Circular RNAs (circRNAs) exhibit superior intrinsic stability and lower immunogenicity compared with linear RNAs, rendering them promising modalities for biomedical and biotechnological applications. Here, we establish the HARBOR (Human-Archaeal RNA Backbone for Overexpression of circular RNA) system, an engineered endogenous circRNA expression platform integrating a human precursor transfer RNA (pre-tRNA) scaffold with an archaeal Bulge-Helix-Bulge (BHB) motif. HARBOR repurposes the endogenous tRNA splicing machinery to generate chimeric pre-tRNA intermediates, which are rapidly degraded following processing, while producing circRNAs ranging from hundreds of nucleotides to 8 knt in cells and in vivo. Notably, it enables robust production of large-sized circRNAs with yields nearly 80-fold than the intronic complementary sequence-mediated back-splicing (3D5) system and can drive protein expression from translatable circRNAs at an efficiency close to 20-fold than the Tornado system. Such prominent properties support diverse applications of HARBOR, including producing therapeutic aptamers targeting PKR-associated neuroinflammation and pathological TDP-43 condensation, as well as protein-coding circRNAs encoding adenine base editors for genome editing and MYOD1 for fibroblast-to-myocyte trans-differentiation. Collectively, HARBOR represents a versatile, scalable, and user-friendly platform that unifies circRNA-based aptamer and protein expression applications, advancing circRNA-enabled engineering and biomedical applications.
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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Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
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.