Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Understanding of the RNA virus and its interactions with host proteins could improve therapeutic interventions for COVID-19. By using icSHAPE, we determined the structural landscape of SARS-CoV-2 RNA in infected human cells and from refolded RNAs, as well as the regulatory untranslated regions of SARS-CoV-2 and six other coronaviruses. We validated several structural elements predicted in silico and discovered structural features that affect the translation and abundance of subgenomic viral RNAs in cells. The structural data informed a deep-learning tool to predict 42 host proteins that bind to SARS-CoV-2 RNA. Strikingly, antisense oligonucleotides targeting the structural elements and FDA-approved drugs inhibiting the SARS-CoV-2 RNA binding proteins dramatically reduced SARS-CoV-2 infection in cells derived from human liver and lung tumors. Our findings thus shed light on coronavirus and reveal multiple candidate therapeutics for COVID-19 treatment.

严重急性呼吸系统综合症冠状病毒 2 (SARS-CoV-2) 是持续的 2019 冠状病毒病 (COVID-19) 大流行的原因。了解 RNA 病毒及其与宿主蛋白的相互作用可以改善 COVID-19 的治疗干预措施。通过使用 icSHAPE，我们确定了受感染人类细胞中 SARS-CoV-2 RNA 和重折叠 RNA 的结构景观，以及 SARS-CoV-2 和其他六种冠状病毒的调节性非翻译区。我们验证了计算机预测的几个结构元素并发现了影响细胞中亚基因组病毒 RNA 翻译和丰度的结构特征。结构数据为深度学习工具提供了信息，可以预测 42 种与 SARS-CoV-2 RNA 结合的宿主蛋白。引人注目的是，针对结构元素的反义寡核苷酸和 FDA 批准的抑制 SARS-CoV-2 RNA 结合蛋白的药物显着降低了 SARS-CoV-2 在源自人类肝脏和肺部肿瘤的细胞中的感染。因此，我们的研究结果阐明了冠状病毒，并揭示了治疗 COVID-19 的多种候选疗法。