Classification of RNA backbone conformation into rotamers using 13C′ chemical shifts: How far we can go?

ABSTRACTThe conformational space of the ribose–phosphate backbone is very complex as is defined in terms of six torsional angles. To help delimit the RNA backbone conformational preferences 46 rotamers have been defined in terms of the 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 use of experimental 13C′ chemical shifts can be used to identify rotamers and discuss some problem with the theoretical computations of 13C′ chemical shifts.

RNA backbone rotamers – finding your way in seven dimensions

Despite the importance of local structural detail for a mechanistic understanding of RNA catalysis and binding functions, RNA backbone conformation has been recalcitrant to analysis. There are too many variable dihedral angles per residue, and their raw empirical distributions are poorly clustered. This study applies quality-filtering techniques (using resolution, crystallographic B factor and all-atom-steric clashes) to the backbone dihedral angle distributions from a selected 8636 residue RNA database. With noise levels significantly decreased, clear signal appears for the underlying angle preferences. We analyse the multidimensional backbone dihedral distributions within sugar-to-sugar ‘suites’ rather than chemical residues due to the greater base interaction and steric interdependence within the suite. The final result is a small library of RNA backbone rotamers, each represented by a data cluster in seven-dimensional dihedral space, which should provide valid conformations for nearly all RNA backbones encountered in experimental structures. We are in the process of improving that library, and developing tools and applications for it in structure determination and analysis.