Computational design of a neutralizing antibody against all SARS-CoV-2 variants

Prof. Byung-Ha Oh’s group at KAIST has generated a therapeutic candidate for COVID-19 and emerging coronaviruses by a computational protein design. This antibody exhibits breadth and potency in binding the receptor-binding domain (RBD) of the virus spike glycoprotein across SARS coronaviruses, including the Omicron, SARS-CoV-1 and pangolin coronaviruses with pico-molar binding affinities. Consistently, the antibody exhibited strong neutralization activity against wild-type SARS-CoV-2 and the Alpha and Delta variants and identified a vulnerable target site on coronaviruses for the development of pan-sarbecovirus nAbs and vaccines. The study was published on 14 February, 2022 (Computational Design of a Neutralizing Antibody with Picomolar Binding Affinity for all Concerning SARS-CoV-2 variants MAbs, 14(1):2013750 (2022)).

“We set out to develop an effective therapeutic antibody that can bind to SARS-CoV-2 and prevent it from infecting cells,” said Byung-Ha Oh, the corresponding author and a professor in the Department of Biological Sciences and the KAIST Institute for the Biocentury at KAIST.

Coronavirus disease 2019, caused by SARS-CoV-2, remains an ongoing pandemic, partly due to the emergence of variant viruses that can “break-through” the protection of the current vaccines and neutralizing antibodies, highlighting the needs for broad nAbs and next-generation vaccines. Initially, a lead antibody was computationally discovered and crystallographically validated to be able to bind to a highly conserved surface of the RBD of wild-type SARS-CoV-2. Subsequently, through experimental affinity enhancements and computational affinity maturation, it was further developed to bind to the RBD of all SARS-CoV-2 variants, SARS-CoV-1 and a pangolin coronavirus with pico-molar binding affinities.

“While all of the broadly protective nAbs were discovered from COVID-19 patients or vaccinated individuals, D27LEY was discovered and developed via a computational antibody design. This suggests that our approach can also be used as a general method for developing mAbs with ultra-potent binding affinities for a given antigen.” Prof. Oh said.
“It took us fair amount of time and effort to develop the computational methods of antibody docking and sequence design of the antigen-binding regions of antibody,” said the first author Bo-Seong Jeong, a graduate student and now a postdoctoral fellow in the Department of Biological Sciences at KAIST. “Once all these were set up, it was even possible to computationally enhance the binding affinity greater than 100 fold.” The reported antibody binds to a conserved surface on the RBD, which explains why it binds to it across branches of sarbecoviruses. “The developed antibody appears to be a strong candidate for development into a therapeutic or prophylactic mAb for COVID-19. Our work here also reveals a rational path toward vaccine design aimed at broad and effective protection against SARS-CoV-2 and other related viruses.”

This work was supported, in part, by the KAIST Mobile Clinic Module Project and by a grant from the National Research Foundation (NRF-2020R1A4A3079755).