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SARS-CoV-2 Spike Glycoprotein Receptor Binding Domain is Subject to Negative Selection with Predicted Positive Selection Mutations
You Li; Ye Wang; Yaping Qiu; Zhen Gong; Lei Deng; Min Pan; Huiping Yang; Jianan Xu; Li Yang; Jin Li.
Affiliation
  • You Li; HitGen Inc.
  • Ye Wang; HitGen Inc.
  • Yaping Qiu; HitGen Inc.
  • Zhen Gong; HitGen Inc.
  • Lei Deng; HitGen Inc.
  • Min Pan; Sichuan Provincial Center for Disease Control and Prevention
  • Huiping Yang; Sichuan Provincial Center for Disease Control and Prevention
  • Jianan Xu; Sichuan Provincial Center for Disease Control and Prevention
  • Li Yang; HitGen Inc.
  • Jin Li; HitGen Inc.
Preprint in En | PREPRINT-BIORXIV | ID: ppbiorxiv-077842
ABSTRACT
COVID-19 is a highly contagious disease caused by a novel coronavirus SARS-CoV-2. The interaction between SARS-CoV-2 spike protein and the host cell surface receptor ACE2 is responsible for mediating SARS-CoV-2 infection. By analyzing the spike-hACE2 interacting surface, we predicted many hot spot residues that make major contributions to the binding affinity. Mutations on most of these residues are likely to be deleterious, leading to less infectious virus strains that may suffer from negative selection. Meanwhile, several residues with mostly advantageous mutations have been predicted. It is more probable that mutations on these residues increase the transmission ability of the virus by enhancing spike-hACE2 interaction. So far, only a limited number of mutations has been reported in this region. However, the list of hot spot residues with predicted downstream effects from this study can still serve as a tracking list for SARS-CoV-2 evolution studies. Coincidentally, one advantageous mutation, p.476G>S, started to surge in the last couple of weeks based on the data submitted to the public domain, indicating that virus strains with increased transmission ability may have already spread.
License
cc_by_nc_nd
Full text: 1 Collection: 09-preprints Database: PREPRINT-BIORXIV Type of study: Prognostic_studies Language: En Year: 2020 Document type: Preprint
Full text: 1 Collection: 09-preprints Database: PREPRINT-BIORXIV Type of study: Prognostic_studies Language: En Year: 2020 Document type: Preprint