@article {Sun2020.07.07.192732,
	author = {Sun, Lei and Li, Pan and Ju, Xiaohui and Rao, Jian and Huang, Wenze and Zhang, Shaojun and Xiong, Tuanlin and Xu, Kui and Zhou, Xiaolin and Ren, Lili and Ding, Qiang and Wang, Jianwei and Zhang, Qiangfeng Cliff},
	title = {In vivo structural characterization of the whole SARS-CoV-2 RNA genome identifies host cell target proteins vulnerable to re-purposed drugs},
	elocation-id = {2020.07.07.192732},
	year = {2020},
	doi = {10.1101/2020.07.07.192732},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {SARS-CoV-2 is an RNA virus of the Coronaviridae family that is the causal pathogen of the ongoing Coronavirus Disease 2019 pandemic. There are currently no antiviral drugs or vaccines to treat COVID-19, and the failure to identify effective interventions can be blamed on our incomplete understanding of the nature of this virus and its host cell infection process. Here, we experimentally determined structural maps of the SARS-CoV-2 RNA genome in infected human cells and also characterized in vitro refolded RNA structures for SARS-CoV-2 and 6 other coronaviruses. Our in vivo data confirms several structural elements predicted from theoretical analysis and goes much further in revealing many previously unknown structural features that functionally impact viral translation and discontinuous transcription in cells. Importantly, we harnessed our in vivo structure data alongside a deep-learning tool and accurately predicted several dozen functionally related host cell proteins that bind to the SARS-CoV-2 RNA genome, none of which were known previously. Thus, our in vivo structural study lays a foundation for coronavirus RNA biology and indicates promising directions for the rapid development of therapeutics to treat COVID-19.HIGHLIGHTSWe mapped the in vivo structure and built secondary structural models of the SARS-CoV-2 RNA genomeWe discovered functionally impactful structural features in the RNA genomes of multiple coronavirusesWe predicted and validated host cell proteins that bind to the SARS-CoV-2 RNA genome based on our in vivo RNA structural data using a deep-learning toolCompeting Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2020/07/08/2020.07.07.192732},
	eprint = {https://www.biorxiv.org/content/early/2020/07/08/2020.07.07.192732.full.pdf},
	journal = {bioRxiv}
}