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  • 1
    Online Resource
    Online Resource
    Modified RNAs and Predictions with the ViennaRNA Package
    Oxford University Press (OUP) ; 2023
    In:  Bioinformatics
    Details
    In: Bioinformatics, Oxford University Press (OUP)
    Abstract: In living organisms, many RNA molecules are modified post-transcriptionally. This turns the widely known four-letter RNA alphabet ACGU into a much larger one with currently more than 300 known distinct modified bases. The roles for the majority of modified bases remain uncertain, but many are already well-known for their ability to influence the preferred structures that an RNA may adopt. In fact, tRNAs sometimes require certain modifications to fold into their cloverleaf shaped structure. However, predicting the structure of RNAs with base modifications is still difficult due to the lack of efficient algorithms that can deal with the extended sequence alphabet, as well as missing parameter sets that account for the changes in stability induced by the modified bases. Results We present an approach to include sparse energy parameter data for modified bases into the ViennaRNA Package. Our method does not require any changes to the underlying efficient algorithms but instead uses a set of plug-in constraints that adapt the predictions in terms of loop evaluation at runtime. These adaptations are efficient in the sense that they are only performed for loops where additional parameters are actually available for. In addition, our approach also facilitates the inclusion of more modified bases as soon as further parameters become available. Availability Source code and documentation are available at https://www.tbi.univie.ac.at/RNA Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btad696
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1468345-3
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  • 2
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    Investigating RNA–RNA interactions through computational and biophysical analysis
    Oxford University Press (OUP) ; 2023
    In:  Nucleic Acids Research Vol. 51, No. 9 ( 2023-05-22), p. 4588-4601
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 51, No. 9 ( 2023-05-22), p. 4588-4601
    Abstract: Numerous viruses utilize essential long-range RNA–RNA genome interactions, specifically flaviviruses. Using Japanese encephalitis virus (JEV) as a model system, we computationally predicted and then biophysically validated and characterized its long-range RNA–RNA genomic interaction. Using multiple RNA computation assessment programs, we determine the primary RNA–RNA interacting site among JEV isolates and numerous related viruses. Following in vitro transcription of RNA, we provide, for the first time, characterization of an RNA–RNA interaction using size-exclusion chromatography coupled with multi-angle light scattering and analytical ultracentrifugation. Next, we demonstrate that the 5′ and 3′ terminal regions of JEV interact with nM affinity using microscale thermophoresis, and this affinity is significantly reduced when the conserved cyclization sequence is not present. Furthermore, we perform computational kinetic analyses validating the cyclization sequence as the primary driver of this RNA–RNA interaction. Finally, we examined the 3D structure of the interaction using small-angle X-ray scattering, revealing a flexible yet stable interaction. This pathway can be adapted and utilized to study various viral and human long-non-coding RNA–RNA interactions and determine their binding affinities, a critical pharmacological property of designing potential therapeutics.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkad223
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 3
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    Mono-valent salt corrections for RNA secondary structures in the ViennaRNA package
    Springer Science and Business Media LLC ; 2023
    In:  Algorithms for Molecular Biology Vol. 18, No. 1 ( 2023-07-29)
    Details
    In: Algorithms for Molecular Biology, Springer Science and Business Media LLC, Vol. 18, No. 1 ( 2023-07-29)
    Abstract: RNA features a highly negatively charged phosphate backbone that attracts a cloud of counter-ions that reduce the electrostatic repulsion in a concentration dependent manner. Ion concentrations thus have a large influence on folding and stability of RNA structures. Despite their well-documented effects, salt effects are not handled consistently by currently available secondary structure prediction algorithms. Combining Debye-Hückel potentials for line charges and Manning’s counter-ion condensation theory, Einert et al. (Biophys J 100: 2745-2753, 2011) modeled the energetic contributions of monovalent cations on loops and helices. Results The model of Einert et al. is adapted to match the structure of the dynamic programming recursion of RNA secondary structure prediction algorithms. An empirical term describing the salt dependence of the duplex initiation energy is added to improve co-folding predictions for two or more RNA strands. The slightly modified model is implemented in the package in such way that only the energy parameters but not the algorithmic structure is affected. A comparison with data from the literature show that predicted free energies and melting temperatures are in reasonable agreement with experiments. Conclusion The new feature in the package makes it possible to study effects of salt concentrations on RNA folding in a systematic manner. Strictly speaking, the model pertains only to mono-valent cations, and thus covers the most important parameter, i.e., the NaCl concentration. It remains a question for future research to what extent unspecific effects of bi- and tri-valent cations can be approximated in a similar manner. Availability Corrections for the concentration of monovalent cations are available in the package starting from version 2.6.0.
