De novo 3D models of SARS-CoV-2 RNA elements from consensus experimental secondary structures. Rangan, R., Watkins, A. M, Chacon, J., Kretsch, R., Kladwang, W., Zheludev, I. N, Townley, J., Rynge, M., Thain, G., & Das, R. Nucleic Acids Research, 49(6):3092-3108, 03, 2021.
De novo 3D models of SARS-CoV-2 RNA elements from consensus experimental secondary structures [link]Paper  doi  abstract   bibtex   
The rapid spread of COVID-19 is motivating development of antivirals targeting conserved SARS-CoV-2 molecular machinery. The SARS-CoV-2 genome includes conserved RNA elements that offer potential small-molecule drug targets, but most of their 3D structures have not been experimentally characterized. Here, we provide a compilation of chemical mapping data from our and other labs, secondary structure models, and 3D model ensembles based on Rosetta's FARFAR2 algorithm for SARS-CoV-2 RNA regions including the individual stems SL1-8 in the extended 5′ UTR; the reverse complement of the 5′ UTR SL1-4; the frameshift stimulating element (FSE); and the extended pseudoknot, hypervariable region, and s2m of the 3′ UTR. For eleven of these elements (the stems in SL1–8, reverse complement of SL1–4, FSE, s2m and 3′ UTR pseudoknot), modeling convergence supports the accuracy of predicted low energy states; subsequent cryo-EM characterization of the FSE confirms modeling accuracy. To aid efforts to discover small molecule RNA binders guided by computational models, we provide a second set of similarly prepared models for RNA riboswitches that bind small molecules. Both datasets (‘FARFAR2-SARS-CoV-2’, https://github.com/DasLab/FARFAR2-SARS-CoV-2; and ‘FARFAR2-Apo-Riboswitch’, at https://github.com/DasLab/FARFAR2-Apo-Riboswitch’) include up to 400 models for each RNA element, which may facilitate drug discovery approaches targeting dynamic ensembles of RNA molecules.
@Article{	  rangan-nucleicacids-2021,
  Author	= {Rangan, Ramya and Watkins, Andrew M and Chacon, Jose and
		  Kretsch, Rachael and Kladwang, Wipapat and Zheludev, Ivan N
		  and Townley, Jill and Rynge, Mats and Thain, Gregory and
		  Das, Rhiju},
  Title		= "{De novo 3D models of SARS-CoV-2 RNA elements from
		  consensus experimental secondary structures}",
  Journal	= {Nucleic Acids Research},
  Volume	= {49},
  Number	= {6},
  Pages		= {3092-3108},
  Year		= {2021},
  Month		= {03},
  Abstract	= "{The rapid spread of COVID-19 is motivating development of
		  antivirals targeting conserved SARS-CoV-2 molecular
		  machinery. The SARS-CoV-2 genome includes conserved RNA
		  elements that offer potential small-molecule drug targets,
		  but most of their 3D structures have not been
		  experimentally characterized. Here, we provide a
		  compilation of chemical mapping data from our and other
		  labs, secondary structure models, and 3D model ensembles
		  based on Rosetta's FARFAR2 algorithm for SARS-CoV-2 RNA
		  regions including the individual stems SL1-8 in the
		  extended 5′ UTR; the reverse complement of the 5′ UTR
		  SL1-4; the frameshift stimulating element (FSE); and the
		  extended pseudoknot, hypervariable region, and s2m of the
		  3′ UTR. For eleven of these elements (the stems in
		  SL1–8, reverse complement of SL1–4, FSE, s2m and 3′
		  UTR pseudoknot), modeling convergence supports the accuracy
		  of predicted low energy states; subsequent cryo-EM
		  characterization of the FSE confirms modeling accuracy. To
		  aid efforts to discover small molecule RNA binders guided
		  by computational models, we provide a second set of
		  similarly prepared models for RNA riboswitches that bind
		  small molecules. Both datasets (‘FARFAR2-SARS-CoV-2’,
		  https://github.com/DasLab/FARFAR2-SARS-CoV-2; and
		  ‘FARFAR2-Apo-Riboswitch’, at
		  https://github.com/DasLab/FARFAR2-Apo-Riboswitch’)
		  include up to 400 models for each RNA element, which may
		  facilitate drug discovery approaches targeting dynamic
		  ensembles of RNA molecules.}",
  ISSN		= {0305-1048},
  DOI		= {10.1093/nar/gkab119},
  URL		= {https://doi.org/10.1093/nar/gkab119},
  EPrint	= {https://academic.oup.com/nar/article-pdf/49/6/3092/36884656/gkab119.pdf}
}
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