Antibodies have become indispensable in biomedical research and are rapidly becoming an important platform for the development of next generation therapeutics. In this context, computational tools can be invaluable to accelerate the rational optimization of initial antibody candidates and minimize experimental screening. Here we introduce AbMuSiC, a structure-based method to predict the change in binding affinity upon mutation (DDG_b) that is specifically designed for antibody-antigen interfaces. AbMuSiC is a physics-based model that linearly combines coarse-grain statistical potentials derived from experimental protein structures. Additionally, the model includes a clash term and an amino acid volume term, which are crucial for the accurate prediction of mutations that fill interface cavities. AbMuSiC takes as input a 3D structure of the wildtype antibody-antigen complex, and can predict the effect on the binding affinity of both single and multiple interface mutations. When evaluated in strict cross-validation on all antibody-antigen mutations from the SKEMPIv2 experimental dataset, AbMuSiC reaches a Pearson correlation of 0.55 and a standard deviation of 1.70 kcal/mol. On AbAgym, our recently published antibody-antigen specific benchmark that contains 35k data points from 68 deep mutational scanning experiments that capture the effect of interface mutations on antibody-antigen binding, AbMuSiC is the best performing method. In conclusion, AbMuSiC is a state-of-the-art physics-based DDG_b prediction method that can help in the rational optimization and design of antibody-antigen interfaces. It will be made freely available for academic use as a Python package.

Co-authors: Gabriel Cia, Fabrizio Pucci, Marianne Rooman