High-throughput mutate-map-rescue evaluates SHAPE-directed RNA structure and uncovers excited states

Abstract

The three-dimensional conformations of noncoding RNAs underpin their biochemical functions but have largely eluded experimental characterization. Here, we report that integrating a classic mutation/rescue strategy with high-throughput chemical mapping enables rapid RNA structure inference with unusually strong validation. We revisit a 16S rRNA domain for which SHAPE (selective 2 ′ -hydroxyl acylation with primer extension) and limited mutational analysis suggested a conformational change between apo-and holo-ribosome conformations. Computational support estimates, data from alternative chemical probes, and mutate-and-map (M 2 ) experiments highlight issues of prior methodology and instead give a nearcrystallographic secondary structure. Systematic interrogation of single base pairs via a high-throughput mutation/rescue approach then permits incisive validation and refinement of the M 2 -based secondary structure. The data further uncover the functional conformation as an excited state (20 ± 10% population) accessible via a single-nucleotide register shift. These results correct an erroneous SHAPE inference of a ribosomal conformational change, expose critical limitations of conventional structure mapping methods, and illustrate practical steps for more incisively dissecting RNA dynamic structure landscapes.

RNA structure inference through chemical mapping after accidental or intentional mutations

“…A total of 33 of these 34 helices matched the crystallographic or conventional structure available in the literature, and none of these cases involved register shifts that have been problems in prior methods (2,7). Despite the observation of other weak signals in these data that do not correspond to helices (Fig.…”

“…Despite these advances, the accuracy of RNA structure inference through chemical mapping and sequencing remains under question (3)(4)(5)(6)(7)(8). For example, models of the 9-kb HIV-1 RNA genome have been repeatedly revised with updates to the selective 2′-OH acylation by primer extension (SHAPE) protocol, data processing, and computational assumptions (2,(9)(10)(11), and the majority of this RNA's helices remain uncertain.…”

“…For example, models of the 9-kb HIV-1 RNA genome have been repeatedly revised with updates to the selective 2′-OH acylation by primer extension (SHAPE) protocol, data processing, and computational assumptions (2,(9)(10)(11), and the majority of this RNA's helices remain uncertain. Even for small RNA domains, SHAPE and dimethyl sulfate (DMS) (methylation of N1 and N3 atoms at A and C) have produced misleading secondary structures for ribosomal domains and blind modeling challenges that have been falsified through crystallography or mutagenesis (3,7,12,13). In alternative approaches based on photoactivated cross-linkers, many helix detections appear to be false positives, based on ribosome data in vitro and in vivo (14,15).…”

RNA structure through multidimensional chemical mapping

“…The information content of chemical mapping is therefore low. Until recently, expert intuition and ad hoc manual comparison of chemical mapping data with phylogenetic information and computational methods have been necessary to integrate chemical data into structure models, sometimes leading to significant errors (Anokhina et al 2013;Dai et al 2008;Deigan et al 2009;Hang et al 2015;Levitt, 1969;Robart et al 2014;Sergiev et al 2001;Tian et al 2014;Whirl-Carrillo et al 2002).…”

“…These experiments raise the prospect of nucleotide-resolution structural portraits of all RNAs being transcribed in an organism -the 'RNA structurome'. Nevertheless, when tested through independent experiments, de novo models derived from chemical mapping and computational modeling have not always given consistently accurate structures, even on small domains folded into well-defined states and probed in vitro (Deigan et al 2009;Kladwang et al 2011c;Leonard et al 2013;Tian et al 2014). These issues can be traced to the poor information content of chemical mapping measurements, which typically give single or few measurements per nucleotide, compared with high-resolution technologies such as crystallography, NMR, or cryo-electron microscopy, which can return datasets with ten or more measurements per nucleotide.…”