    Type of Medium: Online Resource
    ISSN: 1748-7188
    URL: Article
    DOI: 10.1186/s13015-023-00236-0
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2023
    detail.hit.zdb_id: 2224970-9
    SSG: 12
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  • 4
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    Zinc-finger protein CNBP alters the 3-D structure of lncRNA Braveheart in solution
    Springer Science and Business Media LLC ; 2020
    In:  Nature Communications Vol. 11, No. 1 ( 2020-01-09)
    Details
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 11, No. 1 ( 2020-01-09)
    Abstract: Long non-coding RNAs (lncRNAs) constitute a significant fraction of the transcriptome, playing important roles in development and disease. However, our understanding of structure-function relationships for this emerging class of RNAs has been limited to secondary structures. Here, we report the 3-D atomistic structural study of epigenetic lncRNA, Braveheart (Bvht) , and its complex with CNBP (Cellular Nucleic acid Binding Protein). Using small angle X-ray scattering (SAXS), we elucidate the ensemble of Bvht RNA conformations in solution, revealing that Bvht lncRNA has a well-defined, albeit flexible 3-D structure that is remodeled upon CNBP binding. Our study suggests that CNBP binding requires multiple domains of Bvht and the RHT/AGIL RNA motif. We show that RHT/AGIL, previously shown to interact with CNBP, contains a highly flexible loop surrounded by more ordered helices. As one of the largest RNA-only 3-D studies, the work lays the foundation for future structural studies of lncRNA-protein complexes.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    URL: Article
    DOI: 10.1038/s41467-019-13942-4
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2020
    detail.hit.zdb_id: 2553671-0
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  • 5
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    Local RNA folding revisited
    World Scientific Pub Co Pte Ltd ; 2023
    In:  Journal of Bioinformatics and Computational Biology Vol. 21, No. 04 ( 2023-08)
    Details
    In: Journal of Bioinformatics and Computational Biology, World Scientific Pub Co Pte Ltd, Vol. 21, No. 04 ( 2023-08)
    Abstract: Most of the functional RNA elements located within large transcripts are local. Local folding therefore serves a practically useful approximation to global structure prediction. Due to the sensitivity of RNA secondary structure prediction to the exact definition of sequence ends, accuracy can be increased by averaging local structure predictions over multiple, overlapping sequence windows. These averages can be computed efficiently by dynamic programming. Here we revisit the local folding problem, present a concise mathematical formalization that generalizes previous approaches and show that correct Boltzmann samples can be obtained by local stochastic backtracing in McCaskill’s algorithms but not from local folding recursions. Corresponding new features are implemented in the ViennaRNA package to improve the support of local folding. Applications include the computation of maximum expected accuracy structures from RNAplfold data and a mutual information measure to quantify the sensitivity of individual sequence positions.
    Type of Medium: Online Resource
    ISSN: 0219-7200 , 1757-6334
    URL: Article
    DOI: 10.1142/S0219720023500166
    Language: English
    Publisher: World Scientific Pub Co Pte Ltd
    Publication Date: 2023
    SSG: 12
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  • 6
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    DataSheet1.PDF
    Frontiers Media SA ; 2022
    In:  Frontiers in Bioinformatics Vol. 2 ( 2022-7-11)
    Details
    In: Frontiers in Bioinformatics, Frontiers Media SA, Vol. 2 ( 2022-7-11)
    Abstract: Machine learning (ML) and in particular deep learning techniques have gained popularity for predicting structures from biopolymer sequences. An interesting case is the prediction of RNA secondary structures, where well established biophysics based methods exist. The accuracy of these classical methods is limited due to lack of experimental parameters and certain simplifying assumptions and has seen little improvement over the last decade. This makes RNA folding an attractive target for machine learning and consequently several deep learning models have been proposed in recent years. However, for ML approaches to be competitive for de-novo structure prediction, the models must not just demonstrate good phenomenological fits, but be able to learn a (complex) biophysical model. In this contribution we discuss limitations of current approaches, in particular due to biases in the training data. Furthermore, we propose to study capabilities and limitations of ML models by first applying them on synthetic data (obtained from a simplified biophysical model) that can be generated in arbitrary amounts and where all biases can be controlled. We assume that a deep learning model that performs well on these synthetic, would also perform well on real data, and vice versa. We apply this idea by testing several ML models of varying complexity. Finally, we show that the best models are capable of capturing many, but not all, properties of RNA secondary structures. Most severely, the number of predicted base pairs scales quadratically with sequence length, even though a secondary structure can only accommodate a linear number of pairs.
