Abstract：
The dynamic evolution of SARS-CoV-2 variants necessitates ongoing advancements in therapeutic strategies. Despite the promise of monoclonal antibody (mAb) therapies like bebtelovimab, concerns persist regarding resistance mutations, particularly single-to-multipoint mutations in the receptor-binding domain (RBD). Our study addresses this by employing interface-guided computational protein design to predict potential bebtelovimab-resistance mutations. Through extensive physicochemical analysis, mutational preferences, precision-recall metrics, protein-protein docking, and energetic analyses, combined with all-atom, and coarse-grained molecular dynamics (MD) simulations, we elucidated the structural-dynamics-binding features of the bebtelovimab-RBD complexes. Identification of susceptible RBD residues under positive selection pressure, coupled with validation against bebtelovimab-escape mutations, clinically reported resistance mutations, and viral genomic sequences enhances the translational significance of our findings and contributes to a better understanding of the resistance mechanisms of SARS-CoV-2.
摘要：
SARS-CoV-2变体的动态演变需要治疗策略的不断进步。尽管有像bebtelovimab这样的单克隆抗体（mAb）疗法的前景，关于抗性突变的担忧仍然存在，特别是受体结合域（RBD）中的单-多点突变。我们的研究通过采用界面指导的计算蛋白质设计来预测潜在的bebtelovimab抗性突变来解决这一问题。通过广泛的物理化学分析，突变偏好，精确召回指标，蛋白质-蛋白质对接，和能量分析，结合全原子，和粗粒度分子动力学(MD)模拟，我们阐明了bebtelovimab-RBD复合物的结构动力学结合特征。在正选择压力下鉴定敏感的RBD残基，再加上对bebtelovimab逃逸突变的验证，临床报道的耐药突变，和病毒基因组序列增强了我们发现的翻译意义，并有助于更好地理解SARS-CoV-2的抗性机制。
