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Visualization of conformational variability in the domains of long single-stranded RNA molecules.


ABSTRACT: We demonstrate an application of atomic force microscopy (AFM) for the structural analysis of long single-stranded RNA (>1 kb), focusing on 28S ribosomal RNA (rRNA). Generally, optimization of the conditions required to obtain three-dimensional (3D) structures of long RNA molecules is a challenging or nearly impossible process. In this study, we overcome these limitations by developing a method using AFM imaging combined with automated, MATLAB-based image analysis algorithms for extracting information about the domain organization of single RNA molecules. We examined the 5 kb human 28S rRNA since it is the largest RNA molecule for which a 3D structure is available. As a proof of concept, we determined a domain structure that is in accordance with previously described secondary structural models. Importantly, we identified four additional small (200-300 nt), previously unreported domains present in these molecules. Moreover, the single-molecule nature of our method enabled us to report on the relative conformational variability of each domain structure identified, and inter-domain associations within subsets of molecules leading to molecular compaction, which may shed light on the process of how these molecules fold into the final tertiary structure.

SUBMITTER: Gilmore JL 

PROVIDER: S-EPMC5737216 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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Visualization of conformational variability in the domains of long single-stranded RNA molecules.

Gilmore Jamie L JL   Yoshida Aiko A   Hejna James A JA   Takeyasu Kunio K  

Nucleic acids research 20170801 14


We demonstrate an application of atomic force microscopy (AFM) for the structural analysis of long single-stranded RNA (>1 kb), focusing on 28S ribosomal RNA (rRNA). Generally, optimization of the conditions required to obtain three-dimensional (3D) structures of long RNA molecules is a challenging or nearly impossible process. In this study, we overcome these limitations by developing a method using AFM imaging combined with automated, MATLAB-based image analysis algorithms for extracting infor  ...[more]

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