Modeling RNA secondary structure folding ensembles using SHAPE mapping data.
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ABSTRACT:
SUBMITTER: Spasic A
PROVIDER: S-EPMC5758915 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Secondary structure prediction is an important problem in RNA bioinformatics because knowledge of structure is critical to understanding the functions of RNA sequences. Significant improvements in prediction accuracy have recently been demonstrated though the incorporation of experimentally obtained structural information, for instance using selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) mapping. However, such mapping data is currently available only for a limited number of RNA sequences. In this article, we present a method for extending the benefit of experimental mapping data in secondary structure prediction to homologous sequences. Specifically, we propose a method for integrating experimental mapping data into a comparative sequence analysis algorithm for secondary structure prediction of multiple homologs, whereby the mapping data benefits not only the prediction for the specific sequence that was mapped but also other homologs. The proposed method is realized by modifying the TurboFold II algorithm for prediction of RNA secondary structures to utilize basepairing probabilities guided by SHAPE experimental data when such data are available. The SHAPE-mapping-guided basepairing probabilities are obtained using the RSample method. Results demonstrate that the SHAPE mapping data for a sequence improves structure prediction accuracy of other homologous sequences beyond the accuracy obtained by sequence comparison alone (TurboFold II). The updated version of TurboFold II is freely available as part of the RNAstructure software package.
| S-EPMC5529333 | biostudies-literature
Project description:Structure dictates the function of many RNAs, but secondary RNA structure analysis is either labor intensive and costly or relies on computational predictions that are often inaccurate. These limitations are alleviated by integration of structure probing data into prediction algorithms. However, existing algorithms are optimized for a specific type of probing data. Recently, new chemistries combined with advances in sequencing have facilitated structure probing at unprecedented scale and sensitivity. These novel technologies and anticipated wealth of data highlight a need for algorithms that readily accommodate more complex and diverse input sources. We implemented and investigated a recently outlined probabilistic framework for RNA secondary structure prediction and extended it to accommodate further refinement of structural information. This framework utilizes direct likelihood-based calculations of pseudo-energy terms per considered structural context and can readily accommodate diverse data types and complex data dependencies. We use real data in conjunction with simulations to evaluate performances of several implementations and to show that proper integration of structural contexts can lead to improvements. Our tests also reveal discrepancies between real data and simulations, which we show can be alleviated by refined modeling. We then propose statistical preprocessing approaches to standardize data interpretation and integration into such a generic framework. We further systematically quantify the information content of data subsets, demonstrating that high reactivities are major drivers of SHAPE-directed predictions and that better understanding of less informative reactivities is key to further improvements. Finally, we provide evidence for the adaptive capability of our framework using mock probe simulations.
| S-EPMC4931104 | biostudies-literature
Project description:A pseudoknot forms in an RNA when nucleotides in a loop pair with a region outside the helices that close the loop. Pseudoknots occur relatively rarely in RNA but are highly overrepresented in functionally critical motifs in large catalytic RNAs, in riboswitches, and in regulatory elements of viruses. Pseudoknots are usually excluded from RNA structure prediction algorithms. When included, these pairings are difficult to model accurately, especially in large RNAs, because allowing this structure dramatically increases the number of possible incorrect folds and because it is difficult to search the fold space for an optimal structure. We have developed a concise secondary structure modeling approach that combines SHAPE (selective 2'-hydroxyl acylation analyzed by primer extension) experimental chemical probing information and a simple, but robust, energy model for the entropic cost of single pseudoknot formation. Structures are predicted with iterative refinement, using a dynamic programming algorithm. This melded experimental and thermodynamic energy function predicted the secondary structures and the pseudoknots for a set of 21 challenging RNAs of known structure ranging in size from 34 to 530 nt. On average, 93% of known base pairs were predicted, and all pseudoknots in well-folded RNAs were identified.
| S-EPMC3619282 | biostudies-literature
Project description:RNA molecules can populate an ensemble of alternative structural conformations, but the true extent of the RNA secondary structure folding space of a living cell has never been explored. We here generated the first transcriptome-wide maps of RNA secondary structure ensembles in Escherichia coli, both at physiological temperature and upon cold-shock. Our analysis revealed hundreds of novel putative conserved RNA switches.
2024-09-24 | GSE247244 | GEO
Project description:Predicting RNA secondary structure is often the first step to determining the structure of RNA. Prediction approaches have historically avoided searching for pseudoknots because of the extreme combinatorial and time complexity of the problem. Yet neglecting pseudoknots limits the utility of such approaches. Here, an algorithm utilizing structure mapping and thermodynamics is introduced for RNA pseudoknot prediction that finds the minimum free energy and identifies information about the flexibility of the RNA. The heuristic approach takes advantage of the 5' to 3' folding direction of many biological RNA molecules and is consistent with the hierarchical folding hypothesis and the contact order model. Mapping methods are used to build and analyze the folded structure for pseudoknots and to add important 3D structural considerations. The program can predict some well known pseudoknot structures correctly. The results of this study suggest that many functional RNA sequences are optimized for proper folding. They also suggest directions we can proceed in the future to achieve even better results.
