Structural inference of native and partially folded RNA by high-throughput contact mapping

Abstract

The biological behaviors of ribozymes, riboswitches, and numerous other functional RNA molecules are critically dependent on their tertiary folding and their ability to sample multiple functional states. The conformational heterogeneity and partially folded nature of most of these states has rendered their characterization by high-resolution structural approaches difficult or even intractable. Here we introduce a method to rapidly infer the tertiary helical arrangements of large RNA molecules in their native and non-native solution states. Multiplexed hydroxyl radical (⅐OH) cleavage analysis (MOHCA) enables the high-throughput detection of numerous pairs of contacting residues via random incorporation of radical cleavage agents followed by two-dimensional gel electrophoresis. We validated this technology by recapitulating the unfolded and native states of a well studied model RNA, the P4 -P6 domain of the Tetrahymena ribozyme, at subhelical resolution. We then applied MOHCA to a recently discovered third state of the P4 -P6 RNA that is stabilized by high concentrations of monovalent salt and whose partial order precludes conventional techniques for structure determination. The three-dimensional portrait of a compact, non-native RNA state reveals a well ordered subset of native tertiary contacts, in contrast to the dynamic but otherwise similar molten globule states of proteins. With its applicability to nearly any solution state, we expect MOHCA to be a powerful tool for illuminating the many functional structures of large RNA molecules and RNA/protein complexes.hydroxyl radical ͉ molten globule ͉ Tetrahymena ribozyme ͉ two-dimensional gel T he discoveries of catalytic RNAs, silencing RNAs, riboswitches, and a panoply of functional RNA molecules have sweeping implications for our views of evolution from an early ''RNA World'' and for the potential of structured RNAs to act in roles beyond the simple transmission of information laid out in the Central Dogma of Molecular Biology (1). The functions of these RNAs in primitive and modern life are being elucidated at an explosive pace. Nevertheless, a deep understanding of these fundamental biopolymers and their biological roles requires structural portraits of their functional states, and, in this respect, progress has been slow.Our understanding of RNA structure has greatly lagged behind that of protein structure: compared with nearly 40,000 protein structures in the Protein Data Bank, there are currently Ͻ1,000 experimentally determined RNA structures, most of which are small fragments (2). High-resolution approaches using NMR spectroscopy (NMR) and x-ray crystallography have the potential to describe RNA structure at the atomic level, but have been considerably hampered by numerous factors, including limited chemical shift dispersion, the large sizes of structured RNAs, and the poor behavior of RNA at high concentrations.Further enriching and complicating the modeling of RNA behavior is the seemingly pervasive tendency of RNA to form alternative secondary and tert...

RNA design rules from a massive open laboratory

“…Instead, the approach herein enables a vastly larger number of participants to design and execute remote experiments in parallel, while machine learning algorithms sift through the community's catalog of hypotheses. This Massive Open Laboratory template could be generalized to a broad class of biomolecule design problems, including mechanistic dissection of current design rules (26), modeling of pseudoknots, engineering of RNA switches for cellular control (5,6), and 3D modeling and design, all assessed by high-throughput mapping (1,7,16,20,29,30). Other fields, such as taxonomy (31), astronomy (13), and neural mapping (32), are making pioneering efforts in internet-scale scientific discovery games.…”

Large-scale study of long non-coding RNA functions based on structure and expression features

“…For small RNA molecules with lengths less than 30 nt, prediction accuracy of about 4 Å root-mean-square-deviation (RMSD) for the main chains can be achieved. The prediction accuracy of FARNA can be further improved by considering secondary and tertiary structure information [27]. Recently, Baker et al [28] improved FARNA into an all-atomic structure prediction method with high accuracy, FARFAR.…”

RNA Junction Motifs as Scaffolds for Construction of Multifunctional RNA Nanoparticles

“…Experimental methods used to probe through-space distances, such as t-HRP (Das et al, 2008;Gherghe et al, 2009), cross-linking (Harris et al, 1994;Pinard et al, 2001;Yu et al, 2008), and FRET (Rueda et al, 2004), can give high-quality distance information. However, these techniques often require synthesis of specialized RNA constructs, careful controls for unintended structural perturbations, and complex approaches for data interpretation (Hajdin et al, 2010).…”

“…The solvent accessibility of individual nucleotides can also be explored by solution hydroxyl radical probing (HRP) experiments (Cate et al, 1996;Pastor et al, 2000;Tullius and Greenbaum, 2005). Incorporation of experimentally derived structural information with computational modeling can markedly reduce the allowed conformational space and thereby facilitate the computational prediction of native RNA ensembles (Das et al, 2008;Gherghe et al, 2009;Jonikas et al, 2009;Lavender et al, 2010;Yang et al, 2010;Yu et al, 2008).…”

“…For example, the selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) chemistry developed by Weeks and colleagues (Deigan et al, 2009;Weeks, 2010) characterizes the probability of base pairing for each nucleotide. Other experiments such as fluorescence resonance energy transfer (FRET) (Rueda et al, 2004), cross-linking (Harris et al, 1994;Pinard et al, 2001;Yu et al, 2008), and tethered hydroxyl radical probing (t-HRP) (Das et al, 2008;Gherghe et al, 2009) can probe internucleotide distances. The solvent accessibility of individual nucleotides can also be explored by solution hydroxyl radical probing (HRP) experiments (Cate et al, 1996;Pastor et al, 2000;Tullius and Greenbaum, 2005).…”