Influence of two bulge loops on the stability of RNA duplexes

Crowther, Claire V.; Jones, Laura; Morelli, Jessica N.; Mastrogiacomo, Eric M.; Porterfield, Claire; Kent, Jessica L.; Serra, Martin J.

doi:10.1261/rna.056168.116

Cited by 3 publications

(2 citation statements)

References 72 publications

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“…b B stands for dsRNA with bulge loop, and I stands for dsRNA with internal loop. c The experimental data are from(74)(75)(76)(77)(78)(79)(80).dsRNA Structure, Stability & Flexibility Biophysical Journal 115, 1403-1416, October 16, 2018 1411…”

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confidence: 99%

Modeling Structure, Stability, and Flexibility of Double-Stranded RNAs in Salt Solutions

Jin

Shi

Feng

et al. 2018

Biophysical Journal

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Double-stranded (ds) RNAs play essential roles in many processes of cell metabolism. The knowledge of three-dimensional (3D) structure, stability, and flexibility of dsRNAs in salt solutions is important for understanding their biological functions. In this work, we further developed our previously proposed coarse-grained model to predict 3D structure, stability, and flexibility for dsRNAs in monovalent and divalent ion solutions through involving an implicit structure-based electrostatic potential. The model can make reliable predictions for 3D structures of extensive dsRNAs with/without bulge/internal loops from their sequences, and the involvement of the structure-based electrostatic potential and corresponding ion condition can improve the predictions for 3D structures of dsRNAs in ion solutions. Furthermore, the model can make good predictions for thermal stability for extensive dsRNAs over the wide range of monovalent/divalent ion concentrations, and our analyses show that the thermally unfolding pathway of dsRNA is generally dependent on its length as well as its sequence. In addition, the model was employed to examine the salt-dependent flexibility of a dsRNA helix, and the calculated salt-dependent persistence lengths are in good accordance with experiments.

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confidence: 99%

Modeling Structure, Stability, and Flexibility of Double-Stranded RNAs in Salt Solutions

Jin

Shi

Feng

et al. 2018

Biophysical Journal

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“…The current set of RNA folding parameters are enumerated in the nearest neighbor database (NNDB), along with examples of their use (Turner and Mathews 2010). Subsequent to the determination of the latest set of Turner rules, a number of optical melting experiments have demonstrated that improvements in the nearest neighbor parameter models and values are possible (Huynen et al 1997;Znosko et al 2002;Chen et al 2004Chen et al , 2005Chen et al , 2009Chen et al , 2012Vecenie and Serra 2004;Bourdelat-Parks and Wartell 2005;O'Toole et al 2005O'Toole et al , 2006Vecenie et al 2006;Wilkinson et al 2006;Badhwar et al 2007;Blose et al 2007;Davis and Znosko 2007Shankar et al 2007;Carter-O'Connell et al 2008;Znosko 2008, 2009;Clanton-Arrowood et al 2008;Miller et al 2008;Nguyen and Schroeder 2010;Sheehy et al 2010;Thulasi et al 2010;Liu et al 2011;McCann et al 2011;Hausmann and Znosko 2012;Lim et al 2012;Vanegas et al 2012;Kent et al 2014;Murray et al 2014;Kwok et al 2015;Strom et al 2015;Tomcho et al 2015;Crowther et al 2017;Phan et al 2017).…”

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confidence: 99%

Analysis of RNA nearest neighbor parameters reveals interdependencies and quantifies the uncertainty in RNA secondary structure prediction

Zuber

McFadyen

et al. 2018

RNA

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RNA secondary structure prediction is often used to develop hypotheses about structure-function relationships for newly discovered RNA sequences, to identify unknown functional RNAs, and to design sequences. Secondary structure prediction methods typically use a thermodynamic model that estimates the free energy change of possible structures based on a set of nearest neighbor parameters. These parameters were derived from optical melting experiments of small model oligonucleotides. This work aims to better understand the precision of structure prediction. Here, the experimental errors in optical melting experiments were propagated to errors in the derived nearest neighbor parameter values and then to errors in RNA secondary structure prediction. To perform this analysis, the optical melting experimental values were systematically perturbed within the estimates of experimental error and alternative sets of nearest neighbor parameters were then derived from these error-bounded values. Secondary structure predictions using either the perturbed or reference parameter sets were then compared. This work demonstrated that the precision of RNA secondary structure prediction is more robust than suggested by previous work based on perturbation of the nearest neighbor parameters. This robustness is due to correlations between parameters. Additionally, this work identified weaknesses in the parameter derivation that makes accurate assessment of parameter uncertainty difficult. Considerations for experimental design are provided to mitigate these weaknesses are provided.

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Predicting RNA secondary structure by a neural network: what features may be learned?

Grigorashvili

Chervontseva

Gelfand

2022

PeerJ

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Deep learning is a class of machine learning techniques capable of creating internal representation of data without explicit preprogramming. Hence, in addition to practical applications, it is of interest to analyze what features of biological data may be learned by such models. Here, we describe PredPair, a deep learning neural network trained to predict base pairs in RNA structure from sequence alone, without any incorporated prior knowledge, such as the stacking energies or possible spatial structures. PredPair learned the Watson-Crick and wobble base-pairing rules and created an internal representation of the stacking energies and helices. Application to independent experimental (DMS-Seq) data on nucleotide accessibility in mRNA showed that the nucleotides predicted as paired indeed tend to be involved in the RNA structure. The performance of the constructed model was comparable with the state-of-the-art method based on the thermodynamic approach, but with a higher false positives rate. On the other hand, it successfully predicted pseudoknots. t-SNE clusters of embeddings of RNA sequences created by PredPair tend to contain embeddings from particular Rfam families, supporting the predictions of PredPair being in line with biological classification.

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Influence of two bulge loops on the stability of RNA duplexes

Cited by 3 publications

References 72 publications

Modeling Structure, Stability, and Flexibility of Double-Stranded RNAs in Salt Solutions

Modeling Structure, Stability, and Flexibility of Double-Stranded RNAs in Salt Solutions

Analysis of RNA nearest neighbor parameters reveals interdependencies and quantifies the uncertainty in RNA secondary structure prediction

Predicting RNA secondary structure by a neural network: what features may be learned?

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