Biparatopic nanobodies targeting the receptor binding domain efficiently neutralize SARS-CoV-2

P Pymm; SJ Redmond; O Dolezal; F Mordant; E Lopez; JP Cooney; KC Davidson; ER Haycroft; CW Tan; R Seneviratna; SL Grimley; DFJ Purcell; SJ Kent; AK Wheatley; LF Wang; A Leis; A Glukhova; M Pellegrini; AW Chung; K Subbarao; AP Uldrich; WH Tham; DI Godfrey; NA Gherardin

Journal article

Biparatopic nanobodies targeting the receptor binding domain efficiently neutralize SARS-CoV-2

P Pymm, SJ Redmond, O Dolezal, F Mordant, E Lopez, JP Cooney, KC Davidson, ER Haycroft, CW Tan, R Seneviratna, SL Grimley, DFJ Purcell, SJ Kent, AK Wheatley, LF Wang, A Leis, A Glukhova, M Pellegrini, AW Chung, K Subbarao Show all

Iscience | Published : 2022

DOI: 10.1016/j.isci.2022.105259

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Abstract

The development of therapeutics to prevent or treat COVID-19 remains an area of intense focus. Protein biologics, including monoclonal antibodies and nanobodies that neutralize virus, have potential for the treatment of active disease. Here, we have used yeast display of a synthetic nanobody library to isolate nanobodies that bind the receptor-binding domain (RBD) of SARS-CoV-2 and neutralize the virus. We show that combining two clones with distinct binding epitopes within the RBD into a single protein construct to generate biparatopic reagents dramatically enhances their neutralizing capacity. Furthermore, the biparatopic nanobodies exhibit enhanced control over clinically relevant RBD var..

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University of Melbourne Researchers

Samuel Redmond Author

Francesca Mordant Author

Kathryn Davidson Author

Samantha Grimley Author

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Funding Acknowledgements

We thank staff from the flow cytometry facilities at the Department of Microbiology and Immunology at the Peter Doherty Institute. We thank Prof. Andrew C. Kruse (Harvard University, Boston, USA) for provision of the yeast nanobody display library. The authors acknowledge use of the Bio21 Advanced Microscopy Facility, and WEHI Information Technology Services and the WEHI Research Computing Platform for providing facilities and support that contributed to this work. This work was supported by the Jack Ma Foundation, the Ramsay Foundation, The Victorian State Government, Australia, the Medical Research Future Fund (MRFF GNT2002073), the Australian Research Council (ARC; CE140100011) and the National Health and Medical Research Council, Australia (NHMRC; 1113293 and 1145373) and computational resources provided by the Australian Government through MASSIVE HPC facility (www.massive.org.au) under the National Computational Merit Allocation Scheme. N.A.G was supported by an ARC DECRA Fellowship (DE210100705) and D.I.G was supported by an NHMRC Senior Principal Research Fellowship (1117766) and subsequently by an NHMRC investigator grant (2008913). W-H.T. is a Howard Hughes Medical Institute-Wellcome Trust International Research Scholar (208693/Z/17/Z) and is support by an NHMRC Fellowship (1154937). M.P. is supported by an NHMRC Investigator Grant (GNT 1175011). A.G. is a CSL Centenary Fellow. K.S. is supported by an NHMRC Investigator Grant. The Melbourne WHO Collaborating Centre for Reference and Research on Influenza is supported by the Australian Government Department of Health. The work at Duke-NUS is supported by grants from Singapore National Medical Research Council (STPRG-FY19-001, COVID19RF-003, COVID19RF-060 and OFLCG19May-0034).