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1.  Statistical analysis of SHAPE-directed RNA secondary structure modeling 
Biochemistry  2013;52(4):596-599.
The ability to predict RNA secondary structure is fundamental for understanding and manipulating RNA function. The structural information obtained from selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) experiments greatly improves the accuracy of RNA secondary structure prediction. Recently, Das and colleagues [Kladwang et al., Biochemistry 50:8049 (2011)] proposed a “bootstrapping” approach to estimate the variance and helix-by-helix confidence levels of predicted secondary structures based on resampling (randomizing and summing) the measured SHAPE data. We show that the specific resampling approach described by Kladwang et al. introduces systematic errors and underestimates confidence in secondary structure prediction using SHAPE data. Instead, a leave-data-out jackknife approach better estimates the influence of a given experimental dataset on SHAPE-directed secondary structure modeling. Even when 35% of the data were left out in the jackknife approach, the confidence levels of SHAPE-directed secondary structure prediction were significantly higher than those calculated by Das and colleagues using bootstrapping. Helix confidence levels were thus significantly underestimated in the recent study, and resampling approach implemented by Kladwang et al. is not an appropriate metric for assigning confidences in SHAPE-directed secondary structure modeling.
PMCID: PMC3558531  PMID: 23286327
2.  Principles for understanding the accuracy of SHAPE-directed RNA structure modeling 
Biochemistry  2013;52(4):588-595.
Accurate RNA structure modeling is an important, incompletely solved, challenge. Single-nucleotide resolution SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) yields an experimental measurement of local nucleotide flexibility that can be incorporated as pseudo-free energy change constraints to direct secondary structure predictions. Prior work from our laboratory has emphasized both the overall accuracy of this approach and the need for nuanced interpretation of some apparent discrepancies between modeled and accepted structures. Recent studies by Das and colleagues [Kladwang et al., Biochemistry 50:8049 (2011) and Nat. Chem. 3:954 (2011)], focused on analyzing six small RNAs, yielded poorer RNA secondary structure predictions than expected based on prior benchmarking efforts. To understand the features that led to these divergent results, we re-examined four RNAs yielding the poorest results in this recent work – tRNAPhe, the adenine and cyclic-di-GMP riboswitches, and 5S rRNA. Most of the errors reported by Das and colleagues reflected non-standard experiment and data processing choices, and selective scoring rules. For two RNAs, tRNAPhe and the adenine riboswitch, secondary structure predictions are nearly perfect if no experimental information is included but were rendered inaccurate by the Das and colleagues SHAPE data. When best practices were used, single-sequence SHAPE-directed secondary structure modeling recovered ~93% of individual base pairs and greater than 90% of helices in the four RNAs, essentially indistinguishable from the mutate-and-map approach with the exception of a single helix in the 5S rRNA. The field of experimentally-directed RNA secondary structure prediction is entering a phase focused on the most difficult prediction challenges. We outline five constructive principles for guiding this field forward.
PMCID: PMC3578230  PMID: 23316814
3.  Three-Dimensional RNA Structure Refinement by Hydroxyl Radical Probing 
Nature methods  2012;9(6):603-608.
Molecular modeling guided by experimentally-derived structural information is an attractive approach for three-dimensional structure determination of complex RNAs that are not amenable to study by high-resolution methods. Hydroxyl radical probing (HRP), performed routinely in many laboratories, provides a measure of solvent accessibility at individual nucleotides. HRP measurements have, to date, only been used to evaluate RNA models qualitatively. Here, we report development of a quantitative structure refinement approach using HRP measurements to drive discrete molecular dynamics simulations for RNAs ranging in size from 80 to 230 nucleotides. HRP reactivities were first used to identify RNAs that form extensive helical packing interactions. For these RNAs, we achieved highly significant structure predictions, given inputs of RNA sequence and base pairing. This HRP-directed tertiary structure refinement approach generates robust structural hypotheses useful for guiding explorations of structure-function interrelationships in RNA.
PMCID: PMC3422565  PMID: 22504587
4.  Robust and Generic RNA Modeling Using Inferred Constraints: A Structure for the Hepatitis C Virus IRES Pseudoknot Domain 
Biochemistry  2010;49(24):4931-4933.
RNA function is dependent on its structure, yet three-dimensional folds for most biologically important RNAs are unknown. We develop a generic discrete molecular dynamics (DMD)-based modeling system that uses long-range constraints inferred from diverse biochemical or bioinformatic analyses to create statistically significant (p < 0.01) native-like folds for RNAs of known structure ranging from 45 to 158 nucleotides. We then predict the unknown structure of the hepatitis C virus IRES pseudoknot domain. The resulting RNA model rationalizes independent solvent accessibility and cryo-electron microscopy structure information. The pseudoknot positions the AUG start codon near the mRNA channel and is tRNA-like, suggesting the IRES employs molecular mimicry as a functional strategy.
PMCID: PMC2889920  PMID: 20545364
5.  Native-like RNA tertiary structures using a sequence-encoded cleavage agent and refinement by discrete molecular dynamics 
The difficulty of analyzing higher order RNA structure, especially for folding intermediates and for RNAs whose functions require domains that are conformationally flexible, emphasizes the need for new approaches for modeling RNA tertiary structure accurately. Here, we report a concise approach that makes use of facile RNA structure probing experiments that are then interpreted using a computational algorithm, carefully tailored to optimize both the resolution and refinement speed for the resulting structures, without requiring user intervention. The RNA secondary structure is first established using SHAPE chemistry. We then use a sequence-directed cleavage agent, that can be placed arbitrarily in many helical motifs, to obtain high quality inter-residue distances. We interpret this in-solution chemical information using a fast, coarse grained, discrete molecular dynamics engine in which each RNA nucleotide is represented by pseudoatoms for the phosphate, ribose and nucleobase groups. By this approach, we refine base paired positions in yeast tRNAAsp to 4 Å RMSD without any preexisting information or assumptions about secondary or tertiary structures. This blended experimental and computational approach has the potential to yield native-like models for the diverse universe of functionally important RNAs whose structures cannot be characterized by conventional structural methods.
PMCID: PMC2664099  PMID: 19193004

Results 1-5 (5)