Researchers at the Korea Advanced Institute of Science and Technology (KAIST) have developed a computational antibody design and used it to develop a neutralizing antibody that exhibits excellent effects against all Covid-19 variants, including Omicron.
SARS-CoV-2 virus, which causes Covid-19 infection, shows a mechanism to invade cells by binding the receptor-binding site on the spike glycoprotein site to the hACE2 (human Angiotensin Converting Enzyme2) receptor attached to the human cell membrane. Based on this mechanism, researchers at leading pharmaceutical companies have developed neutralizing antibodies, such as Etesevimab and Bamlanivimab, which attach to the receptor-binding site. However, these antibodies have no or less neutralizing ability for Alpha, Beta, and Delta variants.
According to the university, existing antibodies' neutralizing abilities drop because these strains have mutations in the antibody recognition site sequence, neutering their ability to bind properly.
Therefore, the team, led by Professor Oh Byung-ha of the Department of Life Sciences, developed an antibody that strongly binds to the unmutated portion of the viral antigen through a computational protein design method. As a result, the antibody developed showed strong binding to SARS-CoV-1 and pangolin coronavirus, and all known SARS-CoV-2 variants viruses, including Omicron, confirming excellent neutralizing ability indicators.
The research team expects theirs to be a general-purpose Covid-19 treatment antibody candidate that can respond to future coronavirus causing severe respiratory syndrome. Also, the computational antibody design technology is a new method for discovering an antibody that binds to a specific site of an antigen and has wide applicability and high technical value.
“The antibody developed recently has great significance because it can be a therapeutic material that can immediately respond to new and variant coronaviruses emerging in the future by binding to a surface whose amino acid sequence hardly changes,” Professor Oh said. “We expect that the computational antibody design method developed through this study has wide applications in developing antibodies that are difficult to obtain experimentally.”
The study results were published in the latest issue of the journal, mAbs.