Oct 2026· International Journal of Molecular Sciences· 51 references
RNA and protein synthesis mechanisms
Abstract
Identifying the smallest active unit capable of ligating RNA in the absence of proteins is important to elucidate ribozyme evolution. Here, we aimed to construct an evolutionary model of the L1 ligase ribozyme. The miniaturized L1 ligase ribozyme (L1X6c2) consists of three stems: Stems A, B, and C. Mutants with shortened stems B and C were used to examine whether ligation occurred. In the mutant where both stems were simultaneously shortened, no ligation products were observed. We also constructed mutants with nucleotide deletions, measured their ligation-product signals after 24 h, and identified the structures crucial for ligation. A split mutant, formed by cleaving L1X6c2 at stem B, showed a ligation-product signal after 24 h close to that of L1X6c2, whereas another split mutant, formed by cleaving L1X6c2 at stem C, exhibited decreased activity. Although non-Watson–Crick base pairs near the ligation site did not affect ligation, the presence of G1 was important for ligation. Mutational analysis suggested that strong interactions between G1 and A51 in the G1–A51–U38 base triple are responsible for the base specificity of A51. Moreover, consecutive base pairs on each stem subtly contribute to the stability of the ribozyme folded into a compact structure to create a catalytic pocket.
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.