An optimized thermodynamics integration protocol for identifying beneficial mutations in antibody design - Sheng, Zizhang; Bimela, Jude S.; Wang, Maple; Li, Zhiteng; Guo, Yicheng; Ho, David D. (Frontiers in Immunology, 2023)
Accurate identification of beneficial mutations is central to antibody design. Many knowledge-based (KB) computational approaches have been developed to predict beneficial mutations but the accuracies have room for improvement. Thermodynamics integration (TI) is an alchemical free energy algorithm which provides an alternative technique to identify beneficial mutations, but its performance has not been evaluated. In this study, we developed an efficient TI protocol with high accuracy for predicting binding free energy changes of antibody mutations. The improved TI method outperforms KB methods at identifying both beneficial and deleterious mutations. We observed that KB methods have higher accuracies at predicting deleterious mutations than beneficial mutations. A pipeline using KB methods to efficiently exclude deleterious mutations and TI to accurately identify beneficial mutations was developed for high-throughput mutation scanning. The pipeline was applied to optimize the binding affinity of a broadly sarbecovirus neutralizing antibody 10-40 against the circulating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant. Three identified beneficial mutations show strong synergy and improve both binding affinity and neutralization potency of antibody 10-40. Molecular dynamics simulation revealed that the three mutations improve the binding affinity of antibody 10-40 through stabilization of an altered binding mode with increased polar and hydrophobic interactions. Above all, this study develops an accurate and efficient TI-based approach for optimizing antibody and other biomolecules.