Reprogramming the SARS-CoV-1 Neutralizing Antibody S230 to SARS-CoV-2 via Directed Evolution and Molecular Docking-Based Binding Mode Analysis

To reprogram the antigen specificity of the SARS-CoV-1 neutralizing antibody S230 toward SARS-CoV-2, we employed a strategy combining directed evolution with molecular docking-based analysis. An error-prone scFv library was constructed from S230 and screened via bacterial display using the SARS-CoV-2 RBD. Among the enriched clones, IJ36 regained SARS-CoV-2 RBD binding and was further refined to an optimized variant, IJ36-V. IJ36-V efficiently neutralized SARS-CoV-2 infection and retained cross-reactivity with the SARS-CoV-1 RBD, confirming that the reprogrammed antibody preserved the ancestral specificity while extending its functional breadth. Docking simulations revealed that two substitutions, R56W and N57Y, function cooperatively to stabilize the binding interface through favorable hydrogen bonding and aromatic interactions. Reversion analysis confirmed that both mutations are essential for high-affinity binding, with neither being sufficient alone. These findings demonstrate that a limited set of synergistic mutations can reconstitute antigen recognition between evolutionarily divergent but structurally conserved targets. This study establishes a modular and evolvable platform for antibody reprogramming based on synthetic biology principles, enabling rapid adaptation of existing scaffolds to emerging pathogens.

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Publication Details

Journal
ACS Synthetic Biology
Published
2025-11-14
DOI
https://doi.org/10.1021/acssynbio.5c00425
Citations
1
Primary Topic
SARS-CoV-2 and COVID-19 Research
Type
article
Field-Weighted Citation Impact
1.40
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article

Reprogramming the SARS-CoV-1 Neutralizing Antibody S230 to SARS-CoV-2 via Directed Evolution and Molecular Docking-Based Binding Mode Analysis

Dinesh Kumar Sriramulu, Inji Jung, Sang Taek Jung, Sun‐Gu Lee
1 citations
ACS Synthetic Biology
SARS-CoV-2 and COVID-19 Research
1.40
article

Reprogramming the SARS-CoV-1 Neutralizing Antibody S230 to SARS-CoV-2 via Directed Evolution and Molecular Docking-Based Binding Mode Analysis

Dinesh Kumar Sriramulu, Inji Jung, Sang Taek Jung, Sun‐Gu Lee
article en
1 citations

Abstract

To reprogram the antigen specificity of the SARS-CoV-1 neutralizing antibody S230 toward SARS-CoV-2, we employed a strategy combining directed evolution with molecular docking-based analysis. An error-prone scFv library was constructed from S230 and screened via bacterial display using the SARS-CoV-2 RBD. Among the enriched clones, IJ36 regained SARS-CoV-2 RBD binding and was further refined to an optimized variant, IJ36-V. IJ36-V efficiently neutralized SARS-CoV-2 infection and retained cross-reactivity with the SARS-CoV-1 RBD, confirming that the reprogrammed antibody preserved the ancestral specificity while extending its functional breadth. Docking simulations revealed that two substitutions, R56W and N57Y, function cooperatively to stabilize the binding interface through favorable hydrogen bonding and aromatic interactions. Reversion analysis confirmed that both mutations are essential for high-affinity binding, with neither being sufficient alone. These findings demonstrate that a limited set of synergistic mutations can reconstitute antigen recognition between evolutionarily divergent but structurally conserved targets. This study establishes a modular and evolvable platform for antibody reprogramming based on synthetic biology principles, enabling rapid adaptation of existing scaffolds to emerging pathogens.

ACS Synthetic BiologyVol. 14(12)
Biomax Informatics (Germany) (DE), Seoul National University (KR), Korea University (KR), National University College (PR), Bioengineering Center (RU), Korea University (JP), Pusan National University (KR), Graduate School USA (US)
Openalex Percentile: Top 14%
SARS-CoV-2 and COVID-19 Research
1.40
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Reprogramming the SARS-CoV-1 Neutralizing Antibody S230 to SARS-CoV-2 via Directed Evolution and Molecular Docking-Based Binding Mode Analysis — Dinesh Kumar Sriramulu, Inji Jung, et al. · ACS Synthetic Biology (2025) | TGRS Research Map | TGRS