R-chie: a web server and R package for visualizing cis and trans RNA–RNA, RNA–DNA and DNA–DNA interactions

Background: Multiple viruses including HIV, MERS-CoV (coronavirus responsible for Middle East Respiratory Syndrome, MERS), SARS-CoV (coronavirus responsible for SARS) and SARS-CoV-2 (coronavirus responsible for COVID-19) use a mechanism known as -1 programmed ribosomal frameshifting (-1 PRF) to successfully replicate. SARS-CoV-2 possesses a unique RNA pseudoknotted structure that stimulates -1 PRF. Recent experiments identified small molecules as antiviral agents that can bind to the pseudoknot and disrupt its stimulation of -1 PRF. Targeting -1 PRF in SARS-CoV-2 to impair viral replication can improve patients' prognoses.Crucial to developing these successful therapies is modeling the structure of the SARS-CoV-2 -1 PRF pseudoknot.Our goal is to expand knowledge of possible pseudoknot conformations. Results: Following a structural alignment approach, we identify similarities in -1 PRF pseudoknots of SARS-CoV-2, SARS-CoV, and MERS-CoV. 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 helps us to better understand non-native SARS-CoV-2 -1 PRF pseudoknot conformations that are relevant to structure function and may correlate with -1 PRF efficiency. We provide in-depth analysis by investigating the structural landscape for the SARS-CoV-2 -1 PRF pseudoknot, including reference and mutated sequences. To better understand the impact of mutations, we provide insight on SARS-CoV-2 -1 PRF pseudoknot sequence mutations and their effect on the resulting structure. Conclusion: We identify the consensus structure for SARS-CoV, SARS-CoV-2, and MERS-CoV -1 PRF pseudoknots; this similarity in functional RNA structures aids treatment preparation for existing and emergent viruses. Shapify predictions are guided both by SHAPE data and partial structure information. Applied to the SARS-CoV-2 -1 PRF pseudoknot, Shapify unveiled previously unknown pathways from initial stems to pseudoknotted secondary structures. Where SHAPE data is unavailable we provide predictions for noteworthy SARS-CoV-2 -1 PRF mutated pseudoknot sequences. 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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“…3), black arcs represent a suboptimal structure with only slightly higher free energy. Visualization generated using R-chie web server [77]. 2).…”

Section: Input Datamentioning

confidence: 99%

Shapify: Pathways to SARS-CoV-2 Frameshifting Pseudoknot

Trinity

Wark

Lansing

et al. 2022

Preprint

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“…Figure 2 shows the RNA secondary structure of the sRNA RprA and the mRNA csgD and their interaction as R- chie plot (53). Arcs, i.e.…”

Section: Resultsmentioning

confidence: 99%

Investigating the concept of accessibility for predicting novel RNA-RNA interactions

Reißer

2021

Preprint

Self Cite

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State-of-the-art methods for predicting novel trans RNA-RNA interactions use the so-called accessibility as key concept. It estimates whether a region in a given RNA sequence is accessible for forming trans interactions, using a thermodynamic model which quantifies its secondary structure features. RNA-RNA interactions are then predicted by finding the minimum free energy base pairing between the two transcripts, taking into account the accessibility as energy penalty. We investigated the underlying assumptions of this approach using the two methods RNAPLEX and INTARNA on two datasets, containing sRNA-mRNA and snoRNA-rRNA interactions, respectively. We find that (1) known trans RNA-RNA interactions frequently overlap regions containing RNA structure features, (2) the estimated accessibility reflects sRNA structures fairly well, but often disagrees with structures of longer transcripts, (3) the prediction performance of RNA-RNA interaction prediction methods is independent of the quality of the estimated accessibility profiles, and (4) one important overall effect of accessibility profiles is to prevent the thermodynamic model from predicting too long interactions. Based on our findings, we conclude that the accessibility concept to the minimum free energy approach to predicting novel RNA-RNA interactions has conceptual limitations and discuss potential ways of improving the field in the future.

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“…We present III), black arcs represent a suboptimal structure with only slightly higher free energy. Visualization generated using R-chie web server [57].…”

Section: Datamentioning

confidence: 99%

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

Trinity

Lansing

Jabbari

et al. 2021

2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)

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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the COVID-19 pandemic. After over 160 million cases and 3.3 million deaths worldwide as of May 2021, it is still unclear when this crisis will end. Thus, there is an urgent need for treatments that improve the prognosis of seriously affected patients. Multiple viruses including HIV, MERS-CoV (coronavirus responsible for Middle East Respiratory Syndrome, MERS), SARS-CoV (coronavirus responsible for SARS) and SARS-CoV-2 use a mechanism known as -1 programmed ribosomal frameshifting (-1 PRF) to successfully replicate. SARS-CoV and SARS-CoV-2 possess a unique RNA pseudoknotted structure that stimulates -1 PRF. Recent experiments identified small molecules as antiviral agents that can bind to the pseudoknot and disrupt its stimulation of -1 PRF. Targeting -1 PRF in SARS-CoV-2 can be an excellent strategy to impair viral replication and improve patients' prognoses. Crucial to developing these successful therapies is modeling the structure of the SARS-CoV-2 -1 PRF pseudoknot. Following a structural alignment approach, we identify similarities in -1 PRF pseudoknots of SARS-CoV-2, SARS-CoV, and MERS-CoV. In addition, we provide a better understanding of the SARS-CoV-2 -1 PRF pseudoknot by investigating the structural landscape using a hierarchical folding approach. We provide in-depth analysis on alternative structure prediction methods based on SARS-CoV-2 structural reactivity (SHAPE) data to contextualize and motivate future RNA structure-function research. Since understanding the impact of mutations is vital to long-term success of treatments based on predicted RNA functional structures, we provide insight on SARS-CoV-2 -1 PRF pseudoknot sequence mutations and their effect on the resulting structure.

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R-chie: a web server and R package for visualizing cis and trans RNA–RNA, RNA–DNA and DNA–DNA interactions

Cited by 16 publications

References 34 publications

Shapify: Pathways to SARS-CoV-2 Frameshifting Pseudoknot

Shapify: Pathways to SARS-CoV-2 Frameshifting Pseudoknot

Investigating the concept of accessibility for predicting novel RNA-RNA interactions

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

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