Deep learning guided high-throughput virtual screening for in vitro antibody maturation

Antibody affinity maturation is a crucial step in therapeutic antibody discovery. In this study, we present a virtual screening pipeline that integrates protein docking with deep learning-based structural prediction to identify antibody mutants with enhanced binding affinity for the antigen sST2. By introducing random mutations in the CDR3 domain of the wild-type antibody 2B4, we generated 949 mutants and systematically narrowed them down to 14 candidates for experimental validation. Among these, six exhibited higher affinity, while three displayed comparable affinity to 2B4. Notably, the selected mutants shared close interaction sites with each other, providing valuable region for antibody engineering and therapeutic development. Our pipeline is easy for local deployment with less computational resources required, offering a convenient tool for in-silico screening without the need of intensive cluster. The use of advanced deep learning structure prediction model further enhances the accuracy compared to traditional virtual screening pipeline. Consequently, our work significantly reduces the cost and time required for experimental in vitro affinity maturation, effectively combining the interpretability of protein docking with the predictive accuracy of deep learning.