QRNAS: software tool for refinement of nucleic acid structures

Stasiewicz, Juliusz; Mukherjee, Sunandan; Nithin, Chandran; Bujnicki, Janusz M.

doi:10.1186/s12900-019-0103-1

Cited by 66 publications

(71 citation statements)

References 50 publications

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“…For the 1000 best-scored conformations for each segment, all-atom models were generated and their conformational details were refined by running 5000 steps of a steepest-descent minimization with QRNAS ( 36 ) which is an extension of the AMBER simulation method ( 37 ) with additional restraints. For each of the 1000 models, the secondary structure was extracted by running ClaRNA ( 38 ) and the set of 1000 secondary structures was used to generate the consensus structure for each segment, to aid in the data visualization and for comparison with the secondary structure used as restraints.…”

Section: Methodsmentioning

confidence: 99%

Genome-wide mapping of SARS-CoV-2 RNA structures identifies therapeutically-relevant elements

Manfredonia

Nithin

Ponce-Salvatierra

et al. 2020

Nucleic Acids Research

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242

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SARS-CoV-2 is a betacoronavirus with a linear single-stranded, positive-sense RNA genome, whose outbreak caused the ongoing COVID-19 pandemic. The ability of coronaviruses to rapidly evolve, adapt, and cross species barriers makes the development of effective and durable therapeutic strategies a challenging and urgent need. As for other RNA viruses, genomic RNA structures are expected to play crucial roles in several steps of the coronavirus replication cycle. Despite this, only a handful of functionally-conserved coronavirus structural RNA elements have been identified to date. Here, we performed RNA structure probing to obtain single-base resolution secondary structure maps of the full SARS-CoV-2 coronavirus genome both in vitro and in living infected cells. Probing data recapitulate the previously described coronavirus RNA elements (5′ UTR and s2m), and reveal new structures. Of these, ∼10.2% show significant covariation among SARS-CoV-2 and other coronaviruses, hinting at their functionally-conserved role. Secondary structure-restrained 3D modeling of these segments further allowed for the identification of putative druggable pockets. In addition, we identify a set of single-stranded segments in vivo, showing high sequence conservation, suitable for the development of antisense oligonucleotide therapeutics. Collectively, our work lays the foundation for the development of innovative RNA-targeted therapeutic strategies to fight SARS-related infections.

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Section: Methodsmentioning

confidence: 99%

Genome-wide mapping of SARS-CoV-2 RNA structures identifies therapeutically-relevant elements

Manfredonia

Nithin

Ponce-Salvatierra

et al. 2020

Nucleic Acids Research

Self Cite

242

383

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“…SimRNA employs a statistical potential to approximate the energy using additional restraints on the secondary structure taken from miRBase database (52). Finally, the best-scoring model from SimRNA was optimized in the full-atom representation with QRNAS (53). The starting model of the wt33 peptide was generated based on the crystal structure of the TAV2b/siRNA complex (PDB ID: 2ZI0) (37).…”

Section: Modeling Of Wt33 Peptide and Rnasmentioning

confidence: 99%

Constrained Peptides Mimic a Viral Suppressor of RNA Silencing

Kuepper¹,

McLoughlin²,

Neubacher³

et al. 2020

Preprint

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The structure-based design of constrained alpha-helical peptides derived from the viral suppressor of RNA silencing TAV2b is described. We observe that the introduction of two inter-side chain crosslinks provides peptides with increased alpha-helicity and protease stability. One of these modified peptides (B3) shows high affinity for different double-stranded RNA structures including a palindromic siRNA as well as microRNA-21 and its precursor pre-miR-21. Notably, B3 binding to pre-miR-21 inhibits Dicer processing in a biochemical assay. As a further characteristic this peptide also exhibits cellular entry. <br>

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“…Therefore, the results for both SimRNA and HNADOCK may be further improved with more accurate RNA secondary structure or interaction predictions. The HNADOCK models may also be further optimized by using NA refinement tools like QRNAS (49) or RNAfitme (50) to correct the errors introduced by low resolution modeling and/or rigid docking methods.…”

Section: Resultsmentioning

confidence: 99%

HNADOCK: a nucleic acid docking server for modeling RNA/DNA–RNA/DNA 3D complex structures

Wang

Tao

et al. 2019

Nucleic Acids Research

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Interactions between nuclide acids (RNA/DNA) play important roles in many basic cellular activities like transcription regulation, RNA processing, and protein synthesis. Therefore, determining the complex structures between RNAs/DNAs is crucial to understand the molecular mechanism of related RNA/DNA–RNA/DNA interactions. Here, we have presented HNADOCK, a user-friendly web server for nucleic acid (NA)–nucleic acid docking to model the 3D complex structures between two RNAs/DNAs, where both sequence and structure inputs are accepted for RNAs, while only structure inputs are supported for DNAs. HNADOCK server was tested through both unbound structure and sequence inputs on the benchmark of 60 RNA–RNA complexes and compared with the state-of-the-art algorithm SimRNA. For structure input, HNADOCK server achieved a high success rate of 71.7% for top 10 predictions, compared to 58.3% for SimRNA. For sequence input, HNADOCK server also obtained a satisfactory performance and gave a success rate of 83.3% when the bound RNA templates are included or 53.3% when excluding those bound RNA templates. It was also found that inclusion of the inter-RNA base-pairing information from RNA–RNA interaction prediction can significantly improve the docking accuracy, especially for the top prediction. HNADOCK is fast and can normally finish a job in about 10 minutes. The HNADOCK web server is available at http://huanglab.phys.hust.edu.cn/hnadock/.

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QRNAS: software tool for refinement of nucleic acid structures

Cited by 66 publications

References 50 publications

Genome-wide mapping of SARS-CoV-2 RNA structures identifies therapeutically-relevant elements

Genome-wide mapping of SARS-CoV-2 RNA structures identifies therapeutically-relevant elements

Constrained Peptides Mimic a Viral Suppressor of RNA Silencing

HNADOCK: a nucleic acid docking server for modeling RNA/DNA–RNA/DNA 3D complex structures

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