Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies

Daria Beshnova, Yan Fang, Mingjian Du, Yehui Sun, Fenghe Du, Jianfeng Ye, Zhijian James Chen, Bo Li

Research output: Contribution to journalArticlepeer-review

Abstract

Coronavirus disease 2019 (COVID-19) caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide as a severe pandemic and caused enormous global health and economical damage. Since December 2019, more than 197 million cases have been reported, causing 4.2 million deaths. In the settings of pandemic it is an urgent necessity for the development of an effective COVID-19 treatment. While in-vitro screening of hundreds of antibodies isolated from convalescent patients is challenging due to its high cost, use of computational methods may provide an attractive solution in selecting the top candidates. Here, we developed a computational approach (SARS-AB) for binding prediction of spike protein SARS-CoV-2 with monoclonal antibodies. We validated our approach using existing structures in the protein data bank (PDB), and demonstrated its prediction power in antibody-spike protein binding prediction. We further tested its performance using antibody sequences from the literature where crystal structure is not available, and observed a high prediction accuracy (AUC = 99.6%). Finally, we demonstrated that SARS-AB can be used to design effective antibodies against novel SARS-CoV-2 mutants that might escape the current antibody protections. We believe that SARS-AB can significantly accelerate the discovery of neutralizing antibodies against SARS-CoV-2 and its mutants.

Original languageEnglish (US)
Pages (from-to)2212-2222
Number of pages11
JournalComputational and Structural Biotechnology Journal
Volume20
DOIs
StatePublished - Jan 2022

Keywords

  • Binding prediction
  • Modifications of Regdanvimab
  • Regdanvimab antibody
  • SARS-CoV-2 antibodies

ASJC Scopus subject areas

  • Biotechnology
  • Biophysics
  • Structural Biology
  • Biochemistry
  • Genetics
  • Computer Science Applications

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