| Literature DB >> 33064571 |
Jiahui Chen1, Kaifu Gao1, Rui Wang1, Duc Duy Nguyen2, Guo-Wei Wei1,3,4.
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.Entities:
Keywords: COVID-19; SARS-CoV-2; antibody therapy; binding affinity; deep learning; network analysis; persistent homology
Mesh:
Substances:
Year: 2020 PMID: 33064571 DOI: 10.1146/annurev-biophys-062920-063711
Source DB: PubMed Journal: Annu Rev Biophys ISSN: 1936-122X Impact factor: 12.981