Computational design of a neutralizing antibody with picomolar binding affinity for all concerning SARS-CoV-2 variants

Bo Seong Jeong, Jeong Seok Cha, Insu Hwang, Uijin Kim, Jared Adolf-Bryfogle, Brian Coventry, Hyun Soo Cho, Kyun Do Kim, Byung Ha Oh

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Coronavirus disease 2019, caused by SARS-CoV-2, remains an on-going pandemic, partly due to the emergence of variant viruses that can “break-through” the protection of the current vaccines and neutralizing antibodies (nAbs), highlighting the needs for broadly nAbs and next-generation vaccines. We report an antibody that exhibits breadth and potency in binding the receptor-binding domain (RBD) of the virus spike glycoprotein across SARS coronaviruses. Initially, a lead antibody was computationally discovered and crystallographically validated that binds to a highly conserved surface of the RBD of wild-type SARS-CoV-2. Subsequently, through experimental affinity enhancement and computational affinity maturation, it was further developed to bind the RBD of all concerning SARS-CoV-2 variants, SARS-CoV-1 and pangolin coronavirus with pico-molar binding affinities, consistently exhibited strong neutralization activity against wild-type SARS-CoV-2 and the Alpha and Delta variants. These results identify a vulnerable target site on coronaviruses for development of pan-sarbecovirus nAbs and vaccines.

Original languageEnglish
Article number2021601
Issue number1
Publication statusPublished - 2022

Bibliographical note

Funding Information:
This work was supported by the KAIST Mobile Clinic Module Project (Grant No. MCM-2021-N11210036 to B.-H.O.), the National Research Council of Science and Technology grant (CRC-16-01-KRICT to K.-D.K.) and the National Research Foundation grant (NRF-2019M3E5D6063903 to H.-S.C.) through the Korean government (MSIP). The X-ray data were collected on the Beamline 11C at the Pohang Accelerator Laboratory, Korea. All figures presenting protein structures were generated using PyMOL 2.0.

Publisher Copyright:
© 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.

All Science Journal Classification (ASJC) codes

  • Immunology and Allergy
  • Immunology


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