Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Computational approaches to predicting the impact of novel bases on RNA structure and stability.

ABSTRACT: The use of computational modeling techniques to gain insight into nucleobase interactions has been a challenging endeavor to date. Accurate treatment requires the tackling of many challenges but also holds the promise of great rewards. The development of effective computational approaches to predict the binding affinities of nucleobases and analogues can, for example, streamline the process of developing novel nucleobase modifications, which should facilitate the development of new RNAi-based therapeutics. This brief review focuses on available computational approaches to predicting base pairing affinity in RNA-based contexts such as nucleobase-nucleobase interactions in duplexes and nucleobase-protein interactions. The challenges associated with such modeling along with potential future directions for the field are highlighted.

SUBMITTER: Harrison JG

PROVIDER: S-EPMC3856235 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Computational approaches to predicting the impact of novel bases on RNA structure and stability.

Harrison Jason G JG Zheng Yvonne B YB Beal Peter A PA Tantillo Dean J DJ

ACS chemical biology 20131008 11

The use of computational modeling techniques to gain insight into nucleobase interactions has been a challenging endeavor to date. Accurate treatment requires the tackling of many challenges but also holds the promise of great rewards. The development of effective computational approaches to predict the binding affinities of nucleobases and analogues can, for example, streamline the process of developing novel nucleobase modifications, which should facilitate the development of new RNAi-based th ...[more]

PMID: 24063428

Similar Datasets

VfoldCPX Server: Predicting RNA-RNA Complex Structure and Stability.

Project description:RNA-RNA interactions are essential for genomic RNA dimerization, mRNA splicing, and many RNA-related gene expression and regulation processes. The prediction of the structure and folding stability of RNA-RNA complexes is a problem of significant biological importance and receives substantial interest in the biological community. The VfoldCPX server provides a new web interface to predict the two-dimensional (2D) structures of RNA-RNA complexes from the nucleotide sequences. The VfoldCPX server has several novel advantages including the ability to treat RNAs with tertiary contacts (crossing base pairs) such as loop-loop kissing interactions and the use of physical loop entropy parameters. Based on a partition function-based algorithm, the server enables prediction for structure with and without tertiary contacts. Furthermore, the server outputs a set of energetically stable structures, ranked by their stabilities. The results allow users to gain extensive physical insights into RNA-RNA interactions and their roles in RNA function. The web server is freely accessible at "http://rna.physics.missouri.edu/vfoldCPX".

| S-EPMC5033388 | biostudies-literature

Computational approaches to RNA structure prediction, analysis, and design.

Project description:RNA molecules are important cellular components involved in many fundamental biological processes. Understanding the mechanisms behind their functions requires RNA tertiary structure knowledge. Although modeling approaches for the study of RNA structures and dynamics lag behind efforts in protein folding, much progress has been achieved in the past two years. Here, we review recent advances in RNA folding algorithms, RNA tertiary motif discovery, applications of graph theory approaches to RNA structure and function, and in silico generation of RNA sequence pools for aptamer design. Advances within each area can be combined to impact many problems in RNA structure and function.

| S-EPMC3112238 | biostudies-literature

Structure folding of RNA kissing complexes in salt solutions: predicting 3D structure, stability, and folding pathway.

Project description:RNA kissing complexes are essential for genomic RNA dimerization and regulation of gene expression, and their structures and stability are critical to their biological functions. In this work, we used our previously developed coarse-grained model with an implicit structure-based electrostatic potential to predict three-dimensional (3D) structures and stability of RNA kissing complexes in salt solutions. For extensive RNA kissing complexes, our model shows great reliability in predicting 3D structures from their sequences, and our additional predictions indicate that the model can capture the dependence of 3D structures of RNA kissing complexes on monovalent/divalent ion concentrations. Moreover, the comparisons with extensive experimental data show that the model can make reliable predictions on the stability for various RNA kissing complexes over wide ranges of monovalent/divalent ion concentrations. Notably, for RNA kissing complexes, our further analyses show the important contribution of coaxial stacking to the 3D structures and stronger stability than the corresponding kissing-interface duplexes at high salts. Furthermore, our comprehensive analyses for RNA kissing complexes reveal that the thermally folding pathway for a complex sequence is mainly determined by the relative stability of two possible folded states of kissing complex and extended duplex, which can be significantly modulated by its sequence.

| S-EPMC6795135 | biostudies-literature

Computational approaches to 3D modeling of RNA.

Project description:Many exciting discoveries have recently revealed the versatility of RNA and its importance in a variety of functions within the cell. Since the structural features of RNA are of major importance to their biological function, there is much interest in predicting RNA structure, either in free form or in interaction with various ligands, including proteins, metabolites and other molecules. In recent years, an increasing number of researchers have developed novel RNA algorithms for predicting RNA secondary and tertiary structures. In this review, we describe current experimental and computational advances and discuss recent ideas that are transforming the traditional view of RNA folding. To evaluate the performance of the most recent RNA 3D folding algorithms, we provide a comparative study in order to test the performance of available 3D structure prediction algorithms for an RNA data set of 43 structures of various lengths and motifs. We find that the algorithms vary widely in terms of prediction quality across different RNA lengths and topologies; most predictions have very large root mean square deviations from the experimental structure. We conclude by outlining some suggestions for future RNA folding research.

| S-EPMC6286080 | biostudies-literature

Computational approaches for RNA energy parameter estimation.

Project description: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.

| S-EPMC2995392 | biostudies-literature

Determination of instantaneous stability against backward balance loss: two computational approaches.

