Chemical probing experiments, coupled with empirically determined free energy change relationships, can enable accurate modeling of the secondary structures of diverse and complex RNAs. A current frontier lies in modeling large and structurally heterogeneous transcripts, including complex eukaryotic RNAs. To validate and improve on experimentally driven approaches for modeling large transcripts, we obtained high-quality SHAPE data for the protein-free human 18S and 28S ribosomal RNAs (rRNAs). To our surprise, SHAPE-directed structure models for the human rRNAs poorly matched accepted structures. Analysis of predicted rRNA structures based on low-SHAPE and low-entropy (lowSS) metrics revealed that, whereas ∼75% of rRNA sequences form well-determined lowSS secondary structure, only ∼40% of the human rRNAs do. Critically, regions of the human rRNAs that specifically fold into well-determined lowSS structures were modeled to high accuracy using SHAPE data. This work reveals that eukaryotic rRNAs are more unfolded than are those of prokaryotic rRNAs and indeed are largely unfolded overall, likely reflecting increased protein dependence for eukaryotic ribosome structure. In addition, those regions and substructures that are well-determined can be identified and successfully modeled by SHAPE-directed folding.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6687064 | PMC |
http://dx.doi.org/10.1021/acs.biochem.9b00076 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!