51 research outputs found
A mutate-and-map protocol for inferring base pairs in structured RNA
Chemical mapping is a widespread technique for structural analysis of nucleic
acids in which a molecule's reactivity to different probes is quantified at
single-nucleotide resolution and used to constrain structural modeling. This
experimental framework has been extensively revisited in the past decade with
new strategies for high-throughput read-outs, chemical modification, and rapid
data analysis. Recently, we have coupled the technique to high-throughput
mutagenesis. Point mutations of a base-paired nucleotide can lead to exposure
of not only that nucleotide but also its interaction partner. Carrying out the
mutation and mapping for the entire system gives an experimental approximation
of the molecules contact map. Here, we give our in-house protocol for this
mutate-and-map strategy, based on 96-well capillary electrophoresis, and we
provide practical tips on interpreting the data to infer nucleic acid
structure.Comment: 22 pages, 5 figure
Understanding the errors of SHAPE-directed RNA structure modeling
Single-nucleotide-resolution chemical mapping for structured RNA is being
rapidly advanced by new chemistries, faster readouts, and coupling to
computational algorithms. Recent tests have shown that selective 2'-hydroxyl
acylation by primer extension (SHAPE) can give near-zero error rates (0-2%) in
modeling the helices of RNA secondary structure. Here, we benchmark the method
using six molecules for which crystallographic data are available: tRNA(phe)
and 5S rRNA from Escherichia coli, the P4-P6 domain of the Tetrahymena group I
ribozyme, and ligand-bound domains from riboswitches for adenine, cyclic
di-GMP, and glycine. SHAPE-directed modeling of these highly structured RNAs
gave an overall false negative rate (FNR) of 17% and a false discovery rate
(FDR) of 21%, with at least one helix prediction error in five of the six
cases. Extensive variations of data processing, normalization, and modeling
parameters did not significantly mitigate modeling errors. Only one varation,
filtering out data collected with deoxyinosine triphosphate during primer
extension, gave a modest improvement (FNR = 12%, and FDR = 14%). The residual
structure modeling errors are explained by the insufficient information content
of these RNAs' SHAPE data, as evaluated by a nonparametric bootstrapping
analysis. Beyond these benchmark cases, bootstrapping suggests a low level of
confidence (<50%) in the majority of helices in a previously proposed
SHAPE-directed model for the HIV-1 RNA genome. Thus, SHAPE-directed RNA
modeling is not always unambiguous, and helix-by-helix confidence estimates, as
described herein, may be critical for interpreting results from this powerful
methodology.Comment: Biochemistry, Article ASAP (Aug. 15, 2011
Massively Parallel RNA Chemical Mapping with a Reduced Bias MAP-seq Protocol
Chemical mapping methods probe RNA structure by revealing and leveraging
correlations of a nucleotide's structural accessibility or flexibility with its
reactivity to various chemical probes. Pioneering work by Lucks and colleagues
has expanded this method to probe hundreds of molecules at once on an Illumina
sequencing platform, obviating the use of slab gels or capillary
electrophoresis on one molecule at a time. Here, we describe optimizations to
this method from our lab, resulting in the MAP-seq protocol (Multiplexed
Accessibility Probing read out through sequencing), version 1.0. The protocol
permits the quantitative probing of thousands of RNAs at once, by several
chemical modification reagents, on the time scale of a day using a table-top
Illumina machine. This method and a software package MAPseeker
(http://simtk.org/home/map_seeker) address several potential sources of bias,
by eliminating PCR steps, improving ligation efficiencies of ssDNA adapters,
and avoiding problematic heuristics in prior algorithms. We hope that the
step-by-step description of MAP-seq 1.0 will help other RNA mapping
laboratories to transition from electrophoretic to next-generation sequencing
methods and to further reduce the turnaround time and any remaining biases of
the protocol.Comment: 22 pages, 5 figure
Programmable antivirals targeting critical conserved viral RNA secondary structures from influenza A virus and SARS-CoV-2
Influenza A virus’s (IAV’s) frequent genetic changes challenge vaccine strategies and engender resistance to current drugs. We sought to identify conserved and essential RNA secondary structures within IAV’s genome that are predicted to have greater constraints on mutation in response to therapeutic targeting. We identified and genetically validated an RNA structure (packaging stem–loop 2 (PSL2)) that mediates in vitro packaging and in vivo disease and is conserved across all known IAV isolates. A PSL2-targeting locked nucleic acid (LNA), administered 3 d after, or 14 d before, a lethal IAV inoculum provided 100% survival in mice, led to the development of strong immunity to rechallenge with a tenfold lethal inoculum, evaded attempts to select for resistance and retained full potency against neuraminidase inhibitor-resistant virus. Use of an analogous approach to target SARS-CoV-2, prophylactic administration of LNAs specific for highly conserved RNA structures in the viral genome, protected hamsters from efficient transmission of the SARS-CoV-2 USA_WA1/2020 variant. These findings highlight the potential applicability of this approach to any virus of interest via a process we term ‘programmable antivirals’, with implications for antiviral prophylaxis and post-exposure therapy
RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures.:
This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5-3.2 Ã…, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson-Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/
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