TY - JOUR
T1 - Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies
AU - Beshnova, Daria
AU - Fang, Yan
AU - Du, Mingjian
AU - Sun, Yehui
AU - Du, Fenghe
AU - Ye, Jianfeng
AU - Chen, Zhijian James
AU - Li, Bo
N1 - Funding Information:
This work is supported by the following funding sources: CPRIT: RR170079 (BL).
Funding Information:
Yan Fang is supported by CPRIT training grant RP210041.
Publisher Copyright:
© 2022 The Author(s)
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
KW - Binding prediction
KW - Modifications of Regdanvimab
KW - Regdanvimab antibody
KW - SARS-CoV-2 antibodies
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U2 - 10.1016/j.csbj.2022.04.038
DO - 10.1016/j.csbj.2022.04.038
M3 - Article
C2 - 35530743
AN - SCOPUS:85130132002
SN - 2001-0370
VL - 20
SP - 2212
EP - 2222
JO - Computational and Structural Biotechnology Journal
JF - Computational and Structural Biotechnology Journal
ER -