학술논문

Application of an integrated computational antibody engineering platform to design SARS-CoV-2 neutralizers
Research article
Document Type
Academic Journal
Source
Antibody Therapeutics. April 2021, Vol. 4 Issue 2, p109, 14 p.
Subject
France
Language
English
Abstract
INTRODUCTION COVID-19 cases continue to climb rapidly after causing over 160 million infections and 3.3 million deaths since the start of the outbreak. The causing virus, SARS-CoV-2, is identified to [...]
As the COVID-19 pandemic continues to spread, hundreds of new initiatives including studies on existing medicines are running to fight the disease. To deliver a potentially immediate and lasting treatment to current and emerging SARS-CoV-2 variants, new collaborations and ways of sharing are required to create as many paths forward as possible. Here, we leverage our expertise in computational antibody engineering to rationally design/engineer three previously reported SARS-CoV neutralizing antibodies and share our proposal towards anti-SARS-CoV-2 biologics therapeutics. SARS-CoV neutralizing antibodies, m396, 80R and CR-3022 were chosen as templates due to their diversified epitopes and confirmed neutralization potency against SARS-CoV (but not SARS-CoV-2 except for CR3022). Structures of variable fragment (Fv) in complex with receptor binding domain (RBD) from SARS-CoV or SARS-CoV-2 were subjected to our established in silico antibody engineering platform to improve their binding affinity to SARS-CoV-2 and developability profiles. The selected top mutations were ensembled into a focused library for each antibody for further screening. In addition, we convert the selected binders with different epitopes into the trispecific format, aiming to increase potency and to prevent mutational escape. Lastly, to avoid antibody-induced virus activation or enhancement, we suggest application of NNAS and DQ mutations to the Fc region to eliminate effector functions and extend half-life. Statement of Significance: Engineering SARS-CoV antibody for SARS-CoV-2 cross-reactivity is a potentially effective and fast way toward COVID-19 treatment. We utilized computational methods to engineer known antibodies and further formatted them into tri-specific antibody aiming for potent and broad neutralization of SARS-CoV-2. We share our proposal to contribute to the SARS-CoV-2 research community. KEYWORDS: SARS-CoV-2antibody; engineering structure-based; engineering tri-specific; antibodymachine learning