Pocket Restraints Guided by B-Cell Epitope Prediction improves Chai-1 Antibody-Antigen Structure Modeling

Abstract

The accurate prediction of antibody-antigen (AbAg) complexes is a key challenge for computational immunology, with applications in therapeutic antibody design and diagnostics. Current deep learning methods, such as AlphaFold and Chai, have the potential to generate high-confidence AbAg structures. However, these methods often fail to predict the correct AbAg structure, placing the antibody incorrectly on the antigen and converging on repeatedly predicting the same redundant binding mode. Here, we present BepiPocket and DiscoPocket, two simple approaches that integrate B-cell epitope prediction tools to guide antibody-epitope restraints during Chai-1 structure prediction. On a dataset of 1628 antibody-antigen complexes, we demonstrate that using the sequence-based predictor BepiPred-3.0 (BepiPocket) and the structure-based predictor DiscoTope-3.0 (DiscoPocket), we substantially improved both the accuracy and diversity of predicted AbAg complexes compared to standard Chai-1 modeling with random seed variation. We also find that a key driver in these performance gains is the antigen modeling accuracy. The software for both BepiPocket and DiscoPocket algorithms is freely available at https://github.com/mnielLab/BepiPocke