Oct 2026· Science Advances· Vol 12· 0 citations· 50 references
Medicine
Abstract
Ribosomal RNA modification enzymes increase cellular fitness, but it’s little known whether this benefit arises from rRNA modification or a chaperone function of the modification enzyme. Here, we show that E. coli methyltransferase RsmB chaperones the initial recruitment of ribosomal proteins to the 16S 3′ domain rRNA, a key step in assembly of the 30S subunit. Real-time single-molecule assembly experiments show that the RsmB product, 16S m5C967, has little effect on the rRNA affinity for ribosomal proteins. Instead, RsmB facilitates stable recruitment of primary protein uS7 during transcription while blocking the addition of secondary protein uS19. Completion of the methyltransferase cycle promotes RsmB release, allowing assembly to continue. Dimethyl sulfate footprinting of trapped RsmB-rRNA complexes revealed that RsmB restructures a critical rRNA helix junction. Our results illustrate how an RNA modification enzyme favors the proper sequence of protein addition by temporarily reshaping the energy landscape of co-transcriptional rRNA folding.
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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D. Graziotin, Xiaofeng Wang, P. Abrahamsson· SSE@SIGSOFT FSE· 56 citations· ⚡4
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
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.
