ABSTRACT
The continuous emergence of novel SARS-CoV-2 variants poses new challenges to the fight against the COVID-19 pandemic. The newly emerging Omicron strain caused serious immune escape and raised unprecedented concern all over the world. The development of antibody targeting conserved and universal epitope is urgently needed. A subset neutralizing antibody(nAbs) against COVID-19 from convalescent patients were isolated in our previous study. Here in this study, we investigated the accommodation of these nAbs to SARS-CoV-2 variants of concerns (VOCs), revealing that IgG 553-49 neutralizes pseudovirus of SARS-CoV-2 Omicron variant. In addition, we determined the cryo-EM structure of SARS-CoV-2 spike complexed with three antibodies targeting different epitopes, including 553-49, 553-15 and 553-60. Notably, 553-49 targets a novel conserved epitope and neutralizes virus by disassembling spike trimers. 553-15, an antibody that neutralizes all the other VOCs except omicron, cross-links two spike trimers to form trimer dimer, demonstrating that 553-15 neutralizes virus by steric hindrance and virion aggregation. These findings suggest the potential to develop 49 and other antibody targeting this highly conserved epitope as promising cocktail therapeutics reagent for COVID-19.
Subject(s)
COVID-19ABSTRACT
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education. Meanwhile, the COVID-19 condition is very different among the countries (e.g., policies and development of the epidemic), and thus citizens would be interested in news in foreign countries. We build a system for worldwide COVID-19 information aggregation containing reliable articles from 10 regions in 7 languages sorted by topics. Our reliable COVID-19 related website dataset collected through crowdsourcing ensures the quality of the articles. A neural machine translation module translates articles in other languages into Japanese and English. A BERT-based topic-classifier trained on our article-topic pair dataset helps users find their interested information efficiently by putting articles into different categories.