SARS-CoV-2 Ribosomal Frameshifting Pseudoknot: Detection of Inter-viral Structural Similarity

Trinity, Luke; Lansing, Lance; Jabbari, Hosna; Stege, Ulrike

doi:10.1109/ichi52183.2021.00080

Cited by 4 publications

(3 citation statements)

References 60 publications

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“…Our results contribute to RNA structural prediction by providing a sampling of the landscape of notable-and previously unidentified-non-native secondary structures, which may play a role in regulating frameshifting [6,47]. Whereas previous work recognized the importance of non-native pseudoknotted structures, the structural paths delineated here disclose non-native structure formation from initial stable stems.…”

Section: Plos Computational Biologymentioning

confidence: 64%

Shapify: Paths to SARS-CoV-2 frameshifting pseudoknot

et al. 2023

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Multiple coronaviruses including MERS-CoV causing Middle East Respiratory Syndrome, SARS-CoV causing SARS, and SARS-CoV-2 causing COVID-19, use a mechanism known as −1 programmed ribosomal frameshifting (−1 PRF) to replicate. SARS-CoV-2 possesses a unique RNA pseudoknotted structure that stimulates −1 PRF. Targeting −1 PRF in SARS-CoV-2 to impair viral replication can improve patients’ prognoses. Crucial to developing these therapies is understanding the structure of the SARS-CoV-2 −1 PRF pseudoknot. Our goal is to expand knowledge of −1 PRF structural conformations. Following a structural alignment approach, we identify similarities in −1 PRF pseudoknots of SARS-CoV-2, SARS-CoV, and MERS-CoV. We provide in-depth analysis of the SARS-CoV-2 and MERS-CoV −1 PRF pseudoknots, including reference and noteworthy mutated sequences. To better understand the impact of mutations, we provide insight on −1 PRF pseudoknot sequence mutations and their effect on resulting structures. We introduce Shapify, a novel algorithm that given an RNA sequence incorporates structural reactivity (SHAPE) data and partial structure information to output an RNA secondary structure prediction within a biologically sound hierarchical folding approach. Shapify enhances our understanding of SARS-CoV-2 −1 PRF pseudoknot conformations by providing energetically favourable predictions that are relevant to structure-function and may correlate with −1 PRF efficiency. Applied to the SARS-CoV-2 −1 PRF pseudoknot, Shapify unveils previously unknown paths from initial stems to pseudoknotted structures. By contextualizing our work with available experimental data, our structure predictions motivate future RNA structure-function research and can aid 3-D modeling of pseudoknots.

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Section: Plos Computational Biologymentioning

confidence: 64%

Shapify: Paths to SARS-CoV-2 frameshifting pseudoknot

et al. 2023

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“…Shapify (Trinity, Lansing, Jabbari, & Stege, 2021) is a recent method that combines SHAPE reactivity scores with the hierarchical folding hypothesis to improve structure prediction for single RNA molecules. When compared to ShapeKnots, Shapify's set of predicted structures were closer to the MFE than those of ShapeKnots.…”

Section: Methods That Incorporate Experimental Datamentioning

confidence: 99%

Pseudoknots in RNA Structure Prediction

2023

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RNA molecules play active roles in the cell and are important for numerous applications in biotechnology and medicine. The function of an RNA molecule stems from its structure. RNA structure determination is time consuming, challenging, and expensive using experimental methods. Thus, much research has been directed at RNA structure prediction through computational means. Many of these methods focus primarily on the secondary structure of the molecule, ignoring the possibility of pseudoknotted structures. However, pseudoknots are known to play functional roles in many RNA molecules or in their method of interaction with other molecules. Improving the accuracy and efficiency of computational methods that predict pseudoknots is an ongoing challenge for single RNA molecules, RNA-RNA interactions, and RNA-protein interactions. To improve the accuracy of prediction, many methods focus on specific applications while restricting the length and the class of the pseudoknotted structures they can identify. In recent years, computational methods for structure prediction have begun to catch up with the impressive developments seen in biotechnology. Here, we provide a non-comprehensive overview of available pseudoknot prediction methods and their best-use cases.

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“…Structurally it consists of a seven nucleotide slippery site, a five nucleotide spacer, and a pseudoknot; when the processive ribosome encounters the pseudoknot, it retreats by one nucleotide within the slippery site, re-establishing the reading frame for translation and ensuring that ORF1b and downstream genes are read in the correct frame [16]. In SARS-CoV-2 there is some uncertainty as to the exact form the FSE pseudoknot takes as it appears to be dynamic (see [17] for an excellent summary) [18, 19]. The pseudoknot in conjunction with the slippery site stalls ribosome processivity, allowing slippage to occur, but only 15-30% of the time, thus maintaining a ratio of ORF1a:ORF1b where genes encoded by the polyprotein ORF1a are present in excess relative to those encoded by the polyprotein ORF1b [20, 21].…”

Section: Introductionmentioning

confidence: 99%

Prediction of RNA secondary structures in SARS-CoV-2 and comparison with contemporary predictions

Ziesel

Jabbari

2022

Preprint

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SARS-CoV-2, the causative agent of covid-19, is known to exhibit secondary structure in its 5' and 3' untranslated regions, along with the frameshifting stimulatory element situated between ORF1a and 1b. To identify further regions containing conserved structure, multiple sequence alignment with related coronaviruses was used as a starting point from which to apply a modified computational pipeline developed to identify non-coding RNA elements in vertebrate eukaryotes. Three different RNA structural prediction approaches were employed in this modified pipeline. Forty genomic regions deemed likely to harbour structure were identified, ten of which exhibited three-way consensus substructure predictions amongst our predictive utilities. Intracomparison of the pipeline's predictive utilities, along with intercomparison with three previously published SARS-CoV-2 structural datasets, were performed. Limited agreement as to precise structure was observed, although different approaches appear to agree upon regions likely to contain structure in the viral genome.

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SARS-CoV-2 Ribosomal Frameshifting Pseudoknot: Detection of Inter-viral Structural Similarity

Cited by 4 publications

References 60 publications

Shapify: Paths to SARS-CoV-2 frameshifting pseudoknot

Shapify: Paths to SARS-CoV-2 frameshifting pseudoknot

Pseudoknots in RNA Structure Prediction

Prediction of RNA secondary structures in SARS-CoV-2 and comparison with contemporary predictions

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