Knowledge of RNA three-dimensional topological structures provides important insight into the relationship between RNA structural components and function. It is often likely that near-complete sets of biochemical and biophysical data containing structural restraints are not available, but one still wants to obtain knowledge about approximate topological folding of RNA. In this regard, general methods for determining such topological structures with minimum readily available restraints are lacking. Naked RNAs are difficult to crystallize and NMR spectroscopy is generally limited to small RNA fragments. By nature, sequence determines structure and all interactions that drive folding are self-contained within sequence. Nevertheless, there is little apparent correlation between primary sequences and three-dimensional folding unless supplemented with experimental or phylogenetic data. Thus, there is an acute need for a robust high-throughput method that can rapidly determine topological structures of RNAs guided by some experimental data. We present here a novel method (RS3D) that can assimilate the RNA secondary structure information, small-angle X-ray scattering data, and any readily available tertiary contact information to determine the topological fold of RNA. Conformations are firstly sampled at glob level where each glob represents a nucleotide. Best-ranked glob models can be further refined against solvent accessibility data, if available, and then converted to explicit all-atom coordinates for refinement against SAXS data using the Xplor-NIH program. RS3D is widely applicable to a variety of RNA folding architectures currently present in the structure database. Furthermore, we demonstrate applicability and feasibility of the program to derive low-resolution topological structures of relatively large multi-domain RNAs.
Topological Structure Determination of RNA Using Small-Angle X-Ray Scattering.
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作者:Bhandari Yuba R, Fan Lixin, Fang Xianyang, Zaki George F, Stahlberg Eric A, Jiang Wei, Schwieters Charles D, Stagno Jason R, Wang Yun-Xing
| 期刊: | Journal of Molecular Biology | 影响因子: | 4.500 |
| 时间: | 2017 | 起止号: | 2017 Nov 24; 429(23):3635-3649 |
| doi: | 10.1016/j.jmb.2017.09.006 | ||
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