfaces induced by interactions with small molecules, providing a powerful demonstration of generalizability that is uncommon in other deep learning approaches. We anticipate that such designed chemically induced protein interactions will have the potential to expand the sensing repertoire and the assembly of new synthetic pathways in engineered cells for innovative drug-controlled cell-based therapies10.
[学术文献 ] Recent advances in bioinspired multienzyme engineering for food applications 进入全文
Trends in Food Science & Technology
Bioinspired multienzyme engineering provides strong technical support for enhancing food quality, safety, and nutrition. Summarizing and examining its applications and progress in the food industry are essential for exploring the future of food development. Inspired by efficient catalysis of nature at complex reactions, bioinspired multienzyme engineering leveraging the synergistic action of multiple enzymes to achieve targeted outcomes in food processing and analysis. This paper reviews the latest advancements in bioinspired multienzyme engineering within the food sector, highlighting its core operational principles and applications. It provides a systematic analysis of diverse bioinspired multienzyme engineering approaches, exploring assembly strategies, interaction types, and techniques to enhance the enzymes' functionality and catalytic efficiency. The discussion also covers the technology's applications in food biosynthesis, quality monitoring, contaminant degradation, and packaging enhancement. Bioinspired multienzyme engineering is revolutionizing the food industry with its efficiency and eco-friendliness. As protein engineering, synthetic biology, artificial intelligence and machine learning advance, bioinspired multienzyme engineering promises to address stability, activity, scalability, and regulatory challenges, broadening its applications from bioactive compound production and biosensor design to contaminant degradation and smart packaging.
[学术文献 ] S-PLM: Structure-Aware Protein Language Model via Contrastive Learning Between Sequence and Structure 进入全文
Advanced Science
Proteins play an essential role in various biological and engineering processes. Large protein language models (PLMs) present excellent potential to reshape protein research by accelerating the determination of protein functions and the design of proteins with the desired functions. The prediction and design capacity of PLMs relies on the representation gained from the protein sequences. However, the lack of crucial 3D structure information in most PLMs restricts the prediction capacity of PLMs in various applications, especially those heavily dependent on 3D structures. To address this issue, S-PLM is introduced as a 3D structure-aware PLM that utilizes multi-view contrastive learning to align the sequence and 3D structure of a protein in a coordinated latent space. S-PLM applies Swin-Transformer on AlphaFold-predicted protein structures to embed the structural information and fuses it into sequence-based embedding from ESM2. Additionally, a library of lightweight tuning tools is provided to adapt S-PLM for diverse downstream protein prediction tasks. The results demonstrate S-PLM's superior performance over sequence-only PLMs on all protein clustering and classification tasks, achieving competitiveness comparable to state-of-the-art methods requiring both sequence and structure inputs. S-PLM and its lightweight tuning tools are available at https://github.com/duolinwang/S-PLM/.
[学术文献 ] Mechanistic Insight into the Reproductive Toxicity of Trifloxystrobin in Male Sprague-Dawley Rats 进入全文
Environmental Science & Technology
Previous studies have demonstrated the reproductive toxicity of trifluorostrobin (TRI) in male organisms. However, the underlying mechanisms of TRI responsible for testicular damage and hormonal disruption remain elusive. This study elucidated the male reproductive toxicity of TRI at the molecular level under environmentally relevant concentrations and its associations with gut microbiota dysbiosis. The rats were administered TRI (1.5, 15, and 75 mg/kg of body weight/day) continuously via gavage for 90 days. Exposure to 15 mg/kg (below the no-observed adverse effect level (NOAEL) of 30 mg/kg) and 75 mg/kg TRI damaged testicular tissue, reduced sperm count, and lowered serum hormone and total cholesterol levels. Transcriptomics analysis combined with molecular docking simulations and cell proliferation assays showed that exposure to TRI led to testicular damage by inhibiting the expression of cholesterol receptor genes, which, in turn, disrupted steroid hormone biosynthesis. Furthermore, exposure to TRI resulted in a marked decline in the relative abundance of the probiotic bacteria. Consistently, significant reductions in the relative abundance of short-chain fatty acids (SCFAs), retinoic acids, and steroid hormones in the gut were observed. Additionally, a significant correlation was observed between the relative abundance of Parabacteroides and serum testosterone levels, a vital biomarker for reproductive toxicity monitoring. These findings shed light on the mode of action of TRI-induced male reproductive toxicity and highlight the link between testicular injury and gut microbiota.
