Abstract
The conformational space of the ribose-phosphate backbone is very complex as it is defined in terms of six torsional angles. To help delimit the RNA backbone conformational preferences, 46 rotamers have been defined in terms of these torsional angles. In the present work, we use the ribose experimental and theoretical 13C′ chemical shifts data and machine learning methods to classify RNA backbone conformations into rotamers and families of rotamers. We show to what extent the experimental 13C′ chemical shifts can be used to identify rotamers and discuss some problem with the theoretical computations of 13C′ chemical shifts.
Highlights
Nucleic acids are central macromolecules for the storing, flow and regulation of genetic and epigenetic information in cellular organisms
The suite is defined from sugar-to-sugar, and it is contained within the dinucleotide subunit. 13C chemical shifts have been successfully used by our and other groups for protein and glycan structural determination, validation and refinement [4, 5, 6, 7, 8]
Classifiers gave maximal scores above 0.65. This result is in agreement with the fact that backbone 13C chemical shifts are highly sensitive to ribose puckering states [19], since the δ torsional angle keeps a direct relation with the ribose puckering [20]
Summary
Nucleic acids are central macromolecules for the storing, flow and regulation of genetic and epigenetic information in cellular organisms. The classification of RNA backbone conformations into rotamers is a very useful way to delimit the conformational space of RNA structures. This classification was proposed by Richardson et al 2008 [3], and has been achieved after the attempts of different research groups to find a consensus RNA backbone structural classification. There are 55 backbone rotamers, from which 46 are rotamers with well defined torsional angles distributions, and the remaining 9 rotamers were proposed as wannabe rotamers. We study how to use 13C chemical shifts to classify RNA backbone into rotamers
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