RNA hairpin loop stability depends on closing base pair

Serra, Martin J.; Lyttle, Matthew H.; Axenson, Theresa J.; Schadt, Calvin A.; Turner, Douglas H.

doi:10.1093/nar/21.16.3845

Cited by 78 publications

(90 citation statements)

References 26 publications

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“…Thermodynamic parameters are based on the set of Xia et al (44)(45)(46) and Mathews et al (8). Hairpin loop parameters (Tables 1 and 2) are revised on the basis of recent experimental results (47,48) and the previous database of RNA hairpin stabilities (49)(50)(51)(52)(53)(54)(55).…”

Section: Methodsmentioning

confidence: 99%

Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure

Mathews

Disney

Childs

et al. 2004

Proc. Natl. Acad. Sci. U.S.A.

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1,383

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A dynamic programming algorithm for prediction of RNA secondary structure has been revised to accommodate folding constraints determined by chemical modification and to include free energy increments for coaxial stacking of helices when they are either adjacent or separated by a single mismatch. Furthermore, free energy parameters are revised to account for recent experimental results for terminal mismatches and hairpin, bulge, internal, and multibranch loops. To demonstrate the applicability of this method, in vivo modification was performed on 5S rRNA in both Escherichia coli and Candida albicans with 1-cyclohexyl-3-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate, dimethyl sulfate, and kethoxal. The percentage of known base pairs in the predicted structure increased from 26.3% to 86.8% for the E. coli sequence by using modification constraints. For C. albicans, the accuracy remained 87.5% both with and without modification data. On average, for these sequences and a set of 14 sequences with known secondary structure and chemical modification data taken from the literature, accuracy improves from 67% to 76%. This enhancement primarily reflects improvement for three sequences that are predicted with <40% accuracy on the basis of energetics alone. For these sequences, inclusion of chemical modification constraints improves the average accuracy from 28% to 78%. For the 11 sequences with <6% pseudoknotted base pairs, structures predicted with constraints from chemical modification contain on average 84% of known canonical base pairs. R ecent discoveries have shown that RNA plays a larger role in biology than previously realized, e.g., in posttranscriptional regulation (1), development (2, 3), immunity (4, 5), and peptide bond formation (6, 7). It is necessary to determine the native structures of RNAs to understand their mechanisms of action, and determining secondary structure is a crucial step in this process.RNA secondary structure can be predicted by free energy minimization with nearest neighbor parameters to evaluate stability (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18). Previous studies demonstrated that nuclease cleavage data can be used to refine structure prediction and improve accuracy (8, 11). A predicted secondary structure can guide further experiments or comparative sequence analysis (19) and also aid in the design of RNA molecules (20,21).Chemical modification is a technique that reveals solvent accessible nucleotides (22). The nucleotides accessible to 1-cyclohexyl-3-(2-morpholinoethyl) carbodiimide metho-ptoluene sulfonate, dimethyl sulfate, and kethoxal are unpaired, in A-U or G-C pairs at helix ends, in G-U pairs anywhere, or adjacent to G-U pairs. This limited specificity differs from that observed with nucleases, and an algorithm allowing constraints from such chemical modification has not been reported. Chemical modification is used extensively to test hypothesized RNA secondary structures (19,(23)(24)(25)(26)(27)(28). Chemical modification can also be used to deduce possible tertia...

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Section: Methodsmentioning

confidence: 99%

Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure

Mathews

Disney

Childs

et al. 2004

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

1,383

1,721

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“…Exactly opposite effects are anticipated for U·G wobble pairs due to reversal in the sign and magnitude of Δt. Hence, loops closed by G·U pairs (stems beginning with G·U pairs) are more stable than those closed by U·G pairs (stems beginning with U·G pairs) (Serra et al 1993). These factors may explain the high occurrence of G·U and U·G FIGURE 2.…”

