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[学术文献 ] Protein Engineering of Substrate Specificity toward Nitrilases: Strategies and Challenges 进入全文
Journal of Agricultural and Food Chemistry
Nitrilase is extensively applied across diverse sectors owing to its unique catalytic properties. Nevertheless, in industrial production, nitrilases often face issues such as low catalytic efficiency, limited substrate range, suboptimal selectivity, and side reaction products, which have garnered heightened attention. With the widespread recognition that the structure of enzymes has a direct impact on their catalytic properties, an increasing number of researchers are beginning to optimize the functional characteristics of nitrilases by modifying their structures, in order to meet specific industrial or biotechnology application needs. Particularly in the artificial intelligence era, the innovative application of computer-aided design in enzyme engineering offers remarkable opportunities to tailor nitrilases for the widespread production of high-value products. In this discussion, we will briefly examine the structural mechanism of nitrilase. An overview of the protein engineering strategies of substrate preference, regioselectivity and stereoselectivity are explored combined with some representative examples recently in terms of the substrate specificity of enzyme. The future research trends in this field are also prospected.
[学术文献 ] Metabolic Engineering of Escherichia coli for De Novo Biosynthesis of the Platform Chemical Pelletierin 进入全文
ACS Sustainable Chemistry & Engineering
Pelletierine is a versatile plant alkaloid having a C5N–C3 structure from which numerous chemicals can be derived. One notable derivative is huperzine A (HupA) which may alleviate the symptoms of Alzheimer’s disease. Currently, industrial production of pelletierine relies primarily on chemical synthesis and plant extraction. However, chemical synthesis leads to analogues that complicate product separation, and plant extraction is constrained by limited resources. Herein, we report that pelletierine can be produced by recombinant Escherichia coli in which the engineered pelletierine biosynthesis pathway comprises four modules involving seven key genes native to E. coli, three genes from other bacteria, and three genes from plants. To overproduce pelletierine, the intrinsic l-lysine biosynthesis pathway in E. coli was simplified, and a clustered regularly interspaced short palindromic repeats (CRISPR) interference (CRISPRi) system was engineered to minimize the byproducts. Moreover, the transporter MatC was overexpressed to enhance the intracellular concentration of 3-oxoglutaryl ketide, which is another precursor of pelletierine. Based on the aforementioned manipulations, the resulting recombinant E. coli harboring the pelletierine biosynthesis pathway and CRISPRi system produced 3.40 and 8.23 mg/L pelletierine in a shake-flask and a 5 L bioreactor, respectively. This is the first report of microbial production of pelletierine, which represents a sustainable route to produce the precursor of HupA and beyond.
[学术文献 ] Efficient Expression and Activity Optimization of Manganese Peroxidase for the Simultaneous Degradation of Aflatoxins AFB1, AFB2, AFG1, and AFG2 进入全文
Journal of Agricultural and Food Chemistry
Aflatoxins (AFs), notorious mycotoxins that pose significant risks to human and animal health, make biodegradation extremely crucial as they offer a promising approach to managing and reducing their harmful impacts. In this study, we identified a manganese peroxidase from Punctularia strigosozonata (PsMnp) through protein similarity analysis, which has the capability to degrade four AFs (AFB1, AFB2, AFG1, and AFG2) simultaneously. The gene encoding this enzyme was subject to codon optimization, followed by cold shock induction expression using the pColdII vector, leading to the soluble expression of manganese peroxidase (Mnp) in Escherichia coli. This study tackled the problem of inclusion body formation that often occurs during Mnp expression in E. coli. After optimizing the degradation conditions, the degradation rates for AFB1, AFB2, AFG1, and AFG2 were 87.9, 72.8, 77.3, and 85.6%, respectively. Molecular docking and molecular dynamics simulations indicated that PsMnp facilitated the degradation of AFs through hydrophobic and polar interactions among various amino acid residues. This research offers novel insights into the rapid discovery of enzymes capable of degrading AFs and establishes a theoretical foundation for the efficient expression of mycotoxin detoxification enzymes.
[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.