Project description:The purposes of this study were (1) to develop two computational methods and (2) to compare their performance (i.e. the accuracy and the time demand) in determining instantaneous stability, i.e., the shortest distance from an instantaneous center of mass (COM) state to all interpolated points on the stability boundary in the COM position-velocity state space. The intuitive numerical method interpolated the stability boundary against backward balance loss based on four sets of discrete boundary values with a spline function and a preset step size (Deltax). Then a brute-force search approach was used to determine this shortest distance. The analytical method used quadratic polynomial functions to represent the boundaries. The stability was then determined by using the Lagrange multiplier method to find the optimum for the analytical expression of the distance from this given COM state to the boundary. Because reducing Deltax can supposedly improve the accuracy of spline fitting, we used this approach to establish the benchmark employed to determine the accuracy level of all other computations. Based on the results from a database of 67 sit-to-stand-slip trials and 687 gait-slip trials, we would recommend the numerical method with Deltax=0.1 to reach an adequate accuracy level of 10(-2). By changing one order of Deltax from 0.1 to 0.01, however, the accuracy level improved drastically from 10(-2) to 10(-4). Nonetheless to achieve the same higher accuracy at a faster computing speed, we would recommend the analytical method, which costs 80% less time than the numerical approach at the accuracy level of 10(-4).

| S-EPMC2459338 | biostudies-other

Predicting 3D structure and stability of RNA pseudoknots in monovalent and divalent ion solutions.

Project description:RNA pseudoknots are a kind of minimal RNA tertiary structural motifs, and their three-dimensional (3D) structures and stability play essential roles in a variety of biological functions. Therefore, to predict 3D structures and stability of RNA pseudoknots is essential for understanding their functions. In the work, we employed our previously developed coarse-grained model with implicit salt to make extensive predictions and comprehensive analyses on the 3D structures and stability for RNA pseudoknots in monovalent/divalent ion solutions. The comparisons with available experimental data show that our model can successfully predict the 3D structures of RNA pseudoknots from their sequences, and can also make reliable predictions for the stability of RNA pseudoknots with different lengths and sequences over a wide range of monovalent/divalent ion concentrations. Furthermore, we made comprehensive analyses on the unfolding pathway for various RNA pseudoknots in ion solutions. Our analyses for extensive pseudokonts and the wide range of monovalent/divalent ion concentrations verify that the unfolding pathway of RNA pseudoknots is mainly dependent on the relative stability of unfolded intermediate states, and show that the unfolding pathway of RNA pseudoknots can be significantly modulated by their sequences and solution ion conditions.

| S-EPMC6007934 | biostudies-literature

RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Project description:Single nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure and evolutionary conservation features. RegSNPs-intron shows excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of regSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis.

2019-09-30 | GSE138130 | GEO

Predicting pathogenicity for novel hearing loss mutations based on genetic and protein structure approaches.

Project description:Hearing loss is a heterogeneous disorder. Identification of causative mutations is demanding due to genetic heterogeneity. In this study, we investigated the genetic cause of sensorineural hearing loss in patients with severe/profound deafness. After the exclusion of GJB2-GJB6 mutations, we performed whole exome sequencing in 32 unrelated Argentinean families. Mutations were detected in 16 known deafness genes in 20 patients: ACTG1, ADGRV1 (GPR98), CDH23, COL4A3, COL4A5, DFNA5 (GSDDE), EYA4, LARS2, LOXHD1, MITF, MYO6, MYO7A, TECTA, TMPRSS3, USH2A and WSF1. Notably, 11 variants affecting 9 different non-GJB2 genes resulted novel: c.12829C > T, p.(Arg4277*) in ADGRV1; c.337del, p.(Asp109*) and c.3352del, p.(Gly1118Alafs*7) in CDH23; c.3500G > A, p.(Gly1167Glu) in COL4A3; c.1183C > T, p.(Pro395Ser) and c.1759C > T, p.(Pro587Ser) in COL4A5; c.580 + 2 T > C in EYA4; c.1481dup, p.(Leu495Profs*31) in LARS2; c.1939 T > C, p.(Phe647Leu), in MYO6; c.733C > T, p.(Gln245*) in MYO7A and c.242C > G, p.(Ser81*) in TMPRSS3 genes. To predict the effect of these variants, novel protein modeling and protein stability analysis were employed. These results highlight the value of whole exome sequencing to identify candidate variants, as well as bioinformatic strategies to infer their pathogenicity.

| S-EPMC8741999 | biostudies-literature

Predicting 3D Structure, Flexibility, and Stability of RNA Hairpins in Monovalent and Divalent Ion Solutions.

Project description:A full understanding of RNA-mediated biology would require the knowledge of three-dimensional (3D) structures, structural flexibility, and stability of RNAs. To predict RNA 3D structures and stability, we have previously proposed a three-bead coarse-grained predictive model with implicit salt/solvent potentials. In this study, we further develop the model by improving the implicit-salt electrostatic potential and including a sequence-dependent coaxial stacking potential to enable the model to simulate RNA 3D structure folding in divalent/monovalent ion solutions. The model presented here can predict 3D structures of RNA hairpins with bulges/internal loops (<77 nucleotides) from their sequences at the corresponding experimental ion conditions with an overall improved accuracy compared to the experimental data; the model also makes reliable predictions for the flexibility of RNA hairpins with bulge loops of different lengths at several divalent/monovalent ion conditions. In addition, the model successfully predicts the stability of RNA hairpins with various loops/stems in divalent/monovalent ion solutions.

| S-EPMC4701004 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data