    Type of Medium: Online Resource
    ISSN: 2673-7647
    URL: Article
    URL: Article
    DOI: 10.3389/fbinf.2022.835422
    DOI: 10.3389/fbinf.2022.835422.s001
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 3091287-8
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  • 7
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    RNAxplorer: harnessing the power of guiding potentials to sample RNA landscapes
    Oxford University Press (OUP) ; 2021
    In:  Bioinformatics Vol. 37, No. 15 ( 2021-08-09), p. 2126-2133
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 37, No. 15 ( 2021-08-09), p. 2126-2133
    Abstract: Predicting the folding dynamics of RNAs is a computationally difficult problem, first and foremost due to the combinatorial explosion of alternative structures in the folding space. Abstractions are therefore needed to simplify downstream analyses, and thus make them computationally tractable. This can be achieved by various structure sampling algorithms. However, current sampling methods are still time consuming and frequently fail to represent key elements of the folding space. Method We introduce RNAxplorer, a novel adaptive sampling method to efficiently explore the structure space of RNAs. RNAxplorer uses dynamic programming to perform an efficient Boltzmann sampling in the presence of guiding potentials, which are accumulated into pseudo-energy terms and reflect similarity to already well-sampled structures. This way, we effectively steer sampling toward underrepresented or unexplored regions of the structure space. Results We developed and applied different measures to benchmark our sampling methods against its competitors. Most of the measures show that RNAxplorer produces more diverse structure samples, yields rare conformations that may be inaccessible to other sampling methods and is better at finding the most relevant kinetic traps in the landscape. Thus, it produces a more representative coarse graining of the landscape, which is well suited to subsequently compute better approximations of RNA folding kinetics. Availabilityand implementation https://github.com/ViennaRNA/RNAxplorer/. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btab066
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 8
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    DrTransformer : heuristic cotranscriptional RNA folding using the nearest neighbor energy model
    Oxford University Press (OUP) ; 2023
    In:  Bioinformatics Vol. 39, No. 1 ( 2023-01-01)
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 39, No. 1 ( 2023-01-01)
    Abstract: Folding during transcription can have an important influence on the structure and function of RNA molecules, as regions closer to the 5′ end can fold into metastable structures before potentially stronger interactions with the 3′ end become available. Thermodynamic RNA folding models are not suitable to predict structures that result from cotranscriptional folding, as they can only calculate properties of the equilibrium distribution. Other software packages that simulate the kinetic process of RNA folding during transcription exist, but they are mostly applicable for short sequences. Results We present a new algorithm that tracks changes to the RNA secondary structure ensemble during transcription. At every transcription step, new representative local minima are identified, a neighborhood relation is defined and transition rates are estimated for kinetic simulations. After every simulation, a part of the ensemble is removed and the remainder is used to search for new representative structures. The presented algorithm is deterministic (up to numeric instabilities of simulations), fast (in comparison with existing methods), and it is capable of folding RNAs much longer than 200 nucleotides. Availability and implementation This software is open-source and available at https://github.com/ViennaRNA/drtransformer. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btad034
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 1468345-3
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  • 9
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    Insights into the secondary and tertiary structure of the Bovine Viral Diarrhea Virus Internal Ribosome Entry Site
    Informa UK Limited ; 2022
    In:  RNA Biology Vol. 19, No. 1 ( 2022-12-31), p. 496-506
    Details
    In: RNA Biology, Informa UK Limited, Vol. 19, No. 1 ( 2022-12-31), p. 496-506
    Type of Medium: Online Resource
    ISSN: 1547-6286 , 1555-8584
    URL: Article
    DOI: 10.1080/15476286.2022.2058818
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2159587-2
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