Methyl salicylate (MeSA) is a volatile methylated aromatic ester widely used in agriculture, food, and pharmaceuticals. Microbial biosynthesis offers a sustainable alternative to plant extraction and petrochemical synthesis; however, efficient MeSA production remains limited by the low catalytic efficiency of carboxyl methyltransferases, insufficient intracellular supply of S-adenosylmethionine (SAM), and severe product volatilization during fermentation. In this study, we systematically screened salicylate carboxymethyltransferase and identified CbSAMT as a relatively efficient catalyst for salicylic acid (SA). To further improve catalytic performance, we engineered CbSAMT through fusion-protein design. Remarkably, the catalytic efficiency of 7 × His-CbSAMT was 12-fold higher than that of the wild-type. Molecular dynamics simulations and computational analyses revealed that the polyhistidine tag reshaped the catalytic microenvironment by remodeling the hydrogen-bonding network and reducing the distance between SA and key substrate-positioning residues, thereby enhancing substrate binding and catalytic turnover. To further enhance MeSA biosynthesis, we reinforced intracellular SAM regeneration and established an in situ two-phase fermentation system using n-dodecane as the extractant to alleviate product volatilization. Finally, fed-batch fermentation achieved a MeSA titer of 5.12 g/L, representing the highest production level reported to date. Collectively, this study highlights the potential of polyhistidine tags in enzyme engineering and provides an efficient platform for the biosynthesis of volatile methylated natural products.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.