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

ThreshKnot: Thresholded ProbKnot for Improved RNA Secondary Structure Prediction

RNA structure prediction is a challenging problem, especially with pseudoknots. Recently, there has been a shift from the classical minimum free energy-based methods (MFE) to partition function-based ones that assemble structures based on base-pairing probabilities. Two typical examples of the latter group are the popular maximum expected accuracy (MEA) method and the ProbKnot method. ProbKnot is fast heuristic that pairs nucleotides that are reciprocally most probable pairing partners, and unlike MEA, can also predict structures with pseudoknots. However, ProbKnot’s full potential has been largely overlooked. In particular, when introduced, it did not have an MEA-like hyperparameter that can balance between positive predictive value (PPV) and sensitivity. We show that a simple thresholded version of ProbKnot, which we call ThreshKnot, leads to more accurate overall predictions by filtering out unlikely pairs whose probability falls under a given threshold. We also show that on three widely-used folding engines (RNAstructure, Vienna RNAfold, and CONTRAfold), ThreshKnot always outperforms the much more involved MEA algorithm in structure prediction accuracy, in its capability to predict pseudoknots, and in its faster running time. This suggests that ThreshKnot should replace MEA as the default partition function-based structure prediction algorithm

Rich RNA Structure Landscapes Revealed by Mutate-and-Map Analysis

<div><p>Landscapes exhibiting multiple secondary structures arise in natural RNA molecules that modulate gene expression, protein synthesis, and viral. We report herein that high-throughput chemical experiments can isolate an RNA’s multiple alternative secondary structures as they are stabilized by systematic mutagenesis (mutate-and-map, M²) and that a computational algorithm, REEFFIT, enables unbiased reconstruction of these states’ structures and populations. In an <i>in silico</i> benchmark on non-coding RNAs with complex landscapes, M²-REEFFIT recovers 95% of RNA helices present with at least 25% population while maintaining a low false discovery rate (10%) and conservative error estimates. In experimental benchmarks, M²-REEFFIT recovers the structure landscapes of a 35-nt MedLoop hairpin, a 110-nt 16S rRNA four-way junction with an excited state, a 25-nt bistable hairpin, and a 112-nt three-state adenine riboswitch with its expression platform, molecules whose characterization previously required expert mutational analysis and specialized NMR or chemical mapping experiments. With this validation, M²-REEFFIT enabled tests of whether artificial RNA sequences might exhibit complex landscapes in the absence of explicit design. An artificial flavin mononucleotide riboswitch and a randomly generated RNA sequence are found to interconvert between three or more states, including structures for which there was no design, but that could be stabilized through mutations. These results highlight the likely pervasiveness of rich landscapes with multiple secondary structures in both natural and artificial RNAs and demonstrate an automated chemical/computational route for their empirical characterization.</p></div

Computational Tools for Classifying and Visualizing RNA Structure Change in High-Throughput Experimental Data

Mutations (or Single Nucleotide Variants) in folded RiboNucleic Acid (RNA) structures that cause local or global conformational change are riboSNitches. Predicting riboSNitches is challenging, as it requires making two, albeit related, structure predictions. The data most often used to experimentally validate riboSNitch predictions is Selective 2’ Hydroxyl Acylation by Primer Extension, or SHAPE. Experimentally establishing a riboSNitch requires the quantitative comparison of two SHAPE traces: wild-type (WT) and mutant. Historically, SHAPE data was collected on electropherograms and change in structure was evaluated by “gel gazing.” SHAPE data is now routinely collected with next generation sequencing and/or capillary sequencers. We aim to establish a classifier capable of simulating human “gazing” by identifying features of the SHAPE profile that human experts agree “looks” like a riboSNitch. Additionally, when an RNA molecule folds, it does not always adopt a single, well-defined conformation. The folding energy landscape of the RNA is highly dependent on sequence and the molecular environment. Endogenous molecules, especially in the cellular context, will in some cases completely alter the energy landscape and therefore the ensemble of likely low-energy conformations. The effects of these energy landscape changes on the conformational ensemble are particularly challenging to visualize for larger RNAs including most messenger RNAs (mRNAs). We propose here a robust approach for visualizing the conformational ensemble of RNAs particularly well suited for in vitro vs. in vivo comparisons.Doctor of Philosoph