Standardization of RNA Chemical Mapping Experiments

Kladwang, Wipapat; Mann, Thomas H.; Becka, Alexander J.; Tian, Siqi; Kim, Hanjoo; Yoon, Sungroh; Das, Rhiju

doi:10.1021/bi5003426

Cited by 63 publications

(98 citation statements)

References 22 publications

(51 reference statements)

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“…One-dimensional chemical mapping using SHAPE, CMCT, DMS, and hydroxyl radical footprinting and multidimensional chemical mapping measurements based on mutate-and-map (M 2 ) and multiplexed •OH cleavage analysis (MOHCA-seq, for targets 12-14) were acquired as described in Cordero et al (2014), Kladwang et al (2014), andCheng et al (2015a). Data were distributed via the private RNA-Puzzles website or via entries with anonymized sequences in the RNA Mapping Database (Cordero et al 2012); deposition IDs are summarized in Supplemental Table S8.…”

Section: Experimental Data Descriptionmentioning

confidence: 99%

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme

Miao

Adamiak

Antczak

et al. 2017

RNA

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RNA-Puzzles is a collective experiment in blind 3D RNA structure prediction. We report here a third round of RNA-Puzzles. Five puzzles, 4, 8, 12, 13, 14, all structures of riboswitch aptamers and puzzle 7, a ribozyme structure, are included in this round of the experiment. The riboswitch structures include biological binding sites for small molecules (S-adenosyl methionine, cyclic diadenosine monophosphate, 5-amino 4-imidazole carboxamide riboside 5 ′ -triphosphate, glutamine) and proteins (YbxF), and one set describes large conformational changes between ligand-free and ligand-bound states. The Varkud satellite ribozyme is the most recently solved structure of a known large ribozyme. All puzzles have established biological functions and require structural understanding to appreciate their molecular mechanisms. Through the use of fast-track experimental data, including multidimensional chemical mapping, and accurate prediction of RNA secondary structure, a large portion of the contacts in 3D have been predicted correctly leading to similar topologies for the top ranking predictions. Template-based and homologyderived predictions could predict structures to particularly high accuracies. However, achieving biological insights from de novo prediction of RNA 3D structures still depends on the size and complexity of the RNA. Blind computational predictions of RNA structures already appear to provide useful structural information in many cases. Similar to the previous RNA-Puzzles Round II experiment, the prediction of non-Watson-Crick interactions and the observed high atomic clash scores reveal a notable need for an algorithm of improvement. All prediction models and assessment results are available at http://ahsoka.ustrasbg.fr/rnapuzzles/.

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Section: Experimental Data Descriptionmentioning

confidence: 99%

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme

Miao

Adamiak

Antczak

et al. 2017

RNA

Self Cite

181

209

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“…One-dimensional chemical-mapping data and mutate-and-map (M 2 ) data were acquired, quantitated, and normalized as described in Kladwang et al (2014) and Seetin et al (2014), respectively. Three probes were used: 1M7 (a SHAPE reagent, 1-methyl-7-nitroisatoic anhydride, which acylates 2 ′ -hydroxyls of flexible nucleotides); DMS (dimethyl sulfate, reacting with exposed N1/ N3 of adenosine/cytosine; and CMCT (1-cyclohexyl(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate, reacting with exposed N1/N3 of guanosine/uracil) ([Mortimer and Weeks 2007;Cordero et al 2012a] and references therein).…”

Section: Additional Chemical-mapping Datamentioning

confidence: 99%

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

Miao

Adamiak

Blanchet

et al. 2015

RNA

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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 chemicalmapping 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 Å, 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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“…We show that incorporation of probing data from SHAPE 21 and PARS 13 experiments significantly increases the power to discriminate functional structural RNAs from the genomic background.…”

Section: Introductionmentioning

confidence: 98%

Probing-directed identification of novel structured RNAs

et al. 2016

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Transcripts often harbor RNA elements, which regulate cell processes co-or post-transcriptionally. The functions of many regulatory RNA elements depend on their structure, thus it is important to determine the structure as well as to scan genomes for structured elements. State of the art ab initio approaches to predict structured RNAs rely on DNA sequence analysis. They use 2 major types of information inferred from a sequence: thermodynamic stability of an RNA structure and evolutionary footprints of base-pair interactions. In recent years, chemical probing of RNA has arisen as an alternative source of structural information. RNA probing experiments detect positions accessible to specific types of chemicals or enzymes indicating their propensity to be in a paired or unpaired state. There exist several strategies to integrate probing data into RNA secondary structure prediction algorithms that substantially improve the prediction quality. However, whether and how probing data could contribute to detection of structured RNAs remains an open question. We previously developed the energy-based approach RNASurface to detect locally optimal structured RNA elements. Here, we integrate probing data into the RNASurface energy model using a general framework. We show that the use of experimental data allows for better discrimination of ncRNAs from other transcripts. Application of RNASurface to genome-wide analysis of the human transcriptome with PARS data identifies previously undetectable segments, with evidence of functionality for some of them.

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Standardization of RNA Chemical Mapping Experiments

Cited by 63 publications

References 22 publications

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures

Probing-directed identification of novel structured RNAs

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