Computational modeling of RNA 3D structure based on experimental data

Abstract

RNA molecules are master regulators of cells. They are involved in a variety of molecular processes: they transmit genetic information, sense cellular signals and communicate responses, and even catalyze chemical reactions. As in the case of proteins, RNA function is dictated by its structure and by its ability to adopt different conformations, which in turn is encoded in the sequence. Experimental determination of high-resolution RNA structures is both laborious and difficult, and therefore the majority of known RNAs remain structurally uncharacterized. To address this problem, predictive computational methods were developed based on the accumulated knowledge of RNA structures determined so far, the physical basis of the RNA folding, and taking into account evolutionary considerations, such as conservation of functionally important motifs. However, all theoretical methods suffer from various limitations, and they are generally unable to accurately predict structures for RNA sequences longer than 100-nt residues unless aided by additional experimental data. In this article, we review experimental methods that can generate data usable by computational methods, as well as computational approaches for RNA structure prediction that can utilize data from experimental analyses. We outline methods and data types that can be potentially useful for RNA 3D structure modeling but are not commonly used by the existing software, suggesting directions for future development.

Zinc-finger protein CNBP alters the 3-D structure of lncRNA Braveheart in solution

“…In fact, a widely held perspective in the RNA community is that lncRNAs tend to be too flexible and unstable for nuclear magnetic resonance (NMR) and crystallization studies. Interestingly, many biologically important RNAs have dynamically changing conformations, making structure determination challenging 2,15 . However, it has been shown, using chemical probing, that several lncRNAs and portions of lncRNAs adopt well-organized, modular secondary structures [3][4][5]7,9 .…”

Site-specific covalent labeling of large RNAs with nanoparticles empowered by expanded genetic alphabet transcription

“…As XSI can measure much broader distance distributions (ranging from 50 up to 400 Å) than other molecular rulers, such as pulsed electron paramagnetic resonance spectroscopy (EPR) and Förster resonance energy transfer (FRET) that can provide distance information from 20-80 Å (18), XSI can play important roles in structural study of large RNAs and RNA complexes, such as the long non-coding RNAs. For example, the XSI-derived distance distributions can be used to aid in computational 3D modeling of large RNAs (16). Furthermore, we envision that gold nanoparticle-RNA conjugates can be utilized in studying the structure and dynamics of RNAs by cryo-EM microscopy, an EM-based technique called individualparticle electron tomography, or high-resolution AFM (9,48).…”

Shedding light on the dark matter of the biomolecular structural universe: Progress in RNA 3D structure prediction

“…• Combining structure prediction methods or simulations with experimental data such as Selective 2-hydroxyl acylation analyzed by primer extension (SHAPE) [61], Fluorescence Resonance Energy Transfer (FRET) [62] or small angle X-Ray scattering (SAXS) [63,64,65] will allow to probe RNA structures where a single method fails [66].…”