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Modeling and Simulation of a Fully-glycosylated Full-length SARS-CoV-2 Spike Protein in a Viral Membrane
Hyeonuk Woo; Sang-Jun Park; Yeol Kyo Choi; Taeyong Park; Maham Tanveer; Yiwei Cao; Nathan R. Kern; Jumin Lee; Min Sun Yeom; Tristan I. Croll; Chaok Seok; Wonpil Im.
Afiliação
  • Hyeonuk Woo; Seoul National University
  • Sang-Jun Park; Lehigh University
  • Yeol Kyo Choi; Lehigh University
  • Taeyong Park; Seoul National University
  • Maham Tanveer; Lehigh University
  • Yiwei Cao; Lehigh University
  • Nathan R. Kern; Lehigh University
  • Jumin Lee; Lehigh University
  • Min Sun Yeom; Korean Institute of Science and Technology Information
  • Tristan I. Croll; University of Cambridge
  • Chaok Seok; Seoul National University
  • Wonpil Im; Lehigh University
Preprint em En | PREPRINT-BIORXIV | ID: ppbiorxiv-103325
ABSTRACT
This technical study describes all-atom modeling and simulation of a fully-glycosylated full-length SARS-CoV-2 spike (S) protein in a viral membrane. First, starting from PDB6VSB and 6VXX, full-length S protein structures were modeled using template-based modeling, de-novo protein structure prediction, and loop modeling techniques in GALAXY modeling suite. Then, using the recently-determined most occupied glycoforms, 22 N-glycans and 1 O-glycan of each monomer were modeled using Glycan Reader & Modeler in CHARMM-GUI. These fully-glycosylated full-length S protein model structures were assessed and further refined against the low-resolution data in their respective experimental maps using ISOLDE. We then used CHARMM-GUI Membrane Builder to place the S proteins in a viral membrane and performed all-atom molecular dynamics simulations. All structures are available in CHARMM-GUI COVID-19 Archive (http//www.charmm-gui.org/docs/archive/covid19), so researchers can use these models to carry out innovative and novel modeling and simulation research for the prevention and treatment of COVID-19.
Licença
cc_by_nd
Texto completo: 1 Coleções: 09-preprints Base de dados: PREPRINT-BIORXIV Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Preprint
Texto completo: 1 Coleções: 09-preprints Base de dados: PREPRINT-BIORXIV Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Preprint