Improved Antibody‐Specific Epitope Prediction Using AlphaFold and AbAdapt

Antibodies recognize their cognate antigens with high affinity and specificity, but the prediction of binding sites on the antigen (epitope) corresponding to a specific antibody remains a challenging problem. To address this problem, we developed AbAdapt, a pipeline that integrates antibody and antigen structural modeling with rigid docking in order to derive antibody‐antigen specific features for epitope prediction. In this study, we systematically assessed the impact of integrating the state‐of‐the‐art protein modeling method AlphaFold with the AbAdapt pipeline. By incorporating more accurate antibody models, we observed improvement in docking, paratope prediction, and prediction of antibody‐specific epitopes. We further applied AbAdapt‐AF in an anti‐receptor binding domain (RBD) antibody complex benchmark and found AbAdapt‐AF outperformed three alternative docking methods. Also, AbAdapt‐AF demonstrated higher epitope prediction accuracy than other tested epitope prediction tools in the anti‐RBD antibody complex benchmark. We anticipate that AbAdapt‐AF will facilitate prediction of antigen‐antibody interactions in a wide range of applications.