Article ; Online: Rational strategies for enhancing mAb binding to SARS-CoV-2 variants through CDR diversification and antibody-escape prediction.
2023 Volume 14, Page(s) 1113175
Abstract: Since the emergence of SARS-CoV-2, dozens of variants of interest and half a dozen variants of concern (VOCs) have been documented by the World Health Organization. The emergence of these VOCs due to the continuous evolution of the virus is a major ... ...
Abstract | Since the emergence of SARS-CoV-2, dozens of variants of interest and half a dozen variants of concern (VOCs) have been documented by the World Health Organization. The emergence of these VOCs due to the continuous evolution of the virus is a major concern for COVID-19 therapeutic antibodies and vaccines because they are designed to target prototype/previous strains and lose effectiveness against new VOCs. Therefore, there is a need for time- and cost-effective strategies to estimate the immune escape and redirect therapeutic antibodies against newly emerging variants. Here, we computationally predicted the neutralization escape of the SARS-CoV-2 Delta and Omicron variants against the mutational space of RBD-mAbs interfaces. Leveraging knowledge of the existing RBD-mAb interfaces and mutational space, we fine-tuned and redirected CT-p59 (Regdanvimab) and Etesevimab against the escaped variants through complementarity-determining regions (CDRs) diversification. We identified antibodies against the Omicron lineage BA.1 and BA.2 and Delta variants with comparable or better binding affinities to that of prototype Spike. This suggests that CDRs diversification by hotspot grafting, given an existing insight into the Ag-Abs interface, is an exquisite strategy to redirect antibodies against preselected epitopes and combat the neutralization escape of emerging SARS-CoV-2 variants. |
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MeSH term(s) | Humans ; SARS-CoV-2/genetics ; COVID-19 ; Antibodies, Monoclonal/therapeutic use ; Complementarity Determining Regions/genetics |
Chemical Substances | Antibodies, Monoclonal ; Complementarity Determining Regions |
Language | English |
Publishing date | 2023-03-31 |
Publishing country | Switzerland |
Document type | Journal Article ; Research Support, Non-U.S. Gov't |
ZDB-ID | 2606827-8 |
ISSN | 1664-3224 ; 1664-3224 |
ISSN (online) | 1664-3224 |
ISSN | 1664-3224 |
DOI | 10.3389/fimmu.2023.1113175 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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