Cao Shuyuan, Sun Bo, Gao Feng
Department of Physics, School of Science, Tianjin University, Tianjin, China.
State Key Laboratory of Synthetic Biology, Tianjin University, Tianjin, China.
Front Immunol. 2025 Aug 14;16:1637955. doi: 10.3389/fimmu.2025.1637955. eCollection 2025.
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 uses molecular dynamics (MD) simulations to analyze the immune evasion mechanisms of class 1 nanobodies against emerging SARS-CoV-2 variants, and to develop an efficient pipeline for rapid affinity optimization.
We employed MD simulations and binding free energy calculations to investigate the immune evasion mechanisms of four class 1 nanobodies (R14, DL4, V ab6, and Nanosota9) against wild-type (WT) and Omicron variants, including BA.2, JN.1, and KP.3/XEC. Building on these findings, we established a streamlined nanobody optimization pipeline integrating high-throughput mutagenesis of complementarity-determining regions (CDRs) and hotspot residues, protein-protein docking, and MD simulations.
MD analysis 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). Through our pipeline, we identified high-affinity mutants including 3 for R14, 3 for DL4, 11 for VH ab6, and 9 for Nanosota9. The optimized R14 variant L29W/S52C/A101V demonstrated exceptional performance, achieving 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).
This 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.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）奥密克戎变体的快速演变凸显了对能够靶向病毒进化并利用宿主免疫识别机制的治疗策略的迫切需求。本研究使用分子动力学（MD）模拟来分析1类纳米抗体对新出现的SARS-CoV-2变体的免疫逃逸机制，并开发一种高效的快速亲和力优化流程。
我们采用MD模拟和结合自由能计算来研究四种1类纳米抗体（R14、DL4、V ab6和Nanosota9）对野生型（WT）和奥密克戎变体（包括BA.2、JN.1和KP.3/XEC）的免疫逃逸机制。基于这些发现，我们建立了一个简化的纳米抗体优化流程，该流程整合了互补决定区（CDR）和热点残基的高通量诱变、蛋白质-蛋白质对接以及MD模拟。
MD分析证实，KP.3/XEC的免疫逃逸机制与Q493E突变显著相关，该突变削弱了纳米抗体与受体结合域（RBD）之间的静电相互作用。通过我们的流程，我们鉴定出了高亲和力突变体，其中R14有3个、DL4有3个、VH ab6有11个、Nanosota9有9个。优化后的R14变体L29W/S52C/A101V表现出卓越性能，对JN.1的结合能提高了62.6%（与原始R14纳米抗体的-47.3千卡/摩尔相比为-76.88千卡/摩尔），同时在不同变体间保持<15%的亲和力变化（原始R14纳米抗体的这一数值>40%）。
本研究表明，计算机辅助亲和力增强是一种快速且资源高效的方法，可用于重新设计纳米抗体以对抗SARS-CoV-2变体，显著加速亲和力优化，同时减少实验需求。这种计算方法加快了纳米抗体结合亲和力的优化，同时将实验资源需求降至最低。通过提高纳米抗体的效力，我们的方法为开发针对不断演变的SARS-CoV-2变体和其他病原体的靶向对策提供了一个可行的框架。
