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Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies
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 milli...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9059344/ https://www.ncbi.nlm.nih.gov/pubmed/35530743 http://dx.doi.org/10.1016/j.csbj.2022.04.038 |
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author | Beshnova, Daria Fang, Yan Du, Mingjian Sun, Yehui Du, Fenghe Ye, Jianfeng Chen, Zhijian James Li, Bo |
author_facet | Beshnova, Daria Fang, Yan Du, Mingjian Sun, Yehui Du, Fenghe Ye, Jianfeng Chen, Zhijian James Li, Bo |
author_sort | Beshnova, Daria |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9059344 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-90593442022-05-02 Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies Beshnova, Daria Fang, Yan Du, Mingjian Sun, Yehui Du, Fenghe Ye, Jianfeng Chen, Zhijian James Li, Bo Comput Struct Biotechnol J Research Article 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. Research Network of Computational and Structural Biotechnology 2022-05-02 /pmc/articles/PMC9059344/ /pubmed/35530743 http://dx.doi.org/10.1016/j.csbj.2022.04.038 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Article Beshnova, Daria Fang, Yan Du, Mingjian Sun, Yehui Du, Fenghe Ye, Jianfeng Chen, Zhijian James Li, Bo Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies |
title | Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies |
title_full | Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies |
title_fullStr | Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies |
title_full_unstemmed | Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies |
title_short | Computational approach for binding prediction of SARS-CoV-2 with neutralizing antibodies |
title_sort | computational approach for binding prediction of sars-cov-2 with neutralizing antibodies |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9059344/ https://www.ncbi.nlm.nih.gov/pubmed/35530743 http://dx.doi.org/10.1016/j.csbj.2022.04.038 |
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