Quantitative dissection of RNA structure formation reveals a cooperative and modular folding and assembly landscape

Structured RNAs are pervasive in biology with ubiquitous roles in gene expression and regulation. RNAs must fold from a linear chain of nucleotide sequence to attain three-dimensional structure. RNA folding can be described as modular and hierarchical with tiers of structure that form independently: secondary structure forms first and defines the helices followed by formation of tertiary structure. The separation between secondary and tertiary structure is not absolute. Many biological RNAs couple secondary structure changes to RNA tertiary structure formation and link these changes to downstream functional consequences. To predict how these biological RNAs fold requires a deep understanding of the structural intermediates, folding pathways, and mechanisms of cooperativity that promote folding. To test the modularity and predictability of secondary and tertiary RNA folding and assembly, we have investigated the folding and assembly of the P5abc subdomain from the Tetrahymena thermophila Group I intron ribozyme. P5abc folds cooperatively in isolation, binding Mg²⁺ ions and adopting tertiary structure. Mg²⁺ binding is linked to a shift in the secondary structure of seventeen nucleotides and prior work concluded that there is a high degree of cooperativity for this seemingly concerted transition. With the already established principles of RNA modularity in mind, we develop a reconstitution hypothesis to test if cooperative secondary and tertiary folding and assembly of P5abc can be understood from the component pieces. By using rational mutagenesis, we find that higher order folding of P5abc is modular, and we elucidate the physical origins of cooperativity (Chapter 2). With our knowledge of isolated P5abc folding, we demonstrate that the local folding transition within P5abc controls the rate and pathway of assembly with the P5abc-deleted ribozyme core (E[superscript ΔP5abc]), further highlighting the modularity of RNA structure (Chapter 3). Lastly, we show that the kinetics of assembly can be attributed to specific tertiary contacts that form in the assembly transition state such that the rate of a particular folding pathway is dictated by the properties of an individual tertiary contact (Chapter 4). The modularity of RNA structure makes it a reasonable molecule for the origins of life and an adaptable tool for bioengineering applications.Biochemistr

Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes.

RNA plays key regulatory roles in diverse cellular processes, where its functionality often derives from folding into and converting between structures. Many RNAs further rely on co-existence of alternative structures, which govern their response to cellular signals. However, characterizing heterogeneous landscapes is difficult, both experimentally and computationally. Recently, structure profiling experiments have emerged as powerful and affordable structure characterization methods, which improve computational structure prediction. To date, efforts have centered on predicting one optimal structure, with much less progress made on multiple-structure prediction. Here, we report a probabilistic modeling approach that predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data. We demonstrate robust landscape reconstruction and quantitative insights into structural dynamics by analyzing numerous data sets. This work establishes a framework for data-directed characterization of structure landscapes to aid experimentalists in performing structure-function studies

DotAligner:Identification and clustering of RNA structure motifs

Abstract The diversity of processed transcripts in eukaryotic genomes poses a challenge for the classification of their biological functions. Sparse sequence conservation in non-coding sequences and the unreliable nature of RNA structure predictions further exacerbate this conundrum. Here, we describe a computational method, DotAligner, for the unsupervised discovery and classification of homologous RNA structure motifs from a set of sequences of interest. Our approach outperforms comparable algorithms at clustering known RNA structure families, both in speed and accuracy. It identifies clusters of known and novel structure motifs from ENCODE immunoprecipitation data for 44 RNA-binding proteins