Xiong Danyang, Ming Yongfan, Li Yuting, Li Shuhan, Chen Kexin, Liu Jinfeng, Duan Lili, Li Honglin, Li Min, He Xiao
Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200062, China.
School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
J Pharm Anal. 2025 Jun;15(6):101260. doi: 10.1016/j.jpha.2025.101260. Epub 2025 Mar 10.
The identification and optimization of mutations in nanobodies are crucial for enhancing their therapeutic potential in disease prevention and control. However, this process is often complex and time-consuming, which limit its widespread application in practice. In this study, we developed a workflow, named Evolutionary-Nanobody (EvoNB), to predict key mutation sites of nanobodies by combining protein language models (PLMs) and molecular dynamic (MD) simulations. By fine-tuning the ESM2 model on a large-scale nanobody dataset, the ability of EvoNB to capture specific sequence features of nanobodies was significantly enhanced. The fine-tuned EvoNB model demonstrated higher predictive accuracy in the conserved framework and highly variable complementarity-determining regions of nanobodies. Additionally, we selected four widely representative nanobody-antigen complexes to verify the predicted effects of mutations. MD simulations analyzed the energy changes caused by these mutations to predict their impact on binding affinity to the targets. The results showed that multiple mutations screened by EvoNB significantly enhanced the binding affinity between nanobody and its target, further validating the potential of this workflow for designing and optimizing nanobody mutations. Additionally, sequence-based predictions are generally less dependent on structural absence, allowing them to be more easily integrated with tools for structural predictions, such as AlphaFold 3. Through mutation prediction and systematic analysis of key sites, we can quickly predict the most promising variants for experimental validation without relying on traditional evolutionary or selection processes. The EvoNB workflow provides an effective tool for the rapid optimization of nanobodies and facilitates the application of PLMs in the biomedical field.
纳米抗体中突变的鉴定和优化对于增强其在疾病预防和控制中的治疗潜力至关重要。然而，这个过程通常复杂且耗时，限制了其在实际中的广泛应用。在本研究中，我们开发了一种名为进化纳米抗体（EvoNB）的工作流程，通过结合蛋白质语言模型（PLMs）和分子动力学（MD）模拟来预测纳米抗体的关键突变位点。通过在大规模纳米抗体数据集上对ESM2模型进行微调，EvoNB捕获纳米抗体特定序列特征的能力得到显著增强。经过微调的EvoNB模型在纳米抗体的保守框架和高度可变的互补决定区表现出更高的预测准确性。此外，我们选择了四种具有广泛代表性的纳米抗体 - 抗原复合物来验证预测的突变效果。MD模拟分析了这些突变引起的能量变化，以预测它们对与靶标结合亲和力的影响。结果表明，EvoNB筛选出的多个突变显著增强了纳米抗体与其靶标的结合亲和力，进一步验证了该工作流程在设计和优化纳米抗体突变方面的潜力。此外，基于序列的预测通常较少依赖于结构信息的缺失，使其更容易与结构预测工具（如AlphaFold 3）集成。通过突变预测和关键位点的系统分析，我们可以快速预测最有希望进行实验验证的变体，而无需依赖传统的进化或选择过程。EvoNB工作流程为纳米抗体的快速优化提供了一种有效工具，并促进了PLMs在生物医学领域的应用。
