AbEpiTope-1.0: Improved antibody target prediction by use of AlphaFold and inverse folding
Joakim Nøddeskov Clifford, Eve Richardson, Bjoern Peters, Morten Nielsen
Abstract
B cell epitope prediction tools are crucial for designing vaccines and disease diagnostics. However, predicting which antigens a specific antibody binds to and their exact binding sites (epitopes) remains challenging. Here, we present AbEpiTope-1.0, a tool for antibody-specific B cell epitope prediction, using AlphaFold for structural modeling and inverse folding for machine learning models. On a dataset of 1730 antibody-antigen complexes, AbEpiTope-1.0 outperforms AlphaFold in predicting modeled antibody-antigen interface accuracy. By creating swapped antibody-antigen complex structures for each antibody-antigen complex using incorrect antibodies, we show that predicted accuracies are sensitive to antibody input. Furthermore, a model variant optimized for antibody target prediction-differentiating true from swapped complexes-achieved an accuracy of 61.21% in correctly identifying antibody-antigen pairs. The tool evaluates hundreds of structures in minutes, providing researchers with a resource for screening antibodies targeting specific antigens. AbEpiTope-1.0 is freely available as a web server and software.