Episomal plasmids are widely used in yeast metabolic engineering for their flexibility and high transformation efficiency. However, their application in Yarrowia lipolytica is often limited by low copy number and insufficient genetic stability. In this study, a robust episomal plasmid that balanced genetic stability and protein expression capacity was developed in Y. lipolytica. The autonomously replicating sequence (ARS) elements were selected, and ARS1 was identified as a favorable element that balanced genetic stability and gene expression capacity. To improve plasmid copy number, different ubiquitin degrons were used to tune selection marker abundance, and Ubi-R-Δk-mediated marker resulted in an approximately fourfold increase in plasmid copy number (from ∼1 to ∼5 copies per cell). The copy number of the engineered plasmid was one of the highest‑copy episomal systems in Y. lipolytica. To further evaluate the engineered plasmid, we applied it to β‑carotene biosynthesis. The results showed that the β‑carotene titer was 3.43‑fold higher than that of the non‑optimized plasmid. Collectively, this work expands the episomal plasmid toolbox for Y. lipolytica and establishes a generalizable framework for robust protein expression plasmid engineering in non-conventional yeasts.
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 seque...
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.