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Dynamic Interactions of Fully Glycosylated SARS-CoV-2 Spike Protein with Various Antibodies
Yiwei Cao; Yeol Kyo Choi; Martin Frank; Hyeonuk Woo; Sang-Jun Park; Min Sun Yeom; Chaok Seok; Wonpil Im.
Afiliação
  • Yiwei Cao; Lehigh University
  • Yeol Kyo Choi; Lehigh University
  • Martin Frank; Biognos AB
  • Hyeonuk Woo; Seoul National University
  • Sang-Jun Park; Lehigh University
  • Min Sun Yeom; Korean Institute of Science and Technology Information
  • Chaok Seok; Seoul National University
  • Wonpil Im; Lehigh University
Preprint em Inglês | bioRxiv | ID: ppbiorxiv-443519
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ABSTRACT
The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents a public health crisis, and the vaccines that can induce highly potent neutralizing antibodies are essential for ending the pandemic. The spike (S) protein on the viral envelope mediates human angiotensin-converting enzyme 2 (ACE2) binding and thus is the target of a variety of neutralizing antibodies. In this work, we built various S trimer-antibody complex structures on the basis of the fully glycosylated S protein models described in our previous work, and performed all-atom molecular dynamics simulations to get insight into the structural dynamics and interactions between S protein and antibodies. Investigation of the residues critical for S-antibody binding allows us to predict the potential influence of mutations in SARS-CoV-2 variants. Comparison of the glycan conformations between S-only and S-antibody systems reveals the roles of glycans in S-antibody binding. In addition, we explored the antibody binding modes, and the influences of antibody on the motion of S protein receptor binding domains. Overall, our analyses provide a better understanding of S-antibody interactions, and the simulation-based S-antibody interaction maps could be used to predict the influences of S mutation on S-antibody interactions, which will be useful for the development of vaccine and antibody-based therapy.
Texto completo: Disponível Coleções: Preprints Base de dados: bioRxiv Tipo de estudo: Estudo prognóstico Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: bioRxiv Tipo de estudo: Estudo prognóstico Idioma: Inglês Ano de publicação: 2021 Tipo de documento: Preprint
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