Rapid isolation and characterization of high-affinity single-domain antibodies for broad-Spectrum detection of SARS-CoV-2 variants

The rapid evolution of SARS-CoV-2 presents substantial challenges to maintaining diagnostic accuracy. Single-domain antibodies (variable domains of camelid heavy-chain antibodies, VHHs), commonly referred to as nanobodies, are attractive tools for diagnostic applications due to their high specificity, stability, affinity and expression yield. In this study, three high-affinity VHHs that recognize the receptor-binding domain (RBD) of the SARS-CoV-2 BA.2 variant were isolated from a large naïve alpaca VHH phage display library. These nanobodies exhibited nanomolar affinities and high thermal stability. Notably, reformatting one VHH into a bivalent VHH-Fc fusion construct, K115.2-Fc, enhanced binding affinity 24-fold, achieving an equilibrium dissociation constant of 68.3 picomolar. Functional characterization demonstrated that K115.2-Fc cross-reacted with the Wuhan SARS-CoV-2 RBD and multiple variants, including Alpha, Beta, Gamma, Delta, Kappa, BA.1, BA.2, and BA.4/5. In indirect enzyme-linked immunosorbent assay, K115.2-Fc detected full-length spike protein with a limit of detection of 8.1 pM. Moreover, it enabled sensitive detection in western blotting and flow cytometry, highlighting its applicability across diverse diagnostic applications. Collectively, these findings identify K115.2-Fc as a promising candidate with potential for SARS-CoV-2 diagnostic utility.

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Publication Details

Journal
Protein Engineering Design and Selection
Published
2025-12-23
DOI
https://doi.org/10.1093/protein/gzag001
Primary Topic
Monoclonal and Polyclonal Antibodies Research
Type
article
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article

Rapid isolation and characterization of high-affinity single-domain antibodies for broad-Spectrum detection of SARS-CoV-2 variants

J. S. H. Lee, Ah Hyun Cho, Hee Eon Lee, Won-Kyu Lee et al.
Protein Engineering Design and Selection
Monoclonal and Polyclonal Antibodies Research
article

Rapid isolation and characterization of high-affinity single-domain antibodies for broad-Spectrum detection of SARS-CoV-2 variants

J. S. H. Lee, Ah Hyun Cho, Hee Eon Lee, Won-Kyu Lee, Songyeol Yun, Sukmook Lee
article en

Abstract

The rapid evolution of SARS-CoV-2 presents substantial challenges to maintaining diagnostic accuracy. Single-domain antibodies (variable domains of camelid heavy-chain antibodies, VHHs), commonly referred to as nanobodies, are attractive tools for diagnostic applications due to their high specificity, stability, affinity and expression yield. In this study, three high-affinity VHHs that recognize the receptor-binding domain (RBD) of the SARS-CoV-2 BA.2 variant were isolated from a large naïve alpaca VHH phage display library. These nanobodies exhibited nanomolar affinities and high thermal stability. Notably, reformatting one VHH into a bivalent VHH-Fc fusion construct, K115.2-Fc, enhanced binding affinity 24-fold, achieving an equilibrium dissociation constant of 68.3 picomolar. Functional characterization demonstrated that K115.2-Fc cross-reacted with the Wuhan SARS-CoV-2 RBD and multiple variants, including Alpha, Beta, Gamma, Delta, Kappa, BA.1, BA.2, and BA.4/5. In indirect enzyme-linked immunosorbent assay, K115.2-Fc detected full-length spike protein with a limit of detection of 8.1 pM. Moreover, it enabled sensitive detection in western blotting and flow cytometry, highlighting its applicability across diverse diagnostic applications. Collectively, these findings identify K115.2-Fc as a promising candidate with potential for SARS-CoV-2 diagnostic utility.

Protein Engineering Design and SelectionVol. 39
Kookmin University (KR), Palo Alto University (US), Osong Medical Innovation Foundation (KR), Stanford University (US)
Openalex Percentile: Top 28%
Monoclonal and Polyclonal Antibodies Research
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Rapid isolation and characterization of high-affinity single-domain antibodies for broad-Spectrum detection of SARS-CoV-2 variants — J. S. H. Lee, Ah Hyun Cho, et al. · Protein Engineering Design and Selection (2025) | TGRS Research Map | TGRS