Abstract
In the global health emergency caused by coronavirus disease 2019 (COVID-19), efficient and specific therapies are urgently needed. Compared with traditional small-molecular drugs, antibody therapies are relatively easy to develop; they are as specific as vaccines in targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); and they have thus attracted much attention in the past few months. This article reviews seven existing antibodies for neutralizing SARS-CoV-2 with 3D structures deposited in the Protein Data Bank (PDB). Five 3D antibody structures associated with the SARS-CoV spike (S) protein are also evaluated for their potential in neutralizing SARS-CoV-2. The interactions of these antibodies with the S protein receptor-binding domain (RBD) are compared with those between angiotensin-converting enzyme 2 and RBD complexes. Due to the orders of magnitude in the discrepancies of experimental binding affinities, we introduce topological data analysis, a variety of network models, and deep learning to analyze the binding strength and therapeutic potential of the 14 antibody-antigen complexes. The current COVID-19 antibody clinical trials, which are not limited to the S protein target, are also reviewed.
| Original language | English |
|---|---|
| Pages (from-to) | 1-30 |
| Number of pages | 30 |
| Journal | Annual Review of Biophysics |
| Volume | 50 |
| DOIs | |
| State | Published - May 6 2021 |
Bibliographical note
Publisher Copyright:Copyright © 2021 by Annual Reviews. All rights reserved.
Funding
This work was supported in part by National Institutes of Health grant GM126189; National Science Foundation grants DMS-1721024, DMS-1761320, and IIS1900473; theMichigan Economic Development Corporation; George Mason University award PD45722; Bristol-Myers Squibb; and Pfizer. The authors thank the IBM TJWatson Research Center, the COVID-19 High Performance Computing Consortium,NVIDIA, and the Michigan State University High Performance Computing Center for computational assistance.
| Funders | Funder number |
|---|---|
| COVID-19 High Performance Computing Consortium | |
| IBM TJWatson Research Center | |
| National Science Foundation Arctic Social Science Program | IIS1900473, PD45722, DMS-1721024, DMS-1761320 |
| National Institutes of Health (NIH) | |
| National Institute of General Medical Sciences DP2GM119177 Sophie Dumont National Institute of General Medical Sciences | R01GM126189 |
| Bristol-Myers Squibb | |
| Pfizer | |
| Nvidia | |
| Michigan State University |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- COVID-19
- SARS-CoV-2
- antibody therapy
- binding affinity
- deep learning
- network analysis
- persistent homology
ASJC Scopus subject areas
- Bioengineering
- Biophysics
- Structural Biology
- Biochemistry
- Cell Biology
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