Comparative Visualization of the RNA Suboptimal Conformational Ensemble In Vivo

Abstract

When a ribonucleic acid (RNA) molecule folds, it often does not adopt a single, well-defined conformation. The folding energy landscape of an RNA is highly dependent on its nucleotide sequence and molecular environment. Cellular molecules sometimes alter the energy landscape, thereby changing the ensemble of likely low-energy conformations. The effects of these energy landscape changes on the conformational ensemble are particularly challenging to visualize for large RNAs. We have created a robust approach for visualizing the conformational ensemble of RNAs that is well suited for in vitro versus in vivo comparisons. Our method creates a stable map of conformational space for a given RNA sequence. We first identify single point mutations in the RNA that maximally sample suboptimal conformational space based on the ensemble’s partition function. Then, we cluster these diverse ensembles to identify the most diverse partition functions for Boltzmann stochastic sampling. By using, to our knowledge, a novel nestedness distance metric, we iteratively add mutant suboptimal ensembles to converge on a stable 2D map of conformational space. We then compute the selective 2′ hydroxyl acylation by primer extension (SHAPE)-directed ensemble for the RNA folding under different conditions, and we project these ensembles on the map to visualize. To validate our approach, we established a conformational map of the Vibrio vulnificus add adenine riboswitch that reveals five classes of structures. In the presence of adenine, projection of the SHAPE-directed sampling correctly identified the on-conformation; without the ligand, only off-conformations were visualized. We also collected the whole-transcript in vitro and in vivo SHAPE-MaP for human β-actin messenger RNA that revealed similar global folds in both conditions. Nonetheless, a comparison of in vitro and in vivo data revealed that specific regions exhibited significantly different SHAPE-MaP profiles indicative of structural rearrangements, including rearrangement consistent with binding of the zipcode protein in a region distal to the stop codon.

Challenges and approaches to predicting RNA with multiple functional structures

“…The EnsembleRNA program includes aspects of Boltzmann sampling and mutational analysis and utilizes unique metrics for identifying diverse structures (Woods et al 2017). The EnsembleRNA method begins with a sequence and computes the partition function for the wild type and all single point mutations.…”

“…The idea of Shannon entropy, a measure of uncertainty or disorder in information theory, also provides metrics to distinguish different structures. For example, the EnsembleRNA methods use Shannon entropy for base-pairing probabilities as a metric in clustering analysis (Woods et al 2017;Spasic et al 2018). The Shannon entropy can be calculated for base pairs, the full two-dimensional base-pairing probabilities, or thermodynamic structural entropy (Shapiro 1988;Rivas and Eddy 2001;Woods et al 2017).…”

“…For example, the EnsembleRNA methods use Shannon entropy for base-pairing probabilities as a metric in clustering analysis (Woods et al 2017;Spasic et al 2018). The Shannon entropy can be calculated for base pairs, the full two-dimensional base-pairing probabilities, or thermodynamic structural entropy (Shapiro 1988;Rivas and Eddy 2001;Woods et al 2017). The molecular interpretation of the Shannon entropy statistic depends on the type of data, such as RNA sequence alignments, sequence point mutations, SHAPE-MaP data, or thermodynamic calculations.…”

“…The RNAEnsemble approach also introduces a novel metric for measuring nestedness of RNA structures. The nestedness is calculated from the number of inner and outer stacks and loops in structures that are represented using the RNAShapes abstraction for helices (Giegerich et al 2004;Woods et al 2017). This metric is then used in metric multidimensional scaling in order to generate a visual map of RNA ensembles.…”

Probing RNA Conformational Equilibria within the Functional Cellular Context

“…There have been substantial advances in methods to characterize RNA dynamic ensembles in vitro (Salmon et al, 2014;Xue et al, 2015;Shi et al, 2016;Tian and Das, 2016;Nichols et al, 2018). However, despite significant progress (Mahen et al, 2005(Mahen et al, , 2010Spitale et al, 2013;Rogers and Heitsch, 2014;Ruminski et al, 2016;Watters et al, 2016;Lee et al, 2017;Li and Aviran, 2018;Woods et al, 2017;Spasic et al, 2018), probing RNA dynamics in vivo remains challenging. Transcriptomewide structure probing experiments indicate that the cellular environment can affect RNA folding relative to in vitro (Beaudoin et al, 2018;Mustoe et al, 2018;Sun et al, 2019).…”