ORIGINAL RESEARCH article
Front. Immunol.
Sec. Viral Immunology
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1637955
This article is part of the Research TopicViral Surface Spikes: Host Cell Entry, Immune Responses and Evasion, and Implications for Viral Infection, Inhibition and ReboundView all 6 articles
From Immune Evasion to Broad In Silico Binding: Computational Optimization of SARS-CoV-2 RBD-Targeting Nanobody
Provisionally accepted
- Tianjin University, Tianjin, China
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The rapid evolution of SARS-CoV-2 Omicron variants highlights the urgent need for therapeutic strategies that can target viral evolution and leverage host immune recognition mechanisms. This study investigates the binding mechanisms of four class 1 nanobodies (R14, DL4, VH ab6, and Nanosota9) against wild-type (WT) and Omicron variants, including BA.2, JN.1, and KP.3/XEC. Using integrated molecular dynamics (MD) simulations and in silico mutagenesis, we confirmed that the immune evasion mechanism of KP.3/XEC is significantly associated with the Q493E mutation, which weakens electrostatic interactions between the nanobodies and the receptor binding domain (RBD).To overcome this challenge, we developed a streamlined computational pipeline integrating high-throughput mutagenesis of complementarity-determining regions (CDRs) and hotspot residues, protein-protein docking, and molecular dynamics (MD) simulations. This pipeline identified highaffinity mutants for R14 (3), DL4 (3), VH ab6 (11), and Nanosota9 (9), with MD simulations validating the high-affinity and stable mutant L29W; S52C; A101V for R14, showing a 62.6% binding energy improvement against JN.1 (-76.88 kcal/mol compared to -47.3 kcal/mol for original R14 nanobody) while maintaining <15% affinity variation across variants (compared to>40% for original R14 nanobody). Our study demonstrates that in silico affinity enhancement is a rapid and resource-efficient approach to repurpose nanobodies against SARS-CoV-2 variants, significantly accelerating affinity optimization while reducing experimental demands. This computational approach expedites the optimization of nanobody binding affinities while minimizing experimental resource requirements. By enhancing nanobody efficacy, our method provides a viable framework for developing targeted countermeasures against evolving SARS-CoV-2 variants and other pathogens.
Keywords: SARS-CoV-2, Immune Evasion, Nanobody, Computational pipeline, Affinity maturation
Received: 30 May 2025; Accepted: 29 Jul 2025.
Copyright: © 2025 Cao, Sun and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Feng Gao, Tianjin University, Tianjin, China
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