Abstract
RNA is a unique bio-macromolecule that can both record genetic information and perform biological functions in a variety of molecular processes, including transcription, splicing, translation, and even regulating protein function. RNAs adopt specific three-dimensional conformations to enable their functions. Experimental determination of high-resolution RNA structures using x-ray crystallography is both laborious and demands expertise, thus, hindering our comprehension of RNA structural biology. The computational modeling of RNA structure was a milestone in the birth of bioinformatics. Although computational modeling has been greatly improved over the last decade showing many successful cases, the accuracy of such computational modeling is not only length-dependent but also varies according to the complexity of the structure. To increase credibility, various experimental data were integrated into computational modeling. In this review, we summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data. We also demonstrate how computational modeling can help the experimental determination of RNA structure. We highlight the recent advances in computational modeling which can offer reliable structure models using high-throughput experimental data.
Highlights
Reviewed by: Yiliang Ding, John Innes Centre, United Kingdom Peter F
We summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data
We demonstrate how computational modeling can help the experimental determination of RNA structure
Summary
Experiment-Based RNA Structure Modeling example in disease mechanism from recent research (Cammas and Millevoi, 2017) shows RNA G-quadruplexes, folded into a four-stranded conformation, revealing new mechanisms in disease According to these examples, determining and modeling the RNA structure can substantially contribute to our understanding of biological processes, disease mechanisms, and RNA therapies. Instead of the atomistic models, biochemical approaches promote the experimental flexibility and throughput by sacrificing the resolution They determine RNA structure using the computational approaches based on the restraints obtained from experiments. Together with the advances in experiments, computational modeling or prediction methods of RNA secondary and tertiary structure (Magnus et al, 2014; Ponce-Salvatierra et al, 2019) are being developed and improved to help and complement experimental efforts.
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