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Computational simulations reveal the binding dynamics between human ACE2 and the receptor binding domain of SARS-CoV-2 spike protein
Cecylia Lupala; Xuanxuan Li; Jian Lei; Hong Chen; Jianxun Qi; Haiguang Liu; Xiaodong Su.
Afiliação
  • Cecylia Lupala; Beijing Computational Science Research Center
  • Xuanxuan Li; Beijing Computational Science Research Center
  • Jian Lei; West China Hospital, Sichuan, China
  • Hong Chen; Peking University
  • Jianxun Qi; CAS
  • Haiguang Liu; Beijing Computational Science Research Center
  • Xiaodong Su; Peking University
Preprint em Inglês | bioRxiv | ID: ppbiorxiv-005561
ABSTRACT
A novel coronavirus (the SARS-CoV-2) has been identified in January 2020 as the causal pathogen for COVID-19 pneumonia, an outbreak started near the end of 2019 in Wuhan, China. The SARS-CoV-2 was found to be closely related to the SARS-CoV, based on the genomic analysis. The Angiotensin converting enzyme 2 protein (ACE2) utilized by the SARS-CoV as a receptor was found to facilitate the infection of SARS-CoV-2 as well, initiated by the binding of the spike protein to the human ACE2. Using homology modeling and molecular dynamics (MD) simulation methods, we report here the detailed structure of the ACE2 in complex with the receptor binding domain (RBD) of the SARS-CoV-2 spike protein. The predicted model is highly consistent with the experimentally determined complex structures. Plausible binding modes between human ACE2 and the RBD were revealed from all-atom MD simulations. The simulation data further revealed critical residues at the complex interface and provided more details about the interactions between the SARS-CoV-2 RBD and human ACE2. Two mutants mimicking rat ACE2 were modeled to study the mutation effects on RBD binding to ACE2. The simulations showed that the N-terminal helix and the K353 of the human ACE2 alter the binding modes of the CoV2-RBD to the ACE2.
Licença
cc_by_nc_nd
Texto completo: Disponível Coleções: Preprints Base de dados: bioRxiv Tipo de estudo: Estudo prognóstico Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
Texto completo: Disponível Coleções: Preprints Base de dados: bioRxiv Tipo de estudo: Estudo prognóstico Idioma: Inglês Ano de publicação: 2020 Tipo de documento: Preprint
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