Section: Structural Context Of G·u and U·g Wobble Pairs Among Wc Pairsmentioning

confidence: 99%

An innate twist between Crick’s wobble and Watson-Crick base pairs

Prakash

Goldsmith

Yathindra

2013

RNA

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Non-Watson-Crick pairs like the G·U wobble are frequent in RNA duplexes. Their geometric dissimilarity (nonisostericity) with the Watson-Crick base pairs and among themselves imparts structural variations decisive for biological functions. Through a novel circular representation of base pairs, a simple and general metric scheme for quantification of base-pair nonisostericity, in terms of residual twist and radial difference that can also envisage its mechanistic effect, is proposed. The scheme is exemplified by G·U and U·G wobble pairs, and their predicable local effects on helical twist angle are validated by MD simulations. New insights into a possible rationale for contextual occurrence of G·U and other non-WC pairs, as well as the influence of a G·U pair on other non-Watson-Crick pair neighborhood and RNA-protein interactions are obtained from analysis of crystal structure data. A few instances of RNA-protein interactions along the major groove are documented in addition to the well-recognized interaction of the G·U pair along the minor groove. The nonisostericity-mediated influence of wobble pairs for facilitating helical packing through long-range interactions in ribosomal RNAs is also reviewed.

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“…Secondly, we collected a thermodynamic set denoted by T-Full that contains data from 1291 optical melting experiments, published in 53 research articles Sugimoto et al 1986Sugimoto et al , 1987Uhlenbeck 1988, 1989;Longfellow et al 1990;SantaLucia et al 1990SantaLucia et al , 1991aAntao et al 1991;He et al 1991;Peritz et al 1991;Antao and Tinoco 1992;Serra et al 1993Serra et al , 1994Serra et al , 1997Serra et al , 2004Walter et al 1994;Morse and Draper 1995;Wu et al 1995;Laing and Hall 1996;McDowell and Turner 1996;McDowell et al 1997;Schroeder et al 1996Schroeder et al , 2003Xia et al 1997Xia et al , 1998Giese et al 1998;Kierzek et al 1999;Meroueh and Chow 1999;Dale et al 2000;Turner 2000, 2001;Burkard et al 2001;Diamond et al 2001;Mathews and Turner 2002;Proctor et al 2002;Znosko et al 2002Znosko et al , 2004Chen et al 2004Chen et al , 2005Mathews et al 2004;Vecenie and Serra 2004;Bourdélat-Parks and Wartell 2005;…”

Section: Data Setsmentioning

confidence: 99%

Computational approaches for RNA energy parameter estimation

Andronescu

Condon²,

Hoos³

et al. 2010

RNA

122

156

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Methods for efficient and accurate prediction of RNA structure are increasingly valuable, given the current rapid advances in understanding the diverse functions of RNA molecules in the cell. To enhance the accuracy of secondary structure predictions, we developed and refined optimization techniques for the estimation of energy parameters. We build on two previous approaches to RNA free-energy parameter estimation: (1) the Constraint Generation (CG) method, which iteratively generates constraints that enforce known structures to have energies lower than other structures for the same molecule; and (2) the Boltzmann Likelihood (BL) method, which infers a set of RNA free-energy parameters that maximize the conditional likelihood of a set of reference RNA structures. Here, we extend these approaches in two main ways: We propose (1) a max-margin extension of CG, and (2) a novel linear Gaussian Bayesian network that models feature relationships, which effectively makes use of sparse data by sharing statistical strength between parameters. We obtain significant improvements in the accuracy of RNA minimum free-energy pseudoknot-free secondary structure prediction when measured on a comprehensive set of 2518 RNA molecules with reference structures. Our parameters can be used in conjunction with software that predicts RNA secondary structures, RNA hybridization, or ensembles of structures. Our data, software, results, and parameter sets in various formats are freely available at http://www.cs.ubc.ca/labs/beta/Projects/RNA-Params.

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RNA hairpin loop stability depends on closing base pair

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Cited by 78 publications

References 26 publications

Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure

Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure

An innate twist between Crick’s wobble and Watson-Crick base pairs

Computational approaches for RNA energy parameter estimation

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