The constraints imposed by natural antibody affinity maturation often culminate in antibodies with suboptimal binding affinities, thereby limiting their therapeutic efficacy. As such, the augmentation of antibody binding affinity is pivotal for the advancement of efficacious antibody-based therapies. Classical experimental paradigms for antibody engineering are financially and temporally prohibitive due to the extensive combinatorial space of sequence variations in the complementarity-determining regions (CDRs). The advent of computational techniques presents a more expeditious and economical avenue for the systematic design and optimization of antibodies. In this investigation, we assess the performance of AlphaFold2 coupled with the binder hallucination technique for the computational refinement of antibody sequences to elevate the binding affinity of pre-existing antigen-antibody complexes. These methodologies exhibit the capability to predict protein tertiary structures with remarkable fidelity, even in the absence of empirically derived data. Our results intimate that the proposed approach is adept at designing antibodies with improved affinities for antigen-antibody complexes unrepresented in AlphaFold2’s training dataset, underscoring its potential as a robust and scalable strategy for antibody engineering.

天然抗体亲和力成熟所施加的限制通常最终导致抗体具有次优的结合亲和力，从而限制了它们的治疗功效。因此，抗体结合亲和力的增强对于有效的基于抗体的疗法的进展至关重要。由于互补决定区 (CDR) 中序列变异的广泛组合空间，抗体工程的经典实验范式在经济上和时间上都令人望而却步。计算技术的出现为抗体的系统设计和优化提供了更快捷、更经济的途径。在这项研究中，我们评估了 AlphaFold2 与结合剂幻觉技术相结合的性能，用于抗体序列的计算细化，以提高预先存在的抗原抗体复合物的结合亲和力。即使在缺乏经验数据的情况下，这些方法也表现出以惊人的保真度预测蛋白质三级结构的能力。我们的结果表明，所提出的方法擅长设计对 AlphaFold2 训练数据集中未出现的抗原抗体复合物具有改进的亲和力的抗体，强调了其作为抗体工程的强大且可扩展策略的潜力。