| S-EPMC1975678 | biostudies-literature
Project description:Non-coding RNAs perform a wide range of functions inside the living cells that are related to their structures. Several algorithms have been proposed to predict RNA secondary structure based on minimum free energy. Low prediction accuracy of these algorithms indicates that free energy alone is not sufficient to predict the functional secondary structure. Recently, the obtained information from the SHAPE experiment greatly improves the accuracy of RNA secondary structure prediction by adding this information to the thermodynamic free energy as pseudo-free energy.In this paper, a new method is proposed to predict RNA secondary structure based on both free energy and SHAPE pseudo-free energy. For each RNA sequence, a population of secondary structures is constructed and their SHAPE data are simulated. Then, an evolutionary algorithm is used to improve each structure based on both free and pseudo-free energies. Finally, a structure with minimum summation of free and pseudo-free energies is considered as the predicted RNA secondary structure.Computationally simulating the SHAPE data for a given RNA sequence requires its secondary structure. Here, we overcome this limitation by employing a population of secondary structures. This helps us to simulate the SHAPE data for any RNA sequence and consequently improves the accuracy of RNA secondary structure prediction as it is confirmed by our experiments. The source code and web server of our proposed method are freely available at http://mostafa.ut.ac.ir/ESD-Fold/.
| S-EPMC5125645 | biostudies-literature
Project description:Structure mapping experiments (using probes such as dimethyl sulfate [DMS], kethoxal, and T1 and V1 RNases) are used to determine the secondary structures of RNA molecules. The process is iterative, combining the results of several probes with constrained minimum free-energy calculations to produce a model of the structure. We aim to evaluate whether particular probes provide more structural information, and specifically, how noise in the data affects the predictions. Our approach involves generating "decoy" RNA structures (using the sFold Boltzmann sampling procedure) and evaluating whether we are able to identify the correct structure from this ensemble of structures. We show that with perfect information, we are always able to identify the optimal structure for five RNAs of known structure. We then collected orthogonal structure mapping data (DMS and RNase T1 digest) under several solution conditions using our high-throughput capillary automated footprinting analysis (CAFA) technique on two group I introns of known structure. Analysis of these data reveals the error rates in the data under optimal (low salt) and suboptimal solution conditions (high MgCl(2)). We show that despite these errors, our computational approach is less sensitive to experimental noise than traditional constraint-based structure prediction algorithms. Finally, we propose a novel approach for visualizing the interaction of chemical and enzymatic mapping data with RNA structure. We project the data onto the first two dimensions of a multidimensional scaling of the sFold-generated decoy structures. We are able to directly visualize the structural information content of structure mapping data and reconcile multiple data sets.
| S-EPMC2874162 | biostudies-literature
Project description:Thermodynamic folding algorithms and structure probing experiments are commonly used to determine the secondary structure of RNAs. Here we propose a formal framework to reconcile information from both prediction algorithms and probing experiments. The thermodynamic energy parameters are adjusted using 'pseudo-energies' to minimize the discrepancy between prediction and experiment. Our framework differs from related approaches that used pseudo-energies in several key aspects. (i) The energy model is only changed when necessary and no adjustments are made if prediction and experiment are consistent. (ii) Pseudo-energies remain biophysically interpretable and hold positional information where experiment and model disagree. (iii) The whole thermodynamic ensemble of structures is considered thus allowing to reconstruct mixtures of suboptimal structures from seemingly contradicting data. (iv) The noise of the energy model and the experimental data is explicitly modeled leading to an intuitive weighting factor through which the problem can be seen as folding with 'soft' constraints of different strength. We present an efficient algorithm to iteratively calculate pseudo-energies within this framework and demonstrate how this approach can be used in combination with SHAPE chemical probing data to improve secondary structure prediction. We further demonstrate that the pseudo-energies correlate with biophysical effects that are known to affect RNA folding such as chemical nucleotide modifications and protein binding.
| S-EPMC3378861 | biostudies-other
Project description:RNA is a unique bio-macromolecule that can both record genetic information and perform biological functions in a variety of molecular processes, including transcription, splicing, translation, and even regulating protein function. RNAs adopt specific three-dimensional conformations to enable their functions. Experimental determination of high-resolution RNA structures using x-ray crystallography is both laborious and demands expertise, thus, hindering our comprehension of RNA structural biology. The computational modeling of RNA structure was a milestone in the birth of bioinformatics. Although computational modeling has been greatly improved over the last decade showing many successful cases, the accuracy of such computational modeling is not only length-dependent but also varies according to the complexity of the structure. To increase credibility, various experimental data were integrated into computational modeling. In this review, we summarize the experiments that can be integrated into RNA structure modeling as well as the computational methods based on these experimental data. We also demonstrate how computational modeling can help the experimental determination of RNA structure. We highlight the recent advances in computational modeling which can offer reliable structure models using high-throughput experimental data.
| S-EPMC7649352 | biostudies-literature
Project description:SummaryChemical mapping experiments allow for nucleotide resolution assessment of RNA structure. We demonstrate that different strategies of integrating probing data with thermodynamics-based RNA secondary structure prediction algorithms can be implemented by means of soft constraints. This amounts to incorporating suitable pseudo-energies into the standard energy model for RNA secondary structures. As a showcase application for this new feature of the ViennaRNA Package we compare three distinct, previously published strategies to utilize SHAPE reactivities for structure prediction. The new tool is benchmarked on a set of RNAs with known reference structure.Availability and implementationThe capability for SHAPE directed RNA folding is part of the upcoming release of the ViennaRNA Package 2.2, for which a preliminary release is already freely available at http://www.tbi.univie.ac.at/RNA.Contactmichael.wolfinger@univie.ac.atSupplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC4681990 | biostudies-literature