A deep learning approach for rational affinity maturation of anti-VEGF nanobodies

Nanobodies offer several advantages over conventional antibodies due to their lower immunogenicity, enhanced stability, and superior tissue penetration, making them promising candidates for cancer therapy. In this study, we employ deep learning algorithms to design anti-VEGF nanobodies via affinity maturation. Our approach integrates structure-guided mutational modeling and systematic measurement of binding affinity and stability for rational optimization of Complementarity Determining Regions. In addition, we developed a sequence-based melting temperature predictor for nanobodies, ensuring stability of the designed mutants. Our method achieves energy reductions up to -4.92 kcal/mol. Our melting temperature predictor demonstrated a Pearson correlation coefficient of 0.772. These findings emphasize the potential of computational approaches for nanobody affinity maturation and stability prediction, paving the way for more effective therapeutic designs.