Clustering to identify RNA conformations constrained by secondary structure

Abstract

RNA often folds hierarchically, so that its sequence defines its secondary structure (helical base-paired regions connected by single-stranded junctions), which subsequently defines its tertiary fold. To preserve base-pairing and chain connectivity, the threedimensional conformations that RNA can explore are strongly confined compared to when secondary structure constraints are not enforced. Using three examples, we studied how secondary structure confines and dictates an RNA's preferred conformations. We made use of Macromolecular Conformations by SYMbolic programming (MC-Sym) fragment assembly to generate RNA conformations constrained by secondary structure. Then, to understand the correlations between different helix placements and orientations, we robustly clustered all RNA conformations by employing unique methods to remove outliers and estimate the best number of conformational clusters. We observed that the preferred conformation (as judged by largest cluster size) for each type of RNA junction molecule tested is consistent with its biological function. Further, the improved quality of models in our pruned datasets facilitates subsequent discrimination using scoring functions based either on statistical analysis (knowledge based) or experimental data.RNA structure prediction | RNA junctions | clustering | riboswitches | RNA folding R NA is an important molecule in gene regulation both in the presence and absence of proteins (1). The paradigm that "structure affects function," so commonly used on proteins, can also be applied to RNAs, which adopt intricate three-dimensional structures that depend strongly on their in vivo environment. The plethora of RNA types, ranging from small-interfering RNA to ribozymes and riboswitches, points to a need for a strong understanding of how RNA folds in order to better elucidate how RNA functions (2, 3).One test of our understanding of RNA folding is the extent to which we can predict RNA tertiary structure from sequence, a challenging problem that recent research has tackled in a variety of ways (4). Some of the approaches to predicting RNA tertiary structure include assembling RNA fragments from native-like RNA fragment libraries (5-7) or sampling RNA chains using discrete molecular dynamics (8). Such prediction methods are confined to small molecules or require coarse-graining of bases due to the difficulty associated with sufficiently complete all-atom conformational sampling of larger, more complex RNA systems.The sampling process can be simplified because most RNAs fold in a hierarchical fashion (9-11), in which the RNA sequence determines its secondary structure, or the base-pairing within the RNA (Fig. 1A). These base-pairing interactions are preserved during folding to a specific three-dimensional (3D) structure. Therefore, each RNA can generally be regarded as comprising of helical regions (simplified as uniform cylinders) connected by single-stranded junction regions in 3D (Fig. 1B). Hence every RNA molecule is made up of helix-junction-helix motifs.It is comm...

De novo automated design of small RNA circuits for engineering synthetic riboregulation in living cells

“…For the 5'-UTR of the mRNA GFP , we chose a conformation (C1) shown to produce the intended repression (6). Then, our algorithm explored all possible sequences compatible with such specifications (31,37), searching a stable hybridization of the two RNAs where the RBS remained unpaired. …”

Hierarchy of RNA Functional Dynamics

“…For example, theoretical work of a model two-helix junction has demonstrated that inter-helical dynamics strongly discriminate against the formation of tertiary contacts between some helical faces but allow others (Figure 8C) (129). Subsequent studies have since suggested that this property of inter-helical dynamics is broadly used by RNAs to encode their native folds (49, 128, 177–179). Importantly, such a strategy may be how RNAs are able to overcome the limited information content of tertiary interactions, some of which like A-minor motifs appear to have little sequence specificity (180, 181).…”

Predicting RNA 3D structure using a coarse-grain helix-centered model

“…Just as the position of 1 nt relative to the previous can be expressed as a function of the torsion angles and sugar pucker, the position of one coarse-grain helix relative to the previous can be expressed using a set of six different parameters (subsequently referred to as interhelical parameters) (Bailor et al 2011;Sim and The labels in black are names given to distinguish the different secondary structure elements in the graph. The elements f1 and t1 are the 5 ′ and 3 ′ unpaired regions, respectively.…”