Geometric Epitope and Paratope Prediction
This work addresses antibody-antigen interaction prediction for biomedical applications, but it appears incremental as it builds on existing geometric deep learning methods.
The paper tackled the problem of predicting antibody-antigen binding sites by comparing geometric deep learning methods on protein structures, achieving state-of-the-art results with significant performance improvements in O-GEP experiments.
Antibody-antigen interactions play a crucial role in identifying and neutralizing harmful foreign molecules. In this paper, we investigate the optimal representation for predicting the binding sites in the two molecules and emphasize the importance of geometric information. Specifically, we compare different geometric deep learning methods applied to proteins' inner (I-GEP) and outer (O-GEP) structures. We incorporate 3D coordinates and spectral geometric descriptors as input features to fully leverage the geometric information. Our research suggests that surface-based models are more efficient than other methods, and our O-GEP experiments have achieved state-of-the-art results with significant performance